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Tuesday, February 7 • 6:00pm - 9:00pm
Poster Display. Bayesian Hierarchical Modeling of Ozark Mountain Cold Water Stream Temperatures

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AUTHORS: Bridget Whitehead, University of Missouri; Joanna Whittier, University of Missouri; Del Lobb, Missouri Department of Conservation; Jacob Westhoff, Missouri Department of Conservation, and Craig Paukert, U.S. Geological Survey, Missouri Cooperative Fish and Wildlife Research Unit

ABSTRACT: Water temperature is a primary driver of aquatic community distribution and composition. Knowledge of stream water temperature can help inform management practices for trout and other temperature sensitive aquatic species. From 2002 to 2014, the Missouri Department of Conservation collected daily water temperature data at 106 cold water stream sites in the Ozark Region between July 1st and September 15th. Our objective was to examine patterns in water temperature over the collection period and forecast trends in the future. We developed a Bayesian hierarchical model of daily mean water temperature as a function of both static site characteristics and time-varying climatological variables, in which coefficients for regression on air temperature, solar radiation, and precipitation varied based on the site's groundwater influence and upstream watershed area. Groundwater influence was measured by the distance to the nearest upstream spring and, where available, the magnitude of the spring. The model also incorporated AR(1) correlation structure across days, reflecting the residual correlations in water temperatures on nearby days. This structure is crucial for prediction of consecutive day statistics, such as the maximum number of consecutive days above 70F. Based on mean squared prediction error and posterior predictive distributions, the model was able to predict water temperature in the current time period (2002 – 2014) with a reasonable magnitude of uncertainty. In order to predict stream temperatures in the future, we used downscaled climate data which provided future climate simulations at the regional level based on several global climate models. We calibrated the downscaled climate data to match distributions of observed data by applying the Doksum shift function for the period 1990 – 1999. We present inferential results describing relationships between the predictors and water temperature, as well as climate based predictions for summer water temperatures in 2032, 2042, 2062, and 2087.

Tuesday February 7, 2017 6:00pm - 9:00pm CST
Lancaster Ballroom

Attendees (3)