The high-resolution data for the geo-portal and groundwater modeling of the Kabul River Basin. The collected high-resolution data was processed in terms of the river basin boundaries and used to simulate groundwater levels.
Different types of data were retrieved for the Kabul River Basin project’s geoportal. The description of data types and sources are presented in the following sections.
The resolution of the raster datasets is 1x1 km (30 arcsec).
Coordinate reference system Coordinate reference system (crs) is EPSG 4326 (WGS84 geographic coordinate system).
GIS data manipulations such as reading, slicing, reprojection and plotting are done using the open-source tool called geohdf (Zhiyenbek, 2019).
Elevation data is based on SRTM 30+ and ETOPO DEM at 1/120 arc-degrees. The slope data is derived using the DEMSRE3. Elevation in meters, slope in percentage. see the map
The latest climate data was retrieved from the Chelsa (Climatologies at high resolution for the earth’s land surface areas) project (Karger et al., 2019). Using Era Interim model results, they downscaled data into 1x1 km resolution. Downscaling algorithms are based on elevation and wind vectors and can be found in the documentation of the Chelsa project. The monthly datasets are the long term average values from 1979 to 2013. Precipitation in mm/day. Min and Max temperature in Cо. see the climate map
The layer of EVI is enhanced vegetation index, b_4 is Modis band 4, b_7 is Modis band 7. Vegetation indices play an important role in monitoring variations in vegetation. The Enhanced Vegetation Index (EVI) proposed by the MODIS Land Discipline Group and the Normalized Difference Vegetation Index (NDVI) are both global-based vegetation indices aimed at providing consistent spatial and temporal information regarding global vegetation. However, many environmental factors such as atmospheric conditions and soil background may produce errors in these indices. The topographic effect is another very important factor, especially when the indices are used in areas of rough terrain (the article). see the map
The high resolution data was processed with python toolbox by Abdikaiym Zhiyenbek.
The whole report is here