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Water quality profiling of rivers in a data-poor area: Southwest Nigeria.

Omotoso, Toyin

[Thesis]. Manchester, UK: The University of Manchester; 2016.

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Abstract

The current state of the art in water quality profiling is reviewed to lay a foundation in addressing concerns over poor data in developing countries which has not been adequately covered by previous models. A particular focus is made on Ogbese River, southwest Nigeria as a case study. A process-based model with data-filling capability is projected which transforms processes into an event as a reasonably easy way for assessing and predicting river-water quality in the event of constraints in data collection. The structure of the study involves: (i) hydrologic modelling, (ii) hydraulic load modelling and (iii) instream water quality modelling. The hydrologic modelling assesses and makes use of satellite based rainfall estimates subject to processing and reliability tests. A modification to the conceptual relationship of rainfall distribution frequency which makes the model output sensitive to the season was derived. The hydraulic load modelling integrates diffuse sources of pollutant as spatial data in combination with the catchment runoff. A distance decay weighing factor was introduced into the export coefficient to better determine the effective load delivered into the stream. The utility of the model, implemented on WASP platform, was demonstrated by showing how it can be used for scenario testing. Different modelling concepts were evaluated in view of their ability to produce predictions under changing circumstances using the predictions as guide to management. This study promotes a knowledge base in water quality processes by evaluation of the processes which lead to the end product rather than using data monitoring. The study structures understanding of the phenomena that characterises river water quality and tailors it towards regulatory applications and catchment planning. It, also, provides a sustainable strategy to predict the river water quality, evaluate the risks, and take proactive action in setting up an early warning system, for data-poor regions.