Technical Note: Probability Distribution of Extreme Rainfall Series for Conservation Planning in a Sub-Humid Climate

Sharda, V. N.; Das, Purushottam; Ojasvi, P.R.

doi:10.13031/2013.25144

Cited by 1 publication

(1 citation statement)

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“…Based on the correlation analysis of flood characteristics, Copula functions are used to construct multivariate joint distribution functions. The commonly used distributions in hydrological frequency analysis, namely, normal distribution, log-logistic distribution [33], Weibull distribution, Generalized Extreme Value distribution (GEV) [34], and gamma distribution, are fitted to the samples of flood peak, flood volume, duration, peak shape coefficient, peak timing, angle of flood rising point, and coefficient of variation for Fuziling and Xianghongdian Reservoirs. The marginal distributions of flood peak and flood duration for the Fuziling Reservoir are determined to be log-logistic distributions, while the total flood volume and angle of flood rising point are modeled as GEV (Generalized Extreme Value) distributions.…”

Section: Copula Simulation Of Flood Characteristicsmentioning

confidence: 99%

Stochastic Flood Simulation Method Combining Flood Intensity and Morphological Indicators

Fu,

He,

Ding

2023

Sustainability

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The existing flood stochastic simulation methods are mostly applied to the stochastic simulation of flood intensity characteristics, with less consideration for the randomness of the flood hydrograph shape and its correlation with intensity characteristics. In view of this, this paper proposes a flood stochastic simulation method that combines intensity and morphological indicators. Using the Foziling and Xianghongdian reservoirs in the Pi River basin in China as examples, this method utilizes a three-dimensional asymmetric Archimedean M6 Copula to construct stochastic simulation models for peak flow, flood volume, and flood duration. Based on K-means clustering, a multivariate Gaussian Copula is employed to construct a dimensionless flood hydrograph stochastic simulation model. Furthermore, separate two-dimensional symmetric Copula stochastic simulation models are established to capture the correlations between flood intensity characteristics and shape variables such as peak shape coefficient, peak occurrence time, rising inflection point angle, and coefficient of variation. By evaluating the fit between the simulated flood characteristics and the dimensionless flood hydrograph, a complete flood hydrograph is synthesized, which can be applied in flood control dispatch simulations and other related fields. The feasibility and practicality of the proposed model are analyzed and demonstrated. The results indicate that the simulated floods closely resemble natural floods, making the simulation outcomes crucial for reservoir scheduling, risk assessment, and decision-making processes.

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Section: Copula Simulation Of Flood Characteristicsmentioning

confidence: 99%