Observations‐Driven Spatial‐Attention LSTM for Multi‐Depth Soil Moisture Prediction and Dynamic Soil Hydrological Process Connectivity Quantification

Abstract Soil moisture dynamics in vertical profiles result from multiple interacting processes, generating complex temporal behavior and intricate connectivity patterns. However, accurately simulating multi‐depth soil moisture while diagnosing time‐varying vertical connectivity remains challenging. This study develops a Long Short‐Term Memory model with time‐varying spatial attention (LSTM + ATT) to predict multi‐depth soil moisture and quantify vertical hydrological connectivity via attention weights. Evaluated across 19 USCRN (U.S. Climate Reference Network) stations, LSTM + ATT demonstrated superior robustness compared to independent (Ind‐LSTM) and joint (Joint‐LSTM) baselines. While performance was nearly identical at shallow depths (5–20 cm), LSTM + ATT excelled in deeper layers, achieving median NSE values of 0.91 (50 cm) and 0.93 (100 cm), outperforming Ind‐LSTM (0.86–0.89) and Joint‐LSTM (0.90–0.91) while eliminating catastrophic failures (NSE < 0). Learned attention weights revealed physically consistent connectivity patterns, weakening as vertical distance increased. Weight dynamics were strongly linked to soil moisture: influence from upper layers correlated positively ( r : 0.20 to 0.50) with moisture content, while influence from lower layers (50 and 100 cm) correlated negatively ( r : −0.76 to −0.18). During preferential flow, weights reorganized to enhance connectivity with upper layers (increasing 30%–100%) at the expense of self‐influence (decreasing 10%–30%), highlighting distinct hydraulic signatures of preferential pathways. Unlike the implicit relationships in Joint‐LSTM, this dynamic attention framework provides a novel quantitative diagnostic for time‐varying connectivity, effectively bridging the gap between predictive accuracy and process interpretability in subsurface hydrology.