Simulating watershed hydrological response following a wildfire in southeast China with consideration of land cover changes

Wildfires can significantly increase runoff, leading to increased risk of flooding and posing serious threats to life and property. Moreover, wildfires have long-term effects on hydrological processes under climate change. Therefore, long-term monitoring of post-fire runoff is essential. However, the assessment of post-fire runoff through field surveying is time-consuming and expensive. Although model simulation offers an effective means for investigating post-fire runoff, only few studies have focused on hydrological modeling with the full incorporation of changes in vegetation cover and soil properties after wildfire events. This study aimed to simulate the hydrological response of a forest watershed following a wildfire event in southeast China using the Hydrological Simulation Program Fortran model. Land cover changes were updated during model simulation, and model parameters selected using a Generalized Likelihood Uncertainty Estimation method were auto-calibrated using the genetic algorithm method. According to the results of the global sensitivity analysis, seven parameters were identified as the most influential for calibration and validation: lower zone nominal storage, soil infiltration capacity, basic groundwater recession rate, upper zone nominal storage, fraction entering deep groundwater, interflow inflow, and interflow recession. The model exhibited robust performance (R-2 > 0.87 and ENS > 0.83 for burned conditions; R-2 = 0.62 and ENS = 0.46 for unburned conditions). Furthermore, the findings indicated that watershed runoff was notably higher after the fire compared to undisturbed conditions. Additionally, under drought conditions, hydrological responses after the fire can become intensified and persist for an extended duration, approximately spanning 5 to 10 years. Runoff may still increase by 54 % within the first two or three years after the fire. Therefore, it is imperative to enhance long-term monitoring of runoff response following fire events. The established model will serve as a valuable tool in achieving this objective.