Skip to main content

Advertisement

Springer Nature Link
Log in

Vegetation response to climate and climatic extremes in northwest Bangladesh: a quantile regression approach

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

The present study assessed the vegetation response to climate in the water-stressed northwest Bangladesh (NWB). The quantile regression analysis was employed to evaluate the effect of climate and climatic extremes on vegetation. The nonparametric correlation analysis was used to assess the climatic influence on vegetation for various time lags. Besides, the modified Mann–Kendall (MMK) test was conducted to understand the changes in climate and vegetation to anticipate the climate change impacts on vegetation. Satellite estimation of normalized difference vegetation index (NDVI) and observed rainfall and temperature data collected from five locations for the period 1982 − 2018 was used for this purpose. The results revealed a negative effect of rainfall, a positive impact of maximum temperature in monsoon and a positive influence of minimum temperature on vegetation in winter. Quantile regression analysis revealed a significant negative effect of extreme rainfall and a positive impact of maximum temperature on vegetation for the whole NWB. Overall, the study revealed a greater influence of temperature than rainfall on vegetation change in the region. The trend analysis revealed a reduction in rainfall (− 2.56 mm/decade) and a rise in temperature (0.176 °C/decade) and thus an increase in vegetation (0.014 per decade). The results indicate the positive effect of climate change on vegetation, positively impacting the environment and water resources in the study area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data availability

All data used in the study are available in the public domain. Those are also available for sharing on request to the corresponding author.

Code availability

The codes used for the processing of data can be provided on request to the corresponding author.

References

  • Ahammed SJ, Chung ES, Shahid S (2018) Parametric assessment of pre-monsoon agricultural water scarcity in bangladesh. Sustainability 10(3):819

    Article  Google Scholar 

  • Alamgir M, Khan N, Shahid S, Yaseen ZM, Dewan A, Hassan Q, Rasheed B (2020) Evaluating severity–area–frequency (SAF) of seasonal droughts in Bangladesh under climate change scenarios. Stoch Env Res Risk Assess 34(2):447–464

    Article  Google Scholar 

  • Alamgir M, Mohsenipour M, Homsi R, Wang X, Shahid S, Shiru MS, ... & Yuzir A (2019) Parametric assessment of seasonal drought risk to crop production in Bangladesh. Sustainability 11(5):1442

  • Alhumaima AS, Abdullaev SM (2020) Tigris Basin landscapes: sensitivity of vegetation index NDVI to climate variability derived from observational and reanalysis data. Earth Interact 24:1–18. https://doi.org/10.1175/EI-D-20-0002.1

    Article  Google Scholar 

  • Almeida-Ñauñay AF, Benito RM, Quemada M, Losada JC, Tarquis AM (2021) The vegetation–climate system complexity through recurrence analysis. Entropy 23(5):559

    Article  Google Scholar 

  • Basak SR, Basak AC, Rahman MA (2015) Impacts of floods on forest trees and their coping strategies in Bangladesh. Weather Clim Extremes 7:43–48

    Article  Google Scholar 

  • Benayas JMR, Scheiner SM (2002) Plant diversity, biogeography and environment in Iberia: patterns and possible causal factors. J Veg Sci 13:245–258

    Article  Google Scholar 

  • Bi J, Xu L, Samanta A, Zhu ZC, Myneni R (2013) Divergent Arctic-Boreal vegetation changes between North America and Eurasia over the past 30 years. Remote Sens 5:2093–2112

    Article  Google Scholar 

  • Buditama G, Saiya HG, Putri NP (2021) Effects of climate change on dryland agriculture vegetation index in Nangapanda, East Nusa Tenggara. IOP Conf Ser: Earth Environ Sci 716(1):012013 (IOP Publishing)

    Article  Google Scholar 

  • Cao D, Zhang J, Xun L, Yang S, Wang J, Yao F (2021) Spatiotemporal variations of global terrestrial vegetation climate potential productivity under climate change. Sci Total Environ 770:145320

    Article  Google Scholar 

  • Chen A, Huang L, Liu Q, Piao S (2021) Optimal temperature of vegetation productivity and its linkage with climate and elevation on the Tibetan Plateau. Glob Change Biol 27(9):1942–1951

    Article  Google Scholar 

  • Cheng FZ, Han X, Tang S et al (2021) An improved model for evaluating ecosystem service values using land use/cover and vegetation parameters. J Meteorol Res 35:148–156. https://doi.org/10.1007/s13351-021-9199-x

    Article  Google Scholar 

  • David NM (2009) Landscape Design. First Edition 2009. Archimedia Publishing, Ltd, Enugu Nigeria, Arochukwu Ave Independence Layout. P.O. Box 329, Enugu. ISBN 978–073–605

  • Gangashe AT (2020) Assessing the impacts of flooding on vegetation cover in the ShasheLimpopo confluence area using earth observation data. Masters Thesis, University of the Witwatersrand, Johannesburg, South Africa

  • Ge W, Deng L, Wang F, Han J (2021) Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016. Sci Total Environ 773:145648

    Article  Google Scholar 

  • Gould WA (2000) Remote sensing of vegetation, plant species richness, and regional diversity hotspots. Ecol Appl 10:1861–1870

    Article  Google Scholar 

  • Hagfors L, Paraschiv F, Molnar P, Westgaard S (2016) Using quantile regression to analyze the effect of renewables on EEX price formation. Renew Energy Environ Sustain 1:32

    Article  Google Scholar 

  • Hosseini TSM, Hosseini SA, Ghermezcheshmeh B, Sharafati A (2020) Drought hazard depending on elevation and precipitation in Lorestan, Iran. Theor Appl Climatol 142(3):1369–1377

    Article  Google Scholar 

  • Hou WJ, Gao JB, Wu SH, Dai EF (2015) Interannual variations in growing-season NDVI and its correlation with climate variables in the Southwestern Karst Region of China. Remote Sens 7:11105–11124

    Article  Google Scholar 

  • Jiang LL, Jiapaer G, Bao AM, Guo H, Ndayisaba F (2017) Vegetation dynamics and responses to climate change and human activities in Central Asia. Sci Total Environ 599:967–980

    Article  Google Scholar 

  • Kamal ASM, Hossain F, Shahid S (2021) Spatiotemporal changes in rainfall and droughts of Bangladesh for 1.5 and 2° C temperature rise scenarios of CMIP6 models. Theor Appl Climatol 146(1):527–542

    Article  Google Scholar 

  • Kendall MG (1948) Rank correlation methods. Griffin, Oxford

    Google Scholar 

  • Khan N, Shahid S, Juneng L, Ahmed K, Ismail T, Nawaz N (2019) Prediction of heat waves in Pakistan using quantile regression forests. Atmos Res 221:1–11

    Article  Google Scholar 

  • Khwarahm NR, Ararat K, Qader S, Sabir DK (2021) Modeling the distribution of the Near Eastern fire salamander (Salamandra infraimmaculata) and Kurdistan newt (Neurergus derjugini) under current and future climate conditions in Iraq. Ecol Inform 63:101309

    Article  Google Scholar 

  • Kim SW, Chun KW, Kim M, Catani F, Choi B, Seo JI (2021) Effect of antecedent rainfall conditions and their variations on shallow landslide-triggering rainfall thresholds in South Korea. Landslides 18(2):569–582

    Article  Google Scholar 

  • Kimura R (2012) Factors contributing to dust storms in source regions producing the yellow-sand phenomena observed in Japan from 1993 to 2002.J. Arid Environ 80:40–44

    Article  Google Scholar 

  • Koenker R (2005) Quantile regression (no. 38). Cambridge University Press

    Book  Google Scholar 

  • Koenker R, Bassett G (1978) Regression quantiles. Econometrica 46(1):33. https://doi.org/10.2307/1913643

    Article  Google Scholar 

  • Koenker R (2015) Quantreg: QuantileRegression. URL https://CRAN.R-project.org/package=quantreg. R package version 5.19. Accessed 17 Jan 2022

  • Kuan YS, Gamse JT, Schreiber AM, Halpern ME (2007) Selective asymmetry in a conserved forebrain to midbrain projection. J Exp Zool B Mol Dev Evol 308(5):669–678

    Article  Google Scholar 

  • Lamchin M, Wang W, Lim C-H, Ochir A, Ukrainski P, Gebru B, Choi Y, Woo S, Lee WK (2020) Understanding global spatio-temporal trends and the relationship between vegetation greenness and climate factors by land cover during 1982–2014. Glob Ecol Conserv 24(2020):e01299

  • Lee EH, Sohn BJ (2009) Examining the impact of wind and surface vegetation on the Asian dust occurrence over three classified source regions. J Geophys Res 114:D06205

    Google Scholar 

  • Leilei L, Jianrong F, Yang C (2014) The relationship analysis of vegetation cover, rainfall and land surface temperature based on remote sensing in Tibet, China. IOP Conf Ser: Earth Environ Sci 17(1):012034 (IOP Publishing)

    Article  Google Scholar 

  • Levin N, Shmida A, Levanoni O, Tamari H, Kark S (2007) Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices. Divers Distrib 13:692–703

    Article  Google Scholar 

  • Li Z, Qi F, Shang G, Sun L, Xia Y (2018) Spatial-temporal change of vegetation cover and its relationship with SPEI in Hutuo river basin. South-to-North Water Transf Water Sci Technol 16:135–143

    Google Scholar 

  • Li J, Su Z, Jiang J, Chen W, Yu N, Li X, ...& Wei J (2021) Spatial-temporal change in vegetation cover and climate factor drivers of variation in the Haihe River Basin 2003–2016. IOP Conf Ser: Earth Environ Sci 697(1):012005

  • Liu S, Tian Y, Yin Y, An N, Dong S (2015a) Effects of climate change on normalized difference vegetation index based on the multiple analysis of standardized precipitation evapotranspiration index methods in the Lancang River basin. Clima Environ Res 20:705–714

    Google Scholar 

  • Liu XF, Zhu XF, Li SS, Liu YX, Pan YZ (2015b) Changes in growing season vegetation and their associated driving forces in China during 2001–2012. Remote Sens 7:15517–15535

    Article  Google Scholar 

  • Luo M, Xie L, Chakraborty S et al (2021) A five-transgene cassette confers broad-spectrum resistance to a fungal rust pathogen in wheat. Nat Biotechnol 39:561–566. https://doi.org/10.1038/s41587-020-00770-x

    Article  Google Scholar 

  • Madonsela S, Cho MA, Ramoelo A, Mutanga O, Naidoo L (2018) Estimating tree species diversity in the savannah using NDVI and woody canopy cover. Int J Appl Earth ObservGeoinform 66:106–115

    Article  Google Scholar 

  • Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259

    Article  Google Scholar 

  • Markings S (2018) The effect of temperature on the rate of photosynthesis. https://sciencing.com/effect-temperature-rate-photosynthesis-19595.html. Accessed 17 Jan 2022

  • Maselli F (2004) Monitoring forest conditions in a protected Mediterranean coastal area by the analysis of multiyear NDVI data. Remote Sens Environ 89:423–433

    Article  Google Scholar 

  • Measho S, Chen B, Pellikka P, Guo L, Zhang H, Cai D, ...& Ge M (2021) Assessment of vegetation dynamics and ecosystem resilience in the context of climate change and drought in the horn of Africa. Remote Sens 13(9):1668

  • Mishra SN, Gupta HS, Kulkarni N (2021) Impact of climate change on the distribution of Sal species. Ecol Inform 61:101244

    Article  Google Scholar 

  • Mkhabela MS, Bullock P, Raj S, Wang S, Yang Y (2011) Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agric For Meteorol - AGR FOR METEOROL 151:385–393. https://doi.org/10.1016/j.agrformet.2010.11.012

    Article  Google Scholar 

  • Mohsenipour M, Shahid S, Ziarh GF, Yaseen ZM (2020) Changes in monsoon rainfall distribution of Bangladesh using quantile regression model. Theoret Appl Climatol 142(3):1329–1342

    Article  Google Scholar 

  • Nashwan MS, Shahid S, Abd Rahim N (2019) Unidirectional trends in annual and seasonal climate and extremes in Egypt. Theor Appl Climatol 136:457–473

    Article  Google Scholar 

  • Pan S, Zhao X, Yue Y (2019) Spatiotemporal changes of NDVI and correlation with meteorological factors in northern china from 1985–2015. E3S Web Conf 131:01040 (EDP Sciences)

    Article  Google Scholar 

  • Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37:637–669

    Article  Google Scholar 

  • Pei Z, Fang S, Yang W, Wang L, Wu M, Zhang Q, ...& Khoi DN (2019) The relationship between NDVI and climate factors at different monthly time scales: a case study of grasslands in inner Mongolia, China (1982–2015). Sustainability 11(24):7243

  • Pour SH, Shahid S, Chung E-S, Wang X-J (2018) Model output statistics downscaling using support vector machine for the projection of spatial and temporal changes in rainfall of Bangladesh. Atmos Res 213:149–162

    Article  Google Scholar 

  • Pour SH, Wahab AKA, Shahid S, Asaduzzaman M, Dewan A (2020) Low impact development techniques to mitigate the impacts of climate-change-induced urban floods: current trends, issues and challenges. Sustain Cities Soc 62:102373

    Article  Google Scholar 

  • Pumo D, Noto LV (2021) Exploring the linkage between dew point temperature and precipitation extremes: a multi-time-scale analysis on a semiarid Mediterranean region. Atmos Res 254:105508

    Article  Google Scholar 

  • Qi XZ, Jia JH, Liu HY, Lin ZS (2019) Relative importance of climate change and human activities for vegetation changes on China’s silk road economic belt over multiple timescales. Catena 180:224–237 ([CrossRef])

    Article  Google Scholar 

  • Sa’adi Z, Shahid S, Ismail T, Chung ES, Wang XJ (2017) Distributional changes in rainfall and river flow in Sarawak, Malaysia. Asia Pac J Atmos Sci 53(4):489–500

    Article  Google Scholar 

  • Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389

    Article  Google Scholar 

  • Shahid S (2008) Spatial and temporal characteristics of droughts in the western part of Bangladesh. Hydrol Process: Int J 22(13):2235–2247

    Article  Google Scholar 

  • Shahid S (2010) Recent trends in the climate of Bangladesh. Clim Res 42(3):185–193

    Article  Google Scholar 

  • Shahid S (2011) Impact of climate change on irrigation water demand of dry season Boro rice in northwest Bangladesh. Clim Change 105(3):433–453

    Article  Google Scholar 

  • Shahid S, Hazarika MK (2010) Groundwater drought in the northwestern districts of Bangladesh. Water Resour Manage 24(10):1989–2006

    Article  Google Scholar 

  • Sharafati A, Asadollah SBHS, Shahbazi A (2021) Assessing the impact of climate change on urban water demand and related uncertainties: a case study of Neyshabur, Iran. Theor Appl Climatol 145(1):473–487

    Article  Google Scholar 

  • Sharafati A, Pezeshki E (2020) A strategy to assess the uncertainty of a climate change impact on extreme hydrological events in the semi-arid Dehbar catchment in Iran. Theoret Appl Climatol 139(1):389–402

    Article  Google Scholar 

  • Sharafati A, Zahabiyoun B (2014) Rainfall threshold curves extraction by considering rainfall-runoff model uncertainty. Arab J Sci Eng 39(10):6835–6849

    Article  Google Scholar 

  • Shiau J-T, Huang W-H (2015) Detecting distributional changes of annual rainfall indices in Taiwan using quantile regression. J HydroEnviron Res 9:368–438

    Google Scholar 

  • Sun H, Wang J, Xiong J, Bian J, Jin H, Cheng W, Li A (2021) Vegetation change and its response to climate change in Yunnan Province, China. Adv Meteorol 2021:8857589. https://doi.org/10.1155/2021/8857589 (20 pages, 2021)

    Article  Google Scholar 

  • Tan J, Jakob C, Rossow WB, Tselioudis G (2015) Increases in tropical rainfall driven by changes in frequency of organized deep convection. Nature 519:451–454. https://doi.org/10.1038/nature14339

    Article  Google Scholar 

  • Treppiedi D, Cipolla G, Francipane A, Noto LV (2021) Detecting precipitation trend using a multiscale approach based on quantile regression over a Mediterranean area. Int J Climatol 41(13):5938–5955

    Article  Google Scholar 

  • Uddin MA, Kamal ASM, Shahid S, Chung ES (2020) Volatility in rainfall and predictability of droughts in northwest Bangladesh. Sustainability 12(23):9810

    Article  Google Scholar 

  • Van Le H, Hoang DA, Tran CT, Nguyen PQ, Hoang ND, Amiri M, ... & Bui DT (2021) A new approach of deep neural computing for spatial prediction of wildfire danger at tropical climate areas. Ecol Inform 63:101300

  • Walther GR, Post E, Convey P, Menzel A, Parmesan C, Beebee TJC, Fromentin JM, Hoegh-Guldberg O, Bairlein F (2002) Ecological responses to recent climate change. Nature 416:389–395

    Article  Google Scholar 

  • Wang J, Wang K, Zhang M, Zhang C (2015) Impacts of climate change and human activities on vegetation cover in hilly southern China. Ecol Eng 81:451–461

    Article  Google Scholar 

  • Wang S, Liu Q, Huang C (2021) Vegetation change and its response to climate extremes in the arid region of northwest China. Remote Sens 13(7):1230

    Article  Google Scholar 

  • Yan M, Xue M, Zhang L, Tian X, Chen B, Dong Y (2021) A decade’s change in vegetation productivity and its response to climate change over Northeast China. Plants 10(5):821

    Article  Google Scholar 

  • Yoshida T (2021) Extreme value inference for quantile regression with varying coefficients. Commun Stat Theory Methods 50(3):685–710

    Article  Google Scholar 

  • You G, Liu B, Zou C, Li H, McKenzie S, He Y, Gao J, Jia X, Arain MA, Wang S, Wang Z, Xia X, Xu W (2021) Sensitivity of vegetation dynamics to climate variability in a forest-steppe transition ecozone, north-eastern Inner Mongolia, China. Ecol Indic 120:106833. https://doi.org/10.1016/j.ecolind.2020.106833

    Article  Google Scholar 

  • You MJ, Castrillon DH, Bastian BC, O’Hagan RC, Bosenberg MW, Parsons R et al (2002) Genetic analysis of Pten and Ink4a/Arf interactions in the suppression of tumorigenesis in mice. Proc Natl Acad Sci USA 99:1455–1460

    Article  Google Scholar 

  • Zahoor B, Liu X, Kumar L, Dai Y, Tripathy BR, Songer M (2021) Projected shifts in the distribution range of Asiatic black bear (Ursus thibetanus) in the Hindu Kush Himalaya due to climate change. Ecol Inform 63:101312

    Article  Google Scholar 

  • Zarei A, Asadi E, Ebrahimi A, Jafari M, Malekian A, Mohammadi H, Chemura A, Maskell G (2020) Prediction of future grassland vegetation cover fluctuation under climate change scenarios. Ecol Indic 119:106858. https://doi.org/10.1016/j.ecolind.2020.106858

    Article  Google Scholar 

  • Zhong R, Wang P, Mao G, Chen A, Liu J (2021) Spatiotemporal variation of enhanced vegetation index in the Amazon Basin and its response to climate change. Phys Chem Earth Parts A/B/C 123:103024. https://doi.org/10.1016/j.pce.2021.103024

    Article  Google Scholar 

  • Zhao L, Dai A, Dong Bo (2018) Changes in global vegetation activity and its driving factors during 1982–2013. Agric for Meteorol 249:198–209. https://doi.org/10.1016/j.agrformet.2017.11.013

    Article  Google Scholar 

  • Zhao W, Yu X, Jiao C, Xu C, Liu Y, Wu G (2021) Increased association between climate change and vegetation index variation promotes the coupling of dominant factors and vegetation growth. Sci Total Environ 767:144669

    Article  Google Scholar 

  • Zhu W, Mao F, Xu Y, Zheng J, Song L (2019) Analysis on response of vegetation index to climate change and its prediction in the three-rivers-source region. Plateau Meteorol 38:693–704

    Google Scholar 

Download references

Acknowledgements

The authors are grateful to the United States Geological Survey to provide MODIS NDVI data through the web portal. Authors are also grateful to the Bangladesh Meteorological Department to provide rainfall and temperature data.

Author information

Authors and Affiliations

  1. University of Dhaka, Dhaka, 1000, Bangladesh

    Mohammad Ahsan Uddin & A. S. M. Maksud Kamal

  2. School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310, Bahru, Johor, Malaysia

    Shamsuddin Shahid

Authors
  1. Mohammad Ahsan Uddin
    View author publications

    Search author on:PubMed Google Scholar

  2. A. S. M. Maksud Kamal
    View author publications

    Search author on:PubMed Google Scholar

  3. Shamsuddin Shahid
    View author publications

    Search author on:PubMed Google Scholar

Contributions

All the authors contributed to conceptualize and design the study. Data were gathered by Mohammad Ahsan Uddin; the modelling was done by A. S. M. Maksud Kamal and Shamsuddin Shahid; an initial draft of the paper was prepared by A. S. M. Maksud Kamal and Mohammad Ahsan Uddin; the article was repeatedly revised to generate the final version by A. S. M. Maksud Kamal and Shamsuddin Shahid.

Corresponding author

Correspondence to A. S. M. Maksud Kamal.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

All the authors consented to publish the paper.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOC 1001 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Uddin, M.A., Kamal, A.S.M.M. & Shahid, S. Vegetation response to climate and climatic extremes in northwest Bangladesh: a quantile regression approach. Theor Appl Climatol 148, 985–1003 (2022). https://doi.org/10.1007/s00704-022-03968-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-022-03968-y