Hydrological Process Simulation Based on Statistical Simulation and Data Assimilation
In order to solve the load capacity calculation and performance optimization by modelparameter calibration, this paper realizes comprehensive optimization of hydrological simulationby taking the Shahe River Basin in North China as the research object and combining thedistributed time-variable gain h...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Pearl River
2020-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2020.04.008 |
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| _version_ | 1841535419656699904 |
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| author | YIN Jian QIU Yuanhong OU Zhaofan |
| author_facet | YIN Jian QIU Yuanhong OU Zhaofan |
| author_sort | YIN Jian |
| collection | DOAJ |
| description | In order to solve the load capacity calculation and performance optimization by modelparameter calibration, this paper realizes comprehensive optimization of hydrological simulationby taking the Shahe River Basin in North China as the research object and combining thedistributed time-variable gain hydrological model, the response surface provided by PSUADEplatform for parameter optimization, and the ensemble Kalman filtering for error correction & dataassimilation. achieves parameter calibration through using the observation of the outlet flow ofthe basin as the verification data, taking the Nash-Sutcliffe efficiency coefficient, waterbalance coefficient and their comprehensive functions as the evaluation targets, applying thestatistical simulation proxy model of the hydrological model generated by response surface method,performing a sensitivity analysis on the model parameters and reducing the value range, andadopting the global optimization method, and effectively correct the simulation of the runoffprocess in the river basin based on the remote sensing evapotranspiration observation, the ensemble Kalman filtering algorithm and the data assimilation of the hydrological model. Theresults show that the calculation cost of the parameter calibration is reduced with thestatistical calculation method; the simulation trajectory of the model is adjusted with the datasimulation; the water balance coefficient, Nash-Sutcliffe efficiency coefficient and comprehensiveevaluation index of the verification period are 1.102, 0.798, and 0.152, respectively; the effectof overall runoff simulation is good, and the evapotranspiration simulation results are alsoimproved. |
| format | Article |
| id | doaj-art-15a139335e9e4436ac2d00b1ebd82d7b |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-15a139335e9e4436ac2d00b1ebd82d7b2025-01-15T02:32:30ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352020-01-014147653367Hydrological Process Simulation Based on Statistical Simulation and Data AssimilationYIN JianQIU YuanhongOU ZhaofanIn order to solve the load capacity calculation and performance optimization by modelparameter calibration, this paper realizes comprehensive optimization of hydrological simulationby taking the Shahe River Basin in North China as the research object and combining thedistributed time-variable gain hydrological model, the response surface provided by PSUADEplatform for parameter optimization, and the ensemble Kalman filtering for error correction & dataassimilation. achieves parameter calibration through using the observation of the outlet flow ofthe basin as the verification data, taking the Nash-Sutcliffe efficiency coefficient, waterbalance coefficient and their comprehensive functions as the evaluation targets, applying thestatistical simulation proxy model of the hydrological model generated by response surface method,performing a sensitivity analysis on the model parameters and reducing the value range, andadopting the global optimization method, and effectively correct the simulation of the runoffprocess in the river basin based on the remote sensing evapotranspiration observation, the ensemble Kalman filtering algorithm and the data assimilation of the hydrological model. Theresults show that the calculation cost of the parameter calibration is reduced with thestatistical calculation method; the simulation trajectory of the model is adjusted with the datasimulation; the water balance coefficient, Nash-Sutcliffe efficiency coefficient and comprehensiveevaluation index of the verification period are 1.102, 0.798, and 0.152, respectively; the effectof overall runoff simulation is good, and the evapotranspiration simulation results are alsoimproved.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2020.04.008hydrological processsimulationremote sensingbasin |
| spellingShingle | YIN Jian QIU Yuanhong OU Zhaofan Hydrological Process Simulation Based on Statistical Simulation and Data Assimilation Renmin Zhujiang hydrological process simulation remote sensing basin |
| title | Hydrological Process Simulation Based on Statistical Simulation and Data Assimilation |
| title_full | Hydrological Process Simulation Based on Statistical Simulation and Data Assimilation |
| title_fullStr | Hydrological Process Simulation Based on Statistical Simulation and Data Assimilation |
| title_full_unstemmed | Hydrological Process Simulation Based on Statistical Simulation and Data Assimilation |
| title_short | Hydrological Process Simulation Based on Statistical Simulation and Data Assimilation |
| title_sort | hydrological process simulation based on statistical simulation and data assimilation |
| topic | hydrological process simulation remote sensing basin |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2020.04.008 |
| work_keys_str_mv | AT yinjian hydrologicalprocesssimulationbasedonstatisticalsimulationanddataassimilation AT qiuyuanhong hydrologicalprocesssimulationbasedonstatisticalsimulationanddataassimilation AT ouzhaofan hydrologicalprocesssimulationbasedonstatisticalsimulationanddataassimilation |