Flood routing via a copula-based approach

Tahroudi, Mohammad Nazeri; Ramezani, Yousef; Michele, Carlo De; Mirabbasi, Rasoul

doi:10.2166/nh.2021.008

Cited by 22 publications

(3 citation statements)

References 37 publications

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“…Where f is the joint density distribution; f x i i ( ) is the marginal density distribution of the random variable x i , where i d = 1 2 , ,  ; and c is the density of the Copula function. More details about the Copula can be found in Khozeymehnezhad and Nazeri-Tahroudi (2020) and Nazeri-Tahroudi et al (2022). Our article mainly introduced and used the six typical Copula functions that were widely applied (Supplementary Table S1).…”

Section: Vine Copula Functionmentioning

confidence: 99%

Algal proliferation risk assessment using Vine Copula-based coupling methods in the South-to-North Water Diversion Project of China

Nong

Zeng

et al. 2023

Front. Ecol. Evol.

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The Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the longest inter-basin water diversion project (1,432 km) in the world, has delivered more than 60 billion m3 of water resources to North China and benefiting more than 100 million people since December 2014. However, the abnormal algal proliferation in the main canal under low nutrient background has seriously threatened the water quality safety of this mega project. In this research, 3 years of monitoring data matrix, including water temperature (WT), flow discharge (Q), flow velocity (V), dissolved oxygen (DO), and the algal cell density (ACD), from the main canal of the MRSNWDPC were analyzed. The nonlinear relationships were determined based on multiple regression models, and a composite risk analysis model was constructed by Latin hypercube sampling (LHS) method coupled with Vine Copula function. The impacts of different hydrological and environmental factors on algal proliferation were comprehensively analyzed by Bayesian theory. The results showed that the WT gradually decreased from upstream to downstream, with a narrow range of 16.6–17.4°C, and the annual average concentrations of DO showed a gradual increase from upstream to downstream. The flow velocity of MRSNWDPC had a tendency to increase year by year, and the maximum flow velocity exceeds 0.8 m/s upstream, midstream and downstream by 2018. The ACD accumulated along the main canal, and the annual average ACDs of downstream were the highest, ranging from 366.17 to 462.95 × 104 cells/L. The joint early-warning method considering both water temperature and flow velocity conditions is an effective way for algal proliferation risk warning management. When water temperatures of the upstream, midstream, and downstream were below 26, 26, and 23°C, respectively, the algal proliferation risk can be controlled under 50% by the flow velocity at 0.3 m/s; otherwise, the flow velocity needs to be regulated higher than 0.8 m/s. In order to keep the midstream and downstream avoid abnormal algal proliferation events (ACD ≥ 500 × 104 cells/L), the corresponding ACDs of the upstream and midstream need to be controlled lower than 319 × 104 cells/L and 470 × 104 cells/L, respectively. This study provides a scientific reference for the long-distance water diversion project’s algal control and environmental protection. The proposed coupling Vine Copula models can also be widely applied to multivariate risk analysis fields.

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Section: Vine Copula Functionmentioning

confidence: 99%

Algal proliferation risk assessment using Vine Copula-based coupling methods in the South-to-North Water Diversion Project of China

Nong

Zeng

et al. 2023

Front. Ecol. Evol.

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“…Based on this aspect, Tahroudi et al . (2021) and Khashei‐Siuki et al . (2021) used vine copula to simulate flood hydrograph and potential evapotranspiration model, respectively, in their studies.…”

Section: Introductionmentioning

confidence: 99%

“…The complete freedom to select bivariate copula in each step of hierarchical structure provides elevated flexibility in vine copula based multivariate dependence modelling than traditional copulas (Ni et al, 2020). Based on this aspect, Tahroudi et al (2021) and Khashei-Siuki et al (2021) used vine copula to simulate flood hydrograph and potential evapotranspiration model, respectively, in their studies. They found that the efficiency of vine copula in dependence analysis was good and accurate.…”

Section: Introductionmentioning

confidence: 99%

Integrated Drought Index based on Vine Copula Modelling

Kanthavel

Saxena

Singh

2022

Intl Journal of Climatology

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As a natural hazard, drought is a complex multivariate phenomenon that does not depend on only one hydrometeorological variable, as well as, considering a particular kind of drought may not be effective for drought management. Considering this, many multivariate drought indices have been developed based on linearity assumptions or conventional copulas assuming symmetric relationships among univariate drought indices. In this study, D-vine copula was applied to construct a four-dimensional index, named as Integrated Drought Index (IDI), by combining four univariate drought indices (Standardized Precipitation Index [SPI], Reconnaissance Drought Index [RDI], Standardized Soil moisture Index [SSI] and Standardized stream flow Drought Index [SDI]) to better reflect many hydrometeorological variables (precipitation, evapotranspiration, soil moisture and stream flow) and different kinds of drought (meteorological, agricultural and hydrological) simultaneously. Vine copula was used to solve nonlinear and asymmetric relationships among drought indices due to its flexibility over the free selection of copula(s) in each step of hierarchical structure in high dimensional modelling. The IDI was constructed for 1-and 4-month timescales for the upper Tapti basin of the central region in India. The performance of IDI was tested with dependence measures (Pearson's correlation coefficient, Mutual Information) and evaluated against the Terrestrial Water Storage Anomaly data derived from the Gravity Recovery and Climate Experiment (GRACE) mission. Spatial analysis of drought was carried out by fuzzy c-means (FCM) clustering algorithm with IDI. IDI based on vine copula solved the nonlinear and asymmetric relationships among different variables associated with the occurrence of droughts effectively with a reduction of uncertainty as compared to the single drought indices for different kinds of droughts. Analysis revealed spatially different drought risks in the upper and lower river basins. In general, the vine copula addresses nonlinear and asymmetric relationships that exist between the variables associated with natural hazards like drought.

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Application of copula‐based approach as a new data‐driven model for downscaling the mean daily temperature

Tahroudi

Ramezani

Michele

et al. 2022

Intl Journal of Climatology

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Downscaling and simulating various meteorological variables at different time scales are fundamental topics for making climate change studies in a geographic region. Here, a new approach for downscaling the mean daily temperature was implemented using a vine copula-based approach and considering the best CanESM2 predictors. The accuracy of the copula-based approach was compared with genetic programming (GP), optimized support vector regression (OSVR), support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models at Birjand synoptic station in Iran. In the proposed approach, after examining the different vine copulas, the D-vine copula was selected as the best copula according to the evaluation statistics and tree sequences. According to the root-mean-square error (RMSE) and Nash-Sutcliff efficiency (NSE), the accuracy of the ANN model in downscaling the mean daily temperature data was not acceptable and the other considered models were slightly overestimated. The results indicated that the copula-based approach outperformed the other models in downscaling the mean daily temperature with NSE = 0.61. However, given the 99% confidence interval of the simulations, a slightly overestimation at temperatures above 20 C was observed for the copula-based approach, which has better performance than the other considered models. The copula-based approach was able to reduce RMSE by about 82, 20, 24, 47 and 34% compared to ANN, OSVR, GP, SVM and ANFIS models, respectively. The results also showed that the performance of the support vector regression model optimized by the ant colony algorithm is also acceptable and is in the second rank after the copulabased approach. The accuracy of the copula-based approach was also confirmed according to Taylor diagram and violin plot. The proposed approach has a higher accuracy than data-driven models due to use of the conditional density of vine copulas, and the joint distribution of the mean daily temperature and selected predictors.

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Flood routing via a copula-based approach

Cited by 22 publications

References 37 publications

Algal proliferation risk assessment using Vine Copula-based coupling methods in the South-to-North Water Diversion Project of China

Algal proliferation risk assessment using Vine Copula-based coupling methods in the South-to-North Water Diversion Project of China

Integrated Drought Index based on Vine Copula Modelling

Application of copula‐based approach as a new data‐driven model for downscaling the mean daily temperature

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