H11G-0958:
Dual Heuristics for Assessment of Hydrologic Sensitivities to Climate Change in Watersheds of the Lower Colorado Basin
Monday, 15 December 2014
Kevin W Murphy, Arizona State University, School of Geographical Sciences and Urban Planning, Tempe, AZ, United States, Benjamin S Murphy, College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, United States and Andrew W Ellis, Department of Geography, College of Natural Resources, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
Abstract:
Uncertainties surrounding potential impacts of climate change on water resources can be reduced in part by an accurate understanding of a watershed’s hydrologic response to shifts in temperature and precipitation. This has typically been pursued by computationally-intensive land surface modeling involving complex parameterizations. A viable and more efficient alternative lies in two heuristics: temperature sensitivity and precipitation elasticity of runoff. Their comprehensive descriptions are vital for watersheds with distinct seasons, low runoff efficiencies, large coefficients of variation, and highly skewed distributions – such as for the Salt and Verde Rivers of the arid lower Colorado River Basin. Long data records together with an amplified temperature response of these watersheds relative to global trends enable a thorough exploration of temperature sensitivity and precipitation elasticity grounded in observational data. Regression analyses and kriging methods have been employed in this study to develop these seasonal heuristics. While results align with expectations at the mean, trends were revealed across key variables, posing important stream flow implications depending on relative position within the distributions. Winter temperature sensitivity is nearly indistinguishable at low evapotranspiration response, while it is significant in summer with overland flow impairment. It is lessened by an active monsoon season, which also dilutes loss contributions at reservoirs. Precipitation elasticity of runoff is often assumed to be approximately 2.0, but this study revealed higher values in winter and lower ones in summer, with smaller elasticity when approaching the base flow level and in the upper range of runoff efficiency. Descriptive algorithms have been derived that can be readily applied to distribution functions with any climate change assumption to assess stream flow impact and water resource sustainability for the region.
