Analysis of Hydrologic Drought Frequency Using Multivariate Copulas in Shaying River Basin

Abstract

Droughts, considered one of the most dangerous and costly water cycle expressions, always occurs over a certain region, lasting several weeks or months, and involving multiple variables. In this work, a multivariate approach was used for the statistical characterization of hydrological droughts in Shaying River Basin with data from 1959–2008. The standard runoff index (SRI) and the run theory were employed to defined hydrological drought character variables (duration, severity, and intensity peak). Then, a multivariate joint probability analysis with four symmetric and corresponding asymmetric Archimedean Copulas was presented; and the multivariate frequency analysis with the joint return periods (Tand and Tor) were estimated. The results showed that the hydrological droughts have a severity of 4.79 and 5.09, and the drought intensity peak is of 1.35 and 1.50 in Zhoukou station and Luohe station, respectively; the rank correlation coefficients τ are more than 0.5, which means multivariate copulas can effectively describe the joint frequency distributions among multivariate variables. Drought risk shows a spatial variation: the downstream observed at Zhoukou station is characterized by a higher multivariate drought risk. In general, multivariate copulas provide a reliable method when constructing a comprehensive drought index and evaluating multivariate drought characteristics. Thus, this paper can provide useful indications for the multi-dimensional droughts’ risks assessment in Shaying River Basin.

Spatiotemporal monitoring of drought using satellite precipitation products: The case of Borena agro-pastoralists and pastoralists regions, South Ethiopia

“…The magnitude of a drought event is proportional to the cumulative water shortage falling below a certain threshold (SPI ≥ −1) during the drought period [ 18 , 47 , 55 ]. The intensity of drought is the correlation between the degree of drought and the duration of the event [ 47 , 56 , 57 ] and calculated using Eqs. (2) , (3) .…”

Multivariate Drought Monitoring, Propagation, and Projection Using Bias‐Corrected General Circulation Models

“…MSDI examines meteorological and hydrological drought characteristics by establishing the joint distribution with cumulative joint probability of precipitation and runoff using a copula function. Copulas are often used in multivariate frequency analysis, risk assessment, and drought modeling to simulate the dependency patterns of multivariate data (Aas et al., 2009; Hao & AghaKouchak, 2013; Hao et al., 2017; Laux et al., 2011; Ma et al., 2022). Negative MSDI denotes a dry climate, while positive MSDI denotes a wet climate.…”

“…Therefore, the HTWL considers the aggregate number of heatwaves during a specific time frame (Adeyeri et al., 2022) to account for the propagation time. Additionally, multivariate copula functions (Hao et al., 2017; Laux et al., 2011; Ma et al., 2022) were employed for combined drought modelling based on the MSDI. The multivariate drought return period was constructed based on its features using the joint probability derived from the copula functions.…”