As given in Table1, data are of different spatial and temporal resolutions. The basic data source here is GRACE TWSA outputs. Accordingly, other variables were re-gridded using bilinear interpolation technique to ensure the spatial consistency with TWSA data. The bilinear algorithm performs the calculations in both directions (Miao et al, 2018) with the aid of the DNs (Digital Numbers) of the four adjacent cells of each individual pixel of the given imagery. As a result of interpolation, the new imageries were resampled to the grid size of half a degree to resemble the resolution of GRACE dataset. The advantage of re-gridding to the coarser resolution is to guarantee the accuracy of the comparisons of the values derived from different data sources restricting the influences of finer scale noises (Ljungqvist et al, 2019).

The interactions between groundwater storage fluctuations and climatic variables were evaluated using the linear regression method. The correlation and regression coefficients at the significance level of 95% and 0.5-degree resolution were used to show the strength of the relationships of P-GWSA and T-GWSA over the study area at monthly scale.

Since its launch in March 2002, the Gravity Recovery and Climate Experiment [GRACE] satellite mission has provided an alternative and complementary data source for monitoring large-scale mass changes of Earth’s system on different time scales with unprecedented accuracy (Tapely et al, 2004). GRACE is comprised of two identical satellites orbiting the same orbital plane of the earth with an estimated distance of 220 km at an altitude of 500 km (Tapely et al, 2004). It is a unique satellite mission from the viewpoint of its ability to deliver direct estimated measurements of the variations of groundwater storage of the earth. It is based on the principle of the relationship between the earth’s gravitational pull and mass changes at the earth’s surface from which, the TWS variations of the earth can be inferred from using the monthly variations of the gravity field measurements (Hu et al, 2019). Mass movements of the earth bring about the differences in the distance between the two satellites, which are then translated into water storage changes and reported as TWSA ( NASA Facts, 2003). The processing (filtering, truncation, scaling and smoothing) done traditionally on the original signals of GRACE cause decreases in the ratio of signal to noise (S/N) of the data. The newly released version of GRACE products, called mass concentration (mascon), does not need scaling, thus, the uncertainties are less than the original spherical harmonics and the accuracy level is higher (Xu, Chen, Zhang & Chen, 2019).

Terrestrial Water Storage (TWS) is a hydrological component describing the different components of the water cycle. The following equation is used to define the anomalies of TWS over a given area.

\(TWS=\ \ GWS+\ \ SWE+\ \ SMS+\ \ SWS\) [1]

where GWS, SWE, SMS and SWS represent groundwater storage, snow water equivalent, soil moisture storage and surface water storage, respectively. GWS is the main component of TWS that shows the amount of water stored inside the aquifers beneath the ground. The variations in GWS can be isolated from the integral TWSA based on the following equation.

\(GWS=\ \ TWS-[\ \ SWE+\ \ SMS+\ \ SWS]\) [2]

Thus, SWE, SMS and SWA measurements are required for GWS isolation from GRACE TWSA. According to Frappart and Ramillien (2018) in arid and semi-arid regions of the world where TWS is limited to soil water storage, the simplest cases to estimate groundwater changes is to ignore surface water storage. Thus, like many previous studies, the GWS is isolated from GRACE TWS using SWE and SMS variables (Yang, Wang, Chen, Chen &Yu, 2015; Deng and Chen, 2017; Hu et al, 2019) as follows:

\(GWS=\ \ TWS-[\ \ SWE+\ \ SMS]\) [3]

The Global Land Data Assimilation System (GLDAS) is a data assimilation mission of the National Oceanic and Atmospheric Administration [NASA] and the National Centers for Environmental Prediction to simulate different hydro-climatic variables at a higher resolution. Remotely sensed data and in-situ observations are integrated using sophisticated land surface modeling tools and data assimilation techniques (Ramillien, Famiglietti & Wahr, 2008; Rodell, Velicogna & Famiglietti, 2009) culminating in a very useful dataset of land surface states and fluxes especially in hydrology and climatology (Sliwinska, Birylo, Rzepecka, & Nastula, 2019). GLDAS mission simulates the hydro-climatic variables under different land surface models (LSMs) including the Community Land Model [CLM], Variable Infiltration Capacity [VIC] Model, Noah Model, Mosaic Model and Catchment Land Surface Model [CLSM] (Rahaman et al, 2019) with various temporal resolutions ranging from an hour to a month and spatial resolutions from 0.1 to 1.25 degrees (Hu et al, 2019). GLDAS is the most favorable data source to be used for deriving GWSA from GRACE measurements. In this study, the latest version of GLDAS-Noah data model [Noah-2.1 monthly 0.25 degree] was used to deduce the SMS and SWE variables required for GWS isolation. Soil Moisture is modelled in four different depths [0-10, 10-40, 40-100 and 100-200 cm] by GLDAS models. The aggregation of these layers were done to get the SMS variable for the study area. Catchment Land Surface Model [CLSM] is the only LSM model to simulate TWS and GWS values individually. The simulated GWS by GLDAS_CLSM model was also used to compare with the GRACE based GWS variations.