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 ecosystem carbon stocks for California, USA. Carbon stock estimates were derived by linking the Land Use and Carbon Scenario Simulator (LUCAS) model and the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3). 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 ecosystem carbon stocks for California, USA. Carbon stock estimates were derived by linking the Land Use and Carbon Scenario Simulator (LUCAS) model and the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3). 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 consists of modeled projections of land use and land cover and population for the State of California for the period 1970-2101. For the 1970-2001 period, we used the USGS's LUCAS model to "backcast" LULC, beginning with the 2001 initial conditions and ending with 1970. For future projections, the model was initialized in 2001 and run forward on an annual time step to 2100. In total 5 simulations were run with 10 Monte Carlo replications of each simulation. The simulations include: 1) Historical backcast from 2001-1970, 2) Business-as-usual (BAU) projection from 2001-2101, and 3) three modified BAU projections based on California Department of Finance population projections based on high, medium, and low growth rates.

This data series provides tabular output from a series of modeling simulations for the State of California. The methods and results of this research are described in detail in Sleeter et al. (2019). We used the LUCAS model to project changes in ecosystem carbon balance resulting from land use and land use change, climate change, and ecosystem disturbances such as wildfire and drought. The model was run at a 1-km spatial resolution on an annual timestep. We simulated 32 unique scenarios, consisting of 4 land-use scenarios and 2 radiative forcing scenarios as simulated by 4 global climate models. For each scenario, we ran 100 Monte Carlo realizations of the model. Additional details describing the modeling effort can be found in the *Global Change Biology* paper. Results presented here have been aggregated from the individual cell level to either ecoregion or state-wide summaries.

Scenario-based simulation model projections of land use change, ecosystem carbon stocks, and ecosystem carbon fluxes for the State of California from 2001-2101 using the SyncroSim software framework, see http://doc.syncrosim.com/index.php?title=Reference_Guide for software documentation. We explored four land-use scenarios and two radiative forcing scenarios (e.g. Representative Concentration Pathways; RCPs) as simulated by four earth system models (i.e. climate models). Results can be used to understand the drivers of change in ecosystem carbon storage over short, medium, and long (e.g. 100 year) time intervals. See Sleeter et al. (2019) Global Change Biology (doi: 10.1111/gcb.14677) for detailed descriptions of scenarios.

This dataset consists of raster geotiff outputs of 30-year average annual land use and land cover transition probabilities for the California Central Valley modeled for the period 2011-2101 across 5 future scenarios. The full methods and results of this research are described in detail in “Integrated modeling of climate, land use, and water availability scenarios and their impacts on managed wetland habitat: A case study from California’s Central Valley” (2021). Land-use and land-cover change for California's Central Valley were modeled using the LUCAS model and five different scenarios were simulated from 2011 to 2101 across the entirety of the valley. The five future scenario projections originated from the four scenarios developed as part of the Central Valley Landscape Conservation Project (http://climate.calcommons.org/cvlcp ). The 4 original scenarios include a Bad-Business-As-Usual (BBAU; high water availability, poor management), California Dreamin’ (DREAM; high water availability, good management), Central Valley Dustbowl (DUST; low water availability, poor management), and Everyone Equally Miserable (EEM; low water availability, good management). These scenarios represent alternative plausible futures, capturing a range of climate variability, land management activities, and habitat restoration goals. We parameterized our models based on close interpretation of these four scenario narratives to best reflect stakeholder interests, adding a baseline Historical Business-As-Usual scenario (HBAU) for comparison. The TGAP raster maps represent the average annual transition probability of a cell over a specified time period for a specified land use transition group and type. Each filename has the associated scenario ID (scn418 = DUST, scn419 = DREAM, scn420 = HBAU, scn421 = BBAU, and scn426 = EEM), transition group (e.g. FALLOW, URBANIZATION), transition type, model iteration (= it0 in all cases as only 1 Monte Carlo simulation was modeled and no iteration data used in the calculation of the probability value), timestep of the 30-year transition summary end date (ts2041 = average annual 30-year transition probability from modeled timesteps 2012 to 2041, ts2071 = average annual 30-year transition probability from modeled timesteps 2042 to 2071, and ts101 = average annual 30-year transition probability from modeled timesteps 2072 to 2101). For example, the following filename “scn418.tgap_URBANIZATION_ Grass_Shrub to Developed [Type].it0.ts2041.tif” represents 30-year cumulative URBANIZATION transition group, for the Grass/Shrub to Developed transition type, for the 2011 to 2041 model period. More information about the LUCAS model can be found here: https://geography.wr.usgs.gov/LUCC/the_lucas_model.php. For more information on the specific parameter settings used in the model contact Tamara S. Wilson (tswilson@usgs.gov)