Climate change information simulated by global climate models is downscaled using statistical methods to translate spatially course regional projections to finer resolutions needed by researchers and managers to assess local climate impacts. Several statistical downscaling methods have been developed over the past fifteen years, resulting in multiple datasets derived by different methods. We apply a simple monthly water-balance model (MWBM) to demonstrate how the differences among these datasets result in disparate projections of snow loss and future changes in runoff. We apply the MWBM to six statistically downscaled datasets for 14 general circulation models (GCMs) from the Climate Model Intercomparison Program Phase 5 (CMIP5) for the RCP 8.5 emission scenario (1950 - 2099). The statistically downscaled datasets are as follows: BCCA: Bias Corrected Constructed Analogs (Reclamation, 2013) BCSD-C: Bias Corrected Spatial Disaggregation (Reclamation, 2013) BCSD-F: Bias Corrected Spatial Disaggregation (Thrasher et al., 2013) LOCA: Localized Constructed Analogs (Pierce et al., 2014) MACA-L: Multivariate Adaptive Constructed Analogs (Abatzoglou & Brown, 2012, bias corrected by Livneh et al., 2013) MACA-M: Multivariate Adaptive Constructed Analogs (Abatzoglou & Brown, 2012, bias corrected by METDATA, Abatzoglou, 2013) Users interested in the downscaled temperature and precipitation files are referred to the dataset home pages: BCCA, BCSD-C: http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html BCSD-F: https://cds.nccs.nasa.gov/nex/ LOCA: http://loca.ucsd.edu/ MACA-L, MACA-M: http://maca.northwestknowledge.net The GCMs are the following: bcc-csm1-1, CanESM2, CNRM-CM5, CSIRO-Mk3-6-0, GFDL-ESM2G, GFDL-ESM2M, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC-ESM, MIROC-ESM-CHEM, MIROC5, MRI-CGCM3, NorESM1-M

This archive contains daily statistically downscaled climate projections and simulated land surface water and energy fluxes for the western United States and southern British Columbia at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references). The downscaling used is the Modified Delta approach (see Littell et al. 2011), based on 10 models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Documentation home: http://cses.washington.edu/cig/data/wus.shtml Note that time-stamps on these data are not in the future. See the statistical downscaling chapter from this report for more information. http://warm.atmos.washington.edu/2860/r7climate/study_report/CBCCSP_chap4_gcm_final.pdf Reference: This research was sponsored by a grant from the Department of the Interior, USGS NW Climate Science Center, a multi-institution DOI-funded project located at the University of Washington, Oregon State University, and the University of Idaho. We also acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy.

This archive contains daily statistically downscaled climate projections and simulated land surface water and energy fluxes for the western United States and southern British Columbia at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references). The downscaling used is the Modified Delta approach (see Littell et al. 2011), based on 10 models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Documentation home: http://cses.washington.edu/cig/data/wus.shtml Note that time-stamps on these data are not in the future. See the statistical downscaling chapter from this report for more information. http://warm.atmos.washington.edu/2860/r7climate/study_report/CBCCSP_chap4_gcm_final.pdf Reference: This research was sponsored by a grant from the Department of the Interior, CIDA NW Climate Science Center, a multi-institution DOI-funded project located at the University of Washington, Oregon State University, and the University of Idaho. We also acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy.

This data release supports the study by Sexstone and others (2019) and contains simulation output from a hydrological modeling experiment using a specific calibration of the conterminous United States (CONUS) application of the Precipitation-Runoff Modeling System (PRMS) (Hay, 2019) as implemented in the National Hydrologic Model (NHM) infrastructure (Regan and others, 2018). The by hydrologic response unit (byHRU) calibrated, baseline version of the NHM-PRMS (Hay, 2019) was used to evaluate the sensitivity of simulated runoff to the representation of snow depletion curves (SDCs) within the NHM-PRMS across the CONUS. The model experiment consisted of seven NHM-PRMS model simulations using the calibrated NHM-PRMS model parameters from Hay (2019). For each of the model simulations, the calibrated SDCs (Hay, 2019) were replaced with a single derived SDC derived based on a lognormal probability distribution function and assigned snow water equivalent coefficient of variation (CV) value. The seven CV values ranged from 0.1 to 2.0. Each of the simulations were completed at a daily time-step over a 14-year period (water years 2003 – 2016). Detailed methods and results are provided in Sexstone and others (2019). The SDC parameters used in this model experiment are provided by this data release. Furthermore, the attached NHM-PRMS variable table lists the selected output variables included in this data release. The individual *.csv files follow a naming convention of nhru_variable name_CVX.X.csv. The variable names included are defined further in NHM-PRMS variable table. The “CVX.X” denotes the CV value that was used to derive the SDC for the model simulation. The structure of each output file includes a header line which labels the columns by the HRU identification number with each row providing daily outputs. An inventory of the files provided within this data release can be found below. This research used resources provided by the Core Science Analytics, Synthesis, & Libraries (CSASL) Advanced Research Computing (ARC) group at the U.S. Geological Survey. Inventory of data release: NHM-PRMS_SDC_study.xml (1 .xml file): FGDC-compliant metadata file for the data release files. SDC_params.csv (1 .csv file): Table of the seven snow depletion curve parameterizations used in the modeling study. NHM-PRMS_variable_table.docx (1 .docx file): Table describing the selected NHM-PRMS output variables provided in this data release. nhru_gwres_flow_CVX.X.csv (7 .csv files): NHM-PRMS groundwater discharge output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was used in the model simulation. nhru_hru_actet_CVX.X.csv (7 .csv files): NHM-PRMS actual evapotranspiration output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was used in the model simulation. nhru_hru_outflow_CVX.X.csv (7 .csv files): NHM-PRMS total flow output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was used in the model simulation. nhru_hru_ppt_CVX.X.csv (7 .csv files): NHM-PRMS precipitation output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was used in the model simulation. nhru_pkwater_equiv_CVX.X.csv (7 .csv files): NHM-PRMS snow water equivalent output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was used in the model simulation. nhru_potet_CVX.X.csv (7 .csv files): NHM-PRMS potential evapotranspiration output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was

This data release supports the study by Sexstone and others (2019) and contains simulation output from a hydrological modeling experiment using a specific calibration of the conterminous United States (CONUS) application of the Precipitation-Runoff Modeling System (PRMS) (Hay, 2019) as implemented in the National Hydrologic Model (NHM) infrastructure (Regan and others, 2018). The by hydrologic response unit (byHRU) calibrated, baseline version of the NHM-PRMS (Hay, 2019) was used to evaluate the sensitivity of simulated runoff to the representation of snow depletion curves (SDCs) within the NHM-PRMS across the CONUS. The model experiment consisted of seven NHM-PRMS model simulations using the calibrated NHM-PRMS model parameters from Hay (2019). For each of the model simulations, the calibrated SDCs (Hay, 2019) were replaced with a single derived SDC derived based on a lognormal probability distribution function and assigned snow water equivalent coefficient of variation (CV) value. The seven CV values ranged from 0.1 to 2.0. Each of the simulations were completed at a daily time-step over a 14-year period (water years 2003 – 2016). Detailed methods and results are provided in Sexstone and others (2019). The SDC parameters used in this model experiment are provided by this data release. Furthermore, the attached NHM-PRMS variable table lists the selected output variables included in this data release. The individual *.csv files follow a naming convention of nhru_variable name_CVX.X.csv. The variable names included are defined further in NHM-PRMS variable table. The “CVX.X” denotes the CV value that was used to derive the SDC for the model simulation. The structure of each output file includes a header line which labels the columns by the HRU identification number with each row providing daily outputs. An inventory of the files provided within this data release can be found below. This research used resources provided by the Core Science Analytics, Synthesis, & Libraries (CSASL) Advanced Research Computing (ARC) group at the U.S. Geological Survey. Inventory of data release: NHM-PRMS_SDC_study.xml (1 .xml file): FGDC-compliant metadata file for the data release files. SDC_params.csv (1 .csv file): Table of the seven snow depletion curve parameterizations used in the modeling study. NHM-PRMS_variable_table.docx (1 .docx file): Table describing the selected NHM-PRMS output variables provided in this data release. nhru_gwres_flow_CVX.X.csv (7 .csv files): NHM-PRMS groundwater discharge output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was used in the model simulation. nhru_hru_actet_CVX.X.csv (7 .csv files): NHM-PRMS actual evapotranspiration output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was used in the model simulation. nhru_hru_outflow_CVX.X.csv (7 .csv files): NHM-PRMS total flow output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was used in the model simulation. nhru_hru_ppt_CVX.X.csv (7 .csv files): NHM-PRMS precipitation output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was used in the model simulation. nhru_pkwater_equiv_CVX.X.csv (7 .csv files): NHM-PRMS snow water equivalent output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was used in the model simulation. nhru_potet_CVX.X.csv (7 .csv files): NHM-PRMS potential evapotranspiration output for water years 2002 through 2016. Each of the 7 .csv files are labeled with the coefficient of variation (CV) value for the snow depletion curve that was

This archive contains daily dynamically downscaled climate projections and simulated land surface water and energy fluxes for the northwestern United States and part of southern British Columbia (N of about 38 degrees N and W of about 105 degrees W) at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references). The downscaling is based on the Weather Research and Forecasting (WRF) regional model. WRF was run using boundary conditions from the ECHAM5 global model and the SRES A1B emissions scenario, one of the models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Climate simulations were performed using an inner grid resolution of 12-km over the region and a 100-year (1970-2070) simulation. Documentation home: http://cses.washington.edu/cig/data/wus.shtml Reference: This research was sponsored by a grant from the Department of the Interior, CIDA NW Climate Science Center, a multi-institution DOI-funded project located at the University of Washington, Oregon State University, and the University of Idaho. We also acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy.

The U.S. Geological Survey (USGS), in cooperation with the New Hampshire Department of Environmental Services (NHDES) and the Department of Health and Human Services (NHDHHS), has developed data to assess the effects of short- and long-term climate change on hydrology in New Hampshire. A USGS Scientific Investigations Report (SIR) documents the datasets developed by the USGS. The data presented in this data release represent future hydrologic climate projections developed using a calibrated USGS Precipitation Runoff Modeling System (PRMS) model using precipitation and air temperature inputs from five general circulation models (GCMs) for two future climate scenarios for the period 2009 to 2099. The data sets include simulated current and future streamflow, groundwater recharge, and snowfall output datasets. Average monthly streamflow time series data sets are provided for 21 streamgages in New Hampshire, 14 of which also provide daily streamflow time series, Average monthly groundwater recharge and snowfall time series for the same reference time frame and future time frame are also provided for each of the 467 hydrologic response units (HRUs) that compose the model.

The U.S. Geological Survey (USGS), in cooperation with the New Hampshire Department of Environmental Services (NHDES) and the Department of Health and Human Services (NHDHHS), has developed data to assess the effects of short- and long-term climate change on hydrology in New Hampshire. A USGS Scientific Investigations Report (SIR) documents the datasets developed by the USGS. The data presented in this data release represent future hydrologic climate projections developed using a calibrated USGS Precipitation Runoff Modeling System (PRMS) model using precipitation and air temperature inputs from five general circulation models (GCMs) for two future climate scenarios for the period 2009 to 2099. The data sets include simulated current and future streamflow, groundwater recharge, and snowfall output datasets. Average monthly streamflow time series data sets are provided for 21 streamgages in New Hampshire, 14 of which also provide daily streamflow time series, Average monthly groundwater recharge and snowfall time series for the same reference time frame and future time frame are also provided for each of the 467 hydrologic response units (HRUs) that compose the model.

This collection contains three statistically downscaled time series (datasets) for the Red River Basin (South Central U.S.), and one dataset used as historical observations. In particular, three different Global Climate Models (MPI-ESM-LR, CCSM4 and MIROC5) were downscaled using three different quantile mapping methods (CDFt, EDQM and BCQM). We do not recommend the use of the BCQM method, as the CDFt method is considered an improvement of it. The datasets created using the BCQM method are published as a demonstration of the risks of using flawed methods. The variables of interest are: daily maximum and minimum temperature, and daily precipitation. The spatial resolution of the datasets in the collection is 1/10th of a degree (~ 11 km). The statistically downscaled datasets include local climate projections of three different Representative Concentration Pathways (RCPs - 2.6, 4.5 and 8.5) for the 21st century (2006 – 2099), and for the historical (1961-2005) period. The project was funded by the USGS – South Central Climate Science Center.

This collection contains three statistically downscaled time series (datasets) for the Red River Basin (South Central U.S.), and one dataset used as historical observations. In particular, three different Global Climate Models (MPI-ESM-LR, CCSM4 and MIROC5) were downscaled using three different quantile mapping methods (CDFt, EDQM and BCQM). We do not recommend the use of the BCQM method, as the CDFt method is considered an improvement of it. The datasets created using the BCQM method are published as a demonstration of the risks of using flawed methods. The variables of interest are: daily maximum and minimum temperature, and daily precipitation. The spatial resolution of the datasets in the collection is 1/10th of a degree (~ 11 km). The statistically downscaled datasets include local climate projections of three different Representative Concentration Pathways (RCPs - 2.6, 4.5 and 8.5) for the 21st century (2006 – 2099), and for the historical (1961-2005) period. The project was funded by the CIDA – South Central Climate Science Center.