Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation
Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of tem...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2016/2710457 |
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| author | Xinchi Chen Liping Zhang Christopher James Gippel Lijie Shan Shaodan Chen Wei Yang |
| author_facet | Xinchi Chen Liping Zhang Christopher James Gippel Lijie Shan Shaodan Chen Wei Yang |
| author_sort | Xinchi Chen |
| collection | DOAJ |
| description | Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classified Z-R relationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methods were superior. The corresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model. |
| format | Article |
| id | doaj-art-270f85a723184ab6bf58e132d1a36445 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-270f85a723184ab6bf58e132d1a364452025-02-03T05:53:20ZengWileyAdvances in Meteorology1687-93091687-93172016-01-01201610.1155/2016/27104572710457Uncertainty of Flood Forecasting Based on Radar Rainfall Data AssimilationXinchi Chen0Liping Zhang1Christopher James Gippel2Lijie Shan3Shaodan Chen4Wei Yang5State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaAustralian Rivers Institute, Griffith University, Nathan, QLD 4111, AustraliaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaPrecipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classified Z-R relationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methods were superior. The corresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.http://dx.doi.org/10.1155/2016/2710457 |
| spellingShingle | Xinchi Chen Liping Zhang Christopher James Gippel Lijie Shan Shaodan Chen Wei Yang Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation Advances in Meteorology |
| title | Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation |
| title_full | Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation |
| title_fullStr | Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation |
| title_full_unstemmed | Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation |
| title_short | Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation |
| title_sort | uncertainty of flood forecasting based on radar rainfall data assimilation |
| url | http://dx.doi.org/10.1155/2016/2710457 |
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