Pathline Density Distributions in a Null‐Space Monte Carlo Approach to Assess Groundwater Pathways. Issue 2 (28th May 2019)

• Record Type: Journal Article
• Authors: Moeck, Christian; Molson, John; Schirmer, Mario
• Is Part Of: Ground water. Volume 58:Issue 2(2020)
• Page Start: 189
• Page End: 207
• Publication Date: 2019-05-28
• Subjects: Groundwater -- Periodicals; Wells -- Periodicals; Eau souterraine -- Périodiques; Puits -- Périodiques; Grondwater; Eau souterraine; Puits; Electronic journals; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 551.49
• DOI: 10.1111/gwat.12900
• Languages: English
• ISSNs: 0017-467X

Abstract

Abstract: A null‐space Monte‐Carlo (NSMC) approach was applied to account for uncertainty in the calibration of the hydraulic conductivity ( K ) field for a three‐dimensional groundwater flow model of a major water supply system in Switzerland. The approach generates different parameter realizations of the K field using the pilot point methodology. Subsequently, particle tracking (PT) was applied to each calibrated model, and the resulting particles are interpreted as the spatial pathline density distribution of multiple sources. The adopted approach offers advantages over classical PT which does not provide a means for treating uncertainty originating from the incomplete description of subsurface heterogeneity. Uncertainty in the K field is shown to strongly influence the spatial pathline distribution. Pathline spreading is particularly evident in locations where the information content of the head observations does not sufficiently constrain the estimated parameters. Despite the predictive uncertainty, the pumped drinking water at the study site is most likely dominated by artificially‐infiltrated groundwater originating from the local infiltration canals. The model suggests that within the well field, the central pumping wells could be extracting regional groundwater, although the probability is relatively low. Nevertheless, a rigorous uncertainty assessment is still required since only a few realizations resulted in flow paths that support the field observations. Model results should therefore not be based on only one model realization; rather, an uncertainty analysis should be carried out to provide a sufficiently large suite of equally probable simulations that include all potential sources and pathways. Abstract : Article impact statement : An inverse stochastic modeling approach with particle tracking provides probability information beyond classical deterministic approaches.