Bob McKane, USEPA Western Ecology Division

Marc Stieglitz and Feifei Pan, Georgia Tech

Adam Skibbe, Kansas State University

Kansas State UniversitySeptember 25, 2008

A Multi-Model Ecosystem Simulator for Predicting the Effects of Multiple Stressors on

Great Plains Ecosystems

ORD Corvallis – Dr. Bob McKane Region 7 – Brenda Groskinsky and others

A Collaborative EffortA Collaborative Effort

Dr. Marc SteiglitzDr. Feifei Pan

Dr. Ed RastetterBonnie Kwiatkowski

Adam SkibbeDr. John Blair

Dr. Loretta JohnsonMany others…

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

Modeling Goals

Woody Encroachment Air Quality

Rangeland Productivity Water Quality & Quantity

Modeling Approach

Environmental Effects

InteractingStressors

Biogeochemisty(PSM, Plant Soil Model)

Air Quality(BlueSkyRAINS)

Hydrology(GTHM, Georgia Tech

Hydrology Model)

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

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

Fire effects modeling: a collaborative effort involving EPA (ORD & Region 7), KSU, Georgia Tech

http://www.emporia.edu/earthsci/student/lee1/gis.html

Fires (red) andsmoke plume (white)

Flint Hills Ecoregion

Mean Annual Plant Productivity

Total Grass Forbs0

100

200

300

400

500

annually burned

unburned

*

*

*

Abo

vegr

ound

Pro

duct

ion

(g ·

m-2

· yr

-1)

Effect of Fire on Biomass Production

Slide courtesy of John Blair

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

Need to simulate how water controls ecosystem structure and function across multiple scales,

Sala et al. 1988Sala et al. 1988

R2 = 0.90

ANNUAL PRECIPITATION (mm)

Central Great Plains

PR

OD

UC

TIO

N (

g m

-2 y

r-1)

Ojima and Lackett 2002Ojima and Lackett 2002

Precip (in.)

from region…

Heisler & Knapp 2008Heisler & Knapp 2008

Konza Prairie

PR

OD

UC

TIO

N (

g m

-2 y

r-1)

snobear.colorado.edu/IntroHydro/hydro.gif

…to hillslopes

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

Correlation of Soil & Geology

Hydrogeomorphic surfaces, Konza Prairie

Linked H2O, 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

Flint Hills Ecoregion, Kansas~10,000 mi2

Current Landcover of Kansas

TopographyVegetation

SoilClimate

GIS Data Layers

Land Use

30 x 30 mpixels

Ecosystem Simulator

Dynamic Vegetation & Soils Alternative Futures

TopographyVegetation

SoilClimate

GIS Data Layers

Land Use

30 x 30 mpixels

Current Landcover of Kansas

Stressor Scenarios

Ecosystem Simulator

Dynamic Vegetation & Soils Alternative Futures?

Current Landcover of Kansas

Simulated fuel loads provide link to

air quality models

• Data • Collection• Analysis• Management

• Collaboration

• Communication• Web• Metadata• Visualization• “jack of all data”

• Explorer

““GIS Support”GIS Support”

GIS Coverages (30 x 30 m)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

Data IssuesData 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

• Diffuse research team with variedbackgrounds

• They cannot see the landscape…

• How to show them in wayseveryone understands…• Maps• Videos• 3D• KML

CommunicationCommunication

• 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

Knowledge DistributionKnowledge Distributionhttp://epa.adamskibbe.com/

Phase I: Konza Prairie calibration / validation

Phase II:Flint Hills extrapolation

Konza Prairie

Work Plan

Incorporating Ecological Modeling in Incorporating Ecological Modeling in a Decision-Making Frameworka Decision-Making Framework (ENVISION) (ENVISION)

John Bolte, Oregon State University

Landscape Evaluators:

Generate landscape metrics reflecting scarcity

Landscape:Spatial Domain in which land use changes are depicted

Autonomous Change Processes:

Models of nonhuman change

Actions

Policies:Constraints and actions

defining land use management

decisionmaking

PolicySelection

Actors:Decisionmakers making landscape change by selecting

policies responsive to their objectives

Landscape Feedback

Evoland – General Structure

(ES Maps)

Update

Input

Landscape GIS:Maps of current

land use, vegetation, soils,

climateetc.

Human Actions

Policy Selection

Landscape Feedback

Modified from John Bolte, Oregon State University

Changes in Ecological Processes

Ecological Models (GTHM-

PSM)

LandscapeEvaluators:

Generate landscape metrics to assess

outcomes

Actors:Land managers

implement policies responsive to their

objectives

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

Kings Creek Watershed, 11 kmKings Creek Watershed, 11 km22