气候变化领域集成服务门户(CCPortal): Evaluation and machine learning improvement of global hydrological model-based flood simulations

CCPortal

DOI	10.1088/1748-9326/ab4d5e
	Evaluation and machine learning improvement of global hydrological model-based flood simulations
	Yang T.; Sun F.; Gentine P.; Liu W.; Wang H.; Yin J.; Du M.; Liu C.
发表日期	2019
ISSN	17489318
卷号	14 期号:11
英文摘要	A warmer climate is expected to accelerate global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the accuracy and improvement of global hydrological models (GHMs)-based flood simulation is insufficient for most applications. Here we compared flood simulations from five GHMs under the Inter-Sectoral Impact Model Intercomparison Project 2a (ISIMIP2a) protocol, against those calculated from 1032 gauging stations in the Global Streamflow Indices and Metadata Archive for the historical period 1971-2010. A machine learning approach, namely the long short-term memory units (LSTM) was adopted to improve the GHMs-based flood simulations within a hybrid physics- machine learning approach (using basin-averaged daily mean air temperature, precipitation, wind speed and the simulated daily discharge from GHMs-CaMa-Flood model chain as the inputs of LSTM, and observed daily discharge as the output value). We found that the GHMs perform reasonably well in terms of amplitude of peak discharge but are relatively poor in terms of their timing. The performance indicated great discrepancy under different climate zones. The large difference in performance between GHMs and observations reflected that those simulations require improvements. The LSTM used in combination with those GHMs was then shown to drastically improve the performance of global flood simulations (especially in terms of amplitude of peak discharge), suggesting that the combination of classical flood simulation and machine learning techniques might be a way forward for more robust and confident flood risk assessment. © 2019 The Author(s). Published by IOP Publishing Ltd.
英文关键词	flood simulation; global hydrological model; long short-term memory; machine learning
语种	英语
scopus关键词	Brain; Climate models; Learning systems; Long short-term memory; Machine learning; Risk assessment; Wind; Flood risk assessments; Flood simulation; Hydrological modeling; Hydrological models; Intense precipitation; Machine learning approaches; Machine learning techniques; Mean air temperatures; Floods; accuracy assessment; amplitude; discharge; gauge; hydrological cycle; hydrological modeling; machine learning; peak acceleration; peak discharge; risk assessment; streamflow
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154757
作者单位	Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Department of Earth and Environmental Engineering, Columbia University, New York, United States; University of Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China; Akesu National Station of Observation and Research for Oasis Agro-ecosystem, Akesu, Xinjiang, China; Earth Institute, Columbia University, New York, United States; State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
推荐引用方式 GB/T 7714	Yang T.,Sun F.,Gentine P.,et al. Evaluation and machine learning improvement of global hydrological model-based flood simulations[J],2019,14(11).
APA	Yang T..,Sun F..,Gentine P..,Liu W..,Wang H..,...&Liu C..(2019).Evaluation and machine learning improvement of global hydrological model-based flood simulations.Environmental Research Letters,14(11).
MLA	Yang T.,et al."Evaluation and machine learning improvement of global hydrological model-based flood simulations".Environmental Research Letters 14.11(2019).