We investigated, using model simulations, the changes occurring in the distribution of dense non-aqueous phase liquid (DNAPL) mass (Sn) within the source zone during depletion through dissolution, and the resulting changes in the contaminant flux distribution (J) at the source control plane (CP). Two numerical codes (ISCO3D and T2VOC) were used to simulate selected scenarios of DNAPL dissolution and transport in three-dimensional, heterogeneous, spatially correlated, random permeability fields with emplaced sources. Data from the model simulations were interpreted based on population statistics (mean, standard deviation, coefficient of variation) and spatial statistics (centroid, second moments, variograms). The mean and standard deviation of the Sn and J distributions decreased with source mass depletion by dissolution. The decrease in mean and standard deviation was proportional for the J distribution resulting in a constant coefficient of variation (CV), while for the Sn distribution, the mean decreased faster than the standard deviation. The spatial distributions exhibited similar behavior as the population distribution, i.e., the CP flux distribution was more stable (defined by temporally constant second moments and range of variograms) than the Sn distribution. These observations appeared to be independent of the heterogeneity of the permeability (k) field (variance of the log permeability field=1 and 2.45), correlation structure (positive vs. negative correlation between the k and Sn domains) and the DNAPL dissolution model (equilibrium vs. rate-limited), for the cases studied. Analysis of data from a flux monitoring field study (Hill Air Force Base, Utah) at a DNAPL source CP before and after source remediation also revealed temporal invariance of the contaminant flux distribution. These modeling and field observations suggest that the temporal evolution of the contaminant flux distribution can be estimated if the initial distribution is known. However, the findings are preliminary and broader implications to sampling strategies for remediation performance assessment need to be evaluated in additional modeling and experimental studies.