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 the resulting land-use projections for California's Central Coast from 2001-2061 at a resolution of 270-m. Data are provided as (1) annual rasters and (2) summarized as the mean annual transition probability across 10 Monte Carlo iterations. Each package contains folders for five scenarios, which have different sets of management assumptions along two axes: Water management Low/Moderate/High and Land use management Low/Moderate/High. - MM (Moderate/Moderate): a scenario where water demand caps 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): a scenario with no feedbacks between water supplies and development (i.e., land use projections assuming development is not constrained by water availability, closest to a "business-as-usual" continuation of the region's historic trajectory). - WH (Water management High): a scenario that assumes that water demand caps, but with increased caps due to enhanced water supplies proposed under local groundwater agencies' Groundwater Sustainability Plans (i.e., land use projections assuming development stabilizes at a higher water demand). - LL (Land use management Low): a scenario where prime farmland and groundwater recharge areas are not protected from urban sprawl (i.e., land use projections assuming relatively unregulated land use planning, with water sustainability based on current supplies). - LH (Land use management High): a scenario where almost all the state's priority habitats are preserved from urbanization or agricultural expansion (i.e., land use projections assuming a very compact pattern of development, with water sustainability based on current supplies). These projections were created with LUCAS-W, a scenario-based simulation model of coupled land use change and associated water demand. This model is a version of the LUCAS model, which uses the SyncroSim software framework (Software documentation available at http://doc.syncrosim.com/index.php?title=Reference_Guide), that contains a new coupling with statistical software R (https://www.r-project.org/) to enable dynamic feedbacks between land-use change, resulting water demand, and water availability. The model was parameterized with land-use change and water use empirically estimated from county-scale historic data, as well as results from dozens of local agencies’ groundwater modeling efforts. By scaling up studies of local-scale diverse, heterogeneous aquifers and management approaches to a regional level, the model can enable a projection of spatial changes due to shifts in LULC and water management including leakage from land and water use regulated areas into unregulated areas, information that is key to future agency planning for sustainability. See Van Schmidt et al. (2021) Water Resources Research (doi: XXXXXXXXXXXXX) for more details.

This data release provides the resulting land-use projections for California's Central Coast from 2001-2061 at a resolution of 270-m. Data are provided as (1) annual rasters and (2) summarized as the mean annual transition probability across 10 Monte Carlo iterations. Each package contains folders for five scenarios, which have different sets of management assumptions along two axes: Water management Low/Moderate/High and Land use management Low/Moderate/High. - MM (Moderate/Moderate): a scenario where water demand caps 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): a scenario with no feedbacks between water supplies and development (i.e., land use projections assuming development is not constrained by water availability, closest to a "business-as-usual" continuation of the region's historic trajectory). - WH (Water management High): a scenario that assumes that water demand caps, but with increased caps due to enhanced water supplies proposed under local groundwater agencies' Groundwater Sustainability Plans (i.e., land use projections assuming development stabilizes at a higher water demand). - LL (Land use management Low): a scenario where prime farmland and groundwater recharge areas are not protected from urban sprawl (i.e., land use projections assuming relatively unregulated land use planning, with water sustainability based on current supplies). - LH (Land use management High): a scenario where almost all the state's priority habitats are preserved from urbanization or agricultural expansion (i.e., land use projections assuming a very compact pattern of development, with water sustainability based on current supplies). These projections were created with LUCAS-W, a scenario-based simulation model of coupled land use change and associated water demand. This model is a version of the LUCAS model, which uses the SyncroSim software framework (Software documentation available at http://doc.syncrosim.com/index.php?title=Reference_Guide), that contains a new coupling with statistical software R (https://www.r-project.org/) to enable dynamic feedbacks between land-use change, resulting water demand, and water availability. The model was parameterized with land-use change and water use empirically estimated from county-scale historic data, as well as results from dozens of local agencies’ groundwater modeling efforts. By scaling up studies of local-scale diverse, heterogeneous aquifers and management approaches to a regional level, the model can enable a projection of spatial changes due to shifts in LULC and water management including leakage from land and water use regulated areas into unregulated areas, information that is key to future agency planning for sustainability. See Van Schmidt et al. (2021) Water Resources Research (doi: XXXXXXXXXXXXX) for more details.

LUCAS-W is a scenario-based simulation model of coupled land use change and associated water demand for California's Central Coast region from 2001-2061. The model is a verison of the LUCAS model, which uses the SyncroSim software framework (Software documentation available at http://doc.syncrosim.com/index.php?title=Reference_Guide), that contains a new coupling with statistical software R (https://www.r-project.org/) to enable dynamic feedbacks between land-use change, resulting water demand, and water availability. The model was parameterized with land-use change and water use empirically estimated from county-scale historic data, as well as results from dozens of local agencies’ groundwater modeling efforts. It was used to assess a set of five stakeholder-driven scenarios that explored alternative development pathways assuming the continuation of historic land use change rates but with different intensities of water supply and land-use management. Water management strategies were (1) water demand limits, and (2) water supply enhancement, while land use management strategies were (3) urban sprawl limits on recharge areas and prime farmland, and (4) preservation of priority habitat areas. By scaling up studies of local-scale diverse, heterogeneous aquifers and management approaches to a regional level, the model can enable a projection of spatial changes due to shifts in LULC and water management including leakage from land and water use regulated areas into unregulated areas, information that is key to future agency planning for sustainability. The resulting land-use projections provide a range of development projections under different sets of management assumptions: patterns of development that do not stabilize “business-as-usual” (WL), assume that water demand stabilizes at a range of possible sustainable water supply levels (MM, WH), and that assume a relatively unregulated (LL) or tightly compact (LH) pattern of future development. See Van Schmidt et al. (2022) Journal of Hydrology: Regional Studies (https://doi.org/10.1016/j.ejrh.2022.101056) for more details.

LUCAS-W is a scenario-based simulation model of coupled land use change and associated water demand for California's Central Coast region from 2001-2061. The model is a verison of the LUCAS model, which uses the SyncroSim software framework (Software documentation available at http://doc.syncrosim.com/index.php?title=Reference_Guide), that contains a new coupling with statistical software R (https://www.r-project.org/) to enable dynamic feedbacks between land-use change, resulting water demand, and water availability. The model was parameterized with land-use change and water use empirically estimated from county-scale historic data, as well as results from dozens of local agencies’ groundwater modeling efforts. It was used to assess a set of five stakeholder-driven scenarios that explored alternative development pathways assuming the continuation of historic land use change rates but with different intensities of water supply and land-use management. Water management strategies were (1) water demand limits, and (2) water supply enhancement, while land use management strategies were (3) urban sprawl limits on recharge areas and prime farmland, and (4) preservation of priority habitat areas. By scaling up studies of local-scale diverse, heterogeneous aquifers and management approaches to a regional level, the model can enable a projection of spatial changes due to shifts in LULC and water management including leakage from land and water use regulated areas into unregulated areas, information that is key to future agency planning for sustainability. The resulting land-use projections provide a range of development projections under different sets of management assumptions: patterns of development that do not stabilize “business-as-usual” (WL), assume that water demand stabilizes at a range of possible sustainable water supply levels (MM, WH), and that assume a relatively unregulated (LL) or tightly compact (LH) pattern of future development. See Van Schmidt et al. (2022) Journal of Hydrology: Regional Studies (https://doi.org/10.1016/j.ejrh.2022.101056) for more details.

This dataset contains rasters of vegetation refugia and habitat exposure variables for the state of California. Two potential future climate scenarios were used: warmer and wetter (CNRM-CM5), and hotter and drier (MIROC-ESM) & 2 emission scenarios: a higher level one that represents our current trajectory (RCP 8.5) and a lower level one that represents a more optimistic scenario (RCP 4.5). The vegetation exposure models used aims to help in assessing potential climatic stress to vegetation communities and this dataset contains the statewide data for use in assessing the potential risk to each of the California Allotments. Current and future vegetation stress was determined by integrating the hydroclimate data with a detailed 2015 map of the spatial patterns of California’s vegetation community types, and examining how climate conditions will change at those locations using 9 hydroclimatic variables (30-year averages) from the Basin Characterization Model. The main habitat exposure outputs contain rasters all of the climate exposure results: 1 historic run: 1981-2010 and 12 future runs: 3 time periods (2010-2039, 2040-2069, 2070-2099) under 2 emission scenarios and 2 climate scenarios as well as reclassified rasters where the outputs were binned into 5 groups. To distinguish refugia areas from high-stress areas in the climate exposure results above, the team classified the climate frequency distribution for each vegetation type, which are labeled as CA refugia combined 45 and 85 for the respective RCP. Finally, the team looked at the spatial patterns of just refugia for the 2 climate models to identify areas where they align, defined as CA refugia concensus.

Tabular data output from a series of groundwater modeling simulations for five counties along the Central Coast of California, USA. We used a spatially explicit state-and-transition simulation model with stocks and flows that integrates climate, land-use change, human water use, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand at 270-m resolution from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future using output from the Basin Characterization Model, a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual projected changes in groundwater balance were calculated as the difference between land-use related water demand, based on historical estimates, and climate-driven recharge plus agriculture return flows to groundwater from excess irrigation. For each combination of the two climate and two land-use change scenarios, we ran 50 Monte Carlo realizations of the model. Results presented here have been aggregated from the individual cell level and summarized by county.

Tabular data output from a series of groundwater modeling simulations for five counties along the Central Coast of California, USA. We used a spatially explicit state-and-transition simulation model with stocks and flows that integrates climate, land-use change, human water use, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand at 270-m resolution from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future using output from the Basin Characterization Model, a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual projected changes in groundwater balance were calculated as the difference between land-use related water demand, based on historical estimates, and climate-driven recharge plus agriculture return flows to groundwater from excess irrigation. For each combination of the two climate and two land-use change scenarios, we ran 50 Monte Carlo realizations of the model. Results presented here have been aggregated from the individual cell level and summarized by county.

This dataset contains projections of shoreline change and uncertainty bands across California for future scenarios of sea-level rise (SLR). Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations across the state. Scenarios include 25, 50, 75, 100, 125, 150, 175, 200, 250, 300 and 500 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2000. This model shows change in shoreline positions along pre-determined cross-shore transects, considering sea level, wave conditions, along-shore/cross-shore sediment transport, long-term trends due to sediment supply, and estimated variability due to unresolved processes (as described in Vitousek and others, 2021). Variability associated with complex coastal processes (for example, beach cusps/undulations and shore-attached sandbars) are included via a noise parameter in a model, which is tuned using observations of shoreline change at each transect and run in an ensemble of 200 simulations; this approach allows for a representation of statistical variability in a model that is assimilated with sequences of noisy observations. The model synthesizes and improves upon numerous, well-established shoreline models in the scientific literature; processes and methods are described in this metadata (see lineage and process steps), but also described in more detail in Vitousek and others 2017, 2021, and 2023. Output includes different cases covering important model behaviors (cases are described in process steps of this metadata). KMZ data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D features or terrain. For technical users and researchers, shapefile and KMZ data can be ingested into geographic information system (GIS) software such as Global Mapper or QGIS.

This dataset contains projections of shoreline change and uncertainty bands across California for future scenarios of sea-level rise (SLR). Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations across the state. Scenarios include 25, 50, 75, 100, 125, 150, 175, 200, 250, 300 and 500 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2000. This model shows change in shoreline positions along pre-determined cross-shore transects, considering sea level, wave conditions, along-shore/cross-shore sediment transport, long-term trends due to sediment supply, and estimated variability due to unresolved processes (as described in Vitousek and others, 2021). Variability associated with complex coastal processes (for example, beach cusps/undulations and shore-attached sandbars) are included via a noise parameter in a model, which is tuned using observations of shoreline change at each transect and run in an ensemble of 200 simulations; this approach allows for a representation of statistical variability in a model that is assimilated with sequences of noisy observations. The model synthesizes and improves upon numerous, well-established shoreline models in the scientific literature; processes and methods are described in this metadata (see lineage and process steps), but also described in more detail in Vitousek and others 2017, 2021, and 2023. Output includes different cases covering important model behaviors (cases are described in process steps of this metadata). KMZ data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D features or terrain. For technical users and researchers, shapefile and KMZ data can be ingested into geographic information system (GIS) software such as Global Mapper or QGIS.