Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1°) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1° to 0.25°. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales (1-, 3-, and 6-months) and self-calibrated Palmer Drought Severity Index (sc-PDSI). Moreover, the IBIS had experienced increasing drought episodes, e.g., eight drought episodes were detected within the years 2010 and 2016 with WSDI of −1.20 and −1.28 and total WSD of −496.99 mm and −734.01 mm, respectively. The Partial Least Square Regression (PLSR) model between WSDI and climatic variables indicated that potential evaporation had the largest influence on drought after precipitation. The findings of this study will be helpful for drought-related decision-making in IBIS.

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, on the other hand, 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, NDVI from MODIS mission, Surface Runoff (R) and Evapotranspiration (ET) 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 (GDSI, WSDI, SMI, SRI, and NDVI) with the traditional indices (SPI and SPEI) calculated from in-situ observations of precipitation. The results revealed that the GRACE-based Water Storage Deficit Index (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.

Uncontrolled extraction of water from groundwater aquifers causes groundwater depletion, which in turn triggers the formation of sinkholes in many parts of the world. Monitoring and detection of these geomorphologic features are of utmost importance and priority for the decision-makers to minimize significant environmental as well as socio-economic implications of land deformation. In this study, a systematic approach is proposed to investigate the spatio-temporal associations of groundwater storage changes with sinkhole evolution and land deformation by using a number of remotely sensed and modeled data as well as in-situ observations. The proposed approach is implemented and tested in Konya Closed Basin (KCB), Turkey, which is one of the most critical areas in central Turkey concerning sinkhole formation. The results of GRACE (Gravity Recovery and Climate Experiment) estimates suggest that there is a descending trend in the temporal variations of TWSA (Terrestrial Water Storage Anomalies) and GWSA (Groundwater Storage Anomalies) over KCB with an average storage depletion of 4.12 ± 0.34 cm/yr and 3.40 ± 0.61 cm/yr, respectively. The analysis of land deformation from ICESat/ICESat-2 (Ice, Cloud, and Land Elevation Satellite) altimetry data also indicates a descending trend with an estimated average vertical displacement of 5 cm/yr for the study area, which seems to be in rational accord with the sinkhole evolution over KCB. The results further suggest that the sinkhole evolution over KCB has an acceptable association with the variations of groundwater storage, groundwater use, and precipitation. Compared with previous works implemented over KCB, the findings of this study manifest a good performance for the proposed methodology. The integrated analysis of GRACE gravity measures, ICESat/ICESat-2 altimetry data, modeled groundwater abstraction, and in-situ observations of precipitation yields acceptable results for the detection and monitoring of sinkhole events provided that appropriate distribution of the elevation points from ICESat/ICESat-2 is accessible for the given study area.