Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?

Abstract

Freshwater stored on land is an extremely vital resource for all the terrestrial life on Earth. But our ability to record the change of land water storage is weak despite its importance. In this study, we attempt to establish a data‐driven model for simulating terrestrial water storage dynamics by relating climate forcings with terrestrial water storage anomalies (TWSAs) from the Gravity Recovery and Climate Experiment (GRACE) satellites. In the case study in Pearl River basin, China, the relationships were learned by using two ensemble learning algorithms, the Random Forest (RF) and eXtreme Gradient Boost (XGB), respectively. The TWSA in the basin was reconstructed back to past decades and compared with the TWSA derived from global land surface models. As a result, the RF and XGB algorithms both perform well and could nicely reproduce the spatial pattern and value range of GRACE observations, outperforming the land surface models. Temporal behaviors of the reconstructed TWSA time series well capture those of both GRACE and land surface models time series. A multiscale GRACE‐based drought index was proposed, and the index matches the Standardized Precipitation‐Evapotranspiration Index time series at different time scales. The case analysis for years of 1963 and 1998 indicates the ability of the reconstructed TWSA for identifying past drought and flood extremes. The importance of different input variables to the TWSA estimation model was quantified, and the precipitation of the prior 2 months is the most important variable for simulating the TWSA of the current month in the model. Results of this study highlight the great potentials for estimating terrestrial water storage dynamics from climate forcing data by using machine learning to achieve comparable results than complex physical models.

Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin

“…Ensemble learning uses multiple learners and integrates learning methods through specific rules to achieve better results than a single learner. The bootstrap aggregating (bagging) method is essential for assembling weak regressors into strong regressors [23]. Random samples are selected with replacement, and each sample is trained to build the model.…”

“…RF is an ensemble method combining several weak learners to produce a strong learner that yields the optimum results. Additionally, each variable can be sorted by importance, making this model more explanatory [23][24][25]. Combined with LSMs and meteorological forcing data, RF provides a comprehensive perspective on TWSA reconstruction.…”

Groundwater Prediction Using Machine-Learning Tools

“…In addition to these techniques, a range of machine-learning techniques have been applied to the problem, including MLP in [12,[19][20][21][22][23], SVR in [19,24] and recently RFs in [25,26]. The use of XGB is rare in the scheme of groundwater prediction, and is found in only a few studies such as [27,28].…”

“…This leads us to the second approach in which machine-learning techniques can be used for single-output regressing problems. For GRACE ∆TWS image reconstruction, the authors in [27] used both XGB and RFs to acquire the importance of 20 features. It was shown that the precipitation of the two months prior to prediction is the most important variable for estimating the TWS dynamics.…”

“…XGB relies on gradient boosting to form a combined prediction. In XGB, the predictors in a tree are built in a sequential manner, and are trained on the residuals of the previous learners, so that errors are reduced step by step [27].…”