{"id":{"repo_id":"colo-mines","oai_identifier":"oai:repository.mines.edu:11124/469"},"canonical_url":"https://search.dev.ndltd.org/etd/colo-mines/oai:repository.mines.edu:11124/469","repository":{"repo_id":"colo-mines","name":"Colorado School of Mines","base_url":"https://repository.mines.edu/server/oai/request"},"display":{"title":"Development and evaluation of a stream temperature component within the PRMS watershed modeling program","abstract":"Stream temperature is becoming a very important factor in water quality and the health of many aquatic ecosystems. Computer modeling software can help predict the response of watershed and stream systems to changes in climate or other conditions. This thesis project concerns the development of a new module for the deterministic prediction of stream temperature within the United States Geological Survey's (USGS) Precipitation-Runoff Modeling System (PRMS) watershed surface hydrology model. This module is based on the solution found in the United States Fish and Wildlife Service Stream Network Temperature model (SNTemp), coupled with PRMS meteorologic and hydrologic inputs. The module is called within PRMS to predict average daily stream temperature values. The model was validated in the Potato Creek watershed and matched all parameters of a regression curve fit of natural data to within 6 percent with a determination coefficient (R[superscript 2]) of .77. A sensitivity analysis run using the Fourier Amplitude Sensitivity Testing (FAST) technique suggested that the most sensitive factors are solar radiation, air temperature, and rainfall amount. It was concluded that these will be the strongest factors in terms of propagation of errors in the model.","abstract_html":"Stream temperature is becoming a very important factor in water quality and the health of many aquatic ecosystems. Computer modeling software can help predict the response of watershed and stream systems to changes in climate or other conditions. This thesis project concerns the development of a new module for the deterministic prediction of stream temperature within the United States Geological Survey's (USGS) Precipitation-Runoff Modeling System (PRMS) watershed surface hydrology model. This module is based on the solution found in the United States Fish and Wildlife Service Stream Network Temperature model (SNTemp), coupled with PRMS meteorologic and hydrologic inputs. The module is called within PRMS to predict average daily stream temperature values. The model was validated in the Potato Creek watershed and matched all parameters of a regression curve fit of natural data to within 6 percent with a determination coefficient (R[superscript 2]) of .77. A sensitivity analysis run using the Fourier Amplitude Sensitivity Testing (FAST) technique suggested that the most sensitive factors are solar radiation, air temperature, and rainfall amount. It was concluded that these will be the strongest factors in terms of propagation of errors in the model.","abstract_has_math":false,"creators":["Sanders, Michael"],"institution":"Colorado School of Mines. 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Computer modeling software can help predict the response of watershed and stream systems to changes in climate or other conditions. This thesis project concerns the development of a new module for the deterministic prediction of stream temperature within the United States Geological Survey's (USGS) Precipitation-Runoff Modeling System (PRMS) watershed surface hydrology model. This module is based on the solution found in the United States Fish and Wildlife Service Stream Network Temperature model (SNTemp), coupled with PRMS meteorologic and hydrologic inputs. The module is called within PRMS to predict average daily stream temperature values. The model was validated in the Potato Creek watershed and matched all parameters of a regression curve fit of natural data to within 6 percent with a determination coefficient (R[superscript 2]) of .77. 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