Skip to main content

Krista A. Dunne

thumbnail
This is the first of two datasets containing derived data necessary to reproduce the results of the associated journal article: "On the Sensitivity of Annual Streamflow to Air Temperature." This first dataset contains basic basin characteristics of 2,673 gaged basins worldwide, along with associated monthly time series of basin-mean precipitation, air temperature, and net radiation. The streamflow data themselves are available directly from the Global Runoff Data Centre. From the inventory of discharge data holdings of the Global Runoff Data Centre, 2,673 stream gages were selected for which (1) at least 25 complete calendar years of monthly data overlapped in time with available climate data; (2) 500-m-resolution...
thumbnail
This is the second of two datasets containing derived data necessary to reproduce the results of the associated journal article: "On the Sensitivity of Annual Streamflow to Air Temperature," intended for publication in Water Resources Research. The first dataset contains monthly time series of basin-mean precipitation, air temperature, and net radiation, along with basin characteristics. This second dataset, using the first as input, contains empirical and theoretical estimates of annual streamflow sensitivities to precipitation, temperature, and previous-year streamflow. For each basin, a water year was defined by optimization of a streamflow regression against precipitation, temperature, and previous-year streamflow;...
thumbnail
This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Water Model (NWM) Retrospective version 2.1 computed at streamgage benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow (aggregated from an hourly timestep) versus observed daily mean streamflow. Using those errors, the D-score performance benchmark computes the mean squared logarithmic error (MSLE), then decomposes the overall MSLE into orthogonal components such as bias, distribution, and sequence (Hodson and others, 2021). For easier interpretation, the MSLE components can be passed through a scoring function...
ScienceBase brings together the best information it can find about USGS researchers and offices to show connections to publications, projects, and data. We are still working to improve this process and information is by no means complete. If you don't see everything you know is associated with you, a colleague, or your office, please be patient while we work to connect the dots. Feel free to contact sciencebase@usgs.gov.