The role of macropores and multi-resolution soil survey datasets for distributed surface-subsurface flow modeling

Distributed watershed-scale modeling is often used as a framework for exploring the heterogeneity of runoff response and hydrologic performance of the catchment. The objective of this study is to apply this framework to characterizing the impacts of soil hydraulic properties at multiple scales on moisture storage and distributed runoff generation in a forested catchment. The physics-based and fully-coupled Penn State Integrated Hydrologic Model (PIHM) is employed to test a priori and field-measured properties in the modeling of watershed hydrology. PIHM includes an approximate representation of macropore flow that preserves the water holding capacity of the soil matrix while still allowing rapid flow through the macroporous soil under wet conditions. Both phenomena are critical to the overall hydrologic performance of the catchment. Soils data at different scales were identified: Case I STATSGO soils data (uniform or single soil type), Case II STATSGO soils data with macropore effect, and Case III field-based hydropedologic experiment revised distributed soil hydraulic properties and macropore property estimation. Our results showed that the Case I had difficulties in simulating the timing and peakflow of the runoff responses. Case II performed satisfactorily for peakflow at the outlet and internal weir locations. The distributed soils data in Case III demonstrated the model ability of predicting groundwater levels. The analysis suggests the important role of macropore flow to setting the threshold for recharge and runoff response, while still preserving the water holding capability of the soil and plant water availability. The spatial variability in soil hydraulic properties represented by Case III introduces an additional improvement in distributed catchment flow modeling, especially as it relates to subsurface lateral flow. Comparison of the three cases suggests the value of high-resolution soil survey mapping combined with a macropore parameterization can improve distributed watershed models.