IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v117y2023i3d10.1007_s11069-023-05966-3.html
   My bibliography  Save this article

Geo-spatial analysis of drought in The Gambia using multiple models

Author

Listed:
  • Bambo Bayo

    (Government College University)

  • Shakeel Mahmood

    (Government College University)

Abstract

Climate change has made The Gambia vulnerable to drought hazard. Variability and negative trends in rainfall quantity and mid-season dry spells mainly attributed to the impacts of climate change. The inadequacy in hydrometeorological information puts the agricultural sector at a high risk which employs over 70% of the population. The aim of this study was to establish the intensity and spatiotemporal pattern of drought in The Gambia from 2000 to 2020 using multiple drought indices. Rainfall data, satellite images, and government policy documents were analyzed to determine the state of drought in The Gambia. Rainfall data, using Standardized Precipitation Index (SPI) and Precipitation Anomaly Percentage (PAP) were calculated and interpolated, and satellite images were processed using Vegetation Condition Index (VCI) to determine drought intensity and spatial distribution. The findings revealed that drought exists in The Gambia at moderate levels of SPI values (− 1.00 to − 1.49), (35% of PAP), and VCI of no drought intensity of more than 35%. The most drought prone areas in The Gambia are North Bank Region and Eastern parts of country in both north and south of The Gambia River banks. Recommendations of adaptation practice both on-farm and off-farm such as damming and economic diversification was drawn from other parts of the world, to reduce the negative effects of drought hazard in The Gambia.

Suggested Citation

  • Bambo Bayo & Shakeel Mahmood, 2023. "Geo-spatial analysis of drought in The Gambia using multiple models," 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. 117(3), pages 2751-2770, July.
    • Handle: RePEc:spr:nathaz:v:117:y:2023:i:3:d:10.1007_s11069-023-05966-3
    DOI: 10.1007/s11069-023-05966-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-05966-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-023-05966-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Israel R. Orimoloye & Johanes A. Belle & Adeyemi O. Olusola & Emmanuel T. Busayo & Olusola O. Ololade, 2021. "Spatial assessment of drought disasters, vulnerability, severity and water shortages: a potential drought disaster mitigation strategy," 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. 105(3), pages 2735-2754, February.
    2. Dai, Meng & Huang, Shengzhi & Huang, Qiang & Leng, Guoyong & Guo, Yi & Wang, Lu & Fang, Wei & Li, Pei & Zheng, Xudong, 2020. "Assessing agricultural drought risk and its dynamic evolution characteristics," Agricultural Water Management, Elsevier, vol. 231(C).
    3. Anshuka Anshuka & Floris F. van Ogtrop & R. Willem Vervoort, 2019. "Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis," 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. 97(2), pages 955-977, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sheriff Ceesay & Fatima Lambarraa-Lehnhardt & Mohamed Ben Omar Ndiaye & Diatou Thiaw & Mamma Sawaneh & Johannes Schuler, 2025. "Farmers’ Perceptions of the Efficacy of Current Climate Risk Adaptation and Mitigation Strategies on Agriculture in The Gambia," Land, MDPI, vol. 14(3), pages 1-22, March.

    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. Abdol Rassoul Zarei & Mohammad Mehdi Moghimi & Elham Koohi, 2021. "Sensitivity Assessment to the Occurrence of Different Types of Droughts Using GIS and AHP Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3593-3615, September.
    2. Anshuka Anshuka & Floris F. Ogtrop & David Sanderson & Simone Z. Leao, 2022. "A systematic review of agent-based model for flood risk management and assessment using the ODD protocol," 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. 112(3), pages 2739-2771, July.
    3. Tao He & Wenya Zhang & Hanwen Zhang & Jinliang Sheng, 2023. "Estimation of Manure Emissions Issued from Different Chinese Livestock Species: Potential of Future Production," Agriculture, MDPI, vol. 13(11), pages 1-17, November.
    4. Cui, Yi & Zhou, Yuliang & Jin, Juliang & Jiang, Shangming & Wu, Chengguo & Ning, Shaowei, 2023. "Spatiotemporal characteristics and obstacle factors identification of agricultural drought disaster risk: A case study across Anhui Province, China," Agricultural Water Management, Elsevier, vol. 289(C).
    5. Bláhová, Monika & Fischer, Milan & Poděbradská, Markéta & Štěpánek, Petr & Balek, Jan & Zahradníček, Pavel & Kudláčková, Lucie & Žalud, Zdeněk & Trnka, Miroslav, 2024. "Testing the reliability of soil moisture forecast for its use in agriculture," Agricultural Water Management, Elsevier, vol. 304(C).
    6. Israel R. Orimoloye & Adeyemi O. Olusola & Johanes A. Belle & Chaitanya B. Pande & Olusola O. Ololade, 2022. "Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms," 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. 112(2), pages 1085-1106, June.
    7. Alex Dunne & Yuriy Kuleshov, 2023. "Drought risk assessment and mapping for the Murray–Darling Basin, Australia," 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. 115(1), pages 839-863, January.
    8. Lei Zhang & Wei Song & Wen Song, 2020. "Assessment of Agricultural Drought Risk in the Lancang-Mekong Region, South East Asia," IJERPH, MDPI, vol. 17(17), pages 1-24, August.
    9. Djerbouai Salim & Souag-Gamane Doudja & Ferhati Ahmed & Djoukbala Omar & Dougha Mostafa & Benselama Oussama & Hasbaia Mahmoud, 2023. "Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1401-1420, February.
    10. Xiao, Xin & Ming, Wenting & Luo, Xuan & Yang, Luyi & Li, Meng & Yang, Pengwu & Ji, Xuan & Li, Yungang, 2024. "Leveraging multisource data for accurate agricultural drought monitoring: A hybrid deep learning model," Agricultural Water Management, Elsevier, vol. 293(C).
    11. Abiodun A. Ogundeji & Collins C. Okolie, 2022. "Perception and Adaptation Strategies of Smallholder Farmers to Drought Risk: A Scientometric Analysis," Agriculture, MDPI, vol. 12(8), pages 1-18, July.
    12. Xu, Yang & Zhang, Xuan & Hao, Zengchao & Hao, Fanghua & Li, Chong, 2021. "Projections of future meteorological droughts in China under CMIP6 from a three‐dimensional perspective," Agricultural Water Management, Elsevier, vol. 252(C).
    13. Huang, Wenhuan & Wang, Hailong, 2021. "Drought and intensified agriculture enhanced vegetation growth in the central Pearl River Basin of China," Agricultural Water Management, Elsevier, vol. 256(C).
    14. Fatemeh Barzegari Banadkooki & Vijay P. Singh & Mohammad Ehteram, 2021. "Multi-timescale drought prediction using new hybrid artificial neural network models," 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. 106(3), pages 2461-2478, April.
    15. Hua Yang & Jun He & Zhinong Li & Yufang Su & Jianchu Xu, 2024. "Ethnic diversity and divergent perceptions of climate change: a case study in Southwest China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    16. Zhang, Q. & Li, Y.P. & Huang, G.H. & Wang, H. & Li, Y.F. & Shen, Z.Y., 2024. "Multivariate time series convolutional neural networks for long-term agricultural drought prediction under global warming," Agricultural Water Management, Elsevier, vol. 292(C).
    17. Wei Shangguan & Ruqing Zhang & Lu Li & Shulei Zhang & Ye Zhang & Feini Huang & Jianduo Li & Wei Liu, 2022. "Assessment of Agricultural Drought Based on Reanalysis Soil Moisture in Southern China," Land, MDPI, vol. 11(4), pages 1-16, March.
    18. Wang, Shuliang & Gu, Xifeng & Luan, Shengyang & Zhao, Mingwei, 2021. "Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 35(C).
    19. Zhang, Yitong & Hao, Zengchao & Zhang, Yu, 2023. "Agricultural risk assessment of compound dry and hot events in China," Agricultural Water Management, Elsevier, vol. 277(C).
    20. Omidreza Mikaili & Majid Rahimzadegan, 2022. "Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province, Iran)," 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. 111(3), pages 2511-2529, April.

    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:spr:nathaz:v:117:y:2023:i:3:d:10.1007_s11069-023-05966-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.