prediction of land use change and nutrient loading consequences in the west of hudson watersheds to...
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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/
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