Rainfall estimates for urban drainage modelling: an investigation into resolution requirements and radar-rain gauge data merging at the required resolutions
File(s)
Author(s)
Ochoa Rodriguez, Susana
Type
Thesis
Abstract
Rainfall estimates of high accuracy and resolution are required for urban drainage modelling, given the high imperviousness, small size and fast response of urban catchments. Despite significant progress in rainfall measurement in recent decades, the resolution and accuracy of the rainfall estimates typically available from national meteorological services are still insufficient for urban drainage modelling. Moreover, the actual rainfall resolution requirements for these applications are not sufficiently understood.
The first aim of this thesis is to identify critical rainfall input resolution requirements for urban drainage modelling. To this end, a multi-storm, multi-catchment analysis is conducted on the impact of rainfall input resolution on urban stormwater modelling results. Minimum temporal resolutions of 5 min and of cumulative nature, and spatial resolutions of 1 km are found to be required for urban drainage modelling applications.
The second aim of this thesis is to provide guidance on the application of radar rain gauge merging techniques at urban scales, so that merged rainfall products which meet urban drainage modelling accuracy and resolution requirements can be obtained. Three merging techniques, namely Mean Field Bias (MFB) adjustment, kriging with external (KED) and Bayesian (BAY) combination, are selected for testing on grounds of performance and common use. They are tested as they were originally formulated and in combination with two novel treatments identified as having the potential to improve merging applicability for urban hydrology. These are reduction of temporal sampling errors in radar estimates through temporal interpolation, and singularity decomposition of radar estimates prior to merging. All merging methods improve the applicability of radar estimates to urban drainage modelling. Overall, KED displays the best performance, with BAY a close second and MFB providing the smallest benefits. The two special treatments under consideration further improve the overall merging performance at the spatial-temporal resolutions required for urban drainage modelling.
The first aim of this thesis is to identify critical rainfall input resolution requirements for urban drainage modelling. To this end, a multi-storm, multi-catchment analysis is conducted on the impact of rainfall input resolution on urban stormwater modelling results. Minimum temporal resolutions of 5 min and of cumulative nature, and spatial resolutions of 1 km are found to be required for urban drainage modelling applications.
The second aim of this thesis is to provide guidance on the application of radar rain gauge merging techniques at urban scales, so that merged rainfall products which meet urban drainage modelling accuracy and resolution requirements can be obtained. Three merging techniques, namely Mean Field Bias (MFB) adjustment, kriging with external (KED) and Bayesian (BAY) combination, are selected for testing on grounds of performance and common use. They are tested as they were originally formulated and in combination with two novel treatments identified as having the potential to improve merging applicability for urban hydrology. These are reduction of temporal sampling errors in radar estimates through temporal interpolation, and singularity decomposition of radar estimates prior to merging. All merging methods improve the applicability of radar estimates to urban drainage modelling. Overall, KED displays the best performance, with BAY a close second and MFB providing the smallest benefits. The two special treatments under consideration further improve the overall merging performance at the spatial-temporal resolutions required for urban drainage modelling.
Version
Open Access
Date Issued
2016-11
Date Awarded
2017-06
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Onof, Christian
Maksimovic, Cedo
Sponsor
European Commission
Grant Number
RAINGAIN
Publisher Department
Civil and Environmental Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)