Multi-objective optimization to explore trade-offs in a multi-purpose national scale integrated hydrological model

A central element in water management in Denmark is the National Hydrological Model for Denmark (DK-model). The DK-model is a multi-purpose distributed, integrated hydrological model coupling 3D groundwater flow with root zone processes, overland flow and river routing combined with major human impacts such as groundwater abstraction

The model is applied for a range of national scale analysis and provides publicly available data for historic periods, in real time and for future projections. Applications include assessment of available water resources, effects of abstractions, nitrate transport and climate change impact assessments.

The model development and calibration is an ongoing process that seeks to improve performance across a range of model objectives and meet the requirements of endusers.

Recently, the calibration and parameterization of the DK-model has moved towards more spatially distributed parametrization schemes and new calibration targets regarding spatial patterns of evapotranspiration, drain fraction maps and irrigation volumes. This in combination with the large-scale distributed nature and high computational demand of the model system requires a pragmatic optimization approach that allows for both multi-objective and efficient optimization.

This is approached through the Pareto Archived Dynamically Dimensioned Search (PADDS) algorithm allowing a robust global parameter search effective even at a few hundred model runs. In addition, the PADDS approach enables a systematic analysis of tradeoffs between different objectives with minimal a-priori weighting of objective function groups.

In this study we specifically analyse the value of multiple objective functions by comparing optimizations based solely on conventional groundwater head observations and streamflow targets versus a more complex objective function scheme including seasonal groundwater fluctuations, evapotranspiration patterns, drain fractions and irrigation.  

This analysis, illustrates tradeoffs and equifinalities that are relevant for screening behavioral parameter sets for application of a multi-purpose model. In addition, a scheme for selecting an ensemble of parameter sets is illustrated.