Current World Environment

Introduction

The Intergovernmental Panel on Climate Change (IPCC) published the 6^th assessment report (AR6) which is helpful in understanding current global climate. As per the IPCC AR6 report the global warming in the near-term will touch or go beyond 1.5°C even to the lesser scenarios for greenhouse gas emissions ¹. The newest synthesis document from the United Nations Framework Convention on Climate Change (UNFCCC) published in 2022, which includes recent Nationally Determined Contributions (NDCs), indicates that the commitments made under the Paris Agreement by 196 parties are expected to decrease greenhouse gas (GHG) emissions by 0.3% by 2030 year². Globally as per the Sustainable Development Goal (SDG) report published in 2023, the highly vulnerable regions during the period 2010 to 2020 nearly 3.3-3.6 billion people experienced high mortality rates from drought, storms and floods in comparison with very low vulnerable regions³. Water scarcity is arising all over the world due to rainfall extremes and exposed to accumulative pressures^4-6. Land use and land cover (LULC) is the major contributor for the water quality however, sometime, it amplifies due the effect of climate change⁷. The river basin flow patterns were altered by the effects of climate change on rainfall and evapotranspiration⁸. Climate change is one of the factors contributing to the severity of floods⁹. The duration and intensity of rainfall plays a crucial role in flood damages and flood losses¹⁰.

Rainfall extremes and rainfall variability increases the risks of food availability and water security especially in Asian countries¹. The surface temperature of annual average time scale over India are normally 25°C or more in the current climate (1951-2010) and are predicted to escalate by 3 - 5°C in the projected period (2051-2110). The future rainfall predicted in the southwest region having arid and semi-arid climate types shows a rise of 0.5mm/day rainfall amount. The rainfall intensity of wet days and strong rainfall events increase by 7mm/day and higher than 25mm/day respectively¹¹. The Cauvery river basin is the major river basin in Tamil Nadu having tropical and sub-tropical climate. The annual rainfall of approximately 70% is received in North East Monsoon season (NEM) and shows a rising trend¹².

A non-parametric Man-Kendall and Sen’s tests carried out on observed and projected precipitation of Thanjavur delta region situated in the state of Tamil Nadu. The observed precipitation of IMD grid over the period 1970-2014 showed a growing trend¹³. The declining trend is seen in the case of projected precipitation over the 2015-2050 period¹³. The M-K and Sen’s methods are useful in indicating the temporal and spatial patterns in the annual and seasonal rainfall series towards Tamil Nadu state. There is a rise in NEM precipitation in the Western Ghat region and also 4.97% increase in yearly precipitation between the 1901-2015 period¹⁴. The slope from the Sen’s test results showed that a decreasing trend of rainfall with the rate of -1.27 cm per year noticed in coastal Andhra Pradesh region¹⁵. The present research study aims to recognize the rainfall variability through their spatial and temporal trend patterns so as to find hydrological solutions to recover habitable condition of the river basin. For the effective management for water and propose suitable climate change adaptation strategies, the understanding of rainfall trends is essential¹⁶.

The climate extremes study at basin level is in the urgent need because it is having higher impact on regional level sectors like agriculture, environment, water resources, health, social, economic, energy, coastal, atmospheric condition etc. climate extremes studies were carried out and analysed at the side of many research scholars at global level^17,18. The regional level study pertaining to climate studies are very scarce in Indian basins, hence it is an urgent need to analyse rainfall extremes which caused human, land and agricultural loss in the earlier two decades.

Study Area

The Agniyar basin is the flood prone river basin which is situated in the middle and eastern side of Tamil Nadu in the southern part of India is presented in Fig.1. The geographical position of the Agniyar river basin lies between the Latitude 09°55’N and 10°45’N and Longitude 78°15’E and 79°30’E. The spatial area enclosed by the basin is 4663 sq.km. North -Western portion of the basin is occupied by the Cauvery basin, Southern portion of the basin is covered by Pambar and Kottakaraiyar basin, Eartern side is Palk straight and Bay of Bengal is present. The districts covering the Agniyar river basin are Pudukkottai, Thanjavur, Tiruchirapalli, Sivagangai and Dindigul. The basin comprises three sub-basins: Agniyar, Ambuliyar, and South Vellar. Physiographically, the Agniyar basin is distributed into three terrains (i) Western hard rock terrain, (ii) Eastern sedimentary terrain and coastal region, (iii) Central pediplain terrain. About 29.8% of basin land area is agricultural land, 3.9% of land is forest area, 1% is built-up area, 0.18% comprises of water bodies. The main part of 63.5% terrain is waste ground, which encompasses the land impacted by alkalinity, barren space, salt pan, Juliflora grown area, land covered with scrub, shrubs, and exposed rock formations.

The basin is completely a rain-fed area suffering more by the unavailability of irrigation water and domestic water supply. The major types of soil present in this basin namely, Alfisols, Vertisols and Entisols. Agniyar basin is located in the tropical monsoon zone. Based on the hydro meteorological condition of the river basin the year is separated into rainy season (June to December) and non- rainy season (January to May) periods. The monsoon period is categorized into SWM southwest monsoon season occurs from June to September, while the NEM season takes place from October to December. The 60% of yearly water requirement is fulfilled by NEM and it is having an important role in water resources augmentation and agricultural activities¹⁹. The non-monsoon time is further separated into winter spans January to February, and the summer seasons lasts from March to May. The average yearly precipitation of the Agniyar basin is 931 mm.

Figure 1: Index map of Agniyar basin Click here to view Figure

Methodology

Trend detection using M-K and Sen’s Test

The recorded rainfall data of 12 rain gauge was obtained from the State Ground and Surface Water Resources Data Centre, Chennai for the historical period of 1980 to 2021 (42 years). The most influencing rain gauge stations present within and near the Agniyar basin is presented in the Figure 2. The longer duration of available observed data incorporated for the better results.

Figure 2: Thiessen Polygon map of 12 rain gauge stations Click here to view Figure

The M-K test is an extensively adopted non-parametric statistical test^20,21 and the Theil-Sen trend detection test^22,23. Which are employed for the trend analysis of precipitation. The non-parametric median based Theil Sen method provides the slope magnitude which gives the trend of the data series under study. The M-K statistic S and the standardized check statistic Z are expressed as follows,

The statistical parameters are defined as follows: x_u and x_v represent the consecutive data values for years u and v, n is the total number of data points in the record; t_p indicates the number of ties for the pth value; and q represents the count of tied values.

where x_u and x_v are belongs to the series of sequential data values across the years u and v, and the trend’s slope magnitude is denoted by B for the time series values. The trend with increasing values of Z is indicated increasing trend and the decreasing trend values of Z is indicated as declining trend of the associated time series. The H₀ (zero hypothesis) represents that no trend is observed in the series and the Ha (alternate hypothesis) denotes that the trend is existing in the series. If there exists a trend in the series follows the standard normal distribution, the value denoted by Z is computed with the 5% significance level²⁴. The trends were identified by the Mann-Kendall test for the precipitation data. Twelve precipitation gauge stations are incorporated in the current research study as shown in Fig. 2. The M-K test is employed to the sensing of trend in precipitation series for the 12 rain recording stations which situated within and near the basin.

Results and Discussions

Yearly Maximum, Annual and Seasonal Precipitation

The assessed M-K’s S and Z values of each station for yearly maximum, annual, seasonal and time frames are represented in Table 1. The precipitation variable is exposed to high inter-annual variability. Hence the seasonal variations such as monsoon period is more significant than other seasons. The abbreviation of trend results were as I (rising trend), D (Decling trend) and N (No trend) and the outcomes are shown in the Table 2. In the M-K drift test, 5% level is

taken into account for Z to be statistically significant. A significant and statistically acceptable drift is noticed in YMDR for the two stations Alangudi and Manaparai based on the Z values 2.667 and -2.124. The YMDR increasing trend indicates that rainfall will increase in Alangudi region and may cause floods. The decreasing trend of YMDR having Z value as -2.124, in Manaparai area specifies that the drought conditions may prevail due to monsoon failure.

A positive trend is found in Alangudi station for the SWM season. For the NEM season, a growing trend is identified with the Z values in Alangudi (2.492), Kattumavadi (2.23), Kurungulam (2.308) and Pudukkottai (2.286) station as per the statistical values shown in Table 1. The river basin is dependent upon NEM rainfall for the agricultural activities to enhance food security. For the winter and summer season, a non-significant trend is noticed for all the stations. The non-significant trends were observed due to highly erratic nature of the rainfall. The higher Z statistical values specified in Table 1, in yearly rainfall time scale indicates a positive trend in Alangudi (2.667), Kattumavadi (1.39), Pudukkottai (2.449) and Thirumayam (0.704) stations.

The precipitation trend magnitude is calculated by using the Sen’s slope. In annual statistical information, the maximum trend magnitude is arrived in Alangudi and Pudukkottai stations by 12.7 and 10.5 respectively. The maximum trend magnitude of NEM season is identified in Alangudi and Kurungulam stations by 7.55 and 8.61 respectively. The values of most of the rain gauge stations having negative digits in statistics and negative slope for YMDR, SWM, summer and winter indicates the rainfall all over the basin is decreasing. The rainfall is received during the rainy season and the basin remains dry in other seasons prevailing drought conditions.

Table 1: M-K statistics for seasonal, Yearly maximum daily and annual rainfall

Table 2: Trend assessment of seasonal, Yearly maximum daily and annual rainfall

Conclusion

The precipitation variability is due to human influenced activities or change in climate is a crucial part for the development needs related to water resources management at a basin level. The results show that yearly maximum rainfall and NEM seasons has a significant escalating trend in central and north-western part of the basin.  Most of the stations are having negative magnitude of Sen’s slope indicates that the basin is undergoing drought conditions due to the variability of rainfall inter-annually in the current years. The present study concludes that the Agniyar river basin is prone to drought conditions due to global climate change. The streamflow is less during monsoon period and a prolonged dry condition prevails in the basin. The present condition is a serious hydrological threat which can create desertification condition in the Agniyar basin.

Acknowledgement

The author acknowledges the vital help from the Centre for Water resources, Anna University for infrastructure support and providing internet facilities. The author sincerely thank the State ground and Surface Water Resources data centre, Chennai for providing data.

Funding Sources

The author received no financial grant or funding during the course of this research.

Conflict of Interest

The authors do not have any conflict of interest.

Data Availability Statement

The manuscript incorporates temperature datasets of two climate stations received from State ground and Surface Water Resources data centre was examined throughout this research study.

Ethics Statement

This research did not involve human participants, animal subjects, or any material that requires ethical approval

Informed Consent Statement

This study did not involve human participants, and therefore, informed consent was not required.”

Clinical Trial Registration

This research does not involve any clinical trials.

References

1. Pörtner H.O, Roberts D. C, Poloczanska E. S, Mintenbeck K, Tignor M, Alegría A, Craig M, Langsdorf S, Löschke S, Möller V, Okem A, Rama B. IPCC, 2022: Summary for Policymakers, In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, NY, USA, Cambridge University Press. 2022: 3–33. doi:10.1017/9781009325844.001. (Accessed date 22 April, 2024).
   CrossRef
2. UNFCCC Report, 2022 Nationally determined contributions under the Paris Agreement. Synthesis report by the secretariat. FCCC/PA/CMA/2022/4, harm el-Sheikh Climate Change Conference, 1-47.
3. UN DESA. The Sustainable Development Goals Report 2023: New York, USA: UN DESA. 2023. https://unstats.un.org/sdgs/report/2023/. (Accessed date 9 September, 2024).
4. Gleick, P. H. Global freshwater resources: soft-path solutions for the 21st century. Science. 2003; 302(5650): 1524-1528.
   CrossRef
5. Wu X, Guo S, Qian S, Wang Z, Lai C, Li J, Liu P. Long?range precipitation forecast based on multipole and preceding fluctuations of sea surface temperature. International Journal of Climatology. 2022 Dec 15;42(15):8024-39.
   CrossRef
6. Xi Z, Xiaoming Z, Jiawang G, Shuxin L, Tingshan Z. Karst topography paces the deposition of lower Permian, organic-rich, marine–continental transitional shales in the southeastern Ordos Basin, northwestern China. AAPG Bulletin. 2023(20,231,120).
7. Hölzel H, Diekkrüger B. Predicting the impact of linear landscape elements on surface runoff, soil erosion, and sedimentation in the Wahnbach catchment, Germany. Hydrological Processes. 2012 May 30;26(11):1642-54.
   CrossRef
8. Kay AL. Simulation of river flow in Britain under climate change: baseline performance and future seasonal changes. Hydrological Processes. 2021 Apr;35(4):e14137.
   CrossRef
9. Do HX, Westra S, Leonard M. A global-scale investigation of trends in annual maximum streamflow. Journal of hydrology. 2017 Sep 1; 552:28-43.
   CrossRef
10. Guha-Sapir D, Hoyois P, Wallemacq P, Below R. Annual disaster statistical review 2016. The numbers and trends. 2017 Oct:1-91.
11. Nayak S, Takemi T. Assessing the impact of climate change on temperature and precipitation over India. Wadi Flash Floods: Challenges and Advanced Approaches for Disaster Risk Reduction. 2022:121-42.
    CrossRef
12. Panikkar P, Sarkar UK, Das BK. Exploring climate change trends in major river basins and its impact on the riverine ecology, fish catch and fisheries of the Peninsular region of India: Issues and a brief overview. Journal of Water and Climate Change. 2022 Jul 1;13(7):2690-9.
    CrossRef
13. Pavithrapriya S, Ramachandran A, AHAMEDIBRAHIM S, Palanivelu K. Climate variability trend and extreme indices for the Thanjavur Delta region of Tamil Nadu in South India. MAUSAM. 2022 Jun 22;73(2):237-50.
    CrossRef
14. Rangarajan S, Karthik Raja R, Murali A, Thattai D, Kamaraj M, Islam MN. Statistical approach to detect rainfall trend over Tamil Nadu State, India. InIndia II: climate change impacts, mitigation and adaptation in developing countries 2022 May 4 (pp. 407-439). Cham: Springer International Publishing.
    CrossRef
15. Baig MR, Shahfahad, Naikoo MW, Ansari AH, Ahmad S, Rahman A. Spatio-temporal analysis of precipitation pattern and trend using standardized precipitation index and Mann–Kendall test in coastal Andhra Pradesh. Modeling Earth Systems and Environment. 2021:1-20.
    CrossRef
16. Pande CB, Diwate P, Orimoloye IR, Sidek LM, Pratap Mishra A, Moharir KN, Pal SC, Alshehri F, Tolche AD. Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling. Geomatics, Natural Hazards and Risk. 2024 Dec 31;15(1):2290350.
    CrossRef
17. Kiktev D, Sexton DM, Alexander L, Folland CK. Comparison of modeled and observed trends in indices of daily climate extremes. Journal of Climate. 2003 Nov 15;16(22):3560-71.
    CrossRef
18. Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank AM, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour A. Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research: Atmospheres. 2006 Mar 16;111(D5).
    CrossRef
19. Geethalakshmi V, Yatagai A, Palanisamy K, Umetsu C. Impact of ENSO and the Indian Ocean Dipole on the north?east monsoon rainfall of Tamil Nadu State in India. Hydrological Processes: An International Journal. 2009 Feb 15;23(4):633-47.
    CrossRef
20. Mann H. B. Non parametric tests against trend. Econometrica: Journal of the econometric society. 1945;13(3): 245-259.
    CrossRef
21. Kendall M. G. Rank correlation methods. Charles Griffin, London. 1975.
22. Theil H. A rank-invariant method of linear and polynomial regression analysis. Indagationesmathematicae. 1950;12(85):173.
23. Sen P. K. Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association. 1968;63(324):1379.
    CrossRef
24. Palaniswami Supriya, Muthiah Krishnaveni. Change point detection and trend analysis of rainfall and temperature series over the Vellar River basin. Polish Journal of Environmental Studies. 2018; 27(4): 1673-1681.DOI: https://doi.org/10.15244/pjoes/77080.
    CrossRef