A curve number approach to formulate hydrological response units within distributed hydrological modelling

E. Savvidou, A. Efstratiadis, A. D. Koussis, A. Koukouvinos, and D. Skarlatos, A curve number approach to formulate hydrological response units within distributed hydrological modelling, Hydrology and Earth System Sciences Discussions, doi:10.5194/hess-2016-627, 2016.

[doc_id=1673]

[English]

We propose a systematic framework for delineating Hydrological Response Units (HRUs), based on a modified Curve Number (CN) approach. The CN-value accounts for three major physiographic characteristics of a river basin, by means of classes of soil permeability, land use/land cover characteristics, and drainage capacity. A semi-automatic procedure in a GIS environment allows producing basin maps of distributed CN-values as the product of the three classified layers. 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 simultaneously at 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 framework are demonstrated in the hydrological simulation of Nedontas river basin, Greece, in which parameterizations of different complexities are employed in a recently improved version of the HYDROGEIOS modelling framework.

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See also: http://www.hydrol-earth-syst-sci-discuss.net/hess-2016-627/

Remarks:

This discussion paper has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion. An improved version was eventually published in Water (http://www.itia.ntua.gr/1772/). Please, if you wish to cite this work, refer to the peer-reviewed article, not the discussion paper.

Our works referenced by this work:

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Other works that reference this work (this list might be obsolete):

1. Verma, S., R. K. Verma, S. K. Mishra, A. Singh, and G. K. Jayaraj, A revisit of NRCS-CN inspired models coupled with RS and GIS for runoff estimation, Hydrological Sciences Journal, 62(12), 1891-1930, doi:10.1080/02626667.2017.1334166, 2017.
2. D’ Ambrosio, S., A. M. De Girolamo, and M. C. Rulli, Assessing sustainability of agriculture through water footprint analysis and in-stream monitoring activities, Journal of Cleaner Production, 200(1), 454-470, doi:10.1016/j.jclepro.2018.07.229, 2018.
3. D’Ambrosio, E., A. M. De Girolamo, and M. C. Rulli, Coupling the water footprint accounting of crops and in-stream monitoring activities at the catchment scale, MethodsX, 5, 1221-1240, doi:10.1016/j.mex.2018.10.003, 2018.
4. Weber, M., M. Feigl, K. Schulz, and M. Bernhardt, On the ability of LIDAR snow depth measurements to determine or evaluate the HRU discretization in a land surface model, Hydrology, 7(2), 20, doi:10.3390/hydrology7020020, 2020.
5. Prastowo, T., A. Saggaff, and F. Hadinata, A study of watershed characteristics of Tiga Dihaji dam, International Journal of Scientific & Technology Research, 9(4), 1135-1141, 2020.
6. Rodríguez Flores, S., C. Muñoz-Robles, A. J. Ortíz-Rodríguez, J. A. Quevedo Tiznado, and P. Julio-Miranda, Historical and projected changes in hydrological and sediment connectivity under climate change in a tropical catchment of Mexico, Science of The Total Environment, 848, 157731, doi:10.1016/j.scitotenv.2022.157731, 2022.
7. Bodrud-Doza, Md., W. Yang, R. de Queiroga Miranda, A. Martin, B. DeVries, and E. D. G. Fraser, Towards implementing precision conservation practices in agricultural watersheds: A review of the use and prospects of spatial decision support systems and tools, Science of The Total Environment, 905, 167118, doi:10.1016/j.scitotenv.2023.167118, 2023.

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