Are temperature time series measured at hydrometric stations representative of the river’s thermal regime?

Ferchichi, Habiba; St‐Hilaire, André

doi:10.1080/07011784.2023.2216454

Cited by 3 publications

(1 citation statement)

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“…In this region, the prevalence of late snowmelt runoff and glacial melt contributes to consistently cooler stream temperatures even as air temperatures rise during the summer (Bieniek et al., 2014; Brown & Hannah, 2008; Crossman et al., 2013; Curran & Biles, 2021). Although air temperature typically correlates with river temperature, this relationship breaks down in snowmelt and glacier‐dominated areas potentially leading to overestimations in modeled stream temperatures (Ferchichi & St‐Hilaire, 2023; Ficklin et al., 2012; Yan et al., 2021). Additionally, the shorter length of South region rivers, as compared to those in the North and Central regions, allows headwater boundary conditions to propagate extensively (van Vliet et al., 2012), possibly contributing to the pronounced influence of the boundary condition in this region.…”

Section: Discussionmentioning

confidence: 99%

A High‐Resolution, Daily Hindcast (1990–2021) of Alaskan River Discharge and Temperature From Coupled and Optimized Physical Models

Blaskey,

Gooseff,

Cheng

et al. 2024

Water Resources Research

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Water quality and freshwater ecosystems are affected by river discharge and temperature. Models are frequently used to estimate river temperature on large spatial and temporal scales due to limited observations of discharge and temperature. In this study, we use physically based river routing and temperature models to simulate daily discharge and river temperature for rivers in 138 basins in Alaska, including the entire Yukon River basin, from 1990–2021. The river temperature model was optimized for ice free months using a surrogate‐based model optimization method, improving model performance at uncalibrated river gages. A common statistical model relating local air and water temperature was used as a benchmark. The physically based river temperature model exhibited superior performance compared to the benchmark statistical model after optimization, suggesting river temperature model optimization could become more routine. The river temperature model demonstrated high sensitivity to air temperature and model parameterization, and lower sensitivity to discharge. Validation of the models showed a Kling‐Gupta Efficiency of 0.46 for daily river discharge and a root mean square error of 2.04°C for daily river temperature, improving on the non‐optimized physical model and the benchmark statistical model, which had root mean square errors of 3.24 and 2.97°C, respectively. The simulation shows that rivers in northern Alaska have higher maximum summer temperatures and more variability than rivers in the Central and Southern regions. Furthermore, this framework can be readily adapted for use across models and regions.

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