an integrated approach for predicting nutrient transports
TRANSCRIPT
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An Integrated Approach for
Predicting Nutrient Transports
from the Land to the Ocean Z-Y. Yang , C. H. David , A. A. Tavakoly , X. Cai , L.C. Helper, Z.F. Xu, D. Maidment, J. McClelland, P. Montagna, H.J. Xie, and W.M. Hao Jackson School of Geosciences, University of Texas at Austin, Austin, TX, USA (E-mail: [email protected], [email protected] )
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Issues in Riverine Nutrient Export
Research • Lacking integrated climate, extreme weather,
land surface, river flow, biogeochemistry, and ecological models
http://www.wri.org/map/world-hypoxic-and-eutrophic-coastal-areas
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How do we deal with these issues?
• Develop an integrated approach
▫ United States NASA Interdisciplinary Research in Earth Science (IDS)
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Integrated Approach
• Global Climate Model
▫ (CESM)
• Regional Forecast Model
▫ (WRF)
• Land Surface Model
▫ (Noah-MP)
• River Flow Model
▫ (RAPID)
• Simple estuary model
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5
Framework for calculation
Modeling across spatial and temporal scales:
Global Regional Watershed Coastal
Current and future: interannual to hourly
Atmospheric Model
or Dataset
Vector River Network -
High-Performance Computing River
Network Model
Land Surface Model
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6
Framework for calculation
Atmospheric Model
or Dataset
Modeling across spatial and temporal scales:
Global Regional
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The New Dynamic Downscaling (NDD)
method • Central Idea
▫ Correct climatological mean bias in GCM outputs
Run GCM
Correct GCM bias
Run RCM
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Methodology of GCM bias correction
• Bias correction 1:
• Bias correction 2:
'CAMCAMCAM
'NNRPNNRPNNRP
'1 CAMNNRPCAMbc
CAM
NNRP
D
DCAMNNRPCAMbc '2
D: standard deviation
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Annual mean RMSEs in GCM and RCM
RMSE in GCM
Before downscaling
RMSE in RCM
After downscaling
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12
Framework for calculation
Atmospheric Model
or Dataset
Modeling across spatial and temporal scales:
Global Regional Watershed
Land Surface Model
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Noah land surface model with multi-
physics options
13
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Noah LSM with multi-physics options 1. Leaf area index (prescribed; predicted) 2. Turbulent transfer (Noah; NCAR LSM) 3. Soil moisture stress factor for transpiration (Noah; BATS; CLM) 4. Canopy stomatal resistance (Jarvis; Ball-Berry) 5. Snow surface albedo (BATS; CLASS) 6. Frozen soil permeability (Noah; Niu and Yang, 2006) 7. Supercooled liquid water (Noah; Niu and Yang, 2006) 8. Radiation transfer: Modified two-stream:
Gap = F (3D structure; solar zenith angle; ...) ≤ 1-GVF Two-stream applied to the entire grid cell: Gap = 0 Two-stream applied to fractional vegetated area: Gap = 1-GVF
9. Partitioning of precipitation to snowfall and rainfall (CLM; Noah) 10. Runoff and groundwater: TOPMODEL with groundwater
TOPMODEL with an equilibrium water table(Chen&Kumar,2001) Original Noah scheme BATS surface runoff and free drainage
More to be added Niu et al. (2011)
Collaborators: Yang, Niu (UT), Chen (NCAR), Ek/Mitchell (NCEP/NOAA), and others
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Maximum Number of Combinations 1. Leaf area index (prescribed; predicted) 2
2. Turbulent transfer (Noah; NCAR LSM) 2
3. Soil moisture stress factor for transpiration (Noah; BATS; CLM) 4
4. Canopy stomatal resistance (Jarvis; Ball-Berry) 2
5. Snow surface albedo (BATS; CLASS) 2
6. Frozen soil permeability (Noah; Niu and Yang, 2006) 2
7. Supercooled liquid water (Noah; Niu and Yang, 2006) 2
8. Radiation transfer: 3
Modified two-stream:
Gap = F (3D structure; solar zenith angle; ...) ≤ 1-GVF Two-stream applied to the entire grid cell: Gap = 0 Two-stream applied to fractional vegetated area: Gap = 1-GVF
9. Partitioning of precipitation to snowfall and rainfall (CLM; Noah)2
10. Runoff and groundwater: 4
TOPMODEL with groundwater
TOPMODEL with an equilibrium water table(Chen&Kumar,2001) Original Noah scheme BATS surface runoff and free drainage
15
2 x 2 x 3 x 2 x 2 x 2 x 2 x 3 x 2 x 4 = 4608 combinations
Process understanding, probabilistic forecasting, quantifying uncertainties
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17
Framework for calculation
Atmospheric Model
or Dataset
Vector River Network -
High-Performance Computing River
Network Model
Modeling across spatial and temporal scales:
Global Regional Watershed Coastal
Current and future: interannual to hourly
Land Surface Model
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RAPID • Uses mapped rivers • Uses high-performance
parallel computing • Computes everywhere
including ungaged locations
River network modeling
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Routing Application for Parallel computatIon of Discharge
David et al. (2011)
River Network Model: RAPID
Based on Muskingum Method
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RAPID and Noah-MP Performance
Results
20
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Texas Rivers Draining to the Gulf of
Mexico • 01/01/2004 – 12/31/2007 every 3 hours • 4-km grid • NARR meteorological forcing + NEXRAD
rainfall • Noah-MP runoff RAPID routing
• facilitate modeling of nutrient loading, transport, and export to coastal waters
http://www.geo.utexas.edu/scientist/david/rapid.htm
Thanks to Cedric David, Bryan Hong, David Maidment, Ben Hodges, Ahmad Tavakoly, and Adam Kubach of Texas Advanced Computing Center
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http://www.geo.utexas.edu/scientist/david/rapid.htm
Application in the Guadalupe river, Texas River network based on NHDplus
RAPID Routing model • adapted to large scale basin with high spatial resolution • few parameters, inversion process included • numerical efficiency (parallel computation)
(David et al., 2011, HP, JHM)
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Framework; what is missing?
Atmospheric
Model or
Dataset
Vector River Network -
High-Performance Computing
River Network Model
Land Surface Model
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Legend
Guadalupe
San Antonio
Mission
Aransas
Land Use/Land Cover
Open Water
Developed
Rock/Sand/Clay
Forest/Shrub
Grassland/Pasture
Cultivated Crops
Wetland
0 40 80 120 16020
Kilometers
Legend
Guadalupe
San Antonio
Mission
Aransas
Land Use/Land Cover
Open Water
Developed
Rock/Sand/Clay
Forest/Shrub
Grassland/Pasture
Cultivated Crops
Wetland
San Antonio, Guadalupe, Mission, and
Aransas Rivers
Urban/developed Urban/developed
Less Urban/developed
Less Urban
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Observations and Chemistry Sampling
• Sampling targeted to high flow events
▫ potential for high nutrient export
• Stream Gauge data:
▫ Taken from Texas Commission of Environmental Quality (TCEQ)
Gauge data collected at constant time step
▫ Taken from University of Texas Marine Science Institute (UTMSI)
Gauge data collected during high flow events
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Stream Gauge Nitrate Concentration:
Urban vs. Less Urban
• Urban/Developed Location
San Antonio River
10/0
7
2/0
8
6/0
8
10/0
8
2/0
9
6/0
9
10/0
9
Nitra
te c
oncentr
ation (
M)
0
200
400
600
800
1000Guadalupe River
10/0
7
2/0
8
6/0
8
10/0
8
2/0
9
6/0
9
10/0
9
Nitra
te c
oncentr
ato
n (
M)
0
20
40
60
80
100
• Less Urban Location
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Estuary Model Nutrient Transport Study
• Same River Basins • Guadalupe (Less Urban) • San Antonio
(Urban/developed)
• Four HUCs in each basin
• Guadalupe Estuary
• Centrally located along Texas coast
• Microtidal • Small bay area but
large watershed relative to other Texas systems
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Generic Ecosystem Model
(3 components with 2 boundary conditions) • Mass-balance model
• Two boundaries: LGRW & LSRW
• Three components: Nutrient (DIN) –Phytoplankton – Zooplankton
• Re-mineralization and implicit sinking (or horizontal exchange) were assumed to be 50%, respectively
• Δ=1 hr & RK 4th order scheme
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Generic Ecosystem Model Results
• No Loadings (both boundary conditions shut down)
• Lower San Antonio River (Urban/developed region)
• Lower Guadalupe River (Less Urban Region)
Source: Arismendez et al. (2009) Ecol. Informatics 4: 243-253
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Generic Ecosystem Model Conclusions and
Discussion
• Estuary response differs with respect to varying nutrient concentrations.
• Lower San Antonio River (Urban/developed region) is delivering more nutrients and driving greater ranges of ecological response than the Lower Guadalupe River (Less Urban region).
• Increases in nutrient concentrations due to human alterations of the landscape may result in future eutrophic conditions in the Guadalupe Estuary.
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Improving on Nutrient Loading
• Developing a Comprehensive Nitrogen Budget for Texas
▫ Agriculture Sources
Crop fixation, Livestock, and Fertilizer application
▫ Atmospheric
Dry and wet deposition
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Quantification of Sources
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Conclusions
• Predicting nutrient transport from land to coast requires an integrated approach
• Improvement of atmosphere, land, and river flow modeling has lead to better prediction of nutrient fluxes
• Understanding the full pathways of nutrients, with enhanced modeling techniques, will lead to better understanding of sources and solutions
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Future Work
• Land Surface model with leaching (Noah-MP), coupled with regional weather model (WRF)
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Future Work • Expansion beyond the Texas Regional Domain
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Thank You • Zong-Liang Yang (PI)
• David Maidment (co-I), Paul Montagna (co-PI), James McClelland (co-I), Hongjie Xie (co-PI), Wei Min Hao (Co-PI)
• Guo-Yue Niu, Xiaoyan Jiang, Seungbum Hong, Cédric David, Hae-Cheol Kim, Sandra Arismendez, Rae Mooney, Patty Garlough, Rachel Mills, Beibei Yu, Ling Lu, Almoutaz El Hassan, Zhongfeng Xu, Xitian Cai, Ahmad Tavakoly
• Funded by NASA Interdisciplinary Research in Earth Sciences (IDS)
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DON: concentration-runoff relationships
Runoff (mm/day)
0.01 0.1 1 10 100
DO
N c
oncentr
ation (
M)
0
10
20
30
40
50
60
70Guadalupe
San Antonio
Mission
Aransas
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