Journal of Water Resources Planning and Management

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Groundwater salinization caused by natural and anthropogenic processes can increase the salinity above the concentrations recommended for specific uses or environments. Approximately 16% of the total land area on Earth suffers from groundwater salinization, potentially creating significant social, economic, and environmental problems. Global bibliographical research focusing on studies related to the utilization and management of salinization-threatened groundwater was conducted. A database of 520 papers from 85 countries was compiled and discussed. The main salinization sources were identified, and seawater intrusion is the primary studied phenomenon. This paper classifies different state-of-the-art approaches for protecting groundwater resources from salinization. Practical scientific solutions, from technical and engineering solutions to rules or regulatory instruments, are discussed from a global perspective. As salinization risk becomes a relevant problem, more complex initiatives should be applied to meet water demands while minimizing disadvantages. From the lowest to the highest complexity, three approaches were defined: the engineering approach, the discharge management approach, and the water and land management approach. The discussion presents directions for future research opportunities.

Seasonal water supply forecasts are a critical component of regional water resources management planning. Across the United States, multiple modeling tools and operational protocols have evolved over time to address this need. Here, the authors document, assess, and recommend improvements to the current operational water supply forecasting protocols employed in managing flows through, and water levels across, Earth’s largest lake system: the Laurentian Great Lakes. The water resources management actions for this massive system are critically linked to North America’s economy, and to human health and safety, through planning and policy decisions related to hydropower management, commercial navigation, and water level fluctuations. The authors’ assessment indicates that existing operational seasonal water supply forecasting systems for the Great Lakes have moderate skill and that there are several high-priority areas for improvement, including improved representation of initial hydrological conditions, aligning variability in future meteorological forcings with climate-scale projections, and robust representations of forecast uncertainty.

With structural changes in power consumption, peak load demands and peak-valley differences are growing, which motivates peak shaving operations of hydropower systems. A multiobjective mixed-integer nonlinear programming model is presented for short-term hydropower generation scheduling that aims to regulate peak loads and enhance power efficiency. The combined feasible operation zones and the aggregate generation function of each plant are embedded in this model to deal with the operation constraints of units. A set of Pareto solutions of this model is generated by an improved normalized constraint method that narrows the optimization range to ensure the practicality and compact distribution of solutions, each of which represents an aggregated scheduling of cascaded hydropower plants. Then, a mixed integer nonlinear programming formulation is developed to solve the unit commitment problem for each solution, and a fuzzy-based membership value assignment method is employed to extract a best compromise solution for final implementation. A case study of the hydropower plants of the Beipanjiang cascade in China is used to test the proposed approach, verifying its success in generating a practical operating schedule while providing abundant information to decision makers for decision support.

Various studies have demonstrated that the long-term flow regime plays an important role in stimulating fish spawning. The flow regime is altered by the construction and operation of reservoirs. In this study, a flow-regime identification method is proposed, which extracts the rising hydrograph processes with a certain amplitude and duration through multiple passes of denoising and filtering algorithms to filter out random fluctuations with different amplitudes. The identified rising hydrograph processes were then quantitatively characterized using three parameters. A multiobjective optimization model was constructed to mimic the natural flow process in reservoir discharge and to maximize the output value of the power plant using the Nondominated Sorting Genetic Algorithm (NSGA-II). Finally, the proposed model was applied to the planning of a large-scale reservoir on the upper reaches of the Yellow River in China. The derived Pareto front in different hydrological years was analyzed using the analytic hierarchy process (AHP) method, and various operational schemes were evaluated on this basis.

Cascade reservoirs have interrupted the distribution and transportation of nutrients, which brings environmental and ecological problems. However, the current ecological operation methods focusing on the hydrologic regime cannot solve these problems. This paper presents an ecological optimal operation method that aims at restoring the transport pattern of biogenic substances. First, this paper establishes an optimization model that combines multiobjective reservoir operations with a dynamic mass balance calculation of total phosphorus (TP), taking the maximum TP export as the ecological objective, and a traditional model taking hydrologic regime as the ecological objective for further comparison. Then, the upper Mekong River Basin (i.e., the Lancang River Basin) is taken as a case study to investigate the competitive relationship between power generation and TP export under varying minimum outputs. The mechanism of TP export is then analyzed and the concept of optimum period for TP export is proposed. The results and analyses show that a competitive relationship between the power generation originates from the differences between the optimal operation processes for maximum TP export and maximum power generation. Lower water level, lower flow in nonflood season, and higher flow in flood season, especially in the optimum period for TP export are conducive to TP export. This method is then compared to the traditional method, which considers hydrologic regimes as the ecological objective under different conditions. The results show that reservoirs with higher sedimentation coefficient and larger capacity should consider more about the biogenic substances; otherwise, the hydrologic regime and biogenic substances can be substituted for each other to some extent.

An accurate hydraulic model of a water distribution network (WDN) is a critical prerequisite for a multitude of operational, optimization, and planning tasks. The accuracy of a hydraulic model can only be maintained through its periodic calibration and validation with acquired pressure and flow data from a WDN. It is important that this process be robust and computationally efficient. This paper describes the regularization of an inverse problem to deal with data uncertainties and ill-posedness of parameter estimation problems in WDNs. A novel data-driven strategy is presented for tuning the regularization hyperparameter for the inverse problem and also for validating the results on an independent set of operational hydraulic data. A limited-memory quasi-Newton method (L-BFGS-B) is implemented to solve the resulting regularized nonlinear inverse problem. Furthermore, the implemented method utilizes either the Darcy-Weisbach or Hazen-Williams head loss formulas and is investigated both with and without pipe grouping. An extensive experimental program was carried out to acquire unique hydraulic data from an operational WDN in order to investigate the robustness of the proposed parameter estimation method. The hydraulic model of the operational WDN and the acquired hydraulic data are provided as supplementary data to enable the comparison of hydraulic model calibration methods with operational data and encourage reproducible research.

In arid regions, sustainable groundwater management is crucial for the protection of vulnerable local ecosystems but often leads to conflict with the interests of economic development. Management models containing simulation-optimization techniques are widely used to explore sustainable strategies of conjunctive water use, but previous studies often failed to address the system’s complexity as a whole, characterized by many stakeholders, a heterogeneous flow regime, and uncertain driving forces. This article contributes to a many-objective robust decision-making (MORDM) framework that integrates a state-of-the-art multiobjective optimization algorithm (MOEA) with robustness-based decision analysis to advance the field’s knowledge of conjunctive water use. The framework is applied to the trans-regional Heihe River Basin in China, which is facing a number of consequences of unsustainable water management, jeopardizing the downstream ecosystem and agricultural development in the river’s midreach. Results show that the current water management scheme is unsustainable in two respects: It overexploits groundwater resources locally and it cannot fulfill the minimum ecological outflow target. The baseline can be further improved with optimized solutions, but an unresolvable conflict exists between the required environmental outflow and water demand for the irrigated agriculture scheme under the current policy of inflow-proportional water sharing with the downstream. The robustness assessment highlights the key driving forces and how they shape the robustness of the system under different policies and changing conditions. The case study sheds light on the potential of MORDM to solve complex conjunctive water management problems and shows how it can help to improve understanding of the problem itself.

There is growing interest from Afghanistan in using the untapped hydroelectric potential of the Kabul River Basin, on which both Pakistan and Afghanistan are simultaneously upstream and downstream riparians, to meet the energy demands of the country. This study evaluated the hydropower production potential and transboundary flow impacts of water infrastructure development on a major tributary of the Kabul, the Kunar River, under different climate and water management policy scenarios. An existing process-based distributed hydrologic model for the study region was coupled with an optimization-based reservoir operation and routing model developed for this study. Various scenarios representing climatic, water infrastructure development, and management futures were designed, and their relative impacts on energy generation and water availability in each country were evaluated. The results indicate that the reservoir operation objective is the dominant driver of systemwide hydropower and streamflow. Outcomes from potential water management scenarios where either country acts unilaterally are also illustrated. The results shed light on the potential benefits of joint development and operation of water infrastructure in the basin for both Pakistan and Afghanistan.

Frequent, extended, and high fluctuations of internal water pressure are suspected to be causally linked to water main failures. However, there is little solid quantitative evidence to support this for real water systems. This paper examines the influence, extent, and impacts of hydraulic pressure magnitude(s) on the physical integrity of water mains in five selected districts in California. The principal objectives of this study were to: (1) characterize spatial and temporal patterns of relatively high water main failure rates, and (2) statistically test for direct correlative linkages between observed high local pressure regimes and pipe failures. To accomplish these goals, eight years of data from the utility’s annual pressure survey (APS) was collected and analyzed. Areas with a consistently high failure rates showed notably stronger correlations between two variables, which reflects the role of pressure on increased failure rates and likely development of failure clusters. Weaker correlations, however, were observed when analyzing the entire district and nonfailure hotspots. These results can contribute to the development of more comprehensive long-term programmatic water main planning and management strategies.

This case study uses a long short-term memory (LSTM)–based model to predict short-term urban water demands for the Hefei City of China. The performance of the LSTM-based model is compared with the autoregressive integrated moving average (ARIMA) model, the support vector regression (SVR) model, and the random forests (RF) model based on data with time resolutions ranging from $15\min$ to $24\mathrm{h}$. Additionally, this paper investigates the performance of the LSTM-based model in predicting multiple successive data points. Results show that the LSTM-based model can offer predictions with improved accuracy than the other models when dealing with data with high time resolutions, data points with abrupt changes, and data with a relatively high uncertainty level. It is also observed that the LSTM-based model exhibits the best performance in predicting multiple successive water demands with high time resolutions. In addition, the inclusion of external parameters (e.g., temperature) cannot enhance the performance of the LSTM-based model, but it can improve ARIMAX’s prediction ability (ARIMAX is the ARIMA with variables). These observations provide additional and improved evaluations regarding the LSTM-based models used for short-term urban water demand forecasting, thereby enabling their wider adoption in practical applications.

Managing salinity in the Upper Delaware Estuary is an important operational goal within the Delaware River Basin (DRB). High salinity concentrations can create water quality and operational challenges which increase treatment costs for downstream water utilities and cause ecological damage. This study reviews the advantages and limitations of process based empirical models (PBEM) as an alternative to complex hydrodynamic models or statistical models (i.e., multivariate regression) for salinity management. PBEMs involve choosing a parsimonious form of equation(s) that logically reproduces important physical relationships. A PBEM was developed to model specific conductivity (SC) (proxy for salinity) at three locations within the DRB more than 50 years. The resulting models explain most of the variations in historic SC and give comparable performance to a much more complex hydrodynamic model. The PBEM was then combined with streamflow, tidal forecasts, and an error model to develop an operational tool for assessing salinity impacts of potential reservoir releases and for generating ensemble forecasts of chlorinity. The authors also document how such ensemble forecasts can be employed to generate probabilistic forecasts of future salinity levels under various water resource system operating assumptions.