MODELLING SEDIMENT YIELD IN TOFA DAM UNDER THE IMPACT OF ANTHROPOGENIC ACTIVITIES

Morgan Omale, Dr. Al-Amin Danladi Bello, Dr. Umar Alfa Abubakar

Abstract


Sedimentation driven by land-use changes and unsustainable agricultural practices significantly threatens reservoir functionality, reducing water storage capacity and degrading water quality. This study examines sediment dynamics and trapping efficiency in Tofa Dam, Kano State, Nigeria, using geospatial techniques, the Revised Universal Soil Loss Equation (RUSLE), and the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS). Principal Component Analysis (PCA), performed with SPSS, evaluated statistical relationships between sediment yield and influencing factors.Land-use/land-cover (LULC) analysis from 1999 to 2044 revealed drastic changes, with vegetation cover declining from 52.84% in 1999 to 2.21% by 2024, and built-up areas increasing from 3.15% to 39.38%. These changes led to significant sediment load increases. Under contouring cultivation practices, sediment yield rose from 683.2 tonnes in 1999 to 933.6 tonnes in 2024 and is projected to reach 1132.9 tonnes by 2044. Conversely, conservation tillage, as a recommended practice, reduced sediment loads from 253.1 tonnes in 1999 to 345.8 tonnes in 2024 and 419.6 tonnes by 2044, demonstrating its effectiveness in minimizing erosion. PCA results identified the cover management factor (C-factor) and support practice factor (P-factor) as critical determinants of sediment yield, collectively explaining 99.65% of total variance. Sediment yield showed strong correlations with the P-factor (r = 0.947) and moderate correlations with the C-factor (r = 0.276). Regression analysis also highlighted a direct relationship between increasing built-up areas and sediment loads.The study recommends adopting conservation tillage to reduce sediment yield by modifying slope profiles and promoting water infiltration. Additional strategies, including reforestation, vegetative buffers, and conservation tillage, could further reduce sediment loads by up to 30%. These findings demonstrate the effectiveness of integrating geospatial and statistical tools for sustainable sediment management, ensuring the long-term functionality of Tofa Dam and similar hydrological systems.

 

KEYWORDS:  Geospatial Modelling, RUSLE, Soil Erosion, Statistical Analysis, Soil Erosion Factors and Vegetation


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