Coefficient of variation of annual flood peaks: variability of flood peak and rainfall intensity

Abstract

Abstract:Scaling can be a powerful solution for predictions in ungauged basins (PUB). Since becoming a principal scaling tool, the theory concerning the index flood method has been criticized because it requires some scaling conditions that are satisfied in few river basins. In that method, precipitation and flood discharge variability play key roles. Consequently, the coefficient of variation (CV) of annual flood peaks came to be considered after the 1990s. In this paper, we have attempted to clarify true CV characteristics of flood discharges.Using numerical simulations, we attempt to reproduce the empirical characteristics of an increase in CV with increasing catchment area for a small basin (i.e. less than 30-100 km 2 ) and decrease in CV with increasing catchment area for a large basin. First, as a preliminary test, we developed a simple model, which is on the basis of the unit hydrograph. Results obtained using this simple model show that the CV of annual flood peaks shows a constant-increasing-constant pattern, but this model cannot reproduce the empirical characteristics. Secondly, we used an idealized channel network to find the cause of decreasing CV. However, results show that the ability to reproduce the empirical characteristics is unrelated to the presence or absence of a network. This was confirmed using a distributed rainfall-runoff (DRR) model comprising a channel network. The most important cause is decreased CV of rainfall intensity with increasing catchment area.Furthermore, increasing CV of the peak discharge response (PDR) function increases the CV of annual flood peaks. However, the CV of PDR of a partial duration series does not affect the CV of annual flood peaks for smaller basins (0Ð1 km 2 in our simulation condition); hence CV is constant for small basins.

Analysis of Peak Flow Distribution for Bridge Collapse Sites

“…The major benefit of the GEV model is its ability to fit highly skewed data [7]. Therefore, recent hydrologic research has focused on explaining the parameters of the GEV distribution of streamflow extremes [8][9][10][11][12][13][14].If annual maximum exceedances are assumed to be GEV-distributed, the POT exceedances are assumed to be generalized Pareto (GP)-distributed [15], following recommendations in the field of statistics [16,17]. Fitting the GP distribution to exceedances over a high threshold and also estimating the frequency of exceeding the threshold by fitting a Poisson distribution allows for the simultaneous fitting of parameters concerning both the frequency and intensity of extreme events.…”

“…The major benefit of the GEV model is its ability to fit highly skewed data [7]. Therefore, recent hydrologic research has focused on explaining the parameters of the GEV distribution of streamflow extremes [8][9][10][11][12][13][14].…”