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Under the severe impacts of climate change, drought has become one of the most undesirable and complex natural phenomena with critical consequences for the environment, economy and society. The orthodox drought monitoring approaches use observations of meteorological stations, which are typically restricted in time and space. Remote sensing, conversely, provides continuous global coverage of a variety of hydro-meteorological variables that are influential in drought, and data extracted from remote sensing and modeling missions are now considered more practical and alluring for researchers. In this study, we applied a combination of field data, remotely sensed data and modeled data to detect and quantitatively analyze drought phenomena. To achieve this objective, we utilized Terrestrial Water Storage Anomalies (TWSA) estimations from GRACE mission, Normalized Difference Vegetation Index (NDVI) from MODIS mission, Surface Runoff (R) and Evapotranspiration from ERA5 reanalysis datasets and Soil Moisture (SM) from GLDAS data model to evaluate their feasibility in detecting recent droughts over Turkey. We validated the accuracy of several remote sensing-based indices (GRACE Drought Severity Index, Water Storage Deficit Index [WSDI], Soil Moisture Index, Standardized Runoff Index and NDVI) with the traditional indices (SPI and SPEI) calculated from  in situ  observations of precipitation. The results revealed that the GRACE-based WSDI gave the best performance with high correlations with the SPI index both temporally and spatially over Turkey. We also found that monthly and annual time series of WSDI agreed well with the SPI index with correlations of 0.69 and 0.73, respectively. The results of drought analysis also indicated that WSDI could be used as a proxy to standard meteorological drought indices over Turkey as it performed well to detect and characterize the recent droughts of Turkey based on its comparisons to SPI results.

Detection and analysis of drought over Turkey with remote sensing and model-based drought indices