Groundwater Depletion Estimated from GRACE: A Challenge of Sustainable Development in an Arid Region of Central Asia

Abstract

Under climate change and increasing water demands, groundwater depletion has become regional and global threats for water security, which is an indispensable target to achieving sustainable developments of human society and ecosystems, especially in arid and semiarid regions where groundwater is a major water source. In this study, groundwater depletion of 2003–2016 over Xinjiang in China, a typical arid region of Central Asia, is assessed using the gravity recovery and climate experiment (GRACE) satellite and the global land data assimilation system (GLDAS) datasets. In the transition of a warm-dry to a warm-wet climate in Xinjiang, increases in precipitation, soil moisture and snow water equivalent are detected, while GRACE-based groundwater storage anomalies (GWSA) exhibit significant decreasing trends with rates between-3.61 ± 0.85 mm/a of CSR-GWSA and −3.10 ± 0.91 mm/a of JPL-GWSA. Groundwater depletion is more severe in autumn and winter. The decreases in GRACE-based GWSA are in a good agreement with the groundwater statistics collected from local authorities. However, at the same time, groundwater abstraction in Xinjiang doubled, and the water supplies get more dependent on groundwater. The magnitude of groundwater depletion is about that of annual groundwater abstraction, suggesting that scientific exploitation of groundwater is the key to ensure the sustainability of freshwater withdrawals and supplies. Furthermore, GWSA changes can be well estimated by the partial least square regression (PLSR) method based on inputs of climate data. Therefore, GRACE observations provide a feasible approach for local policy makers to monitor and forecast groundwater changes to control groundwater depletion.

Evaluation of the temporal variations of groundwater storage and its interactions with climatic variables using GRACE data and hydrological models: A study from Turkey

“…It is a unique satellite mission from the viewpoint of its ability to deliver direct estimations of the Earth's mass distribution interpreted as TWS. 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 modelled from using the monthly variations of the gravity field measurements (Hu et al, 2019). Mass movements of the Earth create gravitational pulls (as the twin satellites orbit the Earth, areas of slightly stronger gravity [greater mass concentration] affect the lead satellite first, pulling it away from the trailing satellite) that 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).…”

“…According to Frappart and Ramillien (2018), the simplest cases to estimate groundwater changes is to ignore surface water storage particularly in arid and semi‐arid regions of the world where TWS is limited to soil water storage. Thus, like many previous studies, we extracted GWS from GRACE TWS using SWE and SMS variables (Deng & Chen, 2017; Hu et al, 2019; Yang, Wang, Chen, Chen, & Yu, 2015) as follows: …”

“…In GLDAS data model, remotely‐sensed data and in‐situ observations are integrated using sophisticated land surface modelling 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 (Śliwińska et al, 2019). GLDAS mission simulates the hydro‐climatic variables using a range of land surface models (LSMs) including the Community Land Model (CLM), Variable Infiltration Capacity (VIC) Model, Noah Model, Mosaic Model and 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° (Hu et al, 2019). GLDAS is the most favourable data source to be used for deriving GWSA from GRACE measurements.…”

“…Rahaman et al, 2019) with various temporal resolutions ranging from an hour to a month and spatial resolutions from 0.1 to 1.25(Hu et al, 2019). GLDAS is the most favourable data source to be used for deriving GWSA from GRACE measurements.…”

Geo-Spatial Analysis in Hydrology

“…In addition to the topics introduced above, there are many other hydrological topics in which geo-spatial analysis plays a crucial role, such as water quantity and quality evaluation for sustainable management [20,21]. Given the increasing importance of geo-spatial analysis in hydrological studies, the Special Issue aimed to seek studies in a wide range of topics related to the development and application of geo-spatial analysis in hydrology.…”