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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 12, issue 4
Hydrol. Earth Syst. Sci., 12, 989–1006, 2008
https://doi.org/10.5194/hess-12-989-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Modelling strategies across scales

Hydrol. Earth Syst. Sci., 12, 989–1006, 2008
https://doi.org/10.5194/hess-12-989-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  28 Jul 2008

28 Jul 2008

HYDROGEIOS: a semi-distributed GIS-based hydrological model for modified river basins

A. Efstratiadis1, I. Nalbantis2, A. Koukouvinos1, E. Rozos1, and D. Koutsoyiannis1 A. Efstratiadis et al.
  • 1Department of Water Resources and Environment, School of Civil Engineering, National Technical Univ. of Athens, Greece
  • 2Laboratory of Reclamation Works and Water Resources Management, School of Rural and Surveying Engineering, National Technical Univ. of Athens, Greece

Abstract. The HYDROGEIOS modelling framework represents the main processes of the hydrological cycle in heavily modified catchments, with decision-depended abstractions and interactions between surface and groundwater flows. A semi-distributed approach and a monthly simulation time step are adopted, which are sufficient for water resources management studies. The modelling philosophy aims to ensure consistency with the physical characteristics of the system, while keeping the number of parameters as low as possible. Therefore, multiple levels of schematization and parameterization are adopted, by combining multiple levels of geographical data. To optimally allocate human abstractions from the hydrosystem during a planning horizon or even to mimic the allocation occurred in a past period (e.g. the calibration period), in the absence of measured data, a linear programming problem is formulated and solved within each time step. With this technique the fluxes across the hydrosystem are estimated, and the satisfaction of physical and operational constraints is ensured. The model framework includes a parameter estimation module that involves various goodness-of-fit measures and state-of-the-art evolutionary algorithms for global and multiobjective optimization. By means of a challenging case study, the paper discusses appropriate modelling strategies which take advantage of the above framework, with the purpose to ensure a robust calibration and reproduce natural and human induced processes in the catchment as faithfully as possible.

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