impact of land use and land coverfinal
TRANSCRIPT
The Impact of Land Use and Land Cover Metrics and Patterns on Phosphorus Loading in the Southeastern USA
ByTigist Jima
12282015
Contents
1 Introduction
2 Material and Methods
3 Results
4 Discussion
5 Conclusion
6 Recommendation
References
12282015
1 Introduction
bull Non-point source (NPS) pollutions are considered the leading
threat to water quality (Buchanan et al 2013)
bull NPS results in eutrophication causing disturbance to the
quality of the water (Dokulil and Teubner 2010)
bull Agriculture and urban have a greater contribution to diffuse
phosphorus (P) loads to rivers and streams
12282015
Introduction (cont)
bull Degrading surface water quality in the United States
(Badruzzaman et al 2012)
bull Fresh and coastal water quality of the Southeastern United
States is highly threatened by Eutrophication (Garcia et al
2011)
bull Effect of land use land cover (LULC) change on water quality
found to be the main factors influencing nutrient transport
(Tu 2009 You et al 2012 Shen et al 2013)
12282015
Introduction (cont)
bull NPS P pollution is recognized as a landscape phenomenon
bull Landscape metrics have been used to examine the relationship
between landscape pattern and stream health (Carle et al
2005 Roberts and Prince 2010)
bull Roberts and Prince (2010) has found that landscape
composition and configuration metrics improve nutrient
predictions 12282015
Introduction (cont)
bull SPARROW is a water quality model developed by the USGS
(Hoose et al 2008)
bull The model has been applied to assess stream nutrient loading
including TP
bull The Southeast United States regional SPARROW model which
was developed by Garcia et al (2011)
12282015
Introduction (cont)
bull Model accuracy was only 67
bull Landscape metrics were not incorporated into the model
prediction
bull It is of paramount importance to integrate landscape
characteristics that play role in the source transport and
delivery processes of P
12282015
Objectives
bull This study was initiated with the general objective of
improving the Southeast SPARROW TP model accuracy
bull The specific objectives were to
ndash identify landscape metrics that improved the southeast
SPARROW model accuracy
ndash assess the effect of buffering on non-point phosphorus load amp
ndash provide detailed and precise information
12282015
2 Materials and Methods
1 Study Area
The study area includes AL FL GA MS NC SC TN and VA
(Garcia et al 2011) (Figure 21)
Based on
The 2001 NLCD (Homer et al 2007)
Southeast SPARROW model and data (Garcia et al 2011) and
USGS Water Quality Data base (USGS 2013)
12282015
Materials and Methods (Cont)
Figure 21 Study Area Southeast US LULU map (2001)12282015
2 Data Retrieval and Analysis
The study was conducted in four stages including
i Retrieval of the original SPARROW SAS code
ii Retrieval of the 2001 LULC
iii GIS and Fragstats data analysis and
iv Reprogramming and recalibration of the new SE TP
SPARROW model
Materials and Methods (Cont)
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Contents
1 Introduction
2 Material and Methods
3 Results
4 Discussion
5 Conclusion
6 Recommendation
References
12282015
1 Introduction
bull Non-point source (NPS) pollutions are considered the leading
threat to water quality (Buchanan et al 2013)
bull NPS results in eutrophication causing disturbance to the
quality of the water (Dokulil and Teubner 2010)
bull Agriculture and urban have a greater contribution to diffuse
phosphorus (P) loads to rivers and streams
12282015
Introduction (cont)
bull Degrading surface water quality in the United States
(Badruzzaman et al 2012)
bull Fresh and coastal water quality of the Southeastern United
States is highly threatened by Eutrophication (Garcia et al
2011)
bull Effect of land use land cover (LULC) change on water quality
found to be the main factors influencing nutrient transport
(Tu 2009 You et al 2012 Shen et al 2013)
12282015
Introduction (cont)
bull NPS P pollution is recognized as a landscape phenomenon
bull Landscape metrics have been used to examine the relationship
between landscape pattern and stream health (Carle et al
2005 Roberts and Prince 2010)
bull Roberts and Prince (2010) has found that landscape
composition and configuration metrics improve nutrient
predictions 12282015
Introduction (cont)
bull SPARROW is a water quality model developed by the USGS
(Hoose et al 2008)
bull The model has been applied to assess stream nutrient loading
including TP
bull The Southeast United States regional SPARROW model which
was developed by Garcia et al (2011)
12282015
Introduction (cont)
bull Model accuracy was only 67
bull Landscape metrics were not incorporated into the model
prediction
bull It is of paramount importance to integrate landscape
characteristics that play role in the source transport and
delivery processes of P
12282015
Objectives
bull This study was initiated with the general objective of
improving the Southeast SPARROW TP model accuracy
bull The specific objectives were to
ndash identify landscape metrics that improved the southeast
SPARROW model accuracy
ndash assess the effect of buffering on non-point phosphorus load amp
ndash provide detailed and precise information
12282015
2 Materials and Methods
1 Study Area
The study area includes AL FL GA MS NC SC TN and VA
(Garcia et al 2011) (Figure 21)
Based on
The 2001 NLCD (Homer et al 2007)
Southeast SPARROW model and data (Garcia et al 2011) and
USGS Water Quality Data base (USGS 2013)
12282015
Materials and Methods (Cont)
Figure 21 Study Area Southeast US LULU map (2001)12282015
2 Data Retrieval and Analysis
The study was conducted in four stages including
i Retrieval of the original SPARROW SAS code
ii Retrieval of the 2001 LULC
iii GIS and Fragstats data analysis and
iv Reprogramming and recalibration of the new SE TP
SPARROW model
Materials and Methods (Cont)
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
1 Introduction
bull Non-point source (NPS) pollutions are considered the leading
threat to water quality (Buchanan et al 2013)
bull NPS results in eutrophication causing disturbance to the
quality of the water (Dokulil and Teubner 2010)
bull Agriculture and urban have a greater contribution to diffuse
phosphorus (P) loads to rivers and streams
12282015
Introduction (cont)
bull Degrading surface water quality in the United States
(Badruzzaman et al 2012)
bull Fresh and coastal water quality of the Southeastern United
States is highly threatened by Eutrophication (Garcia et al
2011)
bull Effect of land use land cover (LULC) change on water quality
found to be the main factors influencing nutrient transport
(Tu 2009 You et al 2012 Shen et al 2013)
12282015
Introduction (cont)
bull NPS P pollution is recognized as a landscape phenomenon
bull Landscape metrics have been used to examine the relationship
between landscape pattern and stream health (Carle et al
2005 Roberts and Prince 2010)
bull Roberts and Prince (2010) has found that landscape
composition and configuration metrics improve nutrient
predictions 12282015
Introduction (cont)
bull SPARROW is a water quality model developed by the USGS
(Hoose et al 2008)
bull The model has been applied to assess stream nutrient loading
including TP
bull The Southeast United States regional SPARROW model which
was developed by Garcia et al (2011)
12282015
Introduction (cont)
bull Model accuracy was only 67
bull Landscape metrics were not incorporated into the model
prediction
bull It is of paramount importance to integrate landscape
characteristics that play role in the source transport and
delivery processes of P
12282015
Objectives
bull This study was initiated with the general objective of
improving the Southeast SPARROW TP model accuracy
bull The specific objectives were to
ndash identify landscape metrics that improved the southeast
SPARROW model accuracy
ndash assess the effect of buffering on non-point phosphorus load amp
ndash provide detailed and precise information
12282015
2 Materials and Methods
1 Study Area
The study area includes AL FL GA MS NC SC TN and VA
(Garcia et al 2011) (Figure 21)
Based on
The 2001 NLCD (Homer et al 2007)
Southeast SPARROW model and data (Garcia et al 2011) and
USGS Water Quality Data base (USGS 2013)
12282015
Materials and Methods (Cont)
Figure 21 Study Area Southeast US LULU map (2001)12282015
2 Data Retrieval and Analysis
The study was conducted in four stages including
i Retrieval of the original SPARROW SAS code
ii Retrieval of the 2001 LULC
iii GIS and Fragstats data analysis and
iv Reprogramming and recalibration of the new SE TP
SPARROW model
Materials and Methods (Cont)
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Introduction (cont)
bull Degrading surface water quality in the United States
(Badruzzaman et al 2012)
bull Fresh and coastal water quality of the Southeastern United
States is highly threatened by Eutrophication (Garcia et al
2011)
bull Effect of land use land cover (LULC) change on water quality
found to be the main factors influencing nutrient transport
(Tu 2009 You et al 2012 Shen et al 2013)
12282015
Introduction (cont)
bull NPS P pollution is recognized as a landscape phenomenon
bull Landscape metrics have been used to examine the relationship
between landscape pattern and stream health (Carle et al
2005 Roberts and Prince 2010)
bull Roberts and Prince (2010) has found that landscape
composition and configuration metrics improve nutrient
predictions 12282015
Introduction (cont)
bull SPARROW is a water quality model developed by the USGS
(Hoose et al 2008)
bull The model has been applied to assess stream nutrient loading
including TP
bull The Southeast United States regional SPARROW model which
was developed by Garcia et al (2011)
12282015
Introduction (cont)
bull Model accuracy was only 67
bull Landscape metrics were not incorporated into the model
prediction
bull It is of paramount importance to integrate landscape
characteristics that play role in the source transport and
delivery processes of P
12282015
Objectives
bull This study was initiated with the general objective of
improving the Southeast SPARROW TP model accuracy
bull The specific objectives were to
ndash identify landscape metrics that improved the southeast
SPARROW model accuracy
ndash assess the effect of buffering on non-point phosphorus load amp
ndash provide detailed and precise information
12282015
2 Materials and Methods
1 Study Area
The study area includes AL FL GA MS NC SC TN and VA
(Garcia et al 2011) (Figure 21)
Based on
The 2001 NLCD (Homer et al 2007)
Southeast SPARROW model and data (Garcia et al 2011) and
USGS Water Quality Data base (USGS 2013)
12282015
Materials and Methods (Cont)
Figure 21 Study Area Southeast US LULU map (2001)12282015
2 Data Retrieval and Analysis
The study was conducted in four stages including
i Retrieval of the original SPARROW SAS code
ii Retrieval of the 2001 LULC
iii GIS and Fragstats data analysis and
iv Reprogramming and recalibration of the new SE TP
SPARROW model
Materials and Methods (Cont)
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Introduction (cont)
bull NPS P pollution is recognized as a landscape phenomenon
bull Landscape metrics have been used to examine the relationship
between landscape pattern and stream health (Carle et al
2005 Roberts and Prince 2010)
bull Roberts and Prince (2010) has found that landscape
composition and configuration metrics improve nutrient
predictions 12282015
Introduction (cont)
bull SPARROW is a water quality model developed by the USGS
(Hoose et al 2008)
bull The model has been applied to assess stream nutrient loading
including TP
bull The Southeast United States regional SPARROW model which
was developed by Garcia et al (2011)
12282015
Introduction (cont)
bull Model accuracy was only 67
bull Landscape metrics were not incorporated into the model
prediction
bull It is of paramount importance to integrate landscape
characteristics that play role in the source transport and
delivery processes of P
12282015
Objectives
bull This study was initiated with the general objective of
improving the Southeast SPARROW TP model accuracy
bull The specific objectives were to
ndash identify landscape metrics that improved the southeast
SPARROW model accuracy
ndash assess the effect of buffering on non-point phosphorus load amp
ndash provide detailed and precise information
12282015
2 Materials and Methods
1 Study Area
The study area includes AL FL GA MS NC SC TN and VA
(Garcia et al 2011) (Figure 21)
Based on
The 2001 NLCD (Homer et al 2007)
Southeast SPARROW model and data (Garcia et al 2011) and
USGS Water Quality Data base (USGS 2013)
12282015
Materials and Methods (Cont)
Figure 21 Study Area Southeast US LULU map (2001)12282015
2 Data Retrieval and Analysis
The study was conducted in four stages including
i Retrieval of the original SPARROW SAS code
ii Retrieval of the 2001 LULC
iii GIS and Fragstats data analysis and
iv Reprogramming and recalibration of the new SE TP
SPARROW model
Materials and Methods (Cont)
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Introduction (cont)
bull SPARROW is a water quality model developed by the USGS
(Hoose et al 2008)
bull The model has been applied to assess stream nutrient loading
including TP
bull The Southeast United States regional SPARROW model which
was developed by Garcia et al (2011)
12282015
Introduction (cont)
bull Model accuracy was only 67
bull Landscape metrics were not incorporated into the model
prediction
bull It is of paramount importance to integrate landscape
characteristics that play role in the source transport and
delivery processes of P
12282015
Objectives
bull This study was initiated with the general objective of
improving the Southeast SPARROW TP model accuracy
bull The specific objectives were to
ndash identify landscape metrics that improved the southeast
SPARROW model accuracy
ndash assess the effect of buffering on non-point phosphorus load amp
ndash provide detailed and precise information
12282015
2 Materials and Methods
1 Study Area
The study area includes AL FL GA MS NC SC TN and VA
(Garcia et al 2011) (Figure 21)
Based on
The 2001 NLCD (Homer et al 2007)
Southeast SPARROW model and data (Garcia et al 2011) and
USGS Water Quality Data base (USGS 2013)
12282015
Materials and Methods (Cont)
Figure 21 Study Area Southeast US LULU map (2001)12282015
2 Data Retrieval and Analysis
The study was conducted in four stages including
i Retrieval of the original SPARROW SAS code
ii Retrieval of the 2001 LULC
iii GIS and Fragstats data analysis and
iv Reprogramming and recalibration of the new SE TP
SPARROW model
Materials and Methods (Cont)
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Introduction (cont)
bull Model accuracy was only 67
bull Landscape metrics were not incorporated into the model
prediction
bull It is of paramount importance to integrate landscape
characteristics that play role in the source transport and
delivery processes of P
12282015
Objectives
bull This study was initiated with the general objective of
improving the Southeast SPARROW TP model accuracy
bull The specific objectives were to
ndash identify landscape metrics that improved the southeast
SPARROW model accuracy
ndash assess the effect of buffering on non-point phosphorus load amp
ndash provide detailed and precise information
12282015
2 Materials and Methods
1 Study Area
The study area includes AL FL GA MS NC SC TN and VA
(Garcia et al 2011) (Figure 21)
Based on
The 2001 NLCD (Homer et al 2007)
Southeast SPARROW model and data (Garcia et al 2011) and
USGS Water Quality Data base (USGS 2013)
12282015
Materials and Methods (Cont)
Figure 21 Study Area Southeast US LULU map (2001)12282015
2 Data Retrieval and Analysis
The study was conducted in four stages including
i Retrieval of the original SPARROW SAS code
ii Retrieval of the 2001 LULC
iii GIS and Fragstats data analysis and
iv Reprogramming and recalibration of the new SE TP
SPARROW model
Materials and Methods (Cont)
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Objectives
bull This study was initiated with the general objective of
improving the Southeast SPARROW TP model accuracy
bull The specific objectives were to
ndash identify landscape metrics that improved the southeast
SPARROW model accuracy
ndash assess the effect of buffering on non-point phosphorus load amp
ndash provide detailed and precise information
12282015
2 Materials and Methods
1 Study Area
The study area includes AL FL GA MS NC SC TN and VA
(Garcia et al 2011) (Figure 21)
Based on
The 2001 NLCD (Homer et al 2007)
Southeast SPARROW model and data (Garcia et al 2011) and
USGS Water Quality Data base (USGS 2013)
12282015
Materials and Methods (Cont)
Figure 21 Study Area Southeast US LULU map (2001)12282015
2 Data Retrieval and Analysis
The study was conducted in four stages including
i Retrieval of the original SPARROW SAS code
ii Retrieval of the 2001 LULC
iii GIS and Fragstats data analysis and
iv Reprogramming and recalibration of the new SE TP
SPARROW model
Materials and Methods (Cont)
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
2 Materials and Methods
1 Study Area
The study area includes AL FL GA MS NC SC TN and VA
(Garcia et al 2011) (Figure 21)
Based on
The 2001 NLCD (Homer et al 2007)
Southeast SPARROW model and data (Garcia et al 2011) and
USGS Water Quality Data base (USGS 2013)
12282015
Materials and Methods (Cont)
Figure 21 Study Area Southeast US LULU map (2001)12282015
2 Data Retrieval and Analysis
The study was conducted in four stages including
i Retrieval of the original SPARROW SAS code
ii Retrieval of the 2001 LULC
iii GIS and Fragstats data analysis and
iv Reprogramming and recalibration of the new SE TP
SPARROW model
Materials and Methods (Cont)
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Materials and Methods (Cont)
Figure 21 Study Area Southeast US LULU map (2001)12282015
2 Data Retrieval and Analysis
The study was conducted in four stages including
i Retrieval of the original SPARROW SAS code
ii Retrieval of the 2001 LULC
iii GIS and Fragstats data analysis and
iv Reprogramming and recalibration of the new SE TP
SPARROW model
Materials and Methods (Cont)
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
2 Data Retrieval and Analysis
The study was conducted in four stages including
i Retrieval of the original SPARROW SAS code
ii Retrieval of the 2001 LULC
iii GIS and Fragstats data analysis and
iv Reprogramming and recalibration of the new SE TP
SPARROW model
Materials and Methods (Cont)
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
2 Materials and Methods
i The original SAS code datasets and files were retrieved from the
architects (Hoose et al 2008 Garcia et al 2011)
ndash 6 source variables 5 landndashto-water delivery variables (Table 21)
P Source Variable P Land-to-water Delivery Variable
Point source P (kgyr) Annual Precipitation (mm)
Urban Land Total Area (Km2) Soil erodability factor (K) (dimensionless)
Agricultural Land Total Area (Km2) Ground water table depth (m)
Natural Phosphorus (ppmKm2) Soil pH (dimensionless)
Phosphate Mines (ppmKm2) Soil Organic Matter ()
Phosphorus from Manure (Kgyr)
Table 21 Original Sparrow model source and delivery variables
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Based on the data
bull There are a total of 8321
watersheds (Figure 22)
bull Upstream drainage areas range
from 143 to 13606 km2
bull 370 water quality monitoring
station (Figure 23)
Materials and Methods (Cont)
Figure 22 LULC classes of one of the 8321 watersheds12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Materials and Methods (Cont)
Figure 23 Water quality monitoring stations in SE US12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
ii Then the NLCD 2001 dataset was downloaded from MRLC
iii Thirdly based on the WS shapefiles the SE LULU data was
extracted using ArcGIS 100 (ESRI 2010)
bull 30 m buffer was also made for each stream and again the LuLC
extracted for each buffer
bull Five landscape metrics called AREA_MN () ECON_MN () PLAND
() NLSI (dimensionless) and CONTAG () were calculated using
Fragstats 41 (McGarigal et al 2012)
Materials and Methods (Cont)
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
AREA_AM at a class level weighs each patch on the basis of its
relative class area
ECON_AM quantifies weighted contrast between adjacent LC patch
types
PLAND represents the percentage of the landscape comprised of a
particular LC type
NLSI is a normalized ratio of edge to area
CONTAG shows the extent to which patch types are aggregated or
clumped
Materials and Methods (Cont)
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
iv Finally fragstats result and previous SPARROW data merged and
converted to SAS format
bull Texts explaining the additional source and delivery variables as well as
the matrix were added to the existing SPARRPOW control file
bull Model was run in SAS 93
bull Post-model evaluation was done by comparing precision of P loading
Materials and Methods (Cont)
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
3 Resultsbull The overall model P yield prediction accuracy of 81 was
attained
bull The coefficients of Natural P P from manure and point source
P were significant (at p value of lt 001 009 and 031
respectively)
bull The coefficients of urban and agricultural LCTs which are
considered the major sources of NPS P were insignificant (p
value of 0449 and 0150 respectively)
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Results (cont)
bull 2 of the former and 16 new land-to-water delivery variables
were found to be significant at 1 amp 5 significant levels
bull Then metrics were categorized and model was re-run
bull AREA_AM
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
bull Buffer
bull ECON_AM
bull PLAND
bull NLSI
bull CONTAG
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Results (cont)Table 31 Area Weighted Mean Patch Size (AREA_AM)
Variable Model coefficien
t
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year)
0648 0123 5290 lt0001
Area weighted mean patch size for Urban land ( Km2)
8965 1924 4659 lt0001
Soil parent rock (ppm Km2) 0056 0007 8111 lt0001Phosphate mines (ppm Km2) 0279 0104 2687 0008Manure (kg year) 0017 0004 3997 lt0001Area weighted mean patch size for Agricultural land ( Km2)
18701 5832 3206 0001
Land-to-Water DeliverySoil erodability factor (dimensionless)
3801 0794 4790 lt0001
Precipitation [log(mm)] 1907 0388 4918 lt0001Organic matter () -0212 0034 -6199 lt0001
Depth to water table (m) -0334 0051 -6525 lt0001Soil pH (dimensionless) 0435 0213 2043 0042
Model diagnostics MSE 029 R2 Load 091Root MSE 054 R2 Yield 067No Observation 370
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Results (cont)
bull Model variable coefficients increased for
ndash agricultural area (AREA_MN) increased from 484 to 18701
Kgkm2yr
ndash urban area (AREA_MN) from 88 to 8965 Kgkm2yr
ndash P from manure from 0013 to 0017 Natural P from 0037 to
0056 KgppmKm2 and
ndash soil organic matter from -017 to -21 () (Garcia et al 2011)
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Results (cont)Table 32 Area Weighted Mean Edge Contrast (ECON_AM)
Variable Model coefficient
Standard Error of
coefficient
t value p-value
SourcesPoint sources (kg year) 060 012 5177 lt0001Urban land (Km2) 5697 2798 2036 0043Soil parent rock (ppm Km2) 005 001 5171 lt0001Phosphate mines (ppm Km2) 022 010 2275 0024Manure (kg year) 0013 0004 2784 0006Agricultural land (Km2) 3630 1577 2301 0022Land-to-Water DeliverySoil erodibility factor (dimensionless) 293 090 3240Precipitation [log(mm)] 157 050 3111 lt0001Organic matter () -019 004 -5041 lt0001Depth to water table (m) -035 007 -5285 lt0001Area Weighted Mean Edge Contrast ()
Barren Land 0004 0002 1716 0087Developed High 0012 0004 2852 0005Emergent Wetland 0006 0003 2099 0037Evergreen Forest -002 0006 -3263 0001
Model diagnostics MSE 027 R2 Load 092Root MSE 052 R2 Yield 071No Observation 37012282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Results (cont)Table 33 Normalized Landscape Shape Index (NLSI)
Variable Model coefficient
Standard Error of coefficient
t value p-value
SourcesPoint sources (kg year) 061 0115 5297 lt0001Urban land (Km2) 9247 25421 3638 lt0001Soil parent rock (ppm Km2) 004 0008 4685 lt0001Phosphate mines (ppm Km2) 052 0201 2563 0011Manure (kg year) 002 0005 3018 0003Agricultural land (Km2) 3941 13974 2820 0005Land-to-Water DeliverySoil erodibility factor (dimensionless) 301 0987 3049 lt0001Precipitation [log(mm)] 174 0473 3685 lt0001Organic matter () -016 0035 -4563 lt0001Depth to water table (m) -026 0073 -3620 lt0001Percent of Land (Dimensionless)
Deciduous Forest -183 0473 -3877lt0001
Evergreen Forest -204 0785 -2601 lt0001Grassland Herbaceous 291 0630 4622 lt0001Mixed Forest -136 0718 -1895 0059Shrub Scrub -160 0529 -3031 0002
Model diagnostics MSE 026 R2 Load 092Root MSE 051 R2 Yield 071No Observation 370
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Results (cont)bull Percent of land resulted in
ndash 70 model yield prediction accuracy
ndash 91 model load prediction accuracy
ndash MSE of 27
ndash RMSE of 52
ndash Only Barren land cover type (p = 006)
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Results (cont)
bull Buffer landscape metrics
ndash model yield prediction accuracy of 70 70 and 69 for
ECON_AM PLAND and NLSI respectively
ndash Similar model load prediction accuracy of 91
ndash Deciduous forest (-078) and grass land herbaceous(-091) NLSI
ndash Evergreen forest (-0013) ECON
ndash Similar MSE (27) and RMSE (52 )
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
4 Discussion bull When all metrics were involved only 21 variables were
significant at 1 5 and 10 significant level and R2 yield 81
bull This could be due to involving too many independent variables
(6 source and 91 delivery)
bull Results where similar to the findings of Schwarz et al (2006)
of the national SPARROW (18 significant variables)
bull RSME of 49 lower than the national 56 (Schwarz et al
2006) and than Mississippi River SPARROW (60) (Alexander
et al 2008)12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Figure 41 Log transformed residual TP yield when all previous model variables and all new land-to-water delivery variables used
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Discussion (Cont)
bull Area weighted mean patch size metrics showed that how
urban area and agricultural land increase the TP load
bull Both have registered higher coefficients compared to only
using the total area (Garcia et al 2011 Jain et al 2014 and
Robert and Price 2010)
bull Contribution of agriculture and urban land in TP load in
southeast US is way significantly higher than presumed (Garcia
et al 2011)
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Figure 42 Log transformed residual TP yield when total agricultural and urban land replaced by their AREA_AM
Figure 43 Map of Residuals for the SPARROW Model Phosphorus Predictions for the Southeast (Garcia et al 2011)
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Discussion (Cont)
bull Three landscape metrics (ECON_AM PLAND NLSI) resulted in
improving the SPARROW model prediction accuracy
bull States the fact that landscape metrics ultimately play a very
crucial role in better predicting TP (Uuemaa et al 2005
Uuemaa et al 2009)
bull LCTs found significant have greater role than the insignificant
once
bull A positive coefficient for a variable explains the fact that the
variable increased phosphorus delivery to the reaches12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
Discussion (Cont)
bull The result obtained for the buffer areas landscape metrics also
improved the overall model prediction accuracy
bull Confirming the fact that riparian buffer zones are important
factor in controlling the movement of NPS pollutants through
surface water systems (Bowers 2009)
bull The spatial pattern of riparian zones is also an especially
powerful landscape indicator (Uuemaa et al 2009)
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
5 Conclusion bull Incorporation all model variables and resulted in 21 of the
variable coefficients significance
bull Overall model TP yield prediction accuracy (R2 yield) of 81
and over estimation error of (RMSE) 49 was also exhibited
bull Using the AREA_AM metrics significantly raised the coefficients
of urban and agricultural lands
bull Other landscape metrics involved also did play paramount role
in the source and delivery process of total and NPS P
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
6 Recommendationbull Applying other class level landscape metrics in the SE US TP
SPARROW may result in a similar or even better total P and
NPS P prediction
bull Similar landscape metrics can be applied to other regional and
local SPARROW models might result in similar findings
bull Researchers with comprehensive SPARROW model should
apply the landscape metrics and observe the result
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
References bull Alexander RB RA Smith GE Schwarz EW Boyer JV Nolan and JW Brakebill 2008 Differences in Phosphorus and
Nitrogen delivery to the Gulf of Mexico from Mississippi River Basin Environmental Science Technology 42 (3) 822-830
bull Buckanan BP JA Archibald ZM Easton SB Shaw RL Schneider and M Todd Walter 2013 A phosphorus index that combines critical source areas and transport pathways using a travel time approach Journal of Hydrology 486123ndash1354
bull Badruzzaman M J Pinzon J Oppenheimer and JG Jacangelo 2012 Sources of Nutrient Impacting Surface Water in FloridaA Review Journal of Environmental Management 109 80-92
bull Bowers J2009 Incorporating Riparian Buffer Metrics into the USGS Southeast SPARROW Model Masters Project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University
bull Dokulil M T and K Teubner 2011 Eutrophication and Climate Change Present Situation and Future Scenarios In Eutrophication causes consequences and control (eds AA Ansari SS Gill GR Lanza W Rast) Springer pp 1 ndash 16
bull Garcia AM Hoos AB and Terziotti S 2011 A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States Journal of the American Resources Association 47(5) 991-1010
bull Homer C Dewitz J Fry J Coan M Hossain N Larson C Herold N McKerrow A VanDriel JN and Wickham J 2007 Completion of the 2001 National Land Cover Database for the Conterminous United States Photogrammetric Engineering and Remote Sensing Vol 73 No 4 pp 337-341 httpwwwmrlcgovnlcd01_dataphp Accessed 862013
bull Hoos AB SE Terziotti G McMahon K Savvas KC Tighe and R Alkons-Wolinsky 2008 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes Southeastern United States 2002 US Geological Survey Open-File Report 2008-1163
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
References bull McGarigal K Cushman SA Neel MC and Ene E 2012 FRAGTSATS Spatial Pattern Analysis Program for Categorical maps
University of Massachusetts Amherst
bull Roberts AD and SD Prince 2010 Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Cheakspea Bay Ecological indicators 10 459-474
bull Schwarz GE AB Hoos RB Alexander and RA Smith 2006 The SPARROW Surface Water-Quality Model Theory Application and User Documentation US Geological Survey Reston Virginia
bull Shen Z L Chen Q Hong J Qui H Xie and R Liu 2013 Assessment of nitrogen and phosphorus loads and causal factors fromdifferent land use and soil types in the Three Gorges Reservoir Area Science of the Total Environment 454ndash455383ndash392
bull Tu J 2009 Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts USA Journal of Hydrology 379268ndash283
bull USGS 2013 httpwaterusgsgovowqdatahtml Accessed 462014
bull Uuemaa E A Marc J Roosaare R Marja and U Mander 2009 Landscape Metrics and Indices An Overview of Their Use in Landscape Research Living Reviews in Landscape Research 3 (1) 1 ndash 28
bull Uuemaa E J Roosaare and U Mander 2006 Landscape metrics as indicators of river water qualityat catchment scale NordicHydrology pp 1-14
bull You YY WB Jin QX Xiong L Xue TC Ai and BL Li 2012 Simulation and validation of Non-point Source Nitrogen and Phosphorus Loads under Different Land Uses in Sihu Basin Hubei Province China Procedia Environmental Sciences 13 1781-1797
12282015
12282015
12282015