ID:
publications-750
Type:
PEER REVIEWED ARTICLE
Year:
2014
Authors:
B. S. Beyene , A. F. Van Loon , H. A. J. Van Lanen , P. J. J. F. Torfs
Title:
Investigation of variable threshold level approaches for hydrological drought identification
Venue/Journal:
DOI:
10.5194/hessd-11-12765-2014
Research type:
Hydrological modeling
Water System:
River Basins
Technical Focus:
Abstract:
Abstract. Threshold level approaches are widely used to identify drought events in time series of hydrometeorological variables. However, the method used for calculating the threshold level can influence the quantification of drought events or even introduce artefact drought events. In this study, four methods of variable threshold calculation have been tested on catchment scale, namely (1) moving average of monthly quantile (M_MA), (2) moving average of daily quantile (D_MA), (3) thirty days moving window quantile (30D) and (4) fast Fourier transform of daily quantile (D_FF). The levels obtained by these methods were applied to hydrometeorological variables that were simulated with a semi-distributed conceptual rainfall-runoff model (HBV) for five European catchments with contrasting catchment properties and climate conditions. There are no physical arguments to prefer one method over the other for drought identification. The only way to investigate this is by applying the methods and visually inspecting the results. Therefore, drought statistics (i.e. number of droughts, mean duration, mean deficit) and time series plots were studied to compare drought propagation patterns determined by different threshold calculation methods. We found that all four approaches are sufficiently suitable to quantify drought propagation in contrasting catchments. Only the D_FF approach showed lower performance in two catchments. The 30D approach seems to be optimal in snow-dominated catchments, because it follows fast changes in discharge caused by snow melt more accurately. The proposed approaches can be successfully applied by water managers in regions where drought quantification and prediction are essential.
Link with Projects:
282769
Link with Tools:
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