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C. Berenbrock

Publications and source records attributed to C. Berenbrock.

2 recordsLinked to original sources

Hydrological response to timber harvest in northern Idaho: Implications for channel scour and persistence of salmonids

The potential for forest harvest to increase snowmelt rates in maritime snow climates is well recognized. However, questions still exist about the magnitude of peak flow increases in basins larger than 10 km2 and the geomorphic and biological consequences of these changes. In this study, we used observations from two nearly adjacent small basins (13 and 30 km2) in the Coeur d'Alene River basin, one with recent, relatively extensive, timber harvest, and the other with little disturbance in the last 50 years to explore changes in peak flows due to timber harvest and their potential effects on fish. Peak discharge was computed for a specitic rain-on-snow event using a series of physical models that linked predicted values of snowmelt input to a runoff-routing model. Predictions indicate that timber harvest caused a 25% increase in the peak flow of the modelled event and increased the frequency of events of this magnitude from a 9-year recurrence interval to a 3-6-year event. These changes in hydrologic regime, with larger discharges at shorter recurrence intervals, are predicted to increase the depth and frequency of streambed scour, causing up to 15% added mortality of bull trout (Salvelinus confluentus) embryos. Mortality from increased scour, although not catastrophic, may have contributed to the extirpation of this species from the Coeur d'Alene basin, given the widespread timber harvest that occurred in this region. Copyright ?? 2008 John Wiley & Sons, Ltd.

Hydrological Processes

A genetic algorithm to reduce stream channel cross section data

A genetic algorithm (GA) was used to reduce cross section data for a hypothetical example consisting of 41 data points and for 10 cross sections on the Kootenai River. The number of data points for the Kootenai River cross sections ranged from about 500 to more than 2,500. The GA was applied to reduce the number of data points to a manageable dataset because most models and other software require fewer than 100 data points for management, manipulation, and analysis. Results indicated that the program successfully reduced the data. Fitness values from the genetic algorithm were lower (better) than those in a previous study that used standard procedures of reducing the cross section data. On average, fitnesses were 29 percent lower, and several were about 50 percent lower. Results also showed that cross sections produced by the genetic algorithm were representative of the original section and that near-optimal results could be obtained in a single run, even for large problems. Other data also can be reduced in a method similar to that for cross section data.

Journal of the American Water Resources Associatio