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Modeling agricultural drought based on the earth observation-derived standardized precipitation evapotranspiration index and vegetation health index in the northeastern highlands of Ethiopia

Author

Listed:
  • Zerihun Chere

    (Dire Dawa University)

  • Dereje Biru Debalke

    (Bonga University)

Abstract

For farmers in the South Wollo Zone, drought has been one of the most devastating natural disasters, making better monitoring of agricultural drought with the aid of earth observation data essential. The main objective of this research is to characterize the spatiotemporal variation, frequency, and trends of agricultural drought from 2001 to 2021 using the earth observation-derived vegetation health index (VHI) and standardized precipitation evapotranspiration index (SPEI). The VHI and SPEI were developed using the following variables: potential evapotranspiration (MOD16A2GF), climatic hazards group infrared precipitation with stations (CHIRPS), surface temperature of the land and emissivity (MOD11A2), and normalized difference vegetation index (MOD13Q1 NDVI). The Mann–Kendall (MK) trends analysis and Pearson correlation were used to identify the trend and the relationship between VHI and SPEI. SPEI and VHI were validated using crop yield data. According to the findings, there were agricultural droughts of varying severity in 2002, 2004, 2009, 2010, 2014, and 2015. The results demonstrated a decreasing SPEI (87.5%) and VHI (57.4%) slope during July. The comparison between the SPEI and VHI was positive and significant on the seasonal scale (r = 0.56, p = 0.01). The regression analysis results showed that detrended crop yields agreed well with VHI (R2/r = 0.49/0.70, P

Suggested Citation

  • Zerihun Chere & Dereje Biru Debalke, 2024. "Modeling agricultural drought based on the earth observation-derived standardized precipitation evapotranspiration index and vegetation health index in the northeastern highlands of Ethiopia," 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. 120(3), pages 3127-3151, February.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:3:d:10.1007_s11069-023-06320-3
    DOI: 10.1007/s11069-023-06320-3
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    References listed on IDEAS

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    1. Amba Shalishe & Anirudh Bhowmick & Kumneger Elias, 2023. "Agricultural drought analysis and its association among land surface temperature, soil moisture and precipitation in Gamo Zone, Southern Ethiopia: a remote sensing approach," 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(1), pages 57-70, May.
    2. Feng, Puyu & Wang, Bin & Liu, De Li & Yu, Qiang, 2019. "Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia," Agricultural Systems, Elsevier, vol. 173(C), pages 303-316.
    3. Thomas Pave Sohnesen, 2020. "Two Sides to Same Drought: Measurement and Impact of Ethiopia’s 2015 Historical Drought," Economics of Disasters and Climate Change, Springer, vol. 4(1), pages 83-101, April.
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