A soil moisture monitoring network to characterize karstic recharge and evapotranspiration at five representative sites across the globe

Abstract

Abstract. Karst systems are characterized by a high subsurface heterogeneity, and their complex recharge processes are difficult to characterize. Experimental methods to study karst systems mostly focus on analysing the entire aquifer. Despite their important role in recharge processes, the soil and epikarst receive limited attention, and the few available studies were performed at sites of similar latitudes. In this paper, we describe a new monitoring network that allows for the improvement of the understanding of soil and epikarst processes by including different karst systems with different land-cover types in different climate regions. Here, we present preliminary data form the network and elaborate on their potential to answer research questions about the role of soil and epikarst on karstic water flow and storage. The network measures soil moisture at multiple points and depths to understand the partitioning of rainfall into infiltration, evapotranspiration, and groundwater recharge processes. We installed soil moisture probes at five different climate regions: Puerto Rico (tropical), Spain (Mediterranean), the United Kingdom (humid oceanic), Germany (humid mountainous), and Australia (dry semi-arid). At each of the five sites, we defined two 20 m×20 m plots with different land-use types (forest and grassland). At each plot, 15 soil moisture profiles were randomly selected and probes at different depths from the topsoil to the epikarst (in total over 400 soil moisture probes) were installed. Covering the spatio-temporal variability of flow processes through a large number of profiles, our monitoring network will allow researchers to develop a new conceptual understanding of evapotranspiration and groundwater recharge processes in karst regions across different climate regions and land-use types, and this will provide the base for quantitative assessment with physically based modelling approaches in the future.