This Resource serves to explain and contain the methodology, R codes, and results of the PRISM freshwater supply key indicator analysis for my thesis. For more information, see my thesis at the USU Digital Commons.

Freshwater availability in the state can be summarized using streamflow, reservoir level, precipitation, and temperature data. Climate data for this study have a period of record greater than 30 years, preferably extending beyond 1950, and are representative of natural conditions at the county-level.

Oregon State University, Northwest Alliance for Computational Science and Engineering PRISM precipitation and temperature gridded data are representative of statewide, to county-level, from 1895-2015. These data are available online from the PRISM Climate Group. Using the R ‘prism’ package, monthly PRISM 4km raster grids were downloaded. Boundary shapefiles of Utah state, and each county, were obtained online from the Utah Geospatial Resource Center webpage. Using the R ‘rgdal’ and ‘sp’ packages, these shapefiles were transformed from their native World Geodetic System 1984 coordinate system to match the PRISM BIL raster’s native North American Datum 1983 coordinate system. Using the R ‘raster’ package, medians of PRISM precipitation grids at each spatial area of interest were calculated and summed for water years and seasons. Medians were also calculated for PRISM temperature grids and averaged over water years and seasons. For analysis of single months, the median results were used for all PRISM indicators. Seasons were analyzed for the calendar year which they are in, Winter being the first season of each year. Freshwater availability key indicators were non-parametrically separated per temporal/spatial delineation into quintiles representing Very Wet/Very High/Hot (top 20% of values), Wet/High/Hot (60-80%), Moderate/Mid-level (40-60%), Dry/Low/Cool (20-40%), to Very Dry/Very Low/Cool (bottom 20%). Each quintile bin was assigned a rank value 1-5, with ‘5’ being the value of the top quintile, in preparation for the Kendall Tau-b correlation analysis. These results, along with USGS irrigation withdrawal and acreage data, were loaded into R. State-level quintile results were matched according to USGS report year. County quintile results were matched with corresponding USGS irrigation withdrawal and acreage county-level data per report year for all other areas of interest. Using the base R function cor(), with the “kendall” method selected (which is, by default, the Kendall Tau-b calculation), relationship correlation matrices were produced for all areas of interest. The USGS irrigation withdrawal and acreage data correlation analysis matrices were created using the R ‘corrplot’ package for all areas of interest.

See Word file for an Example PRISM Analysis, made by Alan Butler at the United States Bureau of Reclamation, which was used as a guide for this analysis.

The PRISM Climate Group gathers climate observations from a wide range of monitoring networks, applies sophisticated quality control measures, and develops spatial climate datasets to reveal short- and long-term climate patterns. The resulting datasets incorporate a variety of modeling techniques and are available at multiple spatial/temporal resolutions, covering the period from 1895 to the present.

This data set consists of PRSIM precipitation climatologies for Alaska in GeoTIFF format. The files in this data set are available from the PRISM Climate Group as text files but have been processed into GeoTIFFs. These are monthly climatologies with a resolution of 771m. Units are millimeters. There are multiple climatological periods currently available through PRISM, but only one is currently available through SNAP in this dataset: 1971-2000.

Climate data provided by the National Water & Climate Center

The PRISM daily and monthly datasets are gridded climate datasets for the conterminous United States, produced by the PRISM Climate Group at Oregon State University. Grids are developed using PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM interpolation routines simulate how weather and climate vary with elevation, and account for coastal effects, temperature inversions, and terrain barriers that can cause rain shadows. Station data are assimilated from many networks across the country. For more information, see the Descriptions of PRISM Spatial Climate Datasets.

Dataset Card for PRISM

PRISM is a diverse human feedback dataset for preference and value alignment in Large Language Models (LLMs). It maps the characteristics and stated preferences of humans from a detailed survey onto their real-time interactions with LLMs and contextual preference ratings

  Dataset Details

There are two sequential stages: first, participants complete a Survey where they answer questions about their demographics and stated preferences, then proceed to… See the full description on the dataset page: https://huggingface.co/datasets/HannahRoseKirk/prism-alignment.

Monthly PRISM datasets covering the conterminous U.S., from 1981-2019 were used to calculate yearly average air temperature and spatially averaged yearly precipitation for selected counties in and near the Permian Basin. Distribution of the measurements was accomplished using the PRISM, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. The aggregated data was used to display and/or analyze spatially distributed yearly average air temperature and spatially averaged yearly precipitation for select counties in and near the Permian Basin from 1981-2019.

Meteorological map products that show annual precipitation (mm/in) and temperature minimum and maximum (C) averages. Please us link to PRISM Home website below for most current PRISM data.

Climate data--including 30-Year-normal data--provided by PRISM Climate Group at Oregon State University. Data is in raster formats.

Monthly 30-year 'normal' dataset covering the conterminous U.S., averaged over the climatological period 1981-2010. Contains spatially gridded average annual precipitation at 800m grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset was heavily peer reviewed, and is available free-of-charge on the PRISM website.

The PRISM NaNDA dataset provides daily weather data—minimum temperature (tmin), maximum temperature (tmax), and precipitation (ppt)—for all census tracts in the contiguous United States (CONUS) from 1981 to 2024. These data are derived from Oregon State University’s PRISM Climate Group (Northwest Alliance for Computational Science & Engineering & Oregon State University, 2025), which produces high-resolution (4 km x 4 km) gridded climate estimates.In addition to daily values, the dataset includes two types of annual tract-level summary measures:Percentiles (0.5th, 1st, 5th, 95th, 99th, and 99.5th), calculated using a rolling 10-year window of historical data, available for tmin, tmax, and ppt. Percents, representing the proportion of days per year that fall above or below these percentile thresholds, available for tmin and tmax only.These features enable robust analyses of long-term environmental trends, extreme weather events, and their potential impacts on population health.

Monthly totals of precipitation in millimeters (mm), monthly means of daily maximum air temperature in degrees Celsius (C), and monthly means of daily minimum air temperature (C) were developed at the 5 arc minute grid level for the conterminous United States (US) for the 1940-2006 period. Also, included are computed monthly mean of daily potential evapotranspiration (mm) and mean grid elevation in meters (m). These data were developed from PRISM (Parameter-elevation Regressions on Independent Slopes Model) data at the 2.5 arc minute scale and aggregated to the 5 arc minute grid scale. The county means were computed using a weighted mean of the 5 arc minute grids within the county.The USDA Forest Service (USFS) produces a periodic assessment of the condition and trends of the Nation's renewable resources as required by the Forest and Rangeland Renewable Resources Planning Act (RPA) of 1974. This RPA Assessment provides a snapshot of current US forest and rangeland conditions and trends on all ownerships, identifies drivers of change, and projects 50 years into the future (//www.fs.fed.us/research/rpa/, accessed 8/16/2009). For 2010 RPA Assessment, an integrated modeling framework is being used in which the potential implications of climate change can be analyzed across some resource areas (Langner in review). The nature of the climate variables needed to address climate change impacts for these resource analyses in the 2010 RPA Assessment were determined to be monthly precipitation and temperature variables at the county level spatial scale and for some resource analyses at the 5 arc minute grid scale.Original metadata date was 08/02/2010. Metadata modified on 04/22/2011 to adjust citation to include the addition of a DOI (digital object identifier). Minor metadata updates on 02/20/2013. Metadata modified on 07/22/2015 to update cross-reference citations and other minor updates. Additional minor metadata updates on 12/13/2016 and 04/19/2018.

Spatially distributed monthly and annual temperature. Each file represents 1 month of 1 year for the period 1895-1997. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Christopher Daly, Director, The PRISM Climate Group, Oregon State University. Care should be taken in estimating temperature values at any single point on the map. Temperature estimated for each grid cell is an average over the entire area of that cell; thus, point temperature can be estimated at a spatial precision no better than half the resolution of a cell. For example, the temperature data were distributed at a resolution of approximately 4km. Therefore, point temperature can be estimated at a spatial precision no better than 2km. However, the overall distribution of temperature features is thought to be accurate. For further information, the online PRISM homepage can be found at URL:http://prism.oregonstate.edu. Further information on the current state of this project can be found at URL:ftp://ftp.ncdc.noaa.gov/pub/data/prism100

Linework of assessment units intersected with PRISM 30-year normal mean temperature values for the period of 1981-2010. This is one input dataset in a model of stream temperatures.

This data layer is an element of the Oregon GIS Framework. Monthly 30-year "normal" dataset covering Oregon, averaged over the climatological period 1991-2020. Contains spatially gridded average annual total precipitation at 800m (30 arc-second) grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset is available free-of-charge on the PRISM website.

This OSU PRISM Group web site provides access to the highest-quality spatial climate data sets currently available. These data sets were created using the PRISM climate mapping system, developed by Dr. Christopher Daly, PRISM Group director. PRISM is unique in that it incorporates a spatial climate knowledge base that accounts for rain shadows, temperature inversions, coastal effects, and more in the climate mapping process. Daily [monthly] total precipitation (rain+melted snow)

This dataset contains PRISM data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which will produce high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and will be used to provide accurate atmospheric correction of the ocean color measurements. Level 1 data is available in netCDF format.

This metadata record describes a raster of unique PRISM (Parameter-elevation Relationships on Independent Slopes Model) identifier (PRISMID) values. The data are in ESRI's ArcInfo ASCII raster format, a non-proprietary text interchange format. PRISM climate data produced by the PRISM group at Oregon State University, such as time series of monthly precipitation and temperature, can be linked to the raster via the unique PRISMID values. In addition, model-estimated water budget components--including runoff (streamflow per unit area), evapotranspiration, snowfall and soil moisture storage--can be linked to the PRISM raster.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This study evaluates the consistency between in-situ measurements and gridded datasets for precipitation and temperature within the Great Salt Lake Basin, highlighting the significant implications for hydrological modelling and climate analysis. We analysed five widely recognized gridded datasets: GRIDMET, DAYMET, PRISM, NLDAS-2, and CONUS404, utilizing statistical metrics such as the Pearson Correlation Coefficient, Root Mean Square Error (RMSE), and Kling-Gupta Efficiency to assess their accuracy and reliability against ground truth data from 30 meteorological stations. Our findings indicate that the PRISM dataset outperformed others, demonstrating the lowest median RMSE values for both precipitation (approximately 1.9 mm/day) and temperature (approximately 0.9°C), which is attributed to its advanced interpolation methods that effectively incorporate orographic adjustments. In contrast, NLDAS-2 and CONUS404, despite their finer temporal resolutions, showed greater error variability and lower performance metrics, which may limit their utility for detailed hydrological applications. Through the use of visual analytical tools such as heatmaps and boxplots, we were able to vividly illustrate the performance disparities across the datasets, thereby providing a clear comparative analysis that underscores the strengths and weaknesses of each dataset. The study emphasizes the need for careful selection of gridded datasets based on specific regional characteristics to improve the accuracy and reliability of hydro climatological studies and supports better-informed decisions in climate-related adaptations and policy-making. The insights gained from this analysis aim to guide researchers and practitioners in selecting the most appropriate datasets that align with the unique climatic and topographical conditions of the Great Salt Lake Basin, enhancing the efficacy of environmental forecasting and resource management strategies.