Increasing knowledge of the transmissivity field of a detrital aquifer by geostatistical merging of different sources of information

Acknowledgements The work reported here was supported by research project PID2019-106435GB-I00 of the Ministerio de Ciencia e Innovación of Spain. We thank Philippe Renard and the two anonymous reviewers who provided constructive comments that have allowed us to improve the final version of this paper. ; Transmissivity is a significant hydrogeological parameter that affects the reliability of groundwater flow and transport models. This study demonstrates the improvement in the estimated transmissivity field of an unconfined detritic aquifer that can be obtained by using geostatistical methods to combine three types of data: hard transmissivity data obtained from pumping tests, soft transmissivity data obtained from lithological information from boreholes, and water head data. The piezometric data can be related to transmissivity by solving the hydrogeology inverse problem, i.e., including the observed water head to determine the unknown model parameters (log transmissivities). The geostatistical combination of all the available information is achieved by using three different geostatistical methodologies: ordinary kriging, ordinary co-kriging and inverse problem universal co-kriging. In addition, there are eight methodological cases to be compared according to which log-transmissivity data are considered as the primary variable in co-kriging and whether two or three variables are used in inverse-problem universal co-kriging. The results are validated by using the performance statistics of the direct modelling of the unconfined groundwater flow and comparing observed water heads with the modelled ones. Although the results show that the two sets of log-transmissivity data are incompatible, the set of log-transmissivity data from the lithofacies provides a good log-transmissivity image that can be improved by inverse modelling. The map provided by inverse-problem universal co-kriging provides the best results. Using three variables, rather than two in the inverse problem, gives worse results because of the ...