Abadefar (2025) Assessment of land suitability and water availability for surface irrigation using fuzzy logic algorithm in GIS, in the case of Upper Awash Basin, Ethiopia

Identification

• Journal: Applied Water Science
• Year: 2025
• Date: 2025-11-18
• Authors: Demelash Debebe Abadefar
• DOI: 10.1007/s13201-025-02649-1

Research Groups

• Water Resources and Irrigation Engineering Department, Woldia University, Woldia, Ethiopia

Short Summary

This study identified suitable potential zones for surface irrigation in the Keleta watershed, Ethiopia, using a fuzzy logic algorithm integrated with GIS. It found that approximately 52% of the watershed is suitable based on surface water sources and 20.34% (highly and moderately suitable) based on groundwater sources, providing a comprehensive assessment for irrigation development.

Objective

• To evaluate the suitability of land resources for surface irrigation in the Keleta watershed by integrating fuzzy logic algorithms within a Geographic Information System (GIS) framework, assessing land suitability using the Fuzzy Analytic Hierarchy Process (FAHP), and delineating potential zones based on both surface water and groundwater resources.

Study Configuration

• Spatial Scale: Keleta watershed, Upper Awash Basin, Ethiopia, covering a drainage area of 811.4 km². The study area's altitude ranges from 1573 to 4191 m above sea level. Maps were generated at a 12.5 m by 12.5 m resolution.
• Temporal Scale: Meteorological data from 1989 to 2018, hydrological data (stream flow and groundwater yield) from 1989 to 2016, Sentinel 2A satellite images from 2022, and DEM data from 2023. Land use/land cover data acquisition was from December 2021.

Methodology and Data

• Models used:
  • Fuzzy Logic Algorithm
  • Geographic Information System (GIS)
  • Fuzzy Analytic Hierarchy Process (FAHP) with Buckley's geometric mean method for weighting factors.
  • Modified Penman–Monteith method for Potential Evapotranspiration (PET) simulation.
  • Thiessen’s polygon method for converting point rainfall to spatial rainfall.
  • Linear regression method for filling missing meteorological data.
  • Supervised maximum likelihood classification for Land Use/Land Cover (LULC) mapping.
  • Euclidean distance for proximity analysis.
  • Fuzzy Gaussian, Large, and Small fuzzification algorithms.
  • GIS-based gamma 0.9 fuzzy weighted overlay operators.
• Data sources:
  • Meteorological data (1989-2018) from the Ethiopian Meteorological Institute (EMI) / Ethiopian National Meteorological Agency (ENMA).
  • Digital Elevation Model (DEM) (12.5 m resolution, 2023) from https://www.asf.alaska.edu.
  • Sentinel 2A satellite images (10 m resolution, 2022) from https://scihub.copernicus.eu for LULC.
  • Soil physical property shapefiles from Kulumsa Agricultural Research Center (KAEC) and the Ministry of Agriculture (MOA).
  • Geological data from the Ethiopian Ministry of Mine (EMOM).
  • Hydrological data (stream flow and groundwater yield, 1989-2016) from the Ethiopian Ministry of Water and Energy (EMWE).
  • Vector data for road networks from the Ethiopian Road Authority.
  • GPS field data and Google Earth for LULC validation.
  • Expert judgment from 14 local specialists (9 consistent responses used) for FAHP weighting.

Main Results

• Surface Water Suitability:
  • Highly suitable: 149.67 km² (18.56%)
  • Moderately suitable: 272.41 km² (33.78%)
  • Marginally suitable: 243.16 km² (30.16%)
  • Not suitable: 135.99 km² (16.86%)
  • Restricted area (environmental/physical limitations): 5.28 km² (0.65%)
  • Approximately 52% (422.08 km²) of the watershed is highly or moderately suitable for surface irrigation.
• Groundwater Suitability:
  • Highly suitable: 67.11 km² (8.34%)
  • Moderately suitable: 97.02 km² (12.00%)
  • Marginally suitable: 265.91 km² (32.87%)
  • Not suitable: 390.37 km² (48.01%)
  • Restricted area: 5.28 km² (0.65%)
  • Approximately 20.34% (164.13 km²) of the watershed is highly or moderately suitable for groundwater-based irrigation.
• Key Factors: Rainfall Deficit and Soil Capability Index (SCI) were the most significant factors for surface water suitability, while Hydrogeology and SCI were most important for groundwater suitability.
• Validation: 35.71% of existing surface irrigation sites were located in highly suitable zones, 42.86% in moderately suitable, and 21.43% in marginally suitable zones, confirming the methodology's applicability.
• Constraints: A total of 5.28 km² (0.65%) was identified as restricted due to major roads (2.13 km²) and settlements (3.15 km²).
• Overall Potential: With some modifications for marginally suitable areas, 670.52 km² (83.14%) of the Keleta watershed is suitable for surface irrigation development from surface water sources, and 627.35 km² (77.93%) from groundwater sources.

Contributions

• Addresses a research gap in the Keleta watershed by applying an integrated geospatial and decision analysis framework (FAHP, fuzzy logic, GIS) for surface irrigation suitability mapping, which was previously unexplored in the region.
• Provides a novel, integrated dual-source assessment, simultaneously mapping suitability based on both surface water and groundwater resources within the same spatial framework, offering a more comprehensive foundation for irrigation planning.
• Offers valuable decision-support tools and scientifically grounded insights for policymakers, planners, and water resource managers to prioritize irrigation development and enhance land suitability.
• Establishes a replicable framework for similar semi-arid regions facing food security and water challenges.
• Serves as an initial point for surface irrigation reconnaissance studies and facilitates collaboration among academics and development agencies to improve land suitability and promote sustainable irrigation practices.

Funding

• Woldia University provided staff time for this research work. No external funding was obtained.

Citation

@article{Abadefar2025Assessment,
  author = {Abadefar, Demelash Debebe},
  title = {Assessment of land suitability and water availability for surface irrigation using fuzzy logic algorithm in GIS, in the case of Upper Awash Basin, Ethiopia},
  journal = {Applied Water Science},
  year = {2025},
  doi = {10.1007/s13201-025-02649-1},
  url = {https://doi.org/10.1007/s13201-025-02649-1}
}