Local and downstream effects of contemporary forest harvesting on streamflow and sediment yield Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/df65vb33j

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  • This dissertation is a collection of three manuscripts that serve to fill the knowledge gaps and advance methods of detecting the effects of contemporary forest harvesting in experimental catchment studies. While there is a preponderance of studies evaluating the environmental effects of forest harvesting in the western United States, few studies consider local and downstream streamflow and sediment changes following contemporary harvesting practices. Further, many previous and current studies using the paired-catchment approach were based on relatively few observations using annual, storm, and more infrequently monthly data, which can increase the likelihood of false/missed detections. The objective of this research was to develop change detection models using time-series records to detect and quantify the effects of forest harvesting on streamflow and sediment yield. To fulfill this objective, it was necessary to characterize streamflow and sediment processes at a temporal scale capable of describing daily, monthly, and seasonal dynamics following forest harvesting; increase sample sizes used to construct regression-based change detection models; and develop alternative methods to the paired-catchment approach in order to discern changes in streamflow and sediment using highly variable time-series data. The paired-catchment approach was used to detect and quantify relative changes in streamflow and sediment yield in 5 harvested catchments. Though not statistically significant in all catchments, relative increases in streamflow and sediment were observed locally and downstream following harvesting in headwater catchments. The ability to detect statistically significant changes at certain time-steps was a function of accounting for all sources of variability in change detection models. In this study, we aimed to develop robust change detection models using time-series data to increase sample size and decrease false/missed detections of true treatment effects. When mean daily streamflow was used as a response variable, there was no statistically significant increase in streamflow (significance level [alpha] = 0.05), when the effects of forest harvesting were detect with monthly streamflow. We hypothesized that this is due to an increase in unexplained variance and wider prediction limits. An alternative method to detect change with daily streamflow that resulted in reduced variance was hydrologic model simulations.
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