Rainfall variability over Nainital district of India using Precipitation concentration index

IJEP 46(2): 173-183 : Vol. 46 Issue. 2 (February 2026)

Full Text

Sunaina Alok1, Pratibha Naithani1, Kishan Singh Rawat2* and Smruti Ranjan Sahu2

1. Graphic Era (Deemed to be University), Department of Environmental Science, Dehradun – 248 002, Uttarakhand, India
2. Graphic Era (Deemed to be University), Department of Civil Engineering, Dehradun- 248 002, Uttarakhand, India

Abstract

In the global context, both floods and droughts occur universally. The main cause of these natural disasters is sudden changes in climate alongwith variations in rainfall patterns. Heavy rainfall in each region leads to flooding, while insufficient or no rainfall results in drought conditions. Therefore, studying changes in rainfall is essential for effective water resource management, using the precipitation concentration index (PCI) as a key method. This study analyzes 23 years of monthly rainfall data from Nainital district in Uttarakhand. The PCI is divided into five categories: annual, pre-monsoon, monsoon, post-monsoon and winter rainfall. The study finds the maximum PCI of 107.30 in 2015 and the minimum of 34.71 in 2019, based on the 23-year data. The average PCI over this period is 67.25. According to the distribution and levels of annual PCI, each year is classified under the concept of significant irregularity of precipitation distribution (SPID). This classification demonstrates the usefulness of PCI in managing and developing natural resources.

Keywords

Floods, Droughts, Rainfall patterns, Precipitation concentration index, Natural resources

References

  1. Kumar, R.G. and Agarwal, A. 2020. Investigation of precipitation variability and extremes using information theory. Env. Sci. Proc., 4(1): 14. doi: 10.3390/ECAS2020-08115.
  2. Jajoria, D.K., Sharma, S.K., Narolia, G.P. and Dotaniya, M.L. 2015. Rainfall variability: A tool for crop planning of Udaipur region of India. National Academy Sci. Letters india. 38: 95-98. doi: 10.10 07/S40009-014-0305-9.
  3. Dhamge, N.R., Khawas, P., Vipul, V., Dighore, S. T., Paul, T. and Meshram, S. 2024. Analysis of rainfall variability using precipitation concentration index for a catchment. J. Geotech. studies. 9(2): 16-21. doi: 10.46610/jogs.2024.v09i02.003.
  4. Rawat, K.S., Behera, A., Bahuguna, A. and Baweja, H.S. 2024. A case study of rainfall variability analysis using precipitation concentration index in talcher region, Odisha, India. AIP Conf. Proc., 3072: 040010. doi: 10.1063/5.0198692.
  5. Das, J., Tapash, M., Piu, S. and Bhattacharya, S.K. 2021. Variability and trends of rainfall using non-parametric approaches: A case study of semi-arid area. Mausam. 7(1): 33–44. doi: 10.54302/MAU SAM.V71I1.4.
  6. Haile, A.T. 2010. Rainfall variability and estimation for hydrologic modeling: a remote sensing based study at the source basin of the upper Blue Nile river. PhD Thesis. University of Twente, The Netherlands.
  7. Kumar, U. 2023. Recent temporal trend and variability of rainfall in mid-Himalaya region of Uttara-khand. Indian Forester. 149(4): 387–396. doi: 10. 36808/if/2023/v149i4/166742.
  8. Pande, B., Joshi, S. and Pande, S. 2019. Statistical study of variability in rainfall and analysis of extreme rainfall events for hill stations of Uttarakhand. J. Mountain Res., 14 (1): 55-58. doi: 10.51 220/JMR.V14I1.13.
  9. Goel, S. and Singh, R.K. 2024. Studies on variation of temperature and rainfall using climatological data for the plain and hill zones of Uttarakhand. Env. Ecol., 42(3A): 1201—1211. doi: 10.60151/envec/oepx5709.
  10. Swain, S., Mishra, S.K., Pandey, A. and Dayal, D. 2021. Spatio-temporal assessment of precipitation variability, seasonality and extreme characteristics over a Himalayan catchment. Theoretical Appl. Climatol., 147: 817–833. doi: 10.1007/S00704-021-03861-0.
  11. Kumar, S. and Suryavanshi, S. 2018. Assessment of long term climatic variability of uttarakhand. Natural Resour. Conser. Res., 1(4): 1-22. doi: 10. 24294/NRCR.V1I4.744.
  12. Dalal, N., Joshi, A., Soragi, B., Chaudhary, S., Sharma, S., Naidu, S. and Kazmi, Y. 2018. People’s perception to climate change in remote Himalayan mountains and rainfall variability in the Kailash sacred landscape-India. J. Climatol. Weather Forecasting. 6(2): 1-3. DOI: 10.4172/2 332-2594.100 0231.
  13. Kad, P. and Ha, K.J. 2023. Recent tangible natural variability of monsoonal orographic rainfall in the eastern Himalayas. J. Geophys. Res. Atmos., 128 (22): e2023JD038759. doi: 10.1029/ 2023jd038 759.
  14. Tiwari, P.C. and Joshi, B. 2013. Rainfall variability, landslides and food security in Himalaya. In Landslide science and practice: Global environmental change (vol 4). Springer Berlin Heidelberg. pp 183-189. doi: 10.1007/978-3-642-313 37-0_24.
  15. Thakural, L.N., Kumar, S., Jain, S. and Ahmad, T. 2018. The impact of climate change on rainfall variability: A study in central Himalayas. In Climate change impacts: Select proceedings of ICWEES-2016. Ed Singh, V.P., Yadav, S. and Yadava, R.N. Springer Singapore. doi: 10.1007/ 978-981-10-5714-4_15.
  16. Yadav, R.P., Panday, S.C., Kumar, J., Bisht, J.K., Meena, V.S., Choudhary, M., Nath, S., Parihar, M. and Meena, R.P. 2020. Climatic variation and its impacts on yield and water requirement of crops in Indian central Himalaya. In Agrometeorology. Ed Meena, R.S. doi: 10.5772/INTE-CHOPEN.94076.
  17. Huang, P.W., Lin, Y.F. and Wu, C.R. 2021. Impact of the southern annular mode on extreme changes in Indian rainfall during the early 1990s. Sci. Reports. 11(1): 2798.
  18. Moron, V., Robertson, a.W. and Ghil, M. 2012. Impact of the modulated annual cycle and intraseasonal oscillation on daily-to-interannual rainfall variability across monsoonal India. Climate Dynamics. 38(11-12): 2409-2435. doi: 10.1007/S00382-011-1253-4.
  19. Chowfin, A., Gluvakovic, N. and Gayh, U. 2024. Effect of rainfall on water parameters in recreational lakes in Heidelberg, Germany. IgMinres., 2(2): 121-126. doi: 10.61927/igmin153.
  20. Yong, W., Li., Y., Liu, Y., Xu, M., Zhang, L. and Deng, Q. 2020. Impacts of rainfall intensity and urbanization on water environment of urban lakes. Ecohydrol. Hydrobiol., 20: 513-524. doi: 10.1016/J.ECOHYD.2020.06.006.
  21. Takehiko, F., Tatsumi, K. and Bunkei, M. 2021. Lake water quality observed after extreme rainfall events: implications for water quality affected by stormy runoff. Discover Appl. Sci., 3: 841. doi: 10.1007/S42452-021-04823-X.
  22. Li, C.H., Ye, C., Li, J.X., Wei, W.W., Zheng, Y., Kong, M. and Wang, H. 2020. Impact of spring freshet flooding and summer rainfall flooding on the water quality of an alpine barrier lake. Env. Sci. Europe. 32: 57. doi: 10.1186/S12302-020-00319-4.
  23. Wu, Q., Xia, X., Li, X. and Mou, X. 2014. Impacts of meteorological variations on urban lake water quality: a sensitivity analysis for 12 urban lakes with different trophic states. Aquatic Sci., 76(3): 339-351. doi: 10.1007/S00027-014-0339-6.
  24. Zhou, Z.Z., Huang, T., Ma, W., Li, Y. and Zeng, K. 2015. Impacts of water quality variation and rainfall runoff on Jinpen reservoir, in northwest China. Water sci. eng., 8(1): 301-308. doi: 10.1016/J.WS E.2015.12.003.
  25. Subehi, L., Wibowo, H. and Jung, K. 2016. Characteristics of rainfall-discharge and water quality at Limboto lake, Gorontalo, Indonesia. J. Eng. Tech. Sci., 48(3):288-300. doi: 10.5614/J.ENG. TECHNOL.SCI.2016.48.3.4.
  26. Larsen, M.L., Baulch, H.M., Schiff, S.L., Simon, D.F., Sauvé, S. and Venkiteswaran, J.J. 2019. Extreme midsummer rainfall event drives early onset Cyano-bacterial bloom. bioRxiv. doi: 10.110 1/570275.
  27. Abegaz, N.T., Tsidu, G.M. and Kifle, B. 2024. Establishing and modelling the causality relationship of hydro-climatic and land cover change variables with water quality over lake Tana, Ethiopia. Total env. adv., 10: 200100. doi: 10.1016/j.teadva.20 24.200100.
  28. Díaz-Torres, O., Lugo-Melchor, O.Y., De Anda, J., Pacheco, A., Yebra-Montes, C., Gradilla-Hernández, M.S. and Senés-Guerrero, C. 2022. Bacterial dynamics and their influence on the biogeochemical cycles in a subtropical hypereutrophic lake during the rainy season. Front. Microbiol.: Aquatic Microbiol., 13. doi: 10.3389/fmicb.2022.832477.
  29. Rawat, K.S., Sahu, S.R., Singh, S.K. and Mishra, A.K. 2022. Cloudburst analysis in the Himalayan region. Research Square. doi: 10.21203/rs.3.rs-1158066/v1.
  30. Wiese, G.R. and Healy, T.W. 1975. Adsorption of Al (III) at the TiO2-H2O interface. J. Colloid Interface Sci., 51(3): 434-442. DOI: 10.1016/0021-9797(75)90140-X.
  31. Singh, A. 2016. Managing the water resources problems of irrigated agriculture through geospatial techniques: An overview. Agric. Water Manage., 174: 2-10.
  32. Chauhan, D., Thiyaharajan, M., Pandey, A., Singh, N., Singh, V., Sen, S. and Pandey, R. 2021. Climate change water vulnerability and adaptation mechanism in constrained touristic Himalayan city, Nainital, India. Env. Sci. Poll. Res. Int., 29(57): 85904-85921. DOI: 10.1007/s11356-021-15713-5.
  33. Salameh, A.A.M. 2024. Using the precipitation concentration index for characterizing the rainfall distribution in the Levant. J. Water Climate Change. 15(4): 1945–1960. doi: 10.2166/wcc.202 4.037.
  34. Xie, Y., Liu, S., Fang, H., Ding, M. and Liu, D.H. 2022. A study on the precipitation concentration in a Chinese region and its relationship with tele-connections indices. J. hydrol., 612: 128203. doi: 10.1016/j.jhydrol.2022.128203.
  35. Saha, S. 2020. Precipitation concentration index (PCI) a tool to evaluate the distribution of rainfall, Barishal, Bangladesh. Int. J. Adv. Geosci., 8(2): 193. doi: 10.14419/IJAG.V8I2.31074.
  36. Rawat, K.S., Pal, R.K. and Singh, S.K. 2021. Rainfall variability analysis using precipitation concentration index: A case study of the western agro-climatic zone of Punjab, India. Indonesian J. Geogr., 53(3): 388-399. DOI: 10.22146/ijg.64890.
  37. Llano, M.P. 2023. Spatio-temporal variability of monthly precipitation concentration in Argentina. J. Southern Hemisphere Earth Systems Sci., 73(2): 168-177.
  38. Pawar, U., Karunathilaka, P. and Rathnayake, U. 2022. Spatio-temporal rainfall variability and concentration over Sri Lanka. Adv. Meteorol., doi: 10.1155/2022/6456761.
  39. Mathbout, S., Jean, A., Lopez-Bustins, D.R., Martin-Vide, J. and Benhamrouche, A. 2020. Spatiotemporal variability of daily precipitation concentration and its relationship to teleconnection patterns over the Mediterranean during 1975–2015. Int. J. Climatol., doi: 10.1002/JOC.6278.
  40. Ghaedi, S. and Shojaian, A. 2020. Spatial and temporal variability of precipitation concentration in Iran. Geogr. Pannonica. 24(4): 244-251. DOI: 10.59 37/gp24-27361.
  41. Tehreem., Z., Ali, Z., Al-Ansari, N., Niax, R., Hussain, I. and Sammen, S.S. 2022. A novel appraisal protocol for spatiotemporal patterns of rainfall by reconnaissance the precipitation concentration index (PCI) with global warming context. Mathematical Problems Eng., 2022: 1-9. doi: 10.1155/2022/3012100.
  42. Desai, A., Shanker, D. and Jakka, R.S. 2015. Earthquake induced landslide hazard zonation of Nainital region. Geosci. J., 5(2): 62-69. DOI: 10.5923/j.geo.20150502.02.
  43. Goswami, K. and Trakroo, M.D. 2019. An overview of qualitative and quantitative composition of phytoplankton community at lake Nainital. J. Pharmacognosy Phytochem., 8(2): 1800-1804.
  44. Sharma, H.K. and Singh, R. 2019. Comparative water quality assessment of Central Himalayan lake: Nainital. J. Pharmacognosy Phytochem., SP3: 26-28.
  45. Bangia, T., Kumar, A., Sagar, R., Agarwal, P.K. and Singh, S.K. 2011. Development of Mie LIDAR system and initial cloud observations over Central Himalayan region. Sci. Res. Essays. 6(4):896-907. doi: 10.5897/SRE10.920.
  46. Dey, J., Sakhre, S., Vijay, R., Bherwani, H. and Kumar, R. 2021. Geospatial assessment of urban sprawl and landslide susceptibility around the Nainital lake, Uttarakhand, India. Env. Develop. Sustain., 23: 3543–3561. doi: 10.1007/S10668-020-00731-Z.
  47. Taloor, A. K., Sharma, S., Suryakiran, S., Sharma, R. and Sharma, M. 2024. Groundwater contamination and health risk assessment in Indian subcontinent: A geospatial approach. Curr. Opinion Env. Sci. Health. 39: 100555. DOI: 10.1016/j.coesh.2024. 100555.
  48. Pargaien, S., Prakash, R. and Dubey, V.P. 2023. Change of agriculture area over the last 20 years: A case study of Nainital district, Uttarakhand, India. J. Resour. Ecol., 14(5): 983-990.
  49. Divyanjali, Thakur, G., Priyanka, Singh, R., Gehlot, A., Twala, B., Priyadarshi, N., Akram, S.V. 2023. Assessment of environmental degradation of lakes of Nainital district: An ecohydrological perspective. SN Appl. Sci., 5(10): 271. DOI: 10.1007/s42452-023-05491-9.
  50. Rettig, J.E., Surace, M., Rose, K.D., Baird, A.J., Baker, Z.D. and Smith, G.R. 2023. Variation in the diets of western mosquitofish (Gambusia affinis) in two ponds: Effects of time and coexistence with Centrarchid fishes. Animal Biol., 73(4): 407-421.
  51. Nagamani, K., Mishra, A.K., Meer, M.S. and Das, J. 2024. Understanding flash flooding in the Himalayan region: A case study. Sci. Reports. 14(1): 7060.
  52. Oliver, J. E. 1980. Monthly precipitation distribution: A comparative index. Professional Geographer. 32(3): 300-309.
  53. Michiels, P., Gabriels, D. and Hartmann, R. 1992. Using the seasonal and temporal precipitation concentration index for characterizing the monthly rainfall distribution in Spain. Catena. 19(1): 43-58.
  54. Tariyal, K. 2017. Climatic fluctuations in Uttarakhand Himalayan region and resulting impacts: A review. Arch. Agric. Env. Sci., 2(2): 124-128.
  55. Sati, V.P. 2018. Cloudburst triggered natural hazards in Uttarakhand Himalaya: Mechanism, prevention and mitigation. Int. J. Geol. Env. Eng., 12(1): 45-48.
  56. Sati, V. P. and Kumar, S. 2022. Environmental and economic impact of cloudburst-triggered debris flows and flash floods in Uttarakhand Himalaya: A case study. Geoenv. Disasters. 9(1): 5.
  57. Rawat, A.S. and Semwal, M.M. 2022. Unveiling the Himalayan nexus: Unraveling anthropogenic triggers and pursuing sustainable development in Uttarakhand. J. Mountain Res., 18(1): 101-115. DOI: 10.51220/jmr.v18i1.11.
  58. Sahu, S.R., Rawat, K.S., Alok, S. and Baweja, H.S. Rainfall variability analysis using precipitation concentration index (PCI) and GIS techniques over Kanchipuram district of Tamil nadu, India. AIP Conf. Proc., 3072: 040013. DOI: 10.1063/5.0198905.