An Entropy-Based Investigation into Bivariate Drought Analysis in China

Zuo, Depeng; Hou, Wei; Jia, Hu

doi:10.3390/w9090632

Cited by 9 publications

(5 citation statements)

References 48 publications

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“…Further, Hao and Singh [84] applied the entropy-based method exclusively to model the drought duration and magnitude to determine their corresponding return periods on monthly streamflow data for the Brazos River at Waco, Texas. There are few other studies using the entropy-based method for catchments in Turkey [85] and in China [86] using SPI as a basis for modeling.…”

Section: Entropy-based Methodsmentioning

confidence: 99%

Current State of Advances in Quantification and Modeling of Hydrological Droughts

Sharma,

Panu

2024

Water

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Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future.

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Section: Entropy-based Methodsmentioning

confidence: 99%

Current State of Advances in Quantification and Modeling of Hydrological Droughts

Sharma,

Panu

2024

Water

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“…That is, according to the relationship between the drought index value and the threshold, to identify the beginning, continuation or end of drought. In this study, drought duration and severity were separated from the drought index series as the drought characteristics factors by using the Theory of Runs [ 34 , 35 , 36 ]. Drought duration is the duration from the beginning to the end of the drought event, and drought severity is the cumulative sum of the difference between the drought index value and its threshold value in the drought event process.…”

Section: Methodsmentioning

confidence: 99%

Analysis and Application of Drought Characteristics Based on Theory of Runs and Copulas in Yunnan, Southwest China

Wang

Zhang

Wang

et al. 2020

IJERPH

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Drought is a complex natural disaster phenomenon. It is of great significance to analyze the occurrence and development of drought events for drought prevention. In this study, two drought characteristic variables (the drought duration and severity) were extracted by using the Theory of Runs based on four drought indexes (i.e., the percentage of precipitation anomaly, the standardized precipitation index, the standardized precipitation evapotranspiration index and the improved comprehensive meteorological drought index). The joint distribution model of drought characteristic variables was built based on four types of Archimedean copulas. The joint cumulative probability and the joint return period of drought events were analyzed and the relationship between the drought characteristics and the actual crop drought reduction area was also studied. The results showed that: (1) The area of the slight drought and the extreme drought were both the zonal increasing distribution from northeast to southwest in Yunnan Province from 1960 to 2015. The area of the high frequency middle drought was mainly distributed in Huize and Zhanyi in Northeast Yunnan, Kunming in Central Yunnan and some areas of Southwest Yunnan, whereas the severe drought was mainly occurred in Deqin, Gongshan and Zhongdian in Northwest Yunnan; (2) The drought duration and severity were fitted the Weibull and Gamma distribution, respectively and the Frank copula function was the optimal joint distribution function. The Drought events were mostly short duration and high severity, long duration and low severity and short duration and low severity. The joint cumulative probability and joint return period were increased with the increase of drought duration and severity; (3) The error range between the theoretical return period and the actual was 0.1–0.4 a. The year of the agricultural disaster can be accurately reflected by the combined return period in Yunnan Province. The research can provide guidelines for the agricultural management in the drought area.

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“…The characteristics of the two-parameter gamma functions and the two-parameter exponential function used in the article have been presented in Table 1 (Zuo et al 2017;Mohsin et al 2021). There are several families of copula functions including the t family: Elliptical, Plackett, Gaussian, and Archimedean.…”

Section: Copula Function Definitionmentioning

confidence: 99%

“…The Archimedean family is employed in numerous studies in water engineering (e.g. Singh et al 2008;Caccamo et al 2011;Hao & Singh 2015;Zuo et al 2017;Chen et al 2019;Vergni et al 2020). Three Archimedean copula functions of Gumbel, Frank, and Clayton were applied in this article to model the correlation of variables.…”

Section: Copula Function Definitionmentioning

confidence: 99%

Analyzing the duration frequency and severity of drought using copula function in the Yazd city

EskandariPour

Soltaninia

2021

Journal of Water and Climate Change

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Duration and severity are the two main variables used in drought analysis. The copula functions are appropriate for multivariate drought analysis, as it lacks the limitations of the classical multivariate distribution function. This study investigated the bivariate frequency analysis of drought duration and severity of Yazd city in Iran synoptic station during 1953–2013. To this end, first, the drought duration and severity variables were derived from the 6-month Standardized Precipitation Index. Then, considering the distribution functions, the gamma distribution function was selected for analyzing the severity and the exponential distribution function was selected for analyzing the duration and then the Clayton copula function was selected out of the three copula functions as the most appropriate one. After conducting bivariate frequency analysis, the joint seasonal and conjunctive return period and conditional return period curves were plotted. The current study well signified that multivariate analyses could present better interpretations of the reality; for example, as it was identified in conditional return period curves of the drought, for every constant duration, the amount of the return period changed as the severity changed. On the contrary, in analyzing the univariate of duration, no effects of severity were observed.

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An Entropy-Based Investigation into Bivariate Drought Analysis in China

Cited by 9 publications

References 48 publications

Current State of Advances in Quantification and Modeling of Hydrological Droughts

Current State of Advances in Quantification and Modeling of Hydrological Droughts

Analysis and Application of Drought Characteristics Based on Theory of Runs and Copulas in Yunnan, Southwest China

Analyzing the duration frequency and severity of drought using copula function in the Yazd city

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