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Bob McKane, USEPA Western Ecology Division Marc Stieglitz and Feifei Pan, Georgia Tech Adam Skibbe, Kansas State University Kansas State University September.

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Presentation on theme: "Bob McKane, USEPA Western Ecology Division Marc Stieglitz and Feifei Pan, Georgia Tech Adam Skibbe, Kansas State University Kansas State University September."— Presentation transcript:

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

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

3 Agenda 1.Seminar (45 minutes) Project overview – McKane GIS database – Skibbe Model description and results to date – Stieglitz 2.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 Woody Encroachment Air Quality Rangeland Productivity Water Quality & Quantity

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

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

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

8 Fire effects modeling: a collaborative effort involving EPA (ORD & Region 7), KSU, Georgia Tech Fires (red) and smoke plume (white) Flint Hills Ecoregion

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

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

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

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

13 Photo credit:

14 Correlation of Soil & Geology Hydrogeomorphic surfaces, Konza Prairie

15 Linked H 2 O, Carbon & Nitrogen Cycles Low productivity sites High productivity sites Low productivity sites High productivity sites Daily outputs of water & nutrients to streams 30 x 30 m pixels With adequate spatial data, GTHM-PSM simulates the cycling & transport of water & nutrients within watersheds

16 Flint Hills Ecoregion, Kansas ~10,000 mi 2 Current Landcover of Kansas Topography Vegetation Soil Climate GIS Data Layers Land Use 30 x 30 m pixels

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

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

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

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 missing soil types Regional vs. local climate Worldclim vs. weather stations

22 Diffuse research team with varied backgrounds They cannot see the landscape… How to show them in ways everyone understands… Maps Videos 3D KML Communication

23 Web-site to distribute all information related to project Archive of all maps, data, metadata, presentations, etc. Always available for access by collaborators Hosted.KML files Knowledge Distribution

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

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

34 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 Agenda

35 Kings Creek Watershed, 11 km 2


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