Journal of The American Water Resources Association最新文献

Improvements in simulating and communicating the evolutionary trajectory of river morphology in response to environmental forcing over multi-decadal timeframes would foreshadow the development of “foresight competency” in river management, whereby resource managers could strategically plan toward the most preferred of several plausible futures. Of the six steps in foresight competency, visioning, which involves translating scientific forecasts into a format useable by resource managers via a user-friendly and interactive decision support tool that supports transparent decision-making, is the least well developed. The approach requires converting forecasting model outputs into metrics of channel evolution that highlight transitions either within or between channel morphology states. Here, seven process-based state transition metrics are proposed covering channel planform, morphological stability, corridor belt width, floodplain connectivity, bank erosion rate, bedform habitat diversity, and ecohydraulic diversity. To aid decision support, the metrics are converted into graphical indicators that are intuitive for management use and assembled into several prototype dashboard-style graphical user interfaces designed to facilitate interactivity. A proof-of-concept illustration is provided and priorities in development toward a fully operational decision support tool are discussed. Such developments are critical in ensuring the practical relevance of geomorphology.

Extreme precipitation-related hazards like flash floods pose a widespread risk to humans and infrastructure around the world. In the current study, the Fisher information was applied to understand the nonstationarity of the extreme precipitation regimes, whereas copula was used to quantify the likelihood of joint occurrence of two extreme precipitation indices and associated risk assessment in the upper Midwestern United States (UMUS). The trend analysis revealed an increasing trend in 37% of the stations in heavy precipitation amount in the UMUS. The regime shift analysis showed the non-stationary nature of extreme precipitation in about half of the total stations in UMUS. Further, the bivariate analysis using copula demonstrated the risk of the joint occurrence of extreme precipitation indices potentially causing flash floods. The risk index analysis indicated about 28.8% of stations under moderate, 10.6% of stations under high and 0.4% of stations under very high risk of flash flooding. The results from the study can provide important insights for the (re)design of resilient and sustainable water infrastructure in the changing climate condition and can also inform managers and planners for better response and preparedness toward extreme precipitation-related hazards in this region. The results from this study can also help in a more accurate risk assessment, especially in the socio-economically vulnerable community.

In 2020, the Chesapeake Bay Program moved to offset impacts from climate change for the 30-year period from 1995 through 2025 by having its seven watershed jurisdictions (Delaware, Maryland, New York, Pennsylvania, Virginia, West Virginia, and the District of Columbia) apply additional nutrient pollutant reduction practices. The climate change assessment was performed with integrated models of the Chesapeake watershed, airshed, and estuary. Scenarios run for the years 2025, 2035, 2045, and 2055 estimated effects from the different future climatic conditions. This article presents the results of that assessment and is intended to provide a guide to assist other modeling practitioners in assessing climate change impacts in coastal watersheds. Major influences of climate change that were quantified include increases in precipitation volume, potential evapotranspiration, watershed nutrient loads, tidal water temperature, and sea level. Minor influences quantified in the climate change analysis include changes in nutrient speciation and increases in wet deposition of nitrogen, CO₂, rainfall intensity, tidal wetland loss, up-estuary salt intrusion, and phytoplankton biomass. To offset climate change impacts from 1995 to 2025 on water quality, the scenarios indicate an additional 2.3 million and 0.3 million kg of nitrogen and phosphorus per annum, respectively, will need to be reduced beyond what is called for in the Chesapeake Total Maximum Daily Load.

Baseflow was proven to be the most unpredictable component of streamflow through various research. However, the recent method for estimating baseflow is due to the development of theoretical and computational techniques. This paper attempted to develop a fully automated baseflow separation system based on a recursive digital filter with an optimization algorithm for the single separation. Most of the previous baseflow separation methods use a single set of a parameter and BFI_max (the maximum value of baseflow index), which can underestimate or overestimate the baseflow; however, the system developed in this study estimates multiple optimized a parameters using seasonality and flow conditions and uses them for BFI_max calculation and baseflow separation. This system derived baseflow results in better understanding of watershed and streamflow tendency characteristics. This study developed a Web-based Hydrograph Analysis Tool 2020 with a user-friendly interface and new separation method regarding the seasonality and flow conditions with a fully automated python module to optimize a parameters and BFI_max. The application to the two area show diverse parameter sets and different baseflow according to seasonality and flow conditions representing the flow characteristics. This study could be a fundamental tool for detailed watershed management decisions regarding water security in the dry season or environmental water for aquatic ecosystems.

Water resources carrying capacity (WRCC) has been evaluated repeatedly to guide sustainable regional development, with the increasing conflicts over water resources between society and nature. Urban underlying surfaces are constantly changing under the rapid development of urbanization, which has changed the WRCC. The chaotic particle swarm genetic algorithm (CPSGA) is proposed in this study to evaluate the WRCC. It combines the genetic algorithm (GA), chaotic optimization algorithm (COA), and particle swarm optimization (PSO), as well as introduces the chaotic mapping of COA and the velocity position update strategy of PSO into the GA framework to strengthen the population quality and improve the algorithm's efficiency. The effectiveness of CPSGA was demonstrated using three typical functions. Nanjing, China, was used as the study area to evaluate the WRCC from 2015 to 2018. The results showed that the comprehensive evaluation scores of the WRCC of Nanjing from 2015 to 2018 were up to 0.83. In addition, the CPSGA had better astringency and stability than GA, COA, and PSO. The application indicated that the proposed methodology is feasible, providing a reference for conducting WRCC research elsewhere.

Altered precipitation and temperature patterns from a changing climate will affect supply, demand, and overall municipal water system operations throughout the arid western U.S. While supply forecasts leverage hydrological models to connect climate influences with surface water availability, demand forecasts typically estimate water use independent of climate and other externalities. Stemming from an increased focus on seasonal water demand management, we use the Salt Lake City, Utah municipal water system as a test bed to assess model accuracy versus complexity trade-offs between simple climate-independent econometric-based models and complex climate-sensitive data-driven models to average to extreme wet and dry climate conditions—representative of a new climate normal. The climate-independent model displayed low performance during extreme dry conditions with predictions exceeding 90% and 40% of the observed monthly and seasonal volumetric demands, respectively, which we attribute to insufficient model complexity. The climate-sensitive models displayed greater accuracy in all conditions, with an ordinary least squares model demonstrating a measurable reduction in prediction bias (3.4% vs. −27.3%) and RMSE (74.0 lpcd vs. 294 lpcd) compared to the climate-independent model. The climate-sensitive workflow increased model accuracy and characterized climate-demand interactions, demonstrating a novel tool to enhance water system management.

Recreational and occupational contact with freshwater harmful algal blooms (HABs) pose human health and economic risks worldwide. Individual U.S. states control monitoring, reporting, and mitigation of recreational exposure to HABs. We surveyed states to catalog responses to HAB problems. We used this data to develop a state-specific HAB response index (HABRI) and compared it to HAB risks derived from empirical nation-wide data and per capita state environmental management and public health spending. States varied in regulations, reporting, monitoring, communication, and mitigation. The HABRI was not correlated with empirically based risk. Several states had no limits on toxin exposure or limits that were higher than recommended by the USEPA or World Health Organization. Other states did not provide public signage or notification when HABs were occurring and recreation could be hazardous. Increased federal involvement, communication among states, and state and federal legislation could minimize this variation and positively influence responses. We identify best practices for addressing HABs in our study that could provide guidance to authorities in any part of the world while developing new programs or enhancing existing efforts.

The effect of IgG4, which constitutes the least of the IgG subclasses, on the pathogenesis and prognosis of lymphoma or solid tumors is one of the research topics of interest in recent years. The role of IgG4, which has been reported to suppress antitumor immunity, in classic Hodgkin's lymphoma (cHL), which is recognized by its pathognomonic microenvironment, is not yet clearly known. The aim of this study was to determine IgG4-positive plasma cell density in the cHL microenvironment and to compare it with histopathological and clinical parameters. In addition, the role of the increase in IgG4-positive cells in the development of relapse after treatment was also investigated. A retrospective cross-sectional study. Ninety-four patients with the initial diagnosis of cHL who had no comorbidity or no treatment history and forty-one reactive lymph nodes with follicular hyperplasia findings were included in the study. Three hot-spot areas were identified with reference to the IgG4 sections. Mean IgG4-positive plasmacyte counts and IgG4/IgG ratios were determined and compared with histopathological characteristics. The mean IgG4 + plasma cell count was 33.57 in cHL cases and 47.04 in the control group (p = 0.233). IgG4/IgG ratio was significantly higher in cHL compared with the control group (0.27 vs. 0.21, p = 0.021). The IgG4/IgG ratio was found to be higher in younger patients with classic Hodgkin lymphoma, with a low correlation (p = 0.028, r =  - 0.226). There was no relationship with gender, lymph node location, histological subtype, EBV positivity and bone marrow infiltration. It was observed that IgG4/IgG ratio was higher in early-stage patients (p = 0.022). No significant IgG4 + cell increase was detected in the initial diagnosis and relapse slides of six patients who developed relapse after standard treatment, resulting in a cure. Novel therapeutic modalities targeting microenvironmental components have been reported to show dramatic effects, particularly in relapsed or refractory patients. Detailed characterization of the cHL inflammatory milieu will be useful for the identification of alternative targets. IgG4 subclass antibodies, which have been described to have anti-inflammatory effects, may have prognostic significance in a proportion of cHL patients.

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.