Reconstruction of terrestrial water storage anomalies in Northwest China during 1948–2002 using GRACE and GLDAS products

Abstract

Commencement of the Gravity Recovery and Climate Experiment (GRACE) provides an alternative way to monitor changes in terrestrial water storage (TWS) at large scales. However, GRACE dataset spans from 2002 to present, which greatly limits the application of GRACE data for long-term hydrological studies. Thus, the general linear model (GLM), random forest (RF), support vector machines (SVM), and artificial neural networks (ANN) methods were used to reconstruct the time series of terrestrial water storage anomalies (TWSA, i.e., remove the average value from the time series) in Northwest China (NWC) during 1948-2002 based on the GRACE TWSA during 2003-2015 and hydrological data from the Global Land Data Assimilation System (GLDAS) during 1948-2010. Theresults showed that soil moisture (SM) anomalies, or the combination of SM, canopy water (CW), and snow water equivalent (SWE) anomalies were better than the other anomalies of GLDAS in NWC. RF method can be regarded as the optimal method to reconstruct TWSA in NWC in the four models.A negative relationship was found between the reconstructed TWSAs and El Niño-Southern Oscillation (ENSO). The method could also offer an approach to reconstruct TWSA and drought events in large river basins during the past several decades.

Long-term total water storage change from a Satellite Water Cycle reconstruction over large southern Asian basins

“…(4). A relaxed constraint is considered (Pellet et al, 2019): the WC budget is closed within an error r that follows a normal distribution with specified uncertainty (Yilmaz et al, 2011). The problem can be written in the following way:…”

Divergent Trends of Water Storage Observed via Gravity Satellite across Distinct Areas in China

“…The cross-test proved that the water storage observed by gravity satellite was an efficient and reliable data supplement, which provided a way to monitor and manage water storage on a large scale. Although some studies indicated that SMS is a traditional and widely used indicator of water storage [44], it was obvious that SMS was not sufficient to reflect the changing pattern of water storage on a large scale. For example, Rodell et al (2009) reported that the Indian subcontinent experienced rapid depletion of water storage due to excessive pumping over the past decade [1], GRACE-based TWS sensitively observed a decline in water storage, while SMS did not detect this.…”

“…Therefore, the increasing water supply and the underlying surface of the forest ecosystem provided a clear SC water storage increase during 2003-2016. In particular, some large-scale water conservancy projects may also lead to the surplus of regional water storage [44]; therefore, the construction of reservoirs and wetlands is also a reason for the obvious raise of TWS. To sum up, the decreases in TWS in NC were mainly driven by human activities, while the decreases in SC were mainly driven by climate factors.…”

Downscaling of GRACE-Derived Groundwater Storage Based on the Random Forest Model

“…In this research, we used level-three data provided by CSR [24] in the form of grid data product, which has undergone a series of processing, such as gaussian smoothing, destriping filter, glacier isostatic adjustment (GIA), and the anomalies relative has been deducted by default to 2004 to 2009 time-mean baseline. The total water storage should be multiplied the scaling factors to restore much of the energy removed by processing [30,37]. Total water storage of this product has a high accuracy and it is suitable to be applied in the research area of this study [49,51].…”

“…Ning et al [28] used the water balance equation of TWS to carry out the statistical downscaling method, which indicated that TWS is comprehensively affected by precipitation, evapotranspiration, runoff, and other hydrological factors. Yang et al [37] used linear regression and machine learning models to reconstruct the total water storage anomalies in Northwest China during 1948-2002 while using GRACE and GLDAS products, indicating that TWS is closely related to soil moisture, snow water equivalent, and canopy water. Therefore, in this study, we selected six modeling factors, including precipitation (P), evapotranspiration (ET), runoff, soil moisture (SM), snow water equivalent (SWE) and canopy water (CW), and analyzed four kinds of machine learning methods: such as random forest (RF) [38], support vector machine (SVR) [39], artificial neural network (ANN) [40], and multiple linear regression (MLR) [41] to conduct a downscaling study on the GRACE-derived TWS and GWS in the long time series.…”