GC51E-1126
Modeling the impacts of climate change on stream water temperature across scales
Friday, 18 December 2015
Poster Hall (Moscone South)
Catalina Segura1, Peter V Caldwell2, Erika Cohen3, Ge Sun3 and Steve G. McNulty4, (1)Oregon State University, Corvallis, OR, United States, (2)Coweeta Hydrologic Laboratory, USDA Forest Service, Otto, NC, United States, (3)USDA Forest Svc, Eastern Forest Environmental Threat Assessment Center, Raleigh, NC, United States, (4)North Carolina State University at Raleigh, Department of Forestry and Environmental Resources, Raleigh, NC, United States
Abstract:
Water temperature is a critical variable to aquatic ecosystems because it controls metabolic rates and the distribution of aquatic organisms. Therefore, understanding the impacts of future climate on stream water temperature is relevant to sustainable management of water resources. Empirical models based on the statistical relation between air and steam water temperature offer a powerful tool for prediction at large scales. We will demonstrate how simple linear regression models based on short-term historical stream temperature (ts) observations and readily available interpolated air temperature (ta) estimates can be used for rapid assessment of historical and future changes in ts. This methodology was applied to 61 sites in the Southeast region of the US. We found that between 2011 and 2060, all sites were projected to experience increases in ts under the three evaluated climate projections (mean of +0.41 °C per decade). We also developed continental scale models to predict mean and maximum ts in ungauged locations across the US. The models linearly describe site relationships between monthly mean and maximum ta and ts as a function of climatic, hydrologic, and land cover variables. The empirical models were derived using data from 171 reference sites. These sites drain areas spanning four orders of magnitude and are located in 32 states and 16 hydrologic regions. Model performances yielded average Nash-Sutcliffe efficiency coefficients between 0.78 and 0.85. These models were incorporated into the Water Supply Stress Index (WaSSI) Ecosystem Services Model developed by the U.S. Forest Service to predict mean and maximum ts under different climatic projections and land cover changes at the USGS 8 digit hydrologic unit code watershed resolution across the US. The results identify regions in the country where significant increases in ts may occur, potentially causing stress to aquatic ecosystems as climate change progresses.
