Event Title

Attributing Causes of Wildfire, Vegetation, and Hydrologic Climate Change Impacts

Presenter Information

Kristen Emmett

Location

USU Eccles Conference Center

Event Website

http://www.restoringthewest.org

Abstract

The potential biases in climate data sets can drastically affect our ability to attribute climate change impacts. The recent release of a bias corrected gridded air temperature dataset for the continental U.S., TopoWx, provided an opportunity to quantify the effects of a climate model bias on ecological process- 25 based simulations. Here we present preliminary results from modeled vegetation metrics, hydrographs, and wildfire frequency driven by Daymet compared to TopoWx regional temperature datasets, using the dynamic vegetation model, LPJ-GUESS. LPJ-GUESS simulates plant physiological and biogeochemical mechanisms to simulate plant establishment, growth, competition, mortality, and soil hydrological processes. In addition, surface and subsurface runoff from each 1km2 gridcell was input into an external routing model to create hydrographs. Simulations were run for the entire Greater Yellowstone Ecosystem to detect potential impacts of artificial climate trends at elevations above 1500 meters.

Comments

Kristen Emmett is a PhD student, Ecosystem Dynamics Lab, Department of Ecology, Montana State University

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Oct 28th, 11:30 AM Oct 28th, 12:00 PM

Attributing Causes of Wildfire, Vegetation, and Hydrologic Climate Change Impacts

USU Eccles Conference Center

The potential biases in climate data sets can drastically affect our ability to attribute climate change impacts. The recent release of a bias corrected gridded air temperature dataset for the continental U.S., TopoWx, provided an opportunity to quantify the effects of a climate model bias on ecological process- 25 based simulations. Here we present preliminary results from modeled vegetation metrics, hydrographs, and wildfire frequency driven by Daymet compared to TopoWx regional temperature datasets, using the dynamic vegetation model, LPJ-GUESS. LPJ-GUESS simulates plant physiological and biogeochemical mechanisms to simulate plant establishment, growth, competition, mortality, and soil hydrological processes. In addition, surface and subsurface runoff from each 1km2 gridcell was input into an external routing model to create hydrographs. Simulations were run for the entire Greater Yellowstone Ecosystem to detect potential impacts of artificial climate trends at elevations above 1500 meters.

http://digitalcommons.usu.edu/rtw/2015/Posters/15