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Prediction and Validation of Rainfall Classes for Vaigai River Catchment Using El Nino

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Prediction and Validation of Rainfall Classes for Vaigai River Catchment Using El Nino International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-2, July 2019 Prediction and Validation of Rainfall Classes for Vaigai River Catchment using El Nino Mahadevan Palanichamy, Ramaswamy Sankaralingam Narayanasamy Abstract: Extraordinary weather patterns are being observed Tamil Nadu state, situated in the southern end of Indian globally during the past 30 years due to climate change resulting sub-continent, collects the largest portion of its rainfall in variations in temperature and rainfall. Studies on long-term during October through December (North East Monsoon). trend pattern of temperature and rainfall since 1980 distinctly The agricultural activities and economic status of farmers shows a rise in mean temperature and declining rainfall trend. mainly depend on North East Monsoon (NEM). The major Due to change of climate at global level change, forecasting of rainfall with the conventional statistical analysis could not challenge faced by the water resources management predict satisfactory results. Among the available processes, the El professional today is allocating the available water for Niño Southern Oscillation (ENSO) cycle is considered efficient. domestic purposes and irrigation, considering the demand. Statistical analysis was carried out in this study so as to Due to spatial and temporal uncertainty of rainfall, the investigate the implication of rainfall data in seven rain gauge estimation of water that may be available through rainfall stations located in Vaigai River Catchment through the period and run-off is a great task. Understanding the patterns and from 1959 to 2016. ENSO Cycle was used also to predict rainfall trends of rainfall is extremely vital for arriving at the for Vaigai River catchment of the Tamil Nadu State, India. quantity of water that may be available for distribution in Quadratic discrimination analysis (QDA) and Neural Network stages throughout the year. Water resources management models are used to identify the class of rainfall classes with reference to ENSO cycle. The patterns recognized on the study practice leads to take the choice of operating the area offer constructive information to administrators of water multipurpose reservoir systems due to the impact of varying resource management, to implement the same for agriculture, rainfall, runoff and storage quantity. water supply and power generation. Bothale and Katpatal [1] have conducted studies using the Oceanic Niño Index (ONI) on rainfall trends in Godavari Index Terms: Climate change, statistical analysis, trend basin of India for Pranhita catchment. pattern, Vaigai river catchment, water resources management. Annarita Mariotti [2] has conducted studies on climatic variations in south-western parts of central Asia (SWCA) I. INTRODUCTION using ENSO. The results suggest that ENSO significantly Climate change and Global warming are being reported as affects the precipitation in South West Central Area Region the threats to society during the recent years in many ways (SWCA). such as flood, cyclones, and droughts causing destruction of Hafez [3] conducted studies on climatic tele-connection the economy. Extraordinary weather patterns are being between the Oceanic Nino Index (ONI), temperature of air observed globally during the past 30 years due to climate at surface, its variability and rate of precipitation, etc., for change resulting in variations in temperature and rainfall. Saudi Arabia (KSA) during 1950 to 2015. They opined that Studies on long-term trend pattern of temperature and weather parameters, temperature and precipitation rates are rainfall since 1980 distinctly shows a rise in mean controlled by Oceanic Nino index (ONI) mostly in the temperature and declining rainfall trend. Forecasting of autumn and winter seasons. rainfall with the conventional statistical analysis could not Abbot and Marohasy [4] correlated climate indices of predict satisfactory results due to the changes in climate at SOI, PDO and Nino 3.4 in association with the historical global level, which has altered the characteristics of extreme rainfall and temperature data by applying ANN to forecast rainfall events. It has been established that, any forecast of rainfall, for Queensland, Australia. They have developed rainfall can be successful if we consider climatic tele- prototype neural network for rainfall prediction and inferred connections, most importantly the El Niño–Southern that it can improve the synthesis of knowledge and the Oscillation (ENSO) and Southern Oscillation Index (SOI). actual seasonal forecast. Also, the prototype neural network Forecasting of rainfall, its quantity i.e. average, or abnormal, has the ability to consider large numbers of climate indices possibility of flood or drought, its class, etc will be useful and other inputs simultaneously to find solutions for the officials managing water resources in terms of independently of assumed relationships. decision-making. Badr et al. [5] conducted studies on forecasting summer rainfall anomalies employing ANNs in the Sahel region of Africa. They inferred that, the predicted rainfall results were promising and added that there are no previous studies which documented the prediction of Sahelian rainfall. Roy SS [6] conducted studies on the impact of ENSO on rainfall during winter for India from 1925 to 1998 and suggested that this study is helpful for forecasting of winter Revised Manuscript Received on July 06, 2019. precipitation. Studies made by Yadav et al. [7] found that Mahadevan Palanichamy, School of Environmental and Construction the inter annual variability of North West India Winter Technology, Kalasalingam Academy of Research and Education, Anand Precipitation is influenced by Nagar, Krishnankoil - 626126, Tamil Nadu, India. Ramaswamy Sankaralingam Narayanasamy, Department of Civil Arctic Oscillation / North Engineering, Anjalai Ammal Mahalingam Engineering College, Atlantic Oscillation Kovilvenni, Thiruvarur District - 614403, Tamil Nadu, India. Published By: Retrieval Number: B2078078219/19©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijrte.B2078.078219 1412 & Sciences Publication El Nino Based Model Studies on Prediction and Validation of Rainfall Classes for Vaigai River Catchment (AO/NAO) and El Nino – Southern Oscillation (ENSO) from January & February) and Summer / Pre-monsoon phenomena. T. De Silva M et al. [8] have made an attempt season (3 months spanning from March to May). to forecast the rainfall of Mahaweli and Kelani River basins This study investigates the rainfall data from 1959 to of Sri Lanka. Cao et al. [9] investigated the rainy-season 2016 and forecasting in seven rain gauge stations located in precipitation in China based on the influence of five El Vaigai River Catchment through the period with ENSO Nino-Southern Oscillations indices. Cycle. The temperature on the surface of sea, its monthly Chandimala and Zubair [10] have done an investigation anomaly (based on 1981–2010 mean) over NINO3, NINO on predicting stream flow for various seasons for Kelani 3.4 and NINO4 (17° E-120° W, 5° S-5° N) region is being River of Sri Lanka using correlation analysis. The used as SST index. The present study also predicts rainfall investigation with ENSO from October to December for Vaigai River catchment of the Tamil Nadu State, India. revealed that rainfall has a better relationship than stream flow. II. DATA AND STUDY AREA DESCRIPTION Surendran et al. [11] stated that the variation of rainfall during monsoon (summer) in India is linked to El Niño- A. Rainfall data Southern oscillation (ENSO) and also the oscillations in Monthly rainfall data of seven identified rain gauge Indian Ocean at Equatorial region. stations (Berijam, Bodynayakkanur, Gudalur, Periyakulam, Singhrattna et al. [12] conducted studies to develop a Uthamapalayam, Vaigai Dam and Veerapandi) on the method to forecast rainfall in Thailand statistically due to boundary of Vaigai basin catchment for the period of 1959– monsoon in summer and established a significant 2016 were collected from the data sets of India relationship with ENSO. Based on the above literature surveys, it is evident that the Meteorological Department (IMD) and from the State prediction of rainfall is characterized with the usage of surface and ground water division of PWD, Tamil Nadu Multivariate ENSO Index (MEI), Southern Oscillation Index State. (SOI) and ENSO as Sea Surface Temperature (SST). Hence, B. ENSO Indices in this study an attempt has been made to model the effect of ENSO cycle on extreme rainfall events of Vaigai River The ENSO cycle is characterized by Sea Surface Catchment area. Temperature (SST), Southern Oscillation Index (SOI), and There are thirty-four river basins in Tamil Nadu State, Multivariate ENSO Index (MEI). The temperature on the India. For hydrological studies, they are grouped into surface of sea, its monthly anomaly (based on 1981–2010 seventeen river basins. The Vaigai River basin is one mean) over NINO 3, NINO 3.4 and NINO 4 (17° E - 120° among them and it lies between the geographic co-ordinates W, 5° S - 5° N) region is used as SST index and MEI Latitude 9° 33’–10° 27’ N and Longitude 77° 10’–79° 10’ E monthly data for the period from (1959 – 2016). The and covers an area of 7031 sq.km. The entire basin is located difference in air pressure between Tahiti and Darwin on in Theni, Dindigul, Madurai, Sivaganga and surface is represented as Southern Oscillation Index (SOI) in Ramanathapuram Districts. The basin is located in
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