Dr.-Ing. Ye Tuo

Research focus

• Catchment scale hydrological modeling
• Satellite remote sensing in hydrology
• Machine learning and deep learning in hydrology
• Long-term drought and flash drought

Professional Career

• Since 2023 | Lecturer, Technical University of Munich (TUM) | New Master Courses: “Advanced hydrological modeling with machine learning and earth observations”.
• Since 2022 | Post-Doctoral researcher, TUM | Project: TUM Innovation Networks: Twin Earth Methodologies for Biodiversity, Natural Hazards, and Urbanization (EarthCare)
• Since 2019 | Lecturer, TUM | Master Courses: “Remote Sensing in Hydrology”, “Hydrological and Environmental River Basin Modelling” and “Rainfall runoff modeling”.
• Since 2018 | Post-Doctoral researcher and lecturer, TUM | Projects: Hios, PROMOS and Danube floodplain.
• 2015 – 2018 | Ph.D. Research associate for EU project-GLOBAQUA.
• 2015 – 2017 | Teaching assistant, TUM.

 

Research Achievements

• Zha, X., Sun, H., Jiang, H., Cao, L., Xue, J., Gui, D., Yan, D. and Tuo, Y., (2023). Coupling Bayesian Network and copula theory for water shortage assessment: A case study in source area of the South-to-North Water Division Project (SNWDP). Journal of Hydrology, 620, p.129434.
• Schaffhauser, T., Lange, S., Tuo, Y. and Disse, M., (2023). Shifted discharge and drier soils: Hydrological projections for a Central Asian catchment. Journal of Hydrology: Regional Studies, 46, p.101338.
• Arias-Rodriguez, L.F., Tüzün, U.F., Duan, Z., Huang, J., Tuo, Y. and Disse, M., (2023). Global Water Quality of Inland Waters with Harmonized Landsat-8 and Sentinel-2 Using Cloud-Computed Machine Learning. Remote Sensing, 15(5), p.1390.
• Tuo, Y., Zhu, X., and Disse, M., (2023). An innovative data driven approach improves drought impact analysis using earth observation data, EGU General Assembly 2023, Vienna, Austria
• Ho, S., Buras, A., and Tuo, Y., (2023).  A Comparison of Agriculture-related Characteristics of Flash and Traditional Drought, EGU General Assembly 2023, Vienna, Austria.
• Hu, X., Tuo, Y., and Disse, M., (2023). Deep learning based coordinates transformations for improving process understanding in hydrological modeling system, EGU General Assembly 2023, Vienna, Austria.
• Song, Z., Yang, E. and Tuo, Y., (2023). Piloting a physical-metric-based Index bench-marked by a social-economical Index measuring Flood Resilience in Ho Chi Minh City, EGU General Assembly 2023, Vienna, Austria.
• Jalan, I., Merk, F., Tuo, Y., and Disse, M., (2023). Hydrological performance evaluation of temperature reanalysis products for the Ouémé River Basin in West Africa, EGU General Assembly 2023, Vienna, Austria.
• Chen, H., Tuo, Y., and Disse, M., (2023). Intensifying Hydrometeorological Extreme Events and Compound Anomalies in a Temperate Region, Germany, EGU General Assembly 2023, Vienna, Austria.
• Yan, D., Chen, L., Sun, H., Liao, W., Chen, H., Wei, G., Zhang, W., Tuo, Y. (2022). Allocation of ecological water rights considering ecological networks in arid watersheds: A framework and case study of Tarim River basin. Agricultural Water Management, 267, 107636.
• Pyarali, K., Peng, J., Disse, M., Tuo, Y. (2022). Development and application of high resolution SPEI drought dataset for Central Asia. Scientific Data, 9(1), 1-14.
• Duan, Z., Duggan, E., Tuo, Y., Li, Y., Dong, J., Liu, J. and Gao, H., (2022). Modelling stream temperature with multiple hydroclimatological temperature models. EGU General Assembly 2022, Vienna, Austria.
• Schaffhauser, T., Lange, S., Tuo, Y., Disse, M. (2022). Change in Climate Impact Assessment from CMIP5 to CMIP6 in a High-Mountainous Catchment of Central Asia. EGU General Assembly 2022, Vienna, Austria.
• Sun, H., Bai, Y., Yang, Y., Lu, M., Yan, D., Tuo, Y. and Zhang, W. (2022). The altered drivers of evapotranspiration trends around the recent warming hiatus in China. International Journal of Climatology.
• Arias-Rodriguez, L. F., Duan, Z., Díaz-Torres, J. d. J., Basilio Hazas, M., Huang, J., Kumar, B. U., Tuo, Y., Disse, M. (2021). Integration of remote sensing and Mexican water quality monitoring system using an extreme learning machine. Sensors, 21(12), 4118.
• Ho, S., Tian, L., Disse, M., Tuo, Y. (2021). A new approach to quantify propagation time from meteorological to hydrological drought. Journal of Hydrology, 603, 127056.
• Lu, M., Sun, H., Yan, D., Xue, J., Yi, S., Gui, D., Tuo, Y., Zhang, W. (2021). Projections of thermal growing season indices over China under global warming of 1.5° C and 2.0° C. Science of The Total Environment, 781, 146774.
• Sun, H., Bai, Y., Lu, M., Wang, J., Tuo, Y., Yan, D., Zhang, W. (2021). Drivers of the water use efficiency changes in China during 1982–2015. Science of The Total Environment, 799, 149145.
• Song, Z., Tuo, Y. (2021). Automated flood depth estimates from online traffic sign images: explorations of a convolutional neural network-based method. Sensors, 21(16), 5614.
• Tian L., Ho S., Disse M., Tuo, Y. (2021). The Temporal Propagation Processes of Multiple Types of Drought in Central Asia. EGU General Assembly 2021, Vienna, Austria.
• Duan, Z., Duggan, E., Qing, Y., Tuo, Y. (2020). Assessing the performance of radar-based and satellite precipitation products in hydrological modelling with SWAT in Vils Basin, Germany. EGU General Assembly 2020, Vienna, Austria.
• Kopp, M., Tuo, Y., Disse, M. (2019). Fully automated snow depth measurements from time-lapse images applying a convolutional neural network. Science of the Total Environment, 697, 134213.
• Wu, S., Zhang, X., Du, J., Zhou, X., Tuo, Y., Li, R., Duan, Z. (2019). The vertical influence of temperature and precipitation on snow cover variability in the Central Tianshan Mountains, Northwest China. Hydrological Processes, 33: 1686– 1697.
• Duan, Z., Tuo, Y., Liu, J., Gao, H., Song, X., Zhang, Z., Lei, Y., Mekonnen, D. F. (2019). Hydrological evaluation of open-access precipitation and air temperature datasets using SWAT in a poorly gauged basin in Ethiopia. Journal of Hydrology, 569: 612-626.
• Chiogna, G., Marcolini, G., Liu W.Y., Ciria T.P., Tuo, Y. (2018). Coupling Hydrological Modeling and Support Vector Regression to Model Hydropeaking in Alpine Catchments. Science of the Total Environmental, 633: 220-229.
• Tuo, Y., Marcolini, G., Disse, M., Chiogna, G. (2018). A Multi-Objective Approach to Improve SWAT Model Calibration in Alpine Catchments. Journal of Hydrology, 559: 347-360.
• Tuo, Y., Marcolini, G., Disse, M., Chiogna, G. (2018). Calibration of Snow Parameters in SWAT: Comparison of Three Approaches in the Upper Adige River Basin. Hydrological Science Journal, 63: 657-678.
• Vigiak, O., Lutz, S., Mentzafou, A., Chiogna, G., Tuo, Y. et al. (2018). Uncertainty of modelled flow regime for flow-ecological assessment in Southern Europe. Science of the Total Environment, 615: 1028-1047.
• Tuo, Y., Chiogna, G., Disse, M. (2017). Applying a multi-objective based procedure to SWAT modelling of the alpine hydrology. 2017 AGU Fall Meeting, New Orleans, USA.
• Tuo, Y., Chiogna, G., Disse, M. (2017). Joint use of snow and discharge time series for SWAT model calibration. 2017 International SWAT Conference and Workshops, Warsaw, Poland.
• Tuo, Y., Duan, Z., Disse, M., Chiogna, G. (2016). Evaluation of precipitation input for SWAT modeling in Alpine catchment: A case study in the Adige river basin (Italy). Science of the Total Environment, 573: 66-82.
• Duan, Z., Liu, J., Tuo, Y., Chiogna, G. et al. (2016). Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. Science of The Total Environment, 573: 1536-1553.
• Tuo, Y., Chiogna, G., Disse, M. (2016). Optimization of precipitation inputs for SWAT modeling in mountainous catchment. EGU General Assembly 2016, Vienna, Austria.
• Tuo, Y., Chiogna. G., Disse, M. (2015). A multi-criteria model selection protocol for practical applications to nutrient transport at the catchment scale. Water, 7: 2851-2880.