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The Columbia River basin is a large transboundary basin located in the Pacific Northwest. The basin spans seven US states and one Canadian province, encompassing a diverse range of hydroclimates. Strong seasonality and complex topography are projected to give rise to spatially heterogeneous climate effects on unregulated streamflow. The basin's water resources are economically critical, and regulation across the domain is extensive. Many sensitivity studies have investigated climate impacts on the basin's naturalized hydrology; however, few have considered the large role of regulation. This study investigates where and when regulation affects projected changes in streamflow by comparing climate outcomes across 80‐member ensembles of unregulated and regulated streamflow projections at 75 sites across the basin. Unregulated streamflow projections are taken from an existing data set of climate projections derived from Coupled Model Intercomparison Project version 5 Global Climate Models. Regulated streamflow projections were modeled by the US Army Corps of Engineers and the US Bureau of Reclamation by using these unregulated flows as input to hydro‐regulation models that simulate operations based on current and historical water demands. Regulation dampens shifts in winter and summer streamflow volumes. Regulation generally attenuates changes in cool‐season high flow extremes but amplifies shifts in warm‐season and annual high flow extremes at historically snow‐dominant headwater reservoirs. Regulation reduces dry‐season low flow changes in headwater tributaries where regulation is large but elsewhere has little effect on changes in low flows. Results highlight the importance of accounting for water management in climate sensitivity analysis particularly in snow‐dominant basins.

Despite efforts to understand the hydrologic impact of hydropower dams, their influence on downstream river temperatures has gone unnoticed in data limited regions. Using 30 years of Landsat thermal infrared observations (1988–2018), we identified a relationship between dry season water temperature cooling trends and dam development in the 3S Basin, a major tributary of the Mekong River. Within a year of the beginning of operations of major dams in the 3S River Basin, rapid decreases in annual average dry season river temperature were observed ranging between 0.7 ° C and 2 ° C. Furthermore,in situwater temperature observations confirmed decreasing river temperature for two major dam development events. Evidence was found that the 3S outflow has been cooling the Mekong River downstream of the confluence, by as much as 0.8 ° C in recent years. Our findings are critically important for understanding how fish and aquatic ecosystems will behave in the future as more hydropower dams are built in the Mekong River Basin.

Deep learning (DL) methods have shown great promise for accurately predicting hydrologic processes but have not yet reached the complexity of traditional process‐based hydrologic models (PBHM) in terms of representing the entire hydrologic cycle. The ability of PBHMs to simulate the hydrologic cycle makes them useful for a wide range of modeling and simulation tasks, for which DL methods have not yet been adapted. We argue that we can take advantage of each of these approaches by embedding DL methods into PBHMs to represent individual processes. We demonstrate that this is viable by developing DL‐based representations of turbulent heat fluxes and coupling them into the Structure for Unifying Multiple Modeling Alternatives (SUMMA), a modular PBHM modeling framework. We developed two DL parameterizations and integrated them into SUMMA, resulting in a one‐way coupled implementation which relies only on model inputs and a two‐way coupled implementation, which also incorporates SUMMA‐derived model states. Our results demonstrate that the DL parameterizations are able to outperform calibrated standalone SUMMA benchmark simulations. Further we demonstrate that the two‐way coupling can simulate the long‐term latent heat flux better than the standalone benchmark and one‐way coupled configuration. This shows that DL methods can benefit from PBHM information, and the synergy between these modeling approaches is superior to either approach individually.

Canopy‐snow unloading is the complex physical process of snow unloading from the canopy through meltwater drip, sublimation to the atmosphere, or solid snow unloading to the snowpack below. This process is difficult to parameterize due to limited observations. Time‐lapse photographs of snow in the canopy were characterized by citizen scientists to create a data set of snow interception observations at multiple locations across the western United States. This novel interception data set was used to evaluate three snow unloading parameterizations in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) modular hydrologic modeling framework. SUMMA was modified to include a third snow unloading parameterization, termed Wind‐Temperature (Roesch et al., 2001,https://doi.org/10.1007/s003820100153), which includes wind‐dependent and temperature‐dependent unloading functions. It was compared to a meltwater drip unloading parameterization, termed Melt (Andreadis et al., 2009,https://doi.org/10.1029/2008wr007042), and a time‐dependent unloading parameterization, termed Exponential‐Decay (Hedstrom & Pomeroy, 1998,https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4). Wind‐Temperature performed well without calibration across sites, specifically in cold climates, where wind dominates unloading and rime accretion is low. At rime prone sites, Wind‐Temperature should be calibrated to account for longer interception events with less sensitivity to wind, otherwise Melt can be used without calibration. The absence of model physics in Exponential‐Decay requires local calibration that can only be transferred to sites with similar unloading patterns. The choice of unloading parameterization can result in 20% difference in SWE on the ground below the canopy and 10% difference in estimated average winter canopy albedo. These novel observations shed light on processes that are often overlooked in hydrology.

Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.

Projections of change in high‐flow extremes with global warming vary widely among, and within, large midlatitude river basins. The spatial variability of these changes is attributable to multiple causes. One possible and little‐studied cause of changes in high‐flow extremes is a change in the synchrony of mainstem and tributary streamflow during high‐flow extremes at the mainstem‐tributary confluence. We examined reconstructed and simulated naturalized daily streamflow at confluences on the Columbia River in western North America, quantifying changes in synchrony in future streamflow projections and estimating the impact of these changes on high‐flow extremes. In the Columbia River basin, projected flow regimes across colder tributaries initially diverge with warming as they respond to climate change at different rates, leading to a general decrease in synchrony, and lower high‐flow extremes, relative to a scenario with no changes in synchrony. Where future warming is sufficiently large to cause most subbasins upstream from a confluence to transition toward a rain‐dominated, warm regime, the decreasing trend in synchrony reverses itself. At one confluence with a major tributary (the Willamette River), where the mainstem and tributary flow regimes are initially very different, warming increases synchrony and, therefore, high‐flow magnitudes. These results may be generalizable to the class of large rivers with large contributions to flood risk from the snow (i.e., cold) regime, but that also receive considerable discharge from tributaries that drain warmer basins.