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A Multi-Model Ecosystem Simulator for Predicting the Effects of Multiple Stressors on Great Plains Ecosystems Bob McKane, USEPA Western Ecology Division.

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Presentation on theme: "A Multi-Model Ecosystem Simulator for Predicting the Effects of Multiple Stressors on Great Plains Ecosystems Bob McKane, USEPA Western Ecology Division."— Presentation transcript:

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

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

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

9 Aboveground Production
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
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,
from region… Precip (in.) Sala et al. 1988 R2 = 0.90 ANNUAL PRECIPITATION (mm) Central Great Plains PRODUCTION (g m-2 yr-1) Ojima and Lackett 2002

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

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

14 Hydrogeomorphic surfaces, Konza Prairie
Correlation of Soil & Geology

15 water & nutrients to streams
With adequate spatial data, GTHM-PSM simulates the cycling & transport of water & nutrients within watersheds Linked H2O, Carbon & Nitrogen Cycles Low productivity sites High productivity sites Low productivity sites High productivity Daily outputs of water & nutrients to streams How do these model linkages work? This slide illustrates how the linkage of a surface hydrology model to a biogeochemistry model can be used to predict the filtering action of soils & vegetation as water, nutrients & contaminants move downslope. This linkage accomplishes two things that the individual models ALONE cannot: First, the hydrology-biogeochemistry linkage more accurately captures landscape-scale patterns of habitat structure and productivity – that is, low productivity sites on drier ridge tops, and high productivity sites in riparian areas. Second, the linked models simulate how disturbances to the terrestrial ecosystem affect daily outputs of water and nutrients to streams, lakes and estuaries. By simulating the inherent capacity of each patch within a watershed to retain or release water & chemicals, we can ask a number of local to regional-scale questions concerning future changes in water quality & quantity: Will the addition of fertilizers and pesticides to parts of the landscape end up in streams?  e.g., fate of 100kg/ha NITRATE fertilizer, from upland areas to riparian zone to stream? What about regional inputs of nitrogen associated with acidic deposition? How will changes in land use or climate affect local & regional water supplies? 30 x 30 m pixels

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

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

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

23 Knowledge Distribution
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

24 Konza Prairie calibration / validation Flint Hills extrapolation
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) 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

34 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

35 Kings Creek Watershed, 11 km2

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