Using LSTMs for climate change assessment studies on droughts and floods

Workshop paper, investigating first ways of using LSTM-based models for climate change related questions in hydrology.

Abstract

Climate change affects occurrences of floods and droughts worldwide. However, predicting climate impacts over individual watersheds is difficult, primarily because accurate hydrological forecasts require models that are calibrated to past data. In this work we present a large-scale LSTM-based modeling approach that – by training on large data sets – learns a diversity of hydrological behaviors. Previous work shows that this model is more accurate than current state-of-the-art models, even when the LSTM-based approach operates out-of-sample and the latter in-sample. In this work, we show how this model can assess the sensitivity of the underlying systems with regard to extreme (high and low) flows in individual watersheds over the continental US.

Paper

Kratzert, F. and Klotz, D. and Brandstetter, J. and Hoedt, P.-J. and Nearing, G. and Hochreiter, S.: “Using LSTMs for climate change assessment studies on droughts and floods”. Workshop on Tackling Climate Change with Machine Learning 33rd Conference on Neural Information Process-ing Systems (NeurIPS 2019), Vancouver, Canada.

ArXiv: https://arxiv.org/abs/1911.03941

Citation

@inproceedings{kratzert2019climate,
  title={Using LSTMs for climate change assessment studies on droughts and floods},
  author={Kratzert, F. and Klotz, D. and Brandstetter, J. and Hoedt, P.-J. and Nearing, G. and Hochreiter, S.},
  booktitle={Workshop on Tackling Climate Change with Machine Learning 33rd Conference on Neural Information Process-ing Systems (NeurIPS 2019)},
  venue={Vancouver, Canada},
  date={8-14 Dec},
  year={2019}
}