In 2024, Louisiana recorded ***** 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 **** 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 **** 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.

The majority of the wettest cities in the United States are located in the Southeast. The major city with the most precipitation is New Orleans, Louisiana, which receives an average of 1592 millimeters (62.7 inches) of precipitation every year, based on an average between 1981 and 2010.

Precipitation in the United States increased to 777.25 mm in 2024 from 738.01 mm in 2023. This dataset includes a chart with historical data for the United States Average Precipitation.

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).

This statistic shows the ten major U.S. cities with the most rainy days per year between 1981 and 2010. Rochester, New York, had an average of about 167 days per year with precipitation. The sunniest city in the U.S. was Phoenix, Arizona, with an average of 85 percent of sunshine per day.

On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the climate data described in the Atlas publications were interpolated to the grid-point locations defined by the original 3-decimal place latitude and longitude values. Interpolating the data to the 6-decimal place latitude and longitude values would in many cases not result in changes to the reported values and for other grid points the changes would be small and insignificant. Similarly, if the digitized Little (1971, 1976, 1977) taxa distribution maps were regridded using the 6-decimal place latitude and longitude values, the changes to the gridded distributions would be minor, with a small number of grid points along the edge of a taxa's digitized distribution potentially changing value from taxa "present" to taxa "absent" (or vice versa). These changes should be considered within the spatial margin of error for the taxa distributions, which are based on hand-drawn maps with the distributions evidently generalized, or represented by a small, filled circle, and these distributions were subsequently hand digitized. Users wanting to use data that exactly match the data in the Atlas volumes should use the 3-decimal place latitude and longitude data provided in the .csv files in this data release to represent the center point of each grid cell. Users for whom an offset of up to 144.2 m from the original grid-point location is acceptable (e.g., users investigating continental-scale questions) or who want to easily visualize the data may want to use the data associated with the 6-decimal place latitude and longitude values in the netCDF files. The variable names in the netCDF files generally match those in the data release .csv files, except where the .csv file variable name contains a forward slash, colon, period, or comma (i.e., "/", ":", ".", or ","). In the netCDF file variable short names, the forward slashes are replaced with an underscore symbol (i.e., "_") and the colons, periods, and commas are deleted. In the netCDF file variable long names, the punctuation in the name matches that in the .csv file variable names. The "country", "state, province, or territory", and "county" data in the .csv files are not included in the netCDF files. Data included in this release: - Geographic scope. The gridded data cover an area that we labelled as "CANUSA", which includes Canada and the USA (excluding Hawaii, Puerto Rico, and other oceanic islands). Note that the maps displayed in the Atlas volumes are cropped at their northern edge and do not display the full northern extent of the data included in this data release. - Elevation. The elevation data were regridded from the ETOPO5 data set (National Geophysical Data Center, 1993). There were 35 coastal grid points in our CANUSA study area grid for which the regridded elevations were below sea level and these grid points were assigned missing elevation values (i.e., elevation = 9999). The grid points with missing elevation values occur in five coastal areas: (1) near San Diego (California, USA; 1 grid point), (2) Vancouver Island (British Columbia, Canada) and the Olympic Peninsula (Washington, USA; 2 grid points), (3) the Haida Gwaii (formerly Queen Charlotte Islands, British Columbia, Canada) and southeast Alaska (USA, 9 grid points), (4) the Canadian Arctic Archipelago (22 grid points), and (5) Newfoundland (Canada; 1 grid point). - Climate. The gridded climatic data provided here are based on the 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and include annual and monthly temperature and precipitation. The CRU CL 2.0 data were interpolated onto the approximately 25-km grid using geographically-weighted regression, incorporating local lapse-rate estimation and correction. Additional bioclimatic variables (growing degree days on a 5 degrees Celsius base, mean temperatures of the coldest and warmest months, and a moisture index calculated as actual evapotranspiration divided by potential evapotranspiration) were calculated using the interpolated CRU CL 2.0 data. Also included are absolute minimum and maximum temperatures for 1951-1980 interpolated in a similar fashion from climate-station data (WeatherDisc Associates, 1989). These climate and bioclimate data were used in Atlas volumes F and G (see Thompson and others, 2015, for a description of the methods used to create the gridded climate data). Note that for grid points with missing elevation values (i.e., elevation values equal to 9999), climate data were created using an elevation value of -120 meters. Users may want to exclude these climate data from their analyses (see the Usage Notes section in the data release readme file). - Plant distributions. The gridded plant distribution data align with Atlas volume G (Thompson and others, 2015). Plant distribution data on the grid include 690 species, as well as 67 groups of related species and genera, and are based on U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977), regional atlases (e.g., Benson and Darrow, 1981), and new maps based on information available from herbaria and other online and published sources (for a list of sources, see Tables 3 and 4 in Thompson and others, 2015). See the "Notes" column in Table 1 (https://pubs.usgs.gov/pp/p1650-g/table1.html) and Table 2 (https://pubs.usgs.gov/pp/p1650-g/table2.html) in Thompson and others (2015) for important details regarding the species and grouped taxa distributions. - Ecoregions. The ecoregion gridded data are the same as in Atlas volumes D and E (Thompson and others, 2006, 2007), and include three different systems, Bailey's ecoregions (Bailey, 1997, 1998), WWF's ecoregions (Ricketts and others, 1999), and Kuchler's potential natural vegetation regions (Kuchler, 1985), that are each based on distinctive approaches to categorizing ecoregions. For the Bailey and WWF ecoregions for North America and the Kuchler potential natural vegetation regions for the contiguous United States (i.e.,

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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).

This dataset contains dekadal rainfall indicators, computed from Climate Hazards Group InfraRed Precipitation satellite imagery with insitu Station data (CHIRPS) version 2 and the CHIRPS-GEFS short term rainfall forecasts, aggregated by subnational administrative units.

Included indicators are (for each dekad):

• 10 day rainfall mm
• rainfall 1-month rolling aggregation mm
• rainfall 3-month rolling aggregation mm
• rainfall long term average mm
• rainfall 1-month rolling aggregation long term average mm
• rainfall 3-month rolling aggregation long term average mm
• rainfall anomaly %
• rainfall 1-month anomaly %
• rainfall 3-month anomaly %

The administrative units used for aggregation are based on WFP data and contain a Pcode reference attributed to each unit. The number of input pixels used to create the aggregates, is provided in the n_pixels column. Finally, the type column indicates if the value is based on a forecast, a preliminary or a final product.

Forecasts are issued on the 6th, 16th, and 26th of each month for the upcoming 10-day period (dekad), then updated with improved versions on the 1st, 11th, and 21st. Preliminary observations replace the previous dekad’s forecast on the 3rd, 13th, and 23rd, and are later replaced by final observations—published mid-month (13th or 23rd)—covering all three dekads of the prior month. Please find a summary below:

Publication Day: Forecast type, Covers (Dekad)

• 1st: Updated forecast, 1–10 of the same month
• 6th: Initial forecast, 11–20 of the same month
• 11th: Updated forecast, 1–10 of the same month
• 16th: Initial forecast, 21–end of the same month
• 21st: Updated forecast, 11–20 of the same month
• 26th: Initial forecast, 1–10 of the following month

For more on CHIRPS-GEFS forecasts, see: https://www.chc.ucsb.edu/data/chirps-gefs

For further details, please see the methodology section.

This dataset replaces the previous Time Bias Corrected Divisional Temperature-Precipitation Drought Index. The new divisional data set (NClimDiv) is based on the Global Historical Climatological Network-Daily (GHCN-D) and makes use of several improvements to the previous data set. For the input data, improvements include additional station networks, quality assurance reviews and temperature bias adjustments. Perhaps the most extensive improvement is to the computational approach, which now employs climatologically aided interpolation. This 5km grid based calculation nCLIMGRID helps to address topographic and network variability. This data set is primarily used by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) to issue State of the Climate Reports on a monthly basis. These reports summarize recent temperature and precipitation conditions and long-term trends at a variety of spatial scales, the smallest being the climate division level. Data at the climate division level are aggregated to compute statewide, regional and national snapshots of climate conditions. For CONUS, the period of record is from 1895-present. Derived quantities such as Standardized precipitation Index (SPI), Palmer Drought Indices (PDSI, PHDI, PMDI, and ZNDX) and degree days are also available for the CONUS sites. In March 2015, data for thirteen Alaskan climate divisions were added to the NClimDiv data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska NClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the NClimGrid data set. As of November 2018, NClimDiv includes county data and additional inventory files.

In late August and early September 2017, Hurricane Harvey made landfall on the southeastern coastline of Texas and produced a record amount of rainfall, leading to widespread flooding. From August 25 through September 1, 2017, some areas in southeastern Texas received more than 60 inches of rain with large areas receiving at least 40 inches of rain. Hurricane Harvey was the largest rainfall event in United States history in terms of spatial extent and rainfall totals since rainfall records began in the 1880s (Watson and others, 2018). The five most heavily flooded river basins in Texas during this storm included the Brazos River, where the U.S. Geological Survey (USGS) collected water-quality samples at the Brazos River near Rosharon, Tex. (USGS station 08116650, hereinafter referred to as the Brazos River site). Two water-quality samples were collected by the USGS at the Brazos River site in response to Hurricane Harvey in August and September 2017. Water-quality samples are also routinely collected at the Brazos River site approximately 14 times a year as part of the USGS National Water-Quality Assessment Project. Concentrations of selected water-quality constituents in the two water-quality samples collected at the Brazos River site during the period of higher discharge associated with Hurricane Harvey are documented in this data release along with the constituent concentrations measured in samples collected at this site immediately before and after Hurricane Harvey for comparison purposes. Water-quality changed in response to the period of higher discharge at the Brazos River site that resulted from the storm; this data release documents those changes. Results from all water-quality analyses of field properties, major ions, nutrients, trace elements, and pesticides are included in this data release. Discharge is computed continuously (15-minute intervals) at the Brazos River site and those values have been related to the water-quality samples based on collection time. This data release documents how specific conductance and concentrations of most of the major ions analyzed (calcium, magnesium, potassium, sodium, bicarbonate, chloride, sulfate, and dissolved solids) as well as selected trace elements (lithium, strontium, and boron) decreased (relative to samples collected before Hurricane Harvey) in the water-quality samples collected during the period of higher discharge that resulted from Hurricane Harvey at the site. Conversely, concentrations of suspended sediment and iron increased (relative to samples collected before Hurricane Harvey) in the water-quality samples collected during the same period of higher discharge. Detections of pesticides generally were not measured in samples collected during the period of higher discharge that resulted from Hurricane Harvey except for atrazine and a few of its degradates, for which lower concentrations were documented in water-quality samples collected during higher discharge at the Brazos River site compared to the concentrations measured during lower discharge before the storm event.

Flash flooding is the top weather-related killer, responsible for an average of 140 deaths per year across the United States. Although precipitation forecasting and understanding of flash flood causes have improved in recent years, there are still many unknown factors that play into flash flooding. Despite having accurate and timely rainfall reports, some river basins simply do not respond to rainfall as meteorologists might expect. The Flash Flood Potential Index (FFPI) was developed in order to gain insight into these “problem basins”, giving National Weather Service (NWS) meteorologists insight into the intrinsic properties of a river basin and the potential for swift and copious rainfall runoff.

NEXRAD is a network of 160 high-resolution Doppler weather radars operated by the NOAA National Weather Service (NWS), the Federal Aviation Administration (FAA), and the U.S. Air Force (USAF). Doppler radars detect atmospheric precipitation and winds, which allow scientists to track and anticipate weather events, such as rain, ice pellets, snow, hail, and tornadoes, as well as some non-weather objects like birds and insects. NEXRAD stations use the Weather Surveillance Radar - 1988, Doppler (WSR-88D) system. The NEXRAD products are divided in two data processing levels. The lower Level 2 data are base products at original resolution. Level 2 data are recorded at all NWS and most USAF and FAA WSR-88D sites. From the Level 2 quantities, computer processing generates numerous meteorological analysis Level 3 products. The Level 3 data consists of reduced resolution, low-bandwidth, base products as well as many derived, post-processed products. Level 3 products are recorded at most U.S. sites, though non-US sites do not have Level 3 products. There are over 40 Level 3 products available from the NCDC. General products for Level 3 include the base and composite reflectivity, storm relative velocity, vertical integrated liquid, echo tops and VAD wind profile. Precipitation products for Level 3 include estimated ground accumulated rainfall amounts for one and three hour periods, storm totals, and digital arrays. Estimates are based on reflectivity to rainfall rate (Z-R) relationships. Overlay products for Level 3 are alphanumeric data that give detailed information on certain parameters for an identified storm cell. These include storm structure, hail index, mesocyclone identification, tornadic vortex signature, and storm tracking information. Radar messages for Level 3 are sent by the radar site to users in order to know more about the radar status and special product data. NEXRAD data are provided to the NOAA National Climatic Data Center for archiving and dissemination to users. Data coverage varies by station and ranges from May 1992 to 1 day from present. Most stations began observing in the mid-1990s, and most period of records are continuous.Daily GHCN is part of the Global Historical Climatology Network - Daily (GHCN-Daily) dataset. GHCN-Daily integrates daily climate observations from approximately 30 different data sources. Version 3 was released in September 2012 with the addition of data from two additional station networks. Changes to the processing system associated with the version 3 release also allowed for updates to occur 7 days a week rather than only on most weekdays. Version 3 contains station-based measurements from well over 90,000 land-based stations worldwide, about two thirds of which are for precipitation measurement only. Other meteorological elements include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, snowfall and snow depth. Over 25,000 stations are regularly updated with observations from within roughly the last month. The dataset is also routinely reconstructed (usually every week) from its roughly 30 data sources to ensure that GHCN-Daily is generally in sync with its growing list of constituent sources. During this process, quality assurance checks are applied to the full dataset. Where possible, GHCN-Daily station data are also updated daily from a variety of data streams. Station values for each daily update also undergo a suite of quality checks.Local Climatological Data (LCD) are summaries of climatological conditions from airport and other prominent weather stations managed by NWS, FAA, and DOD. The product includes hourly observations and associated remarks, and a record of hourly precipitation for the entire month. Also included are daily summaries summarizing temperature extremes, degree days, precipitation amounts and winds. The tabulated monthly summaries in the product include maximum, minimum, and average temperature, temperature departure from normal, dew point temperature, average station pressure, ceiling, visibility, weather type, wet bulb temperature, relative humidity, degree days (heating and cooling), daily precipitation, average wind speed, fastest wind speed/direction, sky cover, and occurrences of sunshine, snowfall and snow depth. The source data is global hourly (DSI 3505) which includes a number of quality control checks.Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches) Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.The global summaries data set contains a monthly (GSOM) resolution of meteorological elements (max temp, snow, etc) from 1763 to present with updates weekly. The major parameters are: monthly mean maximum, mean minimum and mean temperatures; monthly total precipitation and snowfall; departure from normal of the mean temperature and total precipitation; monthly heating and cooling degree days; number of days that temperatures and precipitation are above or below certain thresholds; and extreme daily temperature and precipitation amounts. The primary source data set source is the Global Historical Climatology Network (GHCN)-Daily Data set. The global summaries data set also contains a yearly (GSOY) resolution of meteorological elements. See associated resources for more information. This data is not to be confused with "GHCN-Monthly", "Annual Summaries" or "NCDC Summary of the Month". There are unique elements that are produced globally within the GSOM and GSOY data files. There are also bias corrected temperature data in GHCN-Monthly, which will not be available in GSOM and GSOY. The GSOM and GSOY data set is going to replace the legacy DSI-3220 and expand to include non-U.S. (a.k.a. global) stations. DSI-3220 only included National Weather Service (NWS) COOP Published, or "Published in CD", sites.The global summaries data set contains a yearly (GSOY) resolution of meteorological elements (max temp, snow, etc) from 1763 to present with updates weekly. The major parameters are: monthly mean maximum, mean minimum and mean temperatures; monthly total precipitation and snowfall; departure from normal of the mean temperature and total precipitation; monthly heating and cooling degree days; number of days that temperatures and precipitation are above or below certain thresholds; and extreme daily temperature and precipitation amounts. The primary source data set source is the Global Historical Climatology Network (GHCN)-Daily Data set. The global summaries data set also contains a monthly (GSOM) resolution of meteorological elements. See associated resources for more information. This data is not to be confused with "GHCN-Monthly", "Annual Summaries" or "NCDC Summary of the Month". There are unique elements that are produced globally within the GSOM and GSOY data files. There are also bias corrected temperature data in GHCN-Monthly, which will not be available in GSOM and GSOY. The GSOM and GSOY data set is going to replace the legacy DSI-3220 and expand to include non-U.S. (a.k.a. global) stations. DSI-3220 only included National Weather Service (NWS) COOP Published, or "Published in CD", sites.The U.S. Annual Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters that provide users with many tools to understand typical climate conditions for thousands of locations across the United States, as well as U.S.

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The 2021 National Hydrologic Assessment offers an analysis of flood risk, water supply, and ice break-up and jam flooding for spring 2021 based on late summer, fall, and winter precipitation, frost depth, soil saturation levels, snowpack, current streamflow, and projected spring weather. NOAA's network of 122 Weather Forecast Offices, 13 River Forecast Centers, National Water Center, and other national centers nationwide assess this risk, summarized here at the national scale. Overall, a reduced risk of spring flooding exists this year primarily due to dry fall and winter, along with limited snow still remaining on the ground. Major flooding is not expected this spring season. Minor to moderate flooding is ongoing across portions of the Lower Missouri River Basin with the flood risk predicted to continue through spring. The exception to the reduced risk is over the Coastal Plain of the Carolinas and Lower Ohio River Basin where flooding is predicted this spring, driven by above normal precipitation over the winter months, which has led to ongoing elevated streamflows and flooding and highly saturated soil conditions. This wet pattern is expected to continue across the Coastal Plain of the Carolinas and Lower Ohio River Basin through spring. It is important to note that heavy rainfall at any time can lead to flooding, even in areas where overall risk is considered low. This assessment addresses only spring flood potential on the timescale of weeks to months, not days or hours. Debris flow and flash flooding often associated with burn scars and urban areas can form quickly and occur any time with heavy rainfall events. Nearly every day, flooding happens somewhere in the United States or its territories. Flooding can cause more damage than any other weather-related event...with an annual average direct damage impact of 8 billion dollars a year over the past 40 years, with these impact costs adjusted for inflation. Flooding is one of America's most underrated killers, causing nearly 100 fatalities per year… roughly half of which occur in vehicles. Flowing water can be particularly powerful and dangerous… with just six inches of water able to sweep a person off their feet… and two feet of rushing water able to carry a mid-size car downstream. No vehicle should ever attempt to cross a flooded roadway, and drivers are reminded to “Turn Around, Don’t Drown.” To be prepared, every American should know their flood risk and what to do before, during, and after a flood event. This information is available at www.ready.gov/floods. To remain apprised of your current flood risk, visit weather.gov for the latest official watches and warnings. For detailed hydrologic conditions and forecasts, go to water.weather.gov.

The study described in this report, initiated by the U.S. Geological Survey in 2014, was designed to evaluate fresh groundwater resources within the Ozark Plateaus, central United States, as an area within a broader national assessment of groundwater availability. The goals of the Ozark study were to evaluate historical effects of human activities on water levels and groundwater availability, quantify groundwater resources now and under probable future pumping and climate conditions, and evaluate existing monitoring networks for their value in making better predictions of future groundwater resources. Previous studies include simulation of local-scale groundwater flow under varying temporal scales, or simulation of the regional system under steady-state conditions. While these studies are useful, particularly for the problem for which they were designed, there is a need to look at the larger regional system under transient conditions to fully evaluate the water resource over time. This study focused on multiple spatial and temporal scales to examine changes in groundwater pumping, storage, and water-level declines. The regional scale provides a broad view of the sources and demands on the system with time. The study area covers approximately 68,000 square miles in the central United States in parts of Missouri, Arkansas, Kansas, and Oklahoma and encompasses the Ozark Plateaus Physiographic Province (Ozark Plateaus), including the Salem Plateau, Springfield Plateau, and Boston Mountains. Groundwater is withdrawn from the Ozark Plateaus aquifer system (Ozark system) for public supply and for domestic, agriculture (including irrigation and aquaculture), livestock, and non-agricultural use (including industrial, thermoelectric power generation, mining, and commercial). The Ozark system provides an important drinking-water supply for people living in the Ozark Plateaus because public supply and domestic use combined constitute the largest groundwater use. Precipitation is the ultimate source of freshwater to the Ozark system; most rainfall occurs during April, May, and June, and precipitation increases generally from north to south across the study area. Groundwater use currently accounts for only 10 percent of the total water use in the areas overlying the Ozark system, but provides a critical drinking-water resource because public supply and domestic groundwater withdrawals are largely from groundwater resources. The 380 million gallons per day of groundwater withdrawn from the Ozark system in 2010 accounts for approximately 2 percent of recharge. Although groundwater use represents a small component of the hydrologic budget, because of low storage in aquifer units, cones of depression with steep water-level gradients can develop quickly around pumping centers. The amount of water entering and leaving the aquifer system from 1900 to about 1965 was relatively constant at a rate of about 13 billion gallons per day (Bgal/d). Much of this inflow of water is discharged through streams in the system to balance the hydrologic budget. Changes in storage over time (from outflows to inflows) reflect the large variability in recharge: if recharge decreases, water levels will decrease, resulting in less groundwater discharge to streams and more water released from aquifer storage. Conversely, when recharge increases, water levels increase, more groundwater discharges to streams, and aquifer storage is replenished. Although pumping generally increased from 1900 to 2016, it does not appear to correlate with the change in storage over the same time period. Regionally, simulated change in groundwater storage corresponds with changes in recharge, more so than with increases in pumping. Average recharge was 11.6 Bgal/d for the period 1900 to 2016. Recharge was generally above average from predevelopment to 1965, followed by a period of below-average recharge from 1965 to about 1980. Recharge remained consistently above average from 1980 to about 1988, after which there was a period of average or below-average recharge, reflected by a decline through the mid-2000s. The implications and potential effects of increased pumping and long-term climate change on the Ozark Plateaus hydrologic system and groundwater availability are a concern for communities and resource managers in the area. Pumping varies from year to year, but is generally expected to moderately increase with population, industrial, and agricultural needs. Most climate models predict warmer minimum and maximum air temperatures by midcentury in the Ozark Plateaus area, especially from midspring through early fall. Three scenarios were developed to simulate possible future conditions from 2016 to 2060 and assess the potential effects on the hydrologic system and availability of water resources. For each scenario, chang... Visit https://dataone.org/datasets/sha256%3Ad2ceead752a76d7870296bff797950759b2cd259cf5d57c63a318b705ee1492e for complete metadata about this dataset.

These geospatial data sets were developed as part of a new analysis of all known current and historical rain gages in the Luquillo Mountains, Puerto Rico published in the journal article Murphy, S.F., Stallard, R.F., Scholl, M.A., Gonzalez, G., and Torres-Sanchez, A.J., 2017, Reassessing rainfall in the Luquillo Mountains, Puerto Rico: Local and global ecohydrological implications: PLOS One 12(7): e0180987, p. 1-26, https://doi.org/10.1371/journal.pone.0180987. That article provides a revised map of mean annual precipitation developed using elevation regression functions and residual interpolation, and that map is presented here in a raster file. Most previous forest- and watershed-wide estimates of precipitation (and evapotranspiration, as inferred by a water balance) have assumed that precipitation increases consistently with elevation in the Luquillo Mountains; therefore, precipitation in leeward Luquillo watersheds has been overestimated by up to 40%.Because the Luquillo Mount ...

MethodsStudy area: Our initial study area included the entire globe. We began with a seamless grid of cells with a resolution of 0.5 degrees (i.e., ~50 km at the equator). Next, we created polylines representing coastlines using SRTM (Shuttle Radar Topographic Mission) v4.1 global digital elevation model data at a resolution of 250 m (Reuter et al. 2007). We used these coastline polylines to identify and retain cells that intersected the coast. We excluded 192,227 cells that did not intersect the coast. To avoid cells with minimal potential coastal wetland habitat, we used the coastline data to remove an additional 1,056 coastal cells that contained less than or equal to 5% coverage of land. We also removed 176 cells which did not have suitable climate data; most of these cells were removed because they either did not have minimum air temperature data or they had unrealistic low or high minimum air temperature data relative to their neighboring cells. Collectively, these steps produced a grid (hereafter, study grid) that contained a total of 4,908 cells at a resolution of 0.5 degrees. Biogeographic zone and range limit assignmentsFor biogeographic zone and range limit-specific analyses, we assigned various identification codes to each study grid cell. Biogeographic zone assignments included either Atlantic East Pacific (AEP) or Indo West Pacific (IWP) (sensu Duke et al. 1998). Range limits, defined as areas where mangroves abruptly become absent from coastlines, were assigned individually using a combination of climate data, mangrove presence data, and descriptions in the literature. We conducted a literature review to develop hypotheses regarding the climatic and non-climatic factors that control each range limit (Table 1). We created polygons for 14 focal range limits (Fig. 2), and used these polygons to assign study grid cells to a particular range limit. All range limits spanned a mangrove presence-absence transition. For range limits that were expected to be controlled, at least in part, by winter temperatures, we created polygons that spanned the cold-to-hot transition zone. Where possible, this zone extended from a minimum temperature of -20 °C (cold) up to a maximum temperature of 20 °C (hot). However, due to various constraints, most of these transitions covered smaller temperature gradients. For range limits that were expected to be controlled, at least in part, by precipitation, we created polygons that spanned the wet-to-dry transition zone, as determined via the mean annual precipitation data.Climate dataPrior studies in North America have identified the importance of using air temperature extremes in mangrove distribution and abundance models (Osland et al. 2013, Cavanaugh et al. 2014). For all cells within the study grid, we sought to identify the absolute coldest daily air temperature that occurred across a recent multi-decadal period. Although monthly-based mean minimum air temperature data are readily available, daily minimum air temperature data have historically been more difficult to obtain at the global scale (Donat et al. 2013). Due to the absence of a consistent and seamless global dataset of daily air temperature minima, we used a combination of three different gridded daily minimum air temperature data sources. For cells in the United States, we used 2.5-arcminute resolution data created by the PRISM Climate Group (Oregon State University; http://prism.oregonstate.edu) (Daly et al. 2008), for the period extending from 1981-2010. For all continental cells outside of the United States (i.e., coastal cell connected to large bodies of land on all continents except for the United States), we used 1-degree resolution data created by Sheffield et al. (2006), for the same time period. For most islands, we used 0.5-degree resolution data created by Maurer et al. (2009), for the period extending from 1971-2000. From these three data sources, we created a minimum temperature (MINT) data set for the study grid cells to represent the absolute coldest air temperature that occurred across a recent three to four decade period, depending upon the source. For each study grid cell, we also obtained 30-second resolution mean annual precipitation (MAP) data from the WorldClim Global Climate Data (Hijmans et al. 2005), for the period extending from 1950-2000. We also obtained 5-arcminute resolution global gridded mean annual sea surface temperature data from a dataset produced by UNEP-WCMC (2015), for the period extending from 2009-2013. In addition to the gridded climate data, we obtained station-based air temperature data. For 13 of the 14 focal range limits, we identified a proximate station with a long-term record of daily air temperatures. For each of these stations, we obtained daily minimum air temperature data for the 30-year period extending from 1981-2010. From these data, we ca... Visit https://dataone.org/datasets/14bc274e-12d2-40bc-be06-afd41acc896d for complete metadata about this dataset.

This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/700/1. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of the long-term ANPP study is to monitor long-term net above ground primary production of the shortgrass steppe community by species. There are 6 sites: ridgetop (ridge), midslope (mid), swale, ESA (replicate 1 not 2), Section 25 (SEC 25), and owl-creek (OC). Each site is located in a different landscape position or soil type on the shortgrass steppe and may be grazed or not. Ridgetop, midslope and swale are grazed and are sampled along a catena. Section 25 is grazed and is located in an upload grassland. ESA is an ungrazed upland grassland an is the control from the Ecosystem Stress Area experiment. Owl Creek is ungrazed and is located in the lowland along the owl creek drainage. There are 3 transects with 5 plots in each transect. Plots in the grazed locations are protected by cages. Because this is a monitoring effort, true replicates across the landscape are not available and it is recommended that the transect be used in calculating mean production at each sampling location. The Shortgrass Steppe Long Term Ecological Research (SGS-LTER) project was funded by National Science Foundation as one of the first sites in the US LTER Network in 1982. This collaborative, interdisciplinary research project was established in the Natural Resource Ecology Lab at Colorado State University by ecosystem scientists who learned novel approaches to study grassland ecosystems during the International Biome Program (IBP) (1968-1974). The SGS-LTER project was built upon the foundation of data and information obtained during IBP, as scientists sought to identify and follow, and often manipulate in experiments, important ecosystem processes over the long-term. The objectives of the SGS-LTER project were to investigate what mechanisms regulate processes in the shortgrass steppe. Research questions focused on how biotic and abiotic components of the ecosystem are coupled, where and when ecosystem components are most vulnerable to perturbations, disseminating information that would be helpful for rangeland management and assessing impacts of climate change. Scientists explored variations in the structure and function of the ecosystem over space and time and sought to understand how these aspects are governed by climate, natural disturbance, biota, physiography, and human use. Scientists at the SGS-LTER integrated long-term monitoring data, designed experimental studies, performed and advanced modeling techniques, and synthesized data to conduct innovative research, education, and outreach. The core SGS-LTER research site was established on the Central Plains Experimental Range (CPER) in Nunn, Colorado, part of the United States Department of Agriculture’s Agricultural Research Service. The research site sits in the rain shadow of the Rocky Mountains at the western edge of the shortgrass steppe of North America. The shortgrass steppe ecosystem evolved with grazing by the American bison, which has now been replaced by cattle. Grazing by domestic livestock is the primary land use of native grassland, which occupies about 60% of the land area of the shortgrass steppe. Short grasses dominate the vegetation community, which have adapted to grazing and less than 400 mm of annual rainfall. The topography is characterized by gently rolling hills, broad ephemeral stream courses and low flat-topped terraces. Aspects of physiography regulate the shortgrass steppe ecosystem, including landscape position, soil age, water holding capacity, soil depth and surface texture which, in turn, determine such properties as soil moisture storage, net primary productivity and the distribution of small mammals such as prairie dogs and pocket gophers. SGS-LTER scientists have expanded their research studies beyond the CPER to identify similar or different patterns in ecosystem structure and function in North American grasslands; across the Great Plains re

The 2021 National Hydrologic Assessment offers an analysis of flood risk, water supply, and ice break-up and jam flooding for spring 2021 based on late summer, fall, and winter precipitation, frost depth, soil saturation levels, snowpack, current streamflow, and projected spring weather. NOAA's network of 122 Weather Forecast Offices, 13 River Forecast Centers, National Water Center, and other national centers nationwide assess this risk, summarized here at the national scale. Overall, a reduced risk of spring flooding exists this year primarily due to a mainly dry fall and winter, along with limited snow still remaining on the ground. Major flooding is not expected this spring season. Minor to moderate flooding is ongoing across portions of the Lower Missouri River Basin with the flood risk predicted to continue through spring. The exception to the reduced risk is over the Coastal Plain of the Carolinas and Lower Ohio River Basin where flooding is predicted this spring, driven by above normal precipitation over the winter months, which has led to ongoing flooding, elevated streamflows, and highly saturated soil conditions. This wet pattern is expected to continue across the Coastal Plain of the Carolinas and Lower Ohio River Basin through spring, making these regions vulnerable to spring flooding. It is important to note that heavy rainfall at any time can lead to flooding, even in areas where overall risk is considered low. This assessment addresses only spring flood potential on the timescale of weeks to months, not days or hours. Debris flow and flash flooding often associated with burn scars and urban areas can form quickly and occur any time with heavy rainfall events. Nearly every day, flooding happens somewhere in the United States or its territories. Flooding can cause more damage than any other weather-related event...with an annual average direct damage impact of 8 billion dollars a year over the past 40 years, with these impact costs adjusted for inflation. Flooding is one of America's most underrated killers, causing nearly 100 fatalities per year… roughly half of which occur in vehicles. Flowing water can be particularly powerful and dangerous… with just six inches of water able to sweep a person off their feet… and two feet of rushing water able to carry a mid-size car downstream. No vehicle should ever attempt to cross a flooded roadway, and drivers are reminded to “Turn Around, Don’t Drown.” To be prepared, every American should know their flood risk and what to do before, during, and after a flood event. This information is available at www.ready.gov/floods. To remain apprised of your current flood risk, visit weather.gov for the latest official watches and warnings. For detailed hydrologic conditions and forecasts, go to water.weather.gov.