scholarly journals A Bayesian Copula Approach for Flood Analysis

2021 ◽  
Vol 17 (4) ◽  
pp. 354-364
Author(s):  
Izzat Fakhruddin Kamaruzaman ◽  
Wan Zawiah Wan Zin ◽  
Noratiqah Mohd Ariff

This study aims to provide joint modelling of rainfall characteristics in Peninsular Malaysia using two-dimensional copula. Two commonly regarded as important variables in the field of hydrology, namely rainfall severity and duration were derived using the Standard Precipitation Index (SPI) and their univariate marginal distributions are further identified by fitting into several distributions. The paper uses a Bayesian framework to estimate the parameter values in the marginal and copula model. The approximation of the posterior distribution by random sampling has been done by Monte Carlo Markov Chain (MCMC). Next, the authors compared these findings with those based on the classical procedure. The results indicated that the Bayesian approach can be substantially more reliable in parameter estimation for small samples.

2021 ◽  
Author(s):  
Stephen Turnbull ◽  
Nawa Pradhan ◽  
Ian Floyd

<p>There are several different infiltration, overland flow routing, and channel routing schemes that can be used in conjunction with recommended hydrodynamic and infiltration parameter values, which are found within the literature, to provide critical flooding assessments for stakeholders and decision makers.  We focus on post wildfire debris flow and flood analysis in two tributaries of the Snake River in Idaho, Trapper Creek and Rock Creek.  The Badger Fire started on September 12, 2020 in the Sawtooth National Forest in Idaho, USA, and burned sub-alpine fir, lodgepole pine, juniper, mountain brush and grass communities, in the upper part of both the Trapper Creek and Rock Creek watersheds.  Trapper Creek has a U.S. Geological Gaging station, and there are two snow gaging sites available.   The biggest concern for flooding and debris flow is the result of a wintertime rain-on-snow event, which resulted in the largest storm in the gaging record period.    </p><p>To estimate runoff in ungaged stream locations, existing process-based hydrodynamic models can be applied in a distributed form to solve the governing equations for mass, momentum and energy in a spatially explicit way. The purpose of this study is to predict potentially inundated land areas as a result of a rain-on-snow event, using the data in the gages basin to provide flood analysis information for both the gaged (Trapper Creek) and ungaged watershed (Rock Creek).  Rain-on-snow events are rainfall events that occur on the snowpack and frozen ground, resulting in a larger magnitude and volume of streamflow.  To predict these flows, Gridded Surface Subsurface Hydrologic Analysis (GSSHA) watershed models are prepared and calibrated to simulate rain-on-snow events in both watersheds.  The runoff generated from a two dimensional overland flow grid is transferred over land with a finite volume numerical method into a one dimensional channel network.  The channel network also uses a finite volume method.    The consistency in the identified range of the parametric values and their physical applicability make GSSHA an ideal candidate for this study, as the model equations provide a methods to evaluate a rain-on-snow event.</p>


2018 ◽  
Vol 34 ◽  
pp. 02048
Author(s):  
Zulkarnain Hassan ◽  
Ahmad Haidir ◽  
Farah Naemah Mohd Saad ◽  
Afizah Ayob ◽  
Mustaqqim Abdul Rahim ◽  
...  

The inconsistency in inter-seasonal rainfall due to climate change will cause a different pattern in the rainfall characteristics and distribution. Peninsular Malaysia is not an exception for this inconsistency, in which it is resulting extreme events such as flood and water scarcity. This study evaluates the seasonal patterns in rainfall indices such as total amount of rainfall, the frequency of wet days, rainfall intensity, extreme frequency, and extreme intensity in Peninsular Malaysia. 40 years (1975-2015) data records have been interpolated using Inverse Distance Weighted method. The results show that the formation of rainfall characteristics are significance during the Northeast monsoon (NEM), as compared to Southwest monsoon (SWM). Also, there is a high rainfall intensity and frequency related to extreme over eastern coasts of Peninsula during the NEM season.


MAUSAM ◽  
2021 ◽  
Vol 68 (3) ◽  
pp. 463-474
Author(s):  
Y. WANG ◽  
Z. W. SHILENJE ◽  
P. O. SAGERO ◽  
A. M. NYONGESA ◽  
N. BANDA

 Basic rainfall characteristics and drought over the Horn of Africa (HoA) is investigated, from 1901 to 2010. Standard Precipitation Index (SPI) is used to study drought variability, mainly focusing on 3-month SPI. The dominant mode of variability of seasonal rainfall was analyzed by performing Empirical orthogonal functions (EOF) analysis. Gridded data is sourced from Climate Research Unit (CRU), spanning from 1901 to 2010. The HoA experiences predominantly bimodal rainfall distribution in time; March to May (MAM) and October to December (OND). The spatial component of the first eigenvector (EOF1) shows that the MAM and OND seasonal rainfalls are dominated by negative and positive loadings, respectively. The EOF1 explain 34.5% and 58.9% variance of MAM and OND seasonal rainfall, respectively. The EOF2, 3 and 4 are predominantly positive, explaining less than 25% in total of the seasonal rainfall variance in the two seasons. The last two decades experienced the highest negative anomaly, with OND seasonal rainfall showing higher anomalies as compared to MAM season. The OND season recorded 9% more drought events as compared to MAM season. The frequency of occurrence of moderate, severe and extreme dryness was almost the same in the two seasons. These results give a good basis for regional model validation, as well as mapping out drought hotspots and projections studies in the HoA.


Author(s):  
Nader Trabelsi

The chapter attempts to test the hypothesis that cryptocurrencies are real independent financial instruments that pose no danger to global financial system stability. For the empirical analysis, the authors use data related to bitcoin and widely traded asset classes. They also utilize the copula approach as well as the CoVaR model. The results show a significant role of crypto-asset market in the stability of global markets. Precisely, they find a dependence between bitcoin and oil prices defined by a normal copula model. The empirical results regarding the systemic risk show that extreme changes in bitcoin prices may have an adverse effect on equity and gold markets. There are positive and significant effects of EUR, JPY, and WTI markets when bitcoin goes down. The authors have also shown that after 2016 the virtual market sudden changes are more likely to raise the whole regular financial system losses, except the energy market. These results are important for policymakers and investors.


2012 ◽  
Vol 15 (3) ◽  
pp. 687-699 ◽  
Author(s):  
Guangtao Fu ◽  
Zoran Kapelan

Flood analysis of urban drainage systems plays a crucial role for flood risk management in urban areas. Rainfall characteristics, including the dependence between rainfall variables, have a significant influence on flood frequency. This paper considers the use of copulas to represent the probabilistic dependence structure between rainfall depth and duration in the synthetic rainfall generation process, and the Gumbel copula is fitted for the rainfall data in a case study of sewer networks. The probabilistic representation of rainfall uncertainty is combined with fuzzy representation of model parameters in a unified framework based on Dempster–Shafer theory of evidence. The Monte Carlo simulation method is used for uncertainty propagation to calculate the exceedance probabilities of flood quantities (depth and volume) of the case study sewer network. This study demonstrates the suitability of the Gumbel copula in simulating the dependence of rainfall depth and duration, and also shows that the unified framework can effectively integrate the copula-based probabilistic representation of random variables and fuzzy representation of model parameters for flood analysis.


2011 ◽  
Vol 404 (1-2) ◽  
pp. 99-108 ◽  
Author(s):  
Hamza Varikoden ◽  
B. Preethi ◽  
A.A. Samah ◽  
C.A. Babu

2018 ◽  
Vol 53 (10) ◽  
pp. 1093-1100
Author(s):  
Alysson Jalles da Silva ◽  
Adhemar Sanches ◽  
Andréa Carla Bastos Andrade ◽  
Gustavo Hugo Ferreira de Oliveira ◽  
Antonio Orlando Di Mauro

Abstract: The objective of this work was to compare the Bayesian approach and the frequentist methods to estimate means and genetic parameters in soybean multienvironment trials. Fifty-one soybean lines and four controls were evaluated in a randomized complete block design, in six environments, with three replicates, and soybean grain yield was determined. The half-normal prior and uniform distributions were used in combination with parameters obtained from data of 18 genotypes collected in previous and related experiments. The genotypic values of the genotypes of high- and low-grain yield, clustered by the Bayesian approach, differed from the means obtained by the frequentist inference. Soybean assessed through the Bayesian approach showed genetic parameter values of the mixed model (REML/Blup) close to those of the following variables: mean heritability (h2mg), accuracy of genotype selection (Acgen), coefficient of genetic variation (CVgi%), and coefficient of environmental variation (CVe%). Therefore, the mixed model methodology and the Bayesian approach lead to similar results for genetic parameters in multienvironment trials.


2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xueqian Chen ◽  
Zhanpeng Shen ◽  
Xin’en Liu

As the uncertainty is widely existent in the engineering structure, it is necessary to study the finite element (FE) modeling and updating in consideration of the uncertainty. A FE model updating approach in structural dynamics with interval uncertain parameters is proposed in this work. Firstly, the mathematical relationship between the updating parameters and the output interesting qualities is created based on the copula approach and the vast samples of inputs and outputs are obtained by the Monte Carlo (MC) sampling technology according to the copula model. Secondly, the samples of updating parameters are rechosen by combining the copula model and the experiment intervals of the interesting qualities. Next, 95% confidence intervals of updating parameters are calculated by the nonparameter kernel density estimation (KDE) approach, which is regarded as the intervals of updating parameters. Lastly, the proposed approach is validated in a two degree-of-freedom mass-spring system, simple plates, and the transport mirror system. The updating results evidently demonstrate the feasibility and reliability of this approach.


Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 897
Author(s):  
J. Agustín García ◽  
Mario M. Pizarro ◽  
F. Javier Acero ◽  
M. Isabel Parra

A Bayesian hierarchical framework with a Gaussian copula and a generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. This spatial copula model was applied to extreme summer temperatures over the Extremadura Region, in the southwest of Spain, during the period 1980–2015, and compared with the spatial noncopula model. The Bayesian hierarchical model was implemented with a Monte Carlo Markov Chain (MCMC) method that allows the distribution of the model’s parameters to be estimated. The results show the GEV distribution’s shape parameter to take constant negative values, the location parameter to be altitude dependent, and the scale parameter values to be concentrated around the same value throughout the region. Further, the spatial copula model chosen presents lower deviance information criterion (DIC) values when spatial distributions are assumed for the GEV distribution’s location and scale parameters than when the scale parameter is taken to be constant over the region.


Author(s):  
Izzat Fakhruddin Kamaruzaman ◽  
Wan Zawiah Wan Zin ◽  
Noratiqah Mohd Ariff

This study generalized the best copula to characterize the joint probability distribution between rainfall severity and duration in Peninsular Malaysia using two dimensional copulas. Specifically, to construct copulas, Inference Function for Margins (IFM) and Canonical Maximum Likelihood (CML) methods were specially exploited. For the purpose of achieving copula fitting, the derived rainfall variables by making use of the Standardized Precipitation Index (SPI) were fitted into several distributions. Five copulas, namely Gaussian, Clayton, Frank, Joe and Gumbel were put to the tests to establish the best data fitted copula. The tests produced acknowledged and satisfactory results of copula fitting for rainfall severity and duration. Surveying the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), only three copulas produced a better fit for parametric and semi parametric approaches. Finally, two consistency tests were conducted and the results had shown that Frank Copula produced consistent results.


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