Topological kriging — method of estimation runoff in ungauged basins
In my research I deal with runoff estimation in basins by Top-kriging method. And what is it good for? I would like to explain for you. My focus is on the hydrology, specially for environmental processes. There are many streams worldwide are ungauged and many ways how to figure it out.
Imagine a little stream in some valley or place where you like to go for a walk. Maybe there used to be a pond. In the case, we would like to withhold some water in landscape for the dry season, we probably ask: Is there enough water for realising a water body? It is necessary provide for the inflow but this is an ungauged basin, where no measurements are available. The possibility of setting up a new measurement gauge is offered. However, this option is usually financially demanding, especially if we need more such meter profiles. So, there is a place for mathematical modelling or alternative runoff estimation using interpolation methods.
Kriging is geostatistical interpolation method, which was developped in mining industry in the second half of the 20th century. Afterwards, with new millennium was this knowledge improved to use in hydrology by adding the topology of stream networks (Skøien et al., 2006). Connection of geostatics interpolation and hydrology specialisation brings effective yields. There is a methode which needs only historical runoff data to estimate the runoff (Laaha et al., 2014).
Exceptionality is the direction of estimation, which is surprisingly from downstream to upstream. For example, we have an observation station on the river and we want to know more information about the water flows above this point. Top-kriging calculate approximate distribution of water in the catchment area. There are many studies which compare accurancy of this method and the others — for example regional regression (Laaha et al., 2014). Another study shows the role of station density and its influence on the results rightness (Parajka et al., 2015). The task of science, as well as mine, is to develop the given method into other areas, test its reliability, look for errors and improve its functioning. The main advantage is there is an open-source software. It was presented a package rtop, which is applicable in the statistical environment R (Skøien et al., 2014). Top-kriging was used in the study regions in Austria or French part of the Moselle basin.
I would like to apply the method in the czech conditions and add some new improvement to these problematics. For research was chosen the Otava river catchment. A possible use is the selection of places for small hydroelectric power station locations.
Laaha, G., Skøien, J.O., Blöschl, G., 2014. Spatial prediction on river networks: comparison of top-kriging with regional regression: SPATIAL PREDICTION ON A RIVER NETWORK: TOP-KRIGING VERSUS REGRESSION. Hydrol. Process. 28, 315–324. https://doi.org/10.1002/hyp.9578
Parajka, J., Merz, R., Skøien, J.O., Viglione, A., 2015. The role of station density for predicting daily runoff by top-kriging interpolation in Austria. J. Hydrol. Hydromech. 63, 228–234. https://doi.org/10.1515/johh-2015-0024
Skøien, J.O., Blöschl, G., Laaha, G., Pebesma, E., Parajka, J., Viglione, A., 2014. rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks. Comput. Geosci. 67, 180–190. https://doi.org/10.1016/j.cageo.2014.02.009
Skøien, J.O., Merz, R., Blöschl, G., 2006. Top-kriging — geostatistics on stream networks. Hydrol. Earth Syst. Sci. 10, 277–287. https://doi.org/10.5194/hess-10-277-2006
