Journal of The American Water Resources Association最新文献

Chlorophyll a water quality criteria provide the means for regulating nutrient pollution in surface water systems without many of the technical challenges associated with nitrogen and phosphorus thresholds. However, the development of defensible chlorophyll a criteria is not without its own challenges, particularly in complex coastal ecosystems. We describe the evolution of water column chlorophyll a criteria specific to the Chesapeake Bay estuary, from a general narrative to numeric thresholds developed to meet multiple regulatory management goals. We then present a unique approach to numeric chlorophyll a criteria derivation, one that integrates continuous and underway water quality monitoring datasets. These datasets were used for the dual purposes of characterizing baseline conditions and determining chlorophyll a concentrations that confer unacceptable levels of risk to a specific water body in both space and time. This novel approach was used by the Commonwealth of Virginia to update the numeric chlorophyll a criteria it had originally established for the tidal James River, a subestuary of the Chesapeake Bay, in 2005. Tidal James River chlorophyll a concentrations were evaluated in terms of their relationships with harmful algal blooms, dissolved oxygen, pH, and water clarity. The importance of spatially and temporally intensive monitoring datasets to chlorophyll a criteria development is highlighted in this case study.

Agricultural nitrate pollution is a major threat to water quality in Iowa. Iowa uses a majority of its land for row crop agriculture and maintains a large livestock population, which together cause high nitrate loads in streams. High-frequency stream nitrate data can aid policy decisions for reducing nitrate emissions by identifying streams with high nitrate loads, historical trends of improvement or deterioration in nitrate loads, and land use or practice changes that affect water quality. We developed a time series regression model framework to supplement existing sensor data and predict daily nitrate loads in Iowa streams lacking nitrate monitoring. Using nitrate data from statewide and national resources, this framework was trained and validated using 11 study sites of diverse geography and land use in Iowa. Partial least squares regression (PLSR) was used with geographical predictors, including land use, hydrogeology, and meteorology, to predict streamflow-nitrate load relationships across the study sites. The developed PLSR model, combined with daily streamflow data, was then used to predict daily nitrate loads with high accuracy over a three-year study period with a mean Kling–Gupta Efficiency of 0.74. Our framework was then used to estimate mean nitrate concentrations at 34 sites that lack nitrate sensors, demonstrating a low-cost, facile method for the accurate prediction of daily nitrate loads in Iowa streams.

The multiobjective scheduling of cascade reservoir systems faces challenges due to high-dimensional nonlinearity, where traditional optimization methods struggle to achieve globally balanced solutions. This study proposes a Two-Stage Multi-Objective Particle Swarm Optimization (TSMOPSO) algorithm, incorporating two innovative components to enhance optimization performance. The first component employs Piecewise mapping, adapts weights and introduces two operators to improve optimization efficiency and convergence speed. The second component features a two-stage refinement mechanism, implementing a two-level adjustment of upstream and downstream water levels based on constraint evaluations, effectively alleviating constraint limitations. A case study is conducted on cascade reservoirs system in the Jinsha River Basin of the Upper Yangtze River (JRBUY), with a multiobjective model integrating power generation, power output, and navigation demands. Numerical experiments demonstrate that TSMOPSO achieves remarkable performance under wet-year conditions: power generation of 2087.46 KW h, power output of 16,435.75 MW, and a navigation index of 3052.92 m³/s. Compared wtih other algorithms, TSMOPSO exhibits significant advantages in hypervolume (HV) indicators and solution set coverage. Pareto front analysis reveals competitive mechanisms among the three objectives. This approach provides a novel technical pathway for multiobjective optimization of complex cascade reservoir systems.

Accurate streamflow forecasts are critical for modeling and managing estuarine water quality, as freshwater fluxes significantly influence coastal dynamics. The National Water Model (NWM) provides high-resolution streamflow predictions, which are valuable for hydrodynamic modeling in poorly gauged coastal regions. However, inaccuracies in NWM forecasts can limit our ability to predict estuarine and nearshore water quality effectively. First, this study evaluates the accuracy of NWM predictions for 14 coastal reaches in southwest Florida's Charlotte Harbor and Caloosahatchee River estuaries from 2018 to 2024, where hydrologic management has impacted water quality. NWM forecasts showed varying bias and variance, with Nash-Sutcliffe efficiencies (NSE) ranging from −2.26 to 0.77. Next, hydrodynamic simulations for the flow-managed Caloosahatchee River Estuary (CRE) were performed using both NWM forecasts and observed streamflows, revealing that errors in NWM predictions during high-flow events caused significant deviations in the position of ecologically relevant isohalines, lasting weeks. Finally, to address these issues, a Long Short-Term Memory (LSTM) network was developed to bias-correct NWM forecasts, improving NSE from 0.41 to 0.53. However, the LSTM's inability to “learn” managed discharge schedules highlights the need for advanced data assimilation and simulation techniques in flow-managed coastal systems.

China is facing a serious water shortage. The government's implementation of a water resource tax policy is an important step in the ecological protection of water resources. This paper constructs a stochastic frontier production function model to measure the industrial water resource utilization efficiency in Hebei Province under the consideration of water resource tax. Then, this paper constructs a model of the impact of water resource tax policy on industrial water resource utilization efficiency using the double difference method to evaluate the causal effect of the policy. The research findings of this article are: (1) Through discontinuity regression, it was found that the water resources tax policy has a significant positive correlation with the improvement of water resource utilization efficiency in Hebei Province. The implementation of the water resources tax policy has successfully improved the industrial water resource utilization in Hebei Province. (2) Although the implementation of the water resources tax policy has improved the efficiency of industrial water use in Hebei Province, the overall efficiency of industrial water use in Hebei Province is still low, and many problems have arisen during the advancement of the water resources tax policy. In response to the problems that occurred during the pilot period in Hebei Province, this article proposes some policy solutions to accelerate the advancement of water resource tax policies across the country.