Wet and dry spell analysis using copulas

Abstract

Climate variability modulates spatio‐temporal variability of dry spells (DSs) and wet spells (WSs) within a river basin and will affect water resources management practices leading to various impacts on the socio‐economic development in river basins. In this study, we evaluated spatio‐temporal variability of DS and WS in Huai River basin (HRB), China, by developing copula‐based severity‐duration‐frequency (SDF) curves. The result shows that the upper reach and the southern part of middle reach of HRB are prone to both DS and WS; however, the duration and severity of WS are comparatively higher in comparison to DS. It was observed that DS is more frequent in spring and summer, whereas WS in summer and autumn. The choice of copula plays an important role in deriving the SDF curves, and an inappropriately chosen copula function may result in a large bias of SDF estimation. The arch12 copula was found to be the best choice in the majority of stations for deriving the SDF curves. The constructed SDF curves primarily shows two major patterns for DS and WS, i.e. concave down pattern and convex up pattern. The frequency of extreme DS decreases from 1960s to 1990s, and increases after 2000s, while the frequency of extreme WS increases from 1970s to 1990s and then decreases from 1990s to 2000s. The results in this study can provide useful information for designing conservation structures and to develop water allocation strategies at different temporal scales.

Copula-based drought risk assessment combined with an integrated index in the Wei River Basin, China

“…To overcome this deficiency , many efforts (Ma et al, 2013; have been devoted to investigating drought risk on two-dimensional or even multi-dimensional levels (e.g., duration, severity, and intensity). Among these methods, the copula function, initially introduced by Sklar (1959), has been widely applied in multi-dimensional drought risk analysis because of the flexible and margin-free characteristics of the method (Bazrafshan et al, 2015;Salvadori and De Michele, 2015;She et al, 2016). Song and Singh (2009) used elliptical copulas to model the joint distribution of drought severity and interval time.…”

Copulas‐based risk analysis for inter‐seasonal combinations of wet and dry conditions under a changing climate

“…This study more generally considers combinations of dryness and wetness between successive seasons that comprise the abrupt transitions from dryness (wetness) to wetness (dryness), prolonged dry conditions and prolonged wet conditions (Mu et al, 2014). The four types of drynesswetness combinations have been observed to occur during the past half-century in many regions, especially in climatic transition zones (Wu et al, 2006a;2006b;She et al, 2016;Shan et al, 2018a). However, the definition of drynesswetness combinations is concerned by more recent studies as a result of the occurrence with enhanced frequency and intensity (Shan et al, 2018b).…”

“…During 2012-2014, California was hit by a record-breaking persistent drought that resulted in water use restrictions, rapid decline in groundwater levels and fallowed farmlands (Griffin and Anchukaitis, 2014;Williams et al, 2015). Influenced by the Meiyu front and remnants of typhoons, the Huai River basin, China is historically vulnerable to prolonged flooding in the summer and early autumn (Ye et al, 2014;She et al, 2016). With respect to dryness-wetness transitions, one example was an abrupt transition from the spring drought to the summer flood over the middle-lower reaches of the Yangtze River valley in 2011.…”

Study on the Variation of Terrestrial Water Storage and the Identification of Its Relationship with Hydrological Cycle Factors in the Tarim River Basin, China

“…Worldwide, the hydrological cycle can be identified by precipitation [9], river discharge [10], and other hydrological variables [11]. Generally, while the drought indices are valuable tools for dryness/wetness conditions assessment, the Standardized Precipitation Index (SPI) [12], the Palmer Drought Severity Index (PDSI) [13], the stream flow drought index (SDI) [14], and the surface water supply index (SWSI) [15] are commonly and widely used to qualify the dryness/wetness level. There are multiple drought indices and methods to assess the dryness/wetness level in the TRB [16,17].…”