This data release contains a shapefile of riparian vegetation communities attributed with information on trends in satellite-estimates of vegetation productivity for the period from 2000-2020. Cloud-masked Landsat data were processed from 2000 to 2020 to generate a 21-year growing season (June, July, and August) time series combining data from Landsat 5 (2000-2011), Landsat 7 (2012), and Landsat 8 (2013-2020). We computed the near-infrared reflectance of vegetation (NIRv) which is strongly correlated to vegetation Gross Primary Productivity (GPP). We analyzed growing season time series trends in NIRv by riparian vegetation type at the polygon-level using the Theil-Sen estimator (aka Sen's slope). In addition to the vector dataset is a table containing climate, topographic and land use co-variates used to model the environmental drivers of riparian vegetation change.

This data release contains a shapefile of riparian vegetation communities attributed with information on trends in satellite-estimates of vegetation productivity for the period from 2000-2020. Cloud-masked Landsat data were processed from 2000 to 2020 to generate a 21-year growing season (June, July, and August) time series combining data from Landsat 5 (2000-2011), Landsat 7 (2012), and Landsat 8 (2013-2020). We computed the near-infrared reflectance of vegetation (NIRv) which is strongly correlated to vegetation Gross Primary Productivity (GPP). We analyzed growing season time series trends in NIRv by riparian vegetation type at the polygon-level using the Theil-Sen estimator (aka Sen's slope). In addition to the vector dataset is a table containing climate, topographic and land use co-variates used to model the environmental drivers of riparian vegetation change.

This data release provides 270-m resolution maps of hotspots of vulnerability to projected changes in land-use, water shortages, and climate from 2001-2061 for agricultural, domestic, and ecological communities in the Central Coast of California, USA, under five management scenarios. This data covers the counties of Santa Cruz, San Benito, Monterey, San Luis Obispo, and Santa Barbara counties, but only cover those areas overlying a groundwater basin (because these contain the overwhelming majority of regional anthropogenic land-uses). Data are provided as .zip compressed file packages containing geospatial raster surfaces (.tif format). Each map is the product of one of three types of exposure to change (land, water, or climate) and one of three types of sensitivity to that change (agricultural, domestic, ecological). The resulting vulnerability measures map hotspots of nine vulnerabilities, plus a tenth map that is the sum of all nine measures to identify hotspots of overall vulnerability. See Van Schmidt et al. (2023) in Ecology & Society (doi: TBD) for full methodological details. Briefly, exposure to future land-use change and water shortages were jointly forecast from 2001 to 2061 with the Land Use and Carbon + Water Simulator (LUCAS-W) based on historical empirical rates. Exposure to climate change was calculated from five model-averaged RCP 8.5 forecasts of the Basin Characterization Model (BCM), which estimated change in runoff as surface water, potential recharge to groundwater aquifers, and climatic water deficit (CWD), among other variables. Lastly, sensitivity for communities was obtained from diverse datasets including LUCAS-W cropland projections, crop water demand data, farmland importance rankings, 2017 census data, range maps for imperiled species and subspecies, and wildlife agency reports. Sensitivity and exposure layers were rescaled 0-1 to allow for comparison, and the final vulnerability measures therefore have a possible range from 0 (no vulnerability) up to a maximum of 1 (maximum exposure and maximum sensitivity). The nine measures are as follows: (1) Land-Agricultural: Loss of important farmland; (2) Land-Domestic: Lack of new development in areas with housing needs; (3) Land-Ecological: Loss of critical habitats for endangered species; (4) Water-Agricultural: Increased water demand that cannot be fallowed (orchards/vineyards); (5) Water-Domestic: Household vulnerability to increased water inaffordability; (6) Water-Ecological: Drying of groundwater-dependent habitats for endangered species; (7) Climate-Agricultural: Increased irrigation water needs of crops; (8) Climate-Domestic: Household vulnerability to heat-related health impacts; (9) Climate-Ecological: Loss of runoff & recharge that keeps streams, ponds, and vernal pools wet. Each .zip file is a compressed file package containing maps of each measure under five scenarios, which have different sets of management assumptions along two axes, Water management Low/Moderate/High intensity and Land use management Low/Moderate/High intensity: - MM (Moderate / Moderate management intensity): a scenario where water demand caps under the Sustainable Groundwater Management Act (SGMA) reduce development in overdrafted groundwater basins based on current total water supplies, and where prime farmland and groundwater recharge areas will be protected from urban sprawl (i.e., land use projections assuming development stabilizes at a level sustainable with current water supplies, and urban sprawl limits). The other four scenarios differ from the MM scenario by altering one of these management strategies, while keeping the second strategy at the "Moderate" level. -- WL (Water management Low intensity): a pre-SGMA "business-as-usual" scenario where water demand is uncoupled from land-use change and does not need to stabilize at sustainable levels. -- WH (Water management High intensity): a scenario that assumes that water demand caps, but with increased caps due

This data release provides 270-m resolution maps of hotspots of vulnerability to projected changes in land-use, water shortages, and climate from 2001-2061 for agricultural, domestic, and ecological communities in the Central Coast of California, USA, under five management scenarios. This data covers the counties of Santa Cruz, San Benito, Monterey, San Luis Obispo, and Santa Barbara counties, but only cover those areas overlying a groundwater basin (because these contain the overwhelming majority of regional anthropogenic land-uses). Data are provided as .zip compressed file packages containing geospatial raster surfaces (.tif format). Each map is the product of one of three types of exposure to change (land, water, or climate) and one of three types of sensitivity to that change (agricultural, domestic, ecological). The resulting vulnerability measures map hotspots of nine vulnerabilities, plus a tenth map that is the sum of all nine measures to identify hotspots of overall vulnerability. See Van Schmidt et al. (2023) in Ecology & Society (doi: TBD) for full methodological details. Briefly, exposure to future land-use change and water shortages were jointly forecast from 2001 to 2061 with the Land Use and Carbon + Water Simulator (LUCAS-W) based on historical empirical rates. Exposure to climate change was calculated from five model-averaged RCP 8.5 forecasts of the Basin Characterization Model (BCM), which estimated change in runoff as surface water, potential recharge to groundwater aquifers, and climatic water deficit (CWD), among other variables. Lastly, sensitivity for communities was obtained from diverse datasets including LUCAS-W cropland projections, crop water demand data, farmland importance rankings, 2017 census data, range maps for imperiled species and subspecies, and wildlife agency reports. Sensitivity and exposure layers were rescaled 0-1 to allow for comparison, and the final vulnerability measures therefore have a possible range from 0 (no vulnerability) up to a maximum of 1 (maximum exposure and maximum sensitivity). The nine measures are as follows: (1) Land-Agricultural: Loss of important farmland; (2) Land-Domestic: Lack of new development in areas with housing needs; (3) Land-Ecological: Loss of critical habitats for endangered species; (4) Water-Agricultural: Increased water demand that cannot be fallowed (orchards/vineyards); (5) Water-Domestic: Household vulnerability to increased water inaffordability; (6) Water-Ecological: Drying of groundwater-dependent habitats for endangered species; (7) Climate-Agricultural: Increased irrigation water needs of crops; (8) Climate-Domestic: Household vulnerability to heat-related health impacts; (9) Climate-Ecological: Loss of runoff & recharge that keeps streams, ponds, and vernal pools wet. Each .zip file is a compressed file package containing maps of each measure under five scenarios, which have different sets of management assumptions along two axes, Water management Low/Moderate/High intensity and Land use management Low/Moderate/High intensity: - MM (Moderate / Moderate management intensity): a scenario where water demand caps under the Sustainable Groundwater Management Act (SGMA) reduce development in overdrafted groundwater basins based on current total water supplies, and where prime farmland and groundwater recharge areas will be protected from urban sprawl (i.e., land use projections assuming development stabilizes at a level sustainable with current water supplies, and urban sprawl limits). The other four scenarios differ from the MM scenario by altering one of these management strategies, while keeping the second strategy at the "Moderate" level. -- WL (Water management Low intensity): a pre-SGMA "business-as-usual" scenario where water demand is uncoupled from land-use change and does not need to stabilize at sustainable levels. -- WH (Water management High intensity): a scenario that assumes that water demand caps, but with increased caps due

This dataset provides annual raster maps of historical and projected future land use and land cover (LULC) for California, USA. Changes in LULC over time were simulated using the Land Use and Carbon Scenario Simulator (LUCAS) model. The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future time periods (2021-2100). Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides annual raster maps of historical and projected future land use and land cover (LULC) for California, USA. Changes in LULC over time were simulated using the Land Use and Carbon Scenario Simulator (LUCAS) model. The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future time periods (2021-2100). Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides annual raster maps of transition probability (i.e., land use change or disturbance) for California, USA. Land change transition probabilities were derived from the Land Use and Carbon Scenario Simulator (LUCAS). The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future (2021-2100) time periods. Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides annual raster maps of transition probability (i.e., land use change or disturbance) for California, USA. Land change transition probabilities were derived from the Land Use and Carbon Scenario Simulator (LUCAS). The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future (2021-2100) time periods. Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides annual raster maps of land change transition probability (e.g., urbanization, wildland fire, timber harvest) for California, USA. Land change transition probabilities are from Land Use and Carbon Scenario Simulator (LUCAS) simulations. Each simulation was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future (2021-2100) time periods. Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations per simulation.

This dataset provides annual raster maps of transition probability (i.e., land use change or disturbance) for California, USA. Land change transition probabilities were derived from the Land Use and Carbon Scenario Simulator (LUCAS). The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future (2021-2100) time periods. Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.