Droughts produce a complex set of negative economic, environmental, and social impacts from regional to national scales. The drought impact can be quantified using drought index time series. This study uses the monthly standardized precipitation index (SPI) time series as a drought index. Drought characteristics, namely, drought duration, severity, interval time and minimum SPI values, were determined. Five hundred years of daily rainfall data were simulated for evaluating drought characteristics. Appropriate distributions were selected for modeling drought duration, interval time, drought severity, and minimum SPI value in different drought states. The drought episodes were quantified using multivariate copula methods. Several copulas from the Archimedean and metaelliptical families were applied to construct four-dimensional joint distributions. The dependence structure in each drought state was investigated and drought probabilities and return period were calculated and analyzed based on a four-dimensional copula using the upper Han River basin, China, as a study area.

The coincidence of flood flows of the mainstream and its tributaries may determine flood peaks. This study analyzed the risk of flooding as a result of such flood coincidences by considering flood magnitudes and time (dates) of occurrence. The Pearson Type III (P3) and log-Pearson Type III (LP3) distributions were selected as the marginal distribution of flood magnitude for annual maximum flood series; the mixed von Mises distribution was selected as the marginal distribution of flood occurrence dates. Two four-dimensional (4D) copula functions were developed for the joint distribution of flood magnitudes and occurrence dates. The upper Yangtze River in China and the Colorado River in the United States were selected to evaluate the method of computing risk. The coincidence probabilities of flood magnitudes and dates were calculated, and the conditional probabilities for the Three Gorges Reservoir (TGR) were analyzed. Results show that the von Mises distribution can fit the observed flood dates dat...

Flood events consist of flood peaks and flood volumes that are mutually correlated and need to be described by multivariate analysis methods, of which the copula functions are most desirable. Until now, the multivariate flood frequency analysis methods based on copulas does not consider the historical floods or paleological information. This may underestimate or overestimate the flood quantiles or conditional probabilities corresponding to high return periods, especially when the length of gauged record data series is relatively short. In this paper, a modified inference functions for margins (MIFM) method is proposed and used to estimate the parameters of both marginal distribution and joint distribution with incorporation of historical information. The conditional probabilities of flood volumes given that the peak discharge exceeding various values were derived. The Three Gorges reservoir (TGR) in China was selected as a case study. The bivariate flood quantiles were obtained based on bivariate ...

Information on flood seasonality is required in many practical applications, such as estimation of seasonal design floods, flood forecasting and protection, flood-plain management and operation of water resources systems. Directional statistics (DS) and relative frequency (RF) are two of the popular methods for identifying flood seasonality. A new method, which is an improvement over DS, the IDS method, is proposed to identify flood seasonality and partition the flood season into sub-seasons. The performance of the IDS method was evaluated using synthetic data as well as observed data. The RF, DS and IDS methods were applied to the Geheyan and Baishan reservoir basins in China, and their results were compared and analysed. The results of flood seasonality identified by the IDS method were more objective than those of the RF and DS methods. Editor Z.W. Kundzewicz; Associate editor S. Grimaldi Citation Chen, L., Singh, V.P., Guo, S.-L., Fang, B., and Liu, P., 2013. A new method for identification of flood seasons using directional statistics. Hydrological Sciences Journal, 58 (1), 1–13.