Precipitation is water released from clouds in the form of rain, sleet, snow, or hail. It is the primary source of recharge to the planet's fresh water supplies. This map contains a historical record showing the volume of precipitation that fell during each month from March 2000 to the present. Snow and hail are reported in terms of snow water equivalent - the amount of water that will be produced when they melt. Dataset SummaryThe GLDAS Precipitation layer is a time-enabled image service that shows average monthly precipitation from 2000 to the present, measured in millimeters. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.Variables: This layer has two variables: rainfall and snowfall. By default the two are summed, but you can view either by itself using the multidimensional filter. You must disable time animation on the layer before using its multidimensional filter.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.This layer has query, identify, and export image services available.This layer is part of a larger collection of earth observation maps that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about earth observations layers and the Living Atlas of the World. Follow the Living Atlas on GeoNet.

This map displays the Quantitative Precipitation Forecast (QPF) for the next 72 hours across the contiguous United States. Data are updated hourly from the National Digital Forecast Database produced by the National Weather Service.The dataset includes incremental and cumulative precipitation data in 6-hour intervals. In the ArcGIS Online map viewer you can enable the time animation feature and select either the "Amount by Time" (incremental) layer or the "Accumulation by Time" (cumulative) layer to view a 72-hour animation of forecast precipitation. All times are reported according to your local time zone.Where is the data coming from?The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces forecast data of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.qpf.binWhere can I find other NDFD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Mean monthly temperature and the monthly sum of precipitation from long-term Colorado Climate Center meteorological observations near the study sites.

Thirty-six years of aboveground net primary productivity (ANPP) data collected across a topographic sequence in the semiarid shortgrass steppe of North America to examine patterns and drivers of spatiotemporal variability in ANPP. ANPP data were collected from the 6,500 ha USDA-Central Plains Experimental Range (CPER), which is part of the Long-Term Agroecosystem Research (LTAR; 2012-present; https://ltar.ars.usda.gov/) network, a former Long-Term Ecological Research station (LTER, 1983-2012), and located in the shortgrass steppe of north-central Colorado, USA. Additional information and referenced materials about many of the long-term studies initiated on the CPER can be found: https://dx.doi.org/10.25675/10217/81141. The topography at the CPER is characterized by gently rolling hills, and the topographic positions for data collection were focused along a catena in one of the most common ecological sites on the CPER, Loamy Plains (ID: R067BY002CO; NRCS, 2020). The plant community included four herbaceous plant functional types (PFTs): 1) perennial, warm-season, C4 grasses (primarily Bouteloua gracilis [Willd. ex Kunth] Lag ex Griffiths and B. dactyloides [Nutt.] J.T. Columbus), 2) perennial, cool-season, C3 grasses (primarily Pascopyrum smithii [Rydb] A. Love and Hesperostipa comata [Trin. & Rupr.] Barkworth ssp. comata), 3) cool-season, annual grass (Vulpia octoflora [Walter] Rydb.), and 4) forbs (primarily Sphaeralcea coccinea [Nutt.] Rydb.). Shrubs, subshrubs, and cactus were present but do not represent a large component of total ANPP and were not included in this study. Daily precipitation data were obtained from a long-term (1979-2018) precipitation gauge associated with the National Atmospheric Deposition program (Site ID: NTN-CO22; http://nadp.slh.wisc.edu/), located on site. Missing precipitation data were gap-filled using CPER headquarters data (1939-2018), or from the Soil Climate Analysis Network (SCAN) rain gauge (1997-2018, Site Number: 2017; https://wcc.sc.egov.usda.gov/), depending on proximity and temporal overlap. Following gap-filling, precipitation data were omitted if >10% of the time series was missing for each focal time period (e.g. fall or spring). Resources in this dataset:Resource Title: Gap filled precipitation data from the Central Plains Experimental Range, Nunn, Colorado from 1980-2018. File Name: CPER-PPT_gapfilled_1980-2018.csvResource Title: Data Dictionary for Gap filled precipitation data from the Central Plains Experimental Range, Nunn, Colorado from 1980-2018. File Name: CPER-PPT_DataDictionary.csvResource Title: Long-Term aboveground net primary production for functional group types on the Central Plains Experimental Range, Nunn, Colorado from 1983-2018. File Name: CPER-LTNPP_bypft_1983-2018.csvResource Title: Data Dictionary for Long-Term aboveground net primary production for functional group types on the Central Plains Experimental Range, Nunn, Colorado from 1983-2018. File Name: CPER-LTNPP_bypft_1983-2018_DataDictionary.csvResource Title: Dictionary of species within each functional group type in the LTNPP data collected on the Central Plains Experimental Range, Nunn, Colorado from 1983-2018. File Name: CPER-LTNPP_bypft_1983-2018_SppInFG_DataDictionary.csv

Daily precipitation and temperature data from 18 Global Climate Models (GCM) in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) that were downscaled using an analog regression approach in En-GARD (Gutmann et al. 2022) over the Colorado River Basin from 1950-2099. En-GARD is a statistical downscaling method designed to use information about upper level atmospheric processes (e.g. 500 mb winds) in addition to processes observed at the surface (e.g. precipitation and temperature). Each GCM was downscaled using training data from ERA-Interim reanalysis (Dee et al. 2011) and observations from the Livneh meteorological dataset (Livneh et al. 2015). Daily GCM precipitation and temperature were downscaled independently for each monthly basis (+/- 15 days for training) and on a grid-cell by grid cell basis. The GCM and ERA-Interim data were bilinearly interpolated to the Livneh 1/16 degree grid for input. Input data (Precipitation/Temperature, 500 mb zonal and meridional wind speeds) were quantile mapped to the corresponding ERA-Interim data and the closest 200 analog days, or days in which the input data matched the large-scale surface and upper atmospheric features, were selected independently for each day to be downscaled and used to train a multivariate linear regression to predict the Livneh data from those analog days. For precipitation, occurrence is modeled separately from magnitude by using a logistic regression with the same analog days to predict the probability of precipitation. To preserve realistic spatiotemporal variability, the residual term from the regression model is saved, and this residual is used to condition a stochastic sampling of the probability distribution for the prediction. Each output variable from En-GARD was quantile mapped to the Livneh meteorological data on a monthly basis to be used as input for a hydrological model that was calibrated using the Livneh meteorological data. More description of the En-GARD...

The official climate divisions for the contiguous United States are used for a wide range of purposes, including ongoing climate monitoring, and through NOAA's long-standing nClimDiv dataset. In Colorado, the climate divisions are based around the basins of the large rivers that flow out of the state. However, considering the complex topography and climate of the state, these divisions do not always represent key climate variations and changes. This study builds upon an approach first developed by Wolter and Allured to establish alternate climate divisions that more closely reflect observed climate variability across Colorado. Hierarchical cluster analysis is applied to gridded temperature and precipitation data (NOAA's nClimGrid) from 1950–2021 to identify areas with similar climate variability, then manual inspection is used to establish 11 divisions. These resulting divisions are being used in an updated state-level climate change assessment. The method is flexible and uses open-sour..., Because NOAA's nClimGrid data sometimes changes retrospectively (owing to late-arriving observations, etc.), the version of the data used for the cluster analysis (obtained in April 2022) is provided here., , # Supporting data for "Development of alternate climate divisions for Colorado based on gridded data"

NOAA's nClimGrid monthly temperature and precipitation dataset, originally obtained from NOAA in April 2022 and spatially subset to Colorado.

Description of the data and file structure

Two files are provided, the monthly average temperature and the monthly average precipitation, from 1895-2021 over Colorado. File format is netCDF; data are on a latitude/longitude grid with approximately 4-km horizontal grid spacing.

Sharing/Access information

Data was derived from

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Climate Reconstruction. The data include parameters of climate reconstructions|pollen with a geographic location of Colorado, United States Of America. The time period coverage is from 4145 to -51 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Precipitation forecast for the next 72 hours across the Continental United States.

Of concern to Colorado River management, as operating guidelines post-2026 are being considered, is whether water resource recovery from low flows during 2000–2020 is possible. Here we analyze new simulations from the sixth generation of the Coupled Model Intercomparison Project (CMIP6) to determine plausible climate impacts on Colorado River flows for 2026–2050 when revised guidelines would operate. Constrained by empirical estimates of Lee Ferry gauge (through which over 85% of the river flow passes) flow sensitivity to meteorological variability, effects of CMIP6 projected precipitation and temperature changes are integrated to project Lee Ferry flow. The critical importance of precipitation, hitherto largely discounted, is emphasized. Model projections indicate increased precipitation in the Upper Colorado River basin due to climate change, which alone acts to increase river flows 5%–7% (relative to a 2000–2020 climatology). Depending on the river’s temperature sensitivity, this wet signal compensates some, if not all, of the depleting effects from basin warming. Considerable precipitation variability is demonstrated, driving a much greater range of plausible Colorado River flow changes for 2026–2050 than previously surmised from treatment of temperature impacts alone: precipitation-induced Lee Ferry flow changes of -25% to +40% contrast with a -30% to -5% range from expected warming effects only. Consequently, extreme low and high flows are more likely. Lee Ferry flow projections, conditioned on an initial drought state in 2000–2020, analogous to observations, reveal substantial recovery odds for water resources, albeit with elevated risks of even further flow declines than recent .

Colorado River Basin ensemble median projected precipitation change (inches) 1990s 2070sHydroclimate projections are being made available to provide immediate access for the convenience of interested persons.Technical reference: https://www.usbr.gov/climate/secure/docs/2016secure/wwcra-hydroclimateprojections.pdfSpatial extent: Colorado River BasinSpatial resolution: 1/8° or ~ 12 km grid resolution Attribute Description:GRID_CODE: ensemble median projected precipitation change (inches) 1990s 2070sPOINT_X: longitude value of 1/8° grid cell centerPOINT_Y: latitude value of 1/8° grid cell center

The water-budget components geodatabase contains selected data from maps in the, "Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 through 1949 and 2000 through 2009" report (Stanton and others, 2011).Data were collected and synthesized from existing climate models including the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (Daly and others, 1994), and the Snow accumulation and ablation model (SNOW-17) (Anderson, 2006), and used in soil-water balance models to compute various components of a water budget. The methodologies used to compute the averages and volumes for the data in this geodatabase are slightly different for different components and models.

The water-budget components geodatabase contains selected data from maps in the, "Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 through 1949 and 2000 through 2009" report (Stanton and others, 2011).Data were collected and synthesized from existing climate models including the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (Daly and others, 1994), and the Snow accumulation and ablation model (SNOW-17) (Anderson, 2006), and used in soil-water balance models to compute various components of a water budget. The methodologies used to compute the averages and volumes for the data in this geodatabase are slightly different for different components and models.

The Forecast Systems Laboratory (FSL) operated a 22-station experimental surface mesoscale meteorological network (mesonet) along the front range of Colorado from May 1985 to June 1994.

Precipitation is water released from clouds in the form of rain, sleet, snow, or hail. It is the primary source of recharge to the planet's fresh water supplies. This map contains a historical record showing the volume of precipitation that fell during each month from March 2000 to the present. Snow and hail are reported in terms of snow water equivalent - the amount of water that will be produced when they melt. Dataset SummaryThe GLDAS Precipitation layer is a time-enabled image service that shows average monthly precipitation from 2000 to the present, measured in millimeters. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: PrecipitationUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean. Mean precipitation for a given month is calculated over the entire period of record - 2000 to present. Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. If you do this, make sure to also select a specific variable. The minimum time extent is one month, and the maximum is 8 years. Variables: This layer has three variables: total precipitation, rainfall and snowfall. By default total is shown, but you can select a different variable using the multidimensional filter, or by applying the relevant raster function. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tool.

This tabular data set represents estimated monthly precipitation (in millimeters) over the period 2006-2099 compiled for NHDPlus version 2 data suite (NHDPlusv2) catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlusv2 by the unique identifier COMID. Reach catchments are accumulated upstream through the river network using a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale (Schwarz and Wieczorek, 2018). The source data is Multivariate Adaptive Constructed Analogs (MACA) (Abatzoglou & Brown, 2011). Summaries are provided for five regions corresponding to NHDPlus vector processing units (VPUs): VPU 02, VPU 03w, VPU 04, VPU 14, and VPU 17.

Barro Colorado Island, AVA tower. Precipitation, electronic tipping bucket Established in Mar, 2011 as part of the ForestGEO Tropical Ecology, Assessment and Monitoring (TEAM) project. The original tower was Located near the center of the island at the north west corner of the 50ha plot (9.1568°, -79.8486°). In 2018 this tower was struck by a tree fall and rendered unusable. In Feb. 2019 a new 45m tower was build close by (9.1568°, -79.8486°).

Debris Flow, Precipitation, and Volume Measurements in the Grizzly Creek Burn Perimeter June 2021-September 2022 https://doi.org/10.5066/P9Z7RROL This data release contains data summarizing observations within and adjacent to the Grizzly Creek Fire, which burned from 10 August to 18 December 2020. This monitoring data summarizes precipitation, observations of debris flows, and the volume of sediment eroded during debris flows triggered during the summer monsoonal period in 2021 and 2022. Summary rainfall data 2021 (1a_Storm_matrix_2021_gr1mmhr.csv) are provided in a comma-separated value (CSV) file. These data represent the maximum measured rainfall intensities during the monsoon months of 2021 (June-Sept). The columns in the csv file are: Date (m/dd/yy), Name (11 columns have unique gage names), Max 15 min (this is the maximum 15-minute rainfall intensity in mm/h for the unique gauge), Maximum Value for All Gages (this is the maximum rainfall intensity for all of the gauges in un ...

Precipitation, electronic, Interval totals Barro Colorado Island (BCI),Clearing ('El Claro') Location: 9°9'47.21"N, 79°50'17.64"W The Clearing Station was established in 1972. It is a small, fenced in area with nearby forest on two sides and open spaces on the other two. The surrounding vegetation if periodically pruned to prevent that significant changes to the local micro-climate. The station Consists of a Stevenson screen with max/min thermometers, air pressure sensor and electronic temperature/humidity sensor. Two manual rain gauges and evaporation sensors are located at various locations close to the screen. Historical datasets can be located here: https://smithsonian.figshare.com/articles/dataset/Barro_Colorado_Island_Clearing_Precipitation/10042463

Barro Colorado Island (BCI), Clearing ('El Claro')

Location: 9°9'47.21"N, 79°50'17.64"W

Precipitation, daily

Measurements made 3-5 times per week

Data are prorated using electronic data to fill gaps

The Clearing is a small, open area surrounded by forest and some buildings. Station established in 1972. Consists of a Stevenson screen with max/min thermometers and air pressure sensor. Temperature/humidity sensor, rain gauge and evaporation sensors are located at various locations around the screen.