Enhancing Flood Simulation in Data-Limited Glacial River Basins through Hybrid Modeling and Multi-Source Remote Sensing Data

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-09, Vol.15 (18), p.4527
• Main Authors: Ren, Weiwei, Li, Xin, Zheng, Donghai, Zeng, Ruijie, Su, Jianbin, Mu, Tinghua, Wang, Yingzheng
• Format: Article
• Language: English
• Subjects: Altitude; Artificial neural networks; Base flow; Basins; Calibration; China; Climate change; Computational linguistics; data-scarce; flood; Flood forecasting; Flood predictions; Floods; glacial river basins; Glacier mass balance; Glaciers; Glaciohydrology; Global warming; Harnesses; hybrid modeling; Hydrologic models; Hydrology; Language processing; Long short-term memory; Machine learning; Mass balance; multi-source; Natural language interfaces; Neural networks; Remote sensing; River basins; Rivers; Runoff; Simulation; Snow; Snow cover; Snowmelt; SPHY model; Stream discharge; Stream flow; Streamflow; Support vector machines; Variables
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15184527

Due 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.