Using GRACE to Detect Groundwater Variation in North China Plain after South–North Water Diversion

Abstract

The gravity recovery and climate experiment (GRACE) and its Follow‐On mission provide a versatile tool for monitoring groundwater depletion in North China Plain (NCP). However, intermittent data gaps and inherent coarse spatial resolution have restricted the continuous detection of regional groundwater storage anomaly (GWSA) after 2014, the period of interest during the implementation of the south‐to‐north water diversion middle route project (SNWDP). Here, we investigated the spatiotemporal changes of GWSA in the NCP during 2004 to 2020 based on continuous downscaled GRACE data. First, we derived the continuous terrestrial water storage anomaly from six GRACE and Follow‐On solutions (i.e., spherical harmonics (SH) and mass concentration [mascon] solutions). Second, we employed a long short‐term memory (LSTM) model and water balance equation to downscale GWSA (i.e., 0.25° × 0.25°). Lastly, we investigated its spatiotemporal characteristics before (2004 to 2014) and after (2015 to 2020) the SNWDP operation. We show the applicability of the continuous downscaled GWSA to capture the characteristics of in situ measurements. The GWSA detects groundwater depletion at a significant (p < 0.05) rate of −17.09 ± 1.80 (SH) and −17.87 ± 1.65 (mascon) mm/a during 2004 to 2014, but a recovering trend of 7.18 ± 3.98 (SH) and 8.23 ± 4.99 (mascon) during 2015 to 2018. The subsequent groundwater extraction and precipitation reduction from 2019 to 2020, resulted in the decreasing trend of GWSA from 2015 to 2020, which is −19.11 ± 8.75 (SH) and −19.72 ± 9.08 mm/a (mascon), respectively. Spatially, the overall depletion trends become nonsignificant along the canals of SNWDP compared to the period 2004 to 2014, and groundwater recovering with trends <6 mm/a near Beijing and Tianjin are detected by the mascon solution during 2015 to 2020.

Associations between Surface Deformation and Groundwater Storage in Different Landscape Areas of the Loess Plateau, China

“…Rodell et al confirmed that the groundwater recharge in an area of groundwater pumping irrigation decreased by about 50% [17,69,70]. Extracting groundwater irrigation will cause surface subsidence [54,71], and drainage irrigation (Figure 8b) causes surface fluctuation deformation; however, drainage irrigation not cause groundwater depletion that otherwise causes ground subsidence to form geological disasters. The time series of the Hetao Irrigation District changes from May to October 2020 (Figure 8B).…”

Influence of South-to-North Water Diversion on Land Subsidence in North China Plain Revealed by Using Geodetic Measurements

“…The depletion of groundwater has affected the surface subsidence rate to a certain degree [39], particularly evident at the three GNSS stations like TJBD, TJWQ, and HECX, with severe land subsidence. However, the water resource supply by the SNWD-C changes the situation, partially meeting the needs of human beings while also alleviating the loss of groundwater reserves in the NCP [40]. The evidence indicates that the surface water has supplemented shallow groundwater to a certain extent, yielding a positive impact on shallow groundwater in Beijing [41].…”

Integrating GRACE/GRACE Follow-On and Wells Data to Detect Groundwater Storage Recovery at a Small-Scale in Beijing Using Deep Learning

“…Countries such as the United States and Australia have relatively dense networks of GW monitoring wells, but the specific yields of unconfined aquifers and the storage coefficients of confined aquifers are difficult to determine precisely, leading to inaccurate conversion of water levels from wells into the groundwater storage anomaly (GWSA) expressed as equivalent water height (EWH). Traditional monitoring methods also have other issues, such as high costs, inconsistent data formats, logging errors, and restrictive data-sharing policies between countries and regions worldwide, all of which hinder the effective utilization of measured data for GW studies [12,13].…”

“…It was released by the National Aeronautics and Space Administration's (NASA's) Gotha Aeronautics Center (https://ldas.gsfc.nasa.gov/gldas) (accessed on 5 October 2023). The CLSM has the best performance in capturing spatiotemporal variations and long-term trends of TWSA [9,13], so the simulated TWSA, runoff, surface temperature, SMS, SNS, and CNS by the CLSM L4 daily model were used to reconstruct six types of GRACE-derived TWSAs and downscale GRACE-derived GWSAs.…”