Journal of The American Water Resources Association最新文献

Groundwater serves as a primary source of public-water and agricultural supply in many areas of Alabama, in particular during drought periods. Long-term climatic models for the southeastern United States indicate that the region will be subjected to more intense and more frequent precipitation events, with no overall change in the amount of precipitation, resulting in increased runoff and reduced aquifer recharge. Reliable prediction of groundwater levels would be beneficial to water resources decision makers and stakeholders especially for time-sensitive decisions. This paper uses a compound application of continuous wavelet transform (CWT) analysis and long short-term memory (LSTM) framework to address the major question with regards to groundwater level: “how long does it take for groundwater to respond to major precipitation events and what is the magnitude of the response?” CWT analysis is used to answer the “how long” part in this question, while the LSTM is used to answer the “what is the magnitude” part of the question. The insights from CWT analysis related to the short-term and long-term response in groundwater level were used to set the parameters of the LSTM model. The LSTM model uses daily groundwater levels, precipitation, and maximum/minimum temperatures as input data. The model was able to provide predictions within a 95% confidence interval of actual groundwater levels. The findings of this study suggest a workflow for groundwater level forecasting in the wells of Alabama given a minimum amount of easy-to-measure and widely available data.

In this study, a new remote sensing tool was used in conjunction with sampling of river bank sediments to map channel migration patterns and estimate the net contribution of bank erosion to the sediment and phosphorus (P) budget of the Nishnabotna River in southwestern Iowa. Between the years 2009 and 2018, we found that at least 1.81 ± 0.57 × 10⁷ Mg of sediment and 8.26 ± 2.5 × 10³ Mg of P entered the Nishnabotna River due to channel migration. This equates to 0.87 Mg of sediment per meter of channel per year and 0.40 kg of P per meter of channel per year. Barring additional deposition elsewhere in the river corridor, these values represent as much as 77% of annual suspended sediment and 46% of the annual P export from the watershed. Our results also indicate that the contribution of net sediment and P volume loss by stream order increases sharply from third to sixth order, even though the total channel length is much smaller in the higher orders. These results suggest that bank erosion is an important source of sediment and P within the watershed and that future attempts to decrease riparian exports of sediment and P should focus on high-order reaches.

This paper proposes a modified conditional value-at-risk interval two-stage stochastic programming coordination model (MCITSP) for water allocation and illustrates its advantages in risk aversion and pollution control. We analyze its performance in maintaining the equity of water use in various sectors, which is specifically reflected in the water satisfaction of multiple users. In this paper, the MCITSP model and original ITSP model are applied to the case of the Hanjiang River Basin, and three scenarios of water availability are set up to provide theoretical support for water allocation. Our results show that the MCITSP model with a higher risk coefficient has a stronger ability to avoid risks. The MCITSP model simultaneously controls pollutant discharge and guarantees economic benefits, making it superior to the ITSP model under different scenarios. Water shortages primarily affect the agricultural sector, due to its high water demand and low economic value, and the MCITSP model plays a positive role in maintaining equity and coordinating water conflicts among multiple users. Managers can choose appropriate model parameters according to their preferences to formulate more reasonable decisions.

Water use efficiency (WUE) is critical for conserving water resources and protecting the environment, yet the lack of quantitative analysis of its spatial spillover effects and the spatial heterogeneity of its influencing factors hinders improving and achieving the spatial equilibrium regulation of regional WUE. In the current study, a way is proposed to explore the spatial pattern and driving forces of regional WUE from spatial spillover and heterogeneity perspective, in which the Improved Super-slack-based Model is firstly applied to estimate the WUE, and then, the spatial Markov chain and the geographically and temporally weighted regression model were, respectively, used to explore the spatial spillover effects of WUE and reveal the spatial heterogeneity of the driving forces behind the WUE. Guangdong Province, a region with rapidly developing economy and significant uneven development in China, was chosen as a case study. Results show that there is a radial pattern of high WUE in the Pearl River Delta and there are significant spatial spillover effects among cities, the city with higher WUE exerted positive effects on its neighboring city's WUE. The key driving factors of WUE include per capita GDP, per capita water consumption, proportion of secondary and tertiary industry water use, foreign direct investment, and R&D input, with spatial heterogeneity in their influences. Policies such as enhancing public awareness of water saving, increasing the reuse of wastewater in industrial parks, and promoting the inter-municipal socioeconomic and technological exchanges are recommended to achieve a more coordinated and balanced regional WUE. The results of this study have scientific and practical implications for coordinating regional water resources exploitation and sustainable development.

This study identifies and summarizes potential risks to operations, regulatory compliance, supply chains, and infrastructure of the electric power industry from changing surface water conditions resulting from global climate change. The results help inform companies/utilities seeking to incorporate climate change risk in their planning and decision-making processes by ranking risk severity and likelihood of occurrence on both a regional basis and by risk receptor. The assessment includes identification of potential risks to: (1) thermal generating, (2) hydroelectric, (3) land-based renewable generating, and (4) transmission and distribution assets. These risks may result from such projected changes as reduced water availability (e.g., for hydroelectric or once-through cooling), increased water temperatures (e.g., decrease in cooling efficiency, inability to meet discharge permit conditions), increased flood severity (e.g., increased streambank erosion and/or damage to river-adjacent infrastructure), and decreased water quality (e.g., from increased transport of sediment and dissolved solids). The potential risks identified from this qualitative risk-assessment are documented in a graphical format depicting both severity and likelihood. This approach allows for comparison of risks across a portfolio and for future prioritization of adaptation strategies. A total of 32 risks were identified in the study, including nine risks to infrastructure, six risks to operations, four risks to supply chain, and 13 environmental/regulatory risks.

In recent decades, the western United States (U.S.) has experienced increasing magnitudes and frequencies of natural land cover disturbances that impact water budget partitioning. Post-disturbance hydrologic response is often variable at the stream outlet and is difficult to detect and quantify with traditional before–after control–impact studies. This study uses a modified version of the U.S. Geological Survey's Monthly Water Balance Model (MWBM) to simulate and separate the hydrologic response to several forest disturbances, including (1) wildfire, (2) forest conversion (subalpine to mid-elevation forest) and (3) a climate that is hotter and drier than present in the Rio Grande Headwaters (RGH) in Colorado, U.S. (this climate scenario was derived from an ensemble of climate scenarios from the Coupled Model Intercomparison Project phases 3 and 5, which were selected based on water stress potential in the state of Colorado). We leverage historic post-disturbance vegetation data in the RGH to add quantitative vegetation representation to the MWBM, then modeled synthetic future (2021–2050) streamflow scenarios as both single and compound disturbances. Relative to a baseline scenario, modeled scenarios predict several changes to average annual water trends over the final simulation decade (2041–2050); (1) decreases in average annual water yield under a hot and dry climate (−14%), except during the rising limb of annual snowmelt; (2) increases in average annual water yield (+32%) and peak runoff under a fire simulation; and (3) increases in average annual water yield (+24%) along with earlier and higher peak runoff under compound (fire + hot/dry) conditions. These findings show the strengths of hydrologic models in separating compound disturbance signals at the stream outlet and a need for quantitative vegetation representation within models to adequately represent dynamic disturbance conditions.

Institutional authority and responsibility for allocating water to ecosystems (“ecologically available water” [EAW]) is spread across local, state, and federal agencies, which operate under a range of statutes, mandates, and planning processes. We use a case study of the Upper Missouri Headwaters Basin in southwestern Montana, United States, to illustrate this fragmented institutional landscape. Our goals are to (a) describe the patchwork of agencies and institutional actors whose intersecting authorities and actions influence the EAW in the study basin; (b) describe the range of governance mechanisms these agencies use, including laws, policies, administrative programs, and planning processes; and (c) assess the extent to which the collective governance regime creates gaps in responsibility. We find the water governance regime includes a range of nested mechanisms that in various ways facilitate or hinder the governance of EAW. We conclude the current multilevel governance regime leaves certain aspects of EAW unaddressed and does not adequately account for the interconnections between water in different parts of the ecosystem, creating integrative gaps. We suggest that more intentional and robust coordination could provide a means to address these gaps.

Seasonality and a changing climate exert strong influences on supply and demand in the western United States, challenging municipal water system (MWS) management. Although supply and demand exhibit characteristics of nonstationarity, the commonly used econometric-based models to estimate demands discount the influences of climate variability and trends in seasonal MWS vulnerability assessments. Given the projected impacts of climate change on water resources, we use the documented performance of a real-world MWS with a calibrated systems model to investigate how demands modeled with and without the influences of climate impact system vulnerability indicators—determined by the exceedance of historical daily mean imported water—for MWS planning guidance. Neglecting climatic influences on MWS demands, the model overestimates the volume of imported water by up to 50% and misclassifies vulnerabilities during supply-limiting conditions. The climate-sensitive demand estimates reduced model error (i.e., <3% error) and correctly categorized vulnerabilities. Moreover, the MWS exhibited an average threefold greater sensitivity to percent changes in demand relative to percent changes in supply. The sensitivity to variances in demand emphasizes the need to account for factors influencing supply and demand when investigating the impacts of a changing climate, suggesting future research to examine the coupled influences of modeled supply and demand accuracy on MWS performance.

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.

A better understanding of modeled streamflow errors related to basin reservoir storage is needed for large regions, which normally have many ungaged basins with reservoirs. We quantified the difference between modeled and observed streamflows for one process-based and three statistical-transfer hydrologic models, none of which explicitly accounted for reservoir storage. Streamflow statistics representing low to high flows, seasonality, annual variability, and daily autocorrelation were examined at 1082 study basins across the conterminous USA. All models increasingly overpredict (or decreasingly underpredict) observed annual maximum flows with increasing storage. Correlations between absolute values of errors for low-flow statistics and storage are often larger in magnitude than those for signed errors—additional storage is associated with increases in model errors in both directions even when its overall effect in one direction is weak. The rate of increase in absolute values of model errors was nonlinear for most statistics. For low flows, model errors had a change point to larger errors at 48 days of reservoir storage (relative to long-term mean daily flow); mean and high flows had change points at 147 to 176 days. We present predicted-to-observed errors for nine streamflow statistics over a large range of reservoir storage to help modelers and users of modeled streamflow understand the amount of storage for which explicit reservoir modeling is needed.