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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 4
Hydrol. Earth Syst. Sci., 17, 1575–1588, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Statistical methods for hydrological applications

Hydrol. Earth Syst. Sci., 17, 1575–1588, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 23 Apr 2013

Research article | 23 Apr 2013

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

S. A. Archfield1, A. Pugliese2, A. Castellarin2, J. O. Skøien3, and J. E. Kiang4 S. A. Archfield et al.
  • 1US Geological Survey, Northborough, MA, USA
  • 2Department DICAM, School of Civil Engineering, University of Bologna, Bologna, Italy
  • 3Institute for Environment and Sustainability, Joint Research Centre, European Commission, Italy
  • 4US Geological Survey, Reston, VA, USA

Abstract. In the United States, estimation of flood frequency quantiles at ungauged 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 treatments of spatial correlation when using regression-based or 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.

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