气候变化领域集成服务门户(CCPortal): Discharge hydrograph estimation at upstream-ungauged sections by coupling a Bayesian methodology and a 2-D GPU shallow water model

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DOI	10.5194/hess-22-5299-2018
	Discharge hydrograph estimation at upstream-ungauged sections by coupling a Bayesian methodology and a 2-D GPU shallow water model
	Ferrari A.; D'Oria M.; Vacondio R.; Dal Palù A.; Mignosa P.; Giovanna Tanda M.
发表日期	2018
ISSN	1027-5606
起始页码	5299
结束页码	5316
卷号	22 期号:10
英文摘要	This paper presents a novel methodology for estimating the unknown discharge hydrograph at the entrance of a river reach when no information is available. The methodology couples an optimization procedure based on the Bayesian geostatistical approach (BGA) with a forward self-developed 2-D hydraulic model. In order to accurately describe the flow propagation in real rivers characterized by large floodable areas, the forward model solves the 2-D shallow water equations (SWEs) by means of a finite volume explicit shock-capturing algorithm. The two-dimensional SWE code exploits the computational power of graphics processing units (GPUs), achieving a ratio of physical to computational time of up to 1000. With the aim of enhancing the computational efficiency of the inverse estimation, the Bayesian technique is parallelized, developing a procedure based on the Secure Shell (SSH) protocol that allows one to take advantage of remote high-performance computing clusters (including those available on the Cloud) equipped with GPUs. The capability of the methodology is assessed by estimating irregular and synthetic inflow hydrographs in real river reaches, also taking into account the presence of downstream corrupted observations. Finally, the procedure is applied to reconstruct a real flood wave in a river reach located in northern Italy. © Author(s) 2018.
语种	英语
scopus关键词	Computational efficiency; Computer graphics; Equations of motion; Floods; Graphics processing unit; Hydraulic models; Inverse problems; Program processors; Bayesian geostatistical approaches; Bayesian methodology; Bayesian techniques; Computational power; Discharge hydrograph; High-performance computing clusters; Optimization procedures; Shallow water equation (SWEs); Rivers
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159887
作者单位	Ferrari, A., Department of Engineering and Architecture, University of Parma, Parma, Italy; D'Oria, M., Department of Engineering and Architecture, University of Parma, Parma, Italy; Vacondio, R., Department of Engineering and Architecture, University of Parma, Parma, Italy; Dal Palù, A., Department of Mathematical, Physical and Computer Sciences, University of Parma, Parma, Italy; Mignosa, P., Department of Engineering and Architecture, University of Parma, Parma, Italy; Giovanna Tanda, M., Department of Engineering and Architecture, University of Parma, Parma, Italy
推荐引用方式 GB/T 7714	Ferrari A.,D'Oria M.,Vacondio R.,et al. Discharge hydrograph estimation at upstream-ungauged sections by coupling a Bayesian methodology and a 2-D GPU shallow water model[J],2018,22(10).
APA	Ferrari A.,D'Oria M.,Vacondio R.,Dal Palù A.,Mignosa P.,&Giovanna Tanda M..(2018).Discharge hydrograph estimation at upstream-ungauged sections by coupling a Bayesian methodology and a 2-D GPU shallow water model.Hydrology and Earth System Sciences,22(10).
MLA	Ferrari A.,et al."Discharge hydrograph estimation at upstream-ungauged sections by coupling a Bayesian methodology and a 2-D GPU shallow water model".Hydrology and Earth System Sciences 22.10(2018).