Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures

Kristina Mcnyset,Carol Volk,Chris Jordan

doi:10.3390/w7126660

Abstract

Although water temperature is important to stream biota, it is difficult to collect in a spatially and temporally continuous fashion. We used remotely-sensed Land Surface Temperature (LST) data to estimate mean daily stream temperature for every confluence-to-confluence reach in the John Day River, OR, USA for a ten year period. Models were built at three spatial scales: site-specific, subwatershed, and basin-wide. Model quality was assessed using jackknife and cross-validation. Model metrics for linear regressions of the predicted vs. observed data across all sites and years: site-specific r2 = 0.95, Root Mean Squared Error (RMSE) = 1.25 °C; subwatershed r2 = 0.88, RMSE = 2.02 °C; and basin-wide r2 = 0.87, RMSE = 2.12 °C. Similar analyses were conducted using 2012 eight-day composite LST and eight-day mean stream temperature in five watersheds in the interior Columbia River basin. Mean model metrics across all basins: r2 = 0.91, RMSE = 1.29 °C. Sensitivity analyses indicated accurate basin-wide models can be parameterized using data from as few as four temperature logger sites. This approach generates robust estimates of stream temperature through time for broad spatial regions for which there is only spatially and temporally patchy observational data, and may be useful for managers and researchers interested in stream biota.

Highlights

We provide examples of using public Land Surface Temperature (LST) data to estimate stream water temperature in a spatio-temporally continuous manner in watersheds where we have a set of point-wise stream temperature observations from in-water sensors
We provide examples of using public LST data to estimate stream water temperature makes it an idealincandidate for elucidating spatially variable relationships between land surface a spatio-temporally continuous manner in watersheds where we have a set of point-wise stream and water temperature
The FLoWS toolbox was used to generate reach-contributing area (RCA) polygons representing the non-overlapping land surface area draining into each stream segment

Summary

Introduction

Stream water temperature is one of the most important factors influencing productivity, species composition, community structure, and life history expression of stream organisms.Timing of significant ecological events like invertebrate and fry emergence, onset and duration of photosynthesis, spawning, and migration are all determined in part by temperature (e.g., [1,2]).For example, fish growth and survival are optimized at particular temperatures (e.g., [3]) and these responses can be used in life cycle and bioenergetics modeling frameworks to incorporate fish activity and metabolic response to varied environmental conditions, such as climate change scenarios [4].the ability to track temperatures across multiple seasons and life stages becomes critical to accurately predicting fish response to changing conditions.Measures of daily, seasonal, and annual variation of stream temperature are ubiquitous in stream ecology research. Stream water temperature is one of the most important factors influencing productivity, species composition, community structure, and life history expression of stream organisms. Fish growth and survival are optimized at particular temperatures (e.g., [3]) and these responses can be used in life cycle and bioenergetics modeling frameworks to incorporate fish activity and metabolic response to varied environmental conditions, such as climate change scenarios [4]. The ability to track temperatures across multiple seasons and life stages becomes critical to accurately predicting fish response to changing conditions. Seasonal, and annual variation of stream temperature are ubiquitous in stream ecology research. Annual mean and maximum temperatures, such as seven-day running means and maximums, are commonly used as indicators of stream “health” or habitat quality for

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Conclusion