Journal of The American Water Resources Association最新文献

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.

Fluvial geomorphology, which describes the form and processes of rivers, is increasingly being incorporated into river assessment procedures. However, the complexity of geomorphic processes makes a single universal and standardized assessment protocol a challenging and possibly impractical task. In this paper, we present a set of recommendations for choosing appropriate river assessment procedures and measuring geomorphic indicators to effectively capture important geomorphic processes required to support river management goals. We outline steps for building a river assessment procedure based on an adaptive approach rather than a one-size-fits-all approach, where the geomorphic indicators, spatial and temporal scale, and methodologies used are carefully chosen based on the goals of the management project; the assessment aims to support. Guidance for choosing the appropriate geomorphic indicators is based on their significance (usefulness in characterizing the system), ease of measurement, and temporal scale needs. We also present recommendations on measurement techniques for each indicator while highlighting recent technological and methodological advancements that help overcome resource challenges often faced in river assessment. Given the wealth of scientific and technological developments in the field of geomorphology, it is possible to improve how geomorphic form and function are measured and incorporated into river assessments that support watershed management goals.

Accurate simulation of evapotranspiration (ET) is essential to enhance efficient irrigation management in the maize field. Here, we evaluated the performance of four mathematical models for estimating the ET of maize. The four models based on surface resistance calculate ET from different vapor sources, which are Penman-Monteith (PM) through the “big leaf” model, the Shuttleworth-Wallace (SW) model for distinguishing between soil and canopy, the clumping (C) model for distinguishing between canopy, soils under the canopy and bare soil, and the seasonal clumping (Cj) model for dividing ET into transpiration of sunlit leaves and shaded leaves, evaporation of bare soil surface, sunlit soil surface of canopy gap fraction, and canopy shaded soil surfaces. The models were calibrated by ET measured from a weighing lysimeter, transpiration by the sap flux method, and soil evaporation by micro-lysimeters in 2014, 2015, and 2017. Results showed that the measured daily transpiration was 3.32 mm/day during the full-grown stage of maize, and the mean measured daily soil evaporation was 1.46 mm/day. The performance of the sap flow for transpiration plus micro-lysimeter for soil evaporation method was consistent with the large-weighted lysimeter method in measuring daily ET. For simulating versus measuring hourly transpiration, the Cj model performed better than the C model with a slope of 0.94, determination coefficient (R²) of 0.85, mean absolute error (MAE) of 0.08 mm/h, and modified agreement index (d) of 0.81. In simulating daily soil evaporation, the Cj model also had a higher slope and less MAE than the C and SW models. Nevertheless, the Cj model yielded increased slope and d and decreased MAE between simulated and measured daily ET. The most sensitive environmental factor in the Cj model is temperature. With a 50% increase in temperature, ET, transpiration, and evaporation increase by 45%, 36%, and 69%, respectively. In summary, the Cj model improved the accuracy for hourly and daily ET of maize and helped separate plant transpiration and soil evaporation, thus giving an available approach for precision irrigation in water management of maize planting systems.