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Bob McKane, USEPA Western Ecology Division Marc Stieglitz and Feifei Pan, Georgia Tech

A Multi-Model Ecosystem Simulator for Predicting the Effects of Multiple Stressors on Great Plains Ecosystems. Bob McKane, USEPA Western Ecology Division Marc Stieglitz and Feifei Pan, Georgia Tech Adam Skibbe, Kansas State University Kansas State University September 25, 2008.

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Bob McKane, USEPA Western Ecology Division Marc Stieglitz and Feifei Pan, Georgia Tech

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  1. A Multi-Model Ecosystem Simulator for Predicting the Effects of Multiple Stressors on Great Plains Ecosystems Bob McKane, USEPA Western Ecology Division Marc Stieglitz and Feifei Pan, Georgia Tech Adam Skibbe, Kansas State University Kansas State University September 25, 2008

  2. A Collaborative Effort ORD Corvallis – Dr. Bob McKane Region 7 – Brenda Groskinsky and others Dr. Marc Steiglitz Dr. Feifei Pan Adam Skibbe Dr. John Blair Dr. Loretta Johnson Many others… Dr. Ed Rastetter Bonnie Kwiatkowski

  3. Agenda • Seminar (45 minutes) • Project overview – McKane • GIS database – Skibbe • Model description and results to date – Stieglitz • Open discussion of collaborative opportunities (45 minutes…) • Calibration & analysis of spatial and temporal controls on: • Plant biomass & NPP • Soil C & N dynamics • Fuel load dynamics • Hillslope hydrology & biogeochemistry • Stream water quality & quantity • Linkage of ecohydrology and air quality modeling • Air quality models (BlueSkyRAINS, others?) • Spatial domain for regional assessments • Scenarios: burning strategies, land use, climate • Ecological and air quality endpoints • Collaboration among KSU, EPA, GT researchers

  4. Modeling Goals Air Quality Woody Encroachment Rangeland Productivity Water Quality & Quantity

  5. Air Quality (BlueSkyRAINS) Biogeochemisty (PSM, Plant Soil Model) Hydrology (GTHM, Georgia Tech Hydrology Model) Modeling Approach Environmental Effects Interacting Stressors

  6. Air Quality (BlueSkyRAINS) Biogeochemisty (PSM, Plant Soil Model) Hydrology (GTHM, Georgia Tech Hydrology Model) Modeling Approach • Terrestrial Effects • Vegetation change • Plant productivity • Carbon storage • Fuel loads (input for fire & air quality models) • Stressors • Vegetation change • Climate change • Management • Fire • Grazing • Pesticides • Fertilizers • Aquatic Effects • Water quality & quantity

  7. Air Quality (BlueSkyRAINS) Biogeochemisty (PSM, Plant Soil Model) Hydrology (GTHM, Georgia Tech Hydrology Model) Modeling Approach • Terrestrial Effects • Vegetation change • Plant productivity • Carbon storage • Fuel loads (input for fire & air quality models) • Stressors • Vegetation change • Climate change • Management • Fire • Grazing • Pesticides • Fertilizers • Aquatic Effects • Water quality & quantity

  8. Fire effects modeling: a collaborative effort involving EPA (ORD & Region 7), KSU, Georgia Tech Flint Hills Ecoregion Fires (red) and smoke plume (white) http://www.emporia.edu/earthsci/student/lee1/gis.html

  9. Effect of Fire on Biomass Production Aboveground Production (g ·m-2 ·yr-1) Slide courtesy of John Blair

  10. but, are a source of particulates and ozone remove litter… and increase plant productivity & diversity… Fires prevent woody invasion… Rangeland Fires: What are the ecological and air quality tradeoffs?

  11. Central Great Plains PRODUCTION (g m-2 yr-1) ANNUAL PRECIPITATION (mm) R2 = 0.90 Sala et al. 1988 Need to simulate how water controls ecosystem structure and function across multiple scales, from region… Precip (in.) Ojima and Lackett 2002

  12. Konza Prairie PRODUCTION (g m-2 yr-1) Heisler & Knapp 2008 …to hillslopes snobear.colorado.edu/IntroHydro/hydro.gif

  13. Photo credit: http://www.konza.ksu.edu/gallery/landscape3.JPG

  14. Correlation of Soil & Geology Hydrogeomorphic surfaces, Konza Prairie

  15. Low productivity sites Low productivity sites Linked H2O, Carbon & Nitrogen Cycles High productivity sites High productivity sites Daily outputs of water & nutrients to streams With adequate spatial data, GTHM-PSM simulates the cycling & transport of water & nutrients within watersheds 30 x 30 m pixels

  16. GIS Data Layers Flint Hills Ecoregion, Kansas ~10,000 mi2 30 x 30 m pixels Land Use Climate Soil Topography Vegetation Current Landcover of Kansas

  17. Ecosystem Simulator Dynamic Vegetation & Soils Alternative Futures GIS Data Layers 30 x 30 m pixels Land Use Climate Soil Topography Vegetation Current Landcover of Kansas Stressor Scenarios

  18. Ecosystem Simulator Dynamic Vegetation & Soils Alternative Futures? Simulated fuel loads provide link to air quality models Current Landcover of Kansas

  19. “GIS Support” • Data • Collection • Analysis • Management • Collaboration • Communication • Web • Metadata • Visualization • “jack of all data” • Explorer

  20. GIS Coverages (30 x 30 m) • Elevation • Slope, aspect, etc. • Climate • Precipitation • Temperature • Solar radiation • Relative humidity • Land Use / Land Cover • Vegetation type • Grazing, cropland, etc. • Stream flow • Stream chemistry • Soils • Horizons • Texture, bulk density • Hydraulic conductivity • Total C, N, P • Geology • Bedrock • Impervious surfaces • Permeability • Boundaries • Watersheds • Political

  21. Data Issues • Identifying gaps • Finding workarounds • Soils example • All variables not part of SSURGO • Append SCD pedon data • Surrogates for missingsoil types • Regional vs. local climate • Worldclim vs. weather stations

  22. Communication • Diffuse research team with variedbackgrounds • They cannot see the landscape… • How to show them in wayseveryone understands… • Maps • Videos • 3D • KML

  23. Knowledge Distribution http://epa.adamskibbe.com/ • Web-site to distributeall information related to project • Archive of all maps, data, metadata, presentations, etc. • Always available for access by collaborators • Hosted .KML files

  24. Work Plan Phase I: Konza Prairie calibration / validation Phase II: Flint Hills extrapolation Konza Prairie

  25. Incorporating Ecological Modeling in a Decision-Making Framework (ENVISION) Actors: Land managers implement policies responsive to their objectives Landscape Feedback Landscape Evaluators: Generate landscape metrics to assess outcomes Human Actions Landscape GIS: Maps of current land use, vegetation, soils, climate etc. Update Policy Selection (ES Maps) Ecological Models (GTHM-PSM) Changes in Ecological Processes Input Modified from John Bolte, Oregon State University

  26. Agenda • 2. Open discussion of collaborative opportunities • Calibration & analysis of spatial and temporal controls on: • Plant biomass & NPP • Soil C & N dynamics • Fuel load dynamics • Hillslope hydrology & biogeochemistry • Stream water quality & quantity • Linkage of ecohydrology and air quality modeling • Air quality models (BlueSkyRAINS, others?) • Spatial domain for regional assessments • Scenarios: burning strategies, land use, climate • Ecological and air quality endpoints • Collaboration among KSU, EPA, GT researchers

  27. Kings Creek Watershed, 11 km2

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