The integrated management of river catchments is a challenge to be addressed by many water authorities, where some of them even today do not yet incorporate the aquifers’ status in decision-making. This situation has led to the depletion of many aquifers, deeply affecting the drinking water supply, agriculture, and industries. This challenge is combined with the difficulty of decision-making under highly uncertain long-term weather forecasts.
This thesis research proposes a new water management methodology for the Elqui River basin in Chile by using an optimization model aligned with the water authorities’ main objectives and additionally incorporating the aquifer criteria. The optimization model is validated by comparing the results obtained over the 2010–2020 period with the water management practices employed during the same period.
Furthermore, an analysis of the performance of the model using different moving window lengths is executed by the implementation of a Receding Horizon Control (RHC) methodology, evaluating how well the solution is by comparing it with the historical simulation over the same period. The latter is done by looking at the performance of the key optimization goals and using a RMSE and R2 analysis.
Finally, a weather generator was used to randomly generate weather data, based on the 30-year period between 1990 and 2020. The random weather conditions are incorporated in a hydrological model to translate weather data into water volume into the reservoir. Making use of the optimization model, the RHC methodology, and the weather generator, the proposed methodology is tested, enabling the simulation of the decision-making processes. The results are again compared with the water management practices employed over the simulation period.
The research concludes that the proposed methodology brings significant benefits to the aquifers’ status, with neglectable impact on the Desmarque values. Receding horizon (RH) length plays a crucial role, with a balance between achieving optimal results and avoiding computational delays, recommending a RH length of 360 days for best results. The stochastic weather generator effectively replaces unpredictable forecast data, yielding comparable results to real future weather conditions, with temperature and accumulated snowpack playing important roles.