Office of Research and Development

Abstract*

Hydrologic Processes within Landscapes*

Kings Creek Watershed, Konza Prairie (11 km2)SSURGO soil map, superimposed on orthophoto *

GT Hydrologic Model Simulation, Flint Hills Ecoregion, Kansas100 km2Precip recordDischarge at Stream network (right)DEM (left)Kings CreekWatershed,11 km2*

Dynamic simulations of stream discharge & soil moisture distribution*Soil Moisture (right)DEM (left)

Dynamic simulations of stream discharge & soil moisture distribution*

MODES: a MODular Ecosystem Services model for assessing human impacts on land, air & water resourcesBob McKane, Project Coordinator (EPA-WED) Marc Stieglitz & Feifei Pan (Georgia Institute of Technology) Ed Rastetter & Bonnie Kwiatkowski (Marine Biological Laboratory)Nathan Schumaker (EPA-WED), Brad McRae (NCEA), Allen Solomon (USFS), Richard Busing (USGS) Water Quality & Quantity Forest Ecosystem Services Habitat Quality Wildlife Populations Air Quality & Greenhouse Gases Agricultural Ecosystem Services

The Problem:Human actions affect multiple ecosystem servicesNo single model can capture all stressor effects & ES trade-offsAquaticLife

EffectsMulti-Model ApproachMODES ModelsWildlife PopulationsPlant CommunitiesBiogeochemistryHydrologyStressors

MODESPhilosophyModular: different models for different suites of eco servicesProcess-Based: link effects to stressors (GCC, land use)Simple: few parameters & drivers Broad Applicability: ag, forest, grassland, tundraFlexible Scales: plots watersheds, days centuries Regulatory & Planning Goals Best Management Practices: balancing multiple eco servicesWater Quality: nutrients, contaminantsWater Quantity: too little, too muchGreenhouse gases: CO2, N2O, NOxHabitat & Wildlife: effects of land use & toxics

MODES suite of modelsClimate:*PRISM high resolution climate data*SNOPACK snow accumulation, drifting & meltSOILTEMP soil temperature & permafrost freeze/thaw Hydrology: *GT spatially distributed land surface hydrologyStream Network stream flow accumulation & nutrient attenuationBiogeochemistry: *MEL C, N, P, H2O cycling in plants & soils*PSM plant & soil C & N, losses of DIN, DON*NESIS stable isotope simulatorWildlife Habitat & Populations*FORCLIM plant community / habitat dynamics*PATCH wildlife population dynamics*Developed or modified through EPA-WED

Climate:*PRISM Daly, Smith, Smith & McKane 2007, J. Applied Meteorology & Climatology*SNOPACK Stieglitz 1994, Journal of Climate*SOIL-TEMP Stieglitz, Ducharne, Koster & Suarez 2001, J. HydrometeorologyHydrology: *GT Pan et al. in prep; McKane et al., in reviewStream Network Liu & Weller 2007, Environmental Modeling & AssessmentBiogeochemistry: *MEL Rastetter, Perakis, Shaver & Agren 2005, Ecological Applications*PSM Stieglitz, McKane & Klausmeier 2006, Global Biogeochemical Cycles*NESIS Rastetter , Kwiatkowski & McKane 2005, Ecological ApplicationsWildlife Habitat & Population Dynamics*FORCLIM Busing, Solomon, McKane, Burdick 2007, Ecological Applications*PATCH McRae, Schumaker, McKane, Busing, Solomon, Burdick, Ecol. Mod. in pressRecent Publications

Climate

PRISMClimate ModelDaly, Smith, Smith & McKane 2007

M. Stieglitz 1994 SNOWPACKSnow DynamicsSnowpack Accumulation & Melt Sleepers River Watershed, VT Winter 1970 - 1971 Snow Depth (cm) Dec-70 Jan-71 Feb-71 Mar-71 Apr-71

Snow Accumulation & Drifting in Complex TerrainSNOPACKSnow DynamicsM. StielglitzWind directionWind direction

SOIL TEMPERATURE MODELStieglitz, Ducharne, Koster & Suarez 2001SOIL-TEMPSoil Thermodynamics

Hydrology

Georgia Tech (GT) Hydrology Model Spatially Distributed Hydrologic Processessnobear.colorado.edu/IntroHydro/hydro.gif GTHydrology

GT is relatively simple3 free parameters vs. dozens for some hydrology models (e.g., HSPF)S = storage P = precipitation D = drainage (infiltration)Q = runoffET = evapotranspiration Pan, Stieglitz & McKane in prepGTHydrology

A Forest Application: HJ AndrewsWestern Oregon CascadesPhoto: Al LevnoGTHydrology

A Forest Application: HJ AndrewsWestern Oregon CascadesPhoto: Al Levno Effects of harvest, fire and climate change on: stream water quality and quantity forest productivity carbon sequestrationGTHydrology

GTHydrology*

But, we need to move nutrients with waterStreamNH4, NO3, PO4 DON, DOCTopographic control of H2O, C, N, P cyclingH2ONH4, NO3, PO4DON, DOC, H2O

Biogeochemistry

http://ecosystems.mbl.edu/Research/Models/mel/welcome.html

GT-MELEco-Hydrology

Accumulation of C, N & P during forest successionHJ Andrews WS-10 Ed Rastetter*Nitrogen g/m2 Phase II N g/m2Phosphorus g/m2 Phase II P g/m2YEARSClearcut, BurnGT-MELEco-HydrologyPhase II C g/m2Carbon g/m2

GT-MELEco-HydrologyAn Agricultural ApplicationCrop Production & Water Quality Trade-offsH2ONO3, NH4, DON Nassauer

McKane, Kwiatkowski, Stieglitz, Pan, Rastetter in review Trade-off: Corn Yield vs. Water QualityGT-MELEco-Hydrology*

Where did all the fertilizer N go?What processes were most important for protecting water quality?GT-MELEco-Hydrology

kg N / hakg N / ha (35% less N leaching)20-yr Cumulative N Inputs & LossesGT-MELEco-HydrologyMcKane, Kwiatkowski, Stieglitz, Pan, Rastetter in review

GT-MELEco-Hydrology

Chesapeake WS109Peterjohn & Correll 1984Agricultural Validation Sites for GT-MEL*

Stable Isotope Simulator Tracing H2O & Nutrients within Organisms, Communities & LandscapesRastetter, Kwiatkowski & McKane 2005

Stream Network ModelDownstream Flow Accumulation & Nutrient AttenuationLiu & Weller 2007

Liu & Weller 2007

Stream NetworkModel

Stream Network ModelStreamflow Accumulation & Nutrient Attenuation

Stream NetworkModel

Stream Network ModelStreamflow Accumulation & Nutrient AttenuationLiu & Weller 2007Streamflow (1,000 m3/day)Streamflow (1,000 m3/day) Gage 251 ObservedSimulated Gage 277 ObservedSimulated1600

1200

800

400

010000 Aug-97 Aug-98 Aug-99 Patuxent River Watershed, MD Aug-97 Aug-98 Aug-99 06000400020008000

Habitat & Wildlife

Present DayFORCLIMForest Habitat 2050Busing, Solomon, McKane & Burdick 2007FORCLIM Plant Community ModelClimate & Fire Effects on Forest Habitat500 km2 South Santiam Watershed, Oregon

Wildlife Population Model

PATCH predicts population changes based on:

Habitat qualityContaminantsPesticidesOther human activitiesPATCHWildlife PopulationsN. Schumaker

Potential Human Health Applications

Incorporation of Human Decisions

MODES +ENVISIONIncorporating Eco-hydrology (MODES) in a Decision-Making Framework (ENVISION)(MODES)(ES Maps)UpdateInputLandscape GIS:Maps of current land use, vegetation, soils, climateetc.Human ActionsPolicy SelectionLandscape FeedbackModified from John Bolte, Oregon State UniversityChanges in Ecosystem ProcessesMODES

Client-oriented goals: Mapping Ecosystem Services in Response to Human Decisions

Are MODES GCC modeling goals achievable within next 5 years?

Comparison of Some Models for GCC AssessmentsEcosystemsAgForestArctic

GHGCO2, N2O, NOx, Water QuantityDrinking water, flood mitigation Water Quality N, P, C, sedimentHabitat&WildlifeMODESRHEESysSWAT

AGWA BASINSSPARROW

Scale of Processes

PlotHillslopeWater-shedRegionMODESRHEESysSWATAGWABASINSSPARROW

Questions?Arctic LTER greenhouse experiments near Toolik Lake, AlaskaMcKane et al. 1997a, 1997b, Ecology 78(4)Photo courtesy of Jim Laundre*

Instead, we are linking a series of models that will collectively predict effects for a wide range of watershed services.These models each represent a part of the ecosystem, including:Surface hydrologyCycling and transport of nutrients and contaminantsPlant communities& Wildlife populationsThe linkage of these models allows us to predict how a change in one part of the system affects the others.CLICKIn doing so, the models can be used to forecast the effects of interacting stressors on both the terrestrial and aquatic components of watersheds.For example, the linkage of hydrology and biogeochemistry models can predict how human impacts on the terrestrial ecosystem effect water quality and quantity across a region.The addition of a plant community model can be used to predict changes in habitat quality that in turn feed into a wildlife model for making predictions about the effects of multiple stressors on wildlife populations.All of the models shown here are process-based, as opposed to statistically-based.The process-level information provided by our models will better enable our clients to:Link effects to specific stressors e.g., is a decline in a wildlife population due to a chemical or to habitat change?Help clients to target policy & remediation efforts more effectively.Predict responses for which there are no historical precedents, that is, responses that are beyond bounds of statistically-based models.Instead, we are linking a series of models that will collectively predict effects for a wide range of watershed services.These models each represent a part of the ecosystem, including:Surface hydrologyCycling and transport of nutrients and contaminantsPlant communities& Wildlife populationsThe linkage of these models allows us to predict how a change in one part of the system affects the others.CLICKIn doing so, the models can be used to forecast the effects of interacting stressors on both the terrestrial and aquatic components of watersheds.For example, the linkage of hydrology and biogeochemistry models can predict how human impacts on the terrestrial ecosystem effect water quality and quantity across a region.The addition of a plant community model can be used to predict changes in habitat quality that in turn feed into a wildlife model for making predictions about the effects of multiple stressors on wildlife populations.All of the models shown here are process-based, as opposed to statistically-based.The process-level information provided by our models will better enable our clients to:Link effects to specific stressors e.g., is a decline in a wildlife population due to a chemical or to habitat change?Help clients to target policy & remediation efforts more effectively.Predict responses for which there are no historical precedents, that is, responses that are beyond bounds of statistically-based models.

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Office of Research and Development