Journal of The American Water Resources Association最新文献

To increase the effectiveness of conservation programs focused on reducing agricultural nutrient runoff and targeting management interventions, some have called for greater attention to the role of diversity in both management and physical context. To examine the independent and interactive effects of behavioral and physical heterogeneity on phosphorus loads, a sensitivity analysis was conducted using six different assumptions about distributions of phosphorus fertilizer application rates and soil test phosphorus (STP) levels for hydrologic response units in a SWAT model for the Maumee River Watershed. Results indicated that changing assumptions about behavior and STP levels can significantly affect estimated dissolved reactive phosphorus (DRP) loads and the level of disproportionality, which is a measure of the unequal distribution of pollutant loading. Placing the highest fertilizer application rates on fields with the most excessive STP produced 14% greater estimated DRP load and higher levels of disproportionality compared to a baseline model, where homogeneity in farmer fertilizer behavior and STP were assumed. In contrast, placing the lowest fertilizer application rates on the fields with the most excessive STP led to estimated DRP loads and level of disproportionality that were similar to the baseline model. Results from this analysis suggest that simplistic or uniform assumptions about behavior or STP levels may mask serious environmental risks in agricultural watershed models.

Assessing hydrological dynamics of wetlands is essential for understanding ecological services. This study utilized open-access Sentinel-2 satellite data to enhance conservation management by enabling near-real-time monitoring and assessment of hydrological dynamics in conserved lands across Nebraska, United States. Using machine learning and Google Earth Engine, this research classifies surface water cover rate for different conserved land sites in Nebraska in 2018–2021. The results of the study confirmed successful inundation performance in conserved wetland sites under Wildlife Management Areas (WMA), Wetlands Reserve Program (WRP), and Waterfowl Production Areas (WPA). The WMA sites had the highest inundated area rate of 16.41%, indicating active hydrological inundation of the core conserved land areas. The WRP and WPA sites reached a mean annual surface water cover rate of 8.07% and 7.51%, respectively, demonstrating occasional flooding or periodic inundation of core wetland areas but limited inundation coverages of the surrounding areas. The findings confirmed that wetland conservation practices are functioning very well on the sites with higher inundation rates, but hydrological restoration at the watershed scale could boost conservation performance for the entire conserved land areas. The findings of this research provide robust evidence for obtaining surface water inundation data, which is crucial for sustainable conservation assessment and achieving long-term goals in wetland monitoring, protection, and management.

This research investigates the capability of hydrological site design to mitigate inland flooding. Empirical data for a target watershed characterize interaction among three hydrologic components: stormwater detention ponds; seasonal wetlands; and soils/groundwater. Findings are (a) stormwater ponds' elevation change in response to precipitation events of a given magnitude varies sharply among storms, such that ponds' pre-event elevation and forecast precipitation are not reliable to predict ponds' ability to detain runoff sufficiently to avoid downstream flooding; (b) water table elevation is governed partly by long-term seasonal variation but also responds quickly to specific events, and powerfully affects the system's capacity to detain runoff; (c) water table elevation during wet weather periods common to Southwest Florida can be high enough to breach the soil surface for extended periods, severely reducing the capacity of the system to detain runoff; (d) in the target watershed of the Florida Gulf Coast University campus, depressed surface storage in seasonal wetlands compensates for reduced wet season capacity of ponds and soil storage. That mechanism explains why the campus has successfully mitigated flooding including from high-precipitation events most prone to produce flooding (intense rate, late wet season events), while some downstream communities with components designed to meet the regulatory minimum have experienced inundation.

Many cold water-dependent aquatic organisms are experiencing habitat and population declines from increasing water temperatures. Identifying mechanisms which drive local and regional stream thermal regimes facilitates restoration at ecologically relevant scales. Stream temperatures vary spatially and temporally both within and among river basins. We developed a modeling process to identify statistical relationships between drivers of stream temperature and covariates representing landscape, climate, and management-related processes. The modeling process was tested in three study areas of the Pacific Northwest United States during the growing season (May [start], August [warmest], September [end]). Across all months and study systems, covariates with the highest relative importance represented the physical landscape (elevation [1st], catchment area [3rd], main channel slope [5th]) and climate covariates (mean monthly air temperature [2nd] and discharge [4th]). Two management covariates (groundwater use [6th] and riparian shade [7th]) also had high relative importance. Across the growing season (for all basins), local reach slope had high relative importance in May, but transitioned to a regional main channel slope covariate in August and September. This modeling process identified regionally similar and locally unique relationships among drivers of stream temperature. High relative importance of management-related covariates suggested potential restoration actions for each system.

Altered evaporative demand is a global phenomenon observed over recent decades, however, such change has not been attributed explicitly to specific meteorological drivers, hampering consensus on what has caused such change. Here we investigate exactly how much individual drivers have contributed to long-term grass-reference evapotranspiration (ET_o) change within conterminous United States (CONUS), with an emphasis on agricultural croplands. Using scenarios that constrain individual drivers i.e., air temperatures (T), relative humidity (RH), solar radiation (R_s), and wind speeds (U₂) to their climatologies, we determined their relative contribution toward ET_o change at monthly and annual scales. Annual ET_o increased by 111 mm, or >2 standard deviations (SD) relative to the 1981–2000 baseline, accompanied by strong increase in R_s (2.7 SD), U₂ (2.5 SD), T (1.1 SD), and decreased RH (2.3 SD) in regions that account for one-third of calories produced in the U.S. Annual ET_o increase was attributed primarily to T (relative contribution of 36%), followed by R_s (29%), U₂ (18%), and RH (17%) with significant spatial and seasonal variability. During agriculturally critical summer months, R_s was the dominant driver with a 40%–50% relative contribution, and other three drivers were roughly equally important. These findings address demand-side of agricultural water use and imply long-term change in crop functions and performance, water security, and planning across aridity gradients.

Extreme weather and climate events pose significant risks to rural water and wastewater systems. We examine the vulnerability of the water sector to weather and climate extremes in rural, predominantly Indigenous and underserved coastal areas and analyze how networks support resilience. Drawing on the analysis of 39 web-based questionnaire responses and 19 interviews with rural water and wastewater managers and service providers in southern Louisiana and western Alaska, this article reports a range of interrelated historical, environmental, and social factors that influence vulnerability to extreme weather events. Formal and informal social networks serve multiple roles in building resilience. These roles include building technical and financial capacities, supporting emergency response and operational- to long-term planning, fostering data collection and monitoring, supporting information sharing and innovative research, and providing institutional support. Results from this research enrich our understanding of the social, relational, and networking processes that condition community resilience to extreme weather events.

Extensive streamflow data sources exist beyond the largest streamflow data provider in the United States, the U.S. Geological Survey. We developed and distributed a survey to about 300 individuals and organizations that collect streamflow data across the Pacific Northwest (Idaho, Oregon, Washington). We received 100 responses with 56% of those sufficiently complete to include in the analysis. From these responses, there are about 2000 streamflow monitoring locations in the region beyond the USGS monitoring network. The duration of record for gages is related to the size of the streamflow gaging network, with small and large networks generally operating monitoring locations for less than 5 years and more than 10 years, respectively. Quality assurance and quality control are variable across organizations, with 41% of respondents having at least two review steps and 13% that audit their data for long-term consistency. Results of this survey begin to establish the differing capabilities of large and small stream gaging networks and highlight how supporting the overall quality streamflow data collection and management within the water resources community will improve our ability to harmonize these datasets in the future.

There is growing interest in studying the impact of alternative agricultural management practices on runoff and soil loss under future climate change scenarios. In order to address this interest, it is important to demonstrate that runoff and soil loss can be accurately simulated under existing climates based on comparisons between modeled and experimental results. This study calibrates and validates the Water Erosion Prediction Project (WEPP) model to quantify the accuracy of predicting growing season runoff and soil erosion in agricultural hillslopes based on comparisons with experimental data from five Minnesota hydrologic unit code 12 watersheds. In order to accurately predict runoff and soil erosion in each watershed, the baseline effective hydraulic conductivity (K_be), interrill and rill erodibility (E_IR and E_R), and monthly precipitation standard deviations (P_stdev) were calibrated in WEPP using observed runoff and total suspended solids data from five Minnesota Discovery Farms field sites. Before calibration, Nash–Sutcliffe model efficiency (NSE) and percent bias (PBIAS) values for predicted versus measured monthly average total runoff (R_avg-T), runoff ratios (RR_T), and total soil loss were generally not in acceptable ranges. After calibration, the NSE values showed very good fits between measured and predicted monthly R_avg-T (0.64–0.98), RR_T (0.66–0.93), and soil loss (0.58–0.80). PBIAS values were also within acceptable ranges for R_avg-T and RR_T (±25%) and soil loss (±55%), except for RR_T at site BE1. NSE and PBIAS values during validation were within acceptable ranges, except for RR_T at site BE1. These findings suggest that the WEPP hillslopes calibrated in this study are sufficiently robust to accurately predict monthly runoff and soil erosion in Minnesota agricultural fields during the growing season.