Unveiling Climatic Trends from 1922 to 2022: A Long-Term Time-Series Analysis of Precipitation of Semi-Arid Agra District, Uttar Pradesh, India

نوع مقاله : مقاله پژوهشی

نویسندگان

1 Department of Geography, St. John’s College, DBRAU, Agra-282001, India

2 Department of Geography, Mizoram University, Aizawl - 796 004, India

3 Department of Physics, St. John’s College, DBRAU, Agra-282001, India

چکیده

Background: Rainfall variation is a clear indicator of climate change. IPCC in its multiple assessment reports has raised concerns over the changing climate and rising global average temperatures which could lead to widespread and catastrophic impacts on natural and human systems. The study of long-term patterns is crucial in establishing evidence of shifting climate and informing policy decisions.
Objectives:This study examines the rainfall trend and variability of the semi-arid Agra district and assesses rainfall in the region. Annual rainfall for 101 years from 18 grid data points was statistically analysed for the same.
Methodology: For the projection and analysis of data points, a Thiessen network polygon was drawn using QGIS 3.28.4. Each grid data point was assigned a Thiessen polygon. According to the area each Thiessen polygon covers, it was assigned weights. Then according to the weight of each polygon, annual rain in that area was calculated. Next, the data were tested for homogeneity and breakpoints using the Standard Normal Homogeneity test, Buishand’s Range test, Buishand’s U test and Pettitt’s test. After this the trend of the data was identified using Mann-Kendall and the magnitude was calculated using Theil-Sen’s estimator. R-studio was used for all the statistical analysis and graph plotting.
Results: Upon conducting the Standard Normal Homogeneity test, Buishand’s Range test, Buishand’s U test and Pettitt’s test it was found that the data was non-homogeneous with the breakpoint in the year 1967. The Mann-Kendall test revealed a declining trend in the annual rainfall and the Theil-Sen estimator calculated the magnitude of this declining trend to be -1.63 for the last century.
Conclusion: The findings suggest that climate change is having a significant impact on rainfall in the semi-arid Agra district. The declining trend in rainfall could have several negative consequences for the region, including water scarcity, crop failure, and increased risk of droughts. 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Unveiling Climatic Trends from 1922 to 2022: A Long-Term Time-Series Analysis of Precipitation of Semi-Arid Agra District, Uttar Pradesh, India

نویسندگان [English]

  • Bob Stanley Gardner 1
  • David Durjoy Lal Soren 2
  • Nelia Lois Chauhan 1
  • Brototi Biswas 2
  • Shalomin Gardner 3
1 Department of Geography, St. John’s College, DBRAU, Agra-282001, India
2 Department of Geography, Mizoram University, Aizawl - 796 004, India
3 Department of Physics, St. John’s College, DBRAU, Agra-282001, India
چکیده [English]

Background: Rainfall variation is a clear indicator of climate change. IPCC in its multiple assessment reports has raised concerns over the changing climate and rising global average temperatures which could lead to widespread and catastrophic impacts on natural and human systems. The study of long-term patterns is crucial in establishing evidence of shifting climate and informing policy decisions.
Objectives:This study examines the rainfall trend and variability of the semi-arid Agra district and assesses rainfall in the region. Annual rainfall for 101 years from 18 grid data points was statistically analysed for the same.
Methodology: For the projection and analysis of data points, a Thiessen network polygon was drawn using QGIS 3.28.4. Each grid data point was assigned a Thiessen polygon. According to the area each Thiessen polygon covers, it was assigned weights. Then according to the weight of each polygon, annual rain in that area was calculated. Next, the data were tested for homogeneity and breakpoints using the Standard Normal Homogeneity test, Buishand’s Range test, Buishand’s U test and Pettitt’s test. After this the trend of the data was identified using Mann-Kendall and the magnitude was calculated using Theil-Sen’s estimator. R-studio was used for all the statistical analysis and graph plotting.
Results: Upon conducting the Standard Normal Homogeneity test, Buishand’s Range test, Buishand’s U test and Pettitt’s test it was found that the data was non-homogeneous with the breakpoint in the year 1967. The Mann-Kendall test revealed a declining trend in the annual rainfall and the Theil-Sen estimator calculated the magnitude of this declining trend to be -1.63 for the last century.
Conclusion: The findings suggest that climate change is having a significant impact on rainfall in the semi-arid Agra district. The declining trend in rainfall could have several negative consequences for the region, including water scarcity, crop failure, and increased risk of droughts. 

کلیدواژه‌ها [English]

  • Agra
  • Change Point Detection
  • Homogeneity
  • Rainfall
  • Climate Change Parameter
  • Trend Analysis
  • IMD Grid Data

مراجع

Alexandersson, H. (1986). A homogeneity test applied to precipitation data. Journal of Climatology, 6(6), 661–675. https://doi.org/10.1002/joc.3370060607
Allen, M. R., & Ingram, W. J. (2002). Constraints on future changes in climate and the hydrologic cycle. 419(September).
Asati, S. R. (2012). Analysis of rainfall data for drought investigation at Agra U . P . International Journal of Life Sciences Biotechnology and Pharma Research, 1(4), 81–86.
Bickici Arikan, B., & Kahya, E. (2019). Homogeneity revisited: analysis of updated precipitation series in Turkey. Theoretical and Applied Climatology, 135(1–2), 211–220. https://doi.org/10.1007/s00704-018-2368-x
Buishand, T. A. (1982). Some methods for testing the homogeneity of rainfall records. Journal of Hydrology, 58(1–2), 11–27. https://doi.org/10.1016/0022-1694(82)90066-X
Diop, L., Bodian, A., & Diallo, D. (2016). Spatiotemporal Trend Analysis of the Mean Annual Rainfall in Senegal. European Scientific Journal, ESJ, 12(12), 231. https://doi.org/10.19044/esj.2016.v12n12p231
Eischeid, J. K., Bruce Baker, C., Karl, T. R., & Diaz, H. F. (1995). The Quality Control of Long-Term Climatological Data Using Objective Data Analysis. Journal of Applied Meteorology, 34(12), 2787–2795. https://doi.org/10.1175/1520-0450(1995)034<2787:TQCOLT>2.0.CO;2
Gardner, B. S. (n.d.). How to Download and Convert IMD Gridded Binary Weather Data - Mapping Around. Retrieved July 13, 2023, from https://mappingaround.in/how-to-download-and-convert-gridded-weather-data/
Gardner, B. S., & Biswas, B. (2015). Enhancing merciless euthanasia of Yamuna- Role of Agra. Land & Water Renewable Energy for Sustainable Development, 150–157. https://doi.org/10.5281/zenodo.8159584
Gardner, B. S., Biswas, B., & Majeed, A. (2016). Land use land cover of Agra tehsil: A comparative study from 2002 to 2015. In Scholars Research Library Archives of Applied Science Research (Vol. 8, Issue 12). https://doi.org/10.5281/zenodo.8159898
Gaur, M. K., & Squires, V. R. (2018). Geographic Extent and Characteristics of the World’s Arid Zones and Their Peoples. In Climate Variability Impacts on Land Use and Livelihoods in Drylands (pp. 3–20). Springer International Publishing. https://doi.org/10.1007/978-3-319-56681-8_1
Hamed, K. H., & Ramachandra Rao, A. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1–4), 182–196. https://doi.org/10.1016/S0022-1694(97)00125-X
Houghton, J. T., Jenkins, G. J., & Ephraums, J. J. (1990). Climate change: The IPCC scientific assessment: Vol. 80:6. Cambridge, MA (United States); Cambridge University Press. https://www.osti.gov/biblio/6819363
IPCC. (2018). Summary for Policymakers. In: Global Warming of 1.5°C: An IPCC Special Report on Impacts of Global Warming of 1.5°C above Pre-industrial Levels in Context of Strengthening Response to Climate Change, Sustainable Development, and Efforts to Eradicate Pover. Global Warming of 1.5°C, 1–24. https://www.cambridge.org/core/product/identifier/9781009157940%23prf2/type/book_part
IPCC , 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (2014). In T. E. B. Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, A. N. L. M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, & and L. L. W. (eds. ) S. MacCracken, P.R. Mastrandrea (Eds.), Cambridge University Press. https://www.ipcc.ch/report/ar5/syr/
Kesava Rao, A. V. R., Wani, S. P., Singh, K. K., Irshad Ahmed, M., Srinivas, K., Bairagi, S. D., & Ramadevi, O. (2013). Increased arid and semi-arid areas in India with associated shifts during 1971-2004. Journal of Agrometeorology, 15(1), 11–18.
Mallakpour, I., & Villarini, G. (2016). A simulation study to examine the sensitivity of the Pettitt test to detect abrupt changes in mean. Hydrological Sciences Journal, 61(2), 245–254. https://doi.org/10.1080/02626667.2015.1008482
Mohammed, R., & Scholz, M. (2023). Quality Control and Homogeneity Analysis of Precipitation Time Series in the Climatic Region of Iraq. Atmosphere, 14(2). https://doi.org/10.3390/atmos14020197
Nandi, S., & Patel, P. (2020). iamsaswata/imdlib: a Python library for IMD gridded data. https://doi.org/10.5281/ZENODO.4405233
Partal, T., & Kahya, E. (2006). Trend analysis in Turkish precipitation data. Hydrological Processes, 20(9), 2011–2026. https://doi.org/10.1002/hyp.5993
Pohlert, T. (2020). Trend: Non-Parametric Trend Tests and Change-Point Detection. CRAN Repository, 1–18. https://cran.r-project.org/web/packages/trend/index.html
Romero, R., Guijarro, J. A., Ramis, C., Alonso, S., Group, M., Mallorca, P. De, Meteorolo, C., & Mallorca, P. De. (1998). A 30-YEAR ( 1964 – 1993 ) DAILY RAINFALL DATA BASE FOR THE SPANISH MEDITERRANEAN REGIONS : FIRST EXPLORATORY. 560, 541–560.
Sánchez-Lavega, A., Pérez-Hoyos, S., & Hueso, R. (2004). Clouds in planetary atmospheres: A useful application of the Clausius–Clapeyron equation. American Journal of Physics, 72(6), 767–774. https://doi.org/10.1119/1.1645279
Singh, P. K., & Chudasama, H. (2021). Pathways for climate resilient development: Human well-being within a safe and just space in the 21st century. Global Environmental Change, 68, 102277. https://doi.org/10.1016/J.GLOENVCHA.2021.102277
Theil-Sen | Open Mind. (n.d.). Retrieved May 20, 2023, from https://tamino.wordpress.com/2013/03/02/theil-sen/?utm_source=pocket_saves
Trenberth, K. E., Dai, A., Rasmussen, R. M., & Parsons, D. B. (2003). The Changing Character of Precipitation. Bulletin of the American Meteorological Society, 84(9), 1205–1218. https://doi.org/10.1175/BAMS-84-9-1205
Wang, T., Hamann, A., Spittlehouse, D., & Carroll, C. (2016). Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE, 11(6), 1–17. https://doi.org/10.1371/journal.pone.0156720
Wijngaard, J. B., Klein Tank, A. M. G., & Können, G. P. (2003). Homogeneity of 20th century European daily temperature and precipitation series. International Journal of Climatology, 23(6), 679–692. https://doi.org/10.1002/joc.906
Xie, H., Li, D., & Xiong, L. (2014). Exploring the ability of the Pettitt method for detecting change point by Monte Carlo simulation. Stochastic Environmental Research and Risk Assessment, 28(7), 1643–1655. https://doi.org/10.1007/s00477-013-0814-y
Xu, L., Yu, G., Zhang, W., Tu, Z., & Tan, W. (2020). Change features of time-series climate variables from 1962 to 2016 in Inner Mongolia, China. Journal of Arid Land, 12(1), 58–72. https://doi.org/10.1007/s40333-019-0124-4
جغرافیا و آینده پژوهی منطقه‌ای
دوره 2، شماره 2 - شماره پیاپی 5
تابستان
شهریور 1403
صفحه 78-84

فایل ها

هم رسانی

ارجاع به این مقاله

آمار