Modeling insights from distributed temperature sensing data

Distributed Temperature Sensing (DTS) technology can collect abundant high resolution river temperature data over space and time to improve development and performance of modeled river temperatures. These data can also identify and quantify ther5 mal variability of micro-habitat that temperature modeling and standard temperature sampling do not capture. This allows researchers and practitioners to bracket uncertainty of daily maximum and minimum temperature that occurs in pools, side channels, or as a result of cool or warm inflows. This is demonstrated in a reach of the Shasta River in Northern California that receives irrigation runoff and inflow from small ground10 water seeps. This approach highlights the influence of air temperature on stream temperatures, and indicates that physically-based numerical models may under-represent this important stream temperature driver. This work suggests DTS datasets improve efforts to simulate stream temperatures and demonstrates the utility of DTS to improve model performance and enhance detailed evaluation of hydrologic processes.

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