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Calibration Catchment Selection for Flood Regionalization Modeling

Authors

  • Wan Zurina Wan Jaafar,

    1. Respectively, PhD Candidate (Wan Jaafar) and Professor (Han), Water and Environmental Management Research Centre, Department of Civil Engineering, University of Bristol, Bristol BS8 1TR, United Kingdom, and Staff (Wan Jaafar), Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
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  • Dawei Han

    1. Respectively, PhD Candidate (Wan Jaafar) and Professor (Han), Water and Environmental Management Research Centre, Department of Civil Engineering, University of Bristol, Bristol BS8 1TR, United Kingdom, and Staff (Wan Jaafar), Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
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  • Paper No. JAWRA-10-0209-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until six months from print publication.

(E-Mail/Wan Jaafar: cewzbwj@bristol.ac.uk).

Abstract

Wan Jaafar, Wan Zurina, and Dawei Han, 2012. Calibration Catchment Selection for Flood Regionalization Modeling. Journal of the American Water Resources Association (JAWRA) 48(4): 698-706. DOI: 10.1111/j.1752-1688.2012.00648.x

Abstract:  There are two unsolved problems in flood regionalization model development related to the quantity and quality of calibration catchments: (1) how many calibration catchments should be used? and (2) how to select the calibration catchments? This study explores these two questions through a case study on the median annual maximum flood (QMED) model in the United Kingdom. It has been found that the chance of developing a good QMED model decreases significantly when the number of calibration catchments drops below a critical number (e.g., 60 in the case study). However, no significant improvement is achieved if the number of calibration catchments is above it. This number could be used as a benchmark for choosing randomly selected calibration catchments. Across a broad range of calibration catchment numbers, there are good and poor calibrated models regardless of calibration catchment numbers. High quality models could be developed from a small number of calibration catchments and also poor models from a large number of calibration catchments. This indicates that the number of calibration catchments may not be the dominating factor for developing a high quality regionalization model. Instead, the information content could be more important. The study has demonstrated that the standard deviation values between the best and poorest groups are distinctive and could be used in choosing appropriate calibration catchments.

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