Journal of The American Water Resources Association最新文献

This study examines the effect of alfalfa (Medicago sativa L.) on nitrogen (N) and phosphorus (P) loads in subsurface (tile) drainage across storm events using edge-of-field monitoring data from two paired-field sites (A and B) with a before-after-control-impact (BACI) experimental design, located in the northwest region of Ohio, United States. A k-medians cluster analysis was used to classify 462 storm events at Site A and 684 storm events at Site B based on precipitation amount and antecedent moisture conditions (AMC), defined as the cumulative 7-day precipitation prior to a storm event. Patterns of nutrient loss in tile drainage were compared between fields with alfalfa and fields with cash and cover crops using a difference-in-differences analysis across three identified storm event types: Dry storm events, wet storm events, and large storm events. Compared to the cash and cover crop rotations, alfalfa had the following effects on discharge and water quality: little to no reduction in subsurface discharge across all storm events at both sites; significant reduction of subsurface nitrate and total N loads across all storm event types at Site A (~200%–800% lower), but not at Site B; ~45% reduction of subsurface dissolved reactive P during large events at both sites; and 11% and 110% reduction of total P loads during large events at Sites A and B, respectively. The impact of alfalfa during large storm events is important given that most nutrient export occurs during these events.

The Las Vegas Wash (the Wash) provides a mechanism for delivering recycled indoor water used in the Las Vegas Valley, NV to the most downstream basin of Lake Mead. The Wash introduces different water quality to Lake Mead, including higher nitrogen and phosphorus concentrations, and it may contain constituents common to urban runoff including microbial organisms and trace contaminants. A strong link has been established between the residential population of the Las Vegas Valley and the mean annual flowrate in the Wash; however, with the onset of the SARS-CoV-2 pandemic in 2020, significantly reduced tourism led to reduced flow in the Wash. This work expands previous modeling efforts which project Wash flowrates based on population projections by incorporating tourism numbers, using the individuals employed in hospitality as a surrogate. The resulting model suggests that mean yearly Wash flowrates could increase 20.6%–23.2% (between 1.95 and 2.19 $��m^3�/�s�)�$ by 2060, compared to 2022 levels. Numerical simulations of Lake Mead show that these increased Wash flowrates are not expected to have a significant thermal influence on either the drinking water intake (Intake) or Hoover Dam outflows. The Wash delivers about $��{10}^3�$ MW of heat into Lake Mead, while heat transfer at the Intake and Hoover Dam water columns was two orders of magnitude less. Wash water concentration and salinity increased in the simulations at the Intake and Hoover Dam outflows, respectively, by at most 0.91% and 1.3%.

The majority of the discharge in the Colorado River originates as snowmelt in headwater catchments. Flow from these streams exhibits significant year-to-year variability (Coefficient of Variation: 0.2–1.18), challenging the management of a critical water and energy resource for millions. Understanding the factors driving this variability will become even more important in a rapidly changing climate. To address this need, we examine regional patterns in winter baseflow, a metric for catchment groundwater storage, in 52 USGS-gauged watersheds in the Upper Colorado River Basin. Coherent 12- to 14-year patterns emerge in baseflow in 5 headwater regions, alongside an apparent 8-year periodicity in 2 regions, suggesting groundwater recharge is responding to a regional climate forcing. Subsequent analyses identified a statistically significant, positive relationship between antecedent winter baseflow and runoff efficiency (RE) in 22 of 24 headwaters with concurrent precipitation and streamflow data available since 1980. These relationships indicate that winter baseflow can be used to reduce uncertainty in RE and streamflow predictions months before snowmelt begins.

This study aims to characterize single-family household water consumption utilizing smart-metered subdaily water use data from more than 700 single-family households across the state of Arizona in the United States for the water year 2022. Using statistical evidence, we identify factors that drive household water consumption such as the number of occupants, appliance efficiency, and the presence of a swimming pool. Furthermore, climate and other regional drivers of water use are investigated. The analysis encompasses mixed-effects regression models to assess water use patterns on daily, weekly, monthly, and seasonal time-steps. The findings show that approximately 64% of water consumption in Arizona was used for outdoor purposes. Households with a swimming pool use approximately 56% more water overall than those without a pool. Even indoor water use is nearly 26% greater in households with a swimming pool. Deep analysis of smart-metered water use data offers greater insights into the efficiency levels of appliances in a household. Households with high-efficiency appliances use about 18.5% less water than households without high-efficiency appliances. Analysis indicates that log-linear mixed-effects regression models provide the most robust assessments for relating water consumption with household and regional factors. This study helps water managers identify and implement water conservation and demand reduction strategies in single-family neighborhoods.

Identifying agricultural production systems that balance economic viability, conservation of soil and water resources, and a clean environment requires significant time and financial investments. This paper presents the SWAT-MOEA, a user-friendly standalone tool, which couples the Soil and Water Assessment Tool (SWAT) with a Multi-Objective Evolutionary Algorithm (MOEA) to determine Pareto optimal solutions for sustainable watershed management within a reasonable timeframe. The SWAT-MOEA is unique compared with existing tools in that it is publicly available for download along with a manual, it is user customizable, and can be used for studies of varying spatial scales from field to very large watersheds and daily to annual temporal scales. This integration enables simultaneous optimization of multiple objectives for existing SWAT projects. The tool identifies the placement of management strategies that achieve optimal trade-offs among competing objectives. Key SWAT-MOEA features include the ability to create and execute management practices directly through the SWAT-MOEA interface and to select objectives that reflect regional priorities. Results are stored in Excel for efficient display. SWAT-MOEA creates and stores Pareto graphs and geospatial maps of suggested solutions. A case study in the Fort Cobb Reservoir Experimental watershed located in Oklahoma is provided to demonstrate its use. The SWAT-MOEA executable and user's manual are available at SWAT-MOEA.

This study investigates various operational strategies to enhance flood control in reservoirs, addressing the challenges of balancing flood mitigation and water supply demands in multi-purpose reservoirs. A comprehensive framework is introduced, which includes a pre-release model categorizing reservoirs based on system characteristics and simulates multiple operational scenarios. Using historical data and simulations for 11 reservoirs managed by the USACE Louisville District, the effectiveness of different operational policies is evaluated. Key findings indicate that pre-release operations, particularly with a 72-h lead time, significantly improve reservoir flood control by reducing end-of-flood water levels and shortening the recovery times for design seasonal flood events. A 24-h pre-release policy is identified as a practical solution, offering substantial improvements with low adverse impacts, making it suitable for regular implementation. Additionally, other operational strategies are assessed, and pre-release is suggested as the optimal approach for facilitating the transition of reservoirs from single to multi-purpose functions. This study underscores the importance of integrating flood forecasting with reservoir operational strategies, advocating for the refinement of release policies to account for unique reservoir conditions. These insights provide a foundation for optimizing reservoir operations, contributing to improved flood and water resource management.