Regional Estimation of Flood Quantile at Ungauged Sites

Authors

  • Basri Badyalina

linear regression, ungauged, kriging

Abstract

In this study, Linear Regression (LR) is performance is investigated with and without implementation of Topological Kriging (TK). The aims of this study to determine the used of TK can improve the performance of LR by grouping the basin which have similar hydrology characteristics. Then LR only model the relationship inside the regions. The result show that LR based TK is more reliable in term of estimation accuracy.

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How to Cite

Regional Estimation of Flood Quantile at Ungauged Sites. (2016). Global Journal of Science Frontier Research, 16(H4), 73-74. https://www.journalofscience.org/index.php/GJSFR/article/view/1930

References

M Sivapalan, K Takeuchi, S Franks, V Gupta, H Karambiri, V Lakshmi, X Liang, J Mcdonnell, E Mendiondo, P O'connell, T Oki, J Pomeroy, D Schertzer, S Uhlenbrook, E Zehe (2003) IAHS Decade on Predictions in Ungauged Basins (PUB), 2003-2012: Shaping an exciting future for the hydrological sciences. 48(6), 857-880.

Abdullah Mamun, Alias Hashim, Zalin Amir (2011) Regional Statistical Models for the Estimation of Flood Peak Values at Ungauged Catchments: Peninsular Malaysia. 17(4), 547-553.

V Smakhtin (2001) Low flow hydrology: a review. 240(3-4), 147-186.

J Samuel, P Coulibaly, R Metcalfe (2011) Estimation of continuous streamflow in Ontario ungauged basins: comparison of regionalization methods. 16(5), 447-459.

S Archfield, A Pugliese, A Castellarin, J Skøien, J Kiang (2013) Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?. 17(4), 1575-1588.

Regional Estimation of Flood Quantile at Ungauged Sites

Published

2016-11-22

How to Cite

Regional Estimation of Flood Quantile at Ungauged Sites. (2016). Global Journal of Science Frontier Research, 16(H4), 73-74. https://www.journalofscience.org/index.php/GJSFR/article/view/1930