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Topological and Canonical Kriging for Design-flood Prediction in Ungauged Catchments: an Improvement Over a Traditional Regional Regression Approach? : Volume 9, Issue 10 (30/10/2012)

By Archfield, S. A.

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Book Id: WPLBN0004013142
Format Type: PDF Article :
File Size: Pages 34
Reproduction Date: 2015

Title: Topological and Canonical Kriging for Design-flood Prediction in Ungauged Catchments: an Improvement Over a Traditional Regional Regression Approach? : Volume 9, Issue 10 (30/10/2012)  
Author: Archfield, S. A.
Volume: Vol. 9, Issue 10
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2012
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Skoslash, J. O., Ien,, Castellarin, A., Kiang, J. E., Archfield, S. A., & Pugliese, A. (2012). Topological and Canonical Kriging for Design-flood Prediction in Ungauged Catchments: an Improvement Over a Traditional Regional Regression Approach? : Volume 9, Issue 10 (30/10/2012). Retrieved from http://hawaiilibrary.net/


Description
Description: Massachusetts-Rhode Island Water Science Center, US Geological Survey, Northborough, MA, USA. In the United States, estimation of flood frequency quantiles at ungaged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e. flood flows and low-flow indices) in ungauged catchments. Literature reports successful applications of two techniques, Canonical kriging, CK, (or physiographical-space based interpolation, PSBI) and Topological kriging, TK, (or Top-kriging). CK performs the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descriptors. TK predicts the streamflow statistic along river networks taking both the catchment area and nested nature of catchments into account. It is of interest to understand how these spatial interpolation methods compare with generalized-least squares (GLS) regression, one of the most common approaches to estimate flood quantiles at ungauged locations. By means of a leave-one-out cross validation procedure, the performance of CK and TK was compared to GLS regression equations developed for the prediction of 10-, 50-, 100- and 500-yr floods for 61 streamgauges in the Southeast United States. TK substantially outperforms GLS and CK for the study area, particularly for large catchments. The performance of TK over GLS highlights an important distinction between the treatment of spatial correlation when using regression-based versus spatial interpolation methods to estimate flood quantiles at ungauged locations. The analysis also shows that coupling TK with CK slightly improves the performance of TK; however, the improvement is marginal when compared to the improvement in performance over GLS.

Summary
Topological and canonical kriging for design-flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?

Excerpt
Blöschl, G. and Sivapalan, M.: Process controls on regional flood frequency: coefficient of variation and basin scale, Water Resour. Res., 33, 2967–2980, 1997.; Burn, D. H.: Evaluation of regional flood frequency analysis with a region of influence approach, Water Resour. Res., 26, 2257–2265, 1990.; Castellarin, A., Burn, D., and Brath, A.: Assessing the effectiveness of hydrological similarity measures for flood frequency analysis, J. Hydrol., 241, 270–285, doi:10.1016/S0022-1694(00)00383-8, 2001.; Castiglioni, S., Castellarin, A., and Montanari, A.: Prediction of low-flow indices in ungauged basins through physiographical space-based interpolation, J. Hydrol., 378, 272–280, doi:10.1016/j.jhydrol.2009.09.032, 2009.; Castiglioni, S., Castellarin, A., Montanari, A., Skøien, J. O., Laaha, G., and Blöschl, G.: Smooth regional estimation of low-flow indices: physiographical space based interpolation and top-kriging, Hydrol. Earth Syst. Sci., 15, 715–727, doi:10.5194/hess-15-715-2011, 2011.; Chokmani, K. and Ouarda, T. B. M. J.: Physiographical space-based kriging for regional flood frequency estimation at ungauged sites, Water Resour. Res., 40, W12514, doi:10.1029/2003WR002983, 2004.; Cressie, N.: Statistics for Spatial Data, Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics, J. Wiley, 1993.; Dalrymple, T.: Flood-Frequency Analyses, Manual of Hydrology: Part 3, 1960.; De Marsily, G.: Quantitative Hydrogeology, Academic Press, London, 1986.; De Marsily, G. and Ahmed, S.: Application of kriging techniques in groundwater hydrology, J. Geol. Soc. India, 29, 57–82, 1987.; Di Prinzio, M., Castellarin, A., and Toth, E.: Data-driven catchment classification: application to the pub problem, Hydrol. Earth Syst. Sci., 15, 1921–1935, doi:10.5194/hess-15-1921-2011, 2011.; Eng, K., Chen, Y., and Kiang, J. E.: Users Guide to the Weighted-Multiple-Linear Regression Program (WREG version 1.0), US Geological Survey Techniques and Methods, Vol. 4, Chap. A8, p. 21, 2009.; FEH: Flood estimation handbook, Insitute of Hydrology, Wallingford, Oxfordshire, 1999.; Gabriele, S. and Arnell, N.: A hierarchical approach to regional flood frequency analysis, Water Resour. Res., 27, 1281–1289, 1991.; Gotvald, A. J., Feaster, T. D., and Weaver, J. C.: Magnitude and Frequency of Rural Floods in the Southeastern United States, 2006, Vol. 1, Georgia, 2009-5043, US Geological Survey, Reston, Virginia, USA, 2009.; Hosking, J. and Wallis, J.: Regional Frequency Analysis: An Approach Based on L-Moments, Cambridge University Press, 1997.; Hundecha, Y., Ouarda, T. B. M. J., and Bardossy, A.: Regional estimation of parameters of a rainfall-runoff model at ungauged watersheds using the spatial structures of the parameters within a canonical physiographic-climatic space, Water Resour. Res., 44, W01427, doi:10.1029/2006WR005439, 2008.; Isaaks, E. H. and Srivastava, R.: Applied Geostatistics, Oxford University Press, New York, 1989.; Journel, A. and Huijbregts, C.: Mining Geostatistics, Academic Press, 1978.

 

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