Skip to content
Research Article Open access CC BY 4.0

Trend Analysis of Rainfall in Telangana State (India) Using Advanced Statistical Approaches

Bellamkonda Jyostna, Admala Meena, Santosha Rathod, Tuti, Mangal Deep, Mangal Deep Tuti, Reddy, Bojjareddygari Nanda Kumar, Supriya Kallakuri, D. Bhanusree, Bojjareddygari Nanda Kumar Reddy

International Journal of Environment and Climate Change · pp. 3404–3412 · Published 27 Oct 2022

10.9734/ijecc/2022/v12i111391

Abstract

This study was carried out to analyse the trend analysis of the long-term annual and seasonal rainfall pattern in Telangana state, India. For this study monthly rainfall data of Telangana state from January 1982 to December 2021 was collected from the NASA power website (https://power.Iarc.nasa.gov). The linear regression trend line and the non-parametric tests, such as Mann-Kendall test, Modified-Mann Kendall test and Innovative trend analysis tests, were used to understand the trend present in the rainfall data of Telangana. Wallis and Moore test was used to test the randomness of the rainfall data under consideration. Both increasing and decreasing trend was seen in linear regression trend method for Telangana rainfall data. The significant result was found in the month of May which showed an increasing trend, whereas remaining months showed the non-significant trend in the Modified Mann Kendall test as well as in the Innovative trend analysis. The pre-monsoon, monsoon and post-monsoon periods showed a non-significant trend in the rainfall pattern of Telangana state. The annual rainfall of Telangana showed a non-significant trend pattern by Modified Mann-Kendall test. There was a significant increasing trend of rainfall in the month of May and remaining months showed a non-significant trend. Accurate identification of rainfall patterns over the area may help to create the appropriate policy measures in advance to plan the future climate uncertainties.

Trend rainfall mann-kendall test modified mann-kendall test wallis-moore test innovative trend analysis

References (24)

  1. 1 Nonparametric Tests Against Trend [DOI]
  2. 2 Estimates of the Regression Coefficient Based on Kendall's Tau [DOI]
  3. 3 Increasing Trend of Extreme Rain Events Over India in a Warming Environment [DOI]
  4. 4 Statistical Methods for Environmental Pollution Monitoring. [DOI]
  5. 5 Rank Correlation Methods [DOI]
  6. 6 The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series [DOI]
  7. 7 Theoretical and Applied Climatology
  8. 8 Some evidence of climate change in twentieth-century India [DOI]
  9. 9 Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches [DOI]
  10. 10 Climate Change and its Impact on Agricultural Productivity in India [DOI]
  11. 11 Precipitation trend analysis of Sindh River basin, India, from 102‐year record (1901–2002) [DOI]
  12. 12 TREND ANALYSIS OF RAINFALL, TEMPERATURE AND RUNOFF DATA: A CASE STUDY OF RANGOON WATERSHED IN NEPAL [DOI]
  13. 13 Temperature and precipitation trend over 139 major Indian cities: An assessment over a century [DOI]
  14. 14 Analysis of rainfall trend using non-parametric methods and innovative trend analysis during 1901–2020 in seven states of North East India [DOI]
  15. 15 Trend analysis of long-term rainfall and temperature data for Ethiopia [DOI]
  16. 16 On rising temperature trends at Dehradun in Doon valley of Uttarakhand, India [DOI]
  17. 17 A hybrid spatio-temporal modelling: an application to space-time rainfall forecasting [DOI]
  18. 18 Climate Change Impacts On Seasonal Rainfall Trends in the Regions of Andhra Pradesh and Telangana States, India [DOI]
  19. 19 Long-term Rainfall Analysis towards Detection of Meteorological Drought over Kozhikode District of Kerala [DOI]
  20. 20 A Significance Test for Time Series and Other Ordered Observations
  21. 21 Rainfall and Temperature Trends in Jagtial District of Telangana State [DOI]
  22. 22 Modeling of daily rainfall using gamma probability distribution.
  23. 23 Effect of rainfall patterns on crop yield in southern dry zone of Karnataka.
  24. 24 International Journal of Environment and Climate Change [DOI]

Cited by 19

Trend Analysis of Rainfall in Mahabubnagar District of Telangana State, India

Preethi Ch, Meena A, Rathod S, Balaji Naik B · International Journal of Environment and Climate Change · 2024

Analysis of Rainfall Trends and Change Point Detection in Kodagu District, Karnataka

Karthik V C, Vinay H T, Jagadeesh M S, Ragini H R, Gaddala Prem, Pavithra V · International Journal of Environment and Climate Change · 2024

Innovative polygon trend analyses to detect trends for domestic and export onion prices

Jhade Sunil, Abhishek Singh, M Manjubala · International Journal of Vegetable Science · 2023

Statistical investigation of heatwave trends and change points in Telangana, India

Kona Naga Venkata Lakshmi Laasya, Supriya Kallakuri, Santosha Rathod · Natural Hazards · 2026

Rainfall in Telangana (India): a decadal trend analysis

Kavitha Chandu, A. Dharma Raju, S. V. J. Kumar · Proceedings of the Indian National Science Academy · 2025

Explainable AI for Ground water nitrate content prediction using machine learning Frameworks in Telangana

Keyur Padia, Anshul Sunkari, Abhinav Gupta · 2025 IEEE 6th India Council International Subsections Conference (INDISCON) · 2025

Showing 7 of 19 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

78

Page views

0

PDF downloads

39

Outbound clicks

19

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.