Articles | Volume 22, issue 2
Hydrol. Earth Syst. Sci., 22, 1299–1315, 2018
Hydrol. Earth Syst. Sci., 22, 1299–1315, 2018

Research article 20 Feb 2018

Research article | 20 Feb 2018

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

Mehmet C. Demirel1,6, Juliane Mai2,4, Gorka Mendiguren1,5, Julian Koch1,3, Luis Samaniego2, and Simon Stisen1 Mehmet C. Demirel et al.
  • 1Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
  • 2Department Computational Hydrosystems, UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
  • 3Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
  • 4Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Canada
  • 5Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
  • 6Department of Civil Engineering, Istanbul Technical University, 34469 Maslak, Istanbul, Turkey

Abstract. Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data.

The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

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Short summary
Satellite data offer great opportunities to improve spatial model predictions by means of spatially oriented model evaluations. In this study, satellite images are used to observe spatial patterns of evapotranspiration at the land surface. These spatial patterns are utilized in combination with streamflow observations in a model calibration framework including a novel spatial performance metric tailored to target the spatial pattern performance of a catchment-scale hydrological model.