Many canyon‐bound rivers have been dammed and downstream flow and water temperatures modified. In some regions, climate change is expected to cause lower storage in reservoirs and warmer release temperatures, which may further alter downstream flow and thermal regimes. To anticipate potential future changes, we first need to understand the dominant heat transfer mechanisms in canyon‐bound river systems. Toward this end, we adapt a dynamic process‐based river routing and temperature model to account for complex shading and radiation characteristics found in canyon‐bound rivers. We apply the model to a 362 km segment of the Colorado River in Grand Canyon National Park, USA to simulate temperature over an 18‐year period. Extensive temperature and flow data sets from within the canyon were used to assess model performance. At the most downstream gaging location, root mean square errors of hourly flow routing and temperature predictions were 11.5 m3/s and 0.93°C, respectively. We found that heat fluxes controlling temperatures were highly variable over space and time, primarily due to shortwave radiation dynamics and hydropeaking flow conditions. Additionally, the large differences between air and water temperature during summer periods resulted in high sensible and latent heat fluxes. Sensitivity analyses indicate that reservoir release temperatures are most influential above the RM88 gage (141 km below Glen Canyon Dam), while a combination of discharge, shortwave radiation, and air temperature become more important farther downstream. This study illustrates the importance of understanding the spatial and temporal variability of topographic shading when predicting water temperatures in canyon‐bound rivers.
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.
more » « less- NSF-PAR ID:
- 10504764
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 60
- Issue:
- 4
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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