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Abstract 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. 

Abstract The Arctic hydrological system is an interconnected system that is experiencing rapid change. It is comprised of permafrost, snow, glacier, frozen soils, and inland river systems. In this study, we aim to lower the barrier of using complex land models in regional applications by developing a generalizable optimization methodology and workflow for the Community Terrestrial Systems Model (CTSM), to move them toward a more Actionable Science paradigm. Further end‐user engagement is required to make science such as this “fully actionable.” We applied CTSM across Alaska and the Yukon River Basin at 4‐km spatial resolution. We highlighted several potentially useful high‐resolution CTSM configuration changes. Additionally, we performed a multi‐objective optimization using snow and river flow metrics within an adaptive surrogate‐based model optimization scheme. Four representative river basins across our study domain were selected for optimization based on observed streamflow and snow water equivalent observations at 10 SNOTEL sites. Fourteen sensitive parameters were identified for optimization with half of them not directly related to hydrology or snow processes. Across fifteen out‐of‐sample river basins, 13 had improved flow simulations after optimization and the mean Kling‐Gupta Efficiency of daily flow increased from 0.43 to 0.63 in a 30‐year evaluation. In addition, we adapted the Shapley Decomposition to disentangle each parameter's contribution to streamflow performance changes, with the seven non‐hydrological parameters providing a non‐negligible contribution to performance gains. The snow simulation had limited improvement, likely because snow simulation is influenced more by meteorological forcing than model parameter choices.