Non-point contaminant source identification in an aquifer using the ensemble smoother with multiple data assimilation

Abstract

[EN] Proper identification of groundwater contaminant sources is vital to assess groundwater contamination. However, the majority of previous studies focuses on point source identification; only a few works have been conducted for non-point source parameter identification. Here, we employ the ensemble smoother with multiple data assimilation (ES-MDA) to simultaneously identify the spatial architecture of non-point contaminant sources and the related release information. Three different shapes of non-point contaminant sources are considered, an ellipse, a circle, and an irregular shape. We test the applicability of the ES-MDA for the simultaneous identification using three scenarios in a synthetic confined aquifer by assimilating concentration observations from alltime steps multiple times. The results demonstrate that the ES-MDA is capable to accurately identify both regular and irregular non-point contaminant source information; the accuracy of the identification can be improved by increasing the number of iterations.