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Spreadsheet of normalized Taylor diagram statistics for multi-day extreme precipitation in six regions in the contiguous U.S. (Extreme precipitation statistics.xlsx)
The U.S. Geological Survey (USGS) and the U.S. Army Corps of Engineers (USACE) are collaborating with the U.S. Federal Emergency Management Agency (FEMA) on improving flood-frequency analysis methods to account for mixed populations arising from different flood causal mechanisms. Precipitation data at different timescales are widely used in flood-typing studies. Various gridded precipitation datasets were validated by comparison against station observations to support flood-typing over six pilot regions in the contiguous U.S. (CONUS) where flood-typing approaches will be initially tested. The six pilot regions are (1) the Delaware River, (2) the Iowa River, (3) Puget Sound, (4) the Red River of the North, (5) the Trinity River, and (6) the Upper Colorado River. The datasets were validated by comparison against gage data from the NOAA Global Historical Climatology Network daily (GHCNd) for the periods 1981-2013 and 1998-2013. A Microsoft Excel workbook is provided, which tabulates normalized Taylor diagram statistics for multi-day extreme precipitation in each pilot region for the two periods. Extreme precipitation of 1-, 2-, 3-, 5-, 7-, 10-, and 14-day duration were evaluated. The statistics evaluated include the Pearson correlation coefficient, the standard deviation ratio, the centered root-mean-square difference, the percent bias, and the Kling-Gupta efficiency (KGE) metric. The gridded precipitation datasets include: (1) 20th Century Reanalysis Version 3 (20CRV3) dataset, (2) the Analysis of Record for Calibration (AORC) dataset, (3) the four-kilometer long-term (40-year) regional hydroclimate reanalysis over the conterminous United States dataset (CONUS404), (4) a bias-adjusted version of CONUS404 (CONUS404BA) which uses the Daymet version 3 dataset for bias adjustment, (5) the European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis - Fifth Generation dataset (ERA5), (6) a downscaled version of ERA5 precipitation (ERA5-Land), (7) the gridded precipitation dataset by Livneh et al. (Livneh), (8) a version of the Livneh dataset that does not split reported 24-hour precipitation between subsequent days (Livneh-unsplit), (9) the Parameter-elevation Regression on Independent Slopes Model (PRISM) dataset, and (10) the Stage IV dataset. Stage IV was not included in the 1981–2013 analysis since it is only available since 1997.
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Spreadsheet of normalized Taylor diagram statistics for daily precipitation by season in six regions in the contiguous U.S. (All precipitation statistics.xlsx)
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The U.S. Geological Survey (USGS) and the U.S. Army Corps of Engineers (USACE) are collaborating with the U.S. Federal Emergency Management Agency (FEMA) on improving flood-frequency analysis methods to account for mixed populations arising from different flood causal-mechanisms. Precipitation data at different timescales are widely used in flood-typing studies. Various gridded precipitation datasets were validated by comparison against station observations to support flood-typing over six pilot regions in the contiguous U.S. (CONUS) where flood-typing approaches will be initially tested. The six pilot regions are (1) the Delaware River, (2) the Iowa River, (3) Puget Sound, (4) the Red River of the North, (5) the Trinity River, and (6) the Upper Colorado River. The datasets were validated by comparison against gage data from the NOAA Global Historical Climatology Network daily (GHCNd) for the periods 1981-2013 and 1998-2013. A Microsoft Excel workbook is provided which tabulates normalized Taylor-diagram statistics for daily precipitation by season in each pilot region for the two periods. The statistics evaluated include the Pearson correlation coefficient, the standard deviation ratio, the centered root-mean-square difference, the percent bias, and the Kling-Gupta efficiency (KGE) metric. The gridded precipitation datasets include: (1) 20th Century Reanalysis Version 3 (20CRV3) dataset, (2) the Analysis of Record for Calibration (AORC) dataset, (3) the four-kilometer long-term (40-year) regional hydroclimate reanalysis over the conterminous United States dataset (CONUS404), (4) a bias-adjusted version of CONUS404 (CONUS404BA) which uses the Daymet version 3 dataset for bias adjustment, (5) the European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis - Fifth Generation dataset (ERA5), (6) a downscaled version of ERA5 precipitation (ERA5-Land), (7) the gridded precipitation dataset by Livneh et al. (Livneh), (8) a version of the Livneh dataset that does not split reported 24-hour precipitation between subsequent days (Livneh-unsplit), (9) the Parameter-elevation Regression on Independent Slopes Model (PRISM) dataset, and (10) the Stage IV dataset. Stage IV was not included in the 1981–2013 analysis since it is only available since 1997.
Validation of gridded precipitation datasets for flood-typing in six regions in the contiguous U.S.
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The U.S. Geological Survey (USGS) and the U.S. Army Corps of Engineers (USACE) are collaborating with the U.S. Federal Emergency Management Agency (FEMA) on improving flood-frequency analysis methods to account for mixed populations arising from different flood causal mechanisms. Precipitation data at different timescales are widely used in flood-typing studies. Various gridded precipitation datasets were validated by comparison against station observations to support flood-typing over six pilot regions in the contiguous U.S. (CONUS), where flood-typing approaches will be initially tested. The six pilot regions are (1) the Delaware River, (2) the Iowa River, (3) Puget Sound, (4) the Red River of the North, (5) the Trinity River, and (6) the Upper Colorado River. Various precipitation datasets derived from station, radar, reanalysis data, or combinations thereof, were validated in terms of their ability to capture the spatiotemporal characteristics of daily precipitation as well as multi-day (1–14 day) extreme precipitation events. The datasets were validated by comparison against gage data from the NOAA Global Historical Climatology Network daily (GHCNd) for the periods 1981-2013 and 1998-2013. Taylor diagrams and the Kling-Gupta efficiency (KGE) metric were used for validation. This data release consists of three tables in EXCEL spreadsheet format: -- Normalized Taylor diagram statistics for daily precipitation by season in each pilot region, for the two periods (All_precipitation_statistics.xlsx). -- Normalized Taylor diagram statistics for multi-day extreme precipitation in each pilot region, for the two periods (Extreme_precipitation_statistics.xlsx). -- List of GHCNd stations used for evaluation of daily precipitation by season and for evaluation of multi-day precipitation performance in each pilot region, for the two periods (GHCND_stations.xlsx).
Standardized Precipitation-Evapotranspiration Index for western United States, 2001-2014, derived from gridMET climate estimates
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These data are 30m by 30 m grids of the mean Standardized Precipitation-Evapotranspiration Index (SPEI) between 2001-2014 in the western United States. The SPEI index was developed by Sergio M. Vicente-Serrano and coauthors (https://spei.csic.es/index.html). Source evapotranspiration and precipitation data were generated by gridMET (http://www.climatologylab.org/gridmet.html).
Standardized Precipitation-Evapotranspiration Index for western United States, 2001-2014, derived from gridMET climate estimates
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These data are 30m by 30 m grids of the mean Standardized Precipitation-Evapotranspiration Index (SPEI) between 2001-2014 in the western United States. The SPEI index was developed by Sergio M. Vicente-Serrano and coauthors (https://spei.csic.es/index.html). Source evapotranspiration and precipitation data were generated by gridMET (http://www.climatologylab.org/gridmet.html).
Results from investigating changes in streamflow seasonality associated with hydroclimatic variability in the north-central United States among three discrete temporal periods, 1946–2020
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This data release contains results of a study investigating changes in streamflow seasonality associated with hydroclimatic variability in the north-central United States, including nine States (Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin). Peak-flow records from unregulated U.S. Geological Survey streamgages were used to evaluate changes in streamflow seasonality over 75-, 50-, and 30-year trend periods through water year 2020. The streamgages in each of the nine states used in the analysis and the results of the seasonal characteristics and statistical analyses are provided in tabular form (in csv file format) in file "Results.zip" under "Attached Files" below.
Results from investigating changes in streamflow seasonality associated with hydroclimatic variability in the north-central United States among three discrete temporal periods, 1946–2020
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This data release contains results of a study investigating changes in streamflow seasonality associated with hydroclimatic variability in the north-central United States, including nine States (Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin). Peak-flow records from unregulated U.S. Geological Survey streamgages were used to evaluate changes in streamflow seasonality over 75-, 50-, and 30-year trend periods through water year 2020. The streamgages in each of the nine states used in the analysis and the results of the seasonal characteristics and statistical analyses are provided in tabular form (in csv file format) in file "Results.zip" under "Attached Files" below.
Semiarid grasslands and extreme precipitation events: Do experimental results scale to the landscape?
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,This dataset contains growing season precipitation data collected from an extensive rain gauge network with thirty catch cans distributed on the USDA-Central Plains Experimental Range (CPER) in Nunn, Colorado from 1982-2013. The CPER is a site in the USDA Long-term Agroecosystem Research Network. These data provide records of long-term observations, which can be applied to assess responses to naturally occurring deluges across the 62.7 km2 CPER in NE Colorado, a SGS ecosystem with significant spatial variation in precipitation received during the growing season, soils and grazing management, and where several small-scale deluge experiments have been conducted.,,
Mean annual runoff, precipitation, and evapotranspiration in the glaciated northeastern United States, 1951-80
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Two maps, compiled at 1:1,000,000 scale, depict mean annual runoff, precipitation, and evapotranspiration in the part of the United States east of Cleveland, Ohio and north of the southern limit of glaciation. The maps are mutually consistent in that runoff equals precipitation minus evapotranspiration everywhere. The runoff map is based on records of streamflow from 503 watersheds in the United States and southernmost Canada, adjusted to represent 1951-80 and supplemented by records of precipitation at 459 stations. Precipitation at each station was partitioned into point estimates of runoff and evapotranspiration, which were constrained such that the evapotranspiration estimates varied smoothly across the region and decreased with increasing latitude and altitude, and the runoff estimates were consistent with measured runoff from nearby watersheds. A point estimate of runoff was allowed to equal mean runoff in a nearby watershed, or to be somewhat higher (or lower) if a compensating departure from mean watershed runoff could be inferred in distant parts of the watershed on the basis of altitude or regional trends. Then, precipitation contours were drawn to parallel runoff contours but differ from them by the magnitude of nearby estimates of evapotranspiration. These maps may slightly underrepresent mean precipitation and evapotranspiration in areas of high relief because most precipitation stations in such areas are in valleys. These 3 coverages were used to produce Open-File Report 96-395. Additional information about methodology can be found in this report
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