the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting
Abstract. The quality of the river runoff determines the quality of regional climate projections for coastal oceans or other estuaries. This study presents a novel approach to river runoff forecasting using Convolutional Long Short-Term Memory (ConvLSTM) networks. Our method accurately predicts daily runoff for 97 rivers within the Baltic Sea catchment by modeling runoff as a spatiotemporal sequence defined by atmospheric forcing. The ConvLSTM model performs similarly to traditional hydrological models, effectively capturing the intricate spatial and temporal patterns that influence individual river runoff across the Baltic Sea region. Our model offers the advantages of faster processing and easier integration into climate models.
- Preprint
(8247 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 06 Dec 2024)
-
RC1: 'Comment on egusphere-2024-2685', Anonymous Referee #1, 05 Nov 2024
reply
General Comments: The authors present a machine learning approach to forecasting river runoff
from weather data using convolutional long-short-term memory neural networks. They present
convincing evidence that the utilized ml model shows results of equal quality as its training data. At
the same time the ml method offers faster processing speeds and thus an easier direct integration
into regional climate models. With this approach, they present a scientifically significant and
qualitative contribution to the integration of river runoff forecasting into climate models. While the
manuscript shows great potential, I think that it requires minor revisions. My comments are listed
below.
Minor Comments: As I do not possess a deeper understanding of the river runoff modelling and
come from the machine learning side, I will limit my comments mostly to the technical aspects.
First, to me it does not become clear exactly how well your training data performs in comparison to
other state-of-the-art models. I understand that your ConvLSTM is able to reproduce its training
data’s quality but I’m not fully able to grasp the strengths and weaknesses of the utilized training
model, which I can assumed are transferred to the ML model. It would be helpful to extend the
technical details section or the model section by a short description of the training data and
especially its strengths and weaknesses compared to other possible runoff forecasting models.
Although I see that the point of the paper is more the proof that is able to reproduce a state-of-the-
art river runoff forecasting and not the exact strenghts and weaknesses of the utilized training data,
it would help give perspective to the strengths of your method.
For example, your training data seems to present a bias compared to observational data (Figure 7b),
which the network reproduces.
In connection to that, you describe that you utilize the time period from 1979 to 2011, because they
are not bias corrected. As a bias correction seems to be usually conducted, I would like to know if
that can be similarly performed on the ConvLSTM outputs.
Connected to that, have you tried to train the ConvLSTM on any other runoff models? Training for
400 epochs on daily training data from 32 years is a lot of training input. Just out of interest, have
you tried training on less data and how does the performance of the ConvLSTM differ? I would
guess, that not all hydrological models provide such a comprehensive dataset. Could you thus
comment on how easy it would be to extend this method to other runoff prediction models and how
much training data would be required.
I would also be interested, if all ocean/regional climate models are able to utilize runoff predictions
from similar sources or if they require their own in-model consistent runoff forcing. Because, if
other climate models would require the ConvLSTM to be trained on different runoff predictions, it
would significantly limit this method’s applicability if that runoff model would be required to
possess such a comprehensive training dataset as the EHYPE model presented in your study.
Additionally, I would be interested out of curiosity how many timesteps are necessary for the LSTM
to significantly improve the CNN output. Have you tried training with significantly less than 30
timesteps? What was your reasoning behind choosing these 30 days? Or was it just based on model
performance/loss functions?Finally, you claim that “While the initial training of the model requires
substantial computational resources, it remains significantly less intensive than running
comprehensive hydrological models” (Page 17). Could you give an estimate on how big this“significant” reduction of computational resources is? Because in the end this time saving is the
important improvement of your method compared to other numerical prediction systems/models.
In general I felt the content of the paper was novel and the method would be of interest to others in
the field, but some details should be explained further or lack a bit of background information.Citation: https://doi.org/10.5194/egusphere-2024-2685-RC1
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
104 | 22 | 6 | 132 | 3 | 3 |
- HTML: 104
- PDF: 22
- XML: 6
- Total: 132
- BibTeX: 3
- EndNote: 3
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1