Performance evaluation of groundwater model hydrostratigraphy from airborne electromagnetic data and lithological borehole logs
- 1Department of Environmental Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark
- 2HydroGeophysics Group, Department of Geoscience, Aarhus University, Aarhus, Denmark
- 3Geological Survey of Denmark and Greenland, Copenhagen, Denmark
Abstract. Large-scale hydrological models are important decision support tools in water resources management. The largest source of uncertainty in such models is the hydrostratigraphic model. Geometry and configuration of hydrogeological units are often poorly determined from hydrogeological data alone. Due to sparse sampling in space, lithological borehole logs may overlook structures that are important for groundwater flow at larger scales. Good spatial coverage along with high spatial resolution makes airborne electromagnetic (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 lithological information into large-scale hydrological models. Clay-fraction maps are produced by translating geophysical resistivity into clay-fraction values using lithological borehole information. Voxel models of electrical resistivity and clay fraction are classified into hydrostratigraphic zones using k-means clustering. Hydraulic conductivity values of the zones are estimated by hydrological calibration using hydraulic head and stream discharge observations. The method is applied to a Danish case study. Benchmarking hydrological performance by comparison of performance statistics from comparable hydrological models, the cluster model performed competitively. Calibrations of 11 hydrostratigraphic cluster models with 1–11 hydraulic conductivity zones showed improved hydrological performance with an increasing number of clusters. Beyond the 5-cluster model hydrological performance did not improve. Due to reproducibility and possibility of method standardization and automation, we believe that hydrostratigraphic model generation with the proposed method has important prospects for groundwater models used in water resources management.