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Introduction

During the last few decades, as a result of the climate change¹variation in rainfall pattern has drawn a lot of attention for protection of the food production and available fresh water.²Some rising trend of precipitation including monsoon rainfall is observed in central Asia, whereas a decrease is found in the south Asia.¹The Indian climate is mainly characterized by monsoon rainfall which is well supported by several studies.^3-6Major parts of India are facing decline in precipitation during the monsoon time and also the climate changes have put a massive pressure on water resources in India leading to the analysis on a large as well as regional scale. Punjab, the bread basket of the country, is also facing severe water stress due to the intensified agriculture as reported in many studies.^7-32

In this study, variations in rainfall trend in Punjab, India for 102 years (1901–2002) were analysed using statistical non-parametric tests - the Modified Mann-Kendall (MK) test and Sen’s slope with an objective to examine the rainfall trend from 1901 to 2002. The rainfall trend analysis in 17 districts of Punjab will help the planners to understand the recent scenario of climate change in this area.

Study Area

研究区是旁遮普(图之间的谎言。1) 29.30° - 32.32° N latitudes and 73.55° - 76.50° E longitudes with a coverage of 50,362 sq. km. It comprises of Indo-Gangetic plains and is bordered on the west by Pakistan and surrounded by the states Jammu and Kashmir, Himachal Pradesh, Haryana and Rajasthan. Punjab is an agriculture dominated state and the crops mainly grown are rice, wheat, jowar, soybean, cotton and sugarcane. Presently, Punjab state has 20 districts and is benefitted by number of rivers namely the Sutlej, Beas, Ravi and Ghaggar.

The climate of Punjab is typically subtropical with ranging from 0°C in winters to 47°C in summers with an average annual rainfall in the range of 58 cms to 96 cms. About 80% of the rainfall is recorded in monsoon seasons (June to September); the pre-monsoon season commences from March up to May, post-monsoon in October and November, and winter from December to February.

Figure 1: Study area showing location of 17 districts Click here to View figure

Materials and Methods

Monthly rainfall data of 17 districts of Punjab consisting of on a 0.5 degree latitude-longitude grid interpolated from 1901 to 2002³³available at http://www.indiawaterportal.org/metdata has been used in this study. The raw station data from seven different sources corrected using a modified version of the Global Historical Climatology Network (GHCN) method. The seasonal analysis of rainfall was carried out for pre-monsoon (April-June), monsoon (July-September), post-monsoon (October-November) and winter season (December-March).

The magnitude of the trend in the seasonal and annual series was determined using the Sen’s estimator³⁴t和统计学意义的趋势ime series was analysed using Modified Mann-Kendall (MMK) test.³⁵

For determining the magnitude of trend in a time series Sen’s estimator³⁴is used which has been widely used for determining the magnitude of trend in hydro-meteorological time series.^36-37In this method, the slopes (T_i) of all data pairs are first calculated by

where x_jand x_kare data values at time j and k (j>k) respectively. The median of these N values of T_iis Sen’s estimator of slope which is calculated as

A positive value of β indicates an upward (increasing) trend and a negative value indicates a downward (decreasing) trend in the time series.

To ascertain the presence of statistically significant trend in hydrologic climatic variables such as temperature, precipitation and stream flow with reference to climate change, nonparametric Mann-Kendall (MK) test has been employed by a number of researchers.^38-40The MK method searches for a trend in a time series without specifying whether the trend is linear or non-linear. In the present study, the MK test was also applied. The MK test checks the null hypothesis of no trend versus the alternative hypothesis of the existence of increasing or decreasing trend. No pre-whitening of the data series was carried out as the sample size is large (n ≥ 50) and slope of trend was high ( > 0.01).⁴¹

The statistics (S) is defined as⁴²

where N is number of data points. Assuming (x_j-x_i) = θ, the value ofsgn(θ)is computed as follows:

This statistics represents the number of positive differences minus the number of negative differences for all the differences considered. For large samples (N>10), the test is conducted using a normal distribution⁴³with the mean and the variance as follows:

where n is the number of tied (zero difference between compared values) groups, and t_kis the number of data points in thekth tied group. The standard normal deviate (Z-statistics) is then computed as⁴⁴:

If the computed value of │Z│> z_α/2, the null hypothesis (H_o) is rejected at α level of significance in a two-sided test. In this analysis, the null hypothesis was tested at 95% confidence level.

Trend analysis using the MMK test

In this method, the effect of all significant autocorrelation coefficients is removed from a dataset.⁴⁵For this purpose, a modified variance of S, designated as Var(S)*, was used as follows:

where n* = effective sample size. The n/n* ratio was computed directly from the equation proposed by Hamed and Rao⁴⁵as

where n = actual number of observations, and r_i= lag-i significant autocorrelation coefficient of rank i of time series. Once Var(S) * was computed from Eq. (8), then it was substituted for Var(S) in Eq. (7). Finally, the Mann-Kendall Z was tested for significance of trend, comparing it with threshold levels, i.e. 1.645 for 10 %, 1.96 for 5 % and 2.33 for 1 % level of significance.

Results and Discussion

Temporal and Spatial Distribution of Rainfall

降雨特征显示的意思是季节性的, mean annual and coefficient of variation of the annual rainfall of different districts of Punjab are reported in Table 1. It is evident from Table 1 that Gurdaspur district received the maximum mean annual rainfall (639.9 mm) whereas Muktsar district received the minimum mean annual rainfall (302.5 mm). The contribution of monsoon rainfall varies from 77.5 % (Amritsar) to 83.1% (Fatehgarh Sahib and Patiala). The coefficient of variation (CV) of the annual rainfall varies from 24% (Patiala and Roopnagar to 32% (Amritsar).

Table 1: Seasonal distribution of average rainfall in different districts in Punjab Click here to View table

*CV = coefficient of variation

The percent change in distribution of rainfall in all districts of Punjab is shown in Table 2 indicating a positive change in all the districts in annual, pre-monsoon, monsoon, post-monsoon seasons and in 7 districts in winter season. Similar results were obtained for Punjab by Kumar et al. (2010)⁴⁶but negative trend in north India were observed by Chhabra et al. (2014).

Rainfall Trends

The magnitude of the trend in annual and seasonal rainfall as determined using the Sen’s estimator is given in Table 2. Trends of rainfall variations show that annual and seasonal rainfall (except winter season in Amritsar, Fatehgarh Sahib, Gurdaspur, Hoshiarpur, Jalandhar, Kapurthala, Ludhiana, Moga, Nawanshahr, Patiala and Roopnagar) increased over all districts but the magnitude of trend varies from one district to another. The increase in annual rainfall over different districts varied between 1.459 mm/year (for Amritsar district) to 0.824 mm/year (for Muktsar district) with an overall average of 1.164 mm/year. Further, the increase was, in general, higher for the districts in north-west and the least for the districts in south-west which implies that the rainfall variability in the State is likely to increase in the future.

As evident from table 2 that the rainfall increased in pre-monsoon, monsoon and post-monsoon in all the districts and in winter season in some districts (Bhatinda, Faridkot, Ferozepur, Mansa, Muktsar and Sangrur). In the monsoon season, the maximum increase is of the order of 1.223 mm/year for Amritsar and minimum increase is for Muktsar district (0.632 mm/year). All the districts have shown the same direction of trend for annual and monsoon rainfall, perhaps because the monsoon season has the major contribution of annual rainfall. The decreasing trend in winter season rainfall is found to vary from -0.040 mm/year (Roopnagar) to -0.003 mm/year (Moga).

The Modified Mann-Kendall (MMK) z-test (10%, 5% and 1% level of significance) applied to annual and seasonal rainfall over different districts. The annual and seasonal rainfall at 10% and 5% level was found non-significant in all the districts. The results of MMK z-test at 1% level are shown in Table 3 which indicates that the majority of districts show significant trend in annual, pre-monsoon and monsoon seasons rainfall. Out of the 17 districts studied, annual rainfall of only four districts (Gurdaspur, Hoshiarpur, Nawanshahr and Roopnagar) showed non-significant trend. Monsoon rainfall also indicated positive significant trend for 9 districts while decreasing trend found in eight districts (Fatehgarh Sahib, Gurdaspur, Hoshiarpur, Kapurthala, Muktsar, Nawanshahr, Patiala and Roopnagar). The increasing trend in pre-monsoon rainfall was found significant for 7 districts. The rainfall in post-monsoon and winter season was found non-significant for all the districts.

Table 2: Percent change in magnitude of rainfall in different districts in Punjab usingSen’s slope Click here to View table

Table 3: Modified Mann Kendall (MMK) Z-test for significance of trend (1% level)

S. No	District	Annual	Pre-monsoon	Monsoon	Post Monsoon	Winter
1	Amritsar	S*	NS **	S	NS	NS
2	Bhatinda	S	S	S	NS	NS
3	Faridkot	S	NS	S	NS	NS
4	Fatehgarh Sahib	S	NS	NS	NS	NS
5	Firozpur	S	S	S	NS	NS
6	Gurdaspur	NS	S	NS	NS	NS
7	Hoshiarpur	NS	NS	NS	NS	NS
8	Jalandhar	S	S	S	NS	NS
9	Kapurthala	S	S	NS	NS	NS
10	Ludhiana	S	NS	S	NS	NS
11	Mansa	S	NS	S	NS	NS
12	Moga	S	S	S	NS	NS
13	Muktsar	S	NS	NS	NS	NS
14	Nawanshahr	NS	NS	NS	NS	NS
15	Patiala	S	NS	NS	NS	NS
16	Roopnagar	NS	NS	NS	NS	NS
17	Sangrur	S	S	S	NS	NS

S*= significant, NS**=non-significant

Conclusions

The trend detection analysis of rainfall for 17 districts of Punjab has indicated an increasing rainfall trends in annual, monsoon, pre-monsoon and post-monsoon seasons. However, MKK test indicates that the rainfall trend has been observed only in annual, monsoon and pre-monsoon seasons at 1% level of significance. Winter rainfall is found decreasing in 11 districts. In future, the variability of rainfall is likely to increase in Punjab. Rainfall has a rising trend at some districts in south-west Punjab namely Bhatinda and Moga which currently receive low annual rainfall. The increasing trend in annual and monsoon rainfall was found statistically significant in 11 and 9 districts, respectively. Although the rainfall in post-monsoon and winter season was found to be non-significant for all the districts, any reduction in winter rainfall will result in exploitation of groundwater for irrigating the Rabi crops.

Acknowledgement

Authors thank Director, National Institute of Hydrology for all the support and encouragement.

References

1. IPCC (Intergovernmental Panel for Climate Change). Climate Change 2007—the Scientific Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel for Climate Change (2007).
2. Dore, M.H.I. Climate change and changes in global precipitation patterns: what do we know?Environmental International, 31:1167–1181(2005).
3. Guhathakurta, P. & Rajeevan, M. Trends in the rainfall pattern over India.International Journal of Climatology. 28:1453–1469 (2008).
4. Krishan, G., Rao, M.S., Kumar, B., Kumar, C.P., Kumar, S., Jaiswal, R.K., Rao, Y.R.S., Tripathi, S., Kumar, M., Garg, P.K. & Kumar, P. Monitoring of Southwest Monsoon using isotope analysis of ground level vapour (Glv) in Indian Sub-Continent.Journal of Earth Science and Climate Change. 5:224 (2014). http://dx.doi.org/10.4172/2157-7617.1000224
5. Krishan, Gopal. Research on possible potential of applications of isotopic composition of atmospheric water vapour- a hydrometeorological perspective.Water and Energy International. 58 (2): 37-40 (2015).
6. Krishan, Gopal, Rao, M.S., Kumar, Bhishm, & Kumar, C.P. Possibility of using isotopic composition of ground level vapour (Glv) for monitoring arrival and withdrawal of southwest monsoon.Current Science. 108 (5):784-786 (2015).
7. Chopra, R.P.S. & Krishan, Gopal. Analysis of aquifer characteristics and groundwater quality in southwest Punjab, India.地球科学和引擎ering. 4(10): 597-604 (2014). http://doi: 17265/2159-581X/2014. 10. 001
8. Chopra, R.P.S. & Krishan, Gopal. Assessment of groundwater quality in Punjab.Journal of Earth Science and Climate Change. 5(10):243 (2014). http://dx.doi.org/10.4172/2157-7617.1000243
9. Krishan, Gopal & Chopra, R.P.S. Assessment of water logging in south western (SW) parts of Punjab, India-a case study from Muktsar district.NDC-WWC Journal. 4(1): 7-10 (2015).
10. Krishan, Gopal, Takshi, K.S., Rao, M.S., Kumar, Sudhir & Lohani, A.K. Spatial analysis of groundwater level in Punjab, India. In: Proceedings of an International conference “India Water Week 2015-Water Management for Sustainable Development" (IWW-2015), 13-17 January, 2015 at New Delhi, India. Pp. 125 (2015).
11. 仙、Gopal, A.K.•拉奥,硕士&库马尔,苏dhir. Spatio-temporal variability analysis of groundwater level for water resources development and management in Northern Punjab, India, India. Journal of Environment and Analytical Toxicology. 5(4):279 (2015). http://dx.doi.org/10.4172/2161-0525.1000279
12. Krishan Gopal, Lapworth D. J., Rao M. S., Kumar C. P., Smilovic M. & Semwal P. Natural (Baseline) Groundwater Quality in the Bist-Doab Catchment, Punjab, India: A Pilot Study Comparing Shallow and Deep Aquifers. International Journal of Earth Sciences and Engineering, 7 (01): 16-26 (2014).
13. Krishan Gopal, Lohani A.K., Rao M.S., Kumar C.P. and Takshi K.S. Groundwater fluctuation and trend in Amritsar, Punjab, India. In: Geo-statistical and Geospatial approaches for the characterization of natural resources in the environment: challenges, processes and strategies (Editor: N. Janardhana Raju). Capital Publishing House, New Delhi, Pp. 108-111 (2014). (ISBN. 978-93-81891-25-4).
14. Krishan, G., Rao, M. S., Purushothaman, P., Rawat, Y. S., Kumar, C.P., Gupta, S., Bhatia, A.K., Marwah, S., Kaushik, Y.B., Angurala, M.P. & Singh, G.P. Groundwater Resources in Bist-Doab Region, Punjab, India-an overview. NDC-WWC Journal. 3 (2): 5-13 (2014).
15. Krishan, Gopal, Rao, M.S., Loyal, R.S., Lohani, A.K., Tuli, N.K., Takshi, K.S., Kumar, C.P., Semwal, P & Kumar Sandeep. Groundwater level analyses of Punjab, India: A quantitative approach. Octa Journal of Environmental Research. 2(3): 221-226 (2014).
16. Krishan, Gopal, Lohani, A.K., Rao, M.S. & Kumar, C.P. Prioritization of groundwater monitoring sites using cross-correlation analysis. NDC-WWC Journal. 3 (1): 28-31 (2014).
17. Krishan, Gopal, Rao, M.S., Kumar, C.P., Garg, Pankaj & Semwal, Prabhat. Assessment of salinity and groundwater quality with special emphasis to fluoride in a semi-arid region of India. Journal of Earth Science and Climate Change. 5(6): 149 (2014). http://dx.doi.org/10.4172/2157-7617.S1.016
18. Krishan, Gopal, Garg, P., Takshi, K.S., Lohani, A.K., Rao, M.S., Loyal, R.S., Kumar, P., Tuli, N.K. Singh, M. & Semwal, P. Monitoring of Groundwater Fluctuations and Trend in Parts of Northern Punjab (vern. Majha), India. In: Proceedings of an International conference on “Annual Water Resources Association 2014 AWRA” during 3-6 November, 2014 at Virginia, USA (2014).
19. Krishan, Gopal, Rao, M.S., Lohani, A.K., Kumar, C.P., Takshi, K.S., Tuli, N.K., Loyal, R.S. & Gill, G.S. Assessment of groundwater level in southwest Punjab, India. Hydraulics, Water resources, Coastal & Environmental Engineering-Hydro 2014 (Editors: H.L. Tiwari, S. Suresh, R.K. Jaiswal) Excellent Publishing House, New Delhi. 23: 248-254 (2014).
20. Krishan, Gopal, Lohani, A.K., Rao, M. S., Kumar, C. P. & Semwal, P. Optimization of groundwater monitoring network in Bist-Doab, Punjab. In: International Conference “India Water Week 2013-Efficient Water Management: Challenges and Opportunities" (IWW-2013)”, pp. 274 (2013).
21. Krishan, Gopal, Rao, M. S., Lapworth, D.J. & MacDonald, A.M. 2013b. Indo-gangetic groundwater resilience project- Punjab case study. In: Report of IBG-Groundwater Resilience Project (eds. H.C. Bonsor and A.M. MacDonald) during 4-7 November 2013 at India Habitat Centre, New Delhi. BGS Internal Report, IR/13/060 (2013). http://nora.nerc.ac.uk/505660/1/IR13060.pdf
22. Krishan, Gopal, Rao, M.S., Kumar, C.P. & Semwal, Prabhat, G.S. Identifying Salinization Using Isotopes and ionchemistry in Semi-Arid Region of Punjab, India. Journal of Geology and Geosciences, 2:4 (2013) http://dx.doi.org/10.4172/jgg.1000129
23. Krishan Gopal, Lohani A.K., Rao, M.S., Kumar, C.P. & Semwal, P. Optimization of groundwater monitoring network in Bist-Doab, Punjab. In: International Conference “India Water Week 2013-Efficient Water Management: Challenges and opportunities" (IWW-2013)”, pp. 274 (2013).
24. Lapworth Dan, Krishan, Gopal, Rao, M.S. & MacDonald, Alan. Intensive Groundwater Exploitation in the Punjab – an Evaluation of Resource and Quality Trends. Technical Report. NERC Open Research Archive, BGS-UK. http://nora.nerc.ac.uk/509752/ (2014).
25. Lapworth, D.J., Krishan, G., Macdonald, A.M., Rao, M.S., Gooddy, D.C. & Darling, W.G. Using Environmental Tracers to Understand the Response of Groundwater Resources in Nw India to Sustained Abstraction. In Proc. of 41st International Conf. of International Association of Hydro-geologist (IAH-2014) on Groundwater: Challenges and Strategies during Sep. 18-19, 2014. at Marrakech Morocco (2014).
26. Lohani, A.K. & Krishan, Gopal. Application of artificial neural network for groundwater level simulation in Amritsar and Gurdaspur districts of Punjab, India. Journal of Earth Science and Climate Change. 6:4 (2015). http://dx.doi.org/10.4172/2157-7617.1000274
27. Lohani, A.K. & Krishan, Gopal. Groundwater level simulation using artificial neural network in southeast, Punjab, India. Journal of Geology and Geosciences. 4(3):206 (2015). http://dx.doi.org/10.4172/2329-6755.1000206
28. Lohani, A.K., Krishan, Gopal, Rao, M.S. & Kumar, Sudhir. Groundwater Level Simulation Using Artificial Neural Network: a A Case Study from Punjab, India. In: Proceedings of an International conference “India Water Week 2015-Water Management for Sustainable Development" (IWW-2015), 13-17 January, 2015 at New Delhi, India. pp. 114 (2015).
29. Macdonald, A. M., Bonsor, H. C., Krishan, Gopal , Rao, M. S., Ahmed, K.M., Taylor, R.G., Shamsudduha, M., Steenburgen, F Van, Mackenzie, A.A., Dixit, A, Moench, M & Tucker, J. Groundwater in the Indo‐Gangetic Basin: Evolution of Groundwater Typologies. In Proc. of 41st International Conf. of International Association of Hydro-geologist (IAH-2014) on Groundwater: Challenges and Strategies during Sep. 18-19, 2014. at Marrakech Morocco (2014).
30. Macdonald, Alan, Bonsor, Helen, Rao, M. Someshwar, Krishan, Gopal , Steenburgen, Frank Van, Ahmed, Kazi, Shamsudduha, Mohammad, Dixit, Ajaya & Moench, Marcus. Groundwater Topologies In the Indo Gangetic Basin, In Proc. of International Conf. on Advances in Water Resources Development & Mangement held at PU, Chandigarh during Oct. 23-27, 2013. P: 2 (2013).
31. Rao, M. S., Purushothaman, P., Krishan, Gopal, Rawat, Y. S. & Kumar, C. P. Hydrochemical and Isotopic Investigation of Groundwater Regime in Jalandhar and Kapurthala Districts, Punjab, India.International Journal of Earth Sciences and Engineering, 7 (01): 06-15 (2014).
32. Sharma, Manishi, Rao, M. S., Rathore, D.S. & Krishan, Gopal. An integrated approach to augment the depleting ground water resource in bist- doab, region of Punjab, India.International Journal of Earth Sciences and Engineering, 7 (01): 27-38 (2014).
33. Mitchell, T.D., Jone, P.D. An improved method of constructing a database of monthly climate observations and associated high resolution grids,International Journal of Climatology, 25:693-712 (2005).
34. Sen, P.K. Estimates of the regression coefficient based on Kendall's tau.Journal of the American Statistical Association, 63:1379-1389 (1968).
35. Gajbhiye, Sarita, Meshram, Chandrashekhar, Mirabbasi, Rasoul & Sharma, S. K. Trend analysis of rainfall time series for Sindh river basin in India.Theoretical and Applied Climatology,(2015), DOI 10.1007/s00704-015-1529-4.
36. Partal, T. & Kahya, E. Trend analysis in Turkish precipitation data.Hydrological Processes,20,2011–2026 (2006).
37. Kumar, V., Jain, S.K., & Singh, Y. Analysis of long-term rainfall trends in India,Hydrological Sciences Journal, 55, 484-496 (2010).
38. Burn, D. H., Cunderlik, J. M., & Pietroniro, A. Hydrological trends and variability in the Liard river basin.Hydrological Sciences Journal, 49, 53–67 (2004).
39. Singh, P., Kumar, V., Thomas, T. & Arora, M. Changes in rainfall and relative humidity in different river basins in the northwest and central India,Hydrological Processes. 22: 2982-2992 (2008).
40. Kumar, V., & Jain, S.K. Trends in seasonal and annual rainfall and rainy days in Kashmir valley in the last century,Quaternary International,212, 64-69 (2010).
41. Bayazit, M. & Onoz, B. To prewhiten or not to prewhiten in trend analysis?Hydrological Sciences Journal, 52, 611–624 (2007).
42. Salas, J.D. Analysis and modeling of hydrologic time series. In Handbook of Hydrology. Maidment DR (ed). McGraw-Hill, New York; 19.1-19.72 (1993).
43. Helsel, D.R. & Hirsch, R.M. Statistical Methods in Water Resources.Elsevier, New York (1992).
44. Hirsch, R.M., Helsel, D.R., Cohn, T.A. & Gilroy, E.J. Statistical treatment of hydrologic data. InHandbook of Hydrology.Maidment D.R. (ed). McGraw-Hill, New York; 17.1-17.52 (1993).
45. Hamed, K.H. & Rao, A.R. A modified Mann-Kendall trend test for auto corelated data. Journal of Hydrology, 204:182–196 (1998).
46. Kumar, Vijay. Annual and seasonal rainfall trends over different districts of Punjab. In: Proceedings of the regional workshop on water availability and management in Punjab. Organized by National Institute of Hydrology, Roorkee, India, (2010).
47. Chhabra, A. S., Lohani, A. K., Dwivedi, V. K., Kar, Anil & Joshi, Nitin. Identifying rainfall trends over North India using 1’X1’ gridded rainfall data (1951-2004).International Journal of Earth Sciences and Engineering, 7 (01): 155-163 (2014).