Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)

Abstract

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.

Using Sentinel-2 data to estimate the concentration of heavy metals caused by industrial activities in Ust-Kamenogorsk, Northeastern Kazakhstan

“…Laser-induced breakdown spectroscopy provides high spatial resolution and real-time measurement, but it may have lower sensitivity for low concentrations and be affected by soil properties. The choice of remote sensing technique depends on the specific application and the trade-off between accuracy, speed, cost, and complexity [ [44] , [45] , [46] ].…”

“…Furthermore, Mirzaei et al [ 9 ] confirmed that the recycling received from Sentinel-2 satellite images has an inverse relationship with the concentration of heavy metals. It can be justified that the presence of heavy metals leads to the inactivation of waves in the visible and infrared parts, resulting in the inverse relationship between reflection and detectable amounts of heavy metal concentration [ [44] , [45] , [46] , [47] , [48] , [49] , [50] ]. Factors such as iron oxides or clays and high levels of organic matter, which have been identified as influencing factors on the number of heavy metals, play a role in the inverse relationship between reflection and detectable amounts of heavy metal concentration [ 52 ].…”

Assessment of Groundwater Storage Depletion using GRACE and Land Surface Models in Mzimba District, North Malawi

“…Downscaling is a common issue in the subject of satellite remote sensing [9]. Normally, the downscaling methods include dynamic and statistical downscaling [3,5,14]. Dynamic downscaling is used to construct a regional numerical model at higher spatial resolution; thus, it can be applied in local areas at smaller scales.…”

Land subsidence analysis using synthetic aperture radar data

“…It includes a bay, which is up to 240 m wide, separating the Western bay called “Paddi Zirr,” which is up to 9.1 m deep, and the Eastern bay called Demi Zirr, which is the deep-water bay, providing the land to Gwadar port [ 45 ]. The Gwadar foreland consists of two of the highest peaks, known as Koh-i-Batel, which is up to 137 m high, spreading up to 11 km, and Koh-i-Mehdi, 340 m tall [ 3 , 10 , 40 , 42 , 46 ].…”