Beyond stand-level analysis : how landscape attributes and logging practices shape flood dynamics

The influence of forest management practices, particularly logging, on peak flow dynamics remains a subject of considerable debate within the field of forest hydrology. This study addresses the limitations of traditional stand-level analyses by emphasizing the importance of landscape-level features in understanding and predicting hydrological responses. The research is conducted within the Dennis Creek watershed, part of the Upper Penticton Creek Experimental Watershed, and employs the Distributed Hydrology Soil Vegetation Model (DHSVM) to simulate long-term flows under various logging scenarios. The analysis compares the Chronological Pairing (CP) and the Frequency Pairing (FP) approaches to examine their effectiveness in evaluating the impact of logging on peak flow events. The results indicate that logging significantly alters both the magnitude and frequency of peak flows, with effects being more pronounced when landscape attributes such as aspect, elevation, and vegetation distribution are considered. Contrary to the long-standing belief that large peak flow events (with return periods exceeding 10 years) are unaffected by forest cover, the FP-based analysis demonstrates that logging can impact peak flow events across all sizes. The findings highlight the limitations of the CP approach, which fails to capture the stochastic and complex interactions between multiple hydrological variables. The FP framework, by contrast, provides a more robust method for predicting the effects of forest management on flood risks, positioning it as a superior approach for both research and practical forest management. Although limited by a sample size of 100 years of extreme events, which may affect upper tail FFC interpretation, this study provides strong evidence that logging influences peak flows across all return periods, with the effects magnified in certain landscape configurations. The study emphasizes the importance of adopting a probabilistic, landscape-level approach that better captures forest-flood interactions and highlights the need for integrating such insights into future management strategies.