Journal of Water Resources Planning and Management

This study introduces a novel sampling design (SD) method for hybrid Machine Learning/Transient-Based (ML/TB) leak detection of pipe networks. The proposed technique exploits the hydraulic responses of the network in the frequency domain and the concept of Filter and Wrapper feature selection as a machine learning approach. It also utilizes multiobjective optimization to handle the trade-off between leak detection error and number of sampling nodes. To apply this method, a transient hydraulic simulation model of the network is developed in the time domain. Then, considering a wide range of leak scenarios, the hydraulic responses of the network are calculated at candidate measurement sites. The responses are then transferred into the frequency domain using the Fast Fourier Transform (FFT) and stored as the train and test datasets. To reduce the dimensions of the initial feature vector, a threshold is applied to the responses to filter the very high frequencies. Finally, a classifier based on a Linear Discriminant Algorithm (LDA) coupled to a binary-coded Non-dominated Sorting Genetic Algorithm (NSGA-II) is applied to preprocessed datasets. Four sampling design methods are adopted from the literature and modified in the frequency domain for more investigations and comparisons. Solving two example pipe networks showed that the proposed method outperforms the existing approaches with respect to higher accuracy leak detection with fewer sampling sites.

Storm surge barriers, levees, and other coastal flood defense megaprojects are currently being proposed as strategies to protect several US cities against coastal storms and rising sea levels. However, social conflict and other political factors add a layer of complexity that casts doubt on their status as practical climate adaptation options. The specific mechanisms responsible for some projects not progressing beyond initial planning stages remains unclear. In this study, we examined the outcome of two USACE storm surge barrier proposals to explore the political reasons why some coastal flood protection megaprojects break ground in the US, while others do not. Using original archive research, we concluded that storm surge barriers are politically challenging climate adaptation options because of modern environmental laws that provide avenues for expression of oppositional views within the decision process and the allure of alternative options that are more aesthetically pleasing and cheaper and faster to implement. To better allocate public resources and utilize the expertise of USACE, future flood protection megaprojects should first achieve broad support from the public, nongovernmental organizations (NGOs), and elected officials before beginning serious planning. This support could be achieved through new innovative designs that simultaneously address adverse environmental impacts and provide cobenefits (e.g., recreation). New designs should be studied to better understand the level of protection offered and their associated reliability so that USACE has confidence in their use.

Water storage reservoirs can be either sustainable or exhaustible. In the absence of sediment management, reservoir storage is an exhaustible resource with long-term consequences. Previous economic planning of reservoirs essentially guaranteed non-sustainable solutions. This paper describes a new economic paradigm for economic assessments of new and existing reservoirs. The new economic paradigm provides a framework for comprehensive accounting of economic benefits and costs over a sufficiently long period of analysis, including cost estimates for dam decommissioning and lost benefits where sediments are not sustainably managed. A case study applies this framework to a hypothetical western US reservoir to quantitatively compare the status quo (i.e., no sediment management) with a selection of sediment management alternatives. Results indicate that sustainable sediment management can be less expensive than the consequences of ignoring sedimentation (e.g., eventual dam decommissioning and replacing lost storage capacity). Additional work shows that beginning sediment management within a decade of reservoir construction prevents the most severe impacts of sedimentation. An investigation of alternative discounting approaches indicates that the approach employed can have a significant impact on economic results.

The effectiveness of irrigation schedules and crop growth models is largely dependent on the accuracies associated with the most uncertain input parameter, viz. weather forecasts. This research is focused on quantifying the inaccuracies associated with the India Meteorological Department (IMD) issuing short-term weather forecasts (with 1–5 days lead time), and their propagation into irrigation models, with an objective to select the optimal parameters for use with simulation. While precipitation (P) forecasts were directly used, remaining meteorological forecasts were converted to reference evapotranspiration (${ET}_0$) forecasts. The effectiveness of the ‘P’ and ‘${ET}_0$’ forecasts was found to be low at all lead times. We applied two popular bias correction methods: linear scaling (LS) and empirical quantile mapping (EQM), and observed a marginal improvement in forecast skill. Bias-corrected, forecast-driven irrigation scenarios, along with conventional irrigation (that ignores weather forecast), and a hypothetical perfect 5-day forecast-based irrigation (as reference) system were tested on a water-intensive paddy crop for two growing seasons (S1: monsoon and S2: winter). Conventional irrigation resulted in the highest use of irrigation water (820 mm in S1, 880 mm in S2) and percolation loss (1,140 mm in S1, 680 mm in S2), while achieving a low relative yield (0.88 in S1, 0.87 in S2). LS-corrected forecasts outperformed other scenarios with $20.24\%\pm 4.21\%$ and $1.25\%\pm 1.51\%$ savings in irrigation costs for S1 and S2, respectively. While IMD forecasts greatly improved irrigation schedules in the monsoon season, their usage for winter crops was found to be trivial. Our findings conclude that LS-corrected IMD forecasts were moderately reliable over multiple lead times and can serve as a valuable addition to irrigation scheduling, provided the contribution of ‘P’ to the total water balance is significant.

Abstract

One of the biggest challenges in characterizing clandestine groundwater pollution sources is correctly estimating the number of such sources and their physical locations. Indiscreet underground disposal of toxic waste leading to groundwater pollution is widespread, and it is extremely difficult to detect the number of such sources and their locations. In this study, a real-life scenario of groundwater pollution in suburban New South Wales, Australia, is solved using a simulated annealing (SA)–based linked simulation optimization (LSO) technique with Benzene, Toluene, Ethylbenzene and Xylene (BTEX). The developed LSO considers the number of pollutant sources and their locations as explicit unknowns. This overcomes a crucial limitation in the earlier methods that limited the search to a few known potential source locations. The results show the applicability of the developed methodology to real-life groundwater pollution problems without making limiting assumptions about the number of sources or potential source locations being known, which has not been addressed to date.

Practical Applications

The developed methodology can potentially improve the process of remediation of polluted groundwater aquifers by simultaneously identifying the number of pollution source locations and release flux histories. The results of the study can be used to assist remediation engineers in developing an appropriate reclamation approach for a polluted aquifer, where knowledge of groundwater pollutant source characteristics is vital. The suggested method will aid in holding polluters accountable, contributing to predictions about the fate and movement of pollution, and developing a strategy for monitoring compliance. When implementing the results from the modeling technique to real-life scenarios of groundwater pollution, the equifinality of the solution cannot be entirely ruled out. The accuracy of the results is governed by assumptions and simplifications made in the model to simulate the aquifer response. Even the model calibration is dependent on the availability of data, which may be noisy and erroneous, resulting in nonunique solutions.

Water resources model development and simulation efforts have seen rapid growth in recent decades to aid evaluations and planning around water scarcity and allocation. Models are typically developed by two distinct communities: (1) large-scale hydrologic modelers emphasizing hydroclimatological processes, and (2) water systems modelers emphasizing environmental, infrastructural, and institutional features that shape water scarcity at the local basin level. This study assesses whether two representative models from these communities produce consistent insights when evaluating the water scarcity vulnerabilities in the Upper Colorado River Basin within the state of Colorado. Results showed that although the regional-scale model [model for scale adaptive river transport (MOSART)—water management (WM)] can capture the aggregate effect of all water operations in the basin, it underestimates the subbasin-scale variability in specific user’s vulnerabilities. The basin-scale water systems model [State of Colorado’s Stream Simulation Model (StateMod)] suggests a larger variance of scarcity across the basin’s water users due to its more detailed accounting of local water allocation infrastructure and institutional processes. This model intercomparison highlights potentially significant limitations of large-scale studies in seeking to evaluate water scarcity and actionable adaptation strategies, as well as ways in which basin-scale water systems model’s information can be used to better inform water allocation and shortage when used in tandem with larger-scale hydrological modeling studies.

Decreases in river flows and rapidly declining reservoir levels pose grave water security risks to urban water utilities reliant upon the Colorado River Basin. This study evaluated multidecadal trends (2000–2020) in urban water use for 28 utilities with a combined service population of 23 million people that depend in part or wholly upon water supplies from the river and its tributaries. It was found that more than half of cities surveyed have substantially decoupled their water demands from population growth by greatly reducing their per capita water use. Total water deliveries to the cities surveyed dropped by 18% even though their combined service population increased by 24%. Median per capita water-use rates for total and residential use decreased by 30% and 28%, respectively. The most common water conservation strategies included toilet replacement rebates and other plumbing retrofits, variable water-pricing structures, and turf removal. Given considerable variability in total per capita water use and rate of per capita reductions, many cities should be able to substantially reduce water demands to attain the per capita water-use levels of top performers. In contrast with overall declines in water use, utilization of the Colorado River Basin as a water source has not declined but instead increased by 1%. These results suggest that opportunity may exist for reducing pressure on the Colorado River Basin if per capita water-use rates continue to decline and utilities increase their use of other water sources—including water reuse, desalination, and stormwater capture as well as other rivers and aquifers—that can be prioritized over the use of Colorado River supplies.

Giant hydrounits are usually accompanied by multiple head-dependent forbidden zones (FZs). FZs intensify hydropower systems’ head effects and affect the safe and economic operations in a deregulated market environment, especially in southwest China. This paper addresses the self-scheduling and bidding strategies of a typical hydroproducer (the Longtan hydroplant, the leading plant on the Hongshuihe River, one of the most important plants in the West-East Electricity Transmission Project in China) with irregular forbidden zones (IFZs) in a day-ahead market. Considering the impacts of crossing IFZs and the head effects, a robust optimization-based approach to maximize profits was developed to establish market-oriented strategies under asymmetric price intervals and is solved by an iterative algorithm.The complicated model with nonconvex and nonlinear features was recast into a robust mixed-integer linear programming (MILP) model. Finally, the optimal self-scheduling of a hydroproducer with IFZs in southwest China was used to show the effectiveness of the proposed model. The optimization results demonstrated that the proposed model can effectively incorporate the head effects and the impacts of crossing IFZs to maximize profits; furthermore, the model significantly reduced the phenomenon of crossing IFZs and provided more executable, stable, and safe generation scheduling. Additionally, the proposed model can obtain more profitable optimal bidding strategies than the model that ignores the impacts of crossing IFZs.

Ethiopia’s agriculture-based economy generates highly seasonal outputs, with most production occurring during the long-rains Meher season and a lesser amount during the short-rains Belg season. Despite numerous studies detailing the economic impacts of Meher precipitation, there is relatively little consideration of other seasonal or subseasonal climate impacts in Ethiopia, with most economic models only considering aggregated annual-scale production to simulate the Ethiopian economy. This paper serves to address both seasonal and subseasonal effects of climate using a partial equilibrium agroeconomic model of Ethiopia. First, an annual-scale model is disaggregated into a seasonal time step, corresponding to the two major cropping seasons in Ethiopia. Second, the effect of yield gains from a subseasonal forecast–based planting strategy is considered in the updated model. We found that crop yields and corresponding economic indicators varied widely by season and location, and that subseasonal forecast–based management strategies can, on average, increase gross domestic product (GDP), per capita calorie consumption, and lead toward reduced poverty, warranting the inclusion of seasonal and subseasonal processes in economic models and agricultural planning.

Hydraulic models of large water distribution networks can have thousands of components, and real-time simulation of these systems can be slowed down by their complexity. Several methods have been developed for which the size of the water network models can be reduced while the hydraulic performance of the reduced systems remains very similar to the original model. Model reduction allows users to model only components of interest, thus saving expensive computation time. However, while model reduction has been widely adopted for control and optimization purposes, few tools are available to reduce models on command. This paper introduces MAGNets, an open-source Python package capable of reducing and aggregating EPANET-compatible water models using the variable elimination reduction method. The package allows the user to specify the operating point around which the model will be reduced, the nodes that must remain in the network, and the maximum nodal degree of nodes removed. The reduced model preserves pressure heads of the original model with high accuracy and results in faster running times. The reduction algorithm iteratively removes nodes from the full model and updates the adjacent nodal demands and pipe properties until the final reduced model is achieved. To test the effect of the order of node removal on the size of the reduced model and computational complexity, we tested three different strategies for reduction order. Results suggest that dynamically updating reduction order greatly improves model performance compared to static and random orders. To increase usability, the Python package includes twelve benchmark networks for testing and validation. MAGNets allows for the quick and efficient reduction of hydraulic models as a way to facilitate time-sensitive tasks, such as real-time state estimation and control.