兰州大学机构库
Enhancing Flood Simulation in Data-Limited Glacial River Basins through Hybrid Modeling and Multi-Source Remote Sensing Data
Ren, Weiwei1; Li, Xin1; Zheng, Donghai1; Zeng, Ruijie2; Su, Jianbin1; Mu, Tinghua3; Wang, Yingzheng4
2023-09
Source PublicationRemote Sensing   Impact Factor & Quartile
ISSN2072-4292
Volume15Issue:18
page numbers21
AbstractDue to the scarcity of observational data and the intricate precipitation-runoff relationship, individually applying physically based hydrological models and machine learning (ML) techniques presents challenges in accurately predicting floods within data-scarce glacial river basins. To address this challenge, this study introduces an innovative hybrid model that synergistically harnesses the strengths of multi-source remote sensing data, a physically based hydrological model (i.e., Spatial Processes in Hydrology (SPHY)), and ML techniques. This novel approach employs MODIS snow cover data and remote sensing-derived glacier mass balance data to calibrate the SPHY model. The SPHY model primarily generates baseflow, rain runoff, snowmelt runoff, and glacier melt runoff. These outputs are then utilized as extra inputs for the ML models, which consist of Random Forest (RF), Gradient Boosting (GDBT), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), Support Vector Machine (SVM) and Transformer (TF). These ML models reconstruct the intricate relationship between inputs and streamflow. The performance of these six hybrid models and SPHY model is comprehensively explored in the Manas River basin in Central Asia. The findings underscore that the SPHY-RF model performs better in simulating and predicting daily streamflow and flood events than the SPHY model and the other five hybrid models. Compared to the SPHY model, SPHY-RF significantly reduces RMSE (55.6%) and PBIAS (62.5%) for streamflow, as well as reduces RMSE (65.8%) and PBIAS (73.51%) for floods. By utilizing bootstrap sampling, the 95% uncertainty interval for SPHY-RF is established, effectively covering 87.65% of flood events. Significantly, the SPHY-RF model substantially improves the simulation of streamflow and flood events that the SPHY model struggles to capture, indicating its potential to enhance the accuracy of flood prediction within data-scarce glacial river basins. This study offers a framework for robust flood simulation and forecasting within glacial river basins, offering opportunities to explore extreme hydrological events in a warming climate.
Keywordflood data-scarce glacial river basins hybrid modeling multi-source SPHY model
PublisherMDPI
DOI10.3390/rs15184527
Indexed BySCIE
Language英语
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:001072272900001
Original Document TypeArticle
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/568371
Collection兰州大学
Corresponding AuthorSu, Jianbin
Affiliation
1.Chinese Acad Sci, Inst Tibetan Plateau Res, Natl Tibetan Plateau Data Ctr TPDC, State Key Lab Tibetan Plateau Earth Syst Sci Envir, Beijing 100101, Peoples R China;
2.Arizona State Univ, Sch Sustainable Engn & Built Environm, Tempe, AZ 85281 USA;
3.China Univ Geosci, Sch Environm Studies, Wuhan 430074, Peoples R China;
4.Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou 730000, Peoples R China
Recommended Citation
GB/T 7714
Ren, Weiwei,Li, Xin,Zheng, Donghai,et al. Enhancing Flood Simulation in Data-Limited Glacial River Basins through Hybrid Modeling and Multi-Source Remote Sensing Data[J]. Remote Sensing,2023,15(18).
APA Ren, Weiwei.,Li, Xin.,Zheng, Donghai.,Zeng, Ruijie.,Su, Jianbin.,...&Wang, Yingzheng.(2023).Enhancing Flood Simulation in Data-Limited Glacial River Basins through Hybrid Modeling and Multi-Source Remote Sensing Data.Remote Sensing,15(18).
MLA Ren, Weiwei,et al."Enhancing Flood Simulation in Data-Limited Glacial River Basins through Hybrid Modeling and Multi-Source Remote Sensing Data".Remote Sensing 15.18(2023).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[Ren, Weiwei]'s Articles
[Li, Xin]'s Articles
[Zheng, Donghai]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ren, Weiwei]'s Articles
[Li, Xin]'s Articles
[Zheng, Donghai]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ren, Weiwei]'s Articles
[Li, Xin]'s Articles
[Zheng, Donghai]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.