Predicting Thermally Stressful Events in Rivers with a Strategy to Evaluate Management Alternatives

Abstract

Water temperature is an important factor in river ecology. Numerous models have been developed to predict river temperature. However, many were not designed to predict thermally stressful periods. Because such events are rare, traditionally applied analyses are inappropriate. Here, we developed two logistic regression models to predict thermally stressful events in the Delaware River at the US Geological Survey gage near Lordville, New York. One model predicted the probability of an event >20.0°C, and a second predicted an event >22.2°C. Both models were strong (independent test data sensitivity 0.94 and 1.00, specificity 0.96 and 0.96) predicting 63 of 67 events in the >20.0°C model and all 15 events in the >22.2°C model. Both showed negative relationships with released volume from the upstream Cannonsville Reservoir and positive relationships with difference between air temperature and previous day's water temperature at Lordville. We further predicted how increasing release volumes from Cannonsville Reservoir affected the probabilities of correctly predicted events. For the >20.0°C model, an increase of 0.5 to a proportionally adjusted release (that accounts for other sources) resulted in 35.9% of events in the training data falling below cutoffs; increasing this adjustment by 1.0 resulted in 81.7% falling below cutoffs. For the >22.2°C these adjustments resulted in 71.1% and 100.0% of events falling below cutoffs. Results from these analyses can help managers make informed decisions on alternative release scenarios.

Monitoring and analysis of vertical thermal structure of the Three Gorges Reservoir

“…Most deep temperate and subtropical reservoirs develop thermal stratification every spring and summer [4] . the stable stratified water column is usually segregated into epilimnion, thermocline and hypolimnion in vertical by the density difference produced via warming of surface water, stratification acts as a barrier restraining mixing of the water column, as a result of incomplete mixing of the water column and lack of light for the photosynthesis at the hypolimnion, the water column can become anoxic [5][6] . Commonly, many reservoirs have fixed-level "off-takes" that are situated below the thermocline, hence releasing waters from the hypolimnion which causes downstream cold-water pollution (CWP) [7] .…”

Construction of confidence intervals for the maximum of the Youden index and the corresponding cutoff point of a continuous biomarker

“…For example, it is used in Hirschfeld and Thielsch (2015) when considering website aesthetic scores, in Srihari and Srinivasan (2008) for automatic fingerprint verification, Maloney, Cole, and Schmid (2016) for prediction of thermally stressful events in rivers and in Newel (2011) andNewel (2013) for assessing the accuracy of species distribution models. For example, it is used in Hirschfeld and Thielsch (2015) when considering website aesthetic scores, in Srihari and Srinivasan (2008) for automatic fingerprint verification, Maloney, Cole, and Schmid (2016) for prediction of thermally stressful events in rivers and in Newel (2011) andNewel (2013) for assessing the accuracy of species distribution models.…”

“…The use of the Youden index and its associated cutoff point is not restricted to the biomarker and/or biomedical literature. For example, it is used in Hirschfeld and Thielsch (2015) when considering website aesthetic scores, in Srihari and Srinivasan (2008) for automatic fingerprint verification, Maloney, Cole, and Schmid (2016) for prediction of thermally stressful events in rivers and in Liu, White, and Newel (2011) and Liu, White, and Newel (2013) for assessing the accuracy of species distribution models. Inference for the Youden index has been considered in several papers.…”