Journal of The American Water Resources Association最新文献

Private groundwater wells can be unmonitored sources of contaminated water that can harm human health. Developing models that predict exposure could allow residents to take action to reduce risk. Machine learning models have been successful in predicting nitrate contamination using geospatial information such as proximity to nitrate sources, but previous models have not considered meteorological factors that change temporally. In this study, we test random forest (regression and classification) and linear regression models to predict nitrate contamination using rainfall, temperature, and readily available soil parameters. We trained and tested models for (1) all of North Carolina, (2) each geographic region in North Carolina, (3) a three-county region with a high density of animal agriculture, and (4) a three-county region with a low density of animal agriculture. All regression models had poor predictive performance (R² < 0.09). The random forest classification model for the coastal plain showed fair agreement (Cohen's κ = 0.23) when trying to predict whether contamination occurred. All other classification models had slight or poor predictive performance. Our results show that temporal changes in rainfall and temperature, or in combination with soil data, are not enough to predict nitrate contamination in most areas of North Carolina. The low level of contamination (<25%) measured during the study could have contributed to the poor performance of the models.

In this paper, we use remotely sensed imagery to identify the location and size of animal feeding operations in the Maumee River Watershed, a key drainage area to Lake Erie's Western Basin, which has recently experienced severe harmful algal blooms. We then estimate the relationship between the intensity of animal feeding operations in the watershed and surface water body concentrations of dissolved reactive phosphorus (DRP), the pollutant most responsible for algal growth. We find that stream reaches with relatively larger increases in upstream animal feeding exposure experience significantly higher increases in concentrations of DRP. The average marginal upstream animal feeding operation in the watershed increases downstream DRP concentrations by between 10% and 15%. In contrast, when restricting the analysis to include only permitted operations, coefficient estimates are practically zero and statistically insignificant. Our work presents evidence that the increasing intensity of animal feeding operations contributes to water quality problems. Permitting and identification of animal feeding operations is therefore important for managing runoff and correctly attributing the causes of excess nutrients in surface water bodies.

The Journal of the American Water Resources Association recognizes the critical role of reviewers in maintaining high standards of the journal and improving the quality of published papers. Starting back in 2020, we have been recognizing those reviewers who have gone above and beyond in providing extensive and comprehensive reviews. The reviewers have been identified by the associate editors during the review process. Our heartfelt thanks to these reviewers for their selfless service to the journal and the scientific community at large.

John Abatzoglou

Nick Martin

Michael Warner

Shan Zuidema

Tamie L. Veith

Jianshi Zhao

Bioenergetics models produce quantitative flow-ecology relationships that summarize changes in habitat and food resources from altered flows. We used a drift-foraging bioenergetics model to quantify the net rate of energetic intake (NREI) for trout above and below a water diversion. NREI is reduced by >95% below the water diversion in July–September, when up to 98% of unimpaired flows are diverted. We then used a risk-based approach to estimate the maximum diversion rate, expressed as a percentage of unimpaired flow, that would produce NREI values that are not significantly lower than values under unimpaired flows throughout a 62-year period. NREI decreased with increased precent-of-flow diversion rates in low-flow months (July–September). Diversion rates of 16% in July and 9% in August and September would maintain NREI within the range of unimpaired flow conditions. In higher flow months, May–June, increasing diversions brought estimated instream flows closer to the peak NREI flow, leading to the assessment that increased diversions would increase NREI. Bioenergetic models can be used to develop protective flow rates at times of the year when fish growth and production would be high under unimpaired flows, which often coincides with when water is diverted. Our study is the first to develop protective percent-of-flow diversion rates for holistic flow management using a quantitative process-based and fish-centric ecological metric.

Increased wildfire activity in the western United States can lead to detrimental cascading effects to water quality. After fires, burned areas may experience significant runoff-induced erosion and sediment transport into rivers and reservoirs, which could rapidly overwhelm existing drinking water treatment plants. This paper couples an assessment of wildfire risk with an evaluation of water utility preparedness to understand where key fire-related drinking water vulnerabilities exist. Wildfire risk assessments were constructed and expanded from a commonly used methodology co-developed between researchers and water managers (Edel et al., 2002), to understand drinking water impacts on water quality after wildfires. A water utility preparedness index was created for this study using publicly available information to contextualize how well utilities may be able to respond to water quality degradation after fires. Results indicate that 22% of utilities studied (10% of the population served) were underprepared for fire and 11% of watersheds used were at greater risk of wildfire (9% of the population served). However, nearly three-quarters of utilities (76% of the population served) showed a moderate risk of fire and some need for improved fire preparedness. Information developed here could provide a useful framework from which utility managers can better assess their likely wildfire risk and preparation plans.

Predictive models of disinfection byproducts (DBPs) formation in treated drinking water have been widely used to guide operational decisions. However, very few studies have addressed the issue of managing DBPs through watershed protection programs and proactive management of water supply systems through predictive modeling of DBP formation potential in source waters. Here, we propose a two-component, simple statistical approach to predict the formation potentials of the sum of five haloacetic acids (HAA5fp) and total trihalomethanes (TTHMfp) in source water streams using environmental variables and ultraviolet absorbance at 254 nm wavelength (UV₂₅₄) as a surrogate for DBP precursors. In the first component of the model, using three feature selection regression models and cross-validation of subsets of the selected predictors, we identified three commonly monitored variables—streamflow, soil temperature, and total phosphorus for predicting UV₂₅₄. In the second component, HAA5fp and TTHMfp are predicted from UV₂₅₄. The approach is successfully demonstrated for two source water streams of the New York City water supply system (R² was 0.8, and 0.7–0.8 for the two model components). Long-term predictions of HAA5fp and TTHMfp showed distinct seasonal patterns that are linked to differences in land uses of the two watersheds. Moreover, sensitivity analysis showed that transport processes were important in one watershed whereas production processes were more important in the other.