The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

In 2024, Louisiana recorded 71.25 inches of precipitation. This was the highest precipitation within the 48 contiguous U.S. states that year. On the other hand, Nevada was the driest state, with only 9.53 inches of precipitation recorded. Precipitation across the United States Not only did Louisiana record the largest precipitation volume in 2024, but it also registered the highest precipitation anomaly that year, around 14.36 inches above the 1901-2000 annual average. In fact, over the last decade, rainfall across the United States was generally higher than the average recorded for the 20th century. Meanwhile, the driest states were located in the country's southwestern region, an area which – according to experts – will become even drier and warmer in the future. How does global warming affect precipitation patterns? Rising temperatures on Earth lead to increased evaporation which – ultimately – results in more precipitation. Since 1900, the volume of precipitation in the United States has increased at an average rate of 0.20 inches per decade. Nevertheless, the effects of climate change on precipitation can vary depending on the location. For instance, climate change can alter wind patterns and ocean currents, causing certain areas to experience reduced precipitation. Furthermore, even if precipitation increases, it does not necessarily increase the water availability for human consumption, which might eventually lead to drought conditions.

Q: Was the month drier or wetter than usual? A: Colors show where and by how much monthly precipitation totals differed from average precipitation for the same month from 1991-2020. Green areas were wetter than the 30-year average for the month and brown areas were drier. White and very light areas had monthly precipitation totals close to the long-term average. Q: Where do these measurements come from? A: Daily measurements of rain and snow come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments gather the data and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly total and plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). To calculate the percent of average precipitation values shown on these maps—also called precipitation anomalies—NCEI scientists take the total precipitation in each 5x5 km grid box for a single month and year, and divide it by its 1991-2020 average for the same month. Multiplying that number by 100 yields a percent of average precipitation. If the result is greater than 100%, the region was wetter than average. Less than 100% means the region was drier than usual. Q: What do the colors mean? A: Shades of brown show places where total precipitation was below the long-term average for the month. Areas shown in shades of green had more liquid water from rain and/or snow than they averaged from 1991 to 2020. The darker the shade of brown or green, the larger the difference from the average precipitation. White and very light areas show where precipitation totals were the same as or very close to the long-term average. Note that snowfall totals are reported as the amount of liquid water they produce upon melting. Thus, a 10-inch snowfall that melts to produce one inch of liquid water would be counted as one inch of precipitation. Q: Why do these data matter? A: Comparing an area’s recent precipitation to its long-term average can tell how wet or how dry the area is compared to usual. Knowing if an area is much drier or much wetter than usual can encourage people to pay close attention to on-the-ground conditions that affect daily life and decisions. People check maps like this to judge crop progress; monitor reservoir levels; consider if lawns and landscaping need water; and to understand the possibilities of flooding. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products; to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on climate data (NClimGrid) produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Total Precipitation NClimGrid Precipitation Normals References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions NCEI Monthly National Analysis Climate at a Glance - Data Information NCEI Climate Monitoring - All ProductsSource: https://www.climate.gov/maps-data/

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Download this data or get more information

Dataset consists of twelve monthly images for 1991-2020, available in small, large, broadcast media, full size zip, and KML archive formats. These images were derived from NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid).Description from Climate.gov:Q:How much rain and snow usually fall this month?A:Based on daily observations from 1991-2020, colors on the map show long-term average precipitation totals in 5x5 km grid cells for the month displayed. The darker the color, the higher the total precipitation.Q:Where do these measurements come from?A:Daily totals of rain and snow come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments gathered the data from 1991 to 2020 and submitted them to the National Centers for Environmental Information (NCEI). After scientists checked the quality of the data to omit any systematic errors, they calculated each station’s monthly total and plotted it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid).Q:What do the colors mean?A:White areas on the map received an average of zero measurable precipitation during the month from 1991-2020. Areas shown in the lightest green received a monthly average of less than one inch of water from rain or snow over the 30-year period. The darker the color on the map, the higher the average precipitation total for the month. Areas shown in dark blue received an average of eight or more inches of water that fell as either rain or snow. Note that snowfall totals are reported as the amount of liquid water they produce upon melting. Thus, a 10-inch snowfall that melts to produce one inch of liquid water would be counted as one inch of precipitation.Q:Why do these data matter?A:Understanding these values provides insight into the “normal” conditions for a month. This type of information is widely used across an array of planning activities, from designing energy distribution networks, to the timing of crop and plant emergence, to choosing the right place and time for recreational activities.Q:How did you produce these snapshots?A:Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on climate data (NClimGrid) produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps.Additional informationThe data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources:NClimGrid Precipitation Normals ReferencesNOAA Monthly U.S. Climate Gridded Dataset (NClimGrid)NOAA Monthly U.S. Climate Divisional Database (NClimDiv)Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions)NCEI Monthly National Analysis)Climate at a Glance - Data Information)NCEI Climate Monitoring - All Products

Mean Annual Precipitation [mm/year] across South America using the Tropical Rainfall Measuring Mission (TRMM 3B43) dataset.

This map layer shows polygons of average annual precipitation in the contiguous United States, for the climatological period 1961-1990. Parameter-elevation Regressions on Independent Slopes Model (PRISM) derived raster data is the underlying data set from which the polygons and vectors were created. PRISM is an analytical model that uses point data and a digital elevation model (DEM) to generate gridded estimates of annual, monthly and event-based climatic parameters.

The Jaeger Surface Rain Gauge Observations data set consists of gridded mean monthly global precipitation values for 1931 to 1960 over the continents and 1955 to 1965 over the oceans. In order to calculate monthly, seasonal, and annual variations of precipitation over the whole globe, both hemispheres, and various meridional zones, a gridding technique was used on data spanning 1931 to 1960 over the continents, and 1955 to 1965 over the oceans. For the continental regions, the grid point values were obtained as eye estimates from isopleth maps prepared from up-to-date climatic atlases containing annual and monthly rainfall values, supplemented by other data sets. Although it was initially intended to use data for the standard period 1931-1960, this did not prove possible for all regions. Moller's (1951) method for estimating rainfall frequencies was adopted to provide ocean precipitation data. Monthly percentage frequencies were extracted from the mapped isolines of the US Marine Climatic Atlas (US Naval Weather Service 1955-1965) and interpolated to the grid points. After re-expressing the monthly frequencies as annual percentages, the values were scaled to rainfall depth units using Geiger's (1965) precipitation map to yield monthly precipitation means.

Hourly Precipitation Data (HPD) is digital data set DSI-3240, archived at the National Climatic Data Center (NCDC). The primary source of data for this file is approximately 5,500 US National Weather Service (NWS), Federal Aviation Administration (FAA), and cooperative observer stations in the United States of America, Puerto Rico, the US Virgin Islands, and various Pacific Islands. The earliest data dates vary considerably by state and region: Maine, Pennsylvania, and Texas have data since 1900. The western Pacific region that includes Guam, American Samoa, Marshall Islands, Micronesia, and Palau have data since 1978. Other states and regions have earliest dates between those extremes. The latest data in all states and regions is from the present day. The major parameter in DSI-3240 is precipitation amounts, which are measurements of hourly or daily precipitation accumulation. Accumulation was for longer periods of time if for any reason the rain gauge was out of service or no observer was present. DSI 3240_01 contains data grouped by state; DSI 3240_02 contains data grouped by year.

The Advanced Weather Interactive Processing System (AWIPS) uses shapefiles for base maps in the system. These shapefiles contain boundaries of areas used by NWS for forecasts and warnings as well as map backgrounds.NWS BordersThe County Warning Area boundaries are the counties/zones for which each Weather Forecast Office (WFO) is responsible for issuing forecasts and warnings. The shapefile was created by aggregating public zones with the same CWA designation into a single polygon and manually adjusting the boundaries of the exceptions to the rule.The NWS county and state borders are background map used internally in NWS.Coastal Marine Zone ForecastThis map layer contains links to NWS marine weather forecasts for coastal or nearshore waters within 20nm of shore out to Day 5. It includes predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas or combined seas, and icing. Air temperature forecasts are optional. The forecasts will also include any marine weather advisories, watches, and/or warnings. The purpose of the forecasts is to support and promote safe transportation across the coastal waters. The forecasts are issued twice per day with updates as necessary by NWS Weather Forecast Offices (WFOs) along the coast and Great Lakes.Offshore Zone ForecastsThis map layer contains links to NWS marine weather forecasts for offshore waters beyond 20 or 30nm of shore out to Day 5. The forecast provides information to mariners who travel on the oceanic waters adjacent to the U.S., its territorial coastal waters and the Caribbean Sea. The forecasts include predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas and likelihood of icing out to Day 5 along with information about any warnings. The offshore forecasts for the Western North Atlantic and Eastern North Pacific Oceans are produced by NWS/NCEP's Ocean Prediction Center. The offshore forecasts for the Gulf of Mexico and Caribbean Sea are issued by the NWS/NCEP National Hurricane Center's Tropical Analysis and Forecast Branch (TAFB). OPC and NHC/TAFB issues the forecasts four times daily at regular intervals, with updates when necessary. The offshore forecast for the waters around Hawaii are issued by the NWS Weather Forecast Office in Honolulu, HI four times daily at regular intervals, with updates when necessary. The offshore forecasts for Alaska waters in the Bering Sea and Gulf of Alaska are issued by NWS Weather Forecast Offices in Alaska at least twice a day with updates as necessary. The WFOs in Alaska include WFO Anchorage, WFO Fairbanks, and WFO Juneau.Public Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS surface weather forecasts, a zone-type forecast providing the average forecast conditions across the zone, usually at the county-scale or sub-county scale. These text forecasts include predictions of weather, sky cover, maximum and minimum surface air temperatures, surface wind direction and speed, and probability of precipitation out to 7 days into the future. In addition, the forecast highlights at the top include any active weather advisories, watches, and/or warnings. These zone predictions are derived from gridded forecasts created by NWS Weather Forecast Offices throughout the U.S. The text weather forecasts are usually issued in the early morning (e.g. 4AM LT) and early evening (4PM LT). They are updated during late mornings and late night and during fast changing weather conditions.Fire Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS Fire Weather Planning Forecasts, a zone-type forecast providing the average fire weather conditions across the zone. According to the NWS, the forecast is "used by land management personnel primarily for input in decision-making related to pre-suppression and other planning." The forecast is valid from the time of issuance through day five and sometimes through day seven and usually has a minimum of three 12-hour time periods. The forecast will have included a discussion of weather patterns affecting the forecast zone or area, identification of any active fire weather watches/warnings and a table of predicted fire weather variables for the next two days: 1) sky/weather conditions, 2) max/min air temperatures, 3) max/min relative humidity, 4) 0-minute average wind direction/speed at 20 feet and sometimes at another height (e.g. 10,000, 15,000 ft), 5) precipitation amount, duration, and timing, 6) mixing height, 7) transport winds, 8) vent category, and 9) several fire weather indices such as Haines Index, Lightning Activity (LAL), Chance of Wetting Rainfall (CWR), Dispersion Index, Low Visibility Occurrence Risk Index (LVORI), and Max LVORI. In addition, it will usually have a forecast in plain text for days 3 to 7. Sometimes an optional outlook of expected conditions for day 6 or possibly for day 6 and 7 is expected. The forecasts are issued by NWS WFOs at least once daily during the local fire season.Metadata:CWA: https://www.weather.gov/gis/CWAmetadataCoastal Marine: https://www.weather.gov/gis/CoastalMarineMetadataOffshore: https://www.weather.gov/gis/OffshoreZoneMetadataPublic Zones: https://www.weather.gov/gis/PublicZoneMetadataFire Zones: https://www.weather.gov/gis/FireZoneMetadataCounties: https://www.weather.gov/gis/CountyMetadataStates: https://www.weather.gov/gis/StateMetadataLink to data download: https://www.weather.gov/gis/AWIPSShapefilesQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled

This map layer shows polygons of average annual precipitation for the conterminous United States from 1961 through 1990. PRISM-derived raster data form the underlying data set from which the polygons and vectors were created. Each polygon represents an area with a constant value for the average annual precipitation, as determined by the PRISM model.

The rainfall-runoff erosivity factor (R-Factor) quantifies the effects of raindrop impacts and reflects the amount and rate of runoff associated with the rain. The R-factor is one of the parameters used by the Revised Unified Soil Loss Equation (RUSLE) to estimate annual rates of erosion. This product is a raster representation of R-Factor derived from isoerodent maps published in the Agriculture Handbook Number 703 (Renard et al.,1997). Lines connecting points of equal rainfall ersoivity are called isoerodents. The iserodents plotted on a map of the coterminous U.S. were digitized, then values between these lines were obtained by linear interpolation. The final R-Factor data are in raster GeoTiff format at 800 meter resolution in Albers Conic Equal Area, GRS80, NAD83.

Consolidated Table of 30+ Year Average Decadal Rainfall. The RFE_LAVG tabular data layer is comprised of 471688 derivative raster precipitation features derived based on 8 kilometers data originally from EDC. The layer provides nominal analytical/mapping at 1:3 500 000. Acronyms and Abbreviations: EDC - USGS EROS (Earth Resources Observation Systems) Data Center.

Consolidated Table of 7 Year Short Average Decadal Rainfall. The RFE_SAVG tabular data layer is comprised of 471688 derivative raster precipitation features derived based on 8 kilometers data originally from EDC. The layer provides nominal analytical/mapping at 1:3 500 000. Acronyms and Abbreviations: EDC - USGS EROS (Earth Resources Observation Systems) Data Center.

The contiguous US exhibits a wide variety of precipitation regimes, first, because of the wide range of latitudes and altitudes. The physiographic units with a basic meridional configuration contribute to the differentiation between east and west in the country while generating some large interior continental spaces. The frequency distribution of daily precipitation amounts almost anywhere conforms to a negative exponential distribution, reflecting the fact that there are many small daily totals and few large ones. Positive exponential curves, which plot the cumulative percentages of days with precipitation against the cumulative percentage of the rainfall amounts that they contribute, can be evaluated through the Concentration Index. The Concentration Index has been applied to the contiguous United States using a gridded climate dataset of daily precipitation data, at a resolution of 0.25°, provided by CPC/NOAA/OAR/Earth System Research Laboratory, for the period between 1956 and 2006. At the same time, other rainfall indices and variables such as the annual coefficient of variation, seasonal rainfall regimes and the probabilities of a day with precipitation have been presented with a view to explaining spatial CI patterns. The spatial distribution of the CI in the contiguous United States is geographically consistent, reflecting the principal physiographic and climatic units of the country. Likewise, linear correlations have been established between the CI and geographical factors such as latitude, longitude and altitude. In the latter case the Pearson correlation coefficient (r) between this factor and the CI is −0.51 (p-value < 0.001). For annual probability of days with precipitation and the CI there is also a significant and negative correlation, r = −0.25 (p-value < 0.001).

Fig. 8. Concentration Index values (1956–2006).

File: ci_raster_USA.tif (geoTIFF)

NOTE: After the publication of the research article we calculate the Concentration Index with the PRISM climate data set, which has a higher resolution with 4km (PRISM Climate Group, Oregon State University). Nevertheless, the temporal coverage is limited to the period from 1981 to 2017.

File: CI_PRISM_USA.tif (geoTIFF)

Fig. 4. Seasonal rainfall regimes (1956–2006) (P, spring, S, summer, A, autumn, W, winter)

File: 1) pulvio_regimes_raster_USA.tif (geoTIFF); 2) pulvio_regimes_id.csv (clasification for regimes)

Map projection details:

EPSG:2163; proj4: "+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"

The U.S. Geological Survey (USGS), in cooperation with the city of Harrisonville, Missouri, assessed flooding of Muddy Creek resulting from varying precipitation magnitudes and durations, antecedent soil moisture conditions, and channel conditions. The precipitation scenarios were used to develop a library of flood-inundation maps that included a 3.8-mile reach of Muddy Creek and tributaries within and adjacent to the city. Hydrologic and hydraulic models of the upper Muddy Creek Basin were used to assess streamflow magnitudes associated with simulated precipitation amounts and the resulting flood-inundation conditions. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS; version 4.4.1) was used to simulate the amount of streamflow produced from a range of rainfall events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS; version 5.0.7) was then used to route streamflows and map resulting areas of flood inundation. The hydrologic and hydraulic models were calibrated to the September 28, 2019; May 27, 2021; and June 25, 2021, runoff events representing a range of antecedent moisture conditions and hydrologic responses. The calibrated HEC–HMS model was used to simulate streamflows from design rainfall events of 30-minute to 24-hour durations and ranging from a 100- to 0.1-percent annual exceedance probability. Flood-inundation maps were produced for USGS streamflow stages of 1.0 feet (ft), or near bankfull, to 4.0 ft, or a stage exceeding the 0.1-percent annual exceedance probability interval precipitation, using the HEC–RAS model. The consequence of each precipitation duration-frequency value was represented by a 0.5-ft increment inundation map based on the generated peak streamflow from that rainfall event and the corresponding stage at the Muddy Creek stage reference _location. Seven scenarios were developed with the HEC–HMS hydrologic model with resulting streamflows routed in a HEC-RAS hydraulic model and these scenarios varied by antecedent soil-moisture and channel conditions. The same precipitation scenarios were used in each of the seven antecedent moisture and channel conditions and the simulation results were assigned to a flood-inundation map condition based on the generated peak flow and corresponding stage at the Muddy Creek reference _location. This data release includes: 1) tables summarizing the model results including the flood-inundation map condition of each model scenario for dry (CNI; Muddy_Creek_summary_table_1_1.csv), normal (CNII; Muddy_Creek_summary_table_1_2.csv), and wet (CNIII; Muddy_Creek_summary_table_1_3.csv) antecedent soil moisture conditions (MuddyCreek_summary_tables.zip); 2) a shapefile dataset of the streamflow inundation extents at Muddy Creek reference _location stages of 1.0 to 4.0 feet (MuddyCreek_inundation_extents.zip containing MudHarMO.shp); 3) a raster dataset of the streamflow depths at Muddy Creek reference _location stages of 1.0 to 4.0 feet (MuddyCreek_inundation_depths.zip containing MudharMO_X.tif where X = 1,2,3,4,5,6,7 corresponding to inundation map stages of 1.0, 1.5 , 2.0, 2.5, 3.0, 3.5, 4.0 feet)); 4) tables of hydrologic and hydraulic model performance and calibration metrics, locations of continuous pressure transducers (PTs; MuddyCreek_PT_locations.zip) and high-water marks (HWMs; MuddCreek_HWM_locations.zip) used in assessment of model calibration and validation, and time series of pressure transducer data (MuddyCreek_PT_time_series.zip) found in MuddyCreek_model_performance_calibration_metrics.zip; 5) hydrologic and hydraulic model run files used in the simulation of dry hydrologic response conditions (CN_I conditions) and effects of proposed detention storage (MuddyCreek_dry_detention.zip); 6) hydrologic and hydraulic model run files used in the simulation and calibration of dry hydrologic response conditions (CN_I conditions) and current (2019) existing channel conditions (MuddyCreek_dry_existing_conditions.zip); 7) hydrologic and hydraulic model run files used in the simulation of normal hydrologic response conditions (CN_II conditions) and effects of cleaned culverts (MuddyCreek_normal_clean_culverts.zip); 8) hydrologic and hydraulic model run files used in the simulation of normal hydrologic response conditions (CN_II conditions) and effects of detention storage (MuddyCreek_normal_detention.zip); 9) hydrologic and hydraulic model run files used in the simulation and calibration of normal hydrologic response conditions (CN_II conditions) and current (2019) existing channel conditions (MuddyCreek_normal_existing_conditions.zip); 10) hydrologic and hydraulic model run files used in the simulation of wet hydrologic response conditions (CN_III conditions) and effects of proposed detention storage (MuddyCreek_wet_detention.zip); 11) hydrologic and hydraulic model run files used in the simulation and calibration of wet hydrologic response conditions (CN_III) and current (2019) existing channel conditions (MuddyCreek_wet_existing_conditions.zip). 12) Service definition files of the Muddy Creek water depths of inundated areas (MuddyCreek_Inundation_depths.sd) and Muddy Creek inundation area polygons (MuddyCreek_inundation_extents.sd) added on September 7, 2022.

The contiguous US exhibits a wide variety of precipitation regimes, first, because of the wide range of latitudes and altitudes. The physiographic units with a basic meridional configuration contribute to the differentiation between east and west in the country while generating some large interior continental spaces. The frequency distribution of daily precipitation amounts almost anywhere conforms to a negative exponential distribution, reflecting the fact that there are many small daily totals and few large ones. Positive exponential curves, which plot the cumulative percentages of days with precipitation against the cumulative percentage of the rainfall amounts that they contribute, can be evaluated through the Concentration Index. The Concentration Index has been applied to the contiguous United States using a gridded climate dataset of daily precipitation data, at a resolution of 0.25°, provided by CPC/NOAA/OAR/Earth System Research Laboratory, for the period between 1956 and 2006. At the same time, other rainfall indices and variables such as the annual coefficient of variation, seasonal rainfall regimes and the probabilities of a day with precipitation have been presented with a view to explaining spatial CI patterns. The spatial distribution of the CI in the contiguous United States is geographically consistent, reflecting the principal physiographic and climatic units of the country. Likewise, linear correlations have been established between the CI and geographical factors such as latitude, longitude and altitude. In the latter case the Pearson correlation coefficient (r) between this factor and the CI is −0.51 (p-value < 0.001). For annual probability of days with precipitation and the CI there is also a significant and negative correlation, r = −0.25 (p-value < 0.001).

Fig. 8. Concentration Index values (1956–2006).

File: ci_raster_USA.tif (geoTIFF)

NOTE: After the publication of the research article we calculate the Concentration Index with the PRISM climate data set, which has a higher resolution with 4km (PRISM Climate Group, Oregon State University). Nevertheless, the temporal coverage is limited to the period from 1981 to 2017.

File: CI_PRISM_USA.tif (geoTIFF)

Fig. 4. Seasonal rainfall regimes (1956–2006) (P, spring, S, summer, A, autumn, W, winter)

File: 1) pulvio_regimes_raster_USA.tif (geoTIFF); 2) pulvio_regimes_id.csv (clasification for regimes)

Map projection details:

EPSG:2163; proj4: "+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.