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.

As hydropower dam construction in rapidly growing economies dislodges communities, rural development experts must help the displaced make their livelihoods in new lacustrine environments. One question is whether the dam infrastructure can directly benefit those who remain within the vicinity of the reservoir. Integrated water resource management seeks to concurrently consider hydrological, socioeconomic, and ecological factors, yet water managers lack the information needed to include livelihoods in their analyses. The objective of this paper is to develop tools and plans for coordinating hydropower reservoir operation and management for rural livelihoods. Specifically, this study investigates how dam management may accommodate vegetable farming on the banks of a reservoir. The intervention investigated is to lower water levels during the cultivation period in order to expose shoreline gardens. Based on the recession agriculture rule, evaluated through simulation of a dam in Lao People’s Democratic Republic, the average annual hydropower production was reduced by between 0.4 and 8.1%, depending on the agricultural goal, with the loss to power occurring mainly in the months April to June. By focusing on hydropower reservoir systems, the techniques developed in this study have the potential to be applied to support communities throughout the world that farm on the shorelines of water reservoirs.

This article presents a new method based on the influence function approach to predict complex ground movement patterns arising from multiple groundwater pumping stations. The method makes use of the experience and prediction techniques that are used by the mining industry for land subsidence estimation. The analysis of land subsidence induced by groundwater extraction involves the evaluation of factors that are often rather complex and practically difficult to assess. Existing approaches to the land subsidence issue are limited in their ability to address effects of geological variation, multiple pumping, and making use of monitoring data. In this article, simplified key input parameters such as equivalent extraction thickness and angle of draw are discussed in relation to the effects of a typical groundwater drawdown profile. The similarities of land subsidence between underground mineral extraction and groundwater extraction are hypothesized, and the application process of using the methodology for horizontal movement as well as vertical settlement calculation is described. Examples are presented to demonstrate the flexibility and adoptability of the method in comparison with other existing methodologies, including presentations of vertical subsidence contours, horizontal movement vectors, and principal strain distributions over the entire affected area. This article shows that the new method is an invaluable tool in assessing groundwater-extraction-related subsidence effects on proximal surface structures and utilities.

In this study, a model predictive control (MPC) system is developed for the goldfield and agricultural water system (GAWS) east of Perth in Western Australia. As part of the study, four months’ water quality and hydraulic data of the system were collected for the development of the MPC system. Two artificial neural network (ANN) models are developed to model the relationships between the control variable, the ammonia dosing rate at the source, and the controlled variables, the total chlorine and free ammonia levels at a designated location (Goomalling pump station) in the network five days later. A two-step process based on both mutual information (MI) and partial mutual information (PMI) is used to select appropriate inputs for the total chlorine and free ammonia models. The total chlorine and free ammonia ANN models perform well, with validation Nash-Sutcliffe efficiencies of 0.84 and 0.62, respectively, and validation root mean square errors (RMSE) of 0.1320 and $0.0106\mathrm{mg}/\mathrm{L}$, respectively. A real-number coded genetic algorithm is then used to find the optimal ammonia dosing rate to achieve the target total chlorine and free ammonia levels at the modeled location. The results demonstrate that the developed MPC system can control the total chlorine and free ammonia levels at Goomalling pump station to be close to their target values by adjusting the ammonia dosing rates at Mundaring pump stations. The errors in the MPC system are mainly due to the relatively weak relationship between the control and controlled variables for this particular system.

Water scarcity and rapid economic growth have increased the pressure on water resources and environment in Northern China, causing decreased groundwater tables, ecosystem degradation, and direct economic losses due to insufficient water supply. The authors applied the water value method, a variant of stochastic dynamic programming, to optimize water resources management in the Ziya River basin. Natural runoff from the upper basin was estimated with a rainfall-runoff model autocalibrated using in situ measured discharge. The runoff serial correlation was described by a Markov chain and used as input for the optimization model. This model was used to assess the economic impacts of ecosystem minimum flow constraints, limited groundwater pumping, and the middle route of the South–North Water Transfer Project (SNWTP). A regional climate shift has exacerbated water scarcity and increased water values, resulting in stricter water management. The results show that the SNWTP reduces the impacts of water scarcity and impacts optimal water management in the basin. The presented modeling framework provides an objective basis for the development of tools to avoid overpumping groundwater resources at minimum costs.

The United States and Brazil both deal with water-related problems associated with being large territorial areas having uneven distribution of water resources and population. Water transfer projects have been widely considered to be feasible solutions to the mitigation of local water shortages. This paper contrasts American and Brazilian water allocation systems and water transfer projects, located in the Colorado and Piracicaba River basins, seeking potential exchanges between these two water management systems and analyzing their adaptability to trends in water demand and climate. This evaluation indicates that the American system could potentially benefit from some of the principles present in Brazilian framework, including (1) participatory approach involving government, users, and citizens; (2) recognition of the economic value of water; and (3) prioritization of drinking water supply during shortage times. In turn, the Brazilian system could benefit from certain characteristics of American water management, including reduced bureaucracy and a more efficient decision and operation process. Interestingly, both countries have found it useful to employ soft-path solutions to ensure system adaptability to future water demand and supply.

Human-induced climate change caused by greenhouse gas (GHG) emissions has become a significant concern. Although water distribution systems (WDSs) provide an essential service, they also contribute to the release of GHG emissions through the use of electricity from fossil fuel sources for pumping purposes. In this paper, the reduction of both costs and GHG emissions associated with the pumping operation of WDSs is considered. Actual (time-varying) emissions factors (EFs) for the South Australia electricity grid from February 2011 to January 2012 with a 5-min time step are used to evaluate pumping operational GHG emissions and are compared with the use of an average EF, which does not consider the time dependency of EFs. An estimated (typical) 24-h EF curve, which aims to replicate the important aspects of the time dependency of actual EFs, is developed and compared for use in place of actual EFs, for when the actual variations in EFs cannot be accurately predicted for the future. Additionally, modified estimated 24-h EF curves, representing different amounts of renewable energy (wind generation) penetration, are considered to test the sensitivity of solution development to the magnitude of the variations of time-dependent EFs. Through the multiobjective optimization of pumping operations of a case study WDS, it is shown that solutions found using actual EFs can minimize GHG emissions by moving pumping to low-EF times of the day. Conversely, solutions found using an average EF can only minimize GHG emissions by pumping more consistently during the day. Additionally, solutions found using the estimated 24-h EF curve are very similar to those found using the actual EFs, suggesting that the estimated 24-h EF curve can accurately replicate the important characteristics of the time dependency of EFs and can be used in place of actual EFs to find solutions of reduced pumping operational costs and GHG emissions. Furthermore, solutions found using the modified estimated 24-h EF curves show that the development of solutions is dependent on the magnitude of the variations of time-dependent EFs.

This paper contributes to the limited literature on the estimation of the total economic value of groundwater using a discrete choice experiment (DCE). An outline of theory and methodology is provided for the organization and application of experiments in the water sector, along with important references on optimizing outcomes and on utilizing previous valuation approaches into the design. The case study area was Viotia, Greece, and in particular the Asopos River basin aquifer, which has been in the center of international attention for the past 10 years, mainly owing to hexavalent chromium pollution. The research results give valuable insight to the respondents’ social profile and to issues valued most in cases of groundwater remediation, and perhaps more importantly to the willingness to pay for various remediation scenarios. In addition, the paper addresses other issues related to DCE, including econometric model appropriateness, selection, and comparisons; treatment of protest responses; and modeling of heterogeneity. The elicited values can be effectively used in policy making, natural resource damage assessment, and remediation planning.

The multidimensional dynamic programming (MDP) algorithm is a traditional method used to solve cascade reservoir operation optimization (CROO) problems, but the high dimensionality called the curse of dimensionalitycannot be ignored. In order to alleviate this problem, this paper proposes a new MDP algorithm named multilayer nested multidimensional dynamic programming (MNDP), which is based on a multilayered, nested structure. MNDP is mainly used to deal with computer memory space and computation complexity problems of MDP in CROO, and its recursive equation of reverse recursion calculation and specific calculation steps are presented in detail. This paper takes the cascade reservoirs of the Li Xianjiang River in China as an example to solve the CROO problem with the proposed MNDP. By comparing with the dynamic programming with successive approximations (DPSA) method, MNDP presents better performance in terms of power generation and the assurance rate in wet, normal, dry, and average years. The global optimality of MNDP is validated by MDP, and the parallel computing results of MNDP are shown in a case study. The MNDP proposed in this paper can reduce not only the programming complexity of MDP, but also the storage of intermediate variables during calculation, thus effectively solving the curse of dimensionality of MDP in CROO and keeping the global convergence feature of MDP.

With hard-to-predict changes in future demand, climate, supply options, technological opportunities, and budgetary constraints, water agency plans should be flexible and robust, designed to meet agency goals over a wide range of plausible future conditions. But current state-of-the-art approaches to water planning make it difficult to craft flexible and robust plans to guide resource allocation and facilitate discussions with the agency’s constituents and ratepayers. This paper describes an innovative effort by one agency, the Metropolitan Water District of Southern California (termed Metropolitan), to begin to address these challenges. Metropolitan’s year 2010 integrated resources plan (IRP) update specifies resource allocations over 25 years and calls for an adaptive management approach to revisit these allocations over time. Using a quantitative decision support approach called robust decision-making (RDM), an enhanced version of Metropolitan’s main planning model was run, over many thousands of cases representing different combinations of assumptions about future demand, conditions in the bay/delta, climate conditions, local resource yields, and implementation challenges. Statistical cluster analysis on the resulting database of model runs identifies scenarios that succinctly summarize the types of future conditions in which the IRP core resources strategy does and does not meet its goals. These scenarios inform early warning indicators that can guide the adaptive management component of the IRP. The robust decision methods presented in this paper should prove broadly useful for Metropolitan in addition to other water agencies seeking to develop robust and adaptive plans in the face of uncertain future conditions.