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

被引:1
|
作者
Bayo, Bambo [1 ]
Mahmood, Shakeel [1 ]
机构
[1] Govt Coll Univ, Dept Geog, Lahore, Pakistan
关键词
Drought; Standardized precipitation index; Precipitation condition index; Vegetation condition index; Agriculture; The Gambia; STANDARDIZED PRECIPITATION INDEX; SPI;
D O I
10.1007/s11069-023-05966-3
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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.
引用
收藏
页码:2751 / 2770
页数:20
相关论文
共 50 条
  • [21] Geo-spatial data mining in the analysis of a demographic database
    M. Yasmina Santos
    L. Alfredo Amaral
    Soft Computing, 2005, 9 : 374 - 384
  • [22] Epidemiological Data Analysis in TerraFly Geo-Spatial Cloud
    Wang, Huibo
    Lu, Yun
    Guang, Yudong
    Edrosa, Erik
    Zhang, Mingjin
    Camarca, Raul
    Yesha, Yelena
    Lucic, Tajana
    Rishe, Naphtali
    2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 2, 2013, : 485 - 490
  • [23] Appraisal of Spatial Distribution of Degraded Lands Using Geo-spatial Techniques
    Tagore, G. S.
    Bairagi, G. D.
    Sharma, R.
    Porte, S. S.
    Vishwakarma, M.
    COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, 2021, 52 (06) : 601 - 612
  • [24] Morphotectonic analysis of Sheer Khadd River basin using geo-spatial tools
    Sharma A.
    Singh P.
    Rai P.K.
    Spatial Information Research, 2018, 26 (4) : 405 - 414
  • [25] Enhancing energy models with geo-spatial data for the analysis of future electrification pathways: The case of Tanzania
    Rocco, Matteo, V
    Fumagalli, Elena
    Vigone, Chiara
    Miserocchi, Ambrogio
    Colombo, Emanuela
    ENERGY STRATEGY REVIEWS, 2021, 34 (34)
  • [26] Designing geo-spatial interfaces to scale process models: the GeoWEPP approach
    Renschler, CS
    HYDROLOGICAL PROCESSES, 2003, 17 (05) : 1005 - 1017
  • [27] Towards interoperable geo-information standards: A comparison of reference models for geo-spatial information
    Jochen Albrecht
    The Annals of Regional Science, 1999, 33 : 151 - 169
  • [29] Temporal Effects on Mobile Stroke "Hot" Zones Using a Geo-Spatial Software Analysis
    Vora, Shivam
    Hake, Joshua
    Willis, Corbin
    Hall, Maelee
    Jennings, Nate
    Katz, Brian S.
    Rai, Vivek
    Loochtan, Aaron
    Hicks, William J.
    Crow, Dan
    STROKE, 2024, 55
  • [30] Analysis and forecasting of municipal solid waste in Nankana City using geo-spatial techniques
    Shakeel Mahmood
    Faiza Sharif
    Atta-ur Rahman
    Amin U Khan
    Environmental Monitoring and Assessment, 2018, 190