Hydrogeological Modeling in Tropical Regions via FeFlow

hydrological modeling is commonly crossed by the solution of inverse problems and the estimation for non-linear parameters techniques. Despite this common scenario, the use of these guidelines is limited to the proper sampling of in-field data. This sampling involves a variety of data that generally have little availability, especially in regions where geographical and climatic variability does not allow a constant measurement. In this article, we present the analysis of a regional underground flow model using two techniques: pilot points (PP) and constant zones (CZ). This methodologies allow identifying properly if there are any biased parameters and heterogeneity of hydraulic properties. For this purpose, we developed a numerical variable density model that is limited with reinterpreted data from real measurements. For the CZ technique, the initial parameters are assigned according to its layer, and every layer is considered constant for parameter values; in contrast for PP technique, the initial parameters are assigned according to interpolations using in-situ point measurements. The developed model was applied in an area under the influence of the ITCZ, located in the middle valley of Magdalena (MMV). This area is important on the development of the country due to its contribution to GDP and has been subject to significant changes in land use, as a result of intense economic activities, for example, agriculture, hydroelectric power, and production of oil and gas. The established model shows a scarce link with the observed state variable (hydraulic head -K), this proves the importance of spatial heterogeneity in K. The model is calibrated in order to establish K (as an anisotropic variable that varies spatially), the porosity (η) and the specific storage capacity (Ss) in the PP and CZ, reducing a “mean square” error of state variable dependable on the observation points. The results show that the PP system approach provides a better heterogeneity representation and shows that each parameter is sensitive, and does not depend on other parameters, giving to the parameter evaluation results factual independence and authenticity. This research compiles a methodology to assertively restrict a highly parameterized inverse model with field data to estimate aquifer parameters that vary spatially at a regional scale