Machine-Learning Based Multi-Layer Soil Moisture Forecasts—An Application Case Study of the Montana 2017 Flash Drought

Abstract

Soil moisture (SM) is an essential climate variable, governing land-atmosphere interactions, runoff generation, and vegetation growth and productivity. Timely forecasts of SM spatial distribution and vertical profiles are needed for early detection and prediction of potential droughts. However, previous studies have primarily concentrated on historical or near real-time soil moisture mapping, with less effort devoted to the development and integration of soil moisture forecast components within drought assessment systems. A satellite-driven machine-learning approach was developed in this study to build complex relationships between diversified predictor data sets and in situ multi-layer SM measurements from the Montana Mesonet, a regionally dense environmental station network in the US upper Missouri and Columbia basins. The resulting 30-m daily SM predictions showed strong performance against in situ SM measurements from 4-, 8- and 20-inch soil layers, and with 1- to 2-week forecast lead times (R > 0.91; RMSE ≤ 0.047 cm3/cm3). The machine-learning model was subsequently applied to the entire Montana region, and the SM deficit forecasts with both 1- and 2-week lead times successfully depicted onset, progression, and termination phases of the 2017 Montana flash drought, which was not effectively identified from prevailing operational systems. The resulting system is capable of delineating local scale SM heterogeneity, and could be extended to predict other critical water cycle variables, potentially enhancing future drought forecasts through multivariate assessments and benefiting water resource management, agricultural practices, and the provision of ecosystem services.