DOI: 10.5194/hess-24-4777-2020
论文题名: Socio-hydrological data assimilation: Analyzing human-flood interactions by model-data integration
作者: Sawada Y. ; Hanazaki R.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期: 10 起始页码: 4777
结束页码: 4791
语种: 英语
Scopus关键词: Clustering algorithms
; Floods
; Risk assessment
; Risk perception
; Uncertainty analysis
; Hydrological data
; Hydrological modeling
; Hydrological models
; Hydrological process
; Observation systems
; Particle Filtering
; Synergistic effect
; Water interactions
; Data integration
; data assimilation
; flood
; hydrological modeling
; nature-society relations
; risk assessment
; simulation
英文摘要: In socio-hydrology, human-water interactions are simulated by mathematical models. Although the integration of these socio-hydrological models and observation data is necessary for improving the understanding of human-water interactions, the methodological development of the model-data integration in socio-hydrology is in its infancy. Here we propose applying sequential data assimilation, which has been widely used in geoscience, to a socio-hydrological model. We developed particle filtering for a widely adopted flood risk model and performed an idealized observation system simulation experiment and a real data experiment to demonstrate the potential of the sequential data assimilation in socio-hydrology. In these experiments, the flood risk model's parameters, the input forcing data, and empirical social data were assumed to be somewhat imperfect. We tested if data assimilation can contribute to accurately reconstructing the historical human-flood interactions by integrating these imperfect models and imperfect and sparsely distributed data. Our results highlight that it is important to sequentially constrain both state variables and parameters when the input forcing is uncertain. Our proposed method can accurately estimate the model's unknown parameters - even if the true model parameter temporally varies. The small amount of empirical data can significantly improve the simulation skill of the flood risk model. Therefore, sequential data assimilation is useful for reconstructing historical socio-hydrological processes by the synergistic effect of models and data. © 2020 Copernicus GmbH. All rights reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162584
Appears in Collections: 气候变化与战略
There are no files associated with this item.
作者单位: Sawada, Y., Institute of Engineering Innovation, School of Engineering, University of Tokyo, Tokyo, Japan; Hanazaki, R., Department of Civil Engineering, School of Engineering, University of Tokyo, Tokyo, Japan
Recommended Citation:
Sawada Y.,Hanazaki R.. Socio-hydrological data assimilation: Analyzing human-flood interactions by model-data integration[J]. Hydrology and Earth System Sciences,2020-01-01,24(10)