This study presents an inverse modeling strategy for organic contaminant source localization. The approach infers the hydraulic conductivity field, the dispersivity, and the source zone location. Beginning with initial observed data of contaminant concentration and hydraulic head, the method follows an iterative strategy of adding new observations and revising the source location estimate. Non-linear optimization using the Gauss-Levenberg-Marquardt Algorithm (PEST++) is tested at a real contaminated site. Then a limited number of drilling locations are added, with their positions guided by the Data Worth analysis capabilities of PYEMU. The first phase of PEST++, with PYEMU guidance, followed by addition of monitoring wells provided an initial source location and identified four additional drilling locations. The second phase confirmed the source location, but the estimated hydraulic conductivity field and the Darcy flux were too far from the measured values. The mismatch led to a revised conceptual site model that included two distinct zones, each with a plume emanating from a separate source. A third inverse modelling phase was conducted with the revised site conceptual model. Finally, the source location was compared to results from a Geoprobe@ MiHPT campaign and historical records, confirming both source locations. By merging measurement and modeling in a coupled, iterative framework, two contaminant sources were located through only two drilling campaigns while also reforming the conceptual model of the site. © 2021 The Authors< Leer menos