An Antecedent-Precipitation-Informed Soil Water Balance and Time-Aware Mamba–MoE Framework for Surface Soil Moisture Forecasting

Abstract

Surface soil moisture forecasting is important for drought monitoring, irrigation management, and land–atmosphere process analysis but remains challenging because near-surface soil moisture is jointly influenced by antecedent precipitation, atmospheric drying, soil properties, vegetation conditions, and irregular multi-source observations. This study proposes an Antecedent-Precipitation-Informed Surface Soil Water Balance and Time-Aware Mamba–Mixture-of-Experts (API-SWB-Mamba-MoE) framework for forecasting in situ volumetric soil moisture at approximately 5 cm depth using only information available before the target time. The framework combines a process-guided API-SWB physical prior, a time-aware Mamba temporal encoder, a context-conditioned MoE residual decoder, and gated residual fusion. The U.S. source-domain stations were evaluated using five independently repeated station-level random holdout splits, with approximately 70%, 15%, and 15% of the stations assigned to training, validation, and testing in each repetition, respectively. The five resulting U.S.-trained models were further applied without target-domain retraining or fine-tuning to six German and French stations for zero-shot transfer evaluation. Across the U.S. test prediction–observation pairs pooled from the five repetitions, the proposed model achieved a Pearson correlation coefficient (R) of 0.934, a root mean square error (RMSE) of 0.035 cm3 cm−3, a Kling–Gupta efficiency (KGE) of 0.922, and a mean bias error (MBE) of −0.001 cm3 cm−3. Pooled zero-shot predictions yielded RMSE values of 0.036 and 0.038 cm3 cm−3 and KGE values of 0.885 and 0.917 for Germany and France, respectively. The pooled U.S. test RMSE was 7.9–22.2% lower than that of the ablation variants and 20.5–32.7% lower than that of the benchmark models. These results suggest that combining a process-guided prior with time-aware sequence modeling and context-conditioned expert routing offers a promising approach for station-scale soil moisture forecasting under irregular multi-source observations. The external results provide preliminary evidence of zero-shot transferability at the six selected sites, although broader regional validation remains necessary.