MONITORING DROUGHT DYNAMICS BASED ON GROUND DATA A DETAILED STUDY OF THE STATE HIMACHAL PRADESH (INDIA)

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 7, Issue 4, July-August 2016, pp Article ID: IJCIET_07_04_013 Available online at ISSN Print: and ISSN Online: IAEME Publication MONITORING DROUGHT DYNAMICS BASED ON GROUND DATA A DETAILED STUDY OF THE STATE HIMACHAL PRADESH (INDIA) Deepak Kumar and Chandra Shekhar Mishra Department of Civil Engineering, SHIATS Allahabad, India ABSTRACT Temperature, wind and relative humidity are also important factors to include in characterizing drought. Drought monitoring also needs to be application-specific because drought impacts will vary between sectors. Drought means different things to different users such as water managers, agricultural producers, hydroelectric power plant operators and wildlife biologists. Even within sectors, there are many different perspectives of drought because impacts may differ markedly. Droughts are commonly classified by type as meteorological, agricultural and hydrological, and differ from one another in intensity, duration and spatial coverage. Key words: Drought; Monsoon; GIS; SPI; SWI; FORTON Cite this Article: Deepak Kumar and Chandra Shekhar Mishra, Monitoring Drought Dynamics Based On Ground Data A Detailed Study of The State Himachal Pradesh (India). International Journal of Civil Engineering and Technology, 7(4), 2016, pp INTRODUCTION Drought is an insidious natural hazard that results from lower levels of precipitations than what is considered normal. When this phenomenon extends over a season or a longer period of time, precipitation is insufficient to meet the demands of human activities and the environment. Drought must be considered a relative, rather than absolute, condition. There are also many different methodologies for monitoring drought. Droughts are regional in extent and each region has specific climatic characteristics. Droughts that occur in the North American Great Plains will differ from those that occur in northeast Brazil, southern Africa, Western Europe, eastern Australia, or the North China Plain. The amount, seasonality and form of precipitation differ widely between each of these locations editor@iaeme.com

2 Deepak Kumar and Chandra Shekhar Mishra Temperature, wind and relative humidity are also important factors to include in characterizing drought. Drought monitoring also needs to be application-specific because drought impacts will vary between sectors. Drought means different things to different users such as water managers, agricultural producers, hydroelectric power plant operators and wildlife biologists. 2. MATERIAL AND METHODS 2.1. Study area Himachal Pradesh, a northern Indian state in the Himalayas, is known for its trekking, climbing and skiing, and scenic mountain towns and resorts such as Dalhousie N, E Area: 55,673 km² Founded: January 25, 1971 Capital: Shimla Population: million (2012) Host to the Dalai Lama, Himachal Pradesh also has a strong Tibetan presence, reflected in its Buddhist temples and monasteries, cuisine heavy on noodles and dumplings, and vibrant Tibetan New Year celebrations. Himachal is in the western Himalayas. Covering an area of 55,673 square kilometres (21,495 sq mi), [6] it is a mountainous state. Most of the state lies on the foothills of the Dhauladhar Range. At 6,816 m Reo Purgyil is the highest mountain peak in the state of Himachal Pradesh. The drainage system of Himachal is composed both of rivers and glaciers. Himalayan rivers criss-cross the entire mountain chain. Himachal Pradesh provides water to both the Indus and Ganges basins. [12] The drainage systems of the region are the Chandra Bhaga or the Chenab, the Ravi, the Beas, the Sutlej, and the Yamuna. These rivers are perennial and are fed by snow and rainfall. They are protected by an extensive cover of natural vegetation. Due to extreme variation in elevation, great variation occurs in the climatic conditions of Himachal. The climate varies from hot and subhumid tropical in the southern tracts to, with more elevation, cold, alpine, and glacial in the northern and eastern mountain ranges. The state has areas like Dharamsala that receive very heavy rainfall, as well as those like Lahaul and Spiti that are cold and almost rainless. Broadly, Himachal experiences three seasons: summer, winter, and rainy season. Summer lasts from mid-april till the end of June and most parts become very hot (except in the alpine zone which experiences a mild summer) with the average temperature ranging from 28 to 32 C (82 to 90 F). Winter lasts from late November till mid March. Snowfall is common in alpine tracts (generally above 2,200 metres (7,218 ft) i.e. in the higher and trans-himalayan region). Himachal Pradesh contributes over 45% to the net state domestic product. It is the main source of income and employment in Himachal. Over 93% of the population in Himachal depends directly upon agriculture, which provides direct employment to 71% of its people. The main cereals grown are wheat, maize, rice and barley. Hydropower is also one of the major sources of income generation for the state. The identified Hydroelectric Potential for the state is 23, MW in five river basins editor@iaeme.com

3 Monitoring Drought Dynamics Based On Ground Data A Detailed Study of The State Himachal Pradesh (India) 2.2. Data used Grided data Monthly precipitation and mean temperature of 3 districts, over period of 40 ( ) years and 3 ( ) years respectively were downloaded from India water portal site ( Figure.3.1 shows the geographical map of study area. In the present study spatial and temporal variability of both parameters are studied at annual and seasonal basis (pre-monsoon, monsoon, post-monsoon and winter) for statistical analysis. Methodology of dividing data into seasonal basis helps in eliminating the effect of seasonality in the time series (Khaled Ahmed & Ramachandra Rao, 1997). It is also necessary to test the quality of time series before conducting major data analysis. The outlier and homogeneity test are carried out by using visual detection of outliers and Standard Normal Homogeneity Test (SNHT) for all 3 stations. By SNHT it is found that data are homogenous for all 3 stations. Further, study is carried out at different time periods , , for mean temperature. In addition, a detailed investigation of district-wise Standard Precipitation Index (SPI) is carried out using monthly precipitation dataa over the period of Furthermore, for design new irrigation requirement of different crops at different agro-climatic zones, the meteorological data such as monthly wind speed, sunshine hour, relative humidity, minimum and maximum temperature were collected for the period of from the Indian Meteorological Department, Pune. In the present study four important crops such maize, rice, barley groundnut and wheat were used for study the changes in irrigation requirement over the study area, because these four crops were practiced in the cropping pattern of all agro-climatic zones. 3. TREND ANALYSIS 3.1. Mann-Kendall testt In the present study a popular non- parametric method Mann-Kendall test is used to detect trend in the rainfall l and temperature at 5% significance level. Because of nonparametric methods are more suitable to detection of trend rather than the parametric methods in hydro-metrological data (Helsel l et al. 2002; Hirsch, 1982; Darshana editor@iaeme.com

4 Deepak Kumar and Chandra Shekhar Mishra and Pandey, 2013, Khaled Ahmed & Ramachandra Rao, 1997). Mann-Kendall test is rank based test, used where autocorrelation is not significant and it can tolerate to outliers, distribution free and has higher power than the other test (Helsel et al. 2002; Hirsch, 1982; Darshana and Pandey, 2013, Khaled Ahmed & Ramachandra Rao, 1997). Since the results of trend test shows miss interpolation when the observed data have autocorrelation with it (Mann, 1945; Kendall, 1975). Therefore in the present study Mann-Kendall test is applied after pre-whitening for all meteorological parameters at annual, pre-monsoon, monsoon, post- monsoon and winter seasons to eliminate the effect of autocorrelation in the data series (Von Storch, 1995; Partal and Kahya, 2006; Mohammad and Manoj Jha, 2014, Yue et al., 2002). The standardized Mann-Kendall test Z value for computation of statistical significance of trend in the time series is given as follow: if S>0 () Z= 0 if S=0 (1) if S<0 () Where S is the statistic value, which possesses normal distribution for large number of sample size (Kendall, 1975; Khaled Ahmed & Ramachandra Rao, 1997) and can be computed as; S =! Sgn (x x ) (2) Where x i is ranked from i = 1, 2, 3 n -1 and x j is ranked from j = i + 1, 2, 3 n in the time series. The values of +Z and Z indicates upward and downward trend respectively. The Z values of Mann-Kendall test accept the null hypotheses of no trend when ± Z Z 1-x/2, where x is the level of significance at two tailed trend test. In the present study test is carried out at 5% significance level, therefore when Z value exceeds ± 1.96 null hypotheses is rejected and show the existence of trend in the series. 4. RESULT AND DISCUSSIONS Table 5.1 District-wise precipitation analysis and SPI value range SPI VALUE YEAR KASOL RAMPUR SUNNI editor@iaeme.com

5 Monitoring Drought Dynamics Based On Ground Data A Detailed Study of The State Himachal Pradesh (India) SPI VALUE YEAR KASOL RAMPUR SUNNI SPI of Kasol editor@iaeme.com

6 Deepak Kumar and Chandra Shekhar Mishra 3 2 SPI of Rampur SPI of Sunni DISCUSSION The present study is carried out to examine the spatial and temporal variability and trends in precipitation and mean temperature at annual and seasonal basis for the 3 districts of Himachal state, India. Along with this district-wise SPI is developed to study changes in abnormal wetness and dryness in the study area under the changing climate. In addition, design new CWR due to recent climate variability by using Drought wet model for the state of himachal. It is found that in the 3 years i.e. for the period of the increase trend in annual precipitation is observed in Kasson, Rampur, Sunni districts at 5% significance level. Results of monthly district-wise SPI showed that there is an increase in precipitation amount in some districts, however, for the same time period other districts showed a decrease in precipitation over the period of Further from the twelve month district-wise SPI of Kasol, Rampur, Sunni District. 6. CONCLUSIONS 1. Kasol is not very much climately varied as I have not observed much rainfall variation in period of 40 years ( ). Therefore as the SPI variation is very less in Kasol; as compared to Rampur Sunni. 2. Therefore humid sub tropical climate zone. A humid subtropical climate in characterized by hot, usually humid summer and to mild to cool winters editor@iaeme.com

7 Monitoring Drought Dynamics Based On Ground Data A Detailed Study of The State Himachal Pradesh (India) 3. This climate zone is typified by long growing season s reliable precipitation not summer and cool winter for growing a wide variety of crops; like orange, apple, sugar, linegraps, soybeans, tobacco, wheat, rice. 7. AGRICULTURE DROUGHT The impact of more difference state that s there is following effects on meteorological drought. 8. METEOROLOGICAL DROUGHT Amount of water for irrigation purpose is reduced (water application for crops) It also account for ground water level declension It also give price rise 9. HYDROLOGICAL DROUGHT Fish aquatic animals are losing their habitats in to atmosphere. Dry environment REFERENCES [1] Khaled H. Hamed and A. Ramachandra Rao, (1998). A modified Mann-Kendall trend test for auto correlated data. Journal of Hydrology 204, [2] Kendall, M.G., (1975). Rank Correlation Methods. Charless Griffin, London. [3] Hirsch, R.M., Slack, J.R., Smith, R.A., (1982). Techniques of trend analysis for monthly water quality data. Water Resour. Res. 18 (1), [4] Mohammad Sayemuzzaman and Manoj K. Jha, (2014). Seasonal and annual precipitation time series trend analysis in North Carolina, United States. Atmospheric Research 137, [5] Partal, T., & Kahya, E. (2006). Trend analysis in Turkish precipitation data. Hydrological Processes, 20, [6] Yue, S., Pilon, P., Cavadias, G., (2002a). Power of the Mann Kendall and Spearman s rho tests for detecting monotonic trends in hydrological series. J. Hydrol. 259, [7] Helsel, D.R., Hirsch, R.M., (2002). Statistical methods in water resources techniques of water resources investigations. Book 4, Chapter A3. U.S. Geological Survey. (522 pages). [8] Duhan and Ashish Pandey, (2013). Statistical analysis of long term spatial and temporal trends of precipitation during at Madhya Pradesh, India. Atmospheric Research 122, [9] Von Storch, H., (1995). Misuses of statistical analysis in climate research. In: Storch, H.V., Navarra, A. (Eds.), Analysis of Climate Variability: Applications of Statistical Techniques. Springer, Berlin, pp [10] G. Sreenivasa Rao, Dr. A. Manjunath and Dr. Mvss Giridhar, Studies on Impact of Mining on Surface and Groundwater. International Journal of Civil Engineering and Technology, 5(6), 2014, pp [11] D.C. Bala, S.S. Jain and R.D. Garg IIT Roorkee, Variability Issues of Road & Its Subsurface Addressed By Ground Coupled GPR. International Journal of Civil Engineering and Technology, 3(2), 2012, pp [12] Partal, T., & Kahya, E. (2006). Trend analysis in Turkish precipitation data. Hydrological Processes, 20, editor@iaeme.com