Experimental setup for scaling the dynamic of infiltration processes in karst aquifers.

Abstract

Advancing the knowledge of infiltration processes in karst aquifers is crucial for a better evaluation of water resources as an adaptation to climate change. Nevertheless, quantitative information on factors related to the variability of effective recharge is still a poorly explored domain in hydrological sciences, as most authors have focused on indirect methods for global recharge estimation. This work introduces a new experimental setup deployed in Sierra de Segura, a karst region in southern Spain, to achieve a better understanding of the dynamics of infiltration processes and subsurface flows at different -temporal and spatial- scales. Three automatic weather stations (WS), suited to mountain climate conditions, and four lysimetric plots (LP) were installed in selected sites with different characteristics regarding altitude, soil thickness, type of vegetation, etc., but also with a specific role in flow processes controlled by spatial scale. Each LP (400m2) contains an average of 45 single probes, vertically arranged in clusters of three (at 5, 10cm depth and soil-rock transition -average depth 25cm-), in randomly chosen profiles. Datasets from plots (soil-moisture and temperature with 15 minute step) were combined with hourly hydrodynamic and hydrothermal responses acquired at an epikarst and several karst springs. From the smallest to the largest scale processes, the main aims are: - To understand the shallowest soil heterogeneity and the partitioning of rainfall snowmelt/evaporation into infiltration. - Precise soil water budget components and evaluate ERT. - Define the transference of the input signal through the vertically distributed compartments of the aquifer . - Study recharge processes with a regional perspective and where duality in recharge mechanisms occurs. From all these results, numerical modelling tools will be developed for a reliable prediction of water resources in carbonate aquifers under different climate change scenarios.