Michelle M. Irizarry-Ortiz 20250301 spreadsheet https://doi.org/10.5066/P13VA3HV 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. The primary purpose of this data release is to provide water-resource researchers and managers with information on the performance of various gridded precipitation datasets over six CONUS regions. This project is a cooperative effort between the USGS, USACE, and FEMA. 19810101 20131231 ground condition Complete None planned -125.0000 -65.0000 50.0000 24.0000 ISO 19115 Topic Category climatologyMeteorologyAtmosphere None extremes precipitation precipitation extremes Delaware River Iowa River Puget Sound Red River of the North Trinity River Upper Colorado River USGS Thesaurus precipitation (atmospheric) storms atmospheric and climatic processes statistical analysis USGS Metadata Identifier USGS:6752157bd34e5c4500cf4363 Common geographic areas Puget Sound Upper Colorado None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Michelle M. Irizarry-Ortiz U.S. Geological Survey, SOUTHEAST REGION Hydrologist mailing and physical

12703 Research Parkway Suite 200

Orlando FL 32826 US 407-803-5533 407-803-5501 mirizarry-ortiz@usgs.gov Prepared by the U.S. Geological Survey, Caribbean-Florida Water Science Center, Orlando in cooperation with the South Florida Water Management District. Microsoft Windows 10 Enterprise; Microsoft Excel Version 2008 The main source of uncertainty is related to the effectiveness of the GHCNd gages to accurately capture different forms of precipitation, especially snowfall during windy conditions. No formal logical accuracy tests were conducted. The dataset is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. No formal positional accuracy tests were conducted. No formal positional accuracy tests were conducted. Slivinski, L.C., Compo, G.P., Whitaker, J.S., and others 2019 tabular digital data https://doi.org/10.1002/qj.3598 https://www.psl.noaa.gov/thredds/catalog/Datasets/20thC_ReanV3/accumsSI/catalog.html Digital and/or Hardcopy 1981 2013 publication date 20CRV3 precip 20CRV3 is one of the the gridded precipitation datasets evaluated. Fall, G., Kitzmiller, D., Pavlovic, and others 2023 tabular digital data https://doi.org/10.1111/1752-1688.13143 https://www.weather.gov/media/owp/operations/aorc_v1_1_methods.pdf https://hydrology.nws.noaa.gov/pub/AORC/V1.1/ Digital and/or Hardcopy 1981 2013 publication date AORC precip AORC is one of the the gridded precipitation datasets evaluated. Rasmussen, R.M., Chen, F., Liu, C.H., and others 2023 tabular digital data https://doi.org/10.1175/BAMS-D-21-0326.1 https://doi.org/10.5066/P9PHPK4F Digital and/or Hardcopy 1981 2013 publication date CONUS404 precip CONUS404 is one of the the gridded precipitation datasets evaluated. Zhang, Y., Grimm, J., Cabell, R., and others 2024 tabular digital data https://doi.org/10.5066/P9JE61P7 Digital and/or Hardcopy 1981 2013 publication date CONUS404BA precip CONUS404BA is one of the the gridded precipitation datasets evaluated. Hersbach, H., Bell, B., Berrisford, P., and others 2024 tabular digital data https://doi.org/10.1002/qj.3803 https://rda.ucar.edu/datasets/ds633-0/ Digital and/or Hardcopy 1981 2013 publication date ERA5 precip ERA5 is one of the the gridded precipitation datasets evaluated. Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., and others 2021 tabular digital data https://doi.org/10.5194/essd-13-4349-2021 https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY Digital and/or Hardcopy 1981 2013 publication date ERA5-Land precip ERA5-Land is one of the the gridded precipitation datasets evaluated. Livneh, B., Bohn, T.J., Pierce, D.W., and others 2015 tabular digital data https://doi.org/10.1038/sdata.2015.42 https://doi.org/10.7289/v5x34vf6 https://www.ncei.noaa.gov/thredds-ocean/catalog/ncei/archive/data/0129374/catalog.html Digital and/or Hardcopy 1981 2013 publication date Livneh precip Livneh is one of the the gridded precipitation datasets evaluated. Pierce, D. W., Su, L., Cayan, D. R., and others 2015 tabular digital data https://doi.org/10.1175/JHM-D-20-0212.1 https://cirrus.ucsd.edu/~pierce/nonsplit_precip/precip/ Digital and/or Hardcopy 1981 2013 publication date Livneh-unsplit precip Livneh-unsplit is one of the the gridded precipitation datasets evaluated. Christopher Daly, Michael Halbleib, Joseph I. Smith, Wayne P. Gibson, Matthew K. Doggett, George H. Taylor, Jan Curtis, and Phillip P. Pasteris 2008 tabular digital data https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.1688 https://prism.oregonstate.edu/ https://prism.oregonstate.edu/downloads/ Digital and/or Hardcopy 1981 2013 publication date PRISM precip PRISM is one of the the gridded precipitation datasets evaluated. Lin, Y., and Mitchell, K.E. 2005 tabular digital data https://ams.confex.com/ams/pdfpapers/83847.pdf https://mesonet.agron.iastate.edu/onsite/stage4 Digital and/or Hardcopy 1998 2013 publication date StageIV precip StageIV is one of the the gridded precipitation datasets evaluated. Menne, M.J., I. Durre, R.S. Vose, and others 2005 tabular digital data https://doi.org/10.1175/JTECH-D-11-00103.1 http://www1.ncdc.noaa.gov/pub/data/ghcn/daily/by_year/ Digital and/or Hardcopy 1981 2013 publication date GHCNd data GHCNd is the gage-precipitation dataset against which the gridded precipitation datasets are evaluated. Downloaded GHCNd precipitation data for stations within each of the six pilot regions with a period of record of at least 10 years (values may be missing during the 10 years) and reporting times between 0900 and 1500 Coordinated Universal Time (UTC). GHCNd data 20231114 Only GHCNd stations with at least 3,653 days of valid data (10 years of values) were retained and the number of years of available data (nyrs) was determined by dividing the number of days with available data by 365.25. Multi-day rolling sums of daily precipitation were computed for each station for each duration of interest (1, 2, 3, 5, 7, 10, and 14 days). Then, the magnitude and dates of the top 3*nyrs multi-day events (which represent the top ~1% of events) were extracted at each station for each duration. GHCNd data 20231114 For each dataset, downloaded netCDF files with gridded hourly precipitation data for each of the six pilot regions. 20CRV3 precip AORC precip CONUS404 precip CONUS404BA precip ERA5 precip ERA5-Land precip StageIV precip 20240625 Computed noon to noon UTC daily precipitation totals from hourly precipitation data for the datasets for which hourly data was downloaded. This allowed for better temporal alignment with precipitation data at GHCNd stations, which were only downloaded for stations with reporting times between 0900 and 1500 UTC. 20CRV3 precip AORC precip CONUS404 precip CONUS404BA precip ERA5 precip ERA5-Land precip StageIV precip 20240625 Downloaded netCDF files with gridded daily precipitation data for each of the six pilot regions. PRISM and Livneh-unsplit assign any 24-hour precipitation totals to the day when it was reported regardless of the reporting time. Livneh pro-rates the data between the day where the 24-hour total precipitation was reported and the previous day based on the reporting time. Livneh precip Livneh-unsplit precip PRISM precip 20240228 Daily precipitation data were extracted for the closest grid cell to each of the GHCNd stations in the six pilot regions from each of the gridded precipitation datasets. 20240621 Performed cross-correlation analysis between the GHCNd station data and the corresponding precipitation from the closest grid cell from each dataset. The lag of maximum cross-correlation was selected among lags of -1 (gridded data lags station data), 0, and 1 (gridded data leads station data). The gridded precipitation data were shifted by the lag of maximum cross-correlation to better align it with the station data. 20240621 Days with missing station or gridded data, and days with station data of bad quality were excluded from further analysis. Multi-day rolling sums of daily precipitation over the durations of interest (1, 2, 3, 5, 7, 10, and 14 days) were computed. Then for each duration and station, multi-day precipitation from each gridded dataset that is closest in magnitude to the selected non-overlapping 3*nyrs at the corresponding GHCNd station (within a search window of the date identified at the GHCNd station for each extreme event) were extracted. The search window was limited to within +/- 1 day from the dates of the non-overlapping 3*nyrs multi-day extreme events identified for the GHCNd station. This accounts for small shifts in the timing of multi-day extreme events at the stations. Then for each duration, the extreme precipitation data for all GHCNd stations within a region were concatenated together into a single vector. Similarly, the gridded multi-day precipitation data corresponding to the station locations were concatenated together into individual vectors for each duration and gridded dataset. 20240621 Finally, the Pearson correlation coefficient, the standard deviation ratio, the centered root-mean-square difference, the percent bias, and the Kling-Gupta efficiency (KGE) metric were computed between the vector with the concatenated station data and the vector with the corresponding gridded data for each gridded dataset and for each duration of interest in each of the six regions. 20240621 1981-2013 Microsoft Excel Worksheet with Table 1. Normalized Taylor diagram statistics for multi-day extreme precipitation in each pilot region for the period 1981–2013. USGS Dataset Gridded precipitation dataset Producer Defined Livneh-unsplit Livneh-unsplit Producer defined 20CRV3 20CRV3 Producer defined ERA5 ERA5 Producer defined PRISM PRISM Producer defined CONUS404 CONUS404 Producer defined AORC AORC Producer defined Livneh Livneh Producer defined ERA5-Land ERA5-Land Producer defined CONUS404BA CONUS404BA Producer defined Duration Duration of the extreme precipitation event (days) Producer Defined 1 14 Delaware River, R Pearson correlation coefficient for the Delaware River for the Dataset and Season specified Producer Defined 0.247 0.919 Delaware River, SD ratio Standard deviation ratio for the Delaware River for the Dataset and Season specified Producer Defined 0.767 1.555 Delaware River, RMSD Centered root-mean-square difference for the Delaware River for the Dataset and Season specified Producer Defined 0.405 1.448 Delaware River, Percent bias Percent bias for the Delaware River for the Dataset and Season specified Producer Defined -54.767 -2.159 Delaware River, KGE Kling-Gupta efficiency for the Delaware River for the Dataset and Season specified Producer Defined 0.131 0.912 Iowa River, R Pearson correlation coefficient for the Iowa River for the Dataset and Season specified Producer Defined 0.032 0.963 Iowa River, SD ratio Standard deviation ratio for the Iowa River for the Dataset and Season specified Producer Defined 0.511 1.664 Iowa River, RMSD Centered root-mean-square difference for the Iowa River for the Dataset and Season specified Producer Defined 0.269 1.785 Iowa River, Percent bias Percent bias for the Iowa River for the Dataset and Season specified Producer Defined -70.714 -1.854 Iowa River, KGE Kling-Gupta efficiency for the Iowa River for the Dataset and Season specified Producer Defined -0.295 0.953 Puget Sound, R Pearson correlation coefficient for Puget Sound for the Dataset and Season specified Producer Defined 0.375 0.985 Puget Sound, SD ratio Standard deviation ratio for Puget Sound for the Dataset and Season specified Producer Defined 0.41 0.995 Puget Sound, RMSD Centered root-mean-square difference for Puget Sound for the Dataset and Season specified Producer Defined 0.174 0.928 Puget Sound, Percent bias Percent bias for Puget Sound for the Dataset and Season specified Producer Defined -56.945 -1.661 Puget Sound, KGE Kling-Gupta efficiency for Puget Sound for the Dataset and Season specified Producer Defined -0.026 0.957 Red River of the North, R Pearson correlation coefficient for the Red River of the North for the Dataset and Season specified Producer Defined 0.072 0.92 Red River of the North, SD ratio Standard deviation ratio for the Red River of the North for the Dataset and Season specified Producer Defined 0.578 1.588 Red River of the North, RMSD Centered root-mean-square difference for the Red River of the North for the Dataset and Season specified Producer Defined 0.408 1.79 Red River of the North, Percent bias Percent bias for the Red River of the North for the Dataset and Season specified Producer Defined -66.185 -1.853 Red River of the North, KGE Kling-Gupta efficiency for the Red River of the North for the Dataset and Season specified Producer Defined -0.216 0.91 Trinity River, R Pearson correlation coefficient for the Trinity River for the Dataset and Season specified Producer Defined 0.073 0.897 Trinity River, SD ratio Standard deviation ratio for the Trinity River for the Dataset and Season specified Producer Defined 0.577 1.539 Trinity River, RMSD Centered root-mean-square difference for the Trinity River for the Dataset and Season specified Producer Defined 0.469 1.507 Trinity River, Percent bias Percent bias for the Trinity River for the Dataset and Season specified Producer Defined -71.668 -3.874 Trinity River, KGE Kling-Gupta efficiency for the Trinity River for the Dataset and Season specified Producer Defined -0.201 0.875 Upper Colorado River, R Pearson correlation coefficient for the Upper Colorado River for the Dataset and Season specified Producer Defined 0.184 0.945 Upper Colorado River, SD ratio Standard deviation ratio for the Upper Colorado River for the Dataset and Season specified Producer Defined 0.544 1.193 Upper Colorado River, RMSD Centered root-mean-square difference for the Upper Colorado River for the Dataset and Season specified Producer Defined 0.34 1.102 Upper Colorado River, Percent bias Percent bias for the Upper Colorado River for the Dataset and Season specified Producer Defined -65.584 -2.851 Upper Colorado River, KGE Kling-Gupta efficiency for the Upper Colorado River for the Dataset and Season specified Producer Defined -0.14 0.922 1998-2013 Microsoft Excel Worksheet with Table 2. Normalized Taylor diagram statistics for multi-day extreme precipitation in each pilot region for the period 1998–2013. USGS Dataset Gridded precipitation dataset Producer Defined Livneh-unsplit Livneh-unsplit Producer defined 20CRV3 20CRV3 Producer defined ERA5 ERA5 Producer defined PRISM PRISM Producer defined CONUS404 CONUS404 Producer defined AORC AORC Producer defined Livneh Livneh Producer defined ERA5-Land ERA5-Land Producer defined CONUS404BA CONUS404BA Producer defined StageIV StageIV Producer defined Duration Duration of the extreme precipitation event (days) Producer Defined 1 14 Delaware River, R Pearson correlation coefficient for the Delaware River for the Dataset and Season specified Producer Defined 0.335 0.945 Delaware River, SD ratio Standard deviation ratio for the Delaware River for the Dataset and Season specified Producer Defined 0.781 1.434 Delaware River, RMSD Centered root-mean-square difference for the Delaware River for the Dataset and Season specified Producer Defined 0.335 1.361 Delaware River, Percent bias Percent bias for the Delaware River for the Dataset and Season specified Producer Defined -54.79 -1.55 Delaware River, KGE Kling-Gupta efficiency for the Delaware River for the Dataset and Season specified Producer Defined 0.216 0.937 Iowa River, R Pearson correlation coefficient for the Iowa River for the Dataset and Season specified Producer Defined 0.061 0.96 Iowa River, SD ratio Standard deviation ratio for the Iowa River for the Dataset and Season specified Producer Defined 0.442 1.674 Iowa River, RMSD Centered root-mean-square difference for the Iowa River for the Dataset and Season specified Producer Defined 0.283 1.675 Iowa River, Percent bias Percent bias for the Iowa River for the Dataset and Season specified Producer Defined -72.123 -1.659 Iowa River, KGE Kling-Gupta efficiency for the Iowa River for the Dataset and Season specified Producer Defined -0.309 0.956 Puget Sound, R Pearson correlation coefficient for Puget Sound for the Dataset and Season specified Producer Defined 0.106 0.976 Puget Sound, SD ratio Standard deviation ratio for Puget Sound for the Dataset and Season specified Producer Defined 0.351 0.982 Puget Sound, RMSD Centered root-mean-square difference for Puget Sound for the Dataset and Season specified Producer Defined 0.225 1.028 Puget Sound, Percent bias Percent bias for Puget Sound for the Dataset and Season specified Producer Defined -55.743 -2.765 Puget Sound, KGE Kling-Gupta efficiency for Puget Sound for the Dataset and Season specified Producer Defined -0.186 0.947 Red River of the North, R Pearson correlation coefficient for the Red River of the North for the Dataset and Season specified Producer Defined -0.039 0.949 Red River of the North, SD ratio Standard deviation ratio for the Red River of the North for the Dataset and Season specified Producer Defined 0.61 1.683 Red River of the North, RMSD Centered root-mean-square difference for the Red River of the North for the Dataset and Season specified Producer Defined 0.32 1.875 Red River of the North, Percent bias Percent bias for the Red River of the North for the Dataset and Season specified Producer Defined -66.877 -1.047 Red River of the North, KGE Kling-Gupta efficiency for the Red River of the North for the Dataset and Season specified Producer Defined -0.185 0.947 Trinity River, R Pearson correlation coefficient for the Trinity River for the Dataset and Season specified Producer Defined 0.032 0.882 Trinity River, SD ratio Standard deviation ratio for the Trinity River for the Dataset and Season specified Producer Defined 0.603 1.469 Trinity River, RMSD Centered root-mean-square difference for the Trinity River for the Dataset and Season specified Producer Defined 0.491 1.472 Trinity River, Percent bias Percent bias for the Trinity River for the Dataset and Season specified Producer Defined -71.065 -4.828 Trinity River, KGE Kling-Gupta efficiency for the Trinity River for the Dataset and Season specified Producer Defined -0.174 0.867 Upper Colorado River, R Pearson correlation coefficient for the Upper Colorado River for the Dataset and Season specified Producer Defined 0.244 0.945 Upper Colorado River, SD ratio Standard deviation ratio for the Upper Colorado River for the Dataset and Season specified Producer Defined 0.533 1.215 Upper Colorado River, RMSD Centered root-mean-square difference for the Upper Colorado River for the Dataset and Season specified Producer Defined 0.335 1.104 Upper Colorado River, Percent bias Percent bias for the Upper Colorado River for the Dataset and Season specified Producer Defined -63.241 -3.903 Upper Colorado River, KGE Kling-Gupta efficiency for the Upper Colorado River for the Dataset and Season specified Producer Defined -0.076 0.925 GS ScienceBase U.S. Geological Survey mailing address

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Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P13VA3HV None 20250929 Michelle M. Irizarry-Ortiz U.S. Geological Survey, SOUTHEAST REGION Hydrologist mailing and physical

12703 Research Parkway Suite 200

Orlando FL 32826 US 407-803-5533 407-803-5501 mirizarry-ortiz@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999