Adaptation of trend analysis over a small-scale region for assessing climate change parameters Open Access

Climate change is one of the important challenges the world is currently facing and highly affects the environment and ecosystems. The intensity of climatic changes varies with the geographical locations and anthropogenic activities (Marie et al. 2021). After the globally increasing population, one of the main causes of water and food scarcity is the global climate changes resulting in an average increased temperature and declining rainfall (Gebremicheal et al. 2014). All these parameters eventually influence the agricultural activities. However, agriculture is a prime sustenance of livelihood in India, where half of the population depends on agriculture and its allied sectors (Baanu et al. 2022). Therefore, it is necessary to observe and analyse climatic data, since almost all the crops and agricultural products and by-products are directly linked with the temperature and rainfall (Abeysingha et al. 2014). Due to the persistent rise in water demand, longer production times and lower farm earnings, the agriculture sector is directly affected by climate change and also confronts resource limitations. Therefore, alternate adaptation strategies are required for the conventional systems to overcome climate change issues. The analysis of climatic fluctuations is essential to combat climate crisis, maintain agricultural sustainability, and enhance the farmers' livelihood.

Several researchers have carried out the analysis of the trends in climatic changes. One of the greatest techniques for analysing rainfall and temperature trends is the Mann–Kendal (MK) test (Mann 1945; Kendall 1975), which may also be used as the best water management tool for effective watershed protection (Tabari & Talaee 2011; Jain & Kumar 2012; Nouri et al. 2017; Alhaji et al. 2018; Chand et al. 2020). Jain & Kumar (2012) described the direction and strength of trends using the MK test and Sen's slope estimator. Chand et al. (2020) examined the temperature trends persisting on the Seonath River Basin and found that the summer season had the strongest warming trends, followed by the winter, pre-monsoon, and post-monsoon seasons. The temperature trend increases over time, which might bring about extreme weather events due to climate change, and the rainfall trends are significantly decreasing in many regions and increasing in a few (Palaniswami & Muthiah 2018; Alemu & Dioha 2020; Isabella et al. 2020; Tossou et al. 2021). This rainfall and temperature variability results in the rise in frequency of extreme weather events, also affecting crop production, increased weed and insect proliferation, and groundwater depletion, and their detrimental implications can go beyond the effects of changing rainfall and temperature (Sathischandra et al. 2014; Siddig et al. 2020; Tabari 2020). To reduce the consequences of climatic variability on agriculture, the study recommends the analysis of rainfall and temperature trends to maintain agricultural sustainability.

Even though the MK test is frequently used in hydrological trend analysis, earlier reports suggested that the existence of autocorrelation (the degree to which the time series resembles a lagged version of itself over subsequent time intervals) can affect the capacity to detect trends using MK and Sen's slope (Pastagia & Mehta 2022). Perhaps to overcome the setbacks of the MK test, Sen (2012) created a novel technique called the innovative trend analysis (ITA), to identify the trends in climatic, hydrological, and air pollution variables (Mallick et al. 2021). ITA has gained popularity as a statistical approach since it is independent of sample size and distribution, and another key benefit is that it displays trends in graphical form (Gedefaw et al. 2018). The ITA approach evaluates the data points that are with the 1:1 line in a Cartesian grid.

Based on the literature, it is evident that these researchers either used MK test or ITA test to interpret the various factors regarding climatic change. But no detailed work was reported about the effect of rainfall and temperature during cropping seasons pertaining to the area of study chosen using MK and ITA tests. In this work, it is planned to use the MK and ITA approach to investigate the trends in rainfall and temperature regarding various cropping seasons in a taluk (a subdivision of a district) and compare them to evaluate the most reliable technique for small-scale regions. The findings of the study would help the policymakers and rural farmers increase their preparedness for climate change and assess sustainable solutions. It is a micro-level representation of large regions that examines the patterns of seasonal rainfall and temperature variability using the trend analysis tool and evaluates the most preferred technique.

The climate is generally hot and dry and the annual average temperature ranges between 22 and 40 °C. The region receives an average annual rainfall between 724 and 913 mm/year. The major soil type in the area is red loamy and black clay soil with cotton, paddy, and maize as the most predominant crops. The observed rainfall and temperature data are divided into three important categories based on the state's agricultural cropping seasons; Kuruvai/Samba (June–September), Thaladi/Navarai (October–January), and Sornavari (February–May) seasons.

The variable denotes the variance, n is the number of data points, g is the number of tied groups (a tied group is a set of sample data having the same value), and t_p is the number of data points in the pth group.

The variable Z is the statistic normal distribution value; if the values are positive, there is an increasing trend in the climatological time series, and if negative, then the trend is decreasing in the time series.

From Figure 4 it is evident that the rainfall pattern in the study area does not follow a uniform distribution pattern; the rainfall concentration is higher in few months and lower in most other months with an average annual rainfall of 818.89 mm, which is comparatively less than the state's average rainfall of 960 mm.

From the results of the trend analysis, it is observed that the taluk has an insignificant increasing rainfall trend and a significantly increasing temperature trend. This implies that even though there is an increasing rainfall trend in the region, simultaneously the temperature also increases during the cropping seasons. The results are in alignment with the results of Bharani Baanu & Jinesh Babu (2024). Similarly, the MK test results showed an increasing trend in precipitation, which can be considered as an increase in the supply of water for agriculture in the region through surface or groundwater.

The temperature concurrently shows a significantly increasing trend by the MK test. The increase in temperature has a direct impact on the water demand for irrigation and crop water requirement. An increase in temperature results in an increase in crop evapotranspiration, which will in turn increase the irrigation water requirement. Therefore, the demand–supply gap must be measured by estimating the water available for agriculture from rainfall and the crop water requirement. In case the demand is higher than the supply, water scarcity arises, which in turn affects crop growth and agricultural productivity. This can be counteracted through proper climate change agriculture adaptation strategies such as adopting water harvesting technologies, use of wastewater for agriculture, planting short duration crop varieties, and developing integrated water resources management.

From the ITA of rainfall, it is observed that during the Kuruvai/Samba (Figure 6) and Sornavari seasons (Figure 8), the trends are increasing with more data points falling above the trendline, but for the Thaladi/Navarai season (Figure 7), the trend is decreasing. This clearly states that the MK test and ITA test are not in concordance with each other for rainfall trend analysis.

It is found that the temperature data (Table 5) for all three cropping seasons are similar, but while comparing the rainfall trends (Table 6), Kuruvai/Samba and Sornavari cropping seasons show increasing trends in both methods, while the Thaladi/Navarai season shows a decreasing trend in the ITA technique indicating a decrease in rainfall trend over a period. The Thaladi/Navarai season (October–January) is the most predominant cropping and rainfall season of the region per the observed climatic data with a rainfall record of 443 mm, which is nearly half of the district's average annual rainfall of 960 mm. This season favours the growing of short, medium, and long duration crop varieties with the cropping period of 120–150 days. The increasing trend in rainfall could increase the crop growth and yield, while the increasing temperature trend assists in developing higher temperature-tolerant crop varieties. In this study, it is found that the MK test is the most reliable technique compared with the ITA technique for the analyses of small areas.

In this study, trends of rainfall and temperature over the Rajapalayam taluk for the period of 2007–2021 (15 years) were examined by the application of both MK and ITA techniques. The comparison of the MK and ITA tests drives to the understanding of the characteristics of both the methods. Both the methods are found suitable for the analysis of climate trends. The application of MK test has the ability to detect nonlinear trends and shows robustness to outliers, but it also has certain setbacks such as the detection of trends monotonically, existence of serial autocorrelation which may result in erroneous trends, and data sensitivity. Also, the MK test does not find the maximum and minimum of detected trends. The ITA technique detects both monotonic and non-monotonic trends. The method detects trends in different levels of a given time series, such as the lows and highs. This method can be applied regardless of distribution assumptions, size, and serial correlation. ITA has also been used to determine trends for different climate factors in a graphical representation, providing a comprehensive understanding of climate events over the period. The results of both techniques were compared to identify the most reliable technique for small-scale regions. Climate change impacts such as a rise in temperature in the study area would possibly affect the water supply in the region and increase the irrigation water demand.

The study shows that, on a yearly basis, rainfall and temperature have an increasing trend. In particular, the results of the ITA technique indicate an increasing rainfall trend in the Kuruvai/Samba (June–September) and Sornavari (March–May) cropping seasons but a decreasing trend in the Thaladi/Navarai (October–January) season. The results detected an increasing trend for temperature in all three cropping seasons. During the predominant cropping season, the rainfall has a decreasing trend by the ITA technique, which does not agree with the MK test. Increase in temperature affects the plant development such as growth, early maturity, higher evapotranspiration, and finally alters the metabolic activities of plants. In such conditions, growing higher-temperature-tolerant crops could be a possible adaptation measure. The most common responses to climate change include crop variety changes, inter- and multiple-crop planting, water harvesting technology utilisation, greywater use, and simultaneous use of surface and groundwater, wastewater aquifer recharging, and so on. In addition, adopting soil and water conservation methods for increasing water productivity and mitigating the effects of climate change, understanding farmers’ perceptions towards climate change and elucidating the necessity and preparedness towards climate change, and the establishment of early warning systems will possibly reduce the impacts of climate change in the area.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.