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ZENODO
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MapSWAT

Authors: López-Ballesteros, Adrián; Senent-Aparicio, Javier; Jimeno-Sáez, Patricia; Pérez-Sánchez, Julio;
Abstract

MapSWAT is a QGIS extension developed using Python 3 programming language. This plugin is valid for any QGIS 3 version, although using the last long term release version is highly recommended. MapSWAT allows a quick preparation of input maps for both versions of SWAT (Soil and Water Assessment Tool), SWAT2012 and SWAT+. The input maps required by the SWAT model are: a digital elevation map (DEM), a land use map (LANDUSE) and a soil map (SOIL). Pre-processing of SWAT input maps can be a laborious and long task. Normally, users have to reduce the raw map area and reproject them. Therefore, MapSWAT was developed to optimize and reduce the amount of time spent on pre-processing SWAT input maps as well as, to filter out possible errors due to the required SWAT format. To achieve these goals, the MapSWAT interface is divided in three main parts: (1) User Data Inputs: In this part, users have to introduce their raw input maps and select the source Coordinate Reference System (CRS) of each one. This first part also includes two additional options, one for drawing the study area outlet from coordinates (X-longitude, Y-latitude) and another for merging several DEM grids; (2) Clipping Options: Three different clipping maps options are implemented in MapSWAT (Clip map by new polygon, clip map by existing polygon and clip map by extension from outlet). These options produce a polygon or mask layer to clip raw user maps; (3) SWAT Input Maps Creation: In this last part, user raw maps are clipped by previous generated mask layer and reprojected into a target CRS selected by user. Users have to indicate a metric CRS, e.g. Universal Transverse Mercator (UTM), to be compatible with SWAT format. The MapSWAT plugin can be a useful tool for beginner SWAT users or advance users who want to quickly prepare the SWAT input maps, avoiding possible formatting errors.

Keywords

MapSWAT, QGIS 3, SWAT model, Python

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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