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.
Groundwater Storage Isolation from GRACE Measurements
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]
Global Land Data Assimilation System [GLDAS] Model
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.