Abstract

Rainfall-runoff modelling is of great importance for flood forecast and water management. Hydrological modelling is the traditional and commonly used approach for rainfall-runoff modelling. In recent years, with the development of artificial intelligence technology, deep learning models, such as the long short-term memory (LSTM) model, are increasingly applied to rainfall-runoff modelling. However, current works do not consider the effect of rainfall spatial distribution information on the results, and the same look-back window is applied to all the inputs. Focusing on two catchments from the CAMELS dataset, this study first analyzed and compared the effects of basin mean rainfall and spatially distributed rainfall data on the LSTM models under different look-back windows (7, 15, 30, 180, 365 days). Then the LSTM+1D CNN model was proposed to simulate the situation of short-term look-back windows (3, 10 days) for rainfall combined with the long-term look-back windows (30, 180, 365 days) for other input features. The models were evaluated using the Nash Sutcliffe efficiency coefficient, root mean square error, and error of peak discharge. The results demonstrate the great potential of deep learning models for rainfall runoff simulation. Adding the spatial distribution information of rainfall can improve the simulation results of the LSTM models, and this improvement is more evident under the condition of short look-back windows. The results of the proposed LSTM+1D CNN are comparable to those of the LSTM model driven by basin mean rainfall data and slightly worse than those of spatially distributed rainfall data for corresponding look-back windows. The proposed LSTM+1D CNN provides new insights for runoff simulation by combining short-term spatial distributed rainfall data with long-term runoff data, especially for catchments where long-term rainfall records are absent.

Highlights

  • IntroductionRainfall-runoff simulations are vital for watershed water resources management and risk analysis (Montanari, 2005; Neitsch et al, 2011)

  • The Variable Infiltration Capacity (VIC) is a large-scale distributed hydrological model developed by the University of Washington, the University of California at Berkeley, and Princeton University (Liang et al, 1996)

  • (Hu et al, 2018) compared the difference between Artificial neural networks (ANNs) and LSTM in simulation of flood events, and the results show that LSTM models perform significantly better than ANN models. (Kratzert et al, 2018) trained LSTM models with rainfall-runoff data from several watersheds, demonstrating the potential of LSTM as a regional hydrological model, one of which can predict flows in various watersheds

Read more

Summary

Introduction

Rainfall-runoff simulations are vital for watershed water resources management and risk analysis (Montanari, 2005; Neitsch et al, 2011). The advantage of LSTM is that it can perform better in longer sequences than a normal Recurrent Neural Network (RNN) If this problem is looked at from the point of view of the physical mechanism of rainfall-runoff formation, for example, only the rainfall that occurred in the previous few days usually has an impact on the current moment of discharge. We focus on the simulation of the one-time step, and on the simulation of multiple future time steps To meet this objective, we propose a LSTM+1D CNN model to simulate rainfall-runoff by combining meteorological and discharge data of long look-back window and rainfall data with spatial distribution of short look-back window, and compare the results with the traditional LSTM model.

Objectives
Methods
Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.