The role of different sources of uncertainty on the stochastic quantification of subsurface discharges in heterogeneous aquifers

Abstract

The quantification of subsurface discharges may significantly be affected by multiple sources of uncertainty, especially in highly heterogeneous aquifers. In this work, subsurface discharges within alluvial aquifers located in the province of Lecco (Lombardy, Northern Italy) are quantified relying upon a Monte Carlo (MC) framework under geological and conditioning data uncertainty sources. These are respectively employed through different geological conceptual models, whose facies spatial distributions are simulated through Sequential Indicator Simulations methodology (SISIM), and by conditioning stochastic facies simulations with different data constraints. Stochastic porosity fields are then achieved upon facies simulated fields by associating average porosity values to each local simulated facies. Then, stochastic subsurface discharge fluxes are quantified under steady hydraulic conditions upon saturated hydraulic fields, inferred from porosity ones. Then, the study of subsurface discharges is pursued via the Generalized Extreme Values (GEV) approach. This method enables both to interpret peak-over-thresholds (POTs) discharges spatial distribution, and to study the role geological and data uncertainty sources on their quantification, for example studying discharges conveyed towards a lake through the surrounding aquifer. These uncertainty-based analyses show that subsurface discharges clearly convey in correspondence of main alluvial aquifers, as well as the tendency to be collected along preferential pathways. Furthermore, the inspection of POTs empirical probability spatial distributions highlights that the geological uncertainty plays as a primary uncertainty source rather than the conditioning data. The latter plays a minor role, e.g. between different employed models, only in presence of a relevant geological difference between analyzed ones. These results are consistent with other kinds of information, such as hydrogeological sections and available piezometric maps, providing novel contributions to understanding how to quantify subsurface discharges in poorly monitored aquifers or how to treat various sources of uncertainty.