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We analyzed the influence of hillslope flow on projections of climate change by comparing two transient climate simulations with the IPSL climate model between 1980 and 2100. Hillslope flow induces a reorganization and increment of soil moisture (+10%), which increases evapotranspiration (+4%) and precipitation (+1%) and decreases total runoff (−3%) and air temperature (−0.1 °C) on an annual average over land for 1980–2010 when compared to simulation not representing hillslope flow. These changes in land/atmosphere fluxes are not homogenous and depend on regional climate and surface conditions. Hillslope flow also influences climate change projections. On average over land, it amplifies the positive trend of soil moisture (+23%), evapotranspiration (+50%), and precipitation (+7%) and slightly attenuates global warming (−1%), especially for daily maximum air temperature. The role of hillslope flow in supporting surface/atmosphere fluxes is more evident at a regional scale. Where precipitation is projected to decrease, hillslope flow is shown to attenuate the related declines in evapotranspiration, precipitation, and total runoff, regardless of aridity conditions and mean air temperature. Where precipitation is projected to increase, hillslope flow amplifies evapotranspiration enhancement but attenuates the increase in precipitation and total runoff. Warming is generally attenuated, especially in semiarid and cold areas, and humid and warm/temperate regions, but the signal is weak. These results demonstrate the role of hillslope flow in enhancing water and energy fluxes between the surface and the atmosphere. They also suggest that including hillslope flow in climate models would weaken the projected intensification of hydrological extreme events.

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