Statistical Trend Analysis of Rainfall in Amaravathi River Basin using Mann-Kendall Test

Sridhar, Sharath Nittur; Raviraj, A.

doi:10.12944/cwe.12.1.11

Cited by 20 publications

(13 citation statements)

References 11 publications

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“…Cannarozzo et al [34] reported that overall trends in Sicily (Italy) have a negative trend for the entire region by analysing around 250 rain gauges using nonparametric statistical methods. Similar studies for the precipitation (rainfall) trend analysis were carried out over many other regions of the world: Ceballos et al [35] and De Luis et al [36] for Spain; Bunting et al [37] and Hess et al [38] for African countries; Longobardi and Villani [39] and Philandras et al [40] for the Mediterranean area; Ahmad et al [41], Deshmukh and Lunge [42], Kumar et al [43], and Sridhar and Raviraj [44] for the Indian subcontinent; Gemmer et al [45] and Zhai et al [46] for China; Nicholls and Lavery [47] and Salinger and Griffiths [48] for Oceania and de la Casa; and Nasello [49] and Knowles et al [50] for the American continent.…”

Section: Introductionmentioning

confidence: 71%

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Rainfall Trend Analysis in Uma Oya Basin, Sri Lanka, and Future Water Scarcity Problems in Perspective of Climate Variability

Khaniya

Jayanayaka

Jayasanka

et al. 2019

Advances in Meteorology

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The effect of climate variability on the rainfall pattern is canvassed on the Uma Oya river basin, Sri Lanka, consisting of 5 rainfall gauging stations. The Uma Oya basin (720 km2) is given utmost precedence due to environmental concerns seen in the ongoing Uma Oya multipurpose development project (529 million USD worth) which is expected to divert water to the southeast dry zone of the country while adding 231 GWh/year electricity to the national grid. The rainfall data for a period of 26 years (1992–2017) were analysed using Mann–Kendall’s test and Sen’s slope estimator test to identify the rainfall trends. Both of these trend analysis test results depict only one negative trend for Hilpankandura Estate for the month of June; however, the seasonal trend analysis and annual trend analysis do not support this observation. Nevertheless, Mann–Kendall’s test showed potential positive trends for the 3 rainfall gauging stations Kirklees Estate, Ledgerwatte Estate, and Welimada Group only in the 1st intermediate period (March-April), and this is well supported by the monthly trend analysis. Other than these trends, the results do not show any significant negative trends in the Uma Oya catchment. Therefore, the results vividly explain that there is no threat of water scarcity to the catchment area being resistant to changing global climate for the past 26 years.

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Section: Introductionmentioning

confidence: 71%

“…e magnitude of the trend at a given time can be found from Sen's slope estimator [72]. e test is widely used in assessing the trend magnitude for rainfall series over the time [41,43,70]; Mondal et al, 2012 [44]. Slope for all data pairs can be calculated by…”

Section: Sen's Slope Estimator Testmentioning

confidence: 99%

Rainfall Trend Analysis in Uma Oya Basin, Sri Lanka, and Future Water Scarcity Problems in Perspective of Climate Variability

Khaniya

Jayanayaka

Jayasanka

et al. 2019

Advances in Meteorology

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show abstract

“…This test is commonly known as Mann-Kendall test and, as stated above, widely used to check temporal monotonic upward and downward trends of climate (Robson et al 1998;De Luís et al 2000;Cannarozzo et al 2006;Kumar et al 2006;Longobardi and Villani 2009;Ahmad et al 2015;Machina and Sharma 2017;Sridhar and Raviraj 2017). Equation (1) gives the Mann-Kendall statistic S.…”

Section: Mann-kendall Testmentioning

confidence: 99%

“…Sen's slope is used to assess the magnitude of trend at a given time (Sen 1968) and used widely in rainfall trend analysis (Kumar et al 2006;Ahmad et al 2015;Sridhar and Raviraj 2017). Equation (5) is used to calculate slope for all data pairs.…”

Section: Sen's Slope Estimator Testmentioning

confidence: 99%

Impact of climate variability on hydropower generation in an un-gauged catchment: Erathna run-of-the-river hydropower plant, Sri Lanka

Perera¹,

Rathnayake

2019

Appl Water Sci

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Impact of climate change or climate variability on water resources is an exceedingly concerned issue. Hydropower development is one of the most affected industries due to the climatic variability. Therefore, this paper presents the promising results from a study of the impact of climate variability on hydropower generation of Erathna run-of-the-river (ROR) hydropower plant located in Rathnapura district, Sri Lanka. This study was based on surrounded rain gauges outside the catchment as Erathna catchment area is an un-gauged catchment. 30-year rainfall trend analysis from 1988 to 2017 was done using Mann-Kendall and Sen's slope estimator tests to predict the available trends. Pearson's correlation coefficient was used to investigate the relationship between rainfall and Erathna power generation. Results show negative trends for annual rainfalls in several rain gauges, while seasonal trend analyses support that observation. July is the most critical month for most of the rain gauges around the catchment. The results also show a good correlation between the rainfalls and power generation. Therefore, the results conclude the importance of rainfall trend analysis in un-gauged catchments and its forecasting capacity of water resources usage in hydropower development.

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“…To determine the range of the possible trends in the WUI and ECI indicators and their statistical significance during the 2005 to 2015 period, the nonparametric Sen's slope method and Mann-Kendall test were used. Sen's slope was used to verify the range of the possible trends in the statistical sequence [47,48], and the Mann-Kendall test was selected to substantiate the statistical significance [49,50].…”

Section: Temporal Analysismentioning

confidence: 99%

Impact of China’s Urbanization on Water Use and Energy Consumption: An Econometric Method and Spatiotemporal Analysis

Xiao

Wang

Zhao

et al. 2018

Water

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As important subsystems of the urban environment, water resources and energy are necessary for normal urban functions and play an important supporting role in urbanization. The rapid development of China’s economy is increasingly dependent on these two subsystems. Analyses of the relationship between urbanization and water use or energy consumption have become the focus of attention, but researchers have mainly evaluated the impact on the two subsystems separately without providing an integrated analysis, nor have they revealed the link between water use and energy consumption. We addressed this information gap by using an econometric method to empirically investigate the long-term equilibrium relationships and Granger causal relationships among urbanization, water use, and energy consumption in China, and by conducting a spatiotemporal analysis to identify the trends of water use intensity and energy consumption intensity under the effects of urbanization during 2005–2015. We found long-term equilibrium relationships among urbanization, water use, and energy consumption. Granger causality results reveal the presence of a unidirectional Granger causal relationship running from urbanization to energy consumption and to water use, and bidirectional causality between energy consumption and water use. Moreover, water use intensity and energy consumption intensity decreased significantly under urbanization during the study period.

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Statistical Trend Analysis of Rainfall in Amaravathi River Basin using Mann-Kendall Test

Cited by 20 publications

References 11 publications

Rainfall Trend Analysis in Uma Oya Basin, Sri Lanka, and Future Water Scarcity Problems in Perspective of Climate Variability

Rainfall Trend Analysis in Uma Oya Basin, Sri Lanka, and Future Water Scarcity Problems in Perspective of Climate Variability

Impact of climate variability on hydropower generation in an un-gauged catchment: Erathna run-of-the-river hydropower plant, Sri Lanka

Impact of China’s Urbanization on Water Use and Energy Consumption: An Econometric Method and Spatiotemporal Analysis

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