A GLM copula approach for multisite annual streamflow generation

Porto, Victor Costa; Filho, Francisco de Assis de Souza; Carvalho, Taís Maria Nunes; Studart, Ticiana Marinho de Carvalho; Portela, María Manuela

doi:10.1016/j.jhydrol.2021.126226

Cited by 2 publications

(4 citation statements)

References 54 publications

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“…Copulas are flexible in choosing arbitrary marginal distributions, depicting the dependence structure, and permitting a separate consideration of dependence structure and marginal distributions (Brechmann & Schepsmeier, 2013; Joe, 1997; Nelsen, 2006). Copula based models have emerged as an effective approach for addressing interdependence between multiple variables, and research regarding copula functions has become quite prevalent in many fields (Favre et al., 2004; Grimaldi et al., 2016; Jongman et al., 2014; Porto et al., 2021; Renard & Lang, 2007; Tosunoglu & Kisi, 2016). However, single parametric copula function lacks flexibility in modeling complex and diverse dependence structures with high dimensionality, which limits its application in spatial relationship analysis of compound floods based on multisite runoff records (Bevacqua et al., 2017; Chen et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Shen

2023

Water Resources Research

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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.

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Section: Introductionmentioning

confidence: 99%

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

Wang

Shen

2023

Water Resources Research

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“…Porto et al. (2021) presented a multisite annual streamflow generation model that combines the Generalized Linear Model (GLM), for determining the temporal structure, with copulas, for modeling the spatial dependence joint distributions. Wang, Zhou, et al.…”

Section: Introductionmentioning

confidence: 99%

“…However, such cross‐correlation structures cannot be considered into traditional single‐catchment streamflow models (Li et al., 2015; Medda & Bhar, 2019; Roderick & Farquhar, 2011). In this context, the development of multi‐catchment models are necessary to preserve the cross‐correlation (Mathai & Mujumdar, 2019; Medda & Bhar, 2019; Porto et al., 2021). At the same time, identification of factors driving runoff variations in multi‐catchment watershed systems has attracted considerable attention (Liu et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Oppel and Fischer (2020) assessed the clusters of temporal distributions of rainfalls and their coherence with flood types through one clustering approach based on unsupervised learning. Porto et al (2021) presented a multisite annual streamflow generation model that combines the Generalized Linear Model (GLM), for determining the temporal structure, with copulas, for modeling the spatial dependence joint distributions. Wang, Zhou, et al (2021) combined correlated sampling methods and a resampling strategy to generate multisite conditioned streamflow series.…”

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confidence: 99%

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Development of a Stepwise‐Clustered Multi‐Catchment Hydrological Model for Quantifying Interactions in Regional Climate‐Runoff Relationships

Wang

Huang

et al. 2022

Water Resources Research

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The concurrent variations of multi‐catchment runoffs exist widely in natural hydrological systems. Approaching such variations requires integrated analyses of not only the climate‐runoff relationships within individual catchments but also the distributive interactions among multiple catchments. In this study, a stepwise‐clustered multi‐catchment hydrological model (SCMW) is proposed to tackle the interactive relationships among multi‐catchment runoffs and their concurrent variations within a watershed system. Through multivariate inference based on Wilks likelihood ratio criterions and F tests, the proposed model can deal with both continuous and discrete variables as well as nonlinear relations among multiple variables without the assumptions of functional relationships. The proposed SCMW is applied to the Iskut‐Stikine Watershed, Canada. The effects of multiple uncertain factors are traced through multilevel factorial analysis. Overall, the accuracies of SCMW‐simulated mean and interval flows demonstrate that the developed method can well reproduce the distributive and interactive relationships between climatic variables and multi‐catchment runoffs. At the same time, the contributions of climate variables can be quantified; for example, it is found that near‐surface minimum temperature (at Below Johnson Station) can explain 25.6% concurrent variations of multi‐catchment runoffs, and vapor pressure (at Below Johnson Station) and precipitation (at Telegraph Creek Station) can explain 17.5% and 4.6% of such variations, respectively. The results of the multilevel factorial analysis indicate that the uncertainties in the simulated runoff levels are mainly from the modeling approach (43.3%); also, significant effects exist from interactions among multiple impact factors. The molding of concurrent variations of multi‐catchment runoffs through SCMW is helpful for improving simulation accuracy for watersheds with spatially heterogeneous climate‐runoff relationships.

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A GLM copula approach for multisite annual streamflow generation

Cited by 2 publications

References 54 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

Development of a Stepwise‐Clustered Multi‐Catchment Hydrological Model for Quantifying Interactions in Regional Climate‐Runoff Relationships

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