Equally likely inverse solutions to a groundwater flow problem including pattern information from remote sensing images

Abstract

Groundwater flow modeling for large areas in arid and semiarid regions, like the Chobe region in Botswana, suffers from a severe lack of data. This study addresses the usefulness of remote sensing (RS) images to constrain the recharge rate estimates for a region. The estimates derived from METEOSAT and NOAA advanced very high resolution radar (AVHRR) images are correlated with recharge rate values estimated from chloride measurements and used jointly in the generation of multiple, equally likely recharge rate realizations with the colocated cosimulation algorithm. The colocated cosimulation algorithm is very suited to generate stochastic realizations of a parameter that includes information from a correlated covariable given on a regular, dense grid as in RS information. These equally likely recharge rate realizations, together with multiple equally likely transmissivity realizations, are conditioned by inversion to hydraulic head data and a digital elevation model. For the inverse conditioning an additional penalty term was added to the objective function, penalizing too large deviations of the recharge rate pattern from the RS image. As such, the recharge rate pattern observed with the RS images is still honored by the calibrated recharge rate realizations. It was observed that conditioning to the RS information reduces significantly the estimated ensemble variance of the recharge rates.