Transcript KSU Modeling Overview Presentation

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
A Collaborative Effort
ORD Corvallis – Dr. Bob McKane
Region 7 – Brenda Groskinsky and others
Adam Skibbe
Dr. John Blair
Dr. Loretta Johnson
Many others…
Dr. Marc Steiglitz
Dr. Feifei Pan
Dr. Ed Rastetter
Bonnie Kwiatkowski
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
Air Quality
(BlueSkyRAINS)
Interacting
Stressors
Biogeochemisty
(PSM, Plant Soil Model)
Hydrology
(GTHM, Georgia Tech
Hydrology Model)
Environmental
Effects
Modeling Approach
Terrestrial Effects
Stressors
 Vegetation change
Air Quality
(BlueSkyRAINS)
• Fire
• Grazing
• Pesticides
• Fertilizers
 Plant productivity
 Carbon storage
 Climate change
 Management
 Vegetation change
Biogeochemisty
(PSM, Plant Soil Model)
Hydrology
(GTHM, Georgia Tech
Hydrology Model)
 Fuel loads (input
for fire & air
quality models)
Aquatic Effects
 Water quality &
quantity
Modeling Approach
Terrestrial Effects
Stressors
 Vegetation change
Air Quality
(BlueSkyRAINS)
• Fire
• Grazing
• Pesticides
• Fertilizers
 Plant productivity
 Carbon storage
 Climate change
 Management
 Vegetation change
Biogeochemisty
(PSM, Plant Soil Model)
Hydrology
(GTHM, Georgia Tech
Hydrology Model)
 Fuel loads (input
for fire & air
quality models)
Aquatic Effects
 Water quality &
quantity
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
Mean
Productivity
Effect
of Annual
Fire on Plant
Biomass
Production
Aboveground Production
(g · m-2 · yr-1)
500
400
annually burned
unburned
*
300
*
200
*
100
0
Total
Grass
Forbs
Slide courtesy of John Blair
Rangeland Fires:
What are the ecological and air quality tradeoffs?
Fires prevent woody invasion…
remove litter…
and increase plant
productivity & diversity…
but, are a source of particulates and ozone
Need to simulate how water controls ecosystem
structure and function across multiple scales,
from region…
Precip (in.)
PRODUCTION (g m-2 yr-1)
Central Great Plains
R2 = 0.90
ANNUAL PRECIPITATION (mm)
Ojima and Lackett 2002
Sala et al. 1988
…to hillslopes
PRODUCTION (g m-2 yr-1)
Konza Prairie
snobear.colorado.edu/IntroHydro/hydro.gif
Heisler & Knapp 2008
Photo credit: http://www.konza.ksu.edu/gallery/landscape3.JPG
Hydrogeomorphic surfaces, Konza Prairie
With adequate spatial data, GTHM-PSM simulates the cycling
& transport of water & nutrients within watersheds
Low
productivity
Low
productivity sites
sites
Linked H2O, Carbon
& Nitrogen Cycles
30 x 30 m pixels
High
productivity
sites
High
productivity
sites
Daily outputs of
water & nutrients to streams
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
Ecosystem Simulator
Stressor Scenarios
Dynamic Vegetation & Soils
Alternative Futures
GIS Data Layers
30 x 30 m
pixels
Land Use
Climate
Soil
Topography
Vegetation
Current Landcover of Kansas
Ecosystem Simulator
Dynamic Vegetation & Soils
Alternative Futures?
Current Landcover of Kansas
Simulated fuel loads
provide link to
air quality models
“GIS Support”
•
Data
• Collection
• Analysis
• Management
•
Collaboration
•
Communication
• Web
• Metadata
• Visualization
• “jack of all data”
•
Explorer
GIS Coverages (30 x 30 m)
•
Elevation
•
•
•
Vegetation type
Grazing, cropland, etc.
Stream flow
Stream chemistry
•
Horizons
Texture, bulk density
Hydraulic conductivity
Total C, N, P
Geology
•
•
•
Land Use / Land Cover
•
•
•
•
Precipitation
Temperature
Solar radiation
Relative humidity
Soils
•
•
•
•
Slope, aspect, etc.
Climate
•
•
•
•
•
•
Bedrock
Impervious surfaces
Permeability
Boundaries
•
•
Watersheds
Political
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
Communication
•
Diffuse research team with varied
backgrounds
•
They cannot see the landscape…
•
How to show them in ways
everyone understands…
• Maps
• Videos
• 3D
• KML
Knowledge Distribution
http://epa.adamskibbe.com/
•
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
Work Plan
Phase I:
Phase II:
Konza Prairie calibration / validation
Flint Hills extrapolation
Konza
Prairie
Incorporating Ecological Modeling in
a Decision-Making Framework (ENVISION)
Evoland – General Structure
Actors:
Actors:
Landscape Feedback
Feedback
Landscape
Decisionmakers
Land managers
making
landscape
implement
policies
change by selecting
responsive
to theirto
policies
responsive
objectives
their
objectives
Landscape
Landscape
Evaluators:
Evaluators:
Generate
Generate landscape
landscape
metrics
to assess
metrics
reflecting
scarcity
outcomes
Human
Actions
Actions
Landscape:
Landscape
GIS:
Policy
Policy
Selection
Selection
Policies:
Constraints and actions
defining land use
management
decisionmaking
Spatialof
Domain
in
Maps
current
which
landland
use,use
changes are
vegetation,
soils,
depicted
climate
(ES etc.
Maps)
Input
Update
AutonomousModels
Change
Ecological
Processes:
(GTHM-PSM)
Models
of nonhuman
Changes
in
change
Ecological Processes
Modified from John Bolte, Oregon State University
John Bolte, Oregon State Univers
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
Kings Creek Watershed, 11 km2