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

By Stacey A. Archfield, Alessio Pugliese, Attilio Castellarin, J. Skøien, J. Kiang. 2012

61 citations recorded by the source corpus.

In the United States, estimation of flood frequency quantiles at ungauged locations has been largely based on regional regression techniques that relate measurable catch- ment descriptors to flood quantiles. More recently, spatial in- terpolation 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 re- ports successful applications of two techniques, canonical kriging, CK (or physiographical-space-based interpolation, PSBI), and topological kriging, TK (or top-kriging). CK per- forms the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descrip- tors. TK predicts the streamflow statistic along river net- works 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 general- ized least squares (GLS) regression, one of the most com- mon approaches to estimate flood quantiles at ungauged lo- cations. By means of a leave-one-out cross-validation proce- dure, 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 per- formance of TK over GLS highlights an important distinc- tion between the treatments of spatial correlation when us- ing regression-based or spatial interpolation methods to es- timate 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

Subfields: Atmospheric Science, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/c89adf66d6bd1fdefc3e2bc40bd11384961b7788

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Citations in the corpus

Ordinary kriging as a tool to estimate historical daily streamflow records (2016, methodology): “, 2006), percentiles, and other indices of the streamflow distribution (Castiglioni et al., 2011; Archfield et al., 2013) and streamflow signatures (Viglione et al.”
Regional flow duration curves: Geostatistical techniques versus multivariate regression (2015, methodology): “, 2009; Archfield et al., 2013 ). Such techniques, relying on kriging methods, do not require identification of hydrologically homogeneous regions. Using a kriging-based weighting scheme, Castellarin (2014) introduces the prediction of a continuous FDC within a three-dimensional xyz space, where x a”