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Digital Mapping to Determine Sustainability of Dam Construction in Owerri North for Agricultural Productivity

DOI: 10.4236/oalib.1113065, PP. 1-27

Subject Areas: Agricultural Engineering, Information Management, Technology, Machine Learning

Keywords: GIS, Remote Sensing, Mapping, Dam

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Abstract

Agriculture is critical for economic stability and food security, particularly in regions like Owerri North, Nigeria, where inconsistent rainfall, waterlogging, and soil erosion threaten productivity. This study leverages Geographic Information Systems (GIS) and Remote Sensing (RS) to identify suitable dam construction sites to improve agricultural productivity. Key thematic layers such as precipitation, stream density, geomorphology, geology, land use/land cover (LULC), and elevation were analyzed. Data were sourced from global satellite missions like TRMM, Earth Explorer, and CHIRPS, high-resolution DEMs (e.g., SRTM), and local geological surveys. Using the Dam Suitability Stream Model (DSSM), this study employed GIS-based Multi-Criteria Decision Making (MCDM) techniques, including the Analytic Hierarchy Process (AHP), to assign weights to criteria such as stream order, slope, and land use. The analysis generated two suitability maps: Suitability on Stream and Overall Suitability. The Suitability on Stream map highlighted highly suitable zones near high-order streams in the basin’s upper reaches, prioritizing areas with consistent water flow and favorable slopes (5% - 15%). In contrast, the Overall Suitability map expanded the scope to include broader factors like Euclidean distance from streams, identifying additional areas in the lower reaches, though many faced limitations such as lower stream orders and flood risks. Detailed evaluations of two proposed sites revealed that Dam Site 1, located on a third-order stream with a 88 km2 catchment area, was the most viable option for multipurpose use, including irrigation and flood control. Dam Site 2, with a smaller catchment area and second-order stream, showed moderate suitability for smaller-scale projects. 3D surface models and cross-sectional analyses confirmed that Dam Site 1 had higher volumetric potential and better geological stability, making it more sustainable for agricultural water management. Therefore, integrating digital mapping and AHP is an efficient method for sustainable dam site selection, directly addressing water resource challenges and enhancing agricultural resilience. 

Cite this paper

Ugwu, O. J. , Emmanuel, U. A. , Samson, U. M. , Nwodo, G. O. and Okoroji, A. (2025). Digital Mapping to Determine Sustainability of Dam Construction in Owerri North for Agricultural Productivity. Open Access Library Journal, 12, e3065. doi: http://dx.doi.org/10.4236/oalib.1113065.

References

[1]  FAO (2021) The State of Food and Agriculture: Making Agri-Food Systems More Resilient to Shocks and Stresses. Food and Agriculture Organiza-tion.
[2]  World Bank (2022) Agriculture for Development: World Develop-ment Report 2022. World Bank Publications.
[3]  NBS (2021) Nigeria’s Gross Domestic Product Report (Q4 2021). National Bureau of Statistics.
[4]  Akpan, S.B., et al. (2020) Climate Change and Agricultural Sustainability in Nigeria. Af-rican Journal of Agricultural Research, 15, 567-578.
[5]  IPCC (2023) Climate Change 2023: Synthesis Report. Intergovernmental Panel on Climate Change.
[6]  Omer, F.O. and Rasul, A. (2023) Assessing Hydrological Modeling Approaches: A Review of the Soil Conservation Service Curve Number and the Soil and Water Assessment Tool. Advanced GIS, 3, 45-60.
[7]  Choudhary, K., Boori, M.S., Shi, W., Valiev, A. and Kupriyanov, A. (2023) Agricultural Land Suitability Assessment for Sustainable Development Using Remote Sensing Techniques with Analytic Hierarchy Process. Remote Sensing Applications: Soci-ety and Environment, 32, Article ID: 101051. https://doi.org/10.1016/j.rsase.2023.101051
[8]  Zhang, Y., et al. (2023) Machine Learning and GIS Integration for Land Consolidation in Hilly Regions. Frontiers in Plant Science, 14, Article 1120450.
[9]  Abate, S.G. and Anteneh, M.B. (2024) Assessment of Agricultural Land Suitability for Cereal Crops Based on the Analysis of Soil Physico-Chemical Characteristics. Environmental Systems Research, 13, Article No. 6. https://doi.org/10.1186/s40068-024-00333-y
[10]  Oladele, O.I., et al. (2019) Challenges and Opportunities in Nigeria’s Agricultural Sector. Journal of Rural Studies, 68, 240-248.
[11]  Eze, J.E., et al. (2022)- Climate Resilience and Irrigation Potential in the Lower Benue River Basin. Environmental Science & Policy, 135, 104-113.
[12]  Adepoju, K.A., et al. (2021) Geospatial Assessment of Land Suitability for Irrigation Development in Nigeria. Journal of Environ-mental Management, 290, Article ID: 112567.
[13]  Saaty, T.L. (1980) The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill.
[14]  Akpoti, K., Higginbottom, T.P., Foster, T., Adhikari, R. and Zwart, S.J. (2022) Mapping Land Suitability for Informal, Small-Scale Irrigation Development Using Spatial Modelling and Machine Learning in the Upper East Region, Ghana. Science of the Total Environment, 803, Article ID: 149959. https://doi.org/10.1016/j.scitotenv.2021.149959
[15]  Ozsahin, E. and Ozdes, M. (2021) Agricultural Land Suitability Assessment for Agricultural Productivi-ty Based on GIS Modeling and Multi-Criteria Decision Analysis: The Case of Tekirda? Province. Environmental Monitoring and Assessment, 194, Article No. 41. https://doi.org/10.1007/s10661-021-09663-1
[16]  Wu, Q., Ramirez Avi-la, J.J., Yang, J., Ji, C. and Fang, S. (2024) High-Resolution Annual Dynamic Da-taset of Curve Number from 2008 to 2021 over Conterminous United States. Scientific Data, 11, Article No. 207. https://doi.org/10.1038/s41597-024-03044-2
[17]  Valle Junior, L.C.G.d., Rodrigues, D.B.B. and Oliveira, P.T.S.d. (2019) Initial Abstraction Ratio and Curve Number Estimation Using Rainfall and Runoff Data from a Tropical Wa-tershed. RBRH, 24, e5. https://doi.org/10.1590/2318-0331.241920170199
[18]  Zhang, Y., Zhong, T. and Jiang, W. (2023) Assessing Farmland Suitability for Agri-Cultural Ma-chinery in Land Consolidation Schemes in Hilly Terrain in China: A Machine Learning Approach. Frontiers in Plant Science, 14, Article 1084886.

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