Evaluation of trends and multivariate frequency analysis of droughts in three meteorological subdivisions of western India

2013 ◽  
Vol 34 (3) ◽  
pp. 911-928 ◽  
Author(s):  
Poulomi Ganguli ◽  
M. Janga Reddy
Keyword(s):  
Frequency Analysis ◽  
Western India ◽  
Multivariate Frequency Analysis

scholarly journals Joint return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation

2012 ◽  
Vol 9 (5) ◽  
pp. 6781-6828 ◽  
Author(s):  
S. Vandenberghe ◽  
M. J. van den Berg ◽  
B. Gräler ◽  
A. Petroselli ◽  
S. Grimaldi ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Three Dimensional ◽  
Distribution Functions ◽  
Return Periods ◽  
Design Variables ◽  
Joint Return ◽  
Design Values ◽  
Multivariate Frequency Analysis ◽  
Joint Return Period

Abstract. Most of the hydrological and hydraulic studies refer to the notion of a return period to quantify design variables. When dealing with multiple design variables, the well-known univariate statistical analysis is no longer satisfactory and several issues challenge the practitioner. How should one incorporate the dependence between variables? How should the joint return period be defined and applied? In this study, an overview of the state-of-the-art for defining joint return periods is given. The construction of multivariate distribution functions is done through the use of copulas, given their practicality in multivariate frequency analysis and their ability to model numerous types of dependence structures in a flexible way. A case study focusing on the selection of design hydrograph characteristics is presented and the design values of a three-dimensional phenomenon composed of peak discharge, volume and duration are derived. Joint return period methods based on regression analysis, bivariate conditional distributions, bivariate joint distributions, and Kendal distribution functions are investigated and compared highlighting theoretical and practical issues of multivariate frequency analysis. Also an ensemble-based method is introduced. For a given design return period, the method chosen clearly affects the calculated design event. Eventually, light is shed on the practical implications of a chosen method.

Multivariate Frequency Analysis of Meteorological Drought Using Copula

2018 ◽  
Vol 32 (5) ◽  
pp. 1741-1758 ◽  
Author(s):  
Lamneithem Hangshing ◽  
Parmendra P. Dabral
Keyword(s):  
Frequency Analysis ◽  
Meteorological Drought ◽  
Multivariate Frequency Analysis

The interpretation of detection data through direct multivariate frequency analysis.

1983 ◽  
Vol 93 (3) ◽  
pp. 574-585 ◽  
Author(s):  
Lynn A. Olzak ◽  
Thomas D. Wickens
Keyword(s):  
Frequency Analysis ◽  
Multivariate Frequency Analysis

Multivariate frequency analysis of urban rainfall characteristics using three-dimensional copulas

2018 ◽  
Vol 2017 (1) ◽  
pp. 206-218 ◽  
Author(s):  
Chenglin Liu ◽  
Yuwen Zhou ◽  
Jun Sui ◽  
Chuanhao Wu
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Three Dimensional ◽  
Rainfall Data ◽  
Rainfall Characteristics ◽  
Design Rainfall ◽  
Rainfall Depth ◽  
Multivariate Frequency Analysis ◽  
Continuous Rainfall

Abstract Urban runoff is a major cause of urban flooding and is difficult to monitor in the long term. In contrast, long term continuous rainfall data are generally available for any given region. As a result, it has become customary to use design rainfall depth as a proxy for runoff in urban hydrological analyses, with an assumption of the same frequency for runoff and rainfall. However, this approach has lack of overall coordination and cannot fully reflect the variability of rainfall characteristics. To address this issue, this study presents a three-dimensional copula-based multivariate frequency analysis of rainfall characteristics based on a long term (1961–2012) rainfall data from Guangzhou, China. Firstly, continuous rainfall data were divided into individual rainfall events using the rainfall intensity method. Then the characteristic variables of rainfall (design rainfall depth, DRD; total rainfall depth, TRD; peak rainfall depth, PRD) were sampled using the annual maximum method. Finally, a copula method was used to develop the multivariate joint probability distribution and the conditional probability distribution of rainfall characteristics. The results showed that the copula-based method is easy to implement and can better reflect urban rainstorm characteristics. It can serve a scientific reference for urban flood control and drainage planning.

Sign in / Sign up

Export Citation Format

Share Document