Sinore et al. (2025) Agricultural and meteorological drought variability assessment over the Rift Valley Lake Basin of Ethiopia

Identification

• Journal: Environmental Sciences Europe
• Year: 2025
• Date: 2025-11-24
• Authors: Tamrat Sinore, Fei Wang, Serkalem Lemike, Firehiywet Girma
• DOI: 10.1186/s12302-025-01238-y

Research Groups

• State Key Laboratory of Soil and Water Conservation and Desertification Control, College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling, Shaanxi, China
• Department of Natural Resource Management, Wachemo University, Hossana, Ethiopia
• Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi, China
• Natural Resource Management, Bureau of Agriculture, Hossana, Ethiopia
• Department of Geographic Information Science, Hawassa University Wondo Genet College of Forestry and Natural Resources, Shashemene, Ethiopia

Short Summary

This study assessed the spatiotemporal variability of meteorological and agricultural droughts in Ethiopia's Rift Valley Lake Basin using multi-source remote sensing data and advanced statistical models. It revealed significant drought severity variations, with specific major events, and highlighted the compounded effects of thermal and moisture stress on vegetation.

Objective

• To examine the spatiotemporal variability of meteorological drought over 40 years (1985–2024).
• To quantify agricultural drought by analyzing vegetation health using NDVI, VCI, TCI, and VHI over 24 years (2001–2024).
• To assess the spatial relationships between meteorological and agricultural drought events.

Study Configuration

• Spatial Scale: Rift Valley Lake Basin (RVLB) of Ethiopia.
• Temporal Scale: Meteorological drought (SPI) from 1985 to 2024; Agricultural drought (NDVI, VCI, TCI, VHI) from 2001 to 2024.

Methodology and Data

• Models used: Standardized Precipitation Index (SPI-1, SPI-4), Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Spearman correlation, Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), Geographically Integrated Dryness Index (GIDI), Google Earth Engine (GEE).
• Data sources: Climate Hazards Center InfraRed Precipitation with Station Data (CHIRPS Version 2.0 Final), MOD13A2.061 Terra Vegetation Indices 16-Day Global 1 km product (MODIS), SPL4SMGP.007 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture, and ground-based rain gauge measurements.

Main Results

• CHIRPS satellite-based precipitation data showed a strong linear relationship with rain gauge observations (Pearson correlation coefficient = 0.93, Root Mean Square Error = 0.00043, Percent Bias = -1.13%).
• Significant spatiotemporal variability in drought severity was observed, with major events occurring in 1985, 1987, 1990, 2002, 2009, 2016, and 2020.
• The northern basin was the most severely affected by drought, with conditions extending into central and northwestern regions. Very dry events during the Belg season (February–May) occurred in 2000, 2008, 2012, 2019, and 2021.
• Strong negative correlations were identified between Land Surface Temperature (LST) and vegetation indices (VCI, NDVI, TCI, VHI), and between SPI and LST (r = -0.58), indicating that higher temperatures negatively impact vegetation health and exacerbate drought stress.
• The Vegetation Health Index (VHI) classified a larger area under severe drought (10%) and moderate drought (30.26%) compared to the Vegetation Condition Index (VCI) (severe: 1.42%, moderate: 15.37%).
• The Geographically Weighted Regression (GWR) model significantly outperformed Ordinary Least Squares (OLS) (Adjusted R² = 0.95), effectively capturing spatial relationships between TCI, SPI, and root zone soil moisture.
• GWR coefficients showed TCI had a positive coefficient (3.19) in central to northern regions (worsening drought) and a negative coefficient (-4.45) in the south (reducing intensity). SPI-1 exhibited an unexpected positive association with drought severity in the northern basin.
• Predicted drought risk ranged from 0.05 to 5.78, with the central basin showing the highest inherent risk due to short-term rainfall deficits and high temperatures.

Contributions

• Provides a comprehensive spatiotemporal assessment of meteorological and agricultural droughts in the Rift Valley Lake Basin, addressing previous limitations that overlooked unique climatic and topographic features.
• Employs a multi-index approach (SPI, NDVI, VCI, TCI, VHI) and advanced spatial statistical tools (GWR, GIDI) to offer a more holistic and spatially explicit understanding of drought dynamics.
• Demonstrates the superior performance of composite indices like VHI over single indices (VCI) for detecting agricultural drought under complex climatic conditions.
• Uncovers unexpected spatial patterns in drought-driving factors (e.g., localized SPI-1 anomalies, inverse TCI-drought relationship in some southern regions), highlighting the need for geographically weighted models.
• Offers actionable insights for policymakers and stakeholders to develop evidence-based drought monitoring, mitigation, and adaptation strategies for a climate change-vulnerable region.

Funding

No funding for this study.

Citation

@article{Sinore2025Agricultural,
  author = {Sinore, Tamrat and Wang, Fei and Lemike, Serkalem and Girma, Firehiywet},
  title = {Agricultural and meteorological drought variability assessment over the Rift Valley Lake Basin of Ethiopia},
  journal = {Environmental Sciences Europe},
  year = {2025},
  doi = {10.1186/s12302-025-01238-y},
  url = {https://doi.org/10.1186/s12302-025-01238-y}
}