Top-kriging - geostatistics on stream networks

By J. Skøien, R. Merz, Günter Blöschl. 2005

234 citations recorded by the source corpus.

Abstract. We present Top-kriging, or topological kriging, as a method for estimating streamflow-related variables in ungauged catchments. It takes both the area and the nested nature of catchments into account. The main appeal of the method is that it is a best linear unbiased estimator (BLUE) adapted for the case of stream networks without any additional assumptions. The concept is built on the work of Sauquet et al. (2000) and extends it in a number of ways. We test the method for the case of the specific 100-year flood for two Austrian regions. The method provides more plausible and, indeed, more accurate estimates than Ordinary Kriging. For the variable of interest, Top-kriging also provides estimates of the uncertainty. On the main stream the estimated uncertainties are smallest and they gradually increase as one moves towards the headwaters. The method as presented here is able to exploit the information contained in short records by accounting for the uncertainty of each gauge. We suggest that Top-kriging can be used for spatially interpolating a range of streamflow-related variables including mean annual discharge, flood characteristics, low flow characteristics, concentrations, turbidity and stream temperature.

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

View Paper: https://www.semanticscholar.org/paper/e31fed9fa552612505894dbe9775dd7a7fde70eb

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In Atmospheric Science

In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach? (2012, methodology): “In a second step, the point variogram is aggregated to the sizes and positions of the catchments for which the hydrometric index of interest is to be estimated and kriging is performed to predict the variables at the ungauged locations (Skøien et al., 2006; Skøien and Bl̈oschl, 2007).”
Ordinary kriging as a tool to estimate historical daily streamflow records (2016, methodology): “In studies exploring the prediction of mean annual runoff (Skøien et al., 2006), percentiles, and other indices of the streamflow distribution (Castiglioni et al.”
Geostatistical prediction of flow–duration curves in an index-flow framework (2014, methodology): “After this, the point variogram can be back-calculated by fitting aggregated variogram values to the sample variogram (details can be found in Skøien et al., 2006).”