Journal of The American Water Resources Association最新文献

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.

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

We gratefully acknowledge the following reviewers who have generously donated their time and expertise to JAWRA. The list includes all reviewers who supported the journal between October 1, 2022 and September 30, 2023.

Abatzoglou, John

Abdallah, Adel

Abdi, Babak

Adnan, Dr. Muhammad

Ahmadisharaf, Ebrahim

Alamdari, Nasrin

Alarcon, Vladimir J.

Aryal, Niroj

Asarian, Joshua

Avesani, Diego

Ayers, Jessica

Babbar-Sebens, Meghna

Ban, Zhaoxin

Banerji, Aabir

Beechie, Tim

Bennett, Katrina

Bhaskar, Aditi

Bledsoe, Brian

Blumenshine, Steve

Boland, John

Bosch, Darrell

Broman, Daniel

Brookfield, Andrea

Brown, Rocko

Cao, Qian

Cao, Zhigang

Carlson Mazur, Martha

Castellini, Mirko

Castro-Bolinaga, Celso

Chegini, Taher

Chen, Feng

Chen, Weibo

Cherry, Mikaela

Choat, Ben

Christensen, Jay

Collick, Amy

Compton, Jana

Curran, Janet

Dahl, Travis

Dallison, Richard

Debaere, Peter

Delworth, Thomas L.

Dey, Sayan

Du, Erhu

Duan, Huan-Feng

Duan, Kai

Eekhout, Joris

Endreny, Theodore

Evenson, Grey R.

Fairfax, Emily

Fakharian, Ahmad

Farmer, William

Ferencz, Stephen

Flanagan, Dennis

Fleming, Sean

Förster, Kristian

Frei, Allan

Freudiger, Daphné

Fullerton, Aimee

Garg, Manoj Chandra Garg

Ghazi, Babak

Giese, Markus

Gilley, John

Goeking, Sara

Goodrich, David

Goodrum, Gregory

Gordu, Fatih

Graham, Jennifer

Gray, Geneva

Grigg, Neil

Gronewold, Andrew

Guertault, Lucie

Gupta, Abhinav

Gupta, Rishabh

Guthrie, Gregory

Gutzler, David

Haas, Henrique

Hacker, Miriam

Hallum, Douglas

Han, Bangshuai

Harmel, R.

Hayes, John

Hecht, Jory

Helmers, Matthew

Hersh, Eric

Hirsch, Robert

Horsburgh, Jeffery

Huffman, George

Hussain, Mubshar

Jackson, C.

Jakubauskas, Mark

Jeong, Hanseok

Jepson, Wendy

Jha, Manoj

Jiang, Lingmei

Joeckel, Matt

Johnson, Thomas

Jones, Chris

Jones, Julia

Jung, Kichul

Kao, Shih-Chieh

Kaplan, David

Kelleher, Christa

Kelly, Walton

Khan, Manas

Kiffney, Peter

Kikoyo, Duncan

Kim, Dong-Hyun

Kim, Wonsik

King, Tyler

Kinnell, P. I. A.

Kisekka, Isaya

Knightes, Christopher

Kolok, Alan

Kukal, Meetpal

Kumar, Amit

Kumar, Sujay

Kurylyk, Barret

Larsson, Rolf

Lee, Jiwan

Lee, Yonggwan

Li, Changjia

Li, Hong-Yi

Li, Zoe

Lin, Zhulu

Liu, Gang

Liu, Huan

Liu, Ning

Lu, Weisheng

Luiz-Silva, Wanderson

Luo, Hao

Mack, Elizabeth

Magne