A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments

作者：Franssen H J Hendricks^*; Alcolea A; Riva M; Bakr M; van der Wiel N; Stauffer F; Guadagnini A

来源：Advances in Water Resources, 2009, 32(6): 851-872.

DOI：10.1016/j.advwatres.2009.02.011

• Inverse modelling
• Aquifer characterisation
• Well catchments
• Comparison study
• Stochastic simulations
• Conditional estimation

Inverse modelling is a key step in groundwater-related hydrological studies. Several inversion techniques were developed during the last decades, but hardly any comparison between them was presented. We compare seven modern inverse methods for groundwater flow: the Regularised Pilot Points Method (both the estimation, RPPM-CE, and the Monte Carlo (MC) simulation variants, RPPM-CS), the MC variant of the Representer Method (RM), the Sequential Self-Calibration Method (SSC), the Moment Equations Method (MEM), the Zonation Method (ZM) and a non-iterative Semi-Analytical Method (SAM). These methods are applied to a two-dimensional synthetic example, depicting steady-state groundwater flow around a pumping well. Their relative performance is assessed in terms of their ability to characterise the log-transmissivity and hydraulic head fields and to predict the extent of the well catchment, both for a mildly and a strongly heterogeneous transmissivity field. The main conclusions drawn from the comparison are: (1) MC-based methods (RPPM-CS, SSC and PM) yield very similar results, regardless the degree of heterogeneity and despite they use different parameterisation schemes and objective functions; (2) statistical moments of the target quantities provided by MEM and RPPM-CE are similar to those of MC-based methods; (3) ZM and SAM are negatively affected by strong heterogeneity; and (4) in general, observed differences between the performances of all methods are not very large. MC-based inverse methods need considerably more CPU time than the other tested approaches. An advantage of MC-based methods is that they allow computing the posterior probability distribution of the target quantities, which can be directly fed to probabilistic risk-assessment procedures.

• 出版日期2009-6