IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i21p15191-d1265901.html
   My bibliography  Save this article

Spatiotemporal Responses of Vegetation to Hydroclimatic Factors over Arid and Semi-arid Climate

Author

Listed:
  • Brijesh Yadav

    (ICAR—National Bureau of Soil Survey & Land Use Planning, Regional Center, Udaipur 313001, India)

  • Lal Chand Malav

    (ICAR—National Bureau of Soil Survey & Land Use Planning, Regional Center, Udaipur 313001, India)

  • Shruti V. Singh

    (Krishi Vigyan Kendra, ICAR—Indian Institute of Vegetable Research, Kushinagar 274406, India)

  • Sushil Kumar Kharia

    (Department of Soil Sciences, College of Agriculture, Swami Keshwanand Rajasthan Agricultural University, Bikaner 334006, India)

  • Md. Yeasin

    (ICAR—Indian Agricultural Statistics Research Institute, New Delhi 110012, India)

  • Ram Narayan Singh

    (ICAR—National Institute of Abiotic Stress Management, Baramati 413115, India)

  • Mahaveer Nogiya

    (ICAR—National Bureau of Soil Survey & Land Use Planning, Regional Center, Udaipur 313001, India)

  • Roshan Lal Meena

    (ICAR—National Bureau of Soil Survey & Land Use Planning, Regional Center, Udaipur 313001, India)

  • Pravash Chandra Moharana

    (ICAR—National Bureau of Soil Survey & Land Use Planning, Nagpur 440033, India)

  • Nirmal Kumar

    (ICAR—National Bureau of Soil Survey & Land Use Planning, Nagpur 440033, India)

  • Ram Prasad Sharma

    (ICAR—National Bureau of Soil Survey & Land Use Planning, Regional Center, Udaipur 313001, India)

  • Gangalakunta P. Obi Reddy

    (ICAR—National Bureau of Soil Survey & Land Use Planning, Nagpur 440033, India)

  • Banshi Lal Mina

    (ICAR—National Bureau of Soil Survey & Land Use Planning, Regional Center, Udaipur 313001, India)

  • Prakash Kumar Jha

    (Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS 39762, USA)

Abstract

Understanding the dynamics of vegetative greenness and how it interacts with various hydroclimatic factors is crucial for comprehending the implications of global climate change. The present study utilized the MODIS-derived normalized difference vegetation index (NDVI) to understand the vegetation patterns over 21 years (2001–2021) in Rajasthan, India. The rainfall, land surface temperature (LST), and evapotranspiration (ET) were also analyzed. The changes, at a 30 m pixel resolution, were evaluated using Mann–Kendall’s trend test. The results reveal that the NDVI, ET, and rainfall had increasing trends, whereas the LST had a decreasing trend in Rajasthan. The NDVI increased for 96.5% of the total pixels, while it decreased for 3.4% of the pixels, of theh indicates vegetation improvement rather than degradation. The findings of this study provide direct proof of a significant reduction in degraded lands throughout Rajasthan, particularly in the vicinity of the Indira Gandhi Canal command area. Concurrently, there has been a noticeable expansion in the cultivated land area. The trend of vegetation decline, particularly in the metro cities, has occurred as a result of urbanization and industrialization. In contrast to the LST, which has a decreasing gradient from the western to eastern portions, the spatial variability in the NDVI, ET, and rainfall have decreasing gradients from the southern and eastern to western regions. The results of correlations between the vegetative indices and hydroclimatic variables indicate that the NDVI has a strong positive correlation with ET (r 2 = 0.86), and a negative correlation with LST (r 2 = −0.55). This research provides scientific insights into vegetation change across Rajasthan, and may help the state to monitor vegetation changes, conserve ecosystems, and implement sustainable ecosystem management.

Suggested Citation

  • Brijesh Yadav & Lal Chand Malav & Shruti V. Singh & Sushil Kumar Kharia & Md. Yeasin & Ram Narayan Singh & Mahaveer Nogiya & Roshan Lal Meena & Pravash Chandra Moharana & Nirmal Kumar & Ram Prasad Sha, 2023. "Spatiotemporal Responses of Vegetation to Hydroclimatic Factors over Arid and Semi-arid Climate," Sustainability, MDPI, vol. 15(21), pages 1-29, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15191-:d:1265901
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/21/15191/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/21/15191/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hansen, Bruce E., 2005. "Challenges For Econometric Model Selection," Econometric Theory, Cambridge University Press, vol. 21(1), pages 60-68, February.
    2. Biranchi Mahala & Birendra Nayak & Pratap Mohanty, 2015. "Impacts of ENSO and IOD on tropical cyclone activity in the Bay of Bengal," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 1105-1125, January.
    3. Guillaume Lacombe & Matthew McCartney, 2014. "Uncovering consistencies in Indian rainfall trends observed over the last half century," Climatic Change, Springer, vol. 123(2), pages 287-299, March.
    4. Alfredo Huete, 2016. "Vegetation's responses to climate variability," Nature, Nature, vol. 531(7593), pages 181-182, March.
    5. Goyal, R. K., 2004. "Sensitivity of evapotranspiration to global warming: a case study of arid zone of Rajasthan (India)," Agricultural Water Management, Elsevier, vol. 69(1), pages 1-11, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tomohiro Ando & Ruey S. Tsay, 2009. "Model selection for generalized linear models with factor‐augmented predictors," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 207-235, May.
    2. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," Working Papers hal-02790523, HAL.
    3. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
    4. Phogat, V. & Cox, J.W. & Šimůnek, J., 2018. "Identifying the future water and salinity risks to irrigated viticulture in the Murray-Darling Basin, South Australia," Agricultural Water Management, Elsevier, vol. 201(C), pages 107-117.
    5. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    6. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    7. Yongfu Huang, 2005. "What determines financial development?," Bristol Economics Discussion Papers 05/580, School of Economics, University of Bristol, UK.
    8. Tong, Ling & Kang, Shaozhong & Zhang, Lu, 2007. "Temporal and spatial variations of evapotranspiration for spring wheat in the Shiyang river basin in northwest China," Agricultural Water Management, Elsevier, vol. 87(3), pages 241-250, February.
    9. Li, Zhibin & Feng, Bianbian & Wang, Wei & Yang, Xi & Wu, Pute & Zhuo, La, 2022. "Spatial and temporal sensitivity of water footprint assessment in crop production to modelling inputs and parameters," Agricultural Water Management, Elsevier, vol. 271(C).
    10. Toru Kitagawa & Chris Muris, 2013. "Covariate selection and model averaging in semiparametric estimation of treatment effects," CeMMAP working papers 61/13, Institute for Fiscal Studies.
    11. De Luca, Giuseppe & Magnus, Jan R. & Peracchi, Franco, 2018. "Weighted-average least squares estimation of generalized linear models," Journal of Econometrics, Elsevier, vol. 204(1), pages 1-17.
    12. Firmin Doko Tchatoka & Wenjie Wang, 2020. "Uniform Inference after Pretesting for Exogeneity," School of Economics and Public Policy Working Papers 2020-05, University of Adelaide, School of Economics and Public Policy.
    13. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls," Papers 1201.0224, arXiv.org, revised May 2012.
    14. Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023. "Analysis of Opportunities to Improve the Quality of Natural Resource Price by Combining Forecasts Resulting from Methods Based on Regression Estimates of Weights [Анализ Возможностей Улучшения Каче," Russian Economic Development, Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
    15. Maribel Serna Rodríguez & Andrés Ramírez Hassan & Alexander Coad, 2019. "Uncovering Value Drivers of High Performance Soccer Players," Journal of Sports Economics, , vol. 20(6), pages 819-849, August.
    16. Yang, Xiaolin & Gao, Wangsheng & Shi, Quanhong & Chen, Fu & Chu, Qingquan, 2013. "Impact of climate change on the water requirement of summer maize in the Huang-Huai-Hai farming region," Agricultural Water Management, Elsevier, vol. 124(C), pages 20-27.
    17. Gioldasis, Georgios & Musolesi, Antonio & Simioni, Michel, 2023. "Interactive R&D spillovers: An estimation strategy based on forecasting-driven model selection," International Journal of Forecasting, Elsevier, vol. 39(1), pages 144-169.
    18. Bharati, Luna & Bhattarai, Utsav & Khadka, Ambika & Gurung, Pabitra & Neumann, L. E. & Penton, D. J. & Dhaubanjar, Sanita & Nepal, S., 2018. "From the mountains to the plains: impact of climate change on water resources in the Koshi River Basin," IWMI Working Papers H049130, International Water Management Institute.
    19. Garcia-Prats, A. & Carricondo-Anton, J.M. & Jiménez-Bello, M.A. & Manzano Juárez, J. & López-Pérez, E. & Pulido-Velazquez, M., 2023. "Dynamic procedure for daily PM56 ETo mapping conducive to site-specific irrigation recommendations in areas covered by agricultural weather networks," Agricultural Water Management, Elsevier, vol. 287(C).
    20. Andrés Ramírez-Hassan, 2020. "Dynamic variable selection in dynamic logistic regression: an application to Internet subscription," Empirical Economics, Springer, vol. 59(2), pages 909-932, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15191-:d:1265901. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.