A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management

Combined simulation–optimization (CSO) schemes are common in the literature to solve different groundwater management problems, and CSO is particularly well-established in the coastal aquifer management literature. However, with a few exceptions, nearly all previous studies have employed the CSO approach to derive static groundwater management plans that remain unchanged during the entire management period, consequently overlooking the possible positive impacts of dynamic strategies. Dynamic strategies involve division of the planning time interval into several subintervals or periods, and adoption of revised decisions during each period based on the most recent knowledge of the groundwater system and its associated uncertainties. Problem structuring and computational challenges seem to be the main factors preventing the widespread implementation of dynamic strategies in groundwater applications. The objective of this study is to address these challenges by introducing a novel probabilistic Multiperiod CSO approach for dynamic groundwater management. This includes reformulation of the groundwater management problem so that it can be adapted to the multiperiod CSO approach, and subsequent employment of polynomial chaos expansion-based stochastic dynamic programming to obtain optimal dynamic strategies. The proposed approach is employed to provide sustainable solutions for a coastal aquifer storage and recovery facility in Oman, considering the effect of natural recharge uncertainty. It is revealed that the proposed dynamic approach results in an improved performance by taking advantage of system variations, allowing for increased groundwater abstraction, injection and hence monetary benefit compared to the commonly used static optimization approach.

     

A methodology to assess the current situation in irrigated agriculture: An application to the village of Tauste (Spain)

Abstract

This paper presents a simple weighted goal programming model which can be used to predict the current allocation of agricultural enterprises in a particular geographical area. Moreover, it also shows how the approach proposed can be integrated into a multiobjective framework to measure the opportunity cost of maintaining the current situation in terms of several criteria relevant from a private and a public perspective. In this way, the opportunity cost of sustaining the current allocation of agricultural enterprises in the irrigated lands of Tauste (Spain) is assessed in terms of gross margin, employment, seasonal labour and water consumption.     

Welfare consequences of water supply alternatives in rural Tunisia

Many economic studies have addressed the issue of inefficiency of public water supply in rural areas and the potential for improved service by private companies. Many of these analyses focused on identifying either willingness to pay or comparing average service costs and prices paid by customers. This paper performs a welfare analysis of two water supply systems—public and self owned—in rural Tunisia. The paper calculates consumer and producer surplus and compares the performance of the two systems from a social point of view. Results suggest that both systems are inefficient, mainly because of a production level that is low compared with production capacity.     

Managed aquifer recharge using quaternary-treated wastewater: an economic perspective

An excess of 31 million m³/y of tertiary-treated wastewater is expected in Muscat, Oman, by 2015. This paper addresses the technical and cost estimation of managed aquifer recharge after reverse-osmosis treatment. The results indicate that the project is appealing from an economic perspective. The total cost varies between USD 0.353 and USD 0.550 per cubic metre, depending on the cost of electricity, the interest rate and the life span of the project. The project may face rejection from domestic users, who may be unwilling to accept mixing treated wastewater with the current water supply due to health risks. An alternative to indirect potable reuse is the installation of a separate network to service industrial users.     

A Hybrid Fuzzy-Based Multi-Objective PSO Algorithm for Conjunctive Water Use and Optimal Multi-Crop Pattern Planning

This paper focuses on extracting an optimal multi-crop pattern plan through multi-objective conjunctive surface-ground water use management. Minimizing shortages in meeting irrigation demands, maximizing groundwater resources sustainability and maximizing agricultural net benefits are the three main goals of the multi-objective optimization problem solved in this paper. A new robust fuzzy-based multi-objective PSO algorithm called f-MOPSO is adopted and modified to solve a three-objective real-world conjunctive use management problem presented in this paper after testing on standard test problems revealed f-MOPSO superiority as compared to the well-known multi-swarm vector evaluated PSO (VEPSO) algorithm. The f-MOPSO benefits from a well-organized Sugeno fuzzy inference system (SFIS) designed for handling multi-objective nature of the optimization problems. The unique performance of f-MOPSO is not only presenting the better final solutions, but also aggregating the capabilities for measurement of dominance and diversity of the solutions in one stage by one index named comprehensive dominance index, in contrast to a wide range of multi-objective algorithms that evaluate dominance and diversity in two separate stages resulting in excessive computational burden. The optimization model is carried out on a 10-year long-term simulation period, resulting in increasing irrigation efficiency i.e. decreasing water losses, decreasing water consumption per unit cultivated area and increasing water productivity compared to those similar criteria observed in actual operation in the study area. The wheat and rice crops were identified as the dominant crops, while the optimization model was the least interested to onion cultivation, assigning the least average cultivation area to this crop over the whole planning period.     

An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers

Aquifer recharge rates and patterns are often uncertain, especially in arid areas due to sporadic and erratic rainfall. Therefore, determining the optimal groundwater abstraction using classical approaches such as Monte Carlo Simulation (MCS) requires a large number of groundwater simulations and exorbitant computational efforts. The problem becomes even more complex and time consuming for regional coastal aquifers whose domains must be discretized using high-resolution meshes. In fact, even fast evolutionary multi-objective optimization techniques generally require a large number of simulations to determine the Pareto-front among the objectives. This study explores the performance of a Decision Tree (DT) approach for the generation of the Pareto optimal solutions of groundwater extraction. This paper applies the DTs for the optimal management of the Al-Khoud coastal aquifer in Oman. The learning process of the developed DT-based model uses the output of a numerical simulation model to assess the aquifer response based on different abstraction policies. The trained DT network then utilizes the NSGA-II to determine the Pareto-optimal solutions. The simulation show that the general flux pattern in the study area is toward the sea and the hydraulic head following a similar pattern in both best and worst recharging scenarios downstream of the studied recharging dam. Statistical tests showed a good correlation between the DT-based and simulation-based results and demonstrate the capability of the DT approach to obtain high-quality solutions by incorporating a large number of recharge scenarios. Moreover, the required runtime of the DT-based approach is extremely low (5 min) compared to that of the simulation-based method (several days). This means that including additional Monte-Carlo simulations can be readily done in few minutes using the obtained DTs, instead of the long computational time needed by the simulation-based approach.