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Classification of Drainage Basins Based on Readily Available Information

Author

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  • Ino Papageorgaki

    (National Technical University of Athens)

  • Ioannis Nalbantis

    (National Technical University of Athens)

Abstract

Classification of drainage basins into groups with similar response to meteorological forcing can be very helpful in cases of transfer of hydrological information in space such as in streamflow prediction in ungauged basins. It is also critical for the implementation of the Water Framework Directive and related legislative tools of the EU such as the Flood Directive. The focus is testing the ability to classify drainage basins using climate-based variables and geomorphometric characteristics as predictors. Precipitation is selected as the climate-based variable, since this is commonly measured in the majority of basins. Geomorphometric characteristics include, among others, the average ground slope and drainage density; these are derived from a Digital Terrain Model. The employed methodology involves two steps. In the first step we perform unsupervised classification through using the fuzzy c-means method to identify basin classes that serve as the reference classes in the second step of analysis. A set of hydrological signatures is used in the first step, which includes the runoff ratio, the baseflow index, the slope of the flow duration curve, and the snow day ratio. In the second step we perform supervised classification through using the k-Nearest Neighbour method which maps predictors to basin classes. Last, the success rate of the obtained classification is assessed through using jack-knife re-sampling. Twenty-four gauged basins in mainland Greece are used, which are classified into four classes. The employed methodology proved to be successful in more than 95 % of cases of recognition of the class for an ungauged basin.

Suggested Citation

  • Ino Papageorgaki & Ioannis Nalbantis, 2016. "Classification of Drainage Basins Based on Readily Available Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5559-5574, December.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:15:d:10.1007_s11269-016-1410-y
    DOI: 10.1007/s11269-016-1410-y
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    References listed on IDEAS

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    1. Habib Abida & Manel Ellouze, 2006. "Hydrological Delineation of Homogeneous Regions in Tunisia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 961-977, December.
    2. Muhammad Qamar & Daniele Ganora & Pierluigi Claps, 2015. "Monthly Runoff Regime Regionalization Through Dissimilarity-Based Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4735-4751, October.
    3. Zaw Latt & Hartmut Wittenberg & Brigitte Urban, 2015. "Clustering Hydrological Homogeneous Regions and Neural Network Based Index Flood Estimation for Ungauged Catchments: an Example of the Chindwin River in Myanmar," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(3), pages 913-928, February.
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    Cited by:

    1. Gokmen Tayfur & Bihrat Onoz & Antonino Cancelliere & Luis Garrote, 2016. "Editorial: Water Resources Management in a Changing World: Challenges and Opportunities," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5553-5557, December.

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