A Vine Copula‐Based Ensemble Projection of Precipitation Intensity–Duration–Frequency Curves at Sub‐Daily to Multi‐Day Time Scales

Zhang, Boen; Wang, Shuo; Moradkhani, Hamid; Slater, Louise; Liu, Jiafeng

doi:10.1029/2022wr032658

Cited by 14 publications

(11 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2022) proposed a new Tropical Cyclones (TCs) hazard scaling system that takes into account the compound hazard of TCs, and defined the Multihazard Hurricane Index using the non‐exceedance probability from the vine copula model. Similar to existing literature regarding hydrometeorological simulation based on vine copulas (Ahn, 2021; Y. Tao et al., 2021; Vernieuwe et al., 2015; W. Wang et al., 2019; B. Zhang et al., 2022), vine structure in above studies is completely determined by maximum spanning tree algorithm with Kendall’s tau rank correlation coefficient as weight factor, which indicates that the vine structure cannot reflect specific copulae relationships between different variables. Furthermore, few studies have proposed quantitative methods for estimating flood dependence between different tributaries and regions in the compound flood process (Bevacqua et al., 2017).…”

Section: Introductionsupporting

confidence: 69%

A Framework of Dependence Modeling and Evaluation System for Compound Flood Events

Wang

Shen

2023

Water Resources Research

View full text Add to dashboard Cite

The coincidence and superposition of flood processes from different rivers and regions tend to form compound flood events, determined by spatial relationship between diverse flood processes that cannot be accurately depicted and evaluated by existing dependence analysis methods. A framework, integrating multi‐dimensional vine copula model and dependence evaluation system, was developed with a testing‐oriented application to explore underlying dependence between two kinds of extreme runoff series (peak discharge and flood volume) extracted from the identified compound flood events in the upper reaches of the Yangtze River. Multi‐dimensional regular vine (R‐vine) copula models were established to depict the complex and diverse dependence, and corresponding vine structure was specified by the vine structure array that can reflect the sequence of tributaries flowing into the main stream and the spatial locations of different hydrometric stations. Dependence magnitude and association status were calculated and compared according to the optimal R‐vine copula models and information theory. Comparison with existing methods demonstrated that dependence evaluation system could reflect nonlinear and local dependence characteristics and eliminate the effect of extreme runoff series from other hydrometric station on the dependence. The association status between different extreme runoff series of the upper Yangtze River and its tributaries were diverse in view of the impact of tributary flood inflow. The proposed framework can be regarded as an effective way for dependence modeling and evaluation of compound floods, thus providing a scientific reference for the risk analysis of water resources systems.

show abstract

Section: Introductionsupporting

confidence: 69%

A Framework of Dependence Modeling and Evaluation System for Compound Flood Events

Wang

Shen

2023

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…Then,

\hat{u}

samples are converted into forecast values by the inverse CDFs of the mixed distributions (Zhang et al., 2022):

{\text{CDF}}_{M}^{-1}\left(\hat{u}\right)=\left\{\begin{array}{@{}ll@{}}{x}_{L}\hfill & \text{if}\,\hat{u}\le 1-p\hfill \\ {\text{CDF}}^{-1}\left[\frac{\hat{u}-(1-p)}{p}\right]\hfill & \text{if}\,\hat{u} > 1-p\hfill \end{array}\right.

where CDF −1 (•) represents the inverse CDFs for the fitted statistical distribution in Table 2. In this way, uniform samples are transformed into climatological forecasts of which the characteristics are in accordance with the fitted statistical distributions.…”

Section: Methodsmentioning

confidence: 99%

“…Then, 𝐴𝐴 𝐴 𝐴𝐴 samples are converted into forecast values by the inverse CDFs of the mixed distributions (Zhang et al, 2022):…”

Section: Tablementioning

confidence: 99%

Reliability of Ensemble Climatological Forecasts

Huang,

Zhao,

Tian

et al. 2023

Water Resources Research

View full text Add to dashboard Cite

Ensemble climatological forecasts play a critical part in benchmarking the predictive performance of hydroclimatic forecasts. Accounting for the skewness and censoring characteristics of hydroclimatic variables, ensemble climatological forecasts can be generated by the log, Box‐Cox and log‐sinh transformations, by the combinations of the Bernoulli distribution with the Gaussian, Gamma, log‐normal, generalized extreme value, generalized logistic and Pearson type III distributions and by the non‐parametric resampling, empirical cumulative distribution function and kernel density estimation methods. This paper is concentrated on the reliability of the 12 types of ensemble climatological forecasts. Specifically, mathematical formulations are presented and large‐sample tests are devised to verify the forecast reliability for the Multi‐Source Weighted‐Ensemble Precipitation version 2 across the globe. Climatological forecasts of monthly precipitation over 18,425 grid cells are generated for 30 years under leave‐one‐year‐out cross validation, leading to 6,633,000 (12 × 18425 × 30) sets of ensemble climatological forecasts. The results point out that the reliability of climatological forecasts considerably varies across the 12 methods, particularly in regions with high hydroclimatic variability. One observation is that climatological forecasts tend to deviate from the distributions of observations when there is inadequate flexibility to fit precipitation data. Another observation is that ensemble spreads can be overly wide when there exist overfits of sample‐specific noises in cross validation. Through the tests of global precipitation, the robustness of the log‐sinh transformation and the Bernoulli‐Gamma distribution is highlighted. Overall, the investigations can serve as a guidance on the uses of transformations, distributions and non‐parametric methods in generating climatological forecasts.

show abstract

“…We used a copula‐based model to estimate the potential impacts of dry or/and wet extremes on rice yields during the rice‐growing season. Copulas are well suited for accurately capturing extreme events such as drought and floods since they can effectively treat the tails of the distribution (B. Zhang et al., 2022). Therefore, the probabilistic assessment of the impacts of climate extremes on rice yields can be conducted based on the conditional distribution of the severity of dry or wet extremes.…”

Section: Methodsmentioning

confidence: 99%

Compound Dry and Wet Extremes Lead to an Increased Risk of Rice Yield Loss

Chen,

Wang

2023

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Extreme dry and wet events can result in significant crop yield losses. However, the impact of consecutive occurrence of dry and wet extremes on crop yield remains unclear. Here, we investigate the hotspots of compound dry and wet (CDW) extremes across global rice croplands and their impacts on rice yield. We identify a significant increasing trend in the frequency of CDW extremes during 1981–2016. The risk of yield loss caused by CDW extremes can be twice as high as the risk from individual wet and dry extremes. Furthermore, we find that global rice croplands face a 43% higher risk of rice yield loss due to dry‐to‐wet extremes compared to wet‐to‐dry extremes. Our findings provide new insights into the sustainability of global rice production and food security in the face of compound hydrological extremes.

show abstract

A Vine Copula‐Based Ensemble Projection of Precipitation Intensity–Duration–Frequency Curves at Sub‐Daily to Multi‐Day Time Scales

Abstract: The past decade has witnessed a number of rare heavy rainfall events in many countries leading to catastrophic flooding or landslides, as exemplified by recent events in Zhengzhou, China (Yin et al.

Cited by 14 publications

References 83 publications

A Framework of Dependence Modeling and Evaluation System for Compound Flood Events

A Framework of Dependence Modeling and Evaluation System for Compound Flood Events

Reliability of Ensemble Climatological Forecasts

Compound Dry and Wet Extremes Lead to an Increased Risk of Rice Yield Loss

Contact Info

Product

Resources

About