International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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TREND ANALYSIS OF RAINFALL OVER ORISSA REGION OF INDIA Shraddha Yadav a Amity University, Noida -------------------------------------------------------------------------***-----------------------------------------------------------------------ABSTRACT
The present study is mainly focused historical spatiotemporal variability and trend of rainfall on the annual and seasonal time series state of Orissa over a period of 60 years (1954–2013). The state of Orissa showed abrupt variation in rainfall intensity due to reoccurring cyclonic disturbances, throughout the study period. El Nino event during summers led to drier conditions and deficient monsoons (droughts noted during years 2000, 2002, 2004, 2009 and 2010). La-Nina event in year 1995 (September - December) leading to formation of severe cyclonic storms in BOB region. In post-monsoon season, major increase in rainfall was observed in year 1999, with increased rainfall anomaly (+1.78) due to coinciding super cyclone event. A similar cyclonic event (Phailin) in October 2013 positive anomaly (+3.01) in rainfall intensity. Extreme rainfall event was observed over the state of Orissa adjacent to Bay of Bengal; which in turn influenced the rainfall.
KEYWORDS: Rainfall, Mann-Kendall Test, Sen’s slope estimator, Cramer Test, Standardized Anomaly Index. 1. INTRODUCTION
For the investigation of the trends analysis of rainfall in the data series of monthly, seasonal and annual rainfall of Orissa region using standardized rainfall anomaly. Aim of the present study was to identify the trend of rainfall in Orissa region of India, to achieve this, analyse of historic data is carried out.
Global climate changes have been responsible in influencing the long-term rainfall and temperature patterns of a region. Hence, it becomes imperative to attempt for investigating the trend of climatic variables like rainfall and temperature for a country at regional level as well as at the individual stations. The amount of rainfall received over an area is an important factor in assessing availability of water to meet various demands for agriculture, industry, irrigation, generation of hydroelectricity and other anthropogenic activities. Similarly, changes in temperature may affect other hydrological processes including rainfall and such processes, in turn may lead to temperature variability (Jain and Kumar, 2012). It has been emphasized by several researchers that in developing countries, the economy is heavily dependent on low-productivity rain fed agriculture. As a result, rainfall trends and variability are important factors adopted to analyze socioeconomic problems such as food insecurity (Cheung et. al., 2008). As a result, investigation of the temporal dynamics of meteorological variables is imperative to provide necessary input to policymakers and practitioners for making informed decisions. Similarly, characterization of the intra- as well as inter-annual temporal trends of meteorological variables, in the context of a changing climate, is vital for assessment of climate-induced changes and thereby suggest feasible adaptation strategies and agricultural practices (Asfaw et.al, 2018).
2. MATERIAL AND METHOD. In this study, the observed annual/seasonal JanuaryFebruary (J-F), March-April-May (MAM), June-JulyAugust-September (JJAS), October-November-December (OND)and monsoonal month June, July, August, September rainfall are used. Daily gridded rainfall data set (0.25° × 0.25°, latitude × longitude) procured from National Data Centre, Indian Meteorological Department (IMD), Pune. 2.1 Mann-Kendall Trend Analysis The Mann (1945)-Kendall (1975), test, is a nonparametric approach, has been widely used for detection of trend in different fields of research including hydrology and climatology. It is used for identifying trends in time series data. If the data do not confirm to a normal distribution, the Mann-Kendall test can be applied. To perform a Mann -Kendall test, compute the difference between the later-measured value and all earliermeasured values, (yj-yi), where j>i, and assign the integer value of 1,0, or –1 to positive differences, no differences, and negative differences, respectively. The test statistic S, is then computed as the sum of the integers:
In order to identify the trends, the rainfall series was divided into 10-year non- overlapping sub-periods from 1954-2013 and the Cramer’s test was then used to compare the means of the sub-period with the mean of the whole recorded period.
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