Articles | Volume 20, issue 6
https://doi.org/10.5194/hess-20-2383-2016
https://doi.org/10.5194/hess-20-2383-2016
Research article
 | 
20 Jun 2016
Research article |  | 20 Jun 2016

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Elena Toth

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Cited articles

Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., and Wilby, R. L.: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting, Prog. Phys. Geogr., 36, 480–513, https://doi.org/10.1177/0309133312444943, 2012.
Archfield, S. A., Pugliese, A., Castellarin, A., Skøien, J. O., and Kiang, J. E.: Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?, Hydrol. Earth Syst. Sci., 17, 1575–1588, https://doi.org/10.5194/hess-17-1575-2013, 2013.
Aziz, K., Rahman, A., Fang, G., and Shreshtha, S.: Application of Artificial Neural Networks in Regional Flood Frequency Analysis: A Case Study for Australia, Stoch. Environ. Res. Risk A., 28, 541–554, https://doi.org/10.1007/s00477-013-0771-5, 2013.
Bloeschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H. (Eds.): Runff prediction in ungauged basins: Synthesis across processes, places and scales, Cambridge University Press, New York, USA, 490 pp., 2013.
Bocchiola, D., De Michele, C., and Rosso, R.: Review of recent advances in index flood estimation, Hydrol. Earth Syst. Sci., 7, 283–296, https://doi.org/10.5194/hess-7-283-2003, 2003.
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Short summary
Runoff thresholds for activating flood warnings might be estimated with regionally derived relationships between catchment descriptors and assigned flood quantiles. Since the consequences of overestimated thresholds (leading to missing alarms) are generally more severe than those of an underestimation (leading to false alarms), the work proposes to parameterise the regression model with an asymmetric error function, instead of using a traditional, symmetric square errors sum.