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increasing trend in Narmada basin at 95% significant level. ..... The authors express their deepest gratitude towards Centre of Excellence (CoE) on “Water ...
National Conference on Water Resources & Flood Management with special reference to Flood Modelling October 14-15, 2016 SVNIT Surat

INVESTIGATION OF TRENDS IN EXTREME RAINFALL AND RAINY DAYS OVER MIDDLE TAPI BASIN, INDIA Vikash V. Sharma1, V. D. Loliyana2, P. V. Timbadiya3 and P. L. Patel4 1

UG Student, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat – 395007; Email : [email protected] 2 Research Scholar, Centre of Excellence on Water Resources and Flood Management, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat – 395007; Email : [email protected] 3 Assistant Professor, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat – 395007; Email : [email protected] 4 Professor, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat – 395007; Email : [email protected]

ABSTRACT In present study, trend analysis is carried out of annual maximum daily rainfall (AMR) and rainy days (RD) for Middle Tapi basin located in Maharashtra, western part of India, which extends from Hathnur dam to Ukai dam, having an area of 31,767 km2. The AMR and RD series were derived from the daily rainfall data available for a period of 70 years (from 1944-2013) for 27 raingauge stations in the basin. The AMR in the basin varied from 142 to 343 mm of rainfall, while RD varied from 33 to 53 days. The trend have been detected for AMR and RD series by applying non-parametric Mann-Kendall (MK) and Sen’s slope estimator tests. Prior to application of MK test, the data were checked for serial correlation; and pre-whitening was done if data were found to be serially correlated. It was observed that for AMR series, 20 out of 27 stations showed decreasing trends with 4 stations (Chalisgaon, Dhulia, Kalvan, and Pachora) showing a significant decrease. Further, in RD series, 16 stations revealed decreasing trends, while in remaining 11 stations increasing trends were observed, with none of the stations showing significant change. Analysis of rainfall trends of AMR and RD series are particularly important in quantifying the impacts of climate variability on the water resources of the basin. Moreover, the study would be useful in understanding the climate variability of the basin as well as in design of storm water networks of urbanized areas, and water yield in the catchment. Keywords: Annual maximum daily rainfall, Rainy days, Trend analysis, Climate variability, Middle Tapi basin

1. INTRODUCTION The Middle Tapi basin lies in the western part of India and receives the maximum amount of rainfall in southwest monsoon season (June to September). Due to release of greenhouse gases and other anthropogenic activities, many studies in the last two decades indicate a climate change in the end of twentieth century which is expected to continue in 21st century (Intergovernmental Panel on Climatic Change (IPCC) 2007, 2008, 2011; Mullan et al. 2008). Annual maximum daily rainfall (AMR) data represents one of the vital and readily available measures of extreme rainfall, which are used frequently as inputs to assessments of flood risk (Bates et al. 2008; Field et al. 2012). Moreover, number of rainy days (RD) in a year also represents the important criteria to determine the effects of climate variability. Using these scenarios, an availability of water availability can be made in basin in the context of future requirements (Kumar and Jain 2011). Roy and Balling (2004) revealed the increase in the frequency of extreme precipitation events for 3838 stations across India. Singh et al. (2008) indicated an increasing trend in annual rainfall and the maximum increase in rainfall is observed in Indus (lower) followed by the Tapi river basin while most of the river basins have experienced a decreasing trend in RD series. Naidu et al. (2009) studied systematic increasing WRH-62-1

National Conference on Water Resources & Flood Management with special reference to Flood Modelling October 14-15, 2016 SVNIT Surat

and decreasing trend in different segments of time series over 30 meteorological subdivisions of India. Nandargi and Dhar (2011) showed a considerable increase in the frequencies of extreme events of rainfall in different sectors of Himalayas during the year of 1951-2000 while there is a decreasing trend in the present decade 2001-2007. Kumar and Jain (2011) examined trends in annual rainfall and RD series on the river basin scale and the analysis revealed no change in rainy days on the river basin scale. Taxak et al. (2014) concluded a decreasing trend in Wainganga basin from 1949-2012. Haktanir and Citakoglu (2014) concluded no significant trend in extreme precipitation records for all of Turkey. Sonar (2014) revealed decreasing but not significant trend in total amount of rainfall and number of rainy days in a year over Ratnagiri station. Mondal et al. (2015) showed a decreasing trend in annual rainfall except the areas of West Central India and Peninsular India. Thomas et al. (2015) showed a significant increasing trend in Narmada basin at 95% significant level. Keeping the view from the reported study, very few studies have been examined at the catchment scale. The present study focuses on the trends in extreme events of rainfall i.e. AMR and RD series in Middle Tapi basin, Maharashtra, India. The objectives of the present study are aimed as follows: (i) To analyse the spatial and temporal variability of AMR and RD using statistical techniques, i.e. Mann-Kendall and Sen’s slope estimator tests and linear regression technique in Middle Tapi basin. (ii) To quantify the change in magnitude of trend in AMR and RD series in the study basin. 2. STUDY AREA The Tapi river starts near Multai in Betul district of Madhya Pradesh at an elevation of 752 m. The major tributaries are the Suki, the Gomai, the Arunavati and the Aner which join it from right while the Vaghur, the Amravati, the Buray, the Panjhra, the Bori, the Girna, the Purna, the Mona and the Sipna join it from left. The entire Tapi basin is divided into three sub-basins: Upper Tapi Basin up to Hathnur dam confluence of Purna river with the main Tapi (29,430 km2 ), Middle Tapi Basin from Hathnur up to the Ukai dam (31,767 km2 ), and Lower Tapi Basin from the Ukai dam to Arabian sea (3948 km2 ) (CWC, 2014). The Middle Tapi basin extends in east-west direction between 73º 45ʹ E to 76º 21ʹ E longitudes while in northsouth direction, the basin lies between 21º 0 N to 21º 45ʹ N latitudes. The index map of study area, i.e., Middle Tapi basin with 27 rain gauge stations is shown in Figure 1. There are 27 rain gauge stations available which are managed by India Meteorological Department (IMD). The Middle Tapi basin had received maximum total annual rainfall of 1427.45 mm in the year of 1976, while minimum total annual rainfall of 574.94 mm in year of 1991. 3. DATA AND METHODOLOGY The daily rainfall data was collected for 27 raingauge stations for a period of 70 years (19442013) from IMD (India Meteorological Department), Pune. The data series had few missing values and hence, missing data estimation was required and it was calculated using conventional inverse distance weighing method. The annual maximum daily rainfall series was derived by selecting the maximum rainfall received at a station in 24 hours. While, it is considered as a rainy day, when the rainfall received in a day is more than 2.5 mm (Sonar, 2014). The trend in the time series was determined by applying Mann-Kendall, Sen’s slope estimator and linear regression tests. The data were checked for serial correlation before application of Mann-Kendall test. The methodology adopted in the present study can be seen in Figure 2.

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National Conference on Water Resources & Flood Management with special reference to Flood Modelling October 14-15, 2016 SVNIT Surat

Figure 1: Location of raingauge stations in the study area

Preparation of time series - annual maximum daily rainfall (AMR) and number of rainy days (RD) in a year

Analysis of statistical parameters (mean, standard deviation, skewness) of AMR and RD series

Analysis of long term temporal trends in the time series using linear regression technique

Checking the time series for lag-1 auto correlation and if data was found to be serially correlated, then pre-whitening process was done

Analysis of trend in the time series using MannKendall (MK) and Sen’s slope estimator tests

Estimating magnitude of change of trend in rainfall indices over spatial scale Figure 2: Flowchart of methodology adopted in present study

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National Conference on Water Resources & Flood Management with special reference to Flood Modelling October 14-15, 2016 SVNIT Surat

3.1 Missing Data Analysis The analysis of daily rainfall values indicated that, at some stations, few rainfall records were missing which were filled by using Inverse Distance Weighing (IDW) method (Chen and Liu, 2012), see Eq. (1). 𝑅 ∑𝑛𝑖=1 2𝑖 𝑑𝑖 𝑃𝑖 = ⋯ (1) 1 ∑𝑛𝑖=1 2 𝑑𝑖 where, Pi = rainfall value at missing raingauge station, Ri = known rainfall value at ith surrounding raingauge station, n = number of observations, and di = distance between missing raingauge station and ith surrounding raingauge station. 3.2 Trend Tests Prior to the application of Mann-Kendall test, the data were checked for lag-one serial correlation, and if data were found to be correlated, the data were pre-whitened. 3.2.1 Mann-Kendall (MK) test MK test is a non-parametric test for detecting the trend in a time series without specifying the type of trend, i.e., linear or non-linear (Araghinejad, 2013). Considering the annual time series 𝑥𝑡 , t = 1, 2, ……, n. Each value of the series xt is compared with all subsequent values 𝑥𝑡+1, and a new series is generated, and 𝑍𝑀𝐾 is calculated as: 𝑆𝑀𝐾 − 1 𝑖𝑓 𝑆𝑀𝐾 > 0 √𝑉𝑎𝑟(𝑆𝑀𝐾 ) 0 𝑖𝑓 𝑆𝑀𝐾 = 0 𝑍𝑀𝐾 = ⋯ (2) 𝑆𝑀𝐾 + 1 𝑖𝑓 𝑆𝑀𝐾 < 0 √𝑉𝑎𝑟(𝑆𝑀𝐾 ) 𝑛−1

where,

𝑛

𝑆𝑀𝐾 = ∑ ∑ 𝑠𝑔𝑛(𝑥𝑗 − 𝑥𝑘 )

⋯ (3)

𝑘=1 𝑗=𝑘+1

𝑉𝑎𝑟(𝑆𝑀𝐾 ) = 𝑠𝑔𝑛(𝑥) =

𝑛(𝑛 − 1)(2𝑛 + 5) − ∑𝑚 𝑖=1 𝑡𝑖 (𝑡𝑖 − 1)(2𝑡𝑖 + 5) 18 +1 𝑖𝑓 (𝑥𝑗 − 𝑥𝑘 ) > 0 0 𝑖𝑓 (𝑥𝑗 − 𝑥𝑘 ) = 0

⋯ (4) ⋯ (5)

−1 𝑖𝑓 (𝑥𝑗 − 𝑥𝑘 ) < 0 Null hypothesis proposed is that the time series has no trend. If null hypothesis is rejected, the time series of xt is considered to have an increasing trend, if SMK is positive, or a decreasing trend, if SMK is negative. 3.2.2 Sen’s Slope Estimator test It is a non-parametric, robust estimate for quantifying monotonic trend in hydrologic time series given by Hirsch et al. (1982), is given by: 𝑥𝑗 − 𝑥𝑖 𝛽 = 𝑀𝑒𝑑𝑖𝑎𝑛 ( ) ∀𝑖 400m) there is a decreasing trend in AMR series whereas there is no such topography impact on RD series. ii. It is observed that Middle Tapi basin exhibits variability in trends of AMR series with majority of stations showing decreasing trend. The results may not be in agreement with the findings of most of the studies carried out on extreme rainfall, wherein many researchers (Thomas et al., 2015; Roy and Balling, 2004) suggested a steep increase in maximum rainfall. Thus, further investigation is required to determine possible causes of such deviations by studying trends in other meteorological parameters like temperature, relative humidity, etc. iii. From the present study, it is observed that RD series has decreased over the past 70 years with the majority of stations showing decreasing trend. The results are in conjunction with the studies carried out in past by researchers (Kumar and Jain, 2011; Sonar, 2014). The reduction in RD series would imply reduction in wet spells as well as reduction in total rainfall in the basin. However, the risk of occurrence of high intensity storms have also increased in the recent past which can result in flash flooding. iv. The average decreasing trend in AMR and RD series indicates the water scarcity condition in near future and thus leading to water problems in existing life. Hence, suitable measures may be implicated for the water conservation. ACKNOWLEDGEMENT The authors express their deepest gratitude towards Centre of Excellence (CoE) on “Water Resources and Flood Management”, SVNIT Surat under TEQIP-II for resourceful help and financial support for the present study. The authors also wish to acknowledge India Meteorological Department (IMD), Pune and Central Water Commission (CWC), Surat for providing necessary data to carry out the present study.

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National Conference on Water Resources & Flood Management with special reference to Flood Modelling October 14-15, 2016 SVNIT Surat Field, C. B. (Ed.). (2012). Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University Press. Haktanir, T., and Citakoglu, H. (2014). Trend, independence, stationarity, and homogeneity tests on maximum rainfall series of standard durations recorded in Turkey. Journal of Hydrologic Engineering, 19(9), 05014009. Hirsch, R. M., and Slack, J. R. (1984). A nonparametric trend test for seasonal data with serial dependence. Water Resources Research, 20(6), 727-732. IPCC (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B, and Miller, H. L. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. IPCC (2008). Climate Change 2008: Mitigation of global greenhouse gas emissions from waste: conclusions and strategies from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. Working Group III (Mitigation) [Bogner, J., Pipatti, R., Hashimoto, S., Diaz, C., Mareckova, K., Diaz, L., and Zhang, T. (eds.)]. Waste Management & Research, 26(1), 11-32. IPCC (2011). Climate Change 2011: Special report on renewable energy sources and climate change mitigation. Prepared by Working Group III of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S., and von Stechow, C. (eds.). Cambridge University Press, Cambridge, United Kingdom. IS 4987 (1994). Recommendations for establishing network of raingauge stations (first revision), March 2010, New Delhi. Kumar, V., and Jain, S. K. (2011). Trends in rainfall amount and number of rainy days in river basins of India (1951–2004). Hydrology Research, 42(4), 290-306. Mondal, A., Khare, D., and Kundu, S. (2015). Spatial and temporal analysis of rainfall and temperature trend of India. Theoretical and Applied Climatology, 122(1-2), 143-158. Mullan, B., Wratt, D., Dean, S., Hollis, M., Allan, S., Williams, T., and Kenny, G. (2008). Climate change effects and impacts assessment: a guidance manual for local government in New Zealand. MFE07305. Wellington, NIWA. Naidu, C. V., Durgalakshmi, K., Muni Krishna, K., Ramalingeswara Rao, S., Satyanarayana, G. C., Lakshminarayana, P., and Malleswara Rao, L. (2009). Is summer monsoon rainfall decreasing over India in the global warming era?. Journal of Geophysical Research: Atmospheres, 114(D24). Nandargi, S., and Dhar, O. N. (2011). Extreme rainfall events over the Himalayas between 1871 and 2007. Hydrological Sciences Journal, 56(6), 930-945. Roy, S. S., and Balling, R. C. (2004). Trends in extreme daily precipitation indices in India. International Journal of Climatology, 24(4), 457-466. Singh, P., Kumar, V., Thomas, T., and Arora, M. (2008). Changes in rainfall and relative humidity in river basins in northwest and central India. Hydrological Processes, 22(16), 2982-2992. Sonar, R. B. (2014). Observed trends and variations in rainfall events over Ratnagiri (Maharashtra) during Southwest monsoon season. Mausam, 65(2), 171-178. Taxak, A. K., Murumkar, A. R., and Arya, D. S. (2014). Long term spatial and temporal rainfall trends and homogeneity analysis in Wainganga basin, Central India. Weather and Climate Extremes, 4, 50-61. Thomas, T., Gunthe, S. S., Ghosh, N. C., and Sudheer, K. P. (2015). Analysis of monsoon rainfall variability over Narmada basin in Central India: implication of climate change. Journal of Water and Climate Change, 6(3), 615-627.

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