Using LSTMs for climate change assessment studies on droughts and floods
This work addresses the challenge of accurate hydrological forecasting for climate change assessment, which is crucial for water resource management and disaster preparedness, but it appears incremental as it extends an existing LSTM model to sensitivity analysis.
The authors tackled the problem of predicting climate impacts on floods and droughts in individual watersheds by developing a large-scale LSTM-based model trained on extensive datasets, which they show can assess sensitivity to extreme flows across the continental US, building on previous work that demonstrated its higher accuracy compared to state-of-the-art models.
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.