Scientific Investigations Report 2014–5186
Expected climatic changes in air temperature and precipitation patterns across the State of Wisconsin may alter future stream temperature and flow regimes. As a consequence of flow and temperature changes, the composition and distribution of fish species assemblages are expected to change. In an effort to gain a better understanding of how climatic changes may affect stream temperature, an approach was developed to predict and project daily summertime stream temperature under current and future climate conditions for 94,341 stream kilometers across Wisconsin. The approach uses a combination of static landscape characteristics and dynamic time-series climatic variables as input for an Artificial Neural Network (ANN) Model integrated with a Soil-Water-Balance (SWB) Model. Future climate scenarios are based on output from downscaled General Circulation Models (GCMs). The SWB model provided a means to estimate the temporal variability in groundwater recharge and provided a mechanism to evaluate the effect of changing air temperature and precipitation on groundwater recharge and soil moisture. The Integrated Soil-Water-Balance and Artificial Neural Network version 1 (SWB-ANNv1) Model was used to simulate daily summertime stream temperature under current (1990–2008) climate and explained 76 percent of the variation in the daily mean based on validation at 67 independent sites. Results were summarized as July mean water temperature, and individual stream segments were classified by thermal class (cold, cold transition, warm transition, and warm) for comparison of current (1990–2008) with future climate conditions.
Integrating the SWB Model with the ANN Model provided a mechanism by which downscaled global or regional climate model results could be used to estimate the potential effects of climate change on future stream temperature on a daily time step. To address future climate scenarios, statistically downscaled air temperature and precipitation projections from 10 GCMs and 2 time periods were used with the SWB-ANNv1 Model to project future stream temperature. Projections of future stream temperatures at mid- (2046–65) and late- (2081–2100) 21st century showed the July mean water temperature increasing for all stream segments with about 80 percent of stream kilometers increasing by 1 to 2 degrees Celsius (°C) by mid-century and about 99 percent increasing by 1 to 3 °C by late-century. Projected changes in stream temperatures also affected changes in thermal classes with a loss in the total amount of cold-water, cold-transition, and warm-transition thermal habitat and a gain in warm-water and very warm thermal habitat for both mid- and late-21st century time periods. The greatest losses occurred for cold-water streams and the greatest gains for warm-water streams, with a contraction of cold-water streams in the Driftless Area of western and southern Wisconsin and an expansion of warm-water streams across northern Wisconsin. Results of this study suggest that such changes will affect the composition of fish assemblages, with a loss of suitable habitat for cold-water fishes and gain in suitable habitat for warm-water fishes. In the end, these projected changes in thermal habitat attributable to climate may result in a net loss of fisheries, because many warm-water species may be unable to colonize habitats formerly occupied by cold-water species because of other habitat limitations (e.g., stream size, gradient). Although projected stream temperatures may vary greatly, depending on the emissions scenario and models used, the results presented in this report represent one possibility. The relative change in stream temperature can provide useful information for planning for potential climate impacts to aquatic ecosystems. Model results can be used to help identify vulnerabilities of streams to climate change, guide stream surveys and thermal classifications, prioritize the allocation of scarce financial resources, identify approaches to climate adaptation to best protect and enhance resiliency in stream thermal habitat, and provide information to make quantitative assessments of statewide stream resources.
First posted January 22, 2015
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Stewart, J.S., Westenbroek, S.M., Mitro, M.G., Lyons, J.D., Kammel, L.E., and Buchwald, C.A., 2015, A model for evaluating stream temperature response to climate change in Wisconsin: U.S. Geological Survey Scientific Investigations Report 2014–5186, 64 p., http://dx.doi.org/10.3133/sir20145186.
ISSN 2328-0328 (online)
The Soil-Water-Balance Model
The Integrated Soil-Water-Balance and Artificial Neural Network version 1 Stream Temperature Model
Results and Discussion
Applications of the Integrated Soil-Water-Balance and Artificial Neural Network
Summary and Conclusions