Analyzing the long-term variability and trend of aridity in India using non-parametric approach

被引:0
作者
Akshita Choudhary
Susanta Mahato
P. S. Roy
Deep Narayan Pandey
P. K. Joshi
机构
[1] Jawaharlal Nehru University,School of Environmental Science
[2] Jawaharlal Nehru University,Special Center for Disaster Research
[3] World Resources Institute India,undefined
来源
Stochastic Environmental Research and Risk Assessment | 2023年 / 37卷
关键词
Aridity index (AI); Climate change; Thornthwaite; Potential evapotranspiration (PET); Mann–Kendall test;
D O I
暂无
中图分类号
学科分类号
摘要
Aridity is a climatic phenomenon characterized by shortage of water availability in a given time and space resulting in low moisture and reduced carrying capacity of ecosystems. It is represented by a numerical indicator known as Aridity Index (AI), a function of rainfall and temperature. Aridification is a slow and steady effect of climate change and assessing its spread and change is vital in context of global climatic variations. Aridity is predominantly significant for agrarian countries like India, where a slight rise in drylands area can have a significant impact on the economy and community sustenance. AI is an inclusive indicator of climatic conditions in most arid and semi-arid regions. It helps in identifying and interpreting large scale trend in temperature and precipitation; and thus, classifying region into different climatic classes. The present study assessed long-term AI based on precipitation and temperature data obtained from the India Meteorological Department at the resolution of 1 × 1 degree for years 1969–2017. AI is estimated as a ratio of mean precipitation to mean potential evapotranspiration, calculated using Thornthwaite method. The results highlight the trend of aridity over pan-India with Innovative Trend Analysis and Mann–Kendall test. The study concludes that there is a relatively slow, however steadily progressive drier conditions being established in most of the regions. A shift from ‘Semi-arid’ towards ‘Arid’ class appeared in central mainland. The north-eastern Himalaya showed decrease in humid conditions (‘Humid’ to ‘Sub-humid’). The study implies that there is a rising aridity trend over the years due to changing climatic conditions. The shifts in aridity can have serious implications on agriculture, long-term water resource utilization and land use management plans. Our results have scope for future landscape management studies in drylands and better adaptation methods in arid regions.
引用
收藏
页码:3837 / 3854
页数:17
相关论文
共 50 条
  • [31] Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches
    Praveen, Bushra
    Talukdar, Swapan
    Shahfahad
    Mahato, Susanta
    Mondal, Jayanta
    Sharma, Pritee
    Islam, Abu Reza Md. Towfiqul
    Rahman, Atiqur
    [J]. SCIENTIFIC REPORTS, 2020, 10 (01)
  • [32] Changes in tropospheric ozone over India: Variability, long-term trends and climate forcing
    Rathore, A.
    Gopikrishnan, G. S.
    Kuttippurath, J.
    [J]. ATMOSPHERIC ENVIRONMENT, 2023, 309
  • [33] Long-term trend and variability in surface temperatures over Emilia-Romagna from 1962 to 2022
    Sabatani, Davide
    Pavan, Valentina
    Grazzini, Federico
    Antolini, Gabriele
    [J]. THEORETICAL AND APPLIED CLIMATOLOGY, 2024, 155 (07) : 6297 - 6305
  • [34] Winter Rice Trend Analysis and Change Point Detection in Assam's North Bank Plains Zone (NBPZ): A Non-Parametric Approach
    Buragohain, Rabijita
    Saikia, Hemanta
    Das, Dhruba
    [J]. EKOIST-JOURNAL OF ECONOMETRICS AND STATISTICS, 2023, (39): : 1 - 9
  • [35] Driving mechanisms of the variability and long-term trend of the Brazil–Malvinas confluence during the 21st century
    Mihael M. de Souza
    Moritz Mathis
    Thomas Pohlmann
    [J]. Climate Dynamics, 2019, 53 : 6453 - 6468
  • [36] Do recent meteorological drought events in central Italy result from long-term trend or increasing variability?
    Romano, Emanuele
    Petrangeli, Anna Bruna
    Salerno, Franco
    Guyennon, Nicolas
    [J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2022, 42 (07) : 4111 - 4128
  • [37] Spatial and temporal variation of meteorological parameters in the lower Tigris-Euphrates basin, Turkiye: application of non-parametric methods and an innovative trend approach
    Esit, Musa
    Celik, Recep
    Akbas, Ergun
    [J]. WATER SCIENCE AND TECHNOLOGY, 2023, 87 (08) : 1982 - 2004
  • [38] Appraisal of long-term hydrological variability in the Luni River Basin: a quantitative statistical approach
    Deopa, Hritika
    Resmi, M. R.
    [J]. THEORETICAL AND APPLIED CLIMATOLOGY, 2024, 155 (07) : 6709 - 6726
  • [39] Exploring the temporal variability of a food web using long-term biomonitoring data
    Olivier, Pierre
    Frelat, Romain
    Bonsdorff, Erik
    Kortsch, Susanne
    Kroncke, Ingrid
    Moellmann, Christian
    Neumann, Hermann
    Sell, Anne F.
    Nordstrom, Marie C.
    [J]. ECOGRAPHY, 2019, 42 (12) : 2107 - 2121
  • [40] Long-Term Time Series Trend Analysis of Climatic Variable for Jaisalmer District in Thar Desert, Western Rajasthan, India, Using Statistical Approaches
    Singh H.
    Choudhary M.P.
    [J]. Journal of The Institution of Engineers (India): Series A, 2024, 105 (02) : 383 - 398