Exploiting soil moisture, precipitation and streamflow observations to evaluate soil moisture/runoff coupling in land...

Area of Science:

• Hydrology
• Earth System Science
• Remote Sensing

Background:

• Accurate partitioning of precipitation into infiltration and runoff is crucial for land surface models.
• Pre-storm soil moisture significantly influences infiltration capacity and unsaturated storage, affecting runoff generation.
• Existing land surface models (LSMs) face challenges in accurately representing these soil moisture-runoff dynamics.

Purpose of the Study:

• To evaluate the accuracy of land surface models in capturing the relationship between pre-storm soil moisture and event runoff coefficients.
• To identify which hydrological processes and model structures best represent the observed soil moisture-runoff coupling.
• To assess the impact of different runoff generation mechanisms on model performance.

Main Methods:

• Utilized NASA Soil Moisture Active Passive (SMAP) Level-4 soil moisture data.
• Combined SMAP data with ground-based streamflow and precipitation observations.
• Analyzed the rank correlation between pre-storm soil moisture and event runoff coefficients across various land surface models.

Main Results:

• Land surface models generally underestimate the positive correlation between pre-storm soil moisture and event runoff coefficients.
• This underestimation is most pronounced in models using the infiltration-excess (Hortonian) runoff generation approach.
• Models incorporating sub-surface stormflow or saturation-excess runoff generation show stronger, more accurate coupling.

Conclusions:

• Current land surface models require refinement to better capture the influence of soil moisture on runoff generation.
• Explicitly representing subsurface flow and saturation-excess processes improves model accuracy in simulating surface water partitioning.
• Enhanced soil moisture data, like that from SMAP, is valuable for model evaluation and improvement.