A regional neural network ensemble for predicting mean daily river water temperature
Abstract: Water temperature is a fundamental property of river habitat and often a key aspect of river resource
management, but measurements to characterize thermal regimes are not available for most streams
and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean
daily water temperature in 197,402 individual stream reaches during the warm season (May–October)
throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four
models with different groups of predictors to determine how well water temperature could be predicted
by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100
ANNs as our final prediction for each model. The final model included air temperature, landform attributes
and forested land cover and predicted mean daily water temperatures with moderate accuracy
as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009
(RMSE = 1.91 C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010
(RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream
reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air
temperature, and network catchment area according to sensitivity analyses. Forest land cover at both
riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature
averaged for the month of July matched expected spatial trends with cooler temperatures in
headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures
throughout a large region, while other regional efforts have predicted at relatively coarse time
steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled
rivers under current conditions and future projections of climate and land use changes, thereby providing
information that is valuable to management of river ecosystems and biota such as brook trout.
Publication Date: 2012
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