Combining Dynamic Storage and Recovery Techniques with the Use of Smart Water Metering for the Multi-Period Aquifers Management in Qatar

Qatar typifies an arid environment and harsh climatic conditions with extremely limited groundwater resources. The lack of freshwater constitutes a major deterrent to its sustainable development. Groundwater resources, although only partially renewable, still contribute considerably to the total water budget. Evolved from the vital role of groundwater resources in meeting the national water demand, several projects have been initiated to recharge the groundwater systems and/or rationalize its consumption in the agriculture field. The major aim is to create strategic reserves of freshwater resources to meet water demands even during emergency situations. The Aquifer Storage and Recovery (ASR) represents a feasible technique for groundwater management. It consists in injecting water in the aquifer during the periods of water availability and then recovery of the water from the subsurface during the periods of high demand. However, the ASR processes have not been fully understood and examined in Qatar. Achieving successfully a ASR project will help in providing sufficient freshwater resources to meet the essential water demands for certain durations of time, in case of zero water or insufficient supply from desalination plants (sudden technical failure, seawater pollution, ..etc.) and also during peak demand periods (e.g., summer time). The outcomes of the ASR may suggest the need of injecting more water than the capacity of the currently existing wells. In this case, there will be necessity to expand the injection network by digging additional wells. Such a problem can be solved by employing the facility location techniques typically used in the field of logistics, while taking into account the hydrological properties of the land and of the wells and pumping technologies. Combined to such a problem, this project will also investigate the feasibility of adopting smart water meters to measure groundwater extraction in the agriculture sector that currently use 92% of the total abstracted groundwater. The farms consumptions will be then monitored by an online information system to ensure that the agriculture water extraction obeys to the ASR outcomes. Otherwise, further centralized decisions can be taken in order to avoid excessive groundwater pumping. A multi-period dynamic water planning approach allows a sufficient level of flexibility in order to take recourse actions on the extracted volumes whenever is needed. Unavoidably, applying the ASR outcomes to manage water resources in condition of scarcity will involve conflicts between the farmers and the decision makers. Developing solutions that can be implemented in practice is impossible without understanding the incentives and behavior of stakeholders. Game theory provides a strong framework for mapping the incentives of stakeholders, interpret their behavior, and design institutions that can facilitate the implementation of win-win solutions in systems involving multiple stakeholders with competing and often conflicting objectives. One of the commonly used approaches nowadays to solve the ASR planning problems is the Iterative Simulation-Optimization (ISO) approach. However, nearly all previous studies have employed ISO to derive static groundwater management plans. Such plans remain then unchanged during the entire management horizon, overlooking, thus, the possible positive impacts of dynamic strategies. Dynamic programming permits recourse decisions in different time periods based on the most recent knowledge of the groundwater system and its associated uncertainties. Problem structuring and computational challenges are the main factors preventing the widespread use of dynamic recursive strategies in groundwater applications. This project will address these challenges by introducing novel stochastic multi-period ISO approaches for the dynamic groundwater management. The outputs of the ISO approach will help in making insightful decisions for the long-term time horizon on the amounts of groundwater to be abstracted, injected and recovered from Qatari aquifers and the duration of each phase taking into account the stochasticity in data and emergency situations. Within the ISO scheme, the simulation module will be performed by developing an ad-hoc numerical groundwater flow model (based on MODFLOW) to simulate the dynamics of water table fluctuations and seawater intrusion at each interval along the planning horizon. On the other hand, several techniques will be adopted, tested and compared during the implementation of the optimization module. The research team will investigate the use of both conventional (i.e. multi-objective programming, facility location techniques and dynamic stochastic programming) and advanced (artificial intelligence) approaches to optimize the whole system. It is expected that any of the dynamic stochastic approaches should result in an improved performance allowing for increased groundwater availability and hence increased benefits compared to the existing static approaches. This project results to be multi-disciplinary in nature. The expertise form optimization and decision science will be integrated with knowledge in hydrology, resources economics and sustainable environmental policy in order to achieve the project objectives. To the best of our knowledge, this is the first work to integrate the dynamic aquifer management together with smart metering within the same framework while adopting recursive stochastic optimization and artificial intelligence. Its outcomes will represent a valuable contribution to the field of groundwater management and will draw useful guidelines to the decision makers on how to ensure a secure and sustainable groundwater balance for the long-term planning. This project results to be strongly aligned with Qatar National Vision 2030 and also with Kahramaa’s initiatives to ensure water security and rational use of the national natural resources.

Hamad Bin Khalifa University (HBKU)