Predicting Terrestrial Water Storage Anomalies at the Global Scale with a Machine-Learning Model

Changes in the level of the world freshwater storage, the Terrestrial Water Storage Anomalies (TWSA), may be induced by natural variability, climate change, and human activities. Since 2002 the Gravity Recovery and Climate Experiment (GRACE) has been measuring the Earth’s gravity field providing estimates of the TWSA at the global scale.

Here, we aim to develop a machine learning model that can reproduce the GRACE monthly time series covering the period between 2002 and 2017 from climate data, identifying to what extent the TWS fluctuations have been caused by climate variability. We used a Long Short-Term Memory (LSTM) neural network trained with meteorological variables (precipitation, air temperature, solar net radiation, snow cover, relative humidity, and leaf area index) and soil properties data (soil porosity, soil texture, and clay, sand, and silt fractions). Our results show that the model is able to consistently reconstruct the observed freshwater anomalies, especially in the humid regions. Furthermore, we observed that as climate change trends are removed from input data, the bias between model predictions and observed data becomes larger, proving the influence of climate change on TWSA.