Accurate spatiotemporal predictions of daily stream temperature from statistical models accounting for interactions between climate and landscape.

Jared E Siegel,Carol J Volk

doi:10.7717/peerj.7892

Abstract

Spatial and temporal patterns in stream temperature are primary factors determining species composition, diversity and productivity in stream ecosystems. The availability of spatially and temporally continuous estimates of stream temperature would improve the ability of biologists to fully explore the effects of stream temperature on biota. Most statistical stream temperature modeling techniques are limited in their ability to account for the influence of variables changing across spatial and temporal gradients. We identified and described important interactions between climate and spatial variables that approximate mechanistic controls on spatiotemporal patterns in stream temperature. With identified relationships we formed models to generate reach-scale basin-wide spatially and temporally continuous predictions of daily mean stream temperature in four Columbia River tributaries watersheds of the Pacific Northwest, USA. Models were validated with a testing dataset composed of completely distinct sites and measurements from different years. While some patterns in residuals remained, testing dataset predictions of selected models demonstrated high accuracy and precision (averaged RMSE for each watershed ranged from 0.85–1.54 °C) and was only 17% higher on average than training dataset prediction error. Aggregating daily predictions to monthly predictions of mean stream temperature reduced prediction error by an average of 23%. The accuracy of predictions was largely consistent across diverse climate years, demonstrating the ability of the models to capture the influences of interannual climatic variability and extend predictions to timeframes with limited temperature logger data. Results suggest that the inclusion of a range of interactions between spatial and climatic variables can approximate dynamic mechanistic controls on stream temperatures.

Highlights

Spatial and temporal patterns in stream temperature are primary factors determining species composition, diversity, and productivity in stream ecosystems (Vannote et al, 1980).How to cite this article Siegel JE, Volk CJ. 2019
Since we contend that the root mean squared error (RMSE) Test. represents the truest test of the ability of models to account for spatiotemporal dynamics in stream temperature, we focus most of the discussion on this result
Selected climate and spatial variables for estimating stream temperature. In this text we provide a brief explanation of the variables and interactions found to be useful in describing spatiotemporal patterns in stream temperature in the study watersheds (Table 3)

Summary

Introduction

Spatial and temporal patterns in stream temperature are primary factors determining species composition, diversity, and productivity in stream ecosystems (Vannote et al, 1980).How to cite this article Siegel JE, Volk CJ. 2019. Stream temperature impacts biota by affecting the dissolved oxygen content of water and biogeochemical processes such as nutrient cycling, decomposition rates, and eutrophication. Many human activities, such as deforestation (Holtby, 1988) and flow regulation by dams and diversions (Sinokrot & Gulliver, 2014), have major effects on stream temperature. Climate change has already caused increases in stream temperature (Isaak et al, 2010; Isaak et al, 2012; Kaushal et al, 2010; Ruesch et al, 2012) and changes in the timing and magnitude of discharge (Luce & Holden, 2009; Riedel & Larrabee, 2016), with effects on the productivity and spatial distributions of resident species (Lawrence et al, 2014). Due to the ecological importance of stream temperature combined with the potential impact of human activities, there is substantial interest among water and fisheries managers in improving our understanding of stream temperature patterns and influences

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