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Statistical analysis of error propagation from radar rainfall to hydrological models

D Zhu, D. Z Peng, I. D Cluckie, Ian Cluckie

Hydrology and Earth System Sciences, Volume: 17, Issue: 4, Pages: 1445 - 1453

Swansea University Author: Ian Cluckie

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DOI (Published version): 10.5194/hess-17-1445-2013

Abstract

This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation un- certainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed t...

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Published in: Hydrology and Earth System Sciences
ISSN: 1607-7938
Published: 2013
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URI: https://cronfa.swan.ac.uk/Record/cronfa15570
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2013-11-18T17:30:31.0567132</datestamp><bib-version>v2</bib-version><id>15570</id><entry>2013-08-20</entry><title>Statistical analysis of error propagation from radar rainfall to hydrological models</title><swanseaauthors><author><sid>d801af52a3cfb625308bd4301583064e</sid><firstname>Ian</firstname><surname>Cluckie</surname><name>Ian Cluckie</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2013-08-20</date><deptcode>FGSEN</deptcode><abstract>This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation un- certainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realisations of possible combi- nations of normalised errors and normalised bias to reflect the identified radar error and temporal dependence. These realisations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semi- distributed, lumped model TOPMODEL and the unit hydro- graph model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km2 ) in South-East England. The results show that the normalised bias of the radar rainfall estimates was enhanced in the sim- ulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This prelimi- nary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.</abstract><type>Journal Article</type><journal>Hydrology and Earth System Sciences</journal><volume>17</volume><journalNumber>4</journalNumber><paginationStart>1445</paginationStart><paginationEnd>1453</paginationEnd><publisher/><issnPrint>1607-7938</issnPrint><issnElectronic/><keywords/><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2013</publishedYear><publishedDate>2013-12-31</publishedDate><doi>10.5194/hess-17-1445-2013</doi><url/><notes>The work was carried out in association with two major research grants. Initial support was from the EPSRC &#xA3;20M+ Flood Risk Management Research Consortium (FRMRC) that was chaired by Cluckie. Additional funding came from the early phase of the NERC EPIRUS research project that formed part of the FREE program. This work focused on the development of new types of distributed hydrological model that could generate ensembles describing the uncertainty of model output (flow or level) when subjected to severe storms. The aim was to understand the propagation of uncertainty in complex fully distributed modeling systems. IF 3.15.</notes><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2013-11-18T17:30:31.0567132</lastEdited><Created>2013-08-20T15:03:17.7921389</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>D</firstname><surname>Zhu</surname><order>1</order></author><author><firstname>D. Z</firstname><surname>Peng</surname><order>2</order></author><author><firstname>I. D</firstname><surname>Cluckie</surname><order>3</order></author><author><firstname>Ian</firstname><surname>Cluckie</surname><order>4</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2013-11-18T17:30:31.0567132 v2 15570 2013-08-20 Statistical analysis of error propagation from radar rainfall to hydrological models d801af52a3cfb625308bd4301583064e Ian Cluckie Ian Cluckie true false 2013-08-20 FGSEN This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation un- certainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realisations of possible combi- nations of normalised errors and normalised bias to reflect the identified radar error and temporal dependence. These realisations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semi- distributed, lumped model TOPMODEL and the unit hydro- graph model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km2 ) in South-East England. The results show that the normalised bias of the radar rainfall estimates was enhanced in the sim- ulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This prelimi- nary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall. Journal Article Hydrology and Earth System Sciences 17 4 1445 1453 1607-7938 31 12 2013 2013-12-31 10.5194/hess-17-1445-2013 The work was carried out in association with two major research grants. Initial support was from the EPSRC £20M+ Flood Risk Management Research Consortium (FRMRC) that was chaired by Cluckie. Additional funding came from the early phase of the NERC EPIRUS research project that formed part of the FREE program. This work focused on the development of new types of distributed hydrological model that could generate ensembles describing the uncertainty of model output (flow or level) when subjected to severe storms. The aim was to understand the propagation of uncertainty in complex fully distributed modeling systems. IF 3.15. COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2013-11-18T17:30:31.0567132 2013-08-20T15:03:17.7921389 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised D Zhu 1 D. Z Peng 2 I. D Cluckie 3 Ian Cluckie 4
title Statistical analysis of error propagation from radar rainfall to hydrological models
spellingShingle Statistical analysis of error propagation from radar rainfall to hydrological models
Ian Cluckie
title_short Statistical analysis of error propagation from radar rainfall to hydrological models
title_full Statistical analysis of error propagation from radar rainfall to hydrological models
title_fullStr Statistical analysis of error propagation from radar rainfall to hydrological models
title_full_unstemmed Statistical analysis of error propagation from radar rainfall to hydrological models
title_sort Statistical analysis of error propagation from radar rainfall to hydrological models
author_id_str_mv d801af52a3cfb625308bd4301583064e
author_id_fullname_str_mv d801af52a3cfb625308bd4301583064e_***_Ian Cluckie
author Ian Cluckie
author2 D Zhu
D. Z Peng
I. D Cluckie
Ian Cluckie
format Journal article
container_title Hydrology and Earth System Sciences
container_volume 17
container_issue 4
container_start_page 1445
publishDate 2013
institution Swansea University
issn 1607-7938
doi_str_mv 10.5194/hess-17-1445-2013
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 0
active_str 0
description This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation un- certainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realisations of possible combi- nations of normalised errors and normalised bias to reflect the identified radar error and temporal dependence. These realisations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semi- distributed, lumped model TOPMODEL and the unit hydro- graph model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km2 ) in South-East England. The results show that the normalised bias of the radar rainfall estimates was enhanced in the sim- ulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This prelimi- nary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.
published_date 2013-12-31T03:17:43Z
_version_ 1763750406679166976
score 11.013731

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