Evaluating the influence of climate inputs on WEPP predictions for a forest road in the Florida Panhandle, USA
| Authors: | Jingqiu Chen, Shuyuan Wang, William J. Elliot, Bernard A. Engel, Feng Pan, Yaoze Liu, Johnny M. Grace |
| Year: | 2026 |
| Type: | Scientific Journal |
| Station: | Rocky Mountain Research Station |
| DOI: | https://doi.org/10.1016/j.jhydrol.2026.135340 |
| Source: | Journal of Hydrology |
Abstract
Rainfall temporal resolution and climate input formats can influence runoff and soil erosion predictions on forest roads. This study evaluated their effects using the Water Erosion Prediction Project (WEPP) simulations for a forest road in Florida’s Chipola Experimental Forest. High-resolution NOAA-ASOS precipitation data (2000–2024) were used to generate breakpoint, fixed-interval (5–60 min), and time to peak/peak intensity (TPIP) climate inputs. WEPP predictions were generally stable across input types, with modest differences in average annual runoff and soil loss. Finer temporal resolutions (5–10 min) for breakpoint inputs produced slightly smaller deviations, while TPIP showed greater sensitivity to storm irregularity, particularly for high-intensity, highly peaked, or long-duration events. Machine learning analysis identified total precipitation and average intensity as the main drivers of daily runoff differences, with ratio of peak intensity to average intensity (ip), ratio of duration where peak intensity occurs to total storm duration (tp), and duration having minor influence. Across soil erodibility conditions, average treatment effects (ATEs) were near zero for small storms and increased moderately for larger events, especially under higher infiltration capacities. TPIP inputs exhibited greater variability, suggesting potential need for runoff adjustments. Mediation analysis indicated that natural indirect effects (NIEs) could be reduced by improving runoff predictions, whereas direct effects (NDEs) were mainly influenced by critical shear stress (τc). These results highlight the importance of rainfall temporal structure and climate input selection in hydrologic and erosion modeling, providing guidance for improving model predictions and informing future applications.