Multivariate assessment of droughts: Frequency analysis and dynamic return period

Michele, Carlo De; Salvadori, Gianfausto; Vezzoli, Renata; Pecora, Silvano

doi:10.1002/wrcr.20551

Cited by 137 publications

(98 citation statements)

References 43 publications

(70 reference statements)

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“…The impact of drought cannot be fully described by a single variable (e.g. De Michele et al, 2013;Shiau et al, 2007): analyses have been carried out which consider drought severity, duration (Shiau et al, 2007), maximum deficit (Saghafian and Mehdikhani, 2013), as well as the affected area (Serinaldi et al, 2009). Another example of CE includes fluvial floods resulting from extreme rainfall occurring on a wet catchment (Pathiraja et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy)

Bevacqua

Maraun

Haff

et al. 2017

Hydrol. Earth Syst. Sci.

266

304

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Abstract. Compound events (CEs) are multivariate extreme events in which the individual contributing variables may not be extreme themselves, but their joint -dependent -occurrence causes an extreme impact. Conventional univariate statistical analysis cannot give accurate information regarding the multivariate nature of these events. We develop a conceptual model, implemented via pair-copula constructions, which allows for the quantification of the risk associated with compound events in present-day and future climate, as well as the uncertainty estimates around such risk. The model includes predictors, which could represent for instance meteorological processes that provide insight into both the involved physical mechanisms and the temporal variability of compound events. Moreover, this model enables multivariate statistical downscaling of compound events. Downscaling is required to extend the compound events' risk assessment to the past or future climate, where climate models either do not simulate realistic values of the local variables driving the events or do not simulate them at all. Based on the developed model, we study compound floods, i.e. joint storm surge and high river runoff, in Ravenna (Italy). To explicitly quantify the risk, we define the impact of compound floods as a function of sea and river levels. We use meteorological predictors to extend the analysis to the past, and get a more robust risk analysis. We quantify the uncertainties of the risk analysis, observing that they are very large due to the shortness of the available data, though this may also be the case in other studies where they have not been estimated. Ignoring the dependence between sea and river levels would result in an underestimation of risk; in particular, the expected return period of the highest compound flood observed increases from about 20 to 32 years when switching from the dependent to the independent case.

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Section: Introductionmentioning

confidence: 99%

Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy)

Bevacqua

Maraun

Haff

et al. 2017

Hydrol. Earth Syst. Sci.

266

304

View full text Add to dashboard Cite

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“…For design purposes, rainfall intensity-duration-frequency (IDF) curves have been used for a long time to synthesize the design storm. Therefore, many studies have integrated drought severity and duration based on the multivariate theory (Bonaccorso et al, 2003;González and Valdés, 2003;Mishra and Singh, 2009;Song and Singh, 2010a, b;De Michele et al, 2013). However, these studies cannot fully explain droughts without considering the frequency, which resulted in the development of drought isoseverity curves for certain return periods and durations for design purposes.…”

Section: Introductionmentioning

confidence: 99%

Development of streamflow drought severity–duration–frequency curves using the threshold level method

Sung

Chung

2014

Hydrol. Earth Syst. Sci.

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Abstract. This study developed a streamflow drought severity-duration-frequency (SDF) curve that is analogous to the well-known depth-duration-frequency (DDF) curve used for rainfall. Severity was defined as the total water deficit volume to target threshold for a given drought duration. Furthermore, this study compared the SDF curves of four threshold level methods: fixed, monthly, daily, and desired yield for water use. The fixed threshold level in this study is the 70th percentile value (Q 70 ) of the flow duration curve (FDC), which is compiled using all available daily streamflows. The monthly threshold level is the monthly varying Q 70 values of the monthly FDC. The daily variable threshold is Q 70 of the FDC that was obtained from the antecedent 365 daily streamflows. The desired-yield threshold that was determined by the central government consists of domestic, industrial, and agricultural water uses and environmental in-stream flow. As a result, the durations and severities from the desired-yield threshold level were completely different from those for the fixed, monthly and daily levels. In other words, the desired-yield threshold can identify streamflow droughts using the total water deficit to the hydrological and socioeconomic targets, whereas the fixed, monthly, and daily streamflow thresholds derive the deficiencies or anomalies from the average of the historical streamflow. Based on individual frequency analyses, the SDF curves for four thresholds were developed to quantify the relation among the severities, durations, and frequencies. The SDF curves from the fixed, daily, and monthly thresholds have comparatively short durations because the annual maximum durations vary from 30 to 96 days, whereas those from the desired-yield threshold have much longer durations of up to 270 days. For the additional analysis, the return-period-duration curve was also derived to quantify the extent of the drought duration. These curves can be an effective tool to identify streamflow droughts using severities, durations, and frequencies.

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“…In recent years, it has been widely applied to various aspects of hydrological studies [13]. The main applications of the Copula Function include the analysis of: precipitation characteristics [12], the correlation between flood peak and flood volume [14,15], the frequency and recurrence interval of floods [16][17][18], characteristics of storms [19,20], the frequency and recurrence intervals of droughts [21], drought assessment [22], risk assessment [23], and an assessment of environmental hydrological model performance [24]. Due to the specialty of the water table, the establishment of the Copula Function for this purpose is relatively difficult; therefore, no research that uses the Copula Function to analyze the water table has been reported.…”

Section: Introductionmentioning

confidence: 99%

Probability Analysis of the Water Table and Driving Factors Using a Multidimensional Copula Function

You

Liu

2018

Water

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Abstract:The relationship between the water table and driving factors is a reliable theoretical reference for the reasonable planning of surface water resources and the water table. Previous research has neglected the distribution and probabilities of the water table. However, this paper analyzes the relationship between the water table and driving factors from a statistical perspective by correcting the variables and introducing the Kernel Distribution Estimation and the Copula Function. The average data of the buried depth of the phreatic water, annual irrigation volume of the surface water, and precipitation in the Jinghui Irrigation District in China from 1977 to 2013 were adopted. We precisely obtained the two-dimensional (2D) and three-dimensional (3D) Joint Distribution Function of each driving factor and the marginal distribution of the water table, calculate the conditional probability in different ranges, and exactly predict the design value of surface water irrigation giving set conditions. Eventually, we emphasize the importance of probability analysis and prediction in groundwater planning.

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Multivariate assessment of droughts: Frequency analysis and dynamic return period

Cited by 137 publications

References 43 publications

Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy)

Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy)

Development of streamflow drought severity–duration–frequency curves using the threshold level method

Probability Analysis of the Water Table and Driving Factors Using a Multidimensional Copula Function

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