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

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 b

SUBMITTER: Fang J 

PROVIDER: S-EPMC12917148 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature