Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China

Reliable real-time flood forecasting is a challenging prerequisite for successful flood protection. This study developed a flood routing model combined with a particle filter-based assimilation model and a one-dimensional hydrodynamic model. This model was applied to an indoor micro-model, using the...
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Autor*in:	Minghong Chen [verfasserIn] Juanjuan Pang [verfasserIn] Pengxiang Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	flood forecasting data assimilation particle filter algorithm micro-model Lower Yellow River

Übergeordnetes Werk:	In: Water - MDPI AG, 2010, 10(2018), 11, p 1612
Übergeordnetes Werk:	volume:10 ; year:2018 ; number:11, p 1612

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/w10111612

Katalog-ID:	DOAJ011933291

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520			\|a Reliable real-time flood forecasting is a challenging prerequisite for successful flood protection. This study developed a flood routing model combined with a particle filter-based assimilation model and a one-dimensional hydrodynamic model. This model was applied to an indoor micro-model, using the Lower Yellow River (LYR) as prototype. Real-time observations of the water level from the micro-model were used for data assimilation. The results show that, compared to the traditional hydrodynamic model, the assimilation model could effectively update water level, flow discharge, and roughness coefficient in real time, thus yielding improved results. The mean water levels of the particle posterior distribution are closer to the observed values than before assimilation, even when water levels change greatly. In addition, the calculation results for different lead times indicate that the root mean square error of the forecasting water level gradually increases with increasing lead time. This is because the roughness value changes greatly in response to unsteady water flow, and the incurring error accumulates with the predicted period. The results show that the assimilation model can simulate water level changes in the micro-model and provide both research method and technical support for real flood forecasting in the LYR.
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allfields_unstemmed	10.3390/w10111612 doi (DE-627)DOAJ011933291 (DE-599)DOAJ08f85c77fa4f4bd7aa92125070989d35 DE-627 ger DE-627 rakwb eng TC1-978 TD201-500 Minghong Chen verfasserin aut Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reliable real-time flood forecasting is a challenging prerequisite for successful flood protection. This study developed a flood routing model combined with a particle filter-based assimilation model and a one-dimensional hydrodynamic model. This model was applied to an indoor micro-model, using the Lower Yellow River (LYR) as prototype. Real-time observations of the water level from the micro-model were used for data assimilation. The results show that, compared to the traditional hydrodynamic model, the assimilation model could effectively update water level, flow discharge, and roughness coefficient in real time, thus yielding improved results. The mean water levels of the particle posterior distribution are closer to the observed values than before assimilation, even when water levels change greatly. In addition, the calculation results for different lead times indicate that the root mean square error of the forecasting water level gradually increases with increasing lead time. This is because the roughness value changes greatly in response to unsteady water flow, and the incurring error accumulates with the predicted period. The results show that the assimilation model can simulate water level changes in the micro-model and provide both research method and technical support for real flood forecasting in the LYR. flood forecasting data assimilation particle filter algorithm micro-model Lower Yellow River Hydraulic engineering Water supply for domestic and industrial purposes Juanjuan Pang verfasserin aut Pengxiang Wu verfasserin aut In Water MDPI AG, 2010 10(2018), 11, p 1612 (DE-627)611729008 (DE-600)2521238-2 20734441 nnns volume:10 year:2018 number:11, p 1612 https://doi.org/10.3390/w10111612 kostenfrei https://doaj.org/article/08f85c77fa4f4bd7aa92125070989d35 kostenfrei https://www.mdpi.com/2073-4441/10/11/1612 kostenfrei https://doaj.org/toc/2073-4441 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 11, p 1612
allfieldsGer	10.3390/w10111612 doi (DE-627)DOAJ011933291 (DE-599)DOAJ08f85c77fa4f4bd7aa92125070989d35 DE-627 ger DE-627 rakwb eng TC1-978 TD201-500 Minghong Chen verfasserin aut Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reliable real-time flood forecasting is a challenging prerequisite for successful flood protection. This study developed a flood routing model combined with a particle filter-based assimilation model and a one-dimensional hydrodynamic model. This model was applied to an indoor micro-model, using the Lower Yellow River (LYR) as prototype. Real-time observations of the water level from the micro-model were used for data assimilation. The results show that, compared to the traditional hydrodynamic model, the assimilation model could effectively update water level, flow discharge, and roughness coefficient in real time, thus yielding improved results. The mean water levels of the particle posterior distribution are closer to the observed values than before assimilation, even when water levels change greatly. In addition, the calculation results for different lead times indicate that the root mean square error of the forecasting water level gradually increases with increasing lead time. This is because the roughness value changes greatly in response to unsteady water flow, and the incurring error accumulates with the predicted period. The results show that the assimilation model can simulate water level changes in the micro-model and provide both research method and technical support for real flood forecasting in the LYR. flood forecasting data assimilation particle filter algorithm micro-model Lower Yellow River Hydraulic engineering Water supply for domestic and industrial purposes Juanjuan Pang verfasserin aut Pengxiang Wu verfasserin aut In Water MDPI AG, 2010 10(2018), 11, p 1612 (DE-627)611729008 (DE-600)2521238-2 20734441 nnns volume:10 year:2018 number:11, p 1612 https://doi.org/10.3390/w10111612 kostenfrei https://doaj.org/article/08f85c77fa4f4bd7aa92125070989d35 kostenfrei https://www.mdpi.com/2073-4441/10/11/1612 kostenfrei https://doaj.org/toc/2073-4441 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 11, p 1612
allfieldsSound	10.3390/w10111612 doi (DE-627)DOAJ011933291 (DE-599)DOAJ08f85c77fa4f4bd7aa92125070989d35 DE-627 ger DE-627 rakwb eng TC1-978 TD201-500 Minghong Chen verfasserin aut Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reliable real-time flood forecasting is a challenging prerequisite for successful flood protection. This study developed a flood routing model combined with a particle filter-based assimilation model and a one-dimensional hydrodynamic model. This model was applied to an indoor micro-model, using the Lower Yellow River (LYR) as prototype. Real-time observations of the water level from the micro-model were used for data assimilation. The results show that, compared to the traditional hydrodynamic model, the assimilation model could effectively update water level, flow discharge, and roughness coefficient in real time, thus yielding improved results. The mean water levels of the particle posterior distribution are closer to the observed values than before assimilation, even when water levels change greatly. In addition, the calculation results for different lead times indicate that the root mean square error of the forecasting water level gradually increases with increasing lead time. This is because the roughness value changes greatly in response to unsteady water flow, and the incurring error accumulates with the predicted period. The results show that the assimilation model can simulate water level changes in the micro-model and provide both research method and technical support for real flood forecasting in the LYR. flood forecasting data assimilation particle filter algorithm micro-model Lower Yellow River Hydraulic engineering Water supply for domestic and industrial purposes Juanjuan Pang verfasserin aut Pengxiang Wu verfasserin aut In Water MDPI AG, 2010 10(2018), 11, p 1612 (DE-627)611729008 (DE-600)2521238-2 20734441 nnns volume:10 year:2018 number:11, p 1612 https://doi.org/10.3390/w10111612 kostenfrei https://doaj.org/article/08f85c77fa4f4bd7aa92125070989d35 kostenfrei https://www.mdpi.com/2073-4441/10/11/1612 kostenfrei https://doaj.org/toc/2073-4441 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 11, p 1612
language	English
source	In Water 10(2018), 11, p 1612 volume:10 year:2018 number:11, p 1612
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institution	findex.gbv.de
topic_facet	flood forecasting data assimilation particle filter algorithm micro-model Lower Yellow River Hydraulic engineering Water supply for domestic and industrial purposes
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container_title	Water
authorswithroles_txt_mv	Minghong Chen @@aut@@ Juanjuan Pang @@aut@@ Pengxiang Wu @@aut@@
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callnumber-first	T - Technology
author	Minghong Chen
spellingShingle	Minghong Chen misc TC1-978 misc TD201-500 misc flood forecasting misc data assimilation misc particle filter algorithm misc micro-model misc Lower Yellow River misc Hydraulic engineering misc Water supply for domestic and industrial purposes Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China
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topic_title	TC1-978 TD201-500 Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China flood forecasting data assimilation particle filter algorithm micro-model Lower Yellow River
topic	misc TC1-978 misc TD201-500 misc flood forecasting misc data assimilation misc particle filter algorithm misc micro-model misc Lower Yellow River misc Hydraulic engineering misc Water supply for domestic and industrial purposes
topic_unstemmed	misc TC1-978 misc TD201-500 misc flood forecasting misc data assimilation misc particle filter algorithm misc micro-model misc Lower Yellow River misc Hydraulic engineering misc Water supply for domestic and industrial purposes
topic_browse	misc TC1-978 misc TD201-500 misc flood forecasting misc data assimilation misc particle filter algorithm misc micro-model misc Lower Yellow River misc Hydraulic engineering misc Water supply for domestic and industrial purposes
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title	Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China
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title_full	Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China
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author_browse	Minghong Chen Juanjuan Pang Pengxiang Wu
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title_sort	flood routing model with particle filter-based data assimilation for flash flood forecasting in the micro-model of lower yellow river, china
callnumber	TC1-978
title_auth	Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China
abstract	Reliable real-time flood forecasting is a challenging prerequisite for successful flood protection. This study developed a flood routing model combined with a particle filter-based assimilation model and a one-dimensional hydrodynamic model. This model was applied to an indoor micro-model, using the Lower Yellow River (LYR) as prototype. Real-time observations of the water level from the micro-model were used for data assimilation. The results show that, compared to the traditional hydrodynamic model, the assimilation model could effectively update water level, flow discharge, and roughness coefficient in real time, thus yielding improved results. The mean water levels of the particle posterior distribution are closer to the observed values than before assimilation, even when water levels change greatly. In addition, the calculation results for different lead times indicate that the root mean square error of the forecasting water level gradually increases with increasing lead time. This is because the roughness value changes greatly in response to unsteady water flow, and the incurring error accumulates with the predicted period. The results show that the assimilation model can simulate water level changes in the micro-model and provide both research method and technical support for real flood forecasting in the LYR.
abstractGer	Reliable real-time flood forecasting is a challenging prerequisite for successful flood protection. This study developed a flood routing model combined with a particle filter-based assimilation model and a one-dimensional hydrodynamic model. This model was applied to an indoor micro-model, using the Lower Yellow River (LYR) as prototype. Real-time observations of the water level from the micro-model were used for data assimilation. The results show that, compared to the traditional hydrodynamic model, the assimilation model could effectively update water level, flow discharge, and roughness coefficient in real time, thus yielding improved results. The mean water levels of the particle posterior distribution are closer to the observed values than before assimilation, even when water levels change greatly. In addition, the calculation results for different lead times indicate that the root mean square error of the forecasting water level gradually increases with increasing lead time. This is because the roughness value changes greatly in response to unsteady water flow, and the incurring error accumulates with the predicted period. The results show that the assimilation model can simulate water level changes in the micro-model and provide both research method and technical support for real flood forecasting in the LYR.
abstract_unstemmed	Reliable real-time flood forecasting is a challenging prerequisite for successful flood protection. This study developed a flood routing model combined with a particle filter-based assimilation model and a one-dimensional hydrodynamic model. This model was applied to an indoor micro-model, using the Lower Yellow River (LYR) as prototype. Real-time observations of the water level from the micro-model were used for data assimilation. The results show that, compared to the traditional hydrodynamic model, the assimilation model could effectively update water level, flow discharge, and roughness coefficient in real time, thus yielding improved results. The mean water levels of the particle posterior distribution are closer to the observed values than before assimilation, even when water levels change greatly. In addition, the calculation results for different lead times indicate that the root mean square error of the forecasting water level gradually increases with increasing lead time. This is because the roughness value changes greatly in response to unsteady water flow, and the incurring error accumulates with the predicted period. The results show that the assimilation model can simulate water level changes in the micro-model and provide both research method and technical support for real flood forecasting in the LYR.
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title_short	Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China
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Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China

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