DROUGHT ANALYSIS IN THE SEONATH RIVER BASIN USING RECONNAISSANCE DROUGHT INDEX AND STANDARDISED PRECIPITATION INDEX

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 7, Issue 6, NovemberDecember 2016, pp , Article ID: IJCIET_07_06_079 Available online at ISSN Print: and ISSN Online: IAEME Publication DROUGHT ANALYSIS IN THE SEONATH RIVER BASIN USING RECONNAISSANCE DROUGHT INDEX AND STANDARDISED PRECIPITATION INDEX Mani Kant Verma, Dr. M. K. Verma, Dr. L K Yadu, Dr. Meena Murmu Civil Engineering Department, NIT Raipur, Chhattisgarh, India ABSTRACT Drought is a climatic situation, characterized by the less availability of moisture. About 33% land area of India come s under the drought prone zone. Seonath river basin (major source of surface water in Chhattisgarh state, India) was taken as a study area for drought analysis. The present work characterizes the frequency of drought by analyzing Reconnaissance Drought Index (RDI) and Standardized precipitation index (SPI) of Seonath basin (Chhattisgarh). Rainfall data of the thirty three rain gauge stations (year ) were taken as the input data for SPI, Rainfall and Temperature data (year ) for RDI. Key words: DrinC (Drought indices calculator), Standardized Precipitation index (SPI), Reconnaissance Drought Index (RDI). Cite this Article: Mani Kant Verma, Dr. M. K. Verma, Dr. L K Yadu, Dr. Meena Murmu. Drought Analysis in the Seonath River Basin using Reconnaissance Drought Index and Standardised Precipitation Index. International Journal of Civil Engineering and Technology, 7(6), 2016, pp INTRODUCTION The Drought is one of the major natural hazard which affects several sectors such as economy, environment and social impact. The effect of drought is raised due to the disbalance of the hydrological cycle. Using these words, we can create the simplest definition of drought that it is a situation when water is scarce and insufficient in quantity to meet the demand. Now days due to climate change issues droughts are occurring very frequently worldwide and in some regions it became a very severe hazard. In India around 68% area is drought susceptible. If a region receives rainfall less than 750 mm in a year, then it is chronically drought prone area. The major drought years in India were 1877, 1899, 1918, 1972, 1987, 2002, 2009 [1,2]. The potential of DrinC software is also highlighted for arid & semiarid region in some literatures [3]. For arid land, drought characteristics is explained and their consequences on water resources management [4]. Drought is an event which correlates two terms i.e., Demand and Supply. RDI method and its potential are explained for calculating the drought index at Bhavnagar district [5]. For arid and semiarid regions RDI is being considered as a potential drought index calculator [6]

2 Mani Kant Verma, Dr. M. K. Verma, Dr. L K Yadu, Dr. Meena Murmu In this paper SPI and RDI method is used for the drought index over Seonath river basin. Seonath basin is main source of water in Chhattisgarh state. Therefore, drought analysis in Seonath basin (Chhattisgarh) is the objective of this paper. DrinC is used for the study along with Microsoft Excel. 2. STUDY AREA & DATA USED Seonath basin receives about 1150 millimeters of mean annual rainfall, mostly in the monsoon season. Overall climate of the area is subhumid tropical. Major crops grown in the area are paddy and maize in the Kharif season and, gram and mustard in the Rabi season. Therefore, this study is needed to improve the agriculture production. The daily rainfall data of 33 Meteorological Stations over entire Seonath river basin for a period of 1980 to 2012 (33 years) were collected from State Data Centre, Department of Water Resources, Raipur. Figure 1 Thiessen Polygon Map of Seonath River Basin (Thiessen Polygon Map) Table 1 Location of Rain Gauge Station use for SPI calculation S.no. Station Name District Longitude Latitude 1 Ambagarh Chowki Rajnadgaon Balod Durg Bemtara Durg Bilaspur Bilaspur Bodla Kabirdham Chilhati Korba Chirapani Kabirdham Chuikhadan Rajnadgaon Dongargaon Rajnadgaon Dongargarh Rajnadgaon Doundi Lohara Durg Durg Durg Gandai Kabirdham Ghonga Bilaspur Gondly Durg

3 Drought Analysis in the Seonath River Basin using Reconnaissance Drought Index and Standardised Precipitation Index 16 Kawardha Kabirdham Kendiri Raipur Kharkhara Durg Khuria Bilaspur Khutaghat Bilaspur Kota Bilaspur Madiyan Rajnadgaon Mungeli Bilaspur Nawagarh_Durg Durg Newara Raipur Pandaria Bilaspur Pindrawan Raipur Patherdih Raipur Raipur Raipur Semartal Bilaspur Dhamtari Dhamtari Jondhra Raipur Kotni Dhamtari METHODOLOGY In this study, SPI and RDI have been used for analysis of the drought over Seonath river basin (major source of surface water in Chhattisgarh state, India). For the assessment of drought; precipitation and potential evapotranspiration data of the basin were used. Wet and dry periods have been compared using SPI and RDI. In this study Drought Index (DI) was calculated using DrinC & Microsoft Excel software. The SPI and RDI were calculated for 12 months basis. In India, generally a monsoon season is between June to September or June to October and sometime it happens till October and November. SPI/RDI value calculated by using monthly rainfall & PET values for year 1980 to In this paper DrinC is used for drought analysis. As an input, series of daily rainfall data of 33 Meteorological Stations over entire river basin for a period of 1980 to 2013 (34 years) is used in the study. The procedure used for drought analysis by SPI and RDI is discussed below Standardized Precipitation Index (SPI) The SPI and its characteristics is explained by McKee et al. (1993) for drought monitoring and analysis [7]. The input data for SPI calculation is precipitation record at any location and the dataset is fitted on gamma probability density function. SPI is normalized by keeping mean value 0 and standard deviation value unity that is beneficial to identify wet and dry periods equally. For any observed precipitation data, probability is calculated from the gamma function and this is used to estimate the precipitation deviation by SPI normalized. Positive values of SPI shows greater precipitation and negative values shows lesser precipitation than average precipitation. = Where, P i = Precitation value, = average precipitation and, S = standard deviation. The drought event ends when the SPI becomes positive. The ranges of SPI values for different classification of drought conditions are given in table 2 [7]. (1)

4 Mani Kant Verma, Dr. M. K. Verma, Dr. L K Yadu, Dr. Meena Murmu Table 2 Classification of drought conditions according to the SPI values 3.2. Reconnaissance Drought Index (RDI) In this work RDI is used for drought index in addition of SPI. For this standardized form of RDI is used and is a precise technique to characterize a drought event for arid regions [3,5,6]. The RDI is based on two inputs such as cumulative precipitation (based on observed precipitation) and potential evapotranspiration (PET, calculated by Thornthwaite formula). The RDI is calculated in a similar way as explained for SPI index. 4. RESULTS AND DISCUSSION The series result of SPI groups such as Extremely wet, Very wet, Moderately wet, Near normal, Moderately dry, Severely dry and Extremely dry are shown in Figures 2 and 3. From result it can be observed that the actual climate tendency in characterized by increasing of normal and wet periods. According to SPI 12month indicator, only 94% of the last 33 years are characterized by light and medium drought, the doughtiest period being the one between SPI Extremely wet Very wet Moderately wet Near normal Moderately dry Severely dry Extremely dry 0% 3% 0% 3% 0% 0% 94% Figure 2 Frequency of drought periods (SPI 12month) 2.00 spi spi 2.00 Figure 3 12month SPI values for Seonath basin

5 Drought Analysis in the Seonath River Basin using Reconnaissance Drought Index and Standardised Precipitation Index The result of RDI 12 is shown in Figures 4 and 5. From these graphs it is observed that the actual climate tendency in characterized by increasing of normal and wet periods. According to RDI 12month indicator, only 93% of the last 33 years are characterized by light and medium drought, the doughtiest period being the one between RDI Extremely wet Very wet Moderately wet Near normal Moderately dry Severely dry Extremely dry 3% 0% 0% 0% 0% 4% 93% Figure 4 Frequency of drought periods (RDI 12month) RDI RDI Figure 5 12month RDI values for Seonath basin 5. CONCLUSION The result shows from the above analysis that mainly Semertal, Raipur, Bilaspur, Durg, Kendri are drought prone region. The year was the doughtiest year for the Seonath basin. Data driven models, can be effective in forecasting drought in the Seonath River Basin. This software also use as a shortterm drought indicator that is closely linked with agricultural drought, is forecast well, and these forecasts can find great application in the Seonath River Basin as a whole given the importance of agriculture in the region. The forecasts for SPI 12 and RDI are even better and can be utilized as longterm planning tools for water resource managers within the country. REFERENCES [1] Drought in India, Poorest Areas Civil Society (PACS) Programme, [2] [3] Tigkas D, Vangelis H, Tsakiris G. DrinC: software for drought analysis based on drought indices. Earth Science Informatics Sep 1;8(3):

6 Mani Kant Verma, Dr. M. K. Verma, Dr. L K Yadu, Dr. Meena Murmu [4] Maliva RG, Missimer TM. Arid lands water evaluation and management. Springer Science & Business Media; 2012 Jun 9. [5] Shah R, Manekar VL, Christian RA, Mistry NJ. Estimation of Reconnaissance Drought Index (RDI) for Bhavnagar District, Gujarat, India. World Academy of Science, Engineering and Technology, International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering Jul 22;7(7): [6] Vangelis H, Tigkas D, Tsakiris G. The effect of PET method on Reconnaissance Drought Index (RDI) calculation. Journal of Arid Environments Jan 31; 88: [7] McKee TB, Doesken NJ, Kleist J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology 1993 Jan 17 (Vol. 17, No. 22, pp ). Boston, MA: American Meteorological Society. [8] Ali Jassim Mohammed Salih and Dr. Omran Issa Mohammed, Estimating Reference Evapotranspiration for Middle Euphrates Area Using Artificial Neural Networks (ANNs). International Journal of Civil Engineering and Technology (IJCIET), 7(6), 2016, pp [9] Prof. G. Bogayya Naidu, Prof. K. V. SivaKumar Babu and Prof. V. Srinivasulu. Evaluation of Reference Evapotranspiration Estimation Methods and Development of Crop Coefficient Models. International Journal of Civil Engineering and Technology (IJCIET), 6 (11), 2015, pp

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