Copula‐Based Modeling of Flood Control Reservoirs

Balistrocchi, Matteo; Orlandini, Stefano; Ranzi, Roberto; Bacchi, Baldassare

doi:10.1002/2017wr021345

Cited by 27 publications

(23 citation statements)

References 47 publications

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“…Generally, flood control can be faced by a univariate approach relying on peak flow discharge statistics only when the main issue lies within the conveyance capacity of the river cross sections. Otherwise, flood volume is the most significant variable [25]. According to the terrain, river channels, and flood processes characteristics of the study area mentioned in Section 2.1, the flood disaster in the study area is mainly affected by the flood volume, thus, this paper used the flood volume level to determine the flood magnitude.…”

Section: Determination Of Flood Magnitudementioning

confidence: 99%

Assessing the Effects of Urbanization on Flood Events with Urban Agglomeration Polders Type of Flood Control Pattern Using the HEC-HMS Model in the Qinhuai River Basin, China

Fang

Gao

et al. 2018

Water

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Abstract:Urban agglomeration polders type of flood control pattern (UAPFCP) is an extensively used pattern for urban flood control in plain water system areas. Urbanization and polders are two main factors that affect the runoff process in these regions. The Qinhuai River basin, one of the representative watersheds of this flood control pattern in East China, was selected to perform the study. Five urbanization scenarios (the historical, current, and three assumed future urbanization scenarios) of the basin were defined in this paper. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was used to simulate basin runoff. The results indicate that the UAPFCP increased the flood volume (Q v ) and peak flow (Q p ) compared to the results under the condition without polders. With the constant improvement of the urbanization level of the basin, Q v and Q p under the with polder condition increased correspondingly, and the potential changes show a linear relationship. The urbanization and urban agglomeration polders have interactions with flood events. The effect of urbanization on the flood process is weakened because of the existence of urban agglomeration polders. With the constant improvement of the urbanization level, the effect on the flood process caused by urban agglomeration polders becomes gradually weaker.

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Section: Determination Of Flood Magnitudementioning

confidence: 99%

Assessing the Effects of Urbanization on Flood Events with Urban Agglomeration Polders Type of Flood Control Pattern Using the HEC-HMS Model in the Qinhuai River Basin, China

Fang

Gao

et al. 2018

Water

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“…Dependence between sites can be modeled by copulas ( Min & Czado, 2010 ; Aghakouchak, 2014 ; Liu et al, 2015 ; Balistrocchi et al, 2017 ; Aranda & García-Bartual, 2018 ; Addo, Chanda & Metcalfe, 2018 ; Wang et al, 2018 ). Hao & Singh (2013) have proposed the maximum entropy copula for multi-site streamflow simulation and shown a reasonable agreement with observed streamflow.…”

Section: Introductionmentioning

confidence: 99%

Dependence structure analysis of multisite river inflow data using vine copula-CEEMDAN based hybrid model

Nazir

Hussain

Faisal

et al. 2020

PeerJ

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Several data-driven and hybrid models are univariate and not considered the dependance structure of multivariate random variables, especially the multi-site river inflow data, which requires the joint distribution of the same river basin system. In this paper, we proposed a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Vine copula-based approach to address this issue. The proposed hybrid model comprised on two stages: In the first stage, the CEEMDAN is used to extract the high dimensional multi-scale features. Further, the multiple models are used to predict multi-scale components and residuals. In the second stage, the residuals obtained from the first stage are used to model the joint uncertainty of multi-site river inflow data by using Canonical Vine. For the application of the proposed two-step architecture, daily river inflow data of the Indus River Basin is used. The proposed two-stage methodology is compared with only the first stage proposed model, Vector Autoregressive and copula-based Autoregressive Integrated Moving Average models. The four evaluation measures, that is, Mean Absolute Relative Error (MARE), Mean Absolute Deviation (MAD), Nash-Sutcliffe Efficiency (NSE) and Mean Square Error (MSE), are used to observe the prediction performance. The results demonstrated that the proposed model outperforms significantly with minimum MARE, MAD, NSE, and MSE for two case studies having significant joint dependance. Therefore, it is concluded that the prediction can be improved by appropriately modeling the dependance structure of the multi-site river inflow data.

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“…It was found to provide an effective and straightforward strategy for inferring probability functions from multivariate sample data. Powerful tests developed inside copula framework allowed to investigate the empirical dependence structure in an accurate manner, especially with respect to the evaluation of tail dependencies (Balistrocchi, 2017). The dependence of copula model between intensity and rain fall duration, both properties of marginal distribution and dependence between intensity and storm duration were preserved.…”

Section: Introductionmentioning

confidence: 99%

Copula and ARMA based study of controlled outflow at Farakka barrage

Singh

Desai

Sharma

et al. 2018

Preprint

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Abstract. In this study, 25 years mean monthly out flow discharge data of Farakka barrage was used (i.e., from 1949 to 1968). Farakka barrage is located between on Ganga River. Spatial and temporal variation in flow rate for any particular area is very common due to various meteorological and other factors existing in nature. But large variations in these factors cause extreme events (e. g., floods and droughts). Monthly outflow discharge for a particular critical month are predicted using statistical models (ARMA Model and Copula Model). Different Copulas (i.e., Normal, t, Frank, Clayton, Gumbel–Hoggard, Ali–Mikhail–Haq) are used for this purpose and the copula model is selected based on distribution functions (Normal distribution, Lognormal distribution, Extreme value type-1 distribution, Generalized Extreme value type, Gamma distribution, Weibull distribution, Exponential distribution). The distribution is selected based on the Mean square error (MSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The model parameters were computed using the Maximum Likelihood (ML) estimation method.

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Copula‐Based Modeling of Flood Control Reservoirs

Cited by 27 publications

References 47 publications

Assessing the Effects of Urbanization on Flood Events with Urban Agglomeration Polders Type of Flood Control Pattern Using the HEC-HMS Model in the Qinhuai River Basin, China

Assessing the Effects of Urbanization on Flood Events with Urban Agglomeration Polders Type of Flood Control Pattern Using the HEC-HMS Model in the Qinhuai River Basin, China

Dependence structure analysis of multisite river inflow data using vine copula-CEEMDAN based hybrid model

Copula and ARMA based study of controlled outflow at Farakka barrage

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