Extending GRACE terrestrial water storage anomalies by combining the random forest regression and a spatially moving window structure

Abstract

The changes of terrestrial water storage (TWS) is critical for drought monitoring, water and food security, global water cycle and climate change studies. Currently, the Gravity Recovery and Climate Experiment (GRACE) twin satellites are unique means of observing large-scale water storage variations, but the short time series (2002-present) limits their applications in long term climatic and hydrologic studies. Although the TWS can be calculated from global land surface models, large uncertainties arise due to uncertainties of inputs and the limitations of the models. This study developed a reconstruction model for GRACE TWS anomalies (TWSA) based on the Global Land Data Assimilation System (GLDAS) model outputs by using a Random Forest (RF) regression approach. A Spatially Moving Window (SMW) structure was introduced when training the RF model to address the spatial variations of TWSA, and a linear regression approach (LR) was also used for comparison purpose. Long-term TWSA over China land area were generated based on the proposed approaches and results were validated through cross-validation and comparisons with reference datasets. As a result, the RF-based model outperforms the LR-based model, and the reconstructed TWSA by using the two models both well reproduce GRACE dataset and outperform the TWSA that are derived directly from GLDAS models. Moreover, the TWSA produced by using the presented models have good agreements with another global GRACE-based reconstructed TWSA dataset and in-situ soil moisture measurements. Importance value for each variable in the RF model was quantified as well as the spatial coefficients for each variable in the LR model. The importance values and regression coefficients present varying spatial patterns. Rather than modifying the land surface model structure and inputs, this study provides alternative ways of improving the TWS estimations of GLDAS and extending time range of GRACE datasets. The experiments are expected to promote and enrich the methodologies and theories of combining physical and statistical models for optimal simulations in geoscientific research.