Stormflow Response and “Effective” Hydraulic Conductivity of a Degraded Tropical Imperata Grassland Catchment as Evaluated With Two Infiltration Models

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Abstract

Predicting catchment stormflow responses after tropical deforestation remains difficult. We used 5-min rainfall and storm runoff data for 30 events to calibrate the Green–Ampt (GA) and the Spatially Variable Infiltration (SVI) models and predict runoff responses for a small, degraded grassland catchment on Leyte Island (the Philippines), where infiltration-excess overland flow (IOF) is considered the dominant runoff process. SVI replicated individual stormflow hydrographs better than GA, particularly for events with small runoff responses or multiple peaks. Calibrated parameter values of the SVI model (i.e., spatially averaged maximum infiltration capacity, Im and initial abstraction, F0) varied markedly between events, but were statistically significant and negatively correlated with (mid-slope) soil moisture content at 10 cm (SWC10)—as did the “catchment-wide effective” hydraulic conductivity (Ke) of the GA model. Using SWC10-based estimates of F0 and Im in SVI yielded satisfactory to good simulations for 11 out of 17 events with runoff coefficients ≥15%, but failed to reproduce the hydrographs for events with very small runoff amounts (0.25–1 mm) and low runoff coefficients (3%–6%). The median field-measured near-surface Ksat (2 mm hr−1) was distinctly lower than the median Im (32 mm hr−1) and, to a lesser extent, Ke (∼8 mm hr−1), suggesting an underestimation of the spatially averaged Ksat by the field measurements. Application of SVI is expected to give the most realistic results for situations where IOF is dominant, that is, where surface conditions are degraded and rainfall intensities high.

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APA

Cheng, Z., Zhang, J., Yu, B., Chappell, N. A., van Meerveld, H. J., & Bruijnzeel, L. A. (2023). Stormflow Response and “Effective” Hydraulic Conductivity of a Degraded Tropical Imperata Grassland Catchment as Evaluated With Two Infiltration Models. Water Resources Research, 59(5). https://doi.org/10.1029/2022WR033625

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