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|Title:||The curve number concept as a driver for delineating Hydrological Response Units||Authors:||Savvidou, Eleni
Koussis, Antonis D.
|Keywords:||Calibration;Curve number;Distributed hydrological modelling;GIS;HYDROGEIOS modeling framework;Hydrological response units;Parameterization||Category:||Civil Engineering||Field:||Engineering and Technology||Issue Date:||11-Feb-2018||Publisher:||MDPI AG||Source:||Water, 2018, Volume 10, Issue 2, Article number 194||metadata.dc.doi:||https://doi.org/10.3390/w10020194||Abstract:||In this paper, a new methodology for delineating Hydrological Response Units (HRUs), based on the Curve Number (CN) concept, is presented. Initially, a semi-automatic procedure in a GIS environment is used to produce basin maps of distributed CN values as the product of the three classified layers, soil permeability, land use/land cover characteristics and drainage capacity. The map of CN values is used in the context of model parameterization, in order to identify the essential number and spatial extent of HRUs and, consequently, the number of control variables of the calibration problem. The new approach aims at reducing the subjectivity introduced by the definition of HRUs and providing parsimonious modelling schemes. In particular, the CN-based parameterization (1) allows the user to assign as many parameters as can be supported by the available hydrological information, (2) associates the model parameters with anticipated basin responses, as quantified in terms of CN classes across HRUs, and (3) reduces the effort for model calibration, simultaneously ensuring good predictive capacity. The advantages of the proposed approach are demonstrated in the hydrological simulation of the Nedontas River Basin, Greece, where parameterizations of different complexities are employed in a recently improved version of the HYDROGEIOS model. A modelling experiment with a varying number of HRUs, where the parameter estimation problem was handled through automatic optimization, showed that the parameterization with three HRUs, i.e., equal to the number of flow records, ensured the optimal performance. Similarly, tests with alternative HRU configurations confirmed that the optimal scores, both in calibration and validation, were achieved by the CN-based approach, also resulting in parameters values across the HRUs that were in agreement with their physical interpretation.||URI:||http://ktisis.cut.ac.cy/handle/10488/10958||ISSN:||20734441||Rights:||© 2018 by the authors.||Type:||Article|
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checked on Nov 18, 2018
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