Process-based river temperature models are integral in understanding the dominant heat flux mechanisms controlling river thermal regimes and allow us to evaluate how systems may be altered with changes in climate, hydrology, or management practices (King & Neilson, 2019;Meier et al., 2003;Webb & Zhang, 2004). These types of models estimate the energy and water fluxes responsible for temperature patterns using hydraulic (i.e., stream width, depth, gradient, and roughness) and meteorological information (i.e., air temperature, relative humidity, wind speed, and solar radiation). As such, prior river temperature modeling efforts have relied on existing weather station networks (e.g.,