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Evaluation and Use of Stream Temperature Prediction Models for Instream Flow and Fish Habitat ManagementKrause, Colin William 14 February 2002 (has links)
The SNTEMP (U.S. Fish and Wildlife Service), QUAL2E (U.S. Environmental Protection Agency), and RQUAL (Tennessee Valley Authority) stream temperature prediction models were evaluated. All models had high predictive ability with the majority of predictions, >80% for Back Creek (Roanoke County, VA) and >90% for the Smith River tailwater (SRT) (Patrick County, VA), within 3°C of the measured water temperature. Sensitivity of model input parameters was found to differ between model, stream system, and season. The most sensitive of assessed parameters, dependent on model and stream, were lateral inflow, starting-water, air, and wet-bulb temperature. All three models predicted well, therefore, selecting a model to assess alternative water management scenarios was based on model capabilities. The RQUAL model, used to predict SRT temperatures under alternative hydropower release regimes, illustrated potential thermal habitat improvement for brown trout (Salmo trutta) compared to existing conditions. A 7-day/week morning 1 hr release was determined to best concurrently increase occurrence of brown trout optimal growth temperatures (+10.2% mean), decrease 21°C (state standard) exceedances (99% prevention), and decrease hourly changes in temperature (-1.6°C mean) compared to existing thermal conditions. The SNTEMP model was used to assess thermal habitat under flow, shade, and channel width changes occurring from future urbanization within the Back Creek watershed. Predictions reveal that additional urban development could limit thermal habitat for present fish species by elevating summer mean daily temperature up to 1°C and cause 31°C (state standard) exceedances compared to existing conditions. Temperature impacts were lessened by single rather than cumulative changes suggesting mitigation measures may maintain suitable thermal habitat. / Master of Science
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A study of stream temperature using distributed temperature sensing fiber optics technology in Big Boulder Creek, a tributary to the Middle Fork John Day River in eastern OregonArik, Aida D. 08 November 2011 (has links)
The Middle Fork John Day Basin in Northeastern Oregon is prime habitat for spring Chinook salmon and Steelhead trout. In 2008, a major tributary supporting rearing habitat, Big Boulder Creek, was restored to its historic mid-valley channel along a 1 km stretch of stream 800 m upstream of the mouth. Reduction of peak summer stream temperatures was among the goals of the restoration. Using Distributed Temperature Sensing (DTS) Fiber Optic Technology, stream temperature was monitored prior to restoration in June 2008, and after restoration in September 2008, July 2009, and August 2009. Data gathered was used to determine locations of groundwater and hyporheic inflow and to form a stream temperature model of the system. The model was used both to develop an evaluation method to interpret components of model performance, and to better understand the physical processes important to the study reach.
A very clear decreasing trend in surface temperature was seen throughout each of the DTS stream temperature datasets in the downstream 500 m of the study reach. Observed reduction in temperature was 0.5°C (±0.10) in June 2008, 0.3°C (±0.37) in September 2008, 0.6°C (±0.25) in July 2009, and 0.2°C (±0.08) in August 2009. Groundwater inflow was calculated to be 3% of the streamflow for July 2009 and 1% during the August 2009 installation. Statistically significant locations of groundwater and hyporheic inflow were also determined.
July 2009 data was used to model stream temperature of the 1 km (RMSE 0.28°C). The developed model performance evaluation method measures timelag, offset, and amplitude at a downstream observed or simulated point compared with the boundary condition, rather than evaluating the model based on error. These measures are
particularly relevant to small scale models in which error may not be a true reflection of the ability of a model to correctly predict temperature. Breaking down model performance into these three predictive measures was a simple and graphic method to show the model's predictive capability without sorting through large amounts of data. To better understand the model and the stream system, a sensitivity analysis was conducted showing high sensitivity to streamflow, air temperature, groundwater inflow, and relative humidity. Somewhat surprisingly, solar radiation was among the lowest sensitivity. Furthermore, three model scenarios were run: a 25% reduction in water velocity, a 5°C increase in air temperature, and no groundwater inflow. Simulations of removal of groundwater inflows resulted in a 0.5°C increase in average temperature over the modeled time period at the downstream end, further illustrating the importance of groundwater in this stream system to reduce temperatures. / Graduation date: 2012
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