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A Multi-scale Collaborative Approach Linking Terrestrial and Aquatic Long-Term Monitoring: Lessons Learned in the Delaware River Basin and Proposed New DirectionsPeter S. Murdoch, Richard Birdsey, Ken Stolte, Rachael Reimann, Karen Riva-Murray, Jennifer Jenkins
The Delaware River BasinCollaborative Environmental Monitoring and Research Initiative (CEMRI)
Richard BirdseyJohn HomYude PanRachel RiemannMichael HoppusKevin McCulloughKen StolteDave WilliamsMike MontgomeryRakesh MinochaWalter Shortle
Peter MurdochJeff FischerDalia VarankaZhi-Liang ZhuJeff EidenshinkGreg LawrenceJason Siemion
Jennifer Jenkins (U. of Vermont)Richard Evans (NPS)Alan Ambler (NPS)
Forest Health Monitoring MeetingSedona, Arizona: February 12, 2004USDA Forest ServiceUSDI Geological SurveyOther Investigators
Mission of the Delaware Basin Collaborative Environmental Monitoring and Research Initiative (CEMRI)To address regional and watershed-scale issues through testing of potential national-scale collaborative strategies among existing biological, terrestrial, aquatic, and atmospheric monitoring and research programs.
Overview of Delaware River Basin Pilot Monitoring ProgramMulti-agency effort to develop an environmental monitoring frameworkUSGS, FS, NPS, NASA, State and local partners Integrated application of monitoring technology at multiple scalesCapable of addressing multiple issuesTested by addressing 4 specific issues:Calcium depletion and nitrogen depositionForest fragmentation Modeling the effects of N- deposition on water quality Linked Terrestrial-Aquatic Carbon budgets
CEMRI Multi-tier Monitoring DesignScale-appropriate monitoring linked through common indicatorsTier One Intensive Research Areas Relatively small number of specific sites representing important processesTier Two Gradient-based surveysMapping of condition using sites representative of a specific condition class and indicator coverages. Tier Three Extensive Inventories and SurveysStatistical representation of the populationTier Four Remote Sensing and MappingWall-to-wall coverage
Increasing spatial resolutionIncreasing temporal resolution
Tier 1 Intensive research areas: the Neversink, Delaware Gap, and French Creek WatershedsDelaware River BasinMurdoch (GS) and Birdsey, Jenkins, Stolte (FS)*Landscape monitoring nested within a watershed frame of reference
Ca Depletion/N-Saturation Intensification Study: Tier 1 at the Neversink River Watershed in the Delaware River BasinNested USGS streamgagesCollaborative research areasIntensified FHM grid throughout the watershedSoil and forest research plots (birch and sugar maple)Manipulation watershed
Foundation Programs USFS Techn. Devel. Group and Research Lab (Hyperspectral/Aerial Photo Interp.) Tier 4Pennsylvania State University(NTN Research) Tier 4USFS Forest Inventory & Analysis Prog (FIA) Tier 3EPA-EMAP/USGS designed stream surveys Tier 3 USGS/New York City Department Of Environmental Protection QW Monitoring Tier 2 USGS- NAWQA Tier 2USGS District COOP/Basic Data Programs (Research and gaging) Tier 1 (also 2)Forest Service Research Lab- Durham, NH Tier 1USGS Hydrologic Benchmark Network Tier 1
Delaware River Basin: Frost Valley, NY 2000Tier 1 Research plot results: soil and foliar calcium decreased from valley to ridgeMinocha, USFS
Delaware River Basin: Frost Valley, NY 2000Minocha, USFSTree stress increased from valley to ridge
Tier 1:Stream Ca Response to ClearcuttingLarge nitrogen and calcium release despite very low calcium pools in soil
Tier 2 USGS Stream Gages in the Neversink River Intensive Area
Neversink River at Claryville
Long-term Stream Monitoring: Decline in calcium + magnesium concentrations (in microequivalents per liter) in streamwater of the Neversink River, 1952-2002
Research Site ResultsLow calcium in soils and foliage is correlated with indicators of tree stress and dieback.Forest harvesting can release large amounts of Ca from even Ca-poor soilsLong-term trends indicate a decline in stream Ca concentrations since the 1970sStream acidification is correlated with low Ca concentrations in forest soils
NY WatershedsNH Watersheds
Tier 2: Regional gradient studies
Is regional foliar or soil chemistry correlated with stream chemistry?Regional gradient study of stream and foliar Calcium concentrationHallet, USFS
Bs HorizonLawrence, USGSAre regional foliar or soil chemistry correlated with stream chemistry? Yes
R2 = 0.83
Tier 2: Stream and soil sampling at watersheds representing a gradient of stream and soil condition.
Tier 3:Scaling strategyFIA P2/P3 plot networkCEMRI intensive plot networknestedwithin
Tier 4: Nitrogen Deposition to the Delaware River BasinFixed stations used to draw regional maps of N deposition (topo. model).Highest deposition in the eastern Catskills and western Poconos.(Lynch, 2002, written com.)(Note Del valley green)NYC water supply
Calcium concentrations in stream water from 1st-order streams during two high-flow surveys, Delaware River BasinTier 3: Stream Calcium
Stream pHTier 2 stream survey: Stream acidification is greatest in the same sub-region where low soil calcium has been mapped.
Process-based, mechanistic model.
Predicts variables in the terrestrial ecosystems by modeling the basic processes controlling them.
PnET Model: Linking the forest to the streamYude Pan, USFS
1. Gross photosynthesis2. Foliar respiration3. Transfer to mobile C4. Growth and maintain resp.5. Allocation to buds6. Allocation to fine roots7. Allocation to wood8. Foliar production9. Wood production10. Soil production11. Precipitation12. Interception13. Snow-rain partition14. Snowmelt15. Fast flow16. Water uptake17. Transpiration18. Drainage19. Wood litter20 Root litter21. Foliar litter22. Wood decay23. Mineralization24. N uptake25. To soil solutionDiagram of PnET ModelWaterWood C/NWoodDeadWoodSoilFineRootPlant C/NCarbon/NitrogenBudC/NNH4NO3 Soil Water FoliarCanopy1091922202367424
5832125161815Snow121712111314Forest Measurements Made:
Pan and others, in processData integration through GIS modeling
Leveled N-dep model matches current soil Ca and stream pH map for Del basin.
Tier 4: AVIRISAirborne Visible/InfraRed Imaging Spectrometer
The resulting 224 band layer image is known as an image cube. When the data from each band is plotted on a graph, it yields a spectrum. Hallet, USFS
LegendCalcium LevelPredicted Foliar Ca for the WMNF Hallet, USFS
FIA, FHM, NPS, Research, Remote sensingForestFIA/FHM- USGS Soil surveys, ResearchSoilWaterNAWQA, WRD District QW Survey, ResearchClimate Research, NADPIntegrated Regional Assessment of Disturbance Effects on Vegetation, Soil, and Water in Forested LandscapesAir
Forest Fragmentation of the Delaware River BasinTiered structure used with each issue.
Based on NLCD data
Complementary goals National Water Quality Assessment Program (NAWQA) Understand effects of urbanization on streams
Forest Service, Forest Inventory and Analysis (FIA) Determine how best to monitor forest fragmentation over the broad areas inventoried, yet with sufficient detail to reflect the processes at work
--ideally with relevance to land management and planning--ideally with specific relevance to these issues of interest, water quality being one
Tier Structure for Assessing Fragmentation Effects Based on NLCD dataFoundation Programs:USFS- Forest Inventory & Analysis (FIA) NLCD and Aerial Photo Interp. Tier 4USEPA: EMAP-design stream survey Tier 3USFS FIA plot network Tier 3USGS NAWQA Tier 2USGS/NPS Boundary Study (Delaware Gap NRA) Tier 2USGS District COOP Research/Basic Data Tier 1USGS National Mapping Division and NAWQA (French Creek) Tier 1USFS Research Tier 1
Gaps in Data AvailabilityAccuracy of NLCD land-use data uncertain.Lacked a probability-based stream water-quality survey.Lacked temporally-intensive stream data in watersheds of low-, medium, and high human land use (stormflow data).Lacked terrestrial data for non-forest plots.
Tier 3 Random sampling of condition within the Neversink, Delaware Gap, and French Creek Intensive AreasRandom forest plots (FHM) and stream survey points (EMAP design)Delaware River BasinDelaware Water Gap Intensive SiteMurdoch (GS) and Birdsey, Jenkins, Stolte (FS)
Forest Fragmentation Tier 1: The Three Watershed Study in the Delaware Water Gap
Tiers 1and 3: Representative-ness of Intensive Monitoring Areas(dots are intensive sites; yellow are high-flow boron concentrations)BoronChloridePercentage of sites (x0.1) below concentration
Tier 2: Fragmentation Study Watersheds in the Delaware River Basin Base Map is NLCD92 from TM Data Fragmentation estimates from low-altitude CIR aerial photography Water quality data from USGS NAWQA synoptic samples 32 watersheds comprise a factorial experiment: urbanization (5 levels) x EPT richness (3 levels) Riemann (FS) and Murray (GS)NeversinkDelaware Water GapFrench Creek
Tier 2: Gradient StudySite selection for urban intensity gradient43 sites10-60 sq. mi. basinsRiffle/pool channelsPoint sources avoidedRiemann and Riva-Murray, in process
Landscape qualities associated with change in stream condition indicatorsRiemann and Riva-Murray, in process
Magnitude & implications of data source inaccuraciesExample --- % Urban landRiemann and Riva-Murray, in process
Correction helps some variables Land use composition in basinUncorrected Corrected NLCD92 %Photo-interpreted % NLCD92 %Photo-interpreted %Riemann and Riva-Murray, in process
Chart3
90.65962832820.92027354520.57213487276.99731744310.7194865899
62.23657095750.32422035990.206072262736.90342079967.5223247584
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37.371566520412.91253275249.164777578248.700209026430.5221110353
35.68356039531.560497695120.425275106331.994504295174.7893986667
32.793803803146.493058053440.716495216218.370381027781.0118402136
26.060355829173.33533518555.1985586668088.4857919696
43.558448603232.386312268328.00831751223.738812042338.5532667012
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80.26732325018.33123963624.85905230896.65795688667.3094055854
92.85748525961.32623875490.84847322761.406132655810.5368311875
88.42831141534.26568227843.24284139560.25122407655.6333622695
97.27824848171.36087575921.0122216390.50236185051.6093138411
93.09805413751.644629060.8952447390.76312479522.1882295699
94.52745888381.70585200790.70647279471.6114224761.7782422638
96.1118067070.45345335180.4128455890.76816351392.7556103672
87.26711168683.52695028392.55056291720.7850951484.2951137979
89.09692165546.82455543133.94073497711.11202969747.5901981647
%forest
%total urban
%residential
%ag
%dev
pi
nlcd92
Sheet1
nlcd92c1nlcd92nlcd2000PI
nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92nlcd92nlcd92nlcd92nlcd92nlcd92nlcd92nlcd92nlcd92nlcd2000nlcd2000nlcd2000nlcd2000nlcd2000nlcd2000nlcd2000nlcd2000nlcd2000PIPIPIPIPIPIPIPI
Basin%road%water%wetland%forest%total urban%residential%ag%dev%wetland_wforestBasin%road%water%wetland%forest%total urban%residential%ag%dev%wetland_wforestBasin%road%water%wetland%forest%total urban%residential%ag%dev%wetland_wforestBasin%road%water%wetland%forest%total urban%residential%ag%dev
14--10902279314--109111713140118855510
33--0060333639233--006200378233--01355359642330003719184463
37--001476671086337--002066571499437--001970601081437400118251487
39--102937333370039--10371394931039--1120343045791393101367561784
40--102651402273040--103632203275040--0022484130781403002353351575
41--20206964978141--203346411881141--202256522076241020157566883
47--0159476094147--01267355088147--0014776698634700019373099
56--003056511469056--004432282439056--013142362668556200206857979
57--1072772027157--1077002224157--11684227313570106319181635
61--004613124153061--0051334625061--00399852612610003532303264
62--0060773239162--0064113447162--00524243473620004620202850
63--004312104556063--0047424939063--013811851624631003529243564
64--00521083848164--0055314251164--013913748604640003719163963
70--003357511067070--005037311378070--003041362970270000196860981
72--0079280092072--00217866085072--0011826778927200078467093
73--005131271848073--006019162024073--0135232041643730002651441973
74--005027252349074--005912112934074--0040191740592740003739362163
79--103122194568279--103515124943279--0020211758793791002337223776
80--0032766167280--0033216452280--01287464713800002816145471
81--1044774956081--1046115265081--11376356624810102837343067
83--003446381965083--004627192679083--0129403230706833003049321768
92--40933314292--409411128292042867607
95--00991101095--00990001095000972202
96--00861311114196--00945315196200821816018
97--1186109111497--11942114497200821513116
98--418085775
99--318014141161099--3193111111099130573937140
100--616923220247100--618843067100050613434034
101--107623220232101--109711122101010514848048
102--3187881915102--3193211215102030861110011
104--208976193104--209521223104010821411216
105--208116161164105--209600134105020742323023
106--8173181711812106--8187431412106070672323024
107--3079171411816107--3089741817107020761816018
Rachel Riemann:--
Rachel Riemann:11
Rachel Riemann:92
Rachel Riemann:41-43, 91
Rachel Riemann:21-33, 85
Rachel Riemann:21-22
Rachel Riemann:81-82, 61
Rachel Riemann:21-33, 61, 81-85
Rachel Riemann:91, 92
Sheet1
%forest
%total urban
%residential
%ag
%dev
pi
nlcd92
Sheet2
%forest
%total urban
%residential
%ag
%dev
pi
nlcd92-corrected (circ7)
Sheet3
%forest
%total urban
%residential
%ag
%dev
pi
nlcd2000
PInlcd92nlcd92c1nlcd2000PInlcd92nlcd92c1nlcd2000
Basin%forest%forest%forest%forest%residential%residential%residential%residential
14889190512
333762313518063
37112060195157360
39133716205696230
402336142235206741
411533292266413352
47126261473554066
562044203157286436
5763771468180592
613551539303768
624664125220182
6335473038242518
643755723916177
701950463060311236
7272160116766767
732660433544161020
74375952403611817
792335222022125217
8028333328141514
8128461437341703
83304672932198032
928694516127
959799502025
9682944216330
9782943113119
98803256
995793443717
10061883334323
10151973448138
1028693731012
1048295761112
1057496752302
1066787932333
1077689991641
Rachel Riemann:41-43, 91
Rachel Riemann:21-22
Chart2
89.67296554782.11581952921.76768085676.78843423968.9042537688
60.40390163483.00590740492.887759307636.054403077339.0603104822
13.596894170676.371395548767.49844412179.69978958686.0711851347
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26.299772724450.560484854739.552382542122.378304806272.9387896609
19.540560631169.277989593163.62851593418.8386926678.1166822531
5.423766984593.971924029775.8539148713093.9719240297
30.415423560355.650483681451.48268691813.617665672269.2681493536
72.22601786487.02953009946.933373820219.873984665626.903514765
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60.47605284396.79456146066.530221467532.042944711438.8375061719
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51.65115491169.72955736697.882097375338.326324810248.0558821771
32.747860773356.936670312551.3178494079.972334813166.9090051256
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51.169444444430.511111111127.061111111117.983333333348.4944444444
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31.467927915522.094869847418.61147552345.467099357967.5619692053
31.93838817966.53135489545.839633591660.847946795567.3793016909
43.56980709976.81237457096.54259927448.827135446955.6395100179
34.051007407145.975770401938.147580279419.385837994365.3616083961
92.58170992173.03022458092.62248398160.66144586113.691670442
98.63534835330.88783950550.81032566010.39266882211.2805083276
%forest
%total urban
%residential
%ag
%dev
pi
nlcd92-corrected (circ7)
Sheet1
nlcd92c1nlcd92nlcd2000PI
nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92c1nlcd92nlcd92nlcd92nlcd92nlcd92nlcd92nlcd92nlcd92nlcd92nlcd2000nlcd2000nlcd2000nlcd2000nlcd2000nlcd2000nlcd2000nlcd2000nlcd2000PIPIPIPIPIPIPIPI
Basin%road%water%wetland%forest%total urban%residential%ag%dev%wetland_wforestBasin%road%water%wetland%forest%total urban%residential%ag%dev%wetland_wforestBasin%road%water%wetland%forest%total urban%residential%ag%dev%wetland_wforestBasin%road%water%wetland%forest%total urban%residential%ag%dev
14--10902279314--109111713140118855510
33--0060333639233--006200378233--01355359642330003719184463
37--001476671086337--002066571499437--001970601081437400118251487
39--102937333370039--10371394931039--1120343045791393101367561784
40--102651402273040--103632203275040--0022484130781403002353351575
41--20206964978141--203346411881141--202256522076241020157566883
47--0159476094147--01267355088147--0014776698634700019373099
56--003056511469056--004432282439056--013142362668556200206857979
57--1072772027157--1077002224157--11684227313570106319181635
61--004613124153061--0051334625061--00399852612610003532303264
62--0060773239162--0064113447162--00524243473620004620202850
63--004312104556063--0047424939063--013811851624631003529243564
64--00521083848164--0055314251164--013913748604640003719163963
70--003357511067070--005037311378070--003041362970270000196860981
72--0079280092072--00217866085072--0011826778927200078467093
73--005131271848073--006019162024073--0135232041643730002651441973
74--005027252349074--005912112934074--0040191740592740003739362163
79--103122194568279--103515124943279--0020211758793791002337223776
80--0032766167280--0033216452280--01287464713800002816145471
81--1044774956081--1046115265081--11376356624810102837343067
83--003446381965083--004627192679083--0129403230706833003049321768
92--40933314292--409411128292042867607
95--00991101095--00990001095000972202
96--00861311114196--00945315196200821816018
97--1186109111497--11942114497200821513116
98--418085775
99--318014141161099--3193111111099130573937140
100--616923220247100--618843067100050613434034
101--107623220232101--109711122101010514848048
102--3187881915102--3193211215102030861110011
104--208976193104--209521223104010821411216
105--208116161164105--209600134105020742323023
106--8173181711812106--8187431412106070672323024
107--3079171411816107--3089741817107020761816018
Rachel Riemann:--
Rachel Riemann:11
Rachel Riemann:92
Rachel Riemann:41-43, 91
Rachel Riemann:21-33, 85
Rachel Riemann:21-22
Rachel Riemann:81-82, 61
Rachel Riemann:21-33, 61, 81-85
Rachel Riemann:91, 92
Sheet1
%forest
%total urban
%residential
%ag
%dev
pi
nlcd92
Sheet2
%forest
%total urban
%residential
%ag
%dev
pi
nlcd92-corrected (circ7)
Sheet3
%forest
%total urban
%residential
%ag
%dev
pi
nlcd2000
PInlcd92nlcd92c1nlcd2000PInlcd92nlcd92c1nlcd2000
Basin%forest%forest%forest%forest%residential%residential%residential%residential
14889190512
333762313518063
37112060195157360
39133716205696230
402336142235206741
411533292266413352
47126261473554066
562044203157286436
5763771468180592
613551539303768
624664125220182
6335473038242518
643755723916177
701950463060311236
7272160116766767
732660433544161020
74375952403611817
792335222022125217
8028333328141514
8128461437341703
83304672932198032
928694516127
959799502025
9682944216330
9782943113119
98803256
995793443717
10061883334323
10151973448138
1028693731012
1048295761112
1057496752302
1066787932333
1077689991641
Rachel Riemann:41-43, 91
Rachel Riemann:21-22
End Result:A comparatively simple and inexpensive collaboration between the USFS and the USGS resulted in greatly enhanced interpretive power of monitoring data from both agencies, AND created a systematic method for scaling up from intensive research areas to integrator scales.
Conclusion:Linking of terrestrial and aquatic monitoring programs can be done
Whats Next? Rates and Effects of Climate Warming and Permafrost Thawing in the Yukon River Basin
Land Use/Land Cover ChangeForest FireCarbon and Nitrogen to the Coastal OceanYukon RiverCEMRI: A Cost-Effective Strategy for Integration of Terrestrial and Aquatic DataClimate change and Permafrost Thawing
TRIBUTARY STREAMSYUKON RIVERCO2CO2CO2MELTWATERSTREAMSGLACIERSPICPICDOC CaCO3 + CO2 + H2O Ca2+ + 2HCO3- DIC &DOCWETLANDSDIC & DOCC SequestrationC SequestrationC ErosionDIC & DOCC ErosionCO2Where will the permafrost carbon go?VEGETATION & SOILCO2CO2Human ActivityC SequestrationCOASTAL OCEAN
Network of Benchmark Climate Research and Monitoring Watersheds?
NWQMC suggestedMonitoring Frameworkhttp://water.usgs.gov/wicp/acwi/monitoring/Objectives:Define status and trends Assess resource management objectivesEarly detection, assessment, and responseSupport and define coastal oceanographic and hydrologic researchHigh-quality data for interpretive reports and educational materialsParaphrased Vowinkle
USGS Hydrologic Benchmark Stations40 years of discharge and chemistry dataForest Service LandLong-term water quality and stream discharge monitoring sites
Tier 4 Forest Fragmentation:Land cover of Dingmans Falls watershed derived from various remote sensors.Moderate Resolution Imaging Spectro-radiometer (MODIS)High-Resolution aerial photo (2000)NLCD92 and 2000 for upper Delaware regionRiemann (FS) and Murray (GS)
Romanovsky, 1999A. The Yukon permafrost is thawing rapidly now.Soil Warming by 20- 50 C over past 50 years To Temperatures near 0o for Boreal Forest PermafrostWhy the Yukon ?
Rates and Effects of Climate Warming and Permafrost Thawing in the Yukon River Basin: An Arctic Benchmark
A proposal for collaborative research and monitoring? ? ?
? TSINGAClearcutPeak Nitrate Concentration in Episodic RunoffIn August following the loggingAugust 06Percent Basal Area RemovalMicro-equivalents per LiterR
Multi-tier (multiphase) monitoring designs are efficient ways to collect data and estimate variables at different scales. These designs have been used for decades in land inventories.
FIA citationwater quality, forest health and sustainability, wildlife habitat quality, recreation quality, and timber and non-timber harvest opportunity, among other things
Fitting these criteria, we were still able to get a range of urbanization (here using road density as a rough measure), although it is evident here (and became more so), that we needed to utilize basins from both areas to get the full range. The pink are the Piemont basins and the green the Poconos.You can tell there is substantial overlap, but you really need addition of the Poconos area to fill out the lower end, and vice versaThese are variables that were important in the PCA axis that was highly correlated with the macroinvertebrate indicators. There are lots of others that will be individually correlated with these indicators (e.g. percent impervious, percent forest, percent forest in small patches). But these indicated could be viewed as broader categories that encompass numerous particular variables (many are substitutable for each other).
Here are some variables that were highly correlated with these variables
Residential land % (piw_res) Grass cover of urban land (piw_ugr)Impervious cover of urban land (buffer) (pib3_uim)Forest patch lacunarity (pi_21lc3)Forest percentage (buffer) (pib3_for)Forest aggregation (pi_ai3)Forest connectivity (pi_cir3) Correlation coefficients (spearman rank) pi -0.80 (p
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