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Advancing basin-scale drought monitoring: Development of a regional combined drought index using precipitation, soil moisture, and vegetation data

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Listed:
  • Abebe, Asnake Kassahun
  • Zhou, Xiang
  • Lv, Tingting
  • Tao, Zui
  • Bayissa, Yared
  • Zhang, Hongming
  • Elnashar, Abdelrazek

Abstract

Drought remains a critical challenge in Ethiopia’s Awash River Basin (ARB), where rainfed agriculture is highly sensitive to climate variability. This study presents a regional Combined Drought Index (rCDI), integrating the Standard Precipitation Index (SPI-3), soil moisture anomaly (SMA), and Vegetation anomaly (VA) using a Principal Component Analysis (PCA)-based weighting approach. Monthly gridded data from 2001 to 2023 were used to generate dynamic, grid-specific weights, capturing spatiotemporal drought variability across the basin. The rCDI was validated against independent station-based SPI-3 data, detrended crop yields (maize and sorghum), and documented drought events for both the Belg (short rainy) and Kiremt (long rainy) seasons. Analysis combined Google Earth Engine (GEE) with Python via Google Colab. Strong correlations (r > 0.70) were observed with SPI-3 were observed in most areas, though weaker (r > 0.45) in arid Belg Zones. Crop yield analysis revealed stronger rCDI sensitivity to maize in the upper ARB and sorghum in upland/northwestern areas, reflecting crop-climate adaptation. The rCDI effectively captured major droughts (2002/2003, 2008–2012, 2015, and 2022), consistent with reported socio-economic impacts. Seasonal patterns showed Belg experiencing more frequent and severe droughts than Kiremt. Statistical trend analysis confirmed rCDI’s strength in monitoring evolving drought conditions, supporting early warning and sustainable resource management. A statistical downscaling using Artificial Neural Network (ANN) enhanced soil moisture resolution from 10 km to 1 km, improving rCDI's accuracy. By integrating meteorological, agricultural, and ecological dimensions, the rCDI provides a comprehensive tool for basin-scale drought assessment and monitoring in data-scarce, climate-sensitive regions.

Suggested Citation

  • Abebe, Asnake Kassahun & Zhou, Xiang & Lv, Tingting & Tao, Zui & Bayissa, Yared & Zhang, Hongming & Elnashar, Abdelrazek, 2025. "Advancing basin-scale drought monitoring: Development of a regional combined drought index using precipitation, soil moisture, and vegetation data," Agricultural Water Management, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:agiwat:v:318:y:2025:i:c:s0378377425004482
    DOI: 10.1016/j.agwat.2025.109734
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    References listed on IDEAS

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    1. Zhou, Keke & Li, Jianzhu & Zhang, Ting & Kang, Aiqing, 2021. "The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China," Agricultural Water Management, Elsevier, vol. 243(C).
    2. Pan, Ying & Zhu, Yonghua & Lü, Haishen & Yagci, Ali Levent & Fu, Xiaolei & Liu, En & Xu, Haiting & Ding, Zhenzhou & Liu, Ruoyu, 2023. "Accuracy of agricultural drought indices and analysis of agricultural drought characteristics in China between 2000 and 2019," Agricultural Water Management, Elsevier, vol. 283(C).
    3. Desalegn Edossa & Mukand Babel & Ashim Das Gupta, 2010. "Drought Analysis in the Awash River Basin, Ethiopia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(7), pages 1441-1460, May.
    4. Wu, Dong & Li, Zhenhong & Zhu, Yongchao & Li, Xuan & Wu, Yingjie & Fang, Shibo, 2021. "A new agricultural drought index for monitoring the water stress of winter wheat," Agricultural Water Management, Elsevier, vol. 244(C).
    5. Li, Jiale & Li, Yu & Yin, Lei & Zhao, Quanhua, 2024. "A novel composite drought index combining precipitation, temperature and evapotranspiration used for drought monitoring in the Huang-Huai-Hai Plain," Agricultural Water Management, Elsevier, vol. 291(C).
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