From Hindcast to Forecast with Deep Learning Streamflow Models

Deep learning has become the de facto standard for streamflow simulation. While there are examples of deep learning based streamflow forecast models (e.g., 1-5), the majority of the development and research has been done with hindcast models. The primary challenge in using deep learning models for forecasting (e.g., flood forecasting) is that the meteorological input data are drawn from different distributions in hindcast vs. forecast. The (relatively small) amount of research that has been done on deep learning streamflow forecasting has largely used an encoder-decoder approach to account for forecast distribution shifts. This is, for example, what Google’s operational flood forecasting model uses [4]. 

In this work we show that the encoder-decoder approach results in artifacts in forecast trajectories that are not detectable with standard hydrological metrics, but which can cause forecasts to have incorrect trends (e.g., rising when they should be falling and vice-versa).  We solve this problem using regularized embeddings, which remove forecast artifacts without harming overall accuracy. 

Perhaps more importantly, input embeddings allow for training models on spatially and/or temporally incomplete meteorological inputs, meaning that a single model can be trained using input data that does not exist everywhere or does not exist during the entire training or forecast period. This allows models to learn from a significantly larger training data set, which is important for high-accuracy predictions. It also allows large (e.g., global) models to learn from local weather data. We demonstrate how and why this is critical for state-of-the-art global-scale streamflow forecasting. 

 

• Franken, Tim, et al. An operational framework for data driven low flow forecasts in Flanders. No. EGU22-6191. Copernicus Meetings, 2022.
• Kao, I-Feng, et al. "Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting." Journal of Hydrology 583 (2020): 124631.
• Liu, Darong, et al. "Streamflow prediction using deep learning neural network: case study of Yangtze River." IEEE access 8 (2020): 90069-90086.
• Nevo, Sella, et al. "Flood forecasting with machine learning models in an operational framework." Hydrology and Earth System Sciences 26.15 (2022): 4013-4032.
• Girihagama, Lakshika, et al. "Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism." Neural Computing and Applications 34.22 (2022): 19995-20015.