River discharge data for tributaries of the Danube River.
A named list with four components:
data_clusteredA numeric matrix containing preprocessed discharge data for each gauging station.
data_rawA numeric matrix containing daily discharge observations for each gauging station.
infoA data frame containing information on each gauging station and its catchment area.
flow_edgesA two-column numeric matrix; each row gives the indices (in info)
of a pair of gauging stations that are directly connected by a river segment.
The matrix data_clustered was obtained by declustering the daily discharge data
from the summer months between 1960 and 2010 contained in data_raw, yielding
between seven and ten observations per year. Each row corresponds to one observation
from the declustered time series; the non-unique row names indicate the year of
observation. Each column corresponds to a gauging station, with column indices in
data_raw and data_clustered matching row indices in info.
See Asadi et al.\ (2015) for details on the preprocessing and declustering.
The info data frame contains the following variables for each gauging station
or its associated catchment area:
RivNamesName of the river at the gauging station.
Lat, LongGeographic coordinates of the gauging station.
Lat_Center, Long_CenterCoordinates of the center of the corresponding catchment area.
AltMean altitude of the catchment area.
AreaArea of the catchment.
SlopeMean slope of the catchment.
PlotCoordX, PlotCoordYCoordinates used to arrange gauging stations when plotting a flow graph.
Data are available from GitHub
and hence can be gathered using the command dataset("danube").
Asadi, P., Davison, A. C., Engelke, S., and Furrer, R. (2015). Extreme-value modeling of spatially dependent river discharges. Journal of the American Statistical Association, 110, 124--136.
de Carvalho, M., Huser, R., Naveau, P., and Reich, B. J. (2026). Handbook of Statistics of Extremes. Chapman & Hall/CRC, Boca Raton, FL.
Wan, P. and Janßen, A. (2026). Clustering Methods for Multivariate Extremes. In: Handbook of Statistics of Extremes, Chapter 12, pp. 243--262.