Journal of The American Water Resources Association最新文献

This study investigates various operational strategies to enhance flood control in reservoirs, addressing the challenges of balancing flood mitigation and water supply demands in multi-purpose reservoirs. A comprehensive framework is introduced, which includes a pre-release model categorizing reservoirs based on system characteristics and simulates multiple operational scenarios. Using historical data and simulations for 11 reservoirs managed by the USACE Louisville District, the effectiveness of different operational policies is evaluated. Key findings indicate that pre-release operations, particularly with a 72-h lead time, significantly improve reservoir flood control by reducing end-of-flood water levels and shortening the recovery times for design seasonal flood events. A 24-h pre-release policy is identified as a practical solution, offering substantial improvements with low adverse impacts, making it suitable for regular implementation. Additionally, other operational strategies are assessed, and pre-release is suggested as the optimal approach for facilitating the transition of reservoirs from single to multi-purpose functions. This study underscores the importance of integrating flood forecasting with reservoir operational strategies, advocating for the refinement of release policies to account for unique reservoir conditions. These insights provide a foundation for optimizing reservoir operations, contributing to improved flood and water resource management.

As high-frequency sensor networks increasingly enhance data-driven models of water quality, process-based models like the U.S. National Water Model (NWM) are generating accessible forecasts of streamflow at increasingly dense scales. There is now an opportunity to combine these products to construct actionable water quality forecasts. To that end, we couple streamflow forecasts from the NWM to a gradient-boosted decision tree algorithm (LightGBM) trained on 5+ years of high-frequency monitoring data to forecast in-stream turbidity levels in the Catskill Mountains, NY, USA. Results indicate LightGBM models are capable of relatively skillful predictions, which enable robust forecasts for 1–3 days lead times. LightGBM models offer improvements over a simplified linear model across the entire forecast horizon, and more spatially complex models are more resilient to error at shorter lead times (1–3 days). Moreover, interpretation of model features emphasizes high flows as a driver of turbidity in the region. Results suggest that interpretable, flexible, and efficient machine learning algorithms can produce capable water quality forecasts from streamflow forecasts and expand understanding of process dynamics. The use case illustrated here—to our knowledge the first NWM-based water quality forecast—underscores the potential to employ the NWM to expand national water quality forecasting capacity and can overall serve as a guide for similar efforts in basins across the country.

Effective streamflow monitoring networks are crucial for flood mitigation planning and water management operations. In Louisiana, USA, extreme rainfall, flat topography, and coastal-inland interactions necessitate enhancements to the sparse existing monitoring resources. This study introduces a stakeholder-driven approach to designing a streamflow monitoring network by integrating local expertise with geospatial process-based criteria. Our approach combines stakeholder input, gathered via web-based geospatial applications, with an automated scoring system. The system is based on hydrologic and geomorphic factors to prioritize gage placements while balancing regional needs and resource constraints. Implemented as part of the Louisiana Watershed Initiative (LWI), the network design addresses monitoring gaps, particularly in ungauged large watersheds and streams with complex flow regimes. The study highlights the importance of incorporating local knowledge into technical designs to support flood mitigation planning, real-time flood forecasting, and hydrodynamic model calibration. This framework can be adopted by other flood-prone regions worldwide to enhance flood monitoring and mitigation planning efforts.

Despite > 700,000 km of unpaved roads in the western United States, our knowledge of how roads impact instream sediment is unclear. We combined two studies, including (1) a regional analysis linking stream habitat data from a large-scale monitoring program with road density data to identify generalizable relationships between roads and streambed sediment distributions and (2) a targeted field study to evaluate the responses of streambed and suspended sediment collected at locations above and below road–stream connection points to better understand the consistency of responses. Regional analyses indicated a significant positive relationship between road density and fine sediment in pool tails and a significant negative relationship between road density and median particle size. We also found significant relationships between landscape, climate, and local covariates and streambed sediment metrics, where most of the parameter estimates of the covariates were equal to or stronger than those for road density. Field studies suggested higher suspended sediment levels across the seasonal hydrologic regime where roads were open to travel year-round. However, sediment responses to road–stream connection points varied by metric and site. Together, our results indicated negative relationships between increasing road densities and sediment size distributions, but detecting road effects at site scales will be challenging given the effects of covariates that can overwhelm sediment signals.

The Conceptual-Functional Equivalent (CFE) to the National Water Model (NWM) serves as a baseline rainfall-runoff model in the National Oceanic and Atmospheric Administration (NOAA)'s Next Generation National Water Model Framework (NextGen). The CFE model performs similarly to the earlier version of the NWM, allowing comparisons with new models introduced in future versions. In addition to streamflow, the NWM outputs other hydrologic variables including soil moisture. Soil moisture plays a key role in simulating seasonal hydrologic processes in process-based models; therefore, it is imperative to evaluate models against observed data. However, incorporating in situ observed soil moisture data, which is highly spatially variable, into the calibration process may compromise streamflow results. We investigate how model evaluation, including in situ soil moisture observations, affects CFE's ability to reproduce streamflow and soil moisture. We evaluated the CFE model on two experimental watersheds using both traditional and signature-based performance metrics for soil moisture. Results showed that including soil moisture data enhances the reproducibility of overall and seasonal soil moisture patterns without sacrificing the reproducibility of streamflow. Calibration against streamflow alone was insufficient to reproduce soil moisture patterns. We recommend including soil moisture metrics when available in the CFE model calibration to improve seasonal streamflow predictions.