DOI: 10.5194/hess-21-839-2017
Scopus记录号: 2-s2.0-85012922076
论文题名: Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?
作者: Mazzoleni M ; , Verlaan M ; , Alfonso L ; , Monego M ; , Norbiato D ; , Ferri M ; , Solomatine D ; P
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 2 起始页码: 839
结束页码: 861
语种: 英语
Scopus关键词: Flood control
; Forecasting
; Hydrology
; Stream flow
; Water levels
; Water resources
; Hydrological data
; Hydrological modelling
; Hydrological models
; Hydrological variables
; Monitoring stations
; Participatory process
; Technological advances
; Water resources management
; Floods
; accuracy assessment
; data assimilation
; flood
; flood forecasting
; hydrological modeling
; numerical model
; participatory approach
; resource management
; sensor
; streamflow
; water resource
英文摘要: Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate these observations into mathematical water models have also been developed. Besides, in recent years, the continued technological advances, in combination with the growing inclusion of citizens in participatory processes related to water resources management, have encouraged the increase of citizen science projects around the globe. In turn, this has stimulated the spread of low-cost sensors to allow citizens to participate in the collection of hydrological data in a more distributed way than the classic static physical sensors do. However, two main disadvantages of such crowdsourced data are the irregular availability and variable accuracy from sensor to sensor, which makes them challenging to use in hydrological modelling. This study aims to demonstrate that streamflow data, derived from crowdsourced water level observations, can improve flood prediction if integrated in hydrological models. Two different hydrological models, applied to four case studies, are considered. Realistic (albeit synthetic) time series are used to represent crowdsourced data in all case studies. In this study, it is found that the data accuracies have much more influence on the model results than the irregular frequencies of data availability at which the streamflow data are assimilated. This study demonstrates that data collected by citizens, characterized by being asynchronous and inaccurate, can still complement traditional networks formed by few accurate, static sensors and improve the accuracy of flood forecasts. © Author(s) 2017. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79261
Appears in Collections: 气候变化事实与影响
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作者单位: UNESCO-IHE Institute for Water Education, Hydroinformatics Group, Delft, Netherlands; Deltares, Delft, Netherlands; Alto Adriatico Water Authority, Venice, Italy; Delft University of Technology, Water Resources Section, Delft, Netherlands
Recommended Citation:
Mazzoleni M,, Verlaan M,, Alfonso L,et al. Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?[J]. Hydrology and Earth System Sciences,2017-01-01,21(2)