Kesserwani, G. orcid.org/0000-0003-1125-8384, Hajihassanpour, M., Pettersson, P. et al. (1 more author) (2024) What is the most efficient sampling-based uncertainty propagation method in flood modelling? In: Gourbesville, P. and Caignaert, G., (eds.) Advances in Hydroinformatics—SimHydro 2023 Volume 1 New Modelling Paradigms for Water Issues. SimHydro 2023, 08-10 Nov 2023, Chatou, France. Springer Water, 1 . Springer Nature Singapore , pp. 367-386. ISBN 9789819740710
Abstract
Modelling uncertainty propagation in flood modelling manifests in frequency of occurrence, or histograms, for quantities of interest, including the flood extent and hazard rating. Such modelling at the field-scale requires the identification of a more efficient alternative to the Standard Monte Carlo (SMC) method that can reproduce comparable output probability distributions with a reduced sample size. Latin Hypercube Sampling (LHS) is the most evaluated alternative but yields no considerable sample size reduction. Potentially better alternatives include Adaptive Stratified Sampling (ASS), Quasi Monte Carlo (QMC) and Haar-Wavelet Expansion (HWE), which are yet unevaluated for probabilistic flood modelling. In this paper, LHS, ASS, QMC and HWE are compared to quantify sample size reduction to reproduce output detailed histograms—for flood extent, and average and maximum hazard rating—while keeping the difference below 10% to the reference SMC prediction. The comparison is done a synthetic test case with two (i.e., inflow discharge and Manning’s coefficient) and three (i.e., further including the ground elevation) input random variables, and a real case with five input random variables. With two input random variables, all four alternatives yield sample size reductions, with QMC and HWE considerably outperforming the others; with three and more input random variables, HWE becomes inflexible and LHS underperforms. Still, QMC is a better choice than ASS to boost sample size reduction and should be preferred.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © 2024 The Authors. Except as otherwise noted, this author-accepted version of a paper published in Advances in Hydroinformatics—SimHydro 2023 Volume 1 is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Earth Sciences; Physical Geography and Environmental Geoscience |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Mechanical, Aerospace and Civil Engineering |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/R007349/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Nov 2024 12:28 |
Last Modified: | 14 Nov 2024 12:28 |
Status: | Published |
Publisher: | Springer Nature Singapore |
Series Name: | Springer Water |
Refereed: | Yes |
Identification Number: | 10.1007/978-981-97-4072-7_24 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:219577 |