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In recent decades, the phenomenon of drought has become a hazard with increasing frequency, with multiple societal and environmental impacts. One of these impacts concerns water resources and their availability for various uses. Numerous indices have been used over time to quantify the severity of drought and to assess its effects on socio-economic activities and environmental components. Among the spatial indices, the most numerous belong to remote sensing, being easy to use to analyse drought consequences especially on landcover and vegetation. However, regarding the hydrological drought, the indices used are mainly calculated based on hydroclimatic data, without taking into account spatial variables, such as the topographical, geological or pedological characteristics. The aim for this study is to compute a hydrological drought index which integrates several drought control variables, using both GIS and remote sensing techniques in order to map the susceptibility to hydrological drought within the Teleorman watershed. Located in the central-southern part of Romania, the Teleorman River has a length of 169 km and a catchment area of 1.427 km2. The most part of the catchment overlaps the central sector of the Romanian Plain, an important agricultural area, highly sensitive to water deficit. According to Kppen-Geiger classification, the analyzed catchment has a humid continental climate with hot summers (Dfa), meaning that the drought could occur in the basin. A series of free data and information sources has been accessed in order to compute the hydrological drought index, such as: Worldclim, Landsat Archive, Geological Map of Romania, Pedological Map of Romania, Shuttle Radar Topography Mission (SRTM), Topographical Map of Romania. The following parameters were derived from these sources: Topograhic Wetness Index (TWI); Drainage Density (resulted from hydrographic network); Normalized Difference Drough Index (NDDI), resulted from ratio between Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI); Temperature Condition Index (TCI), extracted from Land Surface Temperature (LST); Aridity Index (AI) computed as a ratio between Precipitation and Potential Evapotranspiration (PET); De Martonne Index (based on ratio between Precipitation and Air temperature); Lithology and Soil Texture. Because some of the parameters had a different spatial resolution, the regridding method was used to bring the database to a resolution of 30 meters. Analytic hierarchy process method (AHP) was used to determine the influence of each factor and for the bonitation process. Based on the total obtained score, 5 classes from to lowest to highest hydrological drought susceptibility resulted. Finally, the Weighted Overlay and Raster Calculator tools from ArcGisPro software were used to map the index. The resulted map allows the identification in the studied watershed of areas the most susceptible to hydroclimatic drought allowing the focus in these areas of appropriate actions to improve drought risk management. GIS and Remote sensing proved to be useful tools in spatial analysis of drought based on a composite index integrating several drought control factors. In the future, we intend to improve the method by considering other variables controlling the hydrological drought, such as the streamflow and the groundwater depth.
Costache et al. (Fri,) studied this question.