Predicting the behavior of subsurface environments (e.g., groundwater flow and contaminant transport in groundwater) is subject to staggering uncertainties. The latter mainly arise because the subsurface is highly heterogeneous, and it is virtually impossible to characterize all of its details. Uncertainty can only be quantified via stochastic/probabilistic inverse modeling techniques instead of conventional model calibration schemes. A large variety of (stochastic) inverse methods is available in the literature. However, a conclusive and convincing assessment of their relative merits and drawbacks is still missing. This fact creates a challenging barrier to all current and future research efforts that seek to further improve inverse modeling. A key reason for this is the lack of well-defined benchmark scenarios against which diverse methods can be compared under standardized, controlled and reproducible conditions.
This project aims at overcoming this issue by defining a set of benchmark scenarios with highly accurate reference solutions. A community-wide comparison study based on these benchmarks and reference solutions is also planned. Benchmark scenarios, reference solutions and compared solutions will be made available to the research community on a long-term basis for continued future use. The developed benchmark cases will consider fully-saturated transient groundwater flow, low and high spatial variability and multi-Gaussian as well as non-multi-Gaussian hydraulic conductivity fields. Special dedication will be paid to calculating highly accurate reference solutions for the benchmark cases. The reference solutions will be produced with highly specialized algorithms developed during this project. The algorithms will be grounded on the preconditioned Crank-Nicholson variant of Markov Chain Monte Carlo, equipped with adaptive proposal distributions, multi-tempered parallel chains.