Trend of China land water storage redistribution at medi- and large-spatial scales in recent five years by satellite gravity observations

Evaluation of Groundwater Storage Depletion Using GRACE/GRACE Follow-On Data with Land Surface Models and Its Driving Factors in Haihe River Basin, China

Groundwater storage (GWS) in the Haihe River Basin (HRB), which is one of the most densely populated and largest agricultural areas in China, is of great importance for the ecosystem environment and socio-economic development. In recent years, large-scale overexploitation of groundwater in HRB has made it one of the global hotspots of GWS depletion. In this study, monthly GWS variations in HRB from 2003 to 2020 were estimated using the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) data in combination with three land surface models (LSMs) from the Global Land Data Assimilation System (GLDAS). The results show the following: (1) HRB suffered extensive GWS depletion from 2003 to 2020, which has been aggravated since 2014, with a mean rate of 1.88 cm·yr−1, which is equivalent to a volume of 6 billion m3·yr−1. The GWS depletion is more serious in the plain zone (−2.36 cm·yr−1) than in the mountainous zone (−1.63 cm·yr−1). (2) Climate changes are excluded from the reasons for GWS depletion due to annual precipitation and evaporation being close to normal throughout the period. In addition, GWS changes show a low correlation with meteorological factors. (3) The consumption of groundwater for irrigation and land use/cover changes have been confirmed to be the dominant factors for GWS depletion in HRB. (4) The effects of inter-basin water transfer projects cannot be obviously observed using the GRACE and GRACE-FO; more inter-basin water transfers are needed for recovering the GWS in HRB. Therefore, it is imperative to control groundwater exploitation and develop a more economical agricultural irrigation structure for the sustainability of groundwater resources in HRB.

Improving the Accuracy of Water Storage Anomaly Trends Based on a New Statistical Correction Hydrological Model Weighting Method

The Gravity Recovery and Climate Experiment (GRACE) satellite solutions have been considerably applied to assess the reliability of hydrological models on a global scale. However, no single hydrological model can be suitable for all regions. Here, a New Statistical Correction Hydrological Model Weighting (NSCHMW) method is developed based on the root mean square error and correlation coefficient between hydrological models and GRACE mass concentration (mascon) data. The NSCHMW method can highlight the advantages of good models compared with the previous average method. Additionally, to verify the effect of the NSCHMW method, taking the Haihe River Basin (HRB) as an example, the spatiotemporal patterns of Terrestrial Water Storage Anomalies (TWSA) in HRB are analyzed through a comprehensive comparison of decadal trends (2003–2014) from GRACE and different hydrological models (Noah from GLDAS-2.1, VIC from GLDAS-2.1, CLSM from GLDAS-2.1, CLSM from GLDAS-2.0, WGHM, PCR-GLOBWB, and CLM-4.5). Besides, the NSCHMW method is applied to estimate TWSA trends in the HRB. Results demonstrate that (1) the NSCHMW method can improve the accuracy of TWSA estimation by hydrological models; (2) the TWSA trends continue to decrease through the study period at a rate of 15.7 mm/year; (3) the WGHM and PCR-GLOBWB have positive reliability with respect to GRACE with r > 0.9, while all the other models underestimate TWSA trends; (4) the NSCHMW method can effectively improve RMSE, NES, and r with 3–96%, 35–282%, 1–255%, respectively, by weighting the WGHM and PCR-GLOBWB. Indeed, groundwater depletion in HRB also proves the necessity of the South-North Water Diversion Project, which has already contributed to groundwater recovery.

Using Satellite Gravity and Hydrological Data to Estimate Changes in Evapotranspiration Induced by Water Storage Fluctuations in the Three Gorges Reservoir of China

The change in water storage driven by the Three Gorges Project directly affects the terrestrial water migration and redistribution in the Yangtze River Basin (YRB). As a result, a new water balance is established and regional evapotranspiration (ET) fluctuates in the process. In this paper, data from multiple-sources including from the Gravity Recovery and Climate Experiment (GRACE) satellite, land surface models (LSMs), remote sensing, and in-situ observations were used to monitor the temporal and spatial evolution of terrestrial water and estimate changes in ET in the Three Gorges Reservoir (TGR) from 2002 to 2016. Our results showed that GRACE data scaled using the scale factor method significantly improved the signal amplitude and highlighted its spatial differences in the TGR area. Combining GRACE with surface hydrological observations, ET in the TGR area was estimated to have overall change characteristics highly consistent with results from the MOD16 Moderate Resolution Imaging Spectroradiometer (MODIS), and the uncertainties of monthly ET are mainly from TWS changes derived by GRACE uncertainties such as measurement errors and leakage errors. During our study period, the cyclical ET was mainly driven by climate precipitation but short-term (monthly) ET in the TGR area was also directly affected by human-driven water storage. For example, rising water levels in the three water storage stages (2003, 2006, and 2008) caused an abnormal increase in regional ET (up to 22.4 cm/month, 19.2 cm/month and 29.5 cm/month, respectively). Usually, high precipitation will cause increase in ET but the high precipitation during the water release periods (spring and summer) did not have a significant impact on the increased ET due to the water level in the TGR having decreased 30 m in this stage. Our results also indicate that the short-term fluctuations in flooded area and storage capacity of the TGR, i.e., the man-made mass changes in the main branch and tributaries of the Yangtze River, were the main factors that influenced the ET. This further illustrated that a quantitative estimation of changes in the ET in the TGR allows for a deeper understanding of the water balance in the regional land water cycle process as driven by both climate and human factors.

Human-Induced and Climate-Driven Contributions to Water Storage Variations in the Haihe River Basin, China

Terrestrial water storage (TWS) can be influenced by both climate change and anthropogenic activities. While the Gravity Recovery and Climate Experiment (GRACE) satellites have provided a global view on long-term trends in TWS, our ability to disentangle human impacts from natural climate variability remains limited. Here we present a quantitative method to isolate these two contributions with reconstructed climate-driven TWS anomalies (TWSA) based on long-term precipitation data. Using the Haihe River Basin (HRB) as a case study, we find a higher human-induced water depletion rate (−12.87 ± 1.07 mm/yr) compared to the original negative trend observed by GRACE alone for the period of 2003–2013, accounting for a positive climate-driven TWSA trend (+4.31 ± 0.72 mm/yr). We show that previous approaches (e.g., relying on land surface models) provide lower estimates of the climate-driven trend, and thus likely underestimated the human-induced trend. The isolation method presented in this study will help to interpret observed long-term TWS changes and assess regional anthropogenic impacts on water resources.

Detecting Winter Wheat Irrigation Signals Using SMAP Gridded Soil Moisture Data

The southern part of the Hebei Province is one of China’s major crop-producing regions. Due to the continuous decline in groundwater level, agricultural water use is facing significant challenges. Precision agricultural irrigation management is undoubtedly an effective way to solve this problem. Based on multisource data (time series soil moisture active passive (SMAP) data, Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and evapotranspiration (ET), and meteorological station precipitation), the irrigation signal (frequency, timing and area) is detected in the southern part of the Hebei Province. The SMAP data was processed by the 5-point moving average method to reduce the error caused by the uncertainty of the microwave data derived SM. Irrigation signals can be detected by removing the precipitation effect and setting the SM change threshold. Based on the validation results, the overall accuracy of the irrigation signal detection is 77.08%. Simultaneously, considering the spatial resolution limitation of SMAP pixels, the SMAP irrigation area was downscaled using the winter wheat area extracted from MODIS NDVI. The analytical results of 55 winter wheat samples (5 samples in a group) showed that winter wheat covered by one SMAP pixel had an 82.72% growth consistency in surface water irrigation period, which can indicate a downscaling effectiveness. According to the above statistical analysis, this paper considers that although the spatial resolution of SMAP data is insufficient, it can reflect the change of SM more sensitively. In areas where the crop pattern is relatively uniform, the introduction of high-resolution crop pattern distribution can be used not only to detect irrigation signals but also to validate the effectiveness of irrigation signal detection by analyzing crop growth consistency. Therefore, the downscaling results can indicate the true winter wheat irrigation timing, area and frequency in the study area.

Bridging Terrestrial Water Storage Anomaly During GRACE/GRACE-FO Gap Using SSA Method: A Case Study in China

The terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) is now a significant issue for scientific research in high-resolution time-variable gravity fields. This paper proposes the use of singular spectrum analysis (SSA) to predict the TWSA derived from GRACE. We designed a case study in six regions in China (North China Plain (NCP), Southwest China (SWC), Three-River Headwaters Region (TRHR), Tianshan Mountains Region (TSMR), Heihe River Basin (HRB), and Lishui and Wenzhou area (LSWZ)) using GRACE RL06 data from January 2003 to August 2016 for inversion, which were compared with Center for Space Research (CSR), Helmholtz-Centre Potsdam-German Research Centre for Geosciences (GFZ), Jet Propulsion Laboratory (JPL)’s Mascon (Mass Concentration) RL05, and JPL’s Mascon RL06. We evaluated the accuracy of SSA prediction on different temporal scales based on the correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE), which were compared with that of an auto-regressive and moving average (ARMA) model. The TWSA from September 2016 to May 2019 were predicted using SSA, which was verified using Mascon RL06, the Global Land Data Assimilation System model, and GRACE-FO results. The results show that: (1) TWSA derived from GRACE agreed well with Mascon in most regions, with the highest consistency with Mascon RL06 and (2) prediction accuracy of GRACE in TRHR and SWC was higher. SSA reconstruction improved R, NSE, and RMSE compared with those of ARMA. The R values for predicting TWS in the six regions using the SSA method were 0.34–0.98, which was better than those for ARMA (0.26–0.97), and the RMSE values were 0.03–5.55 cm, which were better than the 2.29–5.11 cm RMSE for ARMA as a whole. (3) The SSA method produced better predictions for obvious periodic and trending characteristics in the TWSA in most regions, whereas the detailed signal could not be effectively predicted. (4) The predicted TWSA from September 2016 to May 2019 were basically consistent with Global Land Data Assimilation System (GLDAS) results, and the predicted TWSA during June 2018 to May 2019 agreed well with GRACE-FO results. The research method in this paper provides a reference for bridging the gap in the TWSA between GRACE and GRACE-FO.