Michelle M. Irizarry-Ortiz 20240530 spreadsheet https://doi.org/10.5066/P9Q3LEIL The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2020-59 (centered in 2040) or to the period 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided that tabulates best models for each CMIP6 downscaled climate dataset. Best models were identified based on how well the models capture the climatology and interannual variability of four climate extreme indices using the Model Climatology Index (MCI) and the Model Variability Index (MVI) of Srivastava and others (2020). The four indices consist of annual maxima consecutive precipitation for durations of 1, 3, 5, and 7 days compared against the same indices computed based on the PRISM and SFWMD gridded precipitation datasets for five climate regions: climate region 1 in Northwest Florida, 2 in North Florida, 3 in North Central Florida, 4 in South Central Florida, and climate region 5 in South Florida. The PRISM dataset is based on the Parameter-elevation Relationships on Independent Slopes Model interpolation method of Daly and others (2008). The South Florida Water Management District’s (SFWMD) precipitation super-grid is a gridded precipitation dataset developed by modelers at the agency for use in hydrologic modeling (SFWMD, 2005). This dataset is considered by the SFWMD as the best available gridded rainfall dataset for south Florida and was used in addition to PRISM to identify best models in the South Central and South Florida climate regions. Best models were selected based on MCI and MVI evaluated within each individual downscaled dataset. Models were not compared across CMIP6 downscaled climate datasets due to all the best models belonging to a single downscaled climate dataset (LOCA2). The primary purpose of this table is to tabulate best models for each downscaled climate dataset and for all downscaled climate datasets considered together. This project is a cooperative effort between the U.S. Geological Survey (USGS) and the Florida Flood Hub for Applied Research and Innovation. 1981 2005 ground condition Complete None planned -87.643620946 -79.989351961 31.16271922 24.416352892 ISO 19115 Topic Category climatologyMeteorologyAtmosphere None extremes precipitation extremes Florida depth-duration-frequency Florida Flood Hub for Applied Research and Innovation USGS Thesaurus precipitation (atmospheric) USGS Metadata Identifier USGS:649f2ac3d34ef77fcb0421d2 Common geographic areas Florida 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 address

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 Florida Flood Hub for Applied Research and Innovation at the University of South Florida. Microsoft Windows 10 Enterprise; Microsoft Excel Version 2008 No formal accuracy tests were conducted. No formal logical accuracy tests were conducted. 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. Peter Gibson 20180309 application/service https://github.com/Peter-Gibson/climate/blob/CLIMATE-937/examples/CPC_ETCCDI_Wrapper.py https://github.com/Peter-Gibson/climate/blob/CLIMATE-937/examples/ETCCDI_precip.py Digital and/or Hardcopy 20180309 publication date Gibson code Original source of Python code used to compute climate extremes indices as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI; http://etccdi.pacificclimate.org/list_27_indices.shtml). Code downloaded on February 12, 2021. The Python code was evaluated to make sure it followed the ETCCDI climate extreme index definitions and cross-validated against the Climate Data Operators (CDO, https://code.mpimet.mpg.de/projects/cdo) ECA climate indices package and additional calculations in R software. The Python code was further modified to include additional indices and perform file looping. The Python code relies on Python bindings to CDO. 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/ Digital and/or Hardcopy 1981 2019 publication date PRISM precip PRISM is the gridded observed precipitation dataset that was used to derive the areal reduction factors (ARF) for climate regions in the state of Florida. South Florida Water Management District (SFWMD) 2005 publication West Palm Beach, Florida South Florida Water Management District (SFWMD) Contact the SFWMD directly for access to the SFWMD supergrid precipitation file. https://www.sfwmd.gov/sites/default/files/documents/sfwmm_final_121605.pdf Digital and/or Hardcopy 1914 2016 publication date SFWMD precip Best models determined based on evaluation of climate extreme indices against those computed based on the SFWMD supergrid gridded precipitation dataset. Abhishekh Srivastava, Richard Grotjahn, and Paul A. Ullrich 2020 publication The Netherlands Elsevier https://www.sciencedirect.com/science/article/pii/S2212094719302464 Digital and/or Hardcopy 2020 publication date Srivastava ref Methodology for selecting climate models based on how well they match the spatial patterns of the climatology and interannual variability of climate extreme indices computed from gridded observational precipitation datasets. Downloaded PRISM precipitation data for the state of Florida for the period 1981-2019 using the prism library in R. PRISM precip 20200731 Obtained SFWMD supergrid precipitation file from the SFWMD. SFWMD precip 20210325 Compute the four climate extreme indices of interest (annual maxima consecutive precipitation for durations of 1, 3, 5, and 7 days) for the PRISM and SFWMD supergrid precipitation grids for the period 1981-2005 using the Python code. PRISM precip SFWMD precip Gibson code 20210330 Compute the four climate extreme indices of interest (annual maxima consecutive precipitation for durations of 1, 3, 5, and 7 days) for each model in the NASA and LOCA2 downscaled climate datasets for the period 1981-2005 using the Python code. Gibson code 20230410 The Climate Data Operators (CDO, https://code.mpimet.mpg.de/projects/cdo) utility remapnn was used to remap the climate index files computed for the NASA and LOCA2 downscaled climate datasets to the grids of the PRISM and SFWMD supergrid datasets using nearest-neighbor interpolation. 20230410 For each model in the NASA and LOCA2 downscaled climate datasets, compute the Model Climatology Index (MCI) and the Model Variability Index (MVI) according to Srivastava and others (2020). The MCI and the MVI define how well a model captures the climatology and interannual variability of the four climate extreme indices compared to the two observational datasets (PRISM and the SFWMD supergrid precipitation datasets). Best models were selected based on MCI and MVI evaluated within each individual downscaled dataset. A model was considered to be among the best if it had a negative MCI and a negative MVI when compared to either the PRISM and SFWMD supergrid observational datasets. Models were not compared across CMIP6 downscaled climate datasets due to all the best models belonging to a single downscaled climate dataset (LOCA2). Srivastava ref 20230524 Best models according to MCI and MVI criteria applied to four climate extreme indices for dataset LOCA2 evaluated among LOCA2 models Microsoft Excel Worksheet Producer Defined GCM General Circulation Model. For more information see Tables 2 and 3 of Datasets_station_information.xlsx. Producer Defined General Circulation Models downscaled by the different downscaled climate datasets evaluated in this project. Table 2 of Datasets_station_information.xlsx Ensemble member Ensemble member downscaled for the given GCM. For more information see Tables 2 and 8 of Datasets_station_information.xlsx. Producer Defined Ensemble member downscaled for the given GCM. Table 8 of Datasets_station_information.xlsx. Climate region Climate region for which the GCM is considered a best model Producer Defined 1 Northwest Florida Producer defined 2 North Florida Producer defined 3 North Central Florida Producer defined 4 South Central Florida Producer defined 5 South Florida Producer defined Best models according to MCI and MVI criteria applied to four climate extreme indices for dataset NASA evaluated among NASA models Microsoft Excel Worksheet Producer Defined GCM General Circulation Model. For more information see Tables 2 and 4 of Datasets_station_information.xlsx. Producer Defined General Circulation Models downscaled by the different downscaled climate datasets evaluated in this project. Table 2 of Datasets_station_information.xlsx Ensemble member Ensemble member downscaled for the given GCM. For more information see Tables 2 and 9 of Datasets_station_information.xlsx. Producer Defined Ensemble member downscaled for the given GCM. Table 9 of Datasets_station_information.xlsx. Climate region Climate region for which the combination of GCM and BC dataset is considered a best model Producer Defined 1 Northwest Florida Producer defined 2 North Florida Producer defined 3 North Central Florida Producer defined 4 South Central Florida Producer defined 5 South Florida Producer defined U.S. Geological Survey - ScienceBase mailing and physical

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 USA 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/P9Q3LEIL None 20250826 Michelle M. Irizarry-Ortiz U.S. Geological Survey, SOUTHEAST REGION Hydrologist mailing address

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