ORIAKHI OROBOSA

Open drain channels play a vital role in stormwater management and flood prevention in urban and agricultural environments. However, factors such as complex channel geometry, surface roughness, sediment accumulation, and turbulence often reduce their efficiency, leading to flooding and waterlogging. This study aimed to analyze fluid flow in an open drainage channel using Computational Fluid Dynamics (CFD) to understand flow behavior, identify hydraulic inefficiencies, and recommend design improvements for enhanced drainage performance. The study focused on an open drainage channel within the University of Benin, Ugbowo Campus, Benin City, Nigeria. Data on channel dimensions, flow conditions, and physical characteristics were obtained through site investigations and measurements. A three-dimensional model of the channel was developed and simulated using CFD techniques in SolidWorks Flow Simulation. The governing equations of fluid flow, including the continuity and Navier-Stokes equations, were solved under appropriate boundary conditions to evaluate velocity distribution and free- surface flow characteristics. The simulation results showed that flow velocity increased gradually along the channel length, with the highest velocities occurring near the outlet region. The free surface remained stable throughout the simulation, indicating efficient water conveyance under the specified flow conditions. The study concluded that CFD is an effective tool for predicting flow behavior and evaluating the hydraulic performance of open drainage systems. The findings provide valuable insights for improving drainage design, reducing flood risks, and promoting sustainable stormwater management.

The degradation of vegetated lands due to modernization, agricultural expansion, and climate change has become a growing environmental concern in Edo State, Nigeria. Vegetation plays a critical role in sustaining biodiversity, regulating local climate, reducing soil erosion, and supporting livelihoods through agriculture and forest resources. However, rapid population growth, increasing demand for land, and infrastructural development have intensified pressure on natural vegetation across the state. This study aims to map and monitor the spatio-temporal dynamics of vegetated lands in Edo State using satellite remote sensing data within the Google Earth Engine (GEE) platform. Multi-temporal satellite imagery of Edo State was acquired and preprocessed using Moderate Resolution Imaging Spectroradiometer (MODIS) data as the primary source. Vegetation indices, particularly the Normalized Difference Vegetation Index (NDVI), were computed to classify and map vegetated areas and to evaluate vegetation health and density over time. Time-series analysis and pixel-based classification techniques were applied to assess vegetation patterns and to detect changes in vegetation cover between 2015, 2020, and 2025. The NDVI-derived vegetation classes were categorized into dense vegetation, sparse vegetation, and non-vegetated or built-up surfaces to enable clearer interpretation of vegetation transformation.