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Large-scale hydrological models are important de- cision support tools in water resources management. The largest source of uncertainty in such models is the hydros- tratigraphic model. Geometry and configuration of hydroge- ological units are often poorly determined from hydrogeo- logical data alone. Due to sparse sampling in space, litholog- ical borehole logs may overlook structures that are impor- tant for groundwater flow at larger scales. Good spatial cov- erage along with high spatial resolution makes airborne elec- tromagnetic (AEM) data valuable for the structural input to large-scale groundwater models. We present a novel method to automatically integrate large AEM data sets and lithologi- cal information into large-scale hydrological models. Clay- fraction maps are produced by translating geophysical re- sistivity into clay-fraction values using lithological borehole information. Voxel models of electrical resistivity and clay fraction are classified into hydrostratigraphic zones usingk- means clustering. Hydraulic conductivity values of the zones are estimated by hydrological calibration using hydraulic head and stream discharge observations. The method is ap- plied to a Danish case study. Benchmarking hydrological per- formance by comparison of performance statistics from com- parable hydrological models, the cluster model performed competitively. Calibrations of 11 hydrostratigraphic cluster models with 1-11 hydraulic conductivity zones showed im- proved hydrological performance with an increasing num- ber of clusters. Beyond the 5-cluster model hydrological per- formance did not improve. Due to reproducibility and pos- sibility of method standardization and automation, we be- lieve that hydrostratigraphic model generation with the pro- posed method has important prospects for groundwater mod- els used in water resources management.

Performance evaluation of groundwater model hydrostratigraphy from airborne electromagnetic data and lithological borehole logs