Abstract
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.
Highlights
An important application of hydrologic science is to provide an accurate estimate of the design flood at a site which lacks sufficient measured hydrological information
Several approaches of regional flood frequency analysis (RFFA) have been proposed, standard accepted techniques have been detailed by Hosking and Wallis (1997) and in the Flood Estimation Handbook (FEH, 1999); in the United States, the US Geological Survey utilizes generalized least squares (GLS) regression (RFFA-GLS) as the standard method for the estimation of flood quantiles at ungauged sites
Since GLS is the current method for flood regionalization, we use it as the benchmark approach, while our study focuses on the application of kriging techniques to the Prediction in Ungauged Basins (PUB) problem
Summary
An important application of hydrologic science is to provide an accurate estimate of the design flood (i.e., the flood quantile associated with a given non-exceedance probability, usually expressed in terms of return period) at a site which lacks sufficient measured hydrological information (see Sivapalan et al, 2003). This problem has been addressed by adopting a number of different approaches that are all characterized by the same fil rouge: transferring hydrologic information or knowledge from gauged catchments to ungauged or poorly gauged ones (e.g., Bloschl and Sivapalan, 1997; Merz and Bloschl, 2008; Pallard et al, 2009). Several approaches of RFFA have been proposed (traditional approaches are illustrated for instance in Dalrymple, 1960; Burn, 1990; Gabriele and Arnell, 1991; Stedinger et al, 1993; Hosking and Wallis, 1997; Castellarin et al, 2001; Merz and Bloschl, 2005), standard accepted techniques have been detailed by Hosking and Wallis (1997) and in the Flood Estimation Handbook (FEH, 1999); in the United States, the US Geological Survey utilizes generalized least squares (GLS) regression (RFFA-GLS) as the standard method for the estimation of flood quantiles at ungauged sites.
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