Understanding and predicting physical clogging at managed aquifer recharge systems: A field-based modeling approach

Abstract

Managed aquifer recharge (MAR) techniques are in demand to cope with water scarcity challenges posed by climate change and groundwater overexploitation. One of the long-lasting technical issues associated with MAR systems is physical clogging. The intrusion and deposition of external fines during water recharge reduce the infiltration capacity of the site over time. Operation and maintenance (O&M) costs are experienced directly at the site to restore the original efficiency of infiltration rates. Thus, investors need reliable estimations of the risk of clogging during the planning of the site. As a rule, in MAR design, the main parameter of concern for physical clogging is the total suspended solids (TSS), and most clogging models rely on experiment calibrations in 1D sand columns. However, secondary processes can control the development and spatial distribution of physical clogging in field conditions. The proposed work aims to detect key clogging factors directly in the field and to model these processes for reproducibility at other sites. The fieldwork is conducted at the two-stage infiltration basin in Suvereto (Tuscany, Italy). Spatial factors are included in the analysis (i.e. basin topography) to explain clogging patterns in the field altered by erosion processes. The observed clogging profiles at two sampled locations exhibiting clogging are replicated by a mathematical model. Based on the computation of annual erosion rates in the pond and fines’ redistribution, the exceeding fines’ contents over depth are validated with an RMSE of 2.53% and 12.53%. The infiltration capacity of the site is estimated to reach a stable value of 90% of the initial infiltration capacity over 20 years, given the Suvereto basin features. The model's parameterisation from field measurements represents a great advantage over existing clogging models due to its transferability to other MAR sites. The assessment of the risk of clogging supported by field characterization and numerical modelling is cost-effective and assists the deduction of O&M schemes for MAR sites.