10 April 2019 / / conference

Using large data sets towards generating a catchment aware hydrological model for global applications

PICO presentation at the EGU General Assembly 2019 on prediction in ungauged basins using LSTM based models.

Abstract

One reason for the success of deep learning across many applications (eg image classification or machine translation) are large and publicly available data sets. In Hydrology, more specifically in the area of rainfall-runoff modelling, such publicly available data sets are still rare but are increasingly emerging from the community (eg CAMELS [1, 2] and CAMELS-CL [3]). Having such data sets allows to test new data-driven approaches for old but still outstanding hydrological problems.

In this study we build upon the idea of Kratzert et al.[4] using LSTM (Long Short-Term Memory) networks as a regional hydrological model. However, substantial changes are made:(a) We train a single model on hundreds of catchments across the USA, not only for a single HUC (hydrological unit) as in 4 We do not only use meteorological time series as inputs but also static catchment characteristics through an adaption of the original LSTM architecture and therefore allow the LSTM to learn a catchment aware hydrological model. Here, we only use catchment characteristics of [2] that do not rely on discharge observation. With this setting we can therefore also investigate, if LSTMs can be used for prediction in ungauged basins. Concretely, we perform 6-fold cross-validation on 531 basins of the CAMELS data set. Out of the 6 splits, 5 are used for training and validation and a single split (approximately 89 basins) as test set for the final evaluation. This mimics an ungauged basin setting by not using any data from the basins in the test set during the entire training period.

Link

Citation

@inproceedings{kratzert2019using,
  title={Using large data sets towards generating a catchment aware hydrologicalmodel for global applications},
  author={Kratzert, Frederik and Klotz, Daniel and Herrnegger, Mathew and Hochreiter, Sepp and Klambauer, G{\"u}nter},
  booktitle={Geophysical Research Abstracts, Vol. 21, EGU2019-13795, EGU General Assembly},
  venue={Vienna, Austria},
  date={8-12 Apr},
  year={2019}
}

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