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Mediterranean California’s water use future based on scenarios of land use change 1992-2062 - Tabular Data


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Wilson, T.S., 2017, Mediterranean California’s water use future under multiple scenarios of developed and agricultural land use change: U.S. Geological Survey data release,


This dataset contains csv files in support of the conclusions published in "Water use demand in Mediterranean California under multiple scenarios of developed and agricultural land use " in the journal PLOS One. We used the USGS's LUCAS model to examine a broad suite of spatially explicit future land use scenarios and their associated county-level water use demand, including the historical (1992-2011) and projected periods (2012-2062) across 40 Monte Carlo simulations.We examined a range of potential water demand futures sampled from a 20-year record of historical (1992-2012) data to develop a suite of potential future land change scenarios from 2012-2062. These scenario simulations include a 1) business-as-usual (BAU), 2) low agriculture [...]


Point of Contact :
Tamara Wilson
Originator :
Tamara Wilson
Metadata Contact :
Tamara Wilson
Publisher :
U.S. Geological Survey
Distributor :
U.S. Geological Survey - ScienceBase

Attached Files

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LULC_net_change_state.csv 2.06 KB text/csv
Original FGDC Metadata

62.44 KB application/fgdc+xml
3.24 GB text/csv
5.4 GB text/csv
water_change_county.csv 122.21 KB text/csv
water_change_state_m3.csv 4.94 KB text/csv


The data were produced by running the Land Use and Carbon Scenario Simulator (LUCAS) model across 7 different future land use scenarios from 1992 to 2062 across 40 Monte Carlo iterations. Data include spreadsheets of each scenario and iteration output for state classes (i.e. land use and land cover) and state attributes (i.e. water use by land use class). Future land use related water demand projections are useful for resource managers and planners to envision potential resource futures. * Please note that 2 of the .csv files are very large (> 3 Gigabytes) and will not open in Microsoft Excel. We recommend using R (freeware download: to open and manipulate the larger .csv files.



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