An Index-Flood Statistical Model for Hydrological Drought Assessment

Abstract

Modelling of hydrological extremes and drought modelling in particular has received much attention over recent decades. The main aim of this study is to apply a statistical model for drought estimation (in this case deficit volume) using extreme value theory and the index-flood method and to reduce the uncertainties in estimation of drought event return levels. Deficit volumes for 133 catchments in the Czech Republic (1901–2015) were simulated by hydrological model BILAN. The validation of severity, intensity and length of simulated drought events revealed good match with the available observed data. To estimate return levels of the deficit volumes, it is assumed (in accord with the index-flood method), that the deficit volumes within a homogeneous region are identically distributed after scaling with a site-specific factor. The parameters of the scaled regional distribution are estimated using L-moments. The goodness-of-fit of the statistical model is assessed by Anderson–Darling test. For the estimation of critical values, sampling methods allowing for handling of years without drought were used. It is shown, that the index-flood model with a Generalized Pareto distribution performs well and substantially reduces the uncertainty related to the estimation of the shape parameter and of the large deficit volume quantiles.

Developing Regional Hydrological Drought Risk Models through Ordinary and Principal Component Regression Using Low-Flow Indexes in Susurluk Basin, Turkey

“…Ref. [59] obtained similar results. However, regional frequency analysis is superior to at-site frequency analysis, even if the regions are heterogeneous.…”

Regional Frequency Analysis of Drought Indexes by L-Moments Method

“…In this study, however, the regression equation could not be developed because there was no strong correlation between meteorological variables, the spatial characteristics of the stations, the basin characteristics and the average drought severity values. Strnad et al, (2020) stated in their study that the relationship of average drought severity values with the characteristics of the area under investigation and the other parameters of the analysis is uncertain. For this reason, instead of the regression equation as in the studies of (Zhang et al 2015;Kaluba et al 2017;Li et al 2022) the average values of drought severity of all stations were multiplied by the standard quantile and maps were obtained by IDW method for all return periods from 5 to 1000 years.…”

“…Kaluba et al, (2017) divided the Czech Republic region into 3 homogeneous sub-regions as a result of the discordant of several stations according to the discordancy criterion. In addition, in the studies conducted by (Kaluba et al, 2017;Núñez et al, 2011;Parvizi et al, 2022;Strnad et al, 2020;Zhang et al, 2015), the results of homogeneity and discordancy criteria were taken into account and the researchers divided the study areas into homogeneous sub-regions. In this study, although some stations failed the discordancy criteria, the Kızılırmak Basin represents the only homogeneous region according to the homogeneity criteria.…”

Diurnal Cycle Model of Lake Ice Surface Albedo: A Case Study of Wuliangsuhai Lake

“…Probability density functions are commonly used to undertake statistical analysis and describe the trends of periodic hydro-meteorological events, and to predict the frequency and time of their occurrence. The greatest advantage of probability density functions is the ability to define event lengths and peak heights, which also reduces the uncertainty in the analysis of the long-term flood distribution [35]. In other connections in surface water and groundwater, ecology, and agriculture, these functions have been applied to the assessment of the impact of climate change on hydrology and the agro-ecological environment [36].…”