Development of a New Generic AI Model for Spatio‐Temporal Prediction of Soil Moisture and Soil Water Isotopes

Abstract

Understanding water fluxes and mixing dynamics in the unsaturated zone is crucial for ecohydrological studies. Yet, observations of soil moisture and soil water isotopes remain limited in spatial extent and temporal resolution, restricting modeling studies on the critical zone. A data‐driven Artificial Intelligence (AI) approach was applied in a first attempt to simulate daily soil moisture and soil water isotopes (δ2H, δ18O) across soil profiles of a mixed land use catchment using parsimonious climate and vegetation predictors. A sequential model combined a Long Short‐Term Memory network for soil moisture and a Random Forest model for soil water isotope simulation, using LSTM‐predicted soil moisture as input. Training used ca. 2 years of daily soil moisture and one year of monthly soil water isotopes from different depths (0–100 cm) under seven land uses in the Demnitzer Millcreek catchment (66 km2), NE Germany. The AI model captured seasonal and depth‐dependent hydrological patterns, outperforming a process‐based model previously applied in the catchment. Soil moisture simulations showed Kling‐Gupta Efficiency of 0.75–0.92 (RMSE: 1.81%–4.50%), while isotopes simulations achieved 0.80–0.88 (RMSE: 4.3–5.8‰ for δ2H, 0.6–0.9‰ for δ18O). However, the performance of AI model depended on the study period and catchment, showing overestimation in drier test periods and underestimation in wetter validation periods. Also, in this evapotranspiration‐dominated site, the model relied heavily on indirect climate predictors (e.g., relative humidity), derived from direct drivers (e.g., precipitation), reducing performance in short‐term hydrological responses. Our sequential model has potential as stand‐alone simulations or as a surrogate for process‐based models.