Drought Risk Assessment in Central Asia Using a Probabilistic Copula Function Approach

Zhang

et al. 2020

IJERPH

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.

Section: Discussionmentioning

confidence: 99%

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

Zhang

et al. 2020

IJERPH

“…In the identification of drought processes, the Theory of Runs is a commonly utilized time series analysis tool ). The Theory of Runs was presented by Yevjevich (Yevjevich 1967) (Wang, et al 2020) as a framework for defining and studying drought. The term "run" refers to a sequence of the same symbol that meets a set of requirements.…”

Section: Drought Indicesmentioning

confidence: 99%

“…Archimedean copulas and Elliptical copulas are the two most commonly utilized copula classes (Qin, et al 2021) (Ayantobo, et al 2019) (Wang, et al 2020) ) (Huang, et al 2014a) (Tsakiris, et al 2016) (Song and Singh 2010b). In this study, nine copula functions such as Clayton, Frank, Gumbel, Joe, Student t copula (t-copula), Gaussian, Survival Clayton, Survival…”

Section: Marginal Distributionmentioning

confidence: 99%

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Copula-based multivariate analysis of hydro-meteorological drought

Shaw

Chithra

2023

Theor Appl Climatol

Droughts have a wide range of adverse impacts on the environment, society, and economy ranging from regional to national levels. Multivariate analysis of drought characteristics (i.e., drought duration, severity, peak, etc.) is necessary to get a picture of the actual drought situation in a region as these characteristics are interrelated. However, such studies are limited. Therefore, the current study developed a framework to investigate the meteorological and hydrological droughts based on a multivariate analysis of drought characteristics in the Pennar river basin of India which is located in a semi-arid region where agricultural activity is dominant. Long-term observations of precipitation, temperature, and streamflow for the period of 1980-2015 were used to calculate Standardized Precipitation Evapotranspiration Index (SPEI) and Streamflow Drought Index (SDI), at a 3-month timescale. Based on these indices, three drought characteristics (i.e., drought duration, severity, and peak) were abstracted using the Run Theory. The best-fit marginal distribution was determined for each of the drought variables and those best-fit marginal distributions were used to get the bivariate joint probability distribution, i.e., the copula function. The Copula function was used to figure out the joint probability distribution of multiple variables. Based on the best-fit copula function, joint multivariate distributions have been established and the joint probabilities and the joint return period have been estimated. Results revealed that, out of the nine copula functions, for the overall region of the river basin, for meteorological drought, Frank Copula is found to be the best-fit copula function for the joint risk of drought duration and severity, whereas Survival Clayton copula is found to be the best-fit copula for the combination of both drought duration and peak as well as drought severity and peak. The probability of occurrence of a drought event with specified characteristics of duration, severity, and peak was obtained for the study area based on Copula-based joint probability analysis. The joint return period of a drought event with characteristics greater than the 25 th percentile of the same for the study area is varying from 1 to 2 years whereas it varies from 1 year to 45 years for the 50 th percentile. This analysis will provide important information required for water management and planning in a region to mitigate droughts.

“…Drought indices based on remote sensing monitoring include the Normalized Difference Vegetation Index(NDVI) (Chu et al, 2019), the Vegetation Temperature Condition Index (VTCI) (Zhou et al, 2020), and the Perpendicular Drought Index (PDI) (Nie et al, 2020), etc. The most common drought indices are the Palmer Drought Severity Index (PDSI) (Wang et al, 2015, Yan et al, 2016, the Standardised Precipitation Index (SPI) (Karimi et al, 2019, Bhunia et al, 2020, and the Standardised Precipitation-Evapotranspiration Index (SPEI) (Zhang et al, 2020a, Musei et al, 2021. PDSI has been widely used as a more mature drought monitoring indicator, but the xed time scale is its limitation.…”

Section: Introductionmentioning

confidence: 99%

Agricultural drought disaster risk assessment in Shandong Province, China

2023

Shandong Province, the main grain-producing area in China, has ranked rst in China in terms of total agricultural output value for many years. However, droughts with high frequency and long duration have been hindering local agricultural production. This paper aims to assess the risk of drought disaster in Shandong Province. Based on the natural disaster system theory, an agricultural drought disaster risk assessment model is developed. This model is applied to assess the agricultural drought hazard, exposure, vulnerability, emergency response and recovery capability, and comprehensive agricultural drought disaster risk year by year from 2012 to 2019. The results show that: (1) There is an aridity trend in Shandong Province. The interannual variation of drought hazard is obvious, and the areas with high drought hazard levels are largely located in the eastern part. (2) Agricultural exposure and vulnerability are mainly concentrated in the western part of Shandong Province. With the decrease in the proportion of the rural population, there is a slight downward trend of agricultural exposure. (3) The interannual variation of emergency response and recovery capability in Shandong Province is relatively stable. Agricultural insurance premiums in all regions have a clear upward trend, which is helpful for disaster risk reduction. (4) Rizhao has been in a high-risk area, moreover, Binzhou, Dezhou, and Liaocheng have gradually become high-risk areas in recent years. The interannual variation characteristics and spatial zoning of agricultural drought risk are explored, it is instructive for risk decision-makers to better develop drought response measures and improve drought resilience.