Model parameter analysis using remotely sensed pattern information in a multi-constraint framework

The development of sophisticated distributed hydrological models that characterize groundwater, surface water and atmospheric interactions, inevitably increases the amount of observational data required to force the model and evaluate simulations. The limitations of using traditional evaluation data and the benefits of including independent and spatially distributed observational constraints are illustrated here through a sensitivity analysis of a coupled surface-water/groundwater/atmosphere model simulated at the catchment scale. In the analyses, model performance based on objective functions using conventional stream flow and groundwater head observations are compared against objective functions that utilize spatially distributed satellite based surface temperature retrievals as the calibration variable. The advantage of incorporating remote sensing based observations into the model evaluation process is their spatially distributed information content, enabling an assessment of the capacity of the model to reproduce observed spatial patterns. Results indicate that employing spatially distributed model parameterizations has limited impact on improving model performance when evaluated against traditional model objectives of stream flow and groundwater head. Indeed, a spatially uniform parameterization produced almost identical model performance. In contrast, objective functions that incorporate remote sensing based surface temperatures, highlighted the comparatively poor reproduction of spatial patterns when using a spatially uniform parameterization. Although lumped observations such as stream discharge contain valuable information regarding the total catchment water budget, such traditional observations should be merged with independent data sets that incorporate spatial pattern information to strengthen the robustness of model performance and model parameter constraint.