scholarly journals A maximum entropy copula-based frequency analysis method for assessing bivariate drought risk: a case study of the Kaidu River Basin

2021 ◽  
Author(s):  
X. Yang ◽  
Y. P. Li ◽  
G. H. Huang
Keyword(s):  
Maximum Entropy ◽  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Drought Risk ◽  
Return Periods ◽  
Drought Frequency ◽  
Joint Return ◽  
Joint Return Period ◽  
Kaidu River

Abstract In this study, a maximum entropy copula-based frequency analysis (MECFA) method is developed through integrating maximum entropy, copulas and frequency analysis into a general framework. The advantages of MECFA are that the marginal modeling requires no assumption and joint distribution preserves the dependence structure of drought variables. MECFA is applied to assessing bivariate drought frequency in the Kaidu River Basin, China. Results indicate that the Kaidu River Basin experienced 28 drought events during 1958–2011, and drought inter-arrival time is 10.8 months. The average duration is 6.2 months (severity 4.6), and the most severe drought event lasts for 35 months (severity 41.2) that occurred from June 1977 to March 1980. Results also disclose that hydrological drought index (HDI) 1 is suitable for drought frequency analysis in target year of return periods of 5 and 10, HDI 3, HDI 6 and HDI 12 are fit for the target year of return periods of 20, 50 and 100. The joint return period can be used as the upper bound of the target return period, and the joint return period that either duration or severity reaches the drought threshold can be used as the lower bound of the target return period.

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.

scholarly journals Development of a Maximum Entropy-Archimedean Copula-Based Bayesian Network Method for Streamflow Frequency Analysis—A Case Study of the Kaidu River Basin, China

Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 42 ◽  
Author(s):  
Xiangming Kong ◽  
Xueting Zeng ◽  
Cong Chen ◽  
Yurui Fan ◽  
Guohe Huang ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Maximum Entropy ◽  
River Basin ◽  
Frequency Analysis ◽  
Tail Dependence ◽  
Archimedean Copula ◽  
Hydrological Extremes ◽  
Monthly Streamflow ◽  
Upper Tail Dependence ◽  
Kaidu River

Frequency analysis of streamflow is critical for water-resources system planning, water conservancy projects and the mitigation of hydrological extremes events. In this study, a maximum entropy-Archimedean copula-based Bayesian network (MECBN) method has been proposed for frequency analysis of monthly streamflow in the Kaidu River Basin, which integrates the maximum entropy-Archimedean copula (MEAC) and Bayesian network methods into a general framework. MECBN is effective for representing the uncertainties that exist in model representation, preserving the distributional characteristics of streamflow records and addressing the correlation structure between streamflow pairs. Application to the Kaidu River Basin shows a good performance of MECBN in describing the historical data of this basin in China. The results indicate that the interactions between two adjacent monthly streamflow pairs are non-linear. There is upper tail dependence between monthly streamflow pairs. The dependence coefficients including Spearman’s rho, Kendall’s tau, and the upper tail dependence coefficient are in inverse proportion of monthly streamflow values in the Kaidu River Basin, due to the fact that other factors (i.e., rainfall, snow melting, evapotranspiration rate and requirement of water use) provide more contributions to the streamflow in the flooding season. These findings can be used for providing vital information in the prevention and control of hydrological extremes and to further water resources planning in Kaidu River Basin.

scholarly journals SESR-based Identification of Flash Drought and Multivariate Drought Risk Probability Assessment in the Pearl River Basin, China

2021 ◽  
Author(s):  
Bei Chen ◽  
Chuanhao Wu ◽  
Pat J.-F. Yeh ◽  
Jiayun Li ◽  
Wenhan Lv ◽  
...  
Keyword(s):  
River Basin ◽  
Marginal Distribution ◽  
Distribution Functions ◽  
Pearl River ◽  
Drought Risk ◽  
Return Periods ◽  
Pearl River Basin ◽  
Joint Return ◽  
The Pearl River ◽  
The Pearl River Basin

Abstract Flash drought (FD) is characterized by the rapid onset and development of drought conditions. It usually occurs during the growing seasons, causing more severe impacts on agriculture and society than the slowly-evolving droughts. Based on the Standard Evaporative Stress Ratio (SESR), this study presents an assessment of the spatio-temporal variability of the joint return periods of FD characteristics in the Pearl River basin (PRB), southern China. Three FD characteristics (i.e., duration D, intensity I, peak P) are extracted at each 0.25o×0.25o grid point over the PRB by the Runs theory. Four marginal distribution functions (Gamma, Exponential, Generalized Extreme Value and Lognormal) are used to fit FD characteristics, while three Archimedean Copula functions (Clayton, Frank and Gumbel) are used for generating the joint distributions of various paired FD characteristics. The results indicate that Lognormal is the best-fitted marginal distribution function of FD characteristics in most parts of PRB, while Frank and Clayton are the best-fitted Copula of the joint PDFs of three pairs of FD characteristics in most parts of PRB. During 1953–2013, the FD events are more frequent in eastern PRB (> 40 events) than western PRB (<10 events), and larger FD characteristics (D and I) are also found in eastern PRB than western PRB. The return period of each FD characteristic is smaller in eastern PRB than western PRB, leading to smaller joint return periods of three paired FD characteristics (D-I, D-P, P-I) in eastern PRB than western PRB. Overall, our results suggest that the risk of FD is gradually increased from the west to the east of the PRB.

scholarly journals Estimations of statistical dependence as joint return period modulator of compound events. Part I: storm surge and wave height

2017 ◽  
Author(s):  
Thomas I. Petroliagkis
Keyword(s):  
Storm Surge ◽  
Wave Height ◽  
Return Period ◽  
Flood Hazard ◽  
Joint Probability ◽  
Irish Sea ◽  
Statistical Dependence ◽  
Joint Return ◽  
Compound Events ◽  
Joint Return Period

Abstract. The possibility of utilising statistical dependence methods in coastal flood hazard calculations is investigated, since flood risk is rarely a function of just one source variable but usually two or more. Source variables in most cases are not independent as they may be driven by the same weather event, so their dependence, which is capable of modulating their joint return period, has to be estimated before the calculation of their joint probability. Dependence and correlation may differ substantially from one another since dependence is focused heavily on tail (extreme) percentiles. The statistical analysis between surge and wave is performed over 32 river ending points along European coasts. Two sets of almost 35-year hindcasts of storm surge and wave height were adapted and results are presented by means of analytical tables and maps referring to both correlation and statistical dependence values. Further, the top 80 compound events were defined for each river ending point. Their frequency of occurrence was found to be distinctly higher during the cold months while their main low-level flow characteristics appear to be mainly in harmony with the transient nature of storms and their tracks. Overall, significantly strong values of positive correlations and dependencies were found over the Irish Sea, English Channel, south coasts of the North Sea, Norwegian Sea and Baltic Sea, with compound events taking place in a zero-lag mode. For the rest, mostly positive moderate dependence values were estimated even if a considerable number of them had correlations of almost zero or even negative value.

scholarly journals Spatial Drought Characterization for Seyhan River Basin in the Mediterranean Region of Turkey

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1331 ◽  
Author(s):  
Yonca Cavus ◽  
Hafzullah Aksoy
Keyword(s):  
Spatial Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Time Scale ◽  
Mediterranean Region ◽  
Eastern Mediterranean ◽  
Eastern Mediterranean Region ◽  
Natural Phenomenon ◽  
Total Probability ◽  
Return Periods

Drought is a natural phenomenon that has great impacts on the economy, society and environment. Therefore, the determination, monitoring and characterization of droughts are of great significance in water resources planning and management. The purpose of this study is to investigate the spatial drought characterizations of Seyhan River basin in the Eastern Mediterranean region of Turkey. The standardized precipitation index (SPI) was calculated from monthly precipitation data at 12-month time scale for 19 meteorological stations scattered over the river basin. Drought with the largest severity in each year is defined as the critical drought of the year. Frequency analysis was applied on the critical drought to determine the best-fit probability distribution function by utilizing the total probability theorem. The sole frequency analysis is insufficient in drought studies unless it is numerically related to other factors such as the severity, duration and intensity. Also, SPI is a technical tool and thus difficult to understand at first glance by end-users and decision-makers. Precipitation deficit defined as the difference between precipitation threshold at SPI = 0 and critical precipitation is therefore more preferable due to its usefulness and for being physically more meaningful to the users. Precipitation deficit is calculated and mapped for 1-, 3-, 6- and 12-month drought durations and 2-, 5-, 10-, 25-, 50- and 100-year return periods at 12-month time scale from the frequency analysis of the critical drought severity. The inverse distance weighted (IDW) interpolation technique is used for the spatial distribution of precipitation deficit over the Seyhan River basin. The spatial and temporal characteristics of drought suggest that the Seyhan River Basin in the Eastern Mediterranean region of Turkey experiences quite mild and severe droughts in terms of precipitation deficit. The spatial distribution would alter greatly with increasing return period and drought duration. While the coastal part of the basin is vulnerable to droughts at all return periods and drought durations, the northern part of the basin would be expected to be less affected by the drought. Another result reached in this study is that it could be common for one point in the basin to suffer dry conditions, whilst surrounding points in the same basin experience normal or even humid conditions. This reinforces the importance of spatial analysis over the basin under investigation instead of the point-scale temporal analysis made in each of the meteorological stations. With the use of spatial mapping of drought, it is expected that the destructive and irreversible effects of hydrological droughts can be realized in a more physical sense.

scholarly journals Multi-factor joint return period of rainstorms and its agricultural risk analysis in Liaoning Province, China

2019 ◽  
Vol 10 (1) ◽  
pp. 1988-2008 ◽  
Author(s):  
Yu Feng ◽  
Ying Li ◽  
Zhiru Zhang ◽  
Shiyu Gong ◽  
Meijiao Liu ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Return Period ◽  
Liaoning Province ◽  
Agricultural Risk ◽  
Joint Return ◽  
Joint Return Period

scholarly journals Extremeness of recent drought events in Switzerland: dependence on variable and return period choice

2019 ◽  
Vol 19 (10) ◽  
pp. 2311-2323 ◽  
Author(s):  
Manuela I. Brunner ◽  
Katharina Liechti ◽  
Massimiliano Zappa
Keyword(s):  
Soil Moisture ◽  
Frequency Analysis ◽  
Return Period ◽  
Threshold Level ◽  
Low Flow ◽  
Drought Event ◽  
Return Periods ◽  
Event Identification ◽  
Groundwater Drought ◽  
Drought Frequency Analysis

Abstract. The 2018 drought event had severe ecological, economic, and social impacts. How extreme was it in Switzerland? We addressed this question by looking at different types of drought, including meteorological, hydrological, agricultural, and groundwater drought, and at the two characteristics deficit and deficit duration. The analysis consisted of three main steps: (1) event identification using a threshold-level approach, (2) drought frequency analysis, and (3) comparison of the 2018 event to the severe 2003 and 2015 events. In Step 2 the variables precipitation, discharge, soil moisture, and low-flow storage were first considered separately in a univariate frequency analysis; pairs of variables were then investigated jointly in a bivariate frequency analysis using a copula model for expressing the dependence between the two variables under consideration. Our results show that the 2018 event was especially severe in north-eastern Switzerland in terms of soil moisture, with return periods locally exceeding 100 years. Slightly longer return periods were estimated when discharge and soil moisture deficits were considered together. The return period estimates depended on the region, variable, and return period considered. A single answer to the question of how extreme the 2018 drought event was in Switzerland is therefore not possible – rather, it depends on the processes one is interested in.

scholarly journals Flood Frequency Analysis at the Downstream of Sg. Perak River Basin using Annual Maximum Flow Discharge Data

2018 ◽  
Vol 7 (4.35) ◽  
pp. 709 ◽  
Author(s):  
Munir Snu ◽  
Sidek L.M ◽  
Haron Sh ◽  
Noh Ns.M ◽  
Basri H ◽  
...  
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Maximum Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Discharge Data ◽  
Flow Discharge

The recent flood event occurred in 2014 had caused disaster in Perak and Sungai Perak is the main river of Perak which is a major natural drainage system within the state. The aim of this paper is to determine the expected discharge to return period downstream for Sg. Perak River Basin in Perak by using annual maximum flow data. Flood frequency analysis is a technique to assume the flow values corresponding to specific return periods or probabilities along the river at a different site. The method involves the observed annual maximum flow discharge data to calculate statistical information such as standard deviations, mean, sum, skewness and recurrence intervals. The flood frequency analysis for Sg. Perak River Basin was used Log Pearson Type-III probability distribution method. The annual maximum peak flow series data varying over period 1961 to 2016. The probability distribution function was applied to return periods (T) where T values are 2years, 5years, 10years, 25years, 50years, and 100years generally used in flood forecasting. Flood frequency curves are plotted after the choosing the best fits probability distribution for annual peak maximum data. The results for flood frequency analysis shows that Sg. Perak at Jambatan Iskandar much higher inflow discharge  which is 3714.45m3/s at the 100years return period compare to Sg. Plus at Kg Lintang and Sg. Kinta at Weir G. With this, the 100years peak flow at Sg Perak river mouth is estimated to be in the range of 4,000 m3/s. Overall, the analysis relates the expected flow discharge to return period for all tributaries of Sg. Perak River Basin.

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