Combining downscaled-GRACE data with SWAT to improve the estimation of groundwater storage and depletion variations in the Irrigated Indus Basin (IIB)

An appraisal of the local‐scale spatio‐temporal variations of drought based on the integrated GRACE/GRACE‐FO observations and fine‐resolution FLDAS model

“…Due to the recent achievements in the field of remote sensing, particularly with the advent of the Gravity Recovery And Climate Experiment (GRACE), uninterrupted, precise, and economic assessment and evaluation of water storage is now possible from regional to global scales. The GRACE offers the estimates of Terrestrial Water Storage Anomalies (TWSA), which include different components of the hydrological cycle such as Groundwater Storage Anomalies (GWSA), Surface Water Storage Anomalies (SWSA), Soil Moisture Storage Anomalies (SMSA), and Snow Water Equivalent Anomalies (SWEA) (Equation ) that is stored above and beneath the land surface (Ali et al, 2021; Ali, Liu, et al, 2022; Ali, Wang, et al, 2022; Arshad et al, 2022; Khorrami et al, 2022; Khorrami & Gündüz, 2023). …”

“…However, the challenging point is that the resulting indicators have the same spatial and temporal (monthly) characteristics as those of GRACE and GRACE‐FO data, making them unsuitable for operational drought monitoring on regional to local scales (Li & Rodell, 2021). Although the GRACE data are more suitable for large‐scale hydrological applications due to their coarse resolution, recent developments in downscaling techniques make GRACE data suitable to assess the local scale variations in water storage in some studies (e.g., Ali et al, 2021; Ali, Liu, et al, 2022; Arshad et al, 2022; Gerdener et al, 2020; Gyawali et al, 2022; Jyolsna et al, 2021; Khorrami et al, 2021; Khorrami, Pirasteh, Ali, et al, 2023; Seo & Lee, 2019; Yin et al, 2022). However, the majority of the downscaling practices have so far focused on medium‐resolution () analysis because of the limitations of fine‐resolution hydrological inputs.…”

A new GRACE downscaling approach for deriving high-resolution groundwater storage changes using ground-based scaling factors

“…Statistical downscaling methods are also widely used in GRACE downscaling (Pulla et al 2023;Arshad et al 2022;Yin et al 2022). Most studies construct correlations with GRACE GWSA using data related to changes in GWS, such as precipitation, soil moisture, evapotranspiration, and soil lithology information (Pulla et al 2023;Chen et al 2019).…”

Constructing GRACE-Based 1 km Resolution Groundwater Storage Anomalies in Arid Regions Using an Improved Machine Learning Downscaling Method: A Case Study in Alxa League, China

“…However, the applicability of dynamic downscaling is limited by the need for abundant and complex observational materials from multiple sources, as well as by low computational efficiency resulting from the complexity of the data assimilation approaches [20]. Statistical downscaling, unlike dynamic downscaling, is applied by establishing relationships between low-resolution input variables (predictors) and a high-resolution target variable (predictand), and it enables flexible modeling, is less time-consuming, relatively simple, and requires few materials, making it potentially useful for data-scarce arid regions [21][22][23]. Machine learning, which involves purely data-driven algorithms, provides effective measures for statistical downscaling [24].…”