Min Gan

Project: Exploration of fluvial-tidal interaction of the Yangtze Estuary

The proposed research will be focused on studying the dynamics of tides in the estuary with significant flow discharge, such as Yangtze Estuary and develop an improved analysis tools for better tides prediction, by considering the non-stationarity of the tide dynamics affected by the fluvial discharge and using advanced numerical model. The research will be also supported by the field data and data assimilation approach to take account for the uncertainties. The recent study on implementing the Auto-regression (AR) analysis to the non-stationary tide prediction model NS_TIDE is illustrated below (https://doi.org/10.1016/j.jhydrol.2020.125386):

Yangtze River

Fig 1. Map of the low reach of Yangtze River and locations of hydrometric stations

Fig. 2 RMSE values of the predicted water levels by the NS_TIDE and NS_TIDE&AR models over durations (L) of 12, 24, 36 and 48 hours for: all water levels (A); high water levels (B); and low water levels (C)

Publications:

  • Gan, M., Lai, X., Guo, Y., Lu, Z., Chen, Y., Pan, S., Pan, H. & Chu, A. (2024). “Unravelling the spatiotemporal variation in the water levels of Poyang Lake with the variational mode decomposition model”. Hydrological Processes 38(7), e15239 [DOI: 10.1002/hyp.15239]
  • Gan, M., Lai, X., Guo, Y., Chen, Y., Pan, S. & Zhang, Y. (2024). “Floodplain Lake Water Level Prediction with Strong River-Lake Interaction Using the Ensemble Learning LightGBM”. Water Resources Management [DOI: 10.1007/s11269-024-03915-8]
  • Gan, M., Chen, Y., Pan, S., Lai, X., Pan, H., Wen, Y. & Xia, M. (2024). “An improved machine learning-based model to predict estuarine water levels”. Ocean Modelling, 102376 [DOI: 10.1016/j.ocemod.2024.102376]
  • Tao, Z., Chen, Y., Chu, A., Pan, S., Gan, M., Chen, Y., Che, Z. & Zhu, Y. (2022). “The Modified Method of Reanalysis Wind Data in Estuarine Areas”. Water 14(11), 1826 [DOI: 10.3390/w14111826]
  • Li, J., Pan, S., Chen, Y. & Gan, M. (2021). “The performance of the copulas in estimating the joint probability of extreme waves and surges along east coasts of the mainland China”. Ocean Engineering 237, 109581 [DOI: 10.1016/j.oceaneng.2021.109581]
  • Gan, M., Pan, S., Chen, Y., Cheng, C., Pan, H. & Zhu, X. (2021). “Application of the Machine Learning LightGBM Model to the Prediction of the Water Levels of the Lower Columbia River”. Journal of Marine Science and Engineering 9(5) [DOI: 10.3390/jmse9050496]
  • Yang, L., Gao, H., Yu, D., Pan, S., Zhou, Y. & Gai, Y. (2021). “Design of a Novel Fully Automatic Ocean Spectra Acquisition and Control System Based on the Real-Time Solar Angle Analyzing and Tracking”. IEEE Access 9, 4752-4768 [DOI: 10.1109/access.2020.3048117]
  • Chen, Y., Gan, M., Pan, S., Pan, H., Zhu, X. & Tao, Z. (2020). “Application of auto-regressive (AR) analysis to improve short-term prediction of water levels in the Yangtze estuary”. Journal of Hydrology 590, 125386 [DOI: 10.1016/j.jhydrol.2020.125386]
  • Gan, M., Chen, Y., Pan, S., Li, J. & Zhou, Z. (2019). “A Modified Nonstationary Tidal Harmonic Analysis Model for the Yangtze Estuarine Tides”. Journal of Atmospheric and Oceanic Technology 36(4), 513-525 [DOI: 10.1175/jtech-d-18-0199.1]
  • Chen, Y., Li, J., Pan, S., Gan, M., Pan, Y., Xie, D. & Clee, S. (2019). “Joint probability analysis of extreme wave heights and surges along China’s coasts”. Ocean Engineering 177, 97-107 [DOI: 10.1016/j.oceaneng.2018.12.010]
  • Gan, M., Chen, Y., Pan, S. & Pan, H. (2022). “The Performance of the Nonstationary Tidal Harmonic Analysis (ns_tide) in the Yangtze Estuary”, Asia Oceania Geosciences Society (AOGS)2022
  • Gan, M., Chen, Y., Pan, S., Liu, Y. & Liu, S. (2019). “Application of Modified Nonstationary Tidal Harmonic Analysis Approach to Data Recovery of Missing Water Level Measurements of Yangtze Estuary”, 29th International Ocean and Polar Engineering Conference. International Society of Offshore and Polar Engineers, Honolulu, Hawaii, USA, p. 6