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MPhil in Engineering for Sustainable Development

global challenges, engineering solutions

Studying at Cambridge

Feriha Mugisha

Catchment Characterisation for Sustainable Water Resources Management

Feriha Mugisha

Catchment Characterisation for Sustainable Water Resources Management

 

Water-resources challenges in catchments are influenced by multiple factors which include physical and hydrological factors, land-use, and anthropogenic considerations.  However, the relationship between the basic catchment attributes and their obtaining water-resources challenges (WRCs) is not fully understood and as a result, inferences on water challenges and corresponding mitigation/remedial actions in individual catchments are often based on intuitive analysis, and can therefore be inaccurate and unreliable.  Moreover, employed micro-scale catchment-simulation approaches are often prohibitive and demanding in application, being data and resource-intensive.

This study therefore develops and validates a system-level, generic methodology, with which a catchment's likely WRCs can be inferred from 11 readily-accessible, basic physical-dimensional, hydro-geological and anthropogenic attributes; building on Wagener et al. (2008)'s Form, Function, and Climate Multivariate Characterisation Framework.  The developed Multivariate Catchment Characterisation (MCC) methodology is unique in the use of both Trained Canonical Discriminant Analysis (CDA) and Untrained Cluster Analysis (AHCA) characterisation techniques, as well as Geovisualisation congruence analysis.  The methodology is developed for 3 WRC criteria: Flood-Occurrence, Nitrate-Concentration (N) and Phosphate-Concentration (P); using 27 of the UK's Catchment Abstraction Management Strategy (CAMS) catchments, with validation on 5 independent catchments.

The inferences indicate that there is a significant relationship between catchments' basic attributes and attendant WRCs, as reflected in the respective 92.6%, 92.6, and 96.3% characterisation-accuracies of the CDA models.  The analysis also reveals that the Trained CDA technique achieves a higher overall characterisation-accuracy with 80% validation accuracy for N and P, while the Untrained AHCA correlates strongly with spatial proximity.  Overall, the result is a generic catchment characterisation mechanism that relatively accurately describes the systemic attribute-WRC relationship, and can be used to predict likely adverse water-resources effects based on basic catchment characteristics.