Abstract
This study investigates the sensitivity and uncertainty of hydrological droughts frequencies and severity in the Weihe Basin, China during 1960–2012, by using six commonly used univariate probability distributions and three Archimedean copulas to fit the marginal and joint distributions of drought characteristics. The Anderson-Darling method is used for testing the goodness-of-fit of the univariate model, and the Akaike information criterion (AIC) is applied to select the best distribution and copula functions. The results demonstrate that there is a very strong correlation between drought duration and drought severity in three stations. The drought return period varies depending on the selected marginal distributions and copula functions and, with an increase of the return period, the differences become larger. In addition, the estimated return periods (both co-occurrence and joint) from the best-fitted copulas are the closet to those from empirical distribution. Therefore, it is critical to select the appropriate marginal distribution and copula function to model the hydrological drought frequency and severity. The results of this study can not only help drought investigation to select a suitable probability distribution and copulas function, but are also useful for regional water resource management. However, a few limitations remain in this study, such as the assumption of stationary of runoff series.
Highlights
Hydrological drought refers to a lack of water in the hydrological system and gives rise to negative impacts on river ecosystems and human lives [1]
The skewness coefficient of drought severity is larger than the drought duration
This study investigated the regional drought frequency analysis in the Weihe River Basin considering the spatio-temporal structure of drought with copula functions
Summary
Hydrological drought refers to a lack of water in the hydrological system and gives rise to negative impacts on river ecosystems and human lives [1]. Hydrological droughts are typically defined as periods when streamflow below a pre-defined threshold, called the threshold level method (TLM) [2]. Advantages of the TLM are (i) no a priori knowledge of probability distributions is required, and (ii) it directly produces drought characteristics (e.g., duration, severity, frequency). When the variable of interest (x) (i.e., soil moisture, groundwater storage, or discharge) is below a predefined threshold (τ), a drought is assumed to have occurred. A constant or a threshold can be used, and because a variable threshold level takes seasonal patterns into account, it has been widely used [3,4].
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call