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The Valley of Puebla aquifer (VPA), at the central region of Mexico, is subject to intensive exploitation to satisfy the urban and industrial demand in the region. As a result of this increased exploitation, a number of state and federal agencies in charge of water management are concerned about the problems associated with the aquifer (decline of groundwater table, deterioration in water quality, poor well productivity and increased pumping and water treatment costs). This study presents a groundwater management model that combines ''MOD-FLOW'' simulation with optimization tools ''MODRSP''. This simulation-optimization model for groundwater evaluates a complex range of management options to identify the strategies that best fit the objectives for allocating resources in the VPA. Four hypothetical scenarios were defined to analyze the response of the hydrogeological system for future pumping schemes. Based on the simulation of flow with the MODFLOW program, promising results for the implementation of the optimization of water quantity were found in scenarios 3 and 4. However, upon comparison and analysis of the feasibility of recovery of the piezometric level (considering the policy of gradual reductions of pumping), scenario 4 was selected for optimization purposes. The response functions of scenario 4 were then obtained and optimized, establishing an extraction rate of 204.92 millions of m 3 /year (Mm 3 /year). The reduction in groundwater extraction will be possible by substituting the volume removed by 35 wells (that should be discontinued) by the same volume of water from another source.

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A test for randomness based on a statistic related to the complexity of finite sequences is presented. Simulation of binary sequences under different stochastic models provides estimates of the power of the test. The results show that the test is sensitive t o a variety of alternatives to randomness and suggest that the proposed test statistic is a reasonable measure of the stochastic complexity of a finite sequence of discrete random variables.

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