Integrated modeling of climate and land change impacts on future dynamic wetland habitat – a case study from California’s Central Valley
Dates
Publication Date
2021-06-24
Start
2011
End
2101
Citation
Wilson, T.S., Matchett, E., Byrd, K., Conlisk, E., Reiter, Wallace, C., Flint, L.E., Flint, A.L., Joyce, B., and Moritsch, M., 2021, Integrated modeling of climate and land change impacts on future dynamic wetland habitat – a case study from California’s Central Valley: U.S. Geological Survey data release, https://doi.org/10.5066/P9BSZM8R.
Summary
This dataset consists of raster geotiff and tabular outputs of annual map projections of land use and land cover for the California Central Valley for the period 2011-2101 across 5 future scenarios. Four of the scenarios were developed as part of the Central Valley Landscape Conservation Project. The 4 original scenarios include a Bad-Business-As-Usual (BBAU; high water, poor management), California Dreamin’ (DREAM; high water, good management), Central Valley Dustbowl (DUST; low water, poor management), and Everyone Equally Miserable (EEM; low water, good management). These scenarios represent alternative plausible futures, capturing a range of climate variability, land management activities, and habitat restoration goals. We parameterized [...]
Summary
This dataset consists of raster geotiff and tabular outputs of annual map projections of land use and land cover for the California Central Valley for the period 2011-2101 across 5 future scenarios. Four of the scenarios were developed as part of the Central Valley Landscape Conservation Project. The 4 original scenarios include a Bad-Business-As-Usual (BBAU; high water, poor management), California Dreamin’ (DREAM; high water, good management), Central Valley Dustbowl (DUST; low water, poor management), and Everyone Equally Miserable (EEM; low water, 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. For these future map projections, the model was initialized in 2011 and run forward on an annual time step to 2101. There are 5 datasets included in this Data Release summarizing data across the 5 scenarios including: 1) State Class Rasters = maps of annual LULC, 2) State Class Spreadsheet = tabular output of annual LULC by region and WEAP zone, 3) State Class Transition Spreadsheet = annual tabular output of changes in LULC, 4) TGAP Class Transition Rasters = annual raster maps by specific LULC transition type, and 5) TGAP Flooding Probability Rasters = maps of 30-year average annual transition probabilties across 32 different flooding classes. The datasets were generated from the LUCAS ST-SIM model as described in the parent manuscript. The data can be used to visualize annual land-use and land-cover (LULC), LULC transitions, probability of fallowing, and probability of flooding across the region for each modeled year and scenario, highlighting the spatial and temporal distribution of potential future ecological and waterbird habitat. The full methods and results of this research are described in detail in the parent manuscript "Integrated modeling of climate and land change impacts on future dynamic wetland habitat – a case study from California’s Central Valley" (2021).
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Purpose
These model outputs can be used to summarize any number of land-use and land-cover change information across specific time frames, spatial geographies, and scenarios to determine potential future landscape summary conditions of stable or changing water bird habitat availability. The data can be used to examine the spatial and temporal distribution of other future ecological habitat under a broad range of climate and land use scenarios within the California Central Valley. Data is being provided to improve the replicability of analysis and meet federal open data standards
