Evapotranspiration (ET) is usually difficult to estimate at the regional scale due to scarce direct measurements. This study uses the water balance equation to calculate the regional ET with ...observations of precipitation, runoff, and terrestrial water storage changes (TWSC) in nine exorheic catchments of China. We compared the regional ET estimates from a water balance perspective with and without considering TWSC (ETWB: ET estimates with considering TWSC, and ETPQ: ET estimates from precipitation minus runoff without considering TWSC). Results show that the regional annual ET ranges from 417.7 mm/yr to 831.5 mm/yr in the nine exorheic catchments based on the water balance equation. The impact of ignoring TWSC on calculating ET is notable, as the root mean square errors (RMSEs) of annual ET between ETWB and ETPQ range from 12.0–105.8 mm/yr (2.6–12.7% in corresponding annual ET) among the exorheic catchments. We also compared the estimated regional ET with other ET products. Different precipitation products are assessed to explain the inconsistency between different ET products and regional ET from a water balance perspective. The RMSEs between ET estimates from Gravity Recovery and Climate Experiment (GRACE) and ET from land surface models can be reduced if the deviation of precipitation forcing data is considered. ET estimates from Global Land Evaporation Amsterdam Model (GLEAM) can be improved by reducing the uncertainty of precipitation forcing data in three semiarid catchments. This study emphasizes the importance of considering TWSC when calculating the regional ET using a water balance equation and provides more accurate ET estimates to help improve modeled ET results.
The West Liaohe River Basin (WLRB) is one of the most sensitive areas to climate change in China and an important grain production base in the Inner Mongolia Autonomous Region of China. Groundwater ...depletion in this region is becoming a critical issue. Here, we used the Gravity Recovery and Climate Experiment (GRACE) satellite data and in situ well observations to estimate groundwater storage (GWS) variations and discussed the driving factors of GWS changes in the WLRB. GRACE detects a GWS decline rate of −0.92 ± 0.49 km3/yr in the WLRB during 2005-2011, consistent with the estimate from in situ observations (−0.96 ± 0.19 km3/yr). This long-term GWS depletion is attributed to reduced precipitation and extensive groundwater overexploitation in the 2000s. Long-term groundwater level observations and reconstructed total water storage variations since 1980 show favorable agreement with precipitation anomalies at interannual timescales, both of which are significantly influenced by the El Niño-Southern Oscillation (ENSO). Generally, the WLRB receives more/less precipitation during the El Niño/La Niña periods. One of the strongest El Niño events on record in 1997-1998 and a subsequent strong La Niña drastically transform the climate of WLRB into a decade-long drought period, and accelerate the groundwater depletion in the WLRB after 1998. This study demonstrates the significance of integrating satellite observations, ground-based measurements, and climatological data for interpreting regional GWS changes from a long-term perspective.
Terrestrial water storage (TWS) is a critical component for sustainable societal development and ecosystem cycles. The Gravity Recovery and Climate Experiment satellites have tracked changes in ...global TWS under the combined effects of various factors with unprecedented accuracy since 2002. In this study, we separate the trends in TWS driven by precipitation and non‐precipitation factors for the Chinese mainland from 2003 to 2016 based on the statistical reconstruction method and linear regression and analyze the driving mechanisms combining with multi‐source data. The results show that: (a) during the study period, TWS shows different degrees of increase in most of the Yangtze River basin, the northern part of the Tibetan Plateau, and part of the Heilongjiang Province, while TWS shows a significant decrease in the Tien Shan Mountains, the southeastern part of the Tibetan Plateau, and the North China Plain; (b) precipitation is one of the dominant factors leading to the rise of TWS, and the construction of reservoirs and dams also contributes. In contrast, anthropogenic activities (agricultural irrigation, industrial water use, etc.) and accelerated glacial melting due to global warming are the dominant factors in the decline of water storage; (c) the contribution of long‐term precipitation change to water storage is significantly larger in the northern China region (north of the 800 mm isopleth). This study provides a feasible method for quantifying the contribution of precipitation and non‐precipitation factors to TWS, which is meaningful for understanding the impact of climate change and anthropogenic factors on water resources.
Plain Language Summary
Water scarcity is a global crisis for social development in the present and future. As the world's most populous country, China's per capita freshwater resources are far below the global average, making the imbalance between water supply and demand in China even more serious. The study of water storage changes contributes to understanding freshwater resources in China. Here, the precipitation‐ and non‐precipitation‐ driven water storage changes in China are separated based on the statistical reconstruction method and long‐term precipitation data. In addition, satellite gravity, model simulations, and in‐situ data are combined to analyze the driving mechanism in regions that experienced significant changes in water storage during the past decades. Human activities and glacier melting are the dominant factors leading to the water storage deficit. Both precipitation and dam construction contribute to the increase in water storage. This study would provide valuable information for the rational allocation of water resources and coordinated development of the economy and ecology.
Key Points
Terrestrial water storage (TWS) trends induced by long‐term precipitation change, precipitation variability, and non‐precipitation factors are separated in China
The TP‐TWS factor is proposed to characterize the impact of long‐term precipitation trends on the TWS trends
The TWS changes in typical regions are rationalized from the perspective of precipitation and non‐precipitation factors
The Huang-Huai-Hai (3H) Plain is the major crop-producing region in China. Due to the long-term overexploitation of groundwater for irrigation, the groundwater funnel is constantly expanding and the ...scarcity of water resources is prominent in this region. In this study, Gravity Recovery and Climate Experiment (GRACE) and hydrological models were used to estimate the spatial-temporal changes of groundwater storage (GWS) and the driving factors of GWS variations were discussed in the 3H Plain. The results showed that GRACE-based GWS was depleted at a rate of -1.14 ± 0.89 cm/y in the 3H Plain during 2003 to 2015. The maximum negative anomaly occurred in spring due to agricultural irrigation activities. Spatially, the loss of GWS in the Haihe River Basin is more serious than that in the Huaihe River Basin, presenting a decreasing trend from south to north. Conversely, the blue water footprint (WF
) of wheat exhibited an increasing trend from south to north. During the drought years of 2006, 2013, and 2014, more groundwater was extracted to offset the surface water shortage, leading to an accelerated decline in GWS. This study demonstrated that GWS depletion in the 3H Plain is well explained by reduced precipitation and groundwater abstraction due to anthropogenic irrigation activities.
Hydrological droughts are events of prolonged water scarcity and cause many devastating impacts. It is, therefore, extremely crucial to understand their spatiotemporal evolution to guide prevention ...and mitigation policies. The Gravity Recovery and Climate Experiment (GRACE, April 2002–June 2017) and GRACE Follow‐On (GRACE‐FO, June 2018 until the present) missions have been used to study large‐scale droughts of almost two decades. But characterizing droughts during the between missions gap period of 2017 and 2018 has not been well addressed and will be covered here. To bridge the gap, an innovative Bayesian convolutional neural network is developed to reconstruct the missing signals from hydroclimatic inputs. The reconstruction fields and existing signals are then used to explore regions that have experienced consecutive water storage deficits during the 2017–2018 gap. We found many regions of the northern midlatitudes exhibiting moderate to exceptional droughts in terms of water storage deficits, among which parts of Pakistan and Afghanistan, and Iberian Peninsula experienced exceptional droughts lasting for more than 1 year with the maximum deficits (−4.4 ± 0.8 and −7.2 ± 1.1 cm, respectively) being over 50% of the seasonal storage variations. Comparisons with climate indicators show that the identified droughts are predominantly caused by continuous below‐normal precipitation. The recovery process correlates generally well with the accumulation rate of precipitation surpluses (the correlation coefficient (R) can be up to 0.92). Besides, the reconstructed signals, which have R > 0.7 with the testing GRACE(‐FO) data in over 90% of the globe, reliably maintain the data continuity and therefore they are recommended for hydro‐climatological studies.
Key Points
The missing terrestrial water storage anomaly signals between the 2017–2018 Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On gap are reconstructed by Bayesian deep learning
Hydrological droughts overlapping with this gap period are characterized and quantified
The northern midlatitudes experienced continuous water storage deficits as a result of precipitation deficiency
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.
As the largest hydroelectric project worldwide, previous studies indicate that the Three Gorges Dam (TGD) affects the local climate because of the changes of hydrological cycle caused by the ...impounding and draining of the TGD. However, previous studies do not analyze the long-term precipitation changes before and after the impoundment, and the variation characteristics of local precipitation remain elusive. In this study, we use precipitation anomaly data derived from the CN05.1 precipitation dataset between 1988 and 2017 to trace the changes of precipitation before and after the construction of the TGD (i.e., 1988–2002 and 2003–2017), in the Three Gorges Reservoir Area (TGRA). Results showed that the annual and dry season precipitation anomaly in the TGRA presented an increasing trend, and the precipitation anomaly showed a slight decrease during the flood season. After the impoundment of TGD, the precipitation concentration degree in the TGRA decreased, indicating that the precipitation became increasingly uniform, and the precipitation concentration period insignificantly increased. A resonance phenomenon between the monthly average water level and precipitation anomaly occurred in the TGRA after 2011 and showed a positive correlation. Our findings revealed the change of local precipitation characteristics before and after the impoundment of TGD and showed strong evidence that this change had a close relationship with the water level.
Zhengzhou and its surrounding areas, located in northern Henan Province, China, receive continuous extreme rainfall from July 17 to July 22, 2021. Northern Henan Province experiences extensive flash ...floods and urban floods, causing severe casualties and property damage. Understanding the variation of hydrologic features during this flood event could be valuable for future flood emergency response work and flood risk management. This study first demonstrates the rainstorm process based on near-real-time precipitation data from the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0). To meet the temporal resolution required for monitoring this short-term flood event, reconstructed daily terrestrial water storage anomalies (TWSAs) based on GRACE and GRACE-FO data and CLDAS-V2.0 datasets are first introduced. The spatial and temporal evolution of the reconstructed daily TWSA is analyzed in the study area during this heavy rainfall event. We further employ a wetness index based on the reconstructed daily TWSA for flood warnings. Furthermore, the modeled soil moisture data and daily runoff data are used for flood monitoring. Results show that the reconstructed daily TWSA increases by 437.7 mm in just six days (from July 17 to July 22, 2021), with a terrestrial water storage increment of 9.4 km 3 . Compared with ITSG-Grace2018, the reconstructed daily TWSA has better potential for near-real-time flood monitoring for short-term events in a small region. The wetness index derived from reconstructed daily TWSA is potential for flood early warning.
•Map landslide in Danba using ascending and descending Sentinl-1 datasets.•Seasonal accelerations of landslides are related with seasonal rainfall.•Rainstorm at 17 June 2017 alter the long term ...displacement trends.•Estimate volumes of Niela and Gaoding landslides using mass conservation.
Landslides are frequent mountainous geohazards induced by multiple factors, such as extreme rainfall, river erosion, and intense anthropogenic activities. On 17 June 2020, a rainstorm hit Danba County, Sichuan Province, and triggered the catastrophic Aniangzhai landslide. The impact of this rainstorm on landslide kinematics in this region has seldom been investigated. We used a time-series interferometric synthetic aperture radar (InSAR) analysis to map the active slopes in Danba County using one ascending (2015–2021) and one descending (2018–2021) Sentinel-1 dataset. A total of 36 landslides (63.1 km2) were detected along riverbanks. The time series of eastward and vertical displacements during 2018–2021 were retrieved by integrating the ascending and descending Sentinel-1 datasets. The maximum eastward and vertical displacement rates obtained are 229 and −75 mm/yr, respectively. Seasonal accelerations are correlated with concentrated rainfall. We suggest that the 2020 rainstorm and the abundant precipitation during the water year 2020 altered the long-term displacement trends of active landslides. We further constrained the volumes of the Niela and Gaoding landslides in the order 3.6–6.9 × 107 m3 and 5.4–6.0 × 108 m3, respectively, using 2D displacement rates and mass conservation equations. Our results demonstrate that multi-temporal and multi-orbit InSAR measurements can provide insights into the evolution and mechanism of landslides.
A catastrophic landslide happened on 15 March 2019 in Xiangning County of Shanxi Province, causing 20 fatalities. Such an event makes us realize the significance of loess slope instability detection. ...Therefore, it is essential to identify the potential active landslides, monitor their displacements, and sort out dominant controlling factors. Synthetic Aperture Radar (SAR) Interferometry (InSAR) has been recognized as an effective tool for geological hazard mapping with wide coverage and high precision. In this study, the time series InSAR analysis method was applied to map the unstable areas in Xiangning County, as well as surrounding areas from C-band Sentinel-1 datasets acquired from March 2017 to 2019. A total number of 597 unstable sites covering 41.7 km2 were identified, among which approximately 70% are located in the mountainous areas which are prone to landslides. In particular, the freezing and thawing cycles might be the primary triggering factor for the failure of the Xiangning landslide. Furthermore, the nonlinear displacements of the active loess slopes within this region were found to be correlated significantly with precipitation. Therefore, a climate-driven displacement model was employed to explore the quantitative relationship between rainfall and nonlinear displacements.
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