Caleb Amoa Buahin

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.

Supporting Data for Buahin et al

Supporting Data for Buahin et al

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.

Urbanization, climate, and ecosystem change represent major challenges for managing water resources. Although water systems are complex, a need exists for a generalized representation of these systems to identify important components and linkages to guide scientific inquiry and aid water management. We developed an integrated Structure‐Actor‐Water framework (iSAW) to facilitate the understanding of and transitions to sustainable water systems. Our goal was to produce an interdisciplinary framework for water resources research that could address management challenges across scales (e.g., plot to region) and domains (e.g., water supply and quality, transitioning, and urban landscapes). The framework was designed to be generalizable across all human–environment systems, yet with sufficient detail and flexibility to be customized to specific cases. iSAW includes three major components: structure (natural, built, and social), actors (individual and organizational), and water (quality and quantity). Key linkages among these components include: (1) ecological/hydrologic processes, (2) ecosystem/geomorphic feedbacks, (3) planning, design, and policy, (4) perceptions, information, and experience, (5) resource access and risk, and (6) operational water use and management. We illustrate the flexibility and utility of the iSAW framework by applying it to two research and management problems: understanding urban water supply and demand in a changing climate and expanding use of green storm water infrastructure in a semi‐arid environment. The applications demonstrate that a generalized conceptual model can identify important components and linkages in complex and diverse water systems and facilitate communication about those systems among researchers from diverse disciplines.

This study compared the sensitivity of water quality in tropical Aguamilpa Reservoir, as represented by normalized algae mass and dissolved oxygen, to selected projected changes from global climate change and development. The sensitivity of reservoir stratification as an indicator of reservoir sensitivity also was analysed. Model simulations indicated the reservoir was more sensitive to changes during the warm‐dry season than at other times. Both indexes (normalized algal mass and dissolved oxygen mass) were more sensitive to changes in air temperature (climate change) and nitrogen loading (development) than to changes in flow. The sensitivity to air temperature was similar to, but generally less than, the sensitivity to nutrient inflow. At the bounding values for change (3 °C for temperature; 50% increase in nitrogen loading), the algae mass sensitivities were 0.15 mg L⁻¹ per 3 °C and 0.2 mg L⁻¹ per 50% nitrogen load increase, and the dissolved oxygen mass sensitivities were 0.7 mg L⁻¹ per 3 °C and 2.0 mg L⁻¹ per 50% load increase. Changes in air temperature and nitrogen loadings affect the reservoir in different ways, air temperature mostly changing the timing of the algal growth with little change in peak values, while nutrient loadings change the peak values with little change in the timing. While the sensitivities are similar, the total algal mass change is significantly larger for nitrogen loading, compared to air temperature changes. These results imply global climate change effects can be partially mitigated by implementing management measures in the surrounding watersheds to minimize nutrient inflows, especially nitrogen in the case of Aguamilpa Reservoir.