Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization
文献类型：期刊论文
| 作者 | Ragettli, S.1; Zhou, J.2; Wang, H.1; Liu, C.3,4; Guo, L.3,4 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2017-12-01 |
| 卷号 | 555页码:330-346 |
| 关键词 | Rainfall runoff modeling Parameter regionalization Decision tree learning Ungauged catchments Flash floods China |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2017.10.031 |
| 通讯作者 | Ragettli, S.(ragettli@hydrosolutions.ch) |
| 英文摘要 | Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. One of the main challenges of setting up such a system is finding appropriate model parameter values for ungauged catchments. Previous studies have shown that the transfer of parameter sets from hydrologically similar gauged catchments is one of the best performing regionalization methods. However, a remaining key issue is the identification of suitable descriptors of similarity. In this study, we use decision tree learning to explore parameter set transferability in the full space of catchment descriptors. For this purpose, a semi-distributed rainfall-runoff model is set up for 35 catchments in ten Chinese provinces. Hourly runoff data from in total 858 storm events are used to calibrate the model and to evaluate the performance of parameter set transfers between catchments. We then present a novel technique that uses the splitting rules of classification and regression trees (CART) for finding suitable donor catchments for ungauged target catchments. The ability of the model to detect flood events in assumed ungauged catchments is evaluated in series of leave-one-out tests. We show that CART analysis increases the probability of detection of 10-year flood events in comparison to a conventional measure of physiographic-climatic similarity by up to 20%. Decision tree learning can outperform other regionalization approaches because it generates rules that optimally consider spatial proximity and physical similarity. Spatial proximity can be used as a selection criteria but is skipped in the case where no similar gauged catchments are in the vicinity. We conclude that the CART regionalization concept is particularly suitable for implementation in sparsely gauged and topographically complex environments where a proximity-based regionalization concept is not applicable. (C) 2017 Elsevier B.V. All rights reserved. |
| 收录类别 | SCI |
| WOS关键词 | HYDROLOGIC SIMILARITY ; LOGISTIC-REGRESSION ; GLOBAL OPTIMIZATION ; RADAR RAINFALL ; RUNOFF MODELS ; PART II ; CLASSIFICATION ; STREAMFLOW ; SIMULATION ; METHODOLOGY |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| WOS类目 | Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000418107600027 |
| 出版者 | ELSEVIER SCIENCE BV |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2558035 |
| 专题 | 寒区旱区环境与工程研究所 |
| 通讯作者 | Ragettli, S. |
| 作者单位 | 1.Hydrosolutions Ltd, Zurich, Switzerland 2.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou, Gansu, Peoples R China 3.China Inst Water Resources & Hydropower Res, Beijing, Peoples R China 4.Minist Water Resources, Res Ctr Flood & Drought Disaster Reduct, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ragettli, S.,Zhou, J.,Wang, H.,et al. Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization[J]. JOURNAL OF HYDROLOGY,2017,555:330-346. |
| APA | Ragettli, S.,Zhou, J.,Wang, H.,Liu, C.,&Guo, L..(2017).Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization.JOURNAL OF HYDROLOGY,555,330-346. |
| MLA | Ragettli, S.,et al."Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization".JOURNAL OF HYDROLOGY 555(2017):330-346. |
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来源：寒区旱区环境与工程研究所
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