Stochastic Hydrology Reveals the Controls of Forest Harvesting - Peakflow Causal Relations in Snow-Transient Environment

Understanding and predicting how forest harvesting alters peakflows is essential for managing downstream risk, as changes in forest cover can intensify extreme events, affecting water quality, infrastructure, ecosystems, and public safety. In light of this, the drivers of snow-transient peakflows must be better characterized. This study presents a probabilistic meta-analysis using nine pairs of control-treatment watersheds in the snow-transient environments of the Pacific Northwest to show how harvesting, in some cases, can dramatically increase the magnitude and frequency of not only the small (≤ 10-yr) but also large (>10-yr) peakflows. Under certain hydro-climate and watershed conditions, the effects on the magnitude and frequency of peakflows increase with increasing event size. Subdued landscapes and drier and warmer environments have emerged as effective indicators of conditions where the peakflow regime is most sensitive to harvesting practices. This probabilistic framework can yield predictive outcomes that differ from long-held deterministic expectations in forest hydrology, particularly regarding how harvesting affects large peakflows. To draw a comparison with the development of statistical mechanics in thermodynamics, it appears the introduction of probability in forest hydrology, too, could suddenly reverse the prevailing perspective of the old deterministic framework and stimulate a paradigm-induced gestalt switch in the physical understanding and prediction of forest harvesting effects on peakflows.