Filters: Tags: Mean squared error (X)
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This data release contains daily mean squared logarithmic error (MSLE), as well as several decompositions of the MSLE, for three streamflow models: nearest-neighbor drainage area ratio (NNDAR), a simple statistical model that re-scales streamflow data from the nearest streamgage; the version 3.0 calibration of the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Modeling System (NHM-PRMS); and version 2.0 of the National Water Model (NWM). Error was determined by evaluating each model daily against streamflow observations from 1,021 ‘reference’ (minimally anthropogenically impacted [Falcone, 2011]) watersheds across the conterminous United States with at least 10 years of observations....
Categories: Data;
Tags: Climatology,
Hydrology,
Mean squared error,
Model evaluation,
Streamflow prediction,
This data release contains the D-score (version 0.1) daily streamflow performance benchmark results for the National Hydrologic Model Infrastructure application of the Precipitation-Runoff Modeling System (NHM) version 1 "byObs" calibration with Muskingum routing computed at streamflow benchmark locations (version 1) as defined by Foks and others (2022). Model error was determined by evaluating predicted daily mean streamflow 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,...
Categories: Data;
Tags: Data Release,
Hydrology,
Mean squared error,
Model evaluation,
Streamflow prediction,
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...
Categories: Data;
Tags: Hydrology,
Mean squared error,
Model evaluation,
Streamflow prediction,
USGS Science Data Catalog (SDC),
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