Predicting Hourly Stream Temperatures Using the Equilibrium Temperature Model

Abstract

Water temperature is a key physical habitat determinant in lotic ecosystems as it influences many physical, chemical and biological properties of rivers. Hence, a good understanding of the thermal regime of rivers is essential for effective management of water and fisheries resources. This study deals with the modeling of hourly stream water temperature using the equilibrium temperature model. This water temperature model was applied on two thermally different watercourses, namely, the Little Southwest Miramichi River (LSWM) and Catamaran Brook (CatBk; New Brunswick). The equilibrium temperature model is a simplified version of a deterministic model. As such, in the equilibrium temperature model the total heat flux at the surface is assumed proportional to the difference between the water temperature and an equilibrium temperature. In the present study, the equilibrium temperature was assumed to vary linearly with hourly air temperature. This study showed that there was a good relationship between the equilibrium and air temperature at the hourly time scale. The root-mean-square error (RMSE) obtained with the hourly equilibrium temperature model was similar to results reported in previous studies with values of 1.05˚C (CatBk) and 1.36˚C (LSWM). The model's performance was best in late summer and autumn when water levels were low. In contrast, the presence of snowmelt in the spring resulted in poorer performances. This study also showed good results in estimating the daily mean (Tmean) and maximum (T max ) water temperatures from the predicted hourly water temperatures, which were often required in fishery management.

An ecohydraulics virtual watershed: Integrating physical and biological variables to quantify aquatic habitat quality

“…RMSEs were 0.72, 0.70, 0.85, and 0.91°C for daily average water temperatures and 1.23, 1.33, 1.58, and 1.43 for 30‐min temporal resolution at the Cow Creek Bridge, Danskin Bridge, Private Bridge, and Canyon locations, respectively (Figure and Table ). The model simulated results (RMSE < 1.58 o C) were consistent with other studies (Hébert et al, ; Loinaz et al, ; Wang, Yang, & Khangaonkar, ).…”

“…The model simulated results (RMSE < 1.58 o C) were consistent with other studies (Hébert et al, 2015;Loinaz et al, 2013;Wang, Yang, & Khangaonkar, 2010).…”

“…These impacts may force changes to water resources development, operation, and management in the future. Interaction between flow releases, timing, and dam operation on stream thermal regimes are key elements for aquatic habitat quality (Carey & Zimmerman, 2014;Danehy, Colson, Parrett, & Duke, 2005;Hébert, Caissie, Satish, & El-Jabi, 2015;Johnson, Saito, Anderson, Andre, & Fontane, 2004;Lessard & Hayes, 2003;Loinaz, Davidsen, Butts, & Bauer-Gottwein, 2013;Torgersen, Price, Li, & McIntosh, 1999). The interaction between key habitat elements is often overlooked and a major gap in current environmental flow assessments (Olden & Naiman, 2010).…”

River temperature modelling: A review of process-based approaches and future directions

“…Despite the generally high degree of accuracy with which modern process-based temperature models are able to simulate thermal processes in rivers (e.g. RMSE ≲1.0 °C at sub-hourly to hourly timesteps over seasonal to annual periods; Garner et al, 2014;Hébert et al, 2015;Woltemade and Hawkins, 2016), there remain several limitations to their application. Primarily, these limitations relate to issues associated with the energy balance calculations or input resolution (see sections 3 and 4.3).…”

StreamFlow 1.0: an extension to the spatially distributed snow model Alpine3D for hydrological modelling and deterministic stream temperature prediction

“…Grillakis et al, 2010;Bürger et al, 2011;Schaefli et al, 2014;Ragettli et al, 2014) or water temperature (e.g. Caldwell et al, 2013;Tung et al, 2014;Hébert et al, 2015;Toffolon and Piccolroaz, 2015), but few are able to simulate the two at the same time (e.g. Loinaz et al, 2013;MacDonald et al, 2014;Comola et al, 2015).…”