Impact of spatial distribution information of rainfall  in runoff simulation using deep learning method

Wang, Yang; Karimi, Hassan A.

doi:https://doi.org/10.5194/hess-26-2387-2022

Articles | Volume 26, issue 9

https://doi.org/10.5194/hess-26-2387-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-26-2387-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 9

Research article

|

09 May 2022

Research article |

| 09 May 2022

Impact of spatial distribution information of rainfall in runoff simulation using deep learning method

Yang Wang and Hassan A. Karimi

Data sets

Catchment attributes for large-sample studies N. Addor, A. Newman, M. Mizukami, and M. P. Clark https://doi.org/10.5065/D6G73C3Q

Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2 P. E. Thornton, M. M. Thornton, B. W. Mayer, N. Wilhelmi, Y. Wei, R. Devarakonda, and R. B. Cook https://daac.ornl.gov/DAYMET/guides/Daymet_Daily_V4.html

Short summary

We found that rainfall data with spatial information can improve the model's performance, especially when simulating the future multi-day discharges. We did not observe that regional LSTM as a regional model achieved better results than LSTM as individual model. This conclusion applies to both one-day and multi-day simulations. However, we found that using spatially distributed rainfall data can reduce the difference between individual LSTM and regional LSTM.