Journal of The American Water Resources Association最新文献

This study investigates the hydrologic connection between a surface water reservoir and surrounding groundwater under changing climate and water demand. Field research at the Wyoming Hereford Ranch Reservoir 2 (WHR2) combined Granger causality tests and MODFLOW modeling to examine temporal relationships and simulate groundwater responses. Causality weakened under wetter conditions due to an increase in recharge. A parsimonious MODFLOW model using reservoir levels as input accurately simulated groundwater levels (NSE: 0.65–0.98; RMSE: 0.09–0.3 m). Scenario analysis revealed minimal groundwater change (±0.5 m) under varying reservoir operations, indicating buffering capacity that supports adjacent wetlands. In contrast, dam removal caused a 2.5 m drop in groundwater near the dam, while changes in recharge affected more distant areas by up to 3 m. Higher recharge also reduced the range of groundwater fluctuations. These findings highlight the sensitivity of groundwater to recharge and reservoir dynamics and highlight the importance of adaptive reservoir management. The framework offers a transferable tool for evaluating reservoir impacts on groundwater under climate variability.

The increasing frequency of hydrological extremes highlights the need for event-based approaches to evaluate hydrological model performance and support water resource management. Traditional long-term continuous simulations often overlook model behavior during critical flood and drought periods, limiting their operational value. To address this gap, we developed a coupled SEED–CSES framework for large-sample, event-based benchmarking. SEED identifies flood and drought events using the Log-Pearson Type III (LP3) distribution for multiple return intervals (2, 5, 10, 25, 50, and 100 years), while CSES evaluates model skill. We demonstrate the framework by assessing the extreme-event prediction performance of the National Water Model (NWM) v3.0 at more than 7000 USGS NWIS stations, including over 600 CAMELS basins. Across the CONUS domain, NWM 3.0 shows higher skill for flood events (median KGE ≈0.20) than for drought events (median KGE ≈−0.78). Wetter eastern, southeastern, and northwestern regions perform better (median KGE ≈0.387), while arid western and southwestern regions show low performance (median KGE ≈−0.447), illustrating how event-based benchmarking reveals hydrological behaviors masked in long-term evaluations. The integrated SEED–CSES framework provides a standardized and automated platform for hydrological model assessment, supporting improved flood forecasting, drought monitoring, and climate resilience.

The Potomac River basin, a critical source of drinking water and home to the U.S. capital, provides a case study in sustainable water resources management. This paper traces the history of planning in the basin, examines the opportunities and challenges of water resources management in a complex, multi-jurisdictional setting, and analyzes the integrated, adaptive process used to develop and implement the Potomac Basin Comprehensive Water Resources Plan. Using a review of planning documents and stakeholder engagement outcomes, the analysis identifies key mechanisms through which adaptive, collaborative planning is operationalized across four analytic dimensions: stakeholder engagement, facilitation, plan components, and planning process. The Potomac case demonstrates how integrative principles can be implemented pragmatically through voluntary, science-based, and locally grounded collaboration—rather than applying Integrated Water Resources Management (IWRM) as a formal or prescriptive framework. The findings highlight how institutional capacity, iterative learning, and cross-jurisdictional coordination enable basin-scale planning to evolve over time. Unlike previous Potomac scientific and planning reports, this paper offers a systematic, reflective analysis of the long-term planning process in the Potomac basin, providing empirically grounded insights and a conceptual framework for others pursuing integrated, sustainable water resources management.

Irrigation organizations (IOs) in the arid US West manage water supply, own water rights, and deliver water shares to their users. In delivering most of the water used for irrigated agriculture, the influence of the water managers who run them on water futures cannot be overstated. Yet, the perspectives of both water managers and IOs regarding nimble strategies for water management under scarcity remain understudied. One water management strategy, water banking, was introduced in Utah in 2020, but formal uptake has been slow. Using data collected from individual agricultural water managers within IOs in Utah, we show that water managers are familiar with water markets, but that they do not believe their IOs are interested in increasing the number they engage in. Further, we find that most IOs would have little to no water to place in a future water bank, and that over half of the water managers surveyed believe none of their shareholders would be interested in participating. Finally, government meddling, fear of forfeiture, and economic impacts are all barriers to banking, but water infrastructure improvements might act as bridges to finding more “wet” water for banking and other transactions. This study helps clarify whether and how water markets might be integrated into a more secure water future for Utah and the arid West. While water banking remains one tool for flexible and adaptive water management, we underscore that barriers to banking may limit its uptake in Utah.

Hydrologic science lacks a comprehensive theory of stormflow generation, preventing the development of a general hydrologic model. Studies show that models focusing on dominant local processes often outperform general models that rely on parameter tuning, leading to higher confidence solutions. For continental-scale hydrologic and hydraulic prediction, regional mosaics of models may outperform a single-model approach. However, variations in model inputs, programming languages, solvers, and discretizations hinder interoperability and comparisons. To address these challenges, we developed the Next Generation Water Resources Modeling Framework (NextGen): a model-agnostic, standards-based architecture for model interoperability and evaluation. Two standards enable the Framework: (1) the Basic Model Interface (BMI) version 2.0, for model control, coupling, and querying; and (2) the Open Geospatial Consortium WaterML 2.0 part 3 Hydrologic Features (HY_Features) conceptual data model to describe the “hydrofabric” of surface water hydrologic and hydraulic features. In the NextGen Framework, models retain their unique solution methods while becoming interoperable through BMI variable exchange tied to a common hydrofabric. The Framework enables scientific evaluation of water prediction models that simulate diverse hydrologic and hydraulic processes. Its design supports models written in multiple programming languages and runs on laptops, cloud and distributed memory supercomputers.

History matching of large hydrologic models is challenging due to data sparsity and non-unique process combinations (and associated parameters) that can produce similar model predictions. We develop an ensemble-based history matching (and uncertainty quantification) approach using an iterative ensemble smoother (iES) method for three cutouts of the National Hydrologic Model (NHM) and qualitatively compare the results and performance to the stepwise history matching approach. In the latter approach, subsets of parameters and observations were sequentially calibrated to a diverse range of observations to mitigate non-uniqueness and local minima. In iES, localization simulates the same causal connections between parameters and observations without the need (and computational cost) of sequential history matching steps. iES uses a weighted sum-of-squared-errors objective function which allows differential weighting of multiple data sources. Formal adoption of range observation also pushes results to within ranges of observation values rather than discrete values. Overall, the ensemble approach performs similarly to the stepwise approach. Both approaches performed poorly for the cutout representing a snowmelt-dominated watershed, indicating a structural issue in the process representation of the model. The main advantage of iES is quantification of uncertainty in both the history matching and the predictions of interest.

The freshwater resource of the Ottawa River basin (ORB) is vital for ecological services and economic activities in the region. With climate change impacts becoming more evident, sustainable management is imperative. As a first step, it is important to assess impacts on hydro-climatology. A coupled hydrological model (WATFLOOD-RAVEN) was set up, calibrated, and validated, with all principal reservoirs implemented using the DZTR principle. The coupled model was forced with high-resolution, bias-corrected future climate projections. Ensemble results show a warmer and wetter future. Under RCP 4.5, which recent studies suggest as the more likely pathway, mean annual precipitation and temperature may increase as much as 8.6% and 3.2°C, respectively. Seasonal and monthly changes are more sporadic. Snowpack is expected to decrease substantially, while actual evapotranspiration increases moderately. Annual streamflow may rise by 7%–10%, but spring freshet streamflow is projected to shift earlier (1.1 days/decade) with a lower (12%) peak. Other indicators, such as time to center of mass and spring pulse onset, also point to earlier shifts. Results further indicate more frequent extreme high (Q10) and low (Q90) flow conditions. Substantial increases in winter reservoir inflows are likely to raise reservoir depths and reduce capacity to store the spring freshet. Better quantification of climate change impacts will aid in developing coordinated responses to climate-related challenges.