气候变化领域集成服务门户(CCPortal): Adaptive clustering: Reducing the computational costs of distributed (hydrological) modelling by exploiting time-variable similarity among model elements

CCPortal

DOI	10.5194/hess-24-4389-2020
	Adaptive clustering: Reducing the computational costs of distributed (hydrological) modelling by exploiting time-variable similarity among model elements
	Ehret U.; Van Pruijssen R.; Bortoli M.; Loritz R.; Azmi E.; Zehe E.
发表日期	2020
ISSN	1027-5606
起始页码	4389
结束页码	4411
卷号	24 期号:9
英文摘要	In this paper we propose adaptive clustering as a new method for reducing the computational efforts of distributed modelling. It consists of identifying similar-acting model elements during runtime, clustering them, running the model for just a few representatives per cluster, and mapping their results to the remaining model elements in the cluster. Key requirements for the application of adaptive clustering are the existence of (i) many model elements with (ii) comparable structural and functional properties and (iii) only weak interaction (e.g. hill slopes, subcatchments, or surface grid elements in hydrological and land surface models). The clustering of model elements must not only consider their time-invariant structural and functional properties but also their current state and forcing, as all these aspects influence their current functioning. Joining model elements into clusters is therefore a continuous task during model execution rather than a one-time exercise that can be done beforehand. Adaptive clustering takes this into account by continuously checking the clustering and re-clustering when necessary. We explain the steps of adaptive clustering and provide a proof of concept at the example of a distributed, conceptual hydrological model fit to the Attert basin in Luxembourg. The clustering is done based on normalised and binned transformations of model element states and fluxes. Analysing a 5-year time series of these transformed states and fluxes revealed that many model elements act very similarly, and the degree of similarity varies strongly with time, indicating the potential for adaptive clustering to save computation time. Compared to a standard, full-resolution model run used as a virtual reality "truth", adaptive clustering indeed reduced computation time by 75%, while modelling quality, expressed as the Nash-Sutcliffe efficiency of subcatchment runoff, declined from 1 to 0.84. Based on this proof-of-concept application, we believe that adaptive clustering is a promising tool for reducing the computation time of distributed models. Being adaptive, it integrates and enhances existing methods of static grouping of model elements, such as lumping or grouped response units (GRUs). It is compatible with existing dynamical methods such as adaptive time stepping or adaptive gridding and, unlike the latter, does not require adjacency of the model elements to be joined. As a welcome side effect, adaptive clustering can be used for system analysis; in our case, analysing the space-time patterns of clustered model elements confirmed that the hydrological functioning of the Attert catchment is mainly controlled by the spatial patterns of geology and precipitation. © 2020 Author(s).
语种	英语
scopus关键词	Earth sciences; Hydrology; Adaptive clustering; Adaptive time stepping; Computational effort; Degree of similarity; Distributed modelling; Functional properties; Hydrological modeling; Land surface models; Catchments; cluster analysis; computer simulation; hydrological modeling; numerical model; Grus
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/159312
作者单位	Ehret, U., Institute of Water Resources and River Basin Management, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Van Pruijssen, R., Institute of Water Resources and River Basin Management, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Bortoli, M., Institute of Water Resources and River Basin Management, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Loritz, R., Institute of Water Resources and River Basin Management, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Azmi, E., Steinbuch Centre for Computing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Zehe, E., Institute of Water Resources and River Basin Management, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
推荐引用方式 GB/T 7714	Ehret U.,Van Pruijssen R.,Bortoli M.,et al. Adaptive clustering: Reducing the computational costs of distributed (hydrological) modelling by exploiting time-variable similarity among model elements[J],2020,24(9).
APA	Ehret U.,Van Pruijssen R.,Bortoli M.,Loritz R.,Azmi E.,&Zehe E..(2020).Adaptive clustering: Reducing the computational costs of distributed (hydrological) modelling by exploiting time-variable similarity among model elements.Hydrology and Earth System Sciences,24(9).
MLA	Ehret U.,et al."Adaptive clustering: Reducing the computational costs of distributed (hydrological) modelling by exploiting time-variable similarity among model elements".Hydrology and Earth System Sciences 24.9(2020).