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.

This dataset includes PRISM derived 1961-1990 climatologies of monthly average, maximum, and minimum temperature and total precipitation across Alaska and Western Canada including the Yukon, British Columbia, Alberta, Saskatchewan, and Manitoba. These were obtained from the PRISM Climate Group and mosaicked into a single continuous transboundary extent. Please cite the PRISM Climate Group when using this data.

This data release provides the underlying data for Kampf et al., in review: "Rethinking the role of the water balance in hydrologic research." Mean annual climatic variables based on the Northern Hemisphere water year (October 1 to September 30) and several watershed properties are provided for 121 USGS reference watersheds smaller than 1,000 square kilometers. For each climatic variable, mean annual values were derived from watershed average annual values.

The columns of the dataset are as follows:

SP- watershed averaged January 1 to July 1 snow persistence as in Hammond et al., 2018 P_mm - watershed averaged total water year precipitation from PRISM, Daly, 2013 Q_mm - total water year water yield from USGS NWIS QdivP - runoff ratio, total water year water yield divided by total water year precipitation PET - watershed averaged total water year potential evapotranspiration from gridMET - Abatzoglou, 2013 PdivPET - the ratio of total water year precipitation to total water year potential evapotranspiration from the sources above. Elev_mean_m - GAGES-II, Falcone, 2011 Area_km2 - GAGES-II, Falcone, 2011

Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131.

Daly, C. (2013). Descriptions of PRISM spatial climate datasets for the conterminous United States (PRISM Doc., 14 p.). Corvallis, OR: PRISM Climate Group, Oregon State University.

Falcone, J. A. (2011). GAGES-II: Geospatial attributes of gages for evaluating streamflow (Digit. Spat. Data set). Reston, VA: U.S. Geological Survey.

Hammond, J. C., Saavedra, F. A., & Kampf, S. K. (2018). How does snow persistence relate to annual streamflow in mountain watersheds of the Western U.S. with wet maritime and dry continental climates? Water Resources Research, 54, 2605–2623. https://doi.org/10.1002/ 2017WR021899

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). Also, included are computed monthly mean of daily potential evapotranspiration (mm) and mean grid elevation in meters (m). These data were developed from climate scenarios used in the Fourth Assessment of the Intergovernmental Panel on Climate Change, specifically the A1B and the A2 SRES (Special Report on Emissions Scenarios) scenarios as modeled by these climate models: CGCM3.1MR, CSIRO-MK3.5, and MIROC3.2MR. The monthly change factors were developed from global model output and downscaled to the 5 arc minute spatial grid using ANUSPLIN. The 30 year mean climatology (1961-1990) was 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 change factors were imposed upon the 30-year period (1961-1990) to develop the projections for each climate scenario.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 07/06/2015). 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 et al. 2012). 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 5 arc minute grid level spatial scale.Original metadata dated 08/02/2010. Minor modifications made to Attribute Accuracy section of metadata on 09/17/2010. Metadata modified on 02/22/2012 to adjust citation to include the addition of a DOI (digital object identifier) and update to the cross-reference section. 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.

Climate data for adaptation and vulnerability assessments — southwest (ClimAVA-SW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-SW dataset encompasses the geopolitical boundaries of the six states in the southwestern United States: California, Nevada, Arizona, New Mexico, Utah, and Colorado, as well as watersheds that run into these states. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.

This digital dataset contains monthly reference evapotranspiration (ETo) data for the Central Valley Hydrologic Model (CVHM). The Central Valley encompasses an approximate 50,000 square-kilometer region of California. The complex hydrologic system of the Central Valley is simulated using the USGS numerical modeling code MODFLOW-FMP (Schmid and others, 2006b). This simulation is referred to here as the CVHM (Faunt, 2009). Utilizing MODFLOW-FMP, the CVHM simulates groundwater and surface-water flow, irrigated agriculture, land subsidence, and other key processes in the Central Valley on a monthly basis from 1961-2003. The total active modeled area is 20,334 square-miles on a finite-difference grid comprising 441 rows and 98 columns. Slightly less that 50 percent of the cells are active. The CVHM grid has a uniform horizontal discretization of 1x1 square mile and is oriented parallel to the valley axis, 34 degrees west of north (Faunt, 2009). The main climatic contributors to the CVHM are ETo and precipitation. Data from Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (Climate Source, 2006) provide the temperature and precipitation on a monthly basis for a 2-kilometer grid. Mapping land-use type and distribution is integral to calculating the crop irrigation demand and, ultimately, the water use in the Central Valley. For a given land use, the demand can be calculated from two variables: crop coefficient (Kc) and reference evapotranspiration (ETo). For the CVHM, ETo is calculated from the daily minimum and maximum air temperatures derived from PRISM data and the extraterrestrial solar radiation (Faunt, 2009, p. 151). The CVHM is the most recent regional-scale model of the Central Valley developed by the U.S. Geological Survey (USGS). The CVHM was developed as part of the USGS Groundwater Resources Program (see "Foreword", Chapter A, page iii, for details).

The Gridded Surface Meteorological dataset provides high spatial resolution (~4-km) daily surface fields of temperature, precipitation, winds, humidity and radiation across the contiguous United States from 1979. The dataset blends the high resolution spatial data from PRISM with the high temporal resolution data from the National Land Data Assimilation System (NLDAS) to produce spatially and temporally continuous fields that lend themselves to additional land surface modeling. This dataset contains provisional products that are replaced with updated versions when the complete source data become available. Products can be distinguished by the value of the 'status' property. At first, assets are ingested with status='early'. After several days, they are replaced by assets with status='provisional'. After about 2 months, they are replaced by the final assets with status='permanent'.

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 county level for the conterminous United States (US). Also, included are computed monthly mean of daily potential evapotranspiration (mm) and mean grid elevation in meters (m). These data were developed from climate scenarios used in the Fourth Assessment of the Intergovernmental Panel on Climate Change, specifically the A1B and the A2 SRES (Special Report on Emissions Scenarios) scenarios as modeled by these climate models: CGCM3.1MR, CSIRO-MK3.5, and MIROC3.2MR. The monthly change factors were developed from global model output and downscaled to the 5 arc minute spatial grid using ANUSPLIN. The 30 year mean climatology (1961-1990) was 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 change factors were imposed upon the 30-year period (1961-1990) to develop the projections for each climate scenario. 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 (https://www.fs.usda.gov/research/inventory/rpaa). 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 et al. 2012). 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 resources, at the 5 arc minute grid scale.Original metadata date was 08/03/2010. Metadata modified on 04/18/2011 to adjust citation to include the addition of a DOI (digital object identifier). Minor metadata updates on 02/19/2013. Metadata modified on 07/22/2015 to update cross-reference citations and other minor updates. Additional minor metadata updates on 12/13/2016, 02/08/2021, and 10/27/2022.

[ Derived from parent entry - See data hierarchy tab ]

Climate data for adaptation and vulnerability assessments — northwest (ClimAVA-NW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-NW dataset encompasses the geopolitical boundaries of the five states in the northwestern United States: Idaho, Oregon, Wyoming, Montana, and Washington. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.

October 31, 2024 (Final DWR Data)

The 2018 Legislation required DWR to provide or otherwise identify data regarding the unique local conditions to support the calculation of an urban water use objective (CWC 10609. (b)(2) (C)). The urban water use objective (UWUO) is an estimate of aggregate efficient water use for the previous year based on adopted water use efficiency standards and local service area characteristics for that year.

UWUO is calculated as the sum of efficient indoor residential water use, efficient outdoor residential water use, efficient outdoor irrigation of landscape areas with dedicated irrigation meter for Commercial, Industrial, and Institutional (CII) water use, efficient water losses, and an estimated water use in accordance with variances, as appropriate. Details of urban water use objective calculations can be obtained from DWR’s Recommendations for Guidelines and Methodologies document (Recommendations for Guidelines and Methodologies for Calculating Urban Water Use Objective - https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/Performance-Measures/UWUO_GM_WUES-DWR-2021-01B_COMPLETE.pdf).

The datasets provided in the links below enable urban retail water suppliers calculate efficient outdoor water uses (both residential and CII), agricultural variances, variances for significant uses of water for dust control for horse corals, and temporary provisions for water use for existing pools (as stated in Water Boards’ draft regulation). DWR will provide technical assistance for estimating the remaining UWUO components, as needed. Data for calculating outdoor water uses include:

• Reference evapotranspiration (ETo) – ETo is evaporation plant and soil surface plus transpiration through the leaves of standardized grass surfaces over which weather stations stand. Standardization of the surfaces is required because evapotranspiration (ET) depends on combinations of several factors, making it impractical to take measurements under all sets of conditions. Plant factors, known as crop coefficients (Kc) or landscape coefficients (KL), are used to convert ETo to actual water use by specific crop/plant. The ETo data that DWR provides to urban retail water suppliers for urban water use objective calculation purposes is derived from the California Irrigation Management Information System (CIMIS) program (https://cimis.water.ca.gov/). CIMIS is a network of over 150 automated weather stations throughout the state that measure weather data that are used to estimate ETo. CIMIS also provides daily maps of ETo at 2-km grid using the Spatial CIMIS modeling approach that couples satellite data with point measurements. The ETo data provided below for each urban retail water supplier is an area weighted average value from the Spatial CIMIS ETo.

• Effective precipitation (Peff) - Peff is the portion of total precipitation which becomes available for plant growth. Peff is affected by soil type, slope, land cover type, and intensity and duration of rainfall. DWR is using a soil water balance model, known as Cal-SIMETAW, to estimate daily Peff at 4-km grid and an area weighted average value is calculated at the service area level. Cal-SIMETAW is a model that was developed by UC Davis and DWR and it is widely used to quantify agricultural, and to some extent urban, water uses for the publication of DWR’s Water Plan Update. Peff from Cal-SIMETAW is capped at 25% of total precipitation to account for potential uncertainties in its estimation. Daily Peff at each grid point is aggregated to produce weighted average annual or seasonal Peff at the service area level. The total precipitation that Cal-SIMETAW uses to estimate Peff comes from the Parameter-elevation Relationships on Independent Slopes Model (PRISM), which is a climate mapping model developed by the PRISM Climate Group at Oregon State University.

• Residential Landscape Area Measurement (LAM) – The 2018 Legislation required DWR to provide each urban retail water supplier with data regarding the area of residential irrigable lands in a manner that can reasonably be applied to the standards (CWC 10609.6.(b)). DWR delivered the LAM data to all retail water suppliers, and a tabular summary of selected data types will be provided here. The data summary that is provided in this file contains irrigable-irrigated (II), irrigable-not-irrigated (INI), and not irrigable (NI) irrigation status classes, as well as horse corral areas (HCL_area), agricultural areas (Ag_area), and pool areas (Pool_area) for all retail suppliers.

This dataset provides code and example data for simulating specimen collections of flowering plants across North America, and for developing phenological predictions of population-level flowering onset and termination for these data. It further presents code for assessing the accuracy of these predictions relaticve to known (simulated) population-level flowering dates at the location of each collection., Creating a reference dataset: generating sample locations representing known population-level phenological distributions and individual phenological parameters We simulated phenological data for 1200 hypothetical “species†in the coterminous USA that varied in the attributes of their individual- and population-level flowering phenology. For each of these simulated species, we selected 1000 locations within the continental United States, each representing a local population observed during a single year from which a simulated specimen was later obtained. The coordinates for each location, year, and associated mean annual temperature in the year of collection were randomly selected without replacement from 4-km2 PRISM pixels (PRISM Climate Group 2011) between the years 1901 to 2020, and were restricted to locations with 1991–2020 temperature normals of 1–20 °C and mean annual precipitation normals for the same period of 60–3800 mm. Each species generated this way was assigned a series of ..., , # Simulated Herbarium data for testing the accuracy with which specimen data

This README.txt file was generated on 2023-10-20 by Isaac Park

GENERAL INFORMATION

1. Title of Dataset: Phenological models inferring population-level flowering onset and termination from simulated herbarium data
2. Author Information witheld for review*
3. Date of data collection 10/10/2023:
4. Geographic location of data collection: NA, simulations based on climate data from continental USA
5. Development of these datasets and associated python code were supported by the National Science Foundation

SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: Public Domain
2. Links to publications that cite or use the data:
3. Links to other publicly accessible locations of the data:
4. Links/relationships to ancillary data sets: Raw observation locations used to underpin simulations in this data can be acquired from USANPN.org. Climate data is available through [https://www.pris...

[ Derived from parent entry - See data hierarchy tab ]

Climate data for adaptation and vulnerability assessments — southwest (ClimAVA-SW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-SW dataset encompasses the geopolitical boundaries of the six states in the southwestern United States: California, Nevada, Arizona, New Mexico, Utah, and Colorado, as well as watersheds that run into these states. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.

[ Derived from parent entry - See data hierarchy tab ]

Climate data for adaptation and vulnerability assessments — southwest (ClimAVA-SW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-SW dataset encompasses the geopolitical boundaries of the six states in the southwestern United States: California, Nevada, Arizona, New Mexico, Utah, and Colorado, as well as watersheds that run into these states. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.

[ Derived from parent entry - See data hierarchy tab ]

Climate data for adaptation and vulnerability assessments — northwest (ClimAVA-NW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-NW dataset encompasses the geopolitical boundaries of the five states in the northwestern United States: Idaho, Oregon, Wyoming, Montana, and Washington. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.

Climate data for adaptation and vulnerability assessments — northwest (ClimAVA-NW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-NW dataset encompasses the geopolitical boundaries of the five states in the northwestern United States: Idaho, Oregon, Wyoming, Montana, and Washington. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.