Diagnosing contrasting runoff responses under similar soil moisture conditions using the DWAT model

Soil moisture is widely used to describe catchment wetness conditions and to support drought-related hydrological interpretation. However, runoff responses under apparently similar soil moisture conditions can differ substantially across catchments and events, suggesting that soil moisture alone may not fully capture the processes controlling runoff generation. This study aims to diagnose the hydrological mechanisms associated with contrasting runoff responses under comparable soil moisture states using the process-based DWAT model. The analysis will use daily catchment-scale DWAT outputs including soil moisture, precipitation, total runoff, baseflow (groundwater flow), recharge, infiltration, and actual evapotranspiration for multiple catchments with contrasting hydrological characteristics. To enable consistent comparison across time, daily soil moisture will be transformed into percentile-based indicators to classify relative soil moisture states without directly implying absolute drought impacts. Runoff response will be quantified using event-based runoff ratios derived from simulated precipitation and discharge, and associated process indicators will be evaluated to interpret differences in runoff behavior. By separating soil moisture state from runoff response and leveraging internal model process variables, this work provides a structured framework to investigate why hydrological responses may diverge under similar dry conditions. The proposed approach is expected to support process understanding relevant for drought analysis and catchment-scale hydrological modeling.

This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Aquatic Ecosystem Conservation Research Program, funded by Korea Ministry of Climate, Energy and Environment(MCEE). (RS-2025-02304832)