[Metadata] Mean Annual Rainfall Isohyets in Millimeters for the Islands of Hawai‘i, Kaho‘olawe, Kaua‘i, Lāna‘i, Maui, Moloka‘i and O‘ahu. Source: 2011 Rainfall Atlas of Hawaii, https://www.hawaii.edu/climate-data-portal/rainfall-atlas. Note that Moloka‘I data/maps were updated in 2014. Please see Rainfall Atlas final report appendix for full method details: https://www.hawaii.edu/climate-data-portal/rainfall-atlas. Statewide GIS program staff downloaded data from UH Geography Department, Rainfall Atlas of Hawaii, February, 2019. Annual and monthly isohyets of mean rainfall were available for download. The statewide GIS program makes available only the annual layer. Both the monthly layers and the original annual layer are available from the Rainfall Atlas of Hawaii website, referenced above. Note: Contour attribute value represents the amount of annual rainfall, in millimeters, for that line/isohyet. For additional information, please see metadata at https://files.hawaii.gov/dbedt/op/gis/data/isohyets.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

This dataset contains gridded monthly rainfall from 1990 to 2019 at 250 m resolution for seven of the eight main Hawaiian Islands (18.849°N, 154.668°W to 22.269°N, 159.816°W; the island of Ni‘ihau is excluded due to lack of data). The gridded data use a World Geographic Coordinate System 1984 (WGS84) and are stored as individual GeoTIFF files for each month-year, as indicated by the GeoTIFF file name. Contained in the dataset is a statewide complete 30-year partially gap filled monthly rainfall dataset for all stations for the entire date range with station names, ID and location. Also included are month year statewide files for rainfall kriging input files which contain station rainfall, station rainfall transformations, station transformed anomaly, and denotation of inclusion in per county kriging process, statewide gridded rainfall, statewide standard error, statewide gridded rainfall anomaly, statewide gridded rainfall anomaly standard errors, and statewide meta-data that contain per county as well as statewide cross validation statistics, station counts, and readable data quality statement. Monthly rainfall grids were created using an optimized geostatistical kriging approach to interpolate relative rainfall anomalies which are then combined with long-term means to develop the climatologically aided gridded estimates. Optimization of the kriging algorithm consists of: 1) determining an offset (constant) to use when log-transforming data; 2) quality controlling data prior to interpolation; 3) using machine learning to detect erroneous maps; and 4) identifying the most appropriate parametrization scheme for fitting the model used in the interpolation. At present, the data are available from 1990 to 2019, but datasets will be updated as new gridded monthly rainfall data become available. Rainfall products and error metrics are also available by county and can be accessed online for easy download through the Hawaiʻi Data Climate Portal available at http://www.hawaii.edu/climate-data-portal.

This dataset was assembled using statistically downscaled climate projections from the NASA Earth Exchange-Global Daily Downscaled Projections (NEX-GDDP) project. These climate change scenarios have been developed using global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and four different future scenarios, known as Shared Socioeconomic Pathways (SSPs). The four SSPs involved in this project are SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The raw NEX-GDDP-CMIP6 data has a spatial resolution of 0.25 degrees and a daily temporal resolution. The NEX-GDDP-CMIP6 data was processed to calculate change in climatic variables for each season (fall, spring, summer, winter) and annually for 30-year periods. The five period comparisons available in the dataset are as follows: 1976-2005 to 2025-2054, 1976-2005 to 2045-2074, 1976-2005 to 2070-2099, 2025-2054 to 2045 to 2074, and 2025-2054 to 2070 to 2099. The six climatic variables included in the dataset are change in: total precipitation [in], total precipitation [%], total potential evapotranspiration [in], total potential evapotranspiration [%], maximum temperature [degF], and minimum temperature [degF]. This data was then used to produce an ensemble median of all available NEX-GDDP downscaled GCMs for each variable. Not all GCMs downscaled in NEX-GDDP had availability for every variable and scenario combination. The ensemble data was summarized by HUC-12 feature classes described above. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheets (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This dataset describes land cover and vegetation for the island of Maui, Hawaii, 2017, hereinafter the 2017 land-cover map. The 2017 land-cover map is a modified version of the 2010 land-cover map included in the geospatial dataset titled "Mean annual water-budget components for the Island of Maui, Hawaii, for average climate conditions, 1978-2007 rainfall and 2010 land cover (version 2.0)" by Johnson (2017). The 2010 land-cover map was generated by intersecting (merging) multiple spatial datasets that characterize the spatial distribution of rainfall, cloud-water (or fog) interception, irrigation, reference evapotranspiration, direct runoff, soil type, and land cover. Land-cover designations in the 2010 land-cover map were derived mainly from the U.S. Geological Survey LANDFIRE Existing Vegetation Type map (LANDFIRE.HI_110EVT, Refresh 2008) for the island of Maui. The 2017 land-cover map retains the merged structure of the 2010 land-cover map but includes modifications mainly related to agricultural land use since the release of the 2010 land-cover map. Modifications to the 2010 land-cover map included updating the land cover and vegetation designations, and the polygon boundaries in the 2010 land-cover map to reflect (1) the cessation of sugarcane cultivation by Hawaiian Commercial & Sugar Company in December 2016, and (2) the agricultural land-use information described in the Statewide Agricultural Land Use Baseline 2015 map by Melrose and others (2016). These modifications affected about 10 percent of the total area in the 2010 land-cover map. The 2017 land-cover map also distinguishes between (1) forested areas that are within the fog-interception zone, assumed to be at elevations of 2,000 feet and higher on Maui, and (2) forested areas that are below the fog-interception zone. The same distinction was included in the analysis of Johnson and others (2018) and in the spatial structure of the 2010 land-cover map, but was omitted from the land-cover names in the attribute table of the 2010 land-cover map.

The rainfall-runoff erosivity factor (R-Factor) quantifies the effects of raindrop impacts and reflects the amount and rate of runoff associated with the rain. The R-factor is one of the parameters used by the Revised Unified Soil Loss Equation (RUSLE) to estimate annual rates of erosion. This product is a raster representation of R-Factor derived from isoerodent maps published in the Agriculture Handbook Number 703 (Renard et al.,1997). Lines connecting points of equal rainfall ersoivity are called isoerodents. The iserodents plotted on a map of the Island of Hawaii were digitized, then values between these lines were obtained by linear interpolation. The final R-Factor data are in raster GeoTiff format at 30 meter resolution in UTM, Zone 4, GRS80, NAD83.

Global downscaled projections are now some of the most widely used climate datasets in the world, however, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we show steps to improve the utility of two such global datasets (CHELSA and WorldClim2) to provide credible climate scenarios for regional climate change impact studies. Our approach is based on three steps: 1) Using a standardized baseline period, comparing available global downscaled projections with regional observation-based datasets and regional downscaled datasets (if available); 2) bias correcting projections using observation-based data; and 3) creating ensembles to make use of the differential strengths of global downscaling datasets. We also explored the patterns and magnitude of change for these regional projected climate shifts to determine their plausibility as future climate scenarios using Hawaiʻi as an example region. While our ensemble projections were shown to largely reduce the deviations between model and observation-based current climate, we show projected climate shifts from these commonly used global datasets can fall well outside the range of future scenarios derived from fine-tuned regional downscaling efforts, and hence should be carefully evaluated. This data release includes a baseline (1983-2012) model as well future climate projections for mid- (2040-2059) and late-century (2060-2079) for three regionally-adapted global datasets (CHELSA, WorldClim2, and an ensemble). We considered mean annual temperature (MAT) and mean annual precipitation (MAP) as our primary variables for comparison since they are the most widely used and desired datasets for climate impact studies. These regionally-downscaled future climate projections are available for various individual Global Circulation Models (GCMs) under four representative concentration pathways (RCPs; 2.6, 4.5, 6.0, and 8.5) for each global dataset.

This is the primary output dataset from the project to access the potential impacts of climate change on vegetation management strategies within Hawaii Volcanoes National Park (HAVO). The key objective of this project was to combine climate projections from the International Pacific Research Center (IPRC) and plant distribution models from Price et al. to produce a series of projected species range maps over the next century. Although the project focused on HAVO, the projected species range maps were created for seven of the main Hawaiian Islands. We stored the model output as rasters (.TIF files); additionally we created multi-panel maps of these rasters that are available separately. In summary, this dataset consists of 4,095 rasters that delineate plant species range, both present and future, for various climate change scenarios and years. The series covers 39 species, 7 islands, and 15 different combinations of climate trajectory and year. The contents of each raster varies slightly, but the contents can be determined from the specific filename. Filenames have a consistent naming convenion, as follows: Species name + island + file type + climate trajectory + year.TIF, where the following definitions apply: Species name = abbreviated code representing genus and species; Island = 1 of the main 7 Hawaiian Islands (Hawaii, Maui, Kahoolwe, Lanai, Molokai, Oahu, and Kauai); File type = one of 3 file types: (1) RANGE = present species range as of year 2000, (2) 80 PCT = binary raster of habitat suitability, (3) CHANGE TO 80 = raster showing the change in suitability between the year 2000 and the year indicated in the file name; Climate trajectory = lower (concave upward trajectory of change in rainfall and temperature over the century), middle (linear change in rainfall and temperature), upper (concave downward trajectory of change in rainfall and temperature), or future (where all three trajectories converge in 2090); Year = one of the following years: 2000, 2040, 2070, or 2090. For example, consider this filename: Acakoa Hawaii 80 pct future2090.tif. This filename defines the following: Species name = Acakoa (Acacia koa), Island = Hawaii island, File type = 80 pct, indicating that it is a binary raster of habitat suitability where a value of 1 means 80% of model iterations forecast suitable habitat, and a value of 0 means less than 80% of model runs project suitability, Climate trajectory = future, which represents the point in the future (2090) where the lower, middle and upper trajectories converge, Year = 2090 (end of century since that's when our climate data set series ends).

One of the determinants of runoff is the occurrence of excess rainfall events where rainfall rates exceed the infiltration capacity of soils. To help understand runoff risks, we calculated the probability of excess rainfall events across the Hawaiian landscape by comparing the probability distributions of projected rainfall frequency and land cover-specific infiltration capacity. We characterized soil infiltration capacity based on different land cover types (bare soil, grasses, and woody vegetation) and compared them to the frequency of large rainfall events under current and future (pseudo-global warming) climate scenarios. This simple analysis allowed us to map the potential risk of excess rainfall across the main Hawaiian Islands. Here we provide rasters that contain the probability of rainfall exceeding infiltration capacity in each grid cell at 90 m. We have included rasters of excess rainfall probabilities for current (2002-2012) and future (2090-2099) scenarios as well as by each individual land cover class considered.

This map provides locations for all of the real-time wind and rainfall data sites used in the Hawaii Rainfall Summary (RRAHFO), the Hawaii One-Hour Rainfall Summary (RR5HFO), and the Hawaii Wind Data (OSOHFO) products. The map replaces the static version, which is still valid, but cannot accommodate the large number of real-time sites that are now available. The data sites belong to the National Oceanic and Atmospheric Administration, the U.S. Geological Survey, National Park Service, Bureau of Land Management, U.S. Fish and Wildlife Service, Department of Defense, State of Hawaii Dept. of Land and Natural Resources, University of Hawaii at Manoa, and the Hawaiian Electric Company.

Global downscaled projections are now some of the most widely used climate datasets in the world, however, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we show steps to improve the utility of two such global datasets (CHELSA and WorldClim2) to provide credible climate scenarios for regional climate change impact studies. Our approach is based on three steps: 1) Using a standardized baseline period, comparing available global downscaled projections with regional observation-based datasets and regional downscaled datasets (if available); 2) bias correcting projections using observation-based data; and 3) creating ensembles to make use of the differential strengths of global downscaling datasets. We also explored the patterns and magnitude of change for these regional projected climate shifts to determine their plausibility as future climate scenarios using Hawaiʻi as an example region. While our ensemble projections were shown to largely reduce the deviations between model and observation-based current climate, we show projected climate shifts from these commonly used global datasets can fall well outside the range of future scenarios derived from fine-tuned regional downscaling efforts, and hence should be carefully evaluated. This data release includes a baseline (1983-2012) model as well future climate projections for mid- (2040-2059) and late-century (2060-2079) for three regionally-adapted global datasets (CHELSA, WorldClim2, and an ensemble). We considered mean annual temperature (MAT) and mean annual precipitation (MAP) as our primary variables for comparison since they are the most widely used and desired datasets for climate impact studies. These regionally-downscaled future climate projections are available for various individual Global Circulation Models (GCMs) under four representative concentration pathways (RCPs; 2.6, 4.5, 6.0, and 8.5) for each global dataset.

One of the most basic ways to visualize global temperature data over time is with what has come to be called "warming stripes." Popularized by Ed Hawkins, this style of graphic is a row of thin vertical stripes, each showing one year's temperature compared to a long-term average. These simple visualizations do not use numbers or dates; the pattern of colors alone tells the story of climate change and variability over time. For our "Climate Stripes: U.S. states" webmap, meteorologist Jared Rennie has produced climate stripes images for temperature and precipitation trends in U.S. states from 1895–2022. Users can click on a location and see a temperature stripes image and a precipitation stripes image based on NOAA climate data. A previous version of this map included Alaska, but not Hawaii or Washington, D.C. This map includes all three. This feature layer holds US state polygons that link to the climate stripes images.Description of DataData originates from NOAA NCEI's climate at a glance page, which uses a 5-kilometer gridded data set, known as nClimgrid. This data set provides temperature and precipitation information for each month back to 1895 for the contiguous United States ("the Lower 48"). Annual estimates since 1895 are derived from the monthly data and aggregated onto each state for the continental United States, including the District of Columbia. For Alaska, data go back to 1925; for Hawaii, the images are based on data from individual stations dating back to 1955. To depict the long term change in temperature and precipitation, annual data are then compared to a 20th-century average (1901-2000). These differences from the long-term average (known as a departure from normal, or anomaly) are then used to produce the climate stripes image. For more information on anomalies, please refer to this FAQ page.