Copulas-based probabilistic characterization of the combination of dry and wet conditions in the Guanzhong Plain, China

Huang, Shengzhi; Hou, Beibei; Chang, Jianxia; Huang, Qiang; Chen, Yutong

doi:10.1016/j.jhydrol.2014.10.039

Cited by 67 publications

(35 citation statements)

References 30 publications

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“…Ultimately, the SPI value is calculated through transforming the cumulative probabilities of the Gamma distribution to the standard normal distribution (McKee et al, 1993;Kao and Govindaraju, 2010). The detailed SPI calculation formulas can be referenced to previous studies (McKee et al, 1993;Logan et al, 2010;Huang et al, 2014b).…”

Section: Standardized Precipitation Index (Spi)mentioning

confidence: 99%

The response of agricultural drought to meteorological drought and the influencing factors: A case study in the Wei River Basin, China

Huang

Chang

et al. 2015

Agricultural Water Management

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131

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The heuristic segmentation method Drought propagation The Budyko hypothesis The Wei River Basin a b s t r a c t It is of importance to investigate the response of agricultural drought to meteorological drought and its influencing factors, which could help better understand drought evolution mechanisms and facilitate agricultural drought monitoring and predictions. As a case study in the Wei River Basin (WRB), China, the heuristic segmentation method was first applied to identify change points of long-term hydrological and metrological conditions for the period of 1960-2007. Then the cross wavelet analysis was utilized to reveal the detailed links between agricultural drought (based on Palmer Drought Severity Index (PDSI)) and meteorological drought (based on Standardized Precipitation Index (SPI)) at the seasonal time scale. The controlling factors governing the agricultural response were then explored through quantitatively examining the corresponding large-scale atmospheric conditions and local-scale land surface characteristics. Results indicate that: (1) the variations of the lag time of agricultural drought in response to meteorological drought is large at the seasonal scale, characterized by fast response in summer and relatively slow response in autumn, and the contrasting response in the time lag is mainly due to the buffering effects of soils; (2) the variations of Arctic Oscillation (AO) was found to have large effects on the lag time, with both positive and negative effects; (3) a negative correlation between the lag time and the parameter w in the Fu' equation within the Budyko framework was found, implying the potential effects of vegetation cover on the drought propagation from meteorological drought to agricultural drought in the WRB.

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Section: Standardized Precipitation Index (Spi)mentioning

confidence: 99%

The response of agricultural drought to meteorological drought and the influencing factors: A case study in the Wei River Basin, China

Huang

Chang

et al. 2015

Agricultural Water Management

Self Cite

131

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“…It is noted that the SPI at different timescales has different physical implications. For instance, the SPI at a 3-month scale can reflect the variation in soil moisture, and, therefore, is useful for monitoring agricultural drought (Huang et al, 2014c). Meanwhile, the SPI benefits the elaborate classification of dry and wet conditions based on different SPI thresholds, as presented in Table 1 (Livada and Assimakopoulos, 2007).…”

Section: Datamentioning

confidence: 99%

“…A marginal distribution is the projection of a joint distribution of random variables onto a subspace defined by a subset of components (Huang et al, 2014c). According to the definition of SPI in section 3.1, there can be both negative and positive values in an SPI series.…”

Section: Marginal Distributionmentioning

confidence: 99%

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Copulas‐based risk analysis for inter‐seasonal combinations of wet and dry conditions under a changing climate

Fang

Huang

et al. 2018

Intl Journal of Climatology

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Abrupt transitions between droughts and floods present greater challenges to water resource management than independent drought or flood events. It is therefore of significant importance to further include drought-flood transitions in the risk analysis of water-related hazards under a changing climate. This study more generally evaluates the risks of combinations of dry and wet conditions between adjacent seasons. First, dry and wet conditions are monitored by the standardized precipitation index (SPI). Then, a copula-based framework is proposed for the deviation of joint return periods of dryness-wetness combinations at different severity levels. In addition, SPI series trend detection is conducted using the Mann-Kendall test to analyse the temporal-spatial changes in dry and wet conditions. Wavelet analysis is applied to investigating correlations of dry and wet conditions with climate variability signals, which may provide predictive signals for dryness-wetness combinations. The results of a case study in the Pearl River basin (PRB), China over the period of 1960-2015 indicate that (a) the flood season (from July to October) tends towards dryness and there are wetting trends in the late autumn and winter; (b) as the joint return period is considered the proxy for the risk of dryness-wetness combination, shorter joint return periods remind a higher risk of suffering from abrupt dryness-wetness transitions in the spring-summer and summer-autumn, as well as the more frequent occurrence of continued dryness/wetness in the autumn-winter and winter-spring; (c) the western and eastern PRB are separately characterized by intensified and reduced risks of the most frequent combinations under a changing climate; and (d) El Niño-Southern Oscillation events, the Pacific Decadal Oscillation and sunspot activities have a close association with dry and wet conditions in the PRB. The study provides a supplement for the current risk map and may benefit the early warning and mitigation of water-related hazards.

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“…At present, the analysis of torrential rain generally uses the design rainfall method, which is a univariate analysis based on precipitation (e.g., the designed maximum one-day precipitation and maximum three-day precipitation). In recent years, researchers realized that the analysis of torrential rain based on the distribution of univariate extremum has certain restrictions [3]. Thus, they are trying to analyze the correlation between different characteristics of rainstorms by using multivariable joint distribution, with a view to providing a more comprehensive description of rainstorm events.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Joint Probability of Different Rainfall Frequencies Based on Copulas

Wang

Chuan-zhe

Liu

et al. 2017

Water

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Abstract:The performance evaluation of a city's flood control system is essentially based on accurate storm designs, where a particular challenge is the development of the joint distributions of dependent rainfall variables. When it comes to the research design for consecutive rainfall, the analytical investigation is only focused on the maximum of consecutive rainfalls, and it does not consider the probabilistic relations between the first day of rainfall and the overall rainfall included in consecutive rainfall events. In this study, the copula method is used to separate the dependence structure of multi-day rainfall from its marginal distribution and analyse the different impacts of the dependence structure and marginal distribution on system performance. Three one-parameter Archimedean copulas, including the Clayton, Gumbel, and Frank families, are fitted and compared for different combinations of marginal distributions that cannot be rejected by statistical tests. The fitted copulas are used to generate rainfall events for a system performance analysis, including the conditional probability and design values for different return periods. The results obtained in this study highlight the importance of taking into account the dependence structure of one-day and multi-day rainfall in the context of storm design evaluations and reveal the different impacts of the dependence structure and the marginal distributions on the probability.

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Copulas-based probabilistic characterization of the combination of dry and wet conditions in the Guanzhong Plain, China

Cited by 67 publications

References 30 publications

The response of agricultural drought to meteorological drought and the influencing factors: A case study in the Wei River Basin, China

The response of agricultural drought to meteorological drought and the influencing factors: A case study in the Wei River Basin, China

Copulas‐based risk analysis for inter‐seasonal combinations of wet and dry conditions under a changing climate

Multivariate Analysis of Joint Probability of Different Rainfall Frequencies Based on Copulas

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