Data to modeling the effects of future climate change on streamflow in Salgado River basin in the Brazilian Caatinga biome

Published: 27 August 2021| Version 2 | DOI: 10.17632/6nvr6k3w66.2
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Description

These are the data used to evaluate LULC changes and the future water yield (WY) in the Salgado River basin (SRB) for the period from 2030−2060. Many procedures were used: (a) calibration and validation of the SWAT model is used on a monthly basis for 1986−2017, with a calibration period from 1986 to 2005 and validation period from 2006−2017, (b) LULCC analysis is performed for 1985−2000, (c) future LULC is predicted using the land change model for 2050, (d) multiple scenarios involving five global circulation models (GCMs) are simulated, and representative concentration pathways (RCPs) are considered, (e) bias correction and downscaling for precipitation, maximum temperature, minimum temperature, are performed, (f) average monthly streamflow is estimated from 2030−2060 using GCMs, (g) future hydrological modeling of the geographical distribution of the water yield is performed for each subbasin of the SRB, and (h) RCPs are compared with the baseline period from 1970−2005.

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The data presented here are all in a format suitable for use in ArcSWAT and in Terrset's LCM module. 1 - In file Atmospheric Circulate Models are data from five different global circulation models that were analyzed for this study. They contain future daily precipitation and temperature data, as well as their respective baselines. All are already in input format for ArcSWAT; 2 - In the SWAT input data file are: climatological files for the characterization of the study area (tabular format); Images of the digital elevation model, soil types and land use and cover (raster format); Daily precipitation data (tabular format); information on the association of land use and land cover classes, as well as soil types with the SWAT database (tabular format); 3- The LCM input data file contains images of land use and land cover used as a basis for modeling and the explanatory variables used in the model.

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