Analyzing rainfall trends in the West Central meteorological division of India using the innovative trend method

Rainfall variability critically influences agriculture, water resources, ecosystems, and disaster management, making long-term trend analysis essential for climate adaptation. The study examines the trend in the monthly, seasonal, and annual rainfall for India's west-central meteorological division using historical data of 122 years (1901 to 2022). The trend detection is performed using the Mann–Kendall (MK), the Sen’s slope (SS), the Modified Mann–Kendall (MMK) method with variance correction proposed by Yue and Wang, and the Innovative Trend Analysis (ITA) method. The MK test’s highest trend (0.25 mm) occurred in Madhya Maharashtra’s seasonal rainfall, while SS’s maximum slope (4.38 mm) was observed in Konkan and Goa’s annual rainfall. The MK and SS methods could not detect the trend in the seasonal and annual rainfall for the North Interior Karnataka subdivision due to significant autocorrelation present in the data, whereas the MMK test successfully finds it. The ITA method captures all trends identified by conventional techniques; it additionally detects the hidden trends that are missed by traditional methods, particularly in Marathwada and Vidharbha, where no significant trends were detected by traditional methods. The statistical analysis is performed using R software version 4.4.1.

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Publication Details

Journal
Discover Atmosphere
Published
2025-10-18
DOI
https://doi.org/10.1007/s44292-025-00057-3
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
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article

Analyzing rainfall trends in the West Central meteorological division of India using the innovative trend method

MG Jadhav, YOGESH R. YEWALE
Discover Atmosphere
Climate variability and models
article

Analyzing rainfall trends in the West Central meteorological division of India using the innovative trend method

MG Jadhav, YOGESH R. YEWALE
article en

Abstract

Rainfall variability critically influences agriculture, water resources, ecosystems, and disaster management, making long-term trend analysis essential for climate adaptation. The study examines the trend in the monthly, seasonal, and annual rainfall for India's west-central meteorological division using historical data of 122 years (1901 to 2022). The trend detection is performed using the Mann–Kendall (MK), the Sen’s slope (SS), the Modified Mann–Kendall (MMK) method with variance correction proposed by Yue and Wang, and the Innovative Trend Analysis (ITA) method. The MK test’s highest trend (0.25 mm) occurred in Madhya Maharashtra’s seasonal rainfall, while SS’s maximum slope (4.38 mm) was observed in Konkan and Goa’s annual rainfall. The MK and SS methods could not detect the trend in the seasonal and annual rainfall for the North Interior Karnataka subdivision due to significant autocorrelation present in the data, whereas the MMK test successfully finds it. The ITA method captures all trends identified by conventional techniques; it additionally detects the hidden trends that are missed by traditional methods, particularly in Marathwada and Vidharbha, where no significant trends were detected by traditional methods. The statistical analysis is performed using R software version 4.4.1.

Discover AtmosphereVol. 3(1)
Dr. Vitthalrao Vikhe Patil Foundation’s Medical College (IN)
Openalex Percentile: Top 72%
Climate variability and models
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