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Title: Accounting for rainfall variability in sediment wash-off modelling using uncertainty propagation
Author: Muthusamy, Manoranjan
ISNI:       0000 0004 7233 8617
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2018
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Urban surface sediment is a major source of pollution as it acts as a transport medium for many contaminants. Accurate modelling of sediment wash-off from urban surfaces requires an understanding of the effect of variability in the external drivers such as rainfall on the wash-off process. This study investigates the uncertainty created due to the urban-scale variability of rainfall, in sediment wash-off predictions. Firstly, a rigorous geostatistical method was developed that quantifies uncertainty due to spatial rainfall variability of rainfall at an urban scale. The new method was applied to a unique high-resolution rainfall dataset collected with multiple paired gauges for a study designed to quantify rainfall uncertainty. Secondly, the correlation between calibration parameters and external drivers - rainfall intensity, surface slope and initial load- was established for a widely used exponential wash-off model using data obtained from new detailed laboratory experiments. Based on this, a new wash-off model where the calibration parameters are replaced with functions of these external drivers was derived. Finally, this new wash-off model was used to investigate the propagation of rainfall uncertainty in wash-off predictions. This work produced for the first time quantitative predictions of the variation in wash-off load that can be linked to the rainfall variability observed at an urban scale. The results show that (1) the assumption of constant spatial rainfall variability across rainfall intensity ranges is invalid for small spatial and temporal scales, (2) wash-off load is sensitive to initial loads and using a constant initial load in wash-off modelling is not valid, (3) the level of uncertainty in predicted wash-off load due to rainfall uncertainty depends on the rainfall intensity range and the “first-flush” effect. The maximum uncertainty in the prediction of peak wash-off load due to rainfall uncertainty within an 8-ha catchment was found to be ~15%.
Supervisor: Schellart, Alma ; Tait, Simon Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available