CCMP Dressler 11 10 06

Hydrologic Forecasting for Characterization of Non-Linear Responses of Freshwater Wetlands to Climatic and Land Use Change in the Susquehanna River Basin : 

Hydrologic Forecasting for Characterization of Non-Linear Responses of Freshwater Wetlands to Climatic and Land Use Change in the Susquehanna River Basin Project Recommended for EPA-STAR Funding Proposed Project Schedule: 12/1/06-11/30/09 Lead PI: Denice Wardrop, PSU Cooperative Wetlands Center Co-PIs: Rob Brooks, Kevin Dressler, Chris Duffy, William Easterling, Raymond Najjar, Richard Ready, James Shortle The Pennsylvania State University

Research Theme: 

Research Theme Nonlinear Responses to Climate and Land Use Change in Wetlands This project extends SRBHOS efforts to Understand roles that climate, land use, terrain, ecology, and geology play in partitioning water across environmental systems, particularly freshwater wetlands Extend hydrologic prediction to ecosystem function and services Goals: Understand and predict wetland source/sink areas for water Understand and predict changes in ecosystem services

Major Research Components: 

Major Research Components Data (CARA/SRBHOS/Cooperative Wetlands Center/RTH-Net) Climate Change Scenarios (A2/B2) Land Cover Change Scenarios (aggregate change/geography) Wetlands inventory (222 sites characterized) PRISM (Parameter-elevation Regressions on Independent Slopes Model; Daly et al., 2002) Forcing Data for PIHM Susquehanna Geodatabase ESRI Geodatabase for entire basin Open FTP access to everyone Penn State Integrated Hydrologic Model Finite volume, irregular mesh simulation Fully coupled process formulation Developed for platform independence and open source

Slide4: 

Source: Tiner 1999

Project Goal 1: Scenarios and Prediction: 

Project Goal 1: Scenarios and Prediction Scenarios (Climate and Land Cover) Incorporate scenarios of climate and land cover change, operating on a scale of decades, relevant to the Susquehanna River Basin (SRB). Hydrologic Prediction Using these scenarios, in conjunction with a coupled surface-ground water model, develop a number of predictive hydrologic scenarios for a collection of 11-digit HUC watersheds representing a range of human-associated land uses in the SRB.

Project Goal 2: Ecosystem Services Relationships and Changes : 

Project Goal 2: Ecosystem Services Relationships and Changes ■ Characterize the relationships between hydrologic and landcover parameters and ecosystem characteristics and services (e.g. habitat, flood storage, etc) in wetlands of various types in the SRB ■ Use scenarios to forecast changes in ecosystem services across the entire Susquehanna River Basin Identifying location and timing of non-linearities and/or thresholds in responses Place a value on those changes in services

Slide7: 

Example Threshold Response Wetland Cover

Slide8: 

Example Threshold Response Hydrologic Conditions Floristic Quality Assessment Index vs. Median Depth to Water

Slide9: 

CARA uses GCM output compiled by the Intergovernmental Panel on Climate Change (IPCC), Special Report on Emissions Scenarios (SRES) (Nakicenovic and Swart, 2000).

Slide13: 

Stone Valley Site

Slide14: 

Stone Valley Wetland Reference Site (0.4 acres)

Slide15: 

Reference Wetlands HGM Classes Provides: Relationships between degree of human disturbance in the surrounding landscape and level of ecological functioning (e.g. hydrology, biogeochemical cycling, biodiversity)

Geodatabase—Land Surface: 

Topography Digital elevation models 30 meter resolution (National Elevation Dataset) Streams Delineated from DEM USGS cross-sectional data correlated with Strahler stream order Channel elevation extracted from DEM Vegetation & Soil VEMAP: LAI and vegetation type Root and leaf dimensions, canopy resistance, vegetation type CONUS: soil type and data Manning’s roughness Reservoirs Flow versus depth data from USDOI Geodatabase—Land Surface

Geodatabase—Observation Wells: 

Geodatabase—Observation Wells

Slide19: 

The finite volume approach uses a TIN (triangular irregular network) to decompose the watershed into elements and projects the TIN downward to form the Finite Volume for modeling. Note that all surface water bodies are represented along edges of the triangular grid allowing accurate representation of stream and lake boundaries while still minimizing the number of elements in the watershed. Example watershed decomposition into a TIN, shown for the case with a nested high resolution sub-watershed and a lake or wetland.

Slide20: 

Rescaled groundwater streamflow relation, West Branch of the Susquehanna River showing shallow “fast”(red) and deep “slow” (green) groundwater relation to streamflow. Nonlinear Surface-Groundwater Relation: Possible Indicator of Wetland Response to Climate

Slide21: 

Build-up of Saturation Area after Rainfall Very Fine Scale (~meters) 20 acre catchment ~700 model cells Assessing Wetland Source Waters – Shale Hills Example

Slide22: 

Hypothetical Land Cover/Hydrology/Functional Model Riverine Wetlands Functional level is on a scale of zero (low function) to one (highest possible functioning).

Questions?: 

Questions? SRBHOS: www.srbhos.psu.edu CWC: www.geog.psu.edu/wetlands/ CARA: www.cara.psu.edu

SRBHOS: 

Federal Mid-Atlantic River Forecast Center NASA Goddard, Hydrological Science Branch Northeast Regional Climate Center USACE- Cold Regions Research and Engineering Laboratory USDA Agricultural Research Service  USGS State Department of Military and Veterans Affairs Environmental Stewardship PA DCNR PA DEP PA Geological Survey PA State Climatologist    Management and Stakeholders Chesapeake Bay Foundation Clearwater Conservancy Pennsylvania Environmental Council Susquehanna Greenway Partnership Susquehanna River Basin Commission Upper Susquehanna Coalition Water Centers New York State Water Resources Institute Center for the Environment Pennsylvania Center for Water Resources Virginia Water Resources Research Center Research Centers Chesapeake Community Model Program Chesapeake Research Consortium Smithsonian Environmental Research Center Stroud Water Research Center   Universities Alfred University Clarkson University Colgate University Columbia University Cornell University Drexel University Duke University Frostburg State University Johns Hopkins University Juniata College Lafayette College Massachusetts Institute of Technology Pennsylvania State University Princeton University Rensselaer Polytechnic Institute Rutgers University San Diego Supercomputer Center Stanford University SUNY – Binghamton SUNY – Buffalo SUNY – Cortland SUNY - New Paltz SUNY – Oneonta    SUNY – Plattsburgh Syracuse University Temple University University of California, Berkeley University of Maryland University of Nevada Las Vegas University of South Carolina For more information see http://www.srbhos.psu.edu/ SRBHOS