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Evaluation of the number of events' influence on model performance and uncertainty in urban data-scarce areas based on behavioral parameter ranking method

文献类型：期刊论文

作者Wu, Yingying3; She, Dunxian2,3,4; Xia, Jun3,4; Zhang, Yongyong1; Zou, Lei1
刊名JOURNAL OF HYDROLOGY
出版日期2024-06-01
卷号636页码:17
关键词GLUE SWMM Model uncertainty Urban flood Flood events
ISSN号0022-1694
DOI10.1016/j.jhydrol.2024.131298
英文摘要Rainfall-runoff data in drainage systems in urban areas is the essential input variable for urban hydrological modeling. Obtaining high quality and sufficient size of urban flood events is always difficult due to the inconvenient underground observation and the untimely capture of the rapid rising and recession stages of urban runoff. It largely constrains the efficiency of model calibration and brings large uncertainty in urban rainfallrunoff simulation. Therefore, the evaluation of the number of events' influence on model performance and uncertainty is of great significance, which can provide useful information to help the decision makers select sufficient useful observation data for model calibration with relatively fewer events. In this study, we constructed a comprehensive method of behavioral parameter ranking of multi-events (BPROME) based on coupling the Generalized Likelihood Uncertainty Estimation (GLUE) algorithm and the Storm Water Management Model (SWMM). The results in the two small demonstration areas (named Case #A and Case #B) in Shenzhen city of China proved the good performance of the BPROME in uncertainty assessment in urban areas. We found the model uncertainty (average bandwidth (B) and average deviation amplitude (D)) and model performance (containing Ratio (CR) and the maximum value of behavioral samples' NSE (NSEmax)) became stable at a certain number of events in both two case areas. The B and D decrease and the CR and NSEmax increase as the number of event increases. In particular, the model performance and uncertainty reach their appropriate state concerning the limited observations at a certain range of numbers of events (both three to five in our two case areas). Our results demonstrate the potential influence of the numbers of events for the urban rainfall-runoff modeling calibration which can balance the model efficiency and data collection cost (the number of input rainfall-runoff data). The findings could help decision-makers seek a trade-off between data investment and acceptable model performance.
WOS关键词FORMAL BAYESIAN METHOD ; HYDROLOGICAL MODELS ; GLUE METHOD ; SWAT MODEL ; CALIBRATION ; IMPACT ; EQUIFINALITY ; RAINFALL ; FUTURE
资助项目Ministry of Water Resources of the People's Republic of China[SKS-2022014] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23040304]
WOS研究方向Engineering ; Geology ; Water Resources
语种英语
WOS记录号WOS:001243010900001
出版者ELSEVIER
资助机构Ministry of Water Resources of the People's Republic of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/206784]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者She, Dunxian
作者单位1.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn Sc, Wuhan 430072, Peoples R China
3.Wuhan Univ, State Key Lab Water Resources Engn & Management, Wuhan 430072, Peoples R China
4.Wuhan Univ, Hubei Key Lab Water Syst Sci Sponge City Construct, Wuhan 430072, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Yingying,She, Dunxian,Xia, Jun,et al. Evaluation of the number of events' influence on model performance and uncertainty in urban data-scarce areas based on behavioral parameter ranking method[J]. JOURNAL OF HYDROLOGY,2024,636:17.
APA Wu, Yingying,She, Dunxian,Xia, Jun,Zhang, Yongyong,&Zou, Lei.(2024).Evaluation of the number of events' influence on model performance and uncertainty in urban data-scarce areas based on behavioral parameter ranking method.JOURNAL OF HYDROLOGY,636,17.
MLA Wu, Yingying,et al."Evaluation of the number of events' influence on model performance and uncertainty in urban data-scarce areas based on behavioral parameter ranking method".JOURNAL OF HYDROLOGY 636(2024):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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