Deep causal learning for soil water dynamics: Exploring latent causality and improving predictive adaptability

Due to its fundamental role in the Earth's hydrological system, understanding and predicting soil water processes has long been important. Rapidly evolving machine learning methods have shown remarkable predictive capabilities, but due to a lack of process understanding, are still insufficient when data is suboptimal or the environment varies. Efforts to integrate prior physical knowledge into learning machines have proven effective in improving the robustness of machine learning models in a complex environment. This improvement has relied heavily on domain-specific knowledge, but the potential for machine learning itself to autonomously understand complex environmental processes, such as soil water movement, is still an intriguing possibility. To explore this possibility, we have developed a novel causal deep learning method specifically designed to capture the underlying causal mechanism of soil hydrodynamics, thereby enabling the model to adaptively predict vertical profiles of soil moisture in changing environments. By using time-series data from heterogeneous environments, our model is trained to understand the causal interplay between meteorological forcing, environmental factors, and various high-level physical processes that govern the spatiotemporal distribution of soil moisture. The key innovation of our method lies in its ability to use the learned causal relationships as a guide. Even when faced with sparse data in new and untested environments, the model can estimate the differences and adapt its predictions accordingly. Our method is expected to not only enhance soil moisture predictions in new environments, but also provide potentially new insights into a deeper synergy between physical knowledge and data-driven learning.