Prediction of Land Use Change Prediction of Land Use Change and Nutrient Loading and Nutrient Loading

Consequences in the West of Consequences in the West of Hudson Watersheds to 2022Hudson Watersheds to 2022

Myrna Hall and Seth MyersMyrna Hall and Seth MyersSUNY College of Environmental Science and ForestrySUNY College of Environmental Science and Forestry

New York City Watershed Science and Technology SymposiumNew York City Watershed Science and Technology SymposiumWest Point, NY September 15, 2009West Point, NY September 15, 2009

AcknowledgementsAcknowledgements FundingFunding

NYS DEC NYS DEC

CollaboratorsCollaborators Mary Tyrrell, Yale Univ.Mary Tyrrell, Yale Univ. Rene Germain, SUNY ESFRene Germain, SUNY ESF

Data and Logistic Data and Logistic SupportSupport Watershed Ag. CouncilWatershed Ag. Council NYC DEPNYC DEP Catskill Ctr. for Cons. and Catskill Ctr. for Cons. and

Development Development

Statistical SupportStatistical Support Eddie Bevilacqua, SUNY Eddie Bevilacqua, SUNY

ESFESF

StudentsStudents Seth MyersSeth Myers Mehmet YavuzMehmet Yavuz Prajjwal PandayPrajjwal Panday

Rationale for this Rationale for this ResearchResearch

Filtration facility costly alternative to natural filtration provided by forest cover and forest soils.

y = -1.4794Ln(x) + 0.0152

R2 = 0.5069

0

1

2

3

45% 60% 75% 90%

Proportion of subbasin area in forest in each subbasin (%)

CannonsvilleNeversinkPepactonRondoutAshokanSchoharie

Med

ian

NO

3N

O2-N

(

μ

g /l)

75% of Land is Non-industrial Private Forest Land

Rationale for this Rationale for this ResearchResearch

YearYear 19841984 20002000

Catskill Catskill CountiesCounties

mean parcel size mean parcel size (ac)(ac)

1818 1414

Delaware Delaware CountyCounty

mean parcel size mean parcel size (ac)(ac)

2727 2323

Watershed Watershed CountiesCounties

mean parcel size mean parcel size (ac)(ac)

1919 1616

Rationale for this researchRationale for this research

LaPierre & Germain 2005; Caron 2008

That change may be accelerated by parcelization trend

Comparison of 1987 and 2002 Comparison of 1987 and 2002 Landsat Classifications to Catskill-Landsat Classifications to Catskill-Delaware Tax Parcels to 1984 – 2001 Delaware Tax Parcels to 1984 – 2001 Tax ParcelsTax Parcels

Total Cats-Del Parcel SampleTotal Cats-Del Parcel Sample

Non-Divided ParcelsNon-Divided Parcels Divided ParcelsDivided Parcels

ForestForest +1.13%+1.13% +0.75%+0.75%

AgricultureAgriculture -4.70%-4.70% -3.50%-3.50%

DevelopedDeveloped +7.21%+7.21% +23.85%+23.85%

Visited ParcelsVisited Parcels

Non-Divided ParcelsNon-Divided Parcels Divided ParcelsDivided Parcels

ForestForest +1.74%+1.74% +1.99%+1.99%

AgricultureAgriculture -6.54%-6.54% -5.77%-5.77%

DevelopedDeveloped +3.24%+3.24% +9.48%+9.48%

Rationale for this researchRationale for this research Croton watershed – east of Hudson

water supply catchments (80% suburbanized) with consequential water quality degradation, requiring filtration

ObjectivesObjectives

Understand rate of landscape Understand rate of landscape change and the pattern of landscape change and the pattern of landscape factors that determine where land factors that determine where land cover/land use is likely to change.cover/land use is likely to change.

Project those patterns forward in Project those patterns forward in time.time.

Project future water quality as a Project future water quality as a function of changing land use land function of changing land use land covercover

Major StepsMajor Steps

2. Determine WHERE change is likely to occur based on factors that have historical influence

3. Project future land use under two rate scenarios

4. Calculate future water quality nutrient loading impacts based on our statistical model of landscape characteristics and nutrient export

1. Quantify HOW MUCH change to expect based on:

I. Quantify HOW MUCH change to expect based on:

A. study of sample of tax parcel data (1984 and 2000) by LaPierre and Germain (

B. on-the-ground visits to 137 properties, parcelized and non-parcelized

C. surveys of both current land owners and previous land owners who parcelized

D. time series analysis of remotely-sensed imagery

E. assessment of relation between parcelization and development rates (from 4 above) and single family new home construction permits

INSTRUCTIONS Thank you for participating in our survey. Your responses are very important to us and will be kept strictly confidential. Responses based on detailed information are preferable, but if you do not have exact figures on hand, your best estimate is fine. Please check the box next to the most appropriate answer for each question.

Part 1: SOME GENERAL INFORMATION For the following questions, please consider the term “roundwood” to include all logs, including veneer, sawlogs, and logs used to make any sawn products, including pallets. Do not include firewood or pulpwood in this category.Consider “stumpage” to be any standing timber that you have purchased. Include both hardwood and softwood in these categories. 1.1 Do you purchase roundwood and/or stumpage on behalf of your company from

landowners, loggers, foresters and/or independent brokers? YES

NO If you answered YES, please continue with this survey. If you answered NO, please pass this survey along to someone directly involved in wood procurement for your company. 1.2 How many sawmills does your company operate? 1

2 3 4 or more

1.3 How many of these sawmills do you personally purchase roundwood and/or

stumpage for? 1

2 3 4 or more

1.4 Of the mills that receive the wood you purchase, what is the location of the mill

that receives the most volume of wood from your work? ______________________________________ ______________________ City State For the rest of the survey, please consider this your “primary mill.” Any questions referring to “your primary mill” or “your mill” are

OBSERVED COVERDOCUMENTED USE

REMOTE SENSING

10 Oct. 1975 MSS

9 May 1987 TM

20 May 1991 TM

23 Sept. 1999 TM

24 April 2002 ETM+

Tax Parcel History Based on Tax Parcel History Based on NY State Office of Real NY State Office of Real

Property Tax Parcel Data Base Property Tax Parcel Data Base 1998 - 20071998 - 2007

Tax Parcels in NY City Watershed

4520045400456004580046000462004640046600468004700047200

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Tax Parcels in NY CityWatershed

Three potential futures based on Three potential futures based on 1975-2002 or 1987-2002 1975-2002 or 1987-2002

development or post-2002 development or post-2002 parcelization trendsparcelization trends

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

1960 1980 2000 2020 2040

Year

Dev

elo

pm

ent

Acr

eag

e

1975-2002 Development

Post 2002 ParcelizationTrend

1987-2002 Development

Linear (1987-2002Development)

Linear (1975-2002Development)

Systems Approach:Systems Approach:Integrated Monitoring/Modeling Integrated Monitoring/Modeling

Framework Framework

Integrated ecological-Integrated ecological-economic assessment economic assessment

toolboxestoolboxes

Projecting Spatial Projecting Spatial LocationLocation of of Future Land Change Using Future Land Change Using

GEOMODGEOMODStatistical analysis and modeling of Statistical analysis and modeling of

spatial location of both:spatial location of both:

New Development New Development

Agriculture to Forest ConversionAgriculture to Forest Conversion

Spatially-distributed Factors Spatially-distributed Factors AnalyzedAnalyzed

Socio-economic Physio-economic

distance from hwy exit

Housing units/ha 1987 lulc distance from local rtes

Median home value aspect distance from sec. rtes

Pcnt occupied HUs slope distance from prim. rtes

Pcnt over 65 elevation distance from village ctr

Pcnt owner occupied HUs distance from 1987 agriculture distance from ski areas

Pcnt seasonal housing distance from 1987 developed distance to NY City

Pcnt under 18 distance from areal hydrologic prime farmland

Population/ha distance from city center depth to bedrock

Seas. Hous. Dens. distance from linear hydrologic soil hydrologic group

Public Lands Excluded Public Lands Excluded in Calibration in in Calibration in

ValidationValidation1975-1987 1991 - 1975-1987 1991 -

2002 2002 Pre-MOA Post-MOAPre-MOA Post-MOA

Candidate for Change

Protected – Masked out

40

60

80

100

Wetland (%)

Urban (%)

Agriculture (%)

Forest (%)

LU

LC

% f

or

each

wat

ersh

ed

1

23

4

Signal to Calibrate (1975-Signal to Calibrate (1975-19871987

Signal to Validate (1987-Signal to Validate (1987-2002)2002)

Receiver Operating Characteristic Used as the Measure of Fit

33

Percent of Available Land by Slope Percent of Available Land by Slope Class Developed 1987 - 2002Class Developed 1987 - 2002

Slope in Degrees

Percent of Available Land by Percent of Available Land by Distance from Water Bodies Distance from Water Bodies

Developed 1987 - 2002Developed 1987 - 2002

Distance in Meters

Final set of “Best-fit” spatially-Final set of “Best-fit” spatially-distributed factors explaining new distributed factors explaining new

development 1991 - 2002 development 1991 - 2002 Driver name

Indiv ROC Weight Sign

distance from 1987 developed lulc 0.7769 28.50 Neg

1990 seasonal housing unit density 0.7001 11.00 Pos

1980 % under age 18 0.6880 15.00 Mix

1990 mean value of HU (in 1990 $) 0.6721 12.75 Mix

distance from village 0.6707 3.75 Neg

distance from hydrologic feature 0.6597 7.25 Neg

elevation 0.6595 11.75 Neg

distance from local rtes 0.6391 7.25 Neg

distance from hwy interchange 0.6153 X

prime farmland 0.5989 X

soil hydrologic group 0.5987 3.75

depth to bedrock 0.5528 X

Cumulative ROC 0.8468

Final set of “Best-fit” spatially-Final set of “Best-fit” spatially-distributed factors explaining distributed factors explaining

forest gains 1991 - 2002forest gains 1991 - 2002Driver Name Ind ROC Weight

slope 0.6758 20.5

soil hydrologic group 0.6067 5.25

elevation 0.5932 8.75

distance from local road 0.5799 X

depth to bedrock 0.5786 X

distance from hydrologic feature 0.5701 X

distance from forest 0.5548 0.25

prime farmland 0.5443 X

distance from secondary road 0.5377 X

distance from edge of agriculture patch 0.5347 X

distance from primary road 0.5245 X

distance from village 0.5225 X

composite ROC 0.6826

2002 Development 2002 Development Likelihood MapLikelihood Map

Change potential per grid cell= (Weight1*CHPOT(factor1) + Weight2*CHPOT (factor2) + Weight3*CHPOT (factor3)…..)/Sum of all weights

2002 Reforestation 2002 Reforestation Likelihood MapLikelihood Map

2022 Projected Development2022 Projected Development

2022 Projected Reforestation2022 Projected Reforestation

Projected increases in Projected increases in Development Acreage Using Development Acreage Using

Parcelization RateParcelization RateTown Total New % increase

2022 02-221 Andes 1472.92 188.81 14.70%2 Ashland 739.24 125.88 20.52%3 Bovina 995.88 157.23 18.75%4 Colchester 207.49 0.00 0.00%5 Conesville 734.57 207.72 39.43%6 Delhi 2616.03 157.23 6.39%7 Denning 235.29 44.03 23.02%8 Deposit28.47 0.00 0.00%9 Fallsburg 34.69 0.00 0.00%10 Franklin 265.98 12.68 5.00%11 Gilboa 760.37 465.69 158.04%12 Halcott 182.36 31.36 20.77%13 Hamden 1635.27 182.59 12.57%14 Hardenburgh 189.26 50.26 36.16%15 Harpersfield 464.81 50.26 12.12%16 Hunter 2239.74 843.32 60.39%

17 Hurley 210.92 8.54 3.89%18 Jefferson 95.64 4.46 4.46%19 Jewett 740.04 59.51 7.44%20 Kortright 828.19 38.68 4.46%21 Lexington 720.26 41.51 5.45%22 Liberty 4.08 1.19 22.57%23 Masonville 598.21 38.44 6.04%24 Meredith 529.01 15.28 2.81%25 Middletown 2334.36 151.77 6.10%26 Neversink 849.73 66.46 7.25%27 Olive 634.90 27.09 4.09%28 Prattsville 473.13 36.13 7.09%29 Rochester 4.23 0.00 0.00%30 Roxbury 1924.24 135.52 6.58%31 Shandaken 1308.81 101.43 7.19%32 Sidney 63.03 3.88 5.80%33 Stamford 1077.00 72.66 6.32%34 Tompkins 2005.94 168.29 7.74%35 Walton 4250.80 431.61 9.22%36 Wawarsing 114.66 5.02 4.19%37 Windham 2232.77 472.52 17.47%38 Woodstock 334.93 33.14 9.00%

Total 34666.19 2937.14 7.81%

Projected increases in Projected increases in Development Acreage Using Development Acreage Using

Remote Sensing RateRemote Sensing RateTown Total New % increase

2022 02-22

1 Andes 1374.52 90.41 6.17%2 Ashland 639.39 26.03 3.91%3 Bovina 904.28 65.63 6.77%4 Colchester 225.87 18.38 7.52%5 Conesville 619.93 93.08 13.05%6 Delhi 2650.95 192.15 6.76%7 Denning 212.21 20.95 8.99%8 Deposit30.63 2.17 6.61%9 Fallsburg 36.81 2.12 5.44%10 Franklin 265.40 12.09 4.36%11 Gilboa 324.63 29.96 8.45%12 Halcott 156.40 5.39 3.33%13 Hamden 1570.05 117.37 6.96%14 Hardenburgh 157.44 18.45 10.49%15 Harpersfield 430.13 15.58 3.50%16 Hunter 1710.65 314.24 15.52%17 Hurley 210.92 8.54 3.89%

18 Jefferson 95.64 4.46 4.46% 19 Jewett 740.04 59.51 7.44% 20 Kortright 828.19 38.68 4.46% 21 Lexington 720.26 41.51 5.45% 22 Liberty 4.08 1.19 22.57% 23 Masonville 598.21 38.44 6.04% 24 Meredith 529.01 15.28 2.81% 25 Middletown 2334.36 151.77 6.10% 26 Neversink 849.73 66.46 7.25% 27 Olive 634.90 27.09 4.09% 28 Prattsville 473.13 36.13 7.09% 29 Rochester 4.23 0.00 0.00% 30 Roxbury 1924.24 135.52 6.58% 31 Shandaken 1308.81 101.43 7.19% 32 Sidney 63.03 3.88 5.80% 33 Stamford 1077.00 72.66 6.32% 34 Tompkins 2005.94 168.29 7.74% 35 Walton 4250.80 431.61 9.22% 36 Wawarsing 114.66 5.02 4.19% 37 Windham 2232.77 472.52 17.47% 38 Woodstock 334.93 33.14 9.00% Total 34666.19 2937.14 7.81%

Variables Retained in Export Load Variables Retained in Export Load Regression Models and Their Regression Models and Their

Explanatory ContributionExplanatory ContributionPartial R2 for Variables Retained in Each Export Load Regression Model

Indpependent Factor TP TDP TN NO3NO2 SRP SUSPSLD NH3

Mean % IS 0.6393 0.4423 0.2495 0.3037 0.5225

% FOR 0.0658

% AGR 0.1020 0.1238 0.0697 0.0776

% WTLND 0.0338

Parcel Density 0.059 0.1298 0.2229 0.1366

Mean Slope 0.0979 0.0556

Mean Elev 0.1125 0.046 0.0385

K_FACT 0.0410 0.044

WWTP 0.2686

Total Model R2 0.7641 0.7151 0.4858 0.7740 0.8748 0.2686 0.0000

TN Change 2002 - 2022TN Change 2002 - 2022

Low Rate High Rate

TP Change 2002 - 2022TP Change 2002 - 2022

Low Rate High Rate

TDP Change 2002 - 2022TDP Change 2002 - 2022

Low Rate High Rate

NONO33NONO2 2 Change 2002 - Change 2002 - 20222022

Low Rate High Rate

Total Projected Cat-Del Total Projected Cat-Del Nutrient Mass Balance Nutrient Mass Balance

(kg/yr)(kg/yr)

SummarySummary

Some good news and some areas Some good news and some areas needing attention with respect to needing attention with respect to planning of future development.planning of future development.

Should be overlaid with DEP map of Should be overlaid with DEP map of targeted areas of concern to assess targeted areas of concern to assess vulnerability. vulnerability.

Model projections can serve as input Model projections can serve as input to DEP’s GWLF forecasting.to DEP’s GWLF forecasting.

Developed urban grass his tory

0

1000

2000

3000

4000

5000

6000

7000

8000

1975 1987 1991 2002

decade

Ashokan

Cannonsville

Neversink

Pepecton

Rondout

Schoharie

Agriculture

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1975 1987 1991 2002

decade

Ashokan

Cannonsville

Neversink

Pepecton

Rondout

Schoharie

forest

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

1975 1987 1991 2002

decade

Ashokan

Cannonsville

Neversink

Pepecton

Rondout

Schoharie

Shrub

0

500

1000

1500

2000

2500

1975 1987 1991 2002

decade

Ashokan

Cannonsville

Neversink

Pepecton

Rondout

Schoharie

w etland

0

200

400

600

800

1000

1200

1975 1987 1991 2002

decade

Ashokan

Cannonsville

Neversink

Pepecton

Rondout

Schoharie

LU

LC

TY

PE v

s B

AS

INS

LU

LC

TY

PE v

s B

AS

INS

11

22

44

SubbasinsSubbasins

PDB

S10CDG

E5

CNB

WSPA

S3

S5I

S7I

ASH

BK

SEKCLDG

PBKG

NB

P-21

S8

SCL

P-13

SS

S4

RB

PDRY

C-7

P-60

E16I

RDOA

SWK

NWBR

EDRA

E10I

P-50

WDLLBK

P-7

CEBG

NEBG

PMSAC-8 S1

P-8

CPB

E3

RGA

BNV

SKTPA

DTPA

NCG

BRD

RGB

WDBN

RD4

RD1

CTNBG

S6I

CDVA

PSR

RRHG

WDHOA

WDSTB

PROXG

SWKHG

NK4

DCDB

E15

ASCHG

EDRBDTPB

CSBG

SBKHG

SSHG

NK6

S2

CCBHG

CTNHG

PMSB

S9SKTPB

TownsTowns

Andes

Delhi

Walton

Shandaken

Denning

Roxbury

Hunter

Olive

Middletown

Lexington

Jewett

Tompkins

Neversink

Bovina

Hamden

StamfordWindham

KortrightConesville

Halcott

Woodstock

AshlandMeredith

HardenburghColchester

Gilboa

Prattsville

Hurley

Wawarsing

Masonville

Franklin

HarpersfieldJefferson

Deposit

Rochester

FallsburgLiberty

Marbletown

Cairo

Broome

Durham

NumCells(town, 2022) = TotProjParcels(town, 2022) * 0.60

* 435 m2 / (0.0392 * 900) Where:—TotProjParcels(town, 2022) is the number of new

parcels projected per town by year 2022.—0.60 represents the proportion of new parcels

between 1984 and 2000 from our on-the ground survey to have become ‘developed,’ i.e. with the addition of impervious surface.

—435 m2 is the average area of impervious surface coverage per ground surveyed ‘developed’ new parcel.

—(0.0392 * 900) is the average number of square meters of impervious surface per ‘low intensity developed’ cell. It is based on an average of 3.92% impervious surface per low intensity develop cell

0%

10%

20%

30%

40%

50%

60%

CT High

land

s

PA High

land

s

Tham

es

Catsk

ill/Delaw

are

Population

Development

Housing1985-2000

1990-2000 1990-2000

1980-2000

Comparison of Population, Development, and Housing Trends

Comparison of Population, Development, and Housing Trends 1990-2000

0%2%4%6%8%

10%12%14%16%

CT Highla

nds

PA Highla

nds

Thames

Catskil

l/Dela

ware

Population

Development

Housing

•79% of 2002 new development came from land forested in 1991

•21% came from agricultural land

•50% of 1975 Agricultural Land has reverted to forest

Percent Increase and Loss per Percent Increase and Loss per LULC class LULC class

per Watershed 1975 to 2002per Watershed 1975 to 2002Percent Increase and Loss per LULC class

per Watershed

-80.00%

-60.00%

-40.00%

-20.00%

0.00%

20.00%

40.00%

60.00%

Watersheds

% C

han

ge

Total Developed

Total Agriculture

Total Forest

Shrub/Old Field

Total Wetland

Barren

0

50

100

150

200

250

300

350

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Year

Count Single Fam New Hs Bldg

Permits

New Parcels

Number of Single Family New Number of Single Family New Construction Building Permits vs. Construction Building Permits vs.

Number of New ParcelsNumber of New Parcels

http://www.city-data.com/