Evaluation of regional Rainfall-Runoff modelling using convolutional long short-term memory: CAMELS dataset in US as a case study.

Rainfall-runoff (RR) modeling remains a challenging task in the field of hydrology especially when it comes to regional scale hydrology. Recently, the Long Short-Term Memory (LSTM) - which is known for its ability to learn sequential and temporal relations - has been widely adopted in RR modeling. The Convolutional Neural Networks (CNN) have matured enough in computer vision tasks, and trials were conducted to use them in hydrological applications. Different combinations of CNN and LSTM have proved to work; however, questions remain about suitability of different model architectures, the input variables needed for the model and the interpretability of the learning process of the models for regional scale.

 

In this work we trained a sequential CNN-LSTM deep learning architecture to predict daily streamflow between 1980 and 2014, regionally and simultaneously, over 86 catchments from CAMELS dataset in the US. The model was forced using year-long spatially distributed (gridded) input with precipitation, maximum temperature and minimum temperature for each day, to predict one day streamflow. The model takes advantage of the CNN to encode the spatial patterns in the input tensor, and feed them to the LSTM for learning the temporal relations between them. The trained model was further fine-tuned to predict for 3 local sub-clusters of the 86 stations. This was made in order to test the significance of fine-tuning in the performance and model learning process. Also, to interpret the spatial patterns learning process, a perturbation was introduced in the gridded input data and the sensitivity of the model output to the perturbation was shown in spatial heat maps. Finally, to evaluate the performance of the model, different benchmark models were trained using -as possible- a similar training setup as for the CNN-LSTM model. These models are CNN without the LSTM part (regional model), LSTM without CNN part (regional model), simple single-layer ANN (regional model), and LSTM trained for individual stations (considered as state of the art). All of these benchmark models have been fined-tuned for the 3 clusters as well.

 

CNN-LSTM model, after being fine-tuned, performed well predicting daily streamflow over the test period with a median Nash-Sutcliffe efficiency (NSE) of 0.62 and 65% of the 86 stations with NSE > 0.6 outperforming all benchmark models that were trained regionally using the same training setup. The model also achieved a comparable performance as for the -state of the art- LSTM trained for individual stations. Fine-tuning improved the performance for all of the models during the test period. The CNN-LSTM model, was shown to be more sensitive to input perturbations near the stations in which the prediction is intended. This was even clearer for the fine-tuned model, indicating that the model is learning spatially relevant information from the input gridded data, and fine tuning is helping on guiding the model to focus more on the relevant input.  

 

This work shows the potential of CNN and LSTM for regional Rainfall-runoff modeling by capturing spatiotemporal patterns involved in RR process. The work, also, contributes toward more physically interpretable data-driven modeling paradigm.