Computational Technology Resources - CCP

Keywords: hydrodynamic, HEC-RAS, rainfall-runoff, kernel density estimation.

Summary

This paper proposes a method for modelling and visualization of possible uncertainties in the outputs of hydrodynamic models. The main purpose of hydrodynamic models in the disaster management context is to provide accurate overview of hydrological situation in a given river catchment. In the event of extreme weather conditions, such as unusually heavy rainfall, these models could provide valuable information about imminent flood risk endangering particular area. One of the methods to model the uncertainties in hydrodynamic models is to base them on the uncertainties of the rainfall-runoff (RR) models that provide data for their computation. This paper describes a method for construction of confidence intervals of the RR model outputs based on the uncertainties in their inputs. These confidence intervals can then be used as an input of hydrodynamic simulations which in turn provides several confidence intervals for the resulting water levels and inundation areas. Flood maps generated form these data can significantly improve information value of the hydrodynamic simulations. However, computational requirements of these simulations can be significant since the method requires execution of a large number of hydrodynamic simulations in order to obtain results. In the disaster management context, time plays a very significant role. Up to date and accurate results obtained in short time can be very helpful. The availability of such results can be significantly improved by utilization of HPC resources and tools. An overview of experimental parallel implementation of the proposed simulation method created as a part of the project Floreon+ is described in this paper. Experimental results of the proposed methods on the actual catchments in the Moravian-Silesian region in the Czech Republic are presented in the paper. Experiments includes rainfall-runoff uncertainty simulations and their influence on the uncertainty of hydrodynamic simulations.

purchase the full-text of this paper (price £20)

go to the previous paper
go to the next paper
return to the table of contents
return to the book description
purchase this book (price £45 +P&P)

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 107 PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, GRID AND CLOUD COMPUTING FOR ENGINEERING Edited by: Paper 10 Uncertainty Modelling in Hydrodynamic Simulations using the Parallel Monte Carlo Method M. Golasowski, Š. Kuchar, M. Podhorányi and D. Vojtek IT4Innovations, VSB - Technical University of Ostrava, Czech Republic doi:10.4203/ccp.107.10 purchase the full-text of this paper Full Bibliographic Reference for this paper , "Uncertainty Modelling in Hydrodynamic Simulations using the Parallel Monte Carlo Method", in , (Editors), "Proceedings of the Fourth International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 10, 2015. doi:10.4203/ccp.107.10 Keywords: hydrodynamic, HEC-RAS, rainfall-runoff, kernel density estimation. Summary This paper proposes a method for modelling and visualization of possible uncertainties in the outputs of hydrodynamic models. The main purpose of hydrodynamic models in the disaster management context is to provide accurate overview of hydrological situation in a given river catchment. In the event of extreme weather conditions, such as unusually heavy rainfall, these models could provide valuable information about imminent flood risk endangering particular area. One of the methods to model the uncertainties in hydrodynamic models is to base them on the uncertainties of the rainfall-runoff (RR) models that provide data for their computation. This paper describes a method for construction of confidence intervals of the RR model outputs based on the uncertainties in their inputs. These confidence intervals can then be used as an input of hydrodynamic simulations which in turn provides several confidence intervals for the resulting water levels and inundation areas. Flood maps generated form these data can significantly improve information value of the hydrodynamic simulations. However, computational requirements of these simulations can be significant since the method requires execution of a large number of hydrodynamic simulations in order to obtain results. In the disaster management context, time plays a very significant role. Up to date and accurate results obtained in short time can be very helpful. The availability of such results can be significantly improved by utilization of HPC resources and tools. An overview of experimental parallel implementation of the proposed simulation method created as a part of the project Floreon+ is described in this paper. Experimental results of the proposed methods on the actual catchments in the Moravian-Silesian region in the Czech Republic are presented in the paper. Experiments includes rainfall-runoff uncertainty simulations and their influence on the uncertainty of hydrodynamic simulations. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £45 +P&P)
Back to top	©Civil-Comp Limited 2023 - terms & conditions