Abstract. The inverse problem of groundwater flow is treated with an automatic method that can produce several alternative solutions at once. During their joint optimization, these solutions can exchange information in order to maintain some diversity and thus avoid a systematic premature convergence toward a single local minimum. Although genetic algorithms are capable of doing this, they have not often been used in groundwater inverse optimization. First, a specific multipopulation genetic algorithm is developed. It is then tested on two synthetic cases of steady state flow with transmissivity values extending over 4 orders of magnitude. The first test is nonparametric and optimizes as many parameters as those used to define the reference case. The second test uses a sort of "pilot point" parametrization. The optimization is carried out on a limited number of perturbations that are interpolated and superimposed on an initial transmissivity field. In view of the good quality of the results, these initial attempts provide incentives to further develop genetic algorithms in groundwater inverse problems.
The purpose of the present paper is to investigate the hydrological components of the Aison River Basin in northern Greece. The orography of the area and the increasing irrigation needs require a specifically adapted hydrological model in order to address water management issues. With this aim in view, a parsimonious lumped simulation-optimization model with a snowmelt routine is elaborated in a monthly time step. The Nelder-Mead algorithm is applied for automatic optimization of the model parameters using the Nash criterion as an objective function. The model results are also evaluated by additional statistical criteria. In order to further reduce data uncertainty influence, annual actual evapotranspiration values are compared with those derived using three empirical methods (Turk, Coutagne and Schreiber methods). Model outputs were shown to be a good estimation of the hydrological cycle components, indicating that water losses represent almost 62% of the total precipitation volume.
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