Reconstruction of Extreme Sea Levels in coastal China using Multiple Deep Learning models.

Journal: Scientific data
Published Date: Jan 19, 2026

Abstract

We present a 1970-2020 dataset of daily maximum coastal water levels reconstructed for 23 tide gauges along China's coast. The product combines storm-surge residuals predicted with an Informer-based deep learning workflow (benchmarked against LSTM, CNN-LSTM, and ConvLSTM) with astronomical tides estimated by UTide from historical observations. Predictors are drawn from ERA5 reanalysis and multi-source tide-gauge records are used for training and validation. For each station, the model with best validation skill generates residuals combined with tidal harmonics to form daily maxima. Across stations, the reconstruction attains a mean correlation coefficient of 0.81 and RMSE of 11.7 cm for daily maxima; for events above the 95th percentile, the mean correlation is 0.68 and RMSE is typically below 20 cm. The release includes metadata, data splits, and skill metrics for transparency and reuse. This dataset enables spatiotemporal analyses of extreme coastal water levels and coastal hazard mitigation in regions with sparse observations. Daily maxima are computed as the sum of the maximum tide and maximum surge. This serves as an upper bound, as the peaks of tide and surge rarely coincide. Using hourly data, we estimate a mean non-coincidence bias of 14.9 cm (14.8%). Additionally, station-specific statistics are provided for user adjustment.

Authors

  • Jiayi Fang
    Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou, 311121, China. [email protected].
  • Jionghao Huang
    Department of Environmental Engineering, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; Zhejiang Provincial Key Laboratory for Water Pollution Control and Environmental Safety, Hangzhou 310058, China.
  • Wanchao Bian
    Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou, 311121, China.
  • Sida Li
    Jiangsu Ocean University, Lianyungang, 222000, China.
  • Shuiqing Li
    Key Laboratory of Ocean Observation and Forecasting, Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China.
  • Zhixu Bai
    College of Civil Engineering and Architecture, Wenzhou University, Wenzhou, 325035, China.
  • Ying Qu
    School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
  • Yanjun Wu
    Institute of Software, Chinese Academy of Sciences.
  • Ye Zhu
    Phase I Clinical Trial Site, Nanjing Gaoxin Hospital, Nanjing, Jiangsu, China.

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