Spatio-temporal analysis of daily, seasonal and annual precipitation concentration in Jharkhand state, India

Zamani, Reza; Mirabbasi, Rasoul; Nazeri, Mohammad; Meshram, Sarita Gajbhiye; Ahmadi, Farshad

doi:10.1007/s00477-017-1447-3

Cited by 64 publications

(32 citation statements)

References 33 publications

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“…As described by Oliver (1980) and Zamani et al (2018), PCI or SPCI values below 10 denote a uniform monthly rainfall distribution throughout the year (low precipitation concentration); values ranging from 11 to 15 indicate a moderate concentration of precipitation; values between 16 and 20 represent an irregular distribution; and values above 20 represent a strong irregularity (high precipitation concentration) in precipitation distribution. As a measure of the annual precipitation variability in relation to the mean, the coefficient of variation (CV, %) for the annual total precipitation was also computed at each station.…”

Section: Precipitation Concentration Indexmentioning

confidence: 99%

“…The PCI is very useful to assess the degree of seasonal precipitation concentration and provides information for the comparison of different climates in terms of precipitation regime for different seasons. Currently, many studies have shown that this information can be used for a wide variety of hydrological cycles, water resources, and as an early warning tool for disaster prevention regarding flooding and erosion (De Luis et al , ; ; ; Coscarelli and Caloiero, ; Duan et al , ; Shi et al , ; Zamani et al , ). Unfortunately, the temporal–spatial changes of the PCI and the influences of geographical parameters (latitude, longitude, and altitude) on the index have rarely been investigated in China.…”

Section: Introductionmentioning

confidence: 99%

“…To quantify daily precipitation heterogeneity throughout a year, the concentration index (CI), as proposed by Martin-Vide (2004), is one of the few indices that can correlate the magnitude of precipitation events with the time of occurrence. The CI has been used to investigate and detect the temporal heterogeneity of the precipitation distribution in many regions of the world, including Iran (Alijani et al, 2008), Xinjiang and other parts of China (Zhang et al, 2009;Li et al, 2011;Shi et al, 2015;Huang et al, 2016), Italy (Coscarelli and Caloiero, 2012), Turkey (Yeşilırmak and Atatanır, 2016), Spain (Serrano-Notivoli et al, 2017), India (Zamani et al, 2018), and southern Russia (Vyshkvarkova et al, 2018). Similarly, to evaluate precipitation heterogeneity at a monthly scale within a year, Oliver (1980) suggested a monthly precipitation concentration index (PCI), which was later adopted by De Luis et al (1997).…”

Section: Introductionmentioning

confidence: 99%

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Various characteristics of precipitation concentration index and its cause analysis in China between 1960 and 2016

Zhang

Yao

Qian

et al. 2019

Intl Journal of Climatology

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The precipitation concentration index (PCI) is a powerful indicator for temporal precipitation distribution and is also very useful for the assessment of seasonal precipitation changes. The primary objectives of this study are to investigate and analyse the temporal–spatial variability patterns of annual and seasonal PCI values based on monthly precipitation data. These data were collected from 597 meteorological stations located throughout China, for the time period of 1960–2016, and were used to assess the impacts of geographical parameters (latitude, longitude, and altitude) on the PCI. Additionally, the possible teleconnection with the large‐scale circulation pattern was investigated. Our results reveal that the variation trend of annual PCI values has decreased significantly at a rate of −.234/10 year (α = .01) in China over the past 57 years. For all studied station records, 434 (72.7%) stations showed decreasing trends of PCI values, and these stations are distributed over large areas in China. On an annual scale, the average PCI value ranged from 11 in Hunan province to 44 in Qinghai province. The precipitation concentration in China can be described as strongly irregular in the western and northern parts of the northwest and in the northern region of the Tibetan Plateau, while it is irregular in the southwest and the north of China, and moderately irregular in some parts of the middle‐lower regions of the Yangtze River and southern China. The regularity of the annual precipitation pattern significantly decreased in spring, autumn, and winter from southeastern to northwestern China, and was the most in winter. However, the summer precipitation dispersion and the pattern in the considered period were more regular than those of the other seasons. Furthermore, changes in the PCI appear to be rather complex and possibly related to global atmospheric characteristics as well as geographical factors (latitude, longitude, and altitude). The results presented in this study indicate that the PCI is an essential feature for water resource planning, prediction of risk due to droughts or floods, and the management of natural resources.

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Section: Precipitation Concentration Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Various characteristics of precipitation concentration index and its cause analysis in China between 1960 and 2016

Zhang

Yao

Qian

et al. 2019

Intl Journal of Climatology

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“…The most important property of JDI is to evaluate the general deficit conditions based on the dependence structure of the deficit indices with different time periods. The trend of changes in JDI values was also examined using the modified Mann-Kendall test (Rezaie et al 2014;Khalili et al 2016;Ahmadi et al 2018;Zamani et al 2018). The main assumption of the Mann-Kendall test is that the sample data has no significant autocorrelation.…”

Section: T]mentioning

confidence: 99%

Analyzing the droughts in Iran and its eastern neighboring countries using copula functions

Ramezani¹,

Tahroudi²,

Ahmadi³

2019

Időjárás

Self Cite

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⎯ As a long-term water deficit condition, drought is a challenging issue in the management of water resources and has been known as a costly and less known natural disaster. Monitoring and predicting droughts, especially accurate determination of their beginning and duration are crucial in management of water resources and planning for mitigating the damaging effects of drought. In this study, the droughts in the southwestern region of Asia (Iran, Afghanistan, Pakistan, and Turkmenistan) were evaluated using the joint deficit index (JDI). Data of monthly and annual precipitation of 1392 downscaled rain gauge stations (by using the Bias Correction Spatial Disaggregation (BCSD method) within the statistical period of 1971-2014 were employed to calculate JDI. The results indicated that in recent years, the number of dry months in the studied region (especially in humid regions of Iran) has significantly increased, such that across all regions in Iran, the percentage of dry months has reached over 50%. The results also showed that in addition to scientific description of the general drought condition, JDI is also able to specify the time of beginning of droughts as well as long-term droughts, allowing investigation of the drought condition on a monthly scale. The results of investigating the trend of changes in the JDI values in the studied region revealed that the variations in these values have decreased on annual scale in the studied region. The extent of reduction in JDI and the increase in the number of dry months within the statistical period of 1971-2014 have been significant (at level of 5%) in Iran, suggesting increased drought in Iran, especially during winter. The values of monthly and annual precipitation in the studied region have been descending, where among the studied countries, Iran has experienced the maximum extent of reduction in precipitation.

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“…The analysis of spatio-temporal features of rainstorm elements has always been one of the key components of meteorological research (Kokilavani et al 2017). Scholars generally choose to analyse spatio-temporal changes and differences of different rainstorm elements including rainstorm volume, rainstorm days, rainstorm intensity, and so on (Zamani et al 2018). Time-series analysis methods include trend analysis (Wu and Qian 2017), the Mann-Kendall mutation test and the moving T-test , wavelet period analysis (Palizdan et al 2017) and so on.…”

Section: Introductionmentioning

confidence: 99%

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

Feng

Zhang

et al. 2019

Geomatics, Natural Hazards and Risk

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Rainstorms are one of the frequent natural disasters which have significant impact on agricultural production and people's lives. Considering the comprehensive influence of various factors on the rainstorm events, this study extracts four elements of rainstorms in the Liaoning Province based on meteorological data collected between 1957 and 2015. Combined four elements of rainstorms by copula function, the cumulative probability of the rainstorm events and the probability of their return period can be analysed. The results show that the joint return period of multiple factors combination is less than the return period of a single factor. The degree of accuracy of the joint return period is higher, which is consistent with the actual situation. During periods of 5, 10, 20, and 50 years, the occurrence probability varies according to the idea 'the central region is higher, while the eastern and western regions are lower'. Special attention must be paid to the low values of the region in future rainstorm disaster prevention and mitigation work. Based on return periods, the agricultural risk grade can be divided into 5 levels. In ascending order, they are level I (Fuxin, Shenyang, Tieling), II (Dalian, Jinzhou, Panjin, Liaoyang), III (Huludao, Fushun), IV (Chaoyang, Yingkou, Anshan, Benxi), and V (Dandong).

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Spatio-temporal analysis of daily, seasonal and annual precipitation concentration in Jharkhand state, India

Cited by 64 publications

References 33 publications

Various characteristics of precipitation concentration index and its cause analysis in China between 1960 and 2016

Various characteristics of precipitation concentration index and its cause analysis in China between 1960 and 2016

Analyzing the droughts in Iran and its eastern neighboring countries using copula functions

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

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