Rainfall variability and trend detection in under changing climatic region for Ahilyanagar district of Maharashtra

Introduction

India's agriculture, food, and energy security depend on adequate water availability and favorable climate conditions. Rainfall is a key determinant of water resources for agriculture, industrial, domestic, and hydroelectric uses. The South-West monsoon provides about 80% of annual rainfall, but its seasonal concentration (June-September) leads to water scarcity during nonmonsoon periods (Sebastiane et al., 2023). Climate change is expected to alter rainfall patterns, increasing the risk of drought and floods, and affecting the hydrological cycle as well as water demand. Due to uneven rainfall distribution and demand-supply mismatch, large storage systems are essential. Therefore, analyzing trends in rainfall and other climatic variables is crucial for developing reliable future climate scenarios (Jain and kumar 2012) [1] . Both parametric and non-parametric techniques have been employed for trend detection. Among these, the Mann-Kendall (MK) test (Mann, 1945; Kendall, 1975) [2,3] is widely used as a non-parametric method for identifying trends in hydrological time series data. Atre and Deore (2013) [4] examined weekly rainfall trends at Rahuri, Maharashtra, using the Mann-Kendall test to detect monotonic changes in rainfall time series. The study found a significant increasing trend only during the 36th week, while most monsoon weeks exhibited no significant trend, indicating that the overall monsoon rainfall pattern at Rahuri remained stable with no long-term trend. Joshi et al. (2019) [5] analyzed 57 years (1961-2017) of rainfall data from nine stations in Solapur District, Maharashtra, India, using the Mann-Kendall test and Sen's slope estimator to assess long-term rainfall trends. The study revealed statistically significant decreasing trends in annual, seasonal, and monthly rainfall across most tahsils, highlighting the need for efficient water-resource management to sustain agricultural production under changing climatic conditions. Barai and Kalunge (2021) [6] studied rainfall trends in Ahmednagar district, Maharashtra using statistical methods like the Mann-Kendall test and Sen's slope to understand changes over time using rainfall data for the period of 19801 to 2012. They found that rainfall in Ahmednagar district generally showed an increasing trend over the 33-year period, especially during August and in annual rainfall, indicating changing rainfall patterns across different tahsils. Upadhye et al., (2022) [7] analyzed the long-term trends in annual rainfall and number of rainy days for Pune district, Maharashtra. Nandgude et al. (2024) [8] examined how climate change has affected rainfall extremes and long-term rainfall trends in the upper Krishna river Basin of Maharashtra. They analysed 52 years (1970-2021) of rainfall data from 15 stations in the upper Krishna river basin of Maharashtra and found an overall increasing trend in monsoon and annual rainfall, along with a rise in extreme rainfall events and heavy rainfall days in recent decades.

The present study is conducted in Ahilayanagar district, located in Maharashtra state. The district comprises 14 tahsils and is characterized by a predominantly hot and dry climate. Most parts of the study area experience high temperatures and arid conditions throughout the year, except during the South-West monsoon season. The summer season extends from March to early June, followed by the south-west monsoon, which continues until the end of September. The months of October and November represent the post-monsoon or retreating monsoon phase. Rainfall in the district is primarily received from the south-west monsoon; however, its spatial distribution is highly uneven. The average annual rainfall of the district is 566 mm, and the region is agro-climatically classified as a scarcity zone. The location map of study area is shown in Fig 1.

Materials and methods

The present investigation covers 14 tahsils of Ahilyanagar district, namely Akole, Jamkhed, Karjat, Kopargaon, Parner, Pathardi, Nagar, Shevgaon, Sangamner, Shrigonda, Shrirampur, Rahata, Rahuri and Newasa. The study area extends between 18.2°N to 19.9°N latitudes and 73.9°E to 57.5°E longitudes encompassing total geographical area 17,048 km².

Mann kendall method

The daily rainfall data of Ahilyanagar district for the period 1961-2025 (65 years) were obtained from the India Meteorological Department (IMD) and Hydrometeorological Department. The data analysed using the Mann-Kendall test and Sen's slope estimator to assess monotonic trends. The test, widely applied for hydro-climatic time series, evaluates the null hypothesis of no trend against the alternative of increasing or decreasing trend (Gilbert, 1987) [9] . For datasets exceeding 10 observations, the normal approximation (Z-test) was adopted. The analysis revealed tahsil-wise variations, indicating both increasing and decreasing rainfal tendencies, with statistical significance observed in selected locations.

Where, S denotes the Mann-Kendall test statistics, xⱼ and xₖ denotes the sequential data value, Sign denotes the signum function.

𝑠𝑖𝑔𝑛(𝑥 𝑗 -𝑥 𝑘 ) = {1 𝑖𝑓 𝑥 𝑗 -𝑥 𝑘 > 00 𝑖𝑓 𝑥 𝑗 -𝑥 𝑘 = 0 -1𝑖𝑓 𝑥 𝑗 -𝑥 𝑘 < 0 (2)

Where q denotes the total number of tied groups, tₚ represents the number of observations in the p th tied group, and n indicates the total length of the data series (number of years). The standardized test statistic Z was then calculated using the following expression.

Z ={

The existence of a statistically significant trend was determined based on the value of the standardized test statistic Z. A positive Z value signifies an increasing trend, whereas a negative Z value denotes a decreasing trend.

Sen's slope method

The magnitude of the trend was quantified using the Sen's slope estimator proposed by Pranab Kumar Sen (1968) [10] . This nonparametric method is robust for detecting monotonic trends in hydrometeorological time series and estimates the true slope as the median of all pairwise slopes. The individual slopes between data points are computed using equation ( 5):

Where X represents the variable of interest, n is the total number of observations, and j, k are time indices. A positive slope indicates an increasing trend, whereas a negative slope denotes a decreasing trend.

Results and Discussion Weekly rainfall variability and trends

Weekly rainfall trends for the period 1961-2025 were analysed using the Mann-Kendall test and Sen's slope estimator as shown in Table 1. Both increasing and decreasing trends were observed in different weeks across the tahsils is shown in Fig. Shrigonda tahsil showes increasing trends in weeks 36 and 37 (1.9 and 1.6 mm per year). These weekly rainfall trend patterns are also illustrated in Fig. 2.

Monthly rainfall variability and trends

The monthly rainfall trends for the different tahsils in Ahilyanagar district was calculated using the Mann-Kendall test and Sen's slope estimator, and the results are shown in Table 2. The analysis showed that in May, a statistically significant decreasing trend was observed only in Shevgaon with a Sen's slope Q(t) of -0.01 mm per year. In June and July, a significant increasing trend was observed only in Akole with Sen's slope values of 1.70 and 2.70 mm per month, respectively. In August, a statistically significant increasing trend was detected in Akole, Kopargaon, Nagar, Parner, Rahata, Rahuri and Shrigonda with Sen's slope Q(t) values of 1.6, 0.6, 1.0, 0.5, 0.7, 0.9, and 0.4 mm per month, respectively. In September, Kopargaon, Parner, Pathardi, Rahata, Sangamner and

References

1. Jain SK, Kumar V. Trend analysis of rainfall and temperature data for India. Current Science. 2012;102(1):37-49.
2. Mann HB. Non-parametric test against trend. Econometrica. 1945;(13):245-259.
3. Kendall MG. Rank Correlation methods. Charles Griffin, London. 1975.
4. Atre AA, Deore SS. Detection of trends in weekly rainfall at Rahuri in scarcity zone of Maharashtra, Theme-IX: Hydrological Response of Watershed Development Programme. 2013.
5. Joshi JK, Upadhye SK, More DD. Long term rainfall trend analysis of different time series in Solapur district of Maharashtra, India. International Journal of Current Microbiology and Applied Sciences. 2019;8(12):23592367.
6. Barai VN, Kalunge RM. Trend analysis of rainfall using Mann-Kendall, Sen's slope, moving average and least square techniques for Ahmednagar district of Maharashtra. International Journal of Agricultural sciences. 2021;17(1):19-25.
7. Upadhye SK, Sthool VA, Munde PS, Jadhav JD, Bagade SV. Trend Analysis of Annual Rainfall and Rainy Days Over Pune District of Maharashtra. Journal of Agriculture Research and Technology. 2022;47:145-152.
8. Nandgude SB, Nagarkar SS, Atre AA, Gorantiwar SD, Patil MR. Climate Change Impacts on Rainfall Extremes and Historical Rainfall Trends Over Upper Krishna River Basin, Maharashtra, India. Indian Journal of Ecology. 2024;51(1):32-37.
9. Gilbert RO. Statistical methods for environmental pollution monitoring. John Wiley and Sons. 1987.
10. Sen PK. Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association. 1968;63(324):1379-1389.
11. Chhabra V, Haris AA. Temperature Trends and their impact on rabi crops in changing climatic scenario of Bihar. Scholars Journal of Agricultural and Veterinary Sciences. 2014;1(4A):230-234.
12. Kumar V, Jain SK, Singh Y. Analysis of long-term rainfall trends in India. Hydrological Sciences Journal. 2010;55(4):484-496.
13. Mandal UK, Nayak DB, Mullick S, Samui A, Jana AK, Mahanta KK, et al. Trend analysis of weather parameters over Indian Sundarbans. Journal of Agrometeorology. 2019;21(3):307-615.
14. Sebastian DE, Murtugudde R, Ghosh S. Soil vegetation moisture capacitor maintains dry season vegetation productivity over India. Scientific Reports. 2023;13:1-10.
15. Upadhye SK, Taley SM, Karunakar AN, Jadhav KA. Trends in rainfall and rainy days of Akola in Vidarbha region. Proceedings of third National Conference on Water, Environment and Society (NCWES). 2016:342-347.