Development of flood regressions and climate change scenarios to explore estimates of future peak flows

A new Web-based application, titled “Application of Flood Regressions and Climate Change Scenarios To Explore Estimates of Future Peak Flows”, has been developed by the U.S. Geological Survey, in cooperation with the New York State Department of Transportation, that allows a user to apply a set of regression equations to estimate the magnitude of future floods for any stream or river in New York State (exclusive of Long Island) and the Lake Champlain Basin in Vermont. The regression equations that are the basis of the current application were developed in previous investigations by the U.S. Geological Survey (USGS) and are described at the USGS StreamStats Web sites for New York (http://water.usgs.gov/osw/streamstats/new_york.html) and Vermont (http://water.usgs.gov/osw/streamstats/Vermont.html). These regression equations include several fixed landscape metrics that quantify aspects of watershed geomorphology, basin size, and land cover as well as a climate variable—either annual precipitation or annual runoff.

The application uses predictions of future annual precipitation from five climate models and two future greenhouse gas emissions scenarios and provides results that are averaged over three future periods—2025 to 2049, 2050 to 2074, and 2075 to 2099. Results are presented in ensemble form as the mean, median, maximum, and minimum values among the five climate models for each greenhouse gas emissions scenario and period. These predictions of future annual precipitation are substituted into either the precipitation variable or a water balance equation for runoff to calculate potential future peak flows. This application is intended to be used only as an exploratory tool because (1) the regression equations on which the application is based have not been adequately tested outside the range of the current climate and (2) forecasting future precipitation with climate models and downscaling these results to a fine spatial resolution have a high degree of uncertainty. This report includes a discussion of the assumptions, uncertainties, and appropriate use of this exploratory application.