Groundwater resources in Brazil: a review of possible impacts caused by climate change

Groundwater has a strategic role in times of climate change mainly because aquifers can provide water for long periods, even during very long and severe drought. The reduction and/or changes on the precipitation pattern can diminish the recharge mainly in unconfined aquifer, causing available groundwater restriction. The expected impact of long-term climate changes on the Brazilian aquifers for 2050 will lead to a severe reduction in 70% of recharge in the Northeast region aquifers (comparing to 2010 values), varying from 30% to 70% in the North region. Data referring to the South and Southeast regions are more favorable, with an increase in the relative recharge values from 30% to 100%. Another expected impact is the increase in demand and the decrease in the surface water availability that will make the population turn to aquifers as its main source of water for public or private uses in many regions of the country. Thus, an integrated use of surface and groundwater must therefore be considered in the water use planning. The solution of water scarcity is based on three factors: society growth awareness, better knowledge on the characteristics of hydraulic and chemical aquifers and effective management actions.

A new method for decision making in multi-objective optimization problems

Many engineering sectors are challenged by multi-objective optimization problems. Even if the idea behind these problems is simple and well established, the implementation of any procedure to solve them is not a trivial task. The use of evolutionary algorithms to find candidate solutions is widespread. Usually they supply a discrete picture of the non-dominated solutions, a Pareto set. Although it is very interesting to know the non-dominated solutions, an additional criterion is needed to select one solution to be deployed. To better support the design process, this paper presents a new method of solving non-linear multi-objective optimization problems by adding a control function that will guide the optimization process over the Pareto set that does not need to be found explicitly. The proposed methodology differs from the classical methods that combine the objective functions in a single scale, and is based on a unique run of non-linear single-objective optimizers.