Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model

Mimeau, Louise; Künne, Annika; Branger, Flora; Kralisch, Sven; Devers, Alexandre; Vidal, Jean-Philippe

doi:https://doi.org/10.5194/egusphere-2023-1322

Preprints

https://doi.org/10.5194/egusphere-2023-1322

Preprints

28 Jul 2023

| 28 Jul 2023

Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model

Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal

Abstract. Rivers are rich in biodiversity and act as ecological corridors for plant and animal species. With climate change and increasing anthropogenic water demand, more frequent and prolonged periods of drying in river systems are expected, endangering biodiversity and river ecosystems. However, understanding and predicting the hydrological mechanisms that control periodic drying and rewetting in rivers is challenging due to a lack of studies and hydrological observations, particularly in non-perennial rivers. Within the framework of the Horizon 2020 DRYvER (Drying River Networks and Climate Change) project, a hydrological modelling study of flow intermittence in rivers is being carried out in 3 European catchments (Spain, Finland, France) characterized by different climate, geology and anthropogenic use. The objective of this study is to represent the spatio-temporal dynamics of flow intermittence at the reach level in meso-scaled river networks (between 120 km² and 350 km²). The daily and spatially distributed flow condition (flowing or dry) is predicted using the J2000 distributed hydrological model coupled with a Random Forest classification model. Observed flow condition data from different sources (water level measurements, photo traps, water temperature measurements, citizen science applications) are used to build the predictive model. This study aims to evaluate the impact of the observed flow condition dataset (sample size, spatial and temporal representativity) on the performance of the predictive model. Results show that the hybrid modelling approach developed in this study allows to accurately predict the spatio-temporal patterns of drying in the 3 catchments. This study shows the value of combining different sources of observed flow condition data to reduce the uncertainty in predicting flow intermittence.

Received: 16 Jun 2023 – Discussion started: 28 Jul 2023

Download & links

Preprint (PDF, 5149 KB)

Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
Preprint (5149 KB)

Supplement (8493 KB)

Download & links

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Journal article(s) based on this preprint

23 Feb 2024

Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model

Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal

Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024,https://doi.org/10.5194/hess-28-851-2024, 2024

Short summary

Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1322', Anonymous Referee #1, 30 Sep 2023

the research is very good. the combing of the hydrological model and machine learning is very Innovative research.

Citation: https://doi.org/10.5194/egusphere-2023-1322-RC1
- AC1: 'Reply on RC1', Louise Mimeau, 29 Nov 2023
  
  Dear referee RC1,
  Thank you for your positive feedback on our paper. Constructive criticism is always valuable, and we remain open to any further suggestions or insights you might have upon closer scrutiny.
  With kind regards,
  Louise Mimeau et al.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1322-AC1
RC2:
'Comment on egusphere-2023-1322', Anonymous Referee #2, 16 Oct 2023

I have reviewed Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model”, which seeks to combine JAMS -J2000 model with statistical learning to predict the daily state of flow at reach scale. I think the approach is clever and the final results are interesting. I have only small comments:

Abstract: add a summary of model performance (quantitative values)
Line 17 replace home with habitat
Line 20 “These ecological corridors can be disrupted when river beds dry up” I think this point should be expanded, it is true that some species cannot live in the presence of water, but non-perennial rivers due to the succession of different flow phases represent biodiversity hotspots.
Line 68 I think it is better recall this section “materials and methods”
Line 128 In cases of simultaneous acquisitions from multiple resources. Was the information coincident? For example, did information from traditional measurement stations coincide with google images? Was the 'flow condition information the same?
Line 200 I suggest you use accuracy, sensitivity, specifcity, and true skill statistic to validated RF

Citation: https://doi.org/10.5194/egusphere-2023-1322-RC2
- AC2: 'Reply on RC2', Louise Mimeau, 29 Nov 2023
  
  Dear referee RC2,
  We thank you for your positive comments regarding our paper.
  We agree with all the suggestions made and will take them into account in the revised version of the manuscript.
  Please find in supplement our response to the main comments.
  With kind regards,
  Louise Mimeau et al.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1322-AC2
RC3:
'Comment on egusphere-2023-1322', Anonymous Referee #3, 27 Oct 2023

This paper aims to simulate flow intermittence using a mix of hydrological modelling and random forest algorithms. The study is performed in three different catchments located across Europe with different climate regimes. The study assesses the impact of the used observed dataset on the performance of the model.

Overall, I think that the idea and approach of the authors is interesting and that it could provide relevant outcomes on the importance of different observation datasets for developing data-driven algorithms. I think that the paper has the quality of being published but there are some point that the authors should address before publishing it.

I think that the paper could be strengthen if the following points are considered:

1. Line 23: A word is missing in the sentence, perhaps “limit”? For example: … flow can endanger ecosystems and limit the access to water resources …”.

2. Line 46: I think that the “a” is extra in the beginning of the sentence: “Another a challenge in …”.

3. Line 51: Change “do” into “to”.

4. Lines 61 to 64: This paragraph is a repetition from the previous paragraph. Please remove one.

5. Line 74: Change the sentence to “…, located in the South of Spain...” or “,… located in southern Spain…”.

6. It would be relevant to include the annual mean temperature and total precipitation in table 1 or in the study areas description section. These would help better understand the climate contrast among the different study sites.

7. Line 83: you could add “input to” and change “modelling” to “model” in the sentence. “.., hydrogeology information needed for input to the spatially distributed hydrological model…”.

8. Please use superscript for units throughout the text (e.g. cubic meters, square meters, etc.).

9. Consider changing the symbology for the Google Earth observations because they are not easily observed as they can be confused with the river network.

10. In Figure 3. It should be “Daily flow state”, a “t” is missing.

11. Line 147. A period is missing after (HRU).

12. Line 157. Not clear if the period that you mention is for all study sites or whether it is for the calibration or validation step. Please clarify.

13. Figure 4: I recommend that you add the definitions of MPS, DPS, LPS and all the abbreviations that you used in the figure. These could go into the caption of the figure.

14. Table 2. The Albarine and Lepsämänjoki sites have a continuous simulation period but the Genal has a gap between the calibration and validation period. Would it be relevant to explain why is that?

15. The metrics in table 3 could also include a metric similar to your POD and FAR but for the hydrological model. For instance, set a low-flow threshold and indicate how many times the model succeeds simulating when the river is below the threshold (POD) or when the model is not simulating it accurately (FAR). This would also give information on how good the hydrological model is to simulate low-flow periods, which I think would add relevant information for your study. Is the hydrological model skillful simulating low-flow periods or is the RF algorithm making most of the work?

16. Figure 5. Along the text you refer to Fig. 5a or Fig. 5b, but in figure 5 there is no division between sections a or b.

17. Lines 167 and 278: Is it possible to infer why the uncertainty for the Genal catchment is higher? Is it that the catchment is very complex to simulate or some other issue?

18. Line 279: Would it be relevant to also include some results for configuration 1 (perhaps in the supplement)? Just for comparison and to include something related to the uncertainty of the results.

19. Figure 11. The name of the Finnish river needs to be corrected.

20. Lines 356 to 260. It is not clear if this paragraph only refers to the Albarine catchment. Please clarify.

21. Line 382: Should it be “are not-known”?

22. Line 439. You could support this claim using previous expert elicitation studies that looked into expert perception uncertainty. For example: https://doi.org/10.5194/hess-26-5605-2022 or https://doi.org/10.1002/2015WR018461

23. Lines 479 to 483. As you state that you will use this approach to assess impacts of climate change, it might good to add a line about the implications of using it for such kind of studies. For instance, mentioning a bit on what you wrote in line 396.

24. Line 265. You repeated “which shows the” in the sentence. Please remove one.

Citation: https://doi.org/10.5194/egusphere-2023-1322-RC3
- AC3: 'Reply on RC3', Louise Mimeau, 29 Nov 2023
  
  Dear referee RC3,
  We thank you for your positive comments and for all your suggestions to improve our paper.
  Please find in supplement our responses to the main comments (all other comments relating to form will also be taken into account in the revised version).
  With kind regards,
  Louise Mimeau et al.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1322-AC3

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1322', Anonymous Referee #1, 30 Sep 2023

the research is very good. the combing of the hydrological model and machine learning is very Innovative research.

Citation: https://doi.org/10.5194/egusphere-2023-1322-RC1
- AC1: 'Reply on RC1', Louise Mimeau, 29 Nov 2023
  
  Dear referee RC1,
  Thank you for your positive feedback on our paper. Constructive criticism is always valuable, and we remain open to any further suggestions or insights you might have upon closer scrutiny.
  With kind regards,
  Louise Mimeau et al.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1322-AC1
RC2:
'Comment on egusphere-2023-1322', Anonymous Referee #2, 16 Oct 2023

I have reviewed Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model”, which seeks to combine JAMS -J2000 model with statistical learning to predict the daily state of flow at reach scale. I think the approach is clever and the final results are interesting. I have only small comments:

Abstract: add a summary of model performance (quantitative values)
Line 17 replace home with habitat
Line 20 “These ecological corridors can be disrupted when river beds dry up” I think this point should be expanded, it is true that some species cannot live in the presence of water, but non-perennial rivers due to the succession of different flow phases represent biodiversity hotspots.
Line 68 I think it is better recall this section “materials and methods”
Line 128 In cases of simultaneous acquisitions from multiple resources. Was the information coincident? For example, did information from traditional measurement stations coincide with google images? Was the 'flow condition information the same?
Line 200 I suggest you use accuracy, sensitivity, specifcity, and true skill statistic to validated RF

Citation: https://doi.org/10.5194/egusphere-2023-1322-RC2
- AC2: 'Reply on RC2', Louise Mimeau, 29 Nov 2023
  
  Dear referee RC2,
  We thank you for your positive comments regarding our paper.
  We agree with all the suggestions made and will take them into account in the revised version of the manuscript.
  Please find in supplement our response to the main comments.
  With kind regards,
  Louise Mimeau et al.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1322-AC2
RC3:
'Comment on egusphere-2023-1322', Anonymous Referee #3, 27 Oct 2023

This paper aims to simulate flow intermittence using a mix of hydrological modelling and random forest algorithms. The study is performed in three different catchments located across Europe with different climate regimes. The study assesses the impact of the used observed dataset on the performance of the model.

Overall, I think that the idea and approach of the authors is interesting and that it could provide relevant outcomes on the importance of different observation datasets for developing data-driven algorithms. I think that the paper has the quality of being published but there are some point that the authors should address before publishing it.

I think that the paper could be strengthen if the following points are considered:

1. Line 23: A word is missing in the sentence, perhaps “limit”? For example: … flow can endanger ecosystems and limit the access to water resources …”.

2. Line 46: I think that the “a” is extra in the beginning of the sentence: “Another a challenge in …”.

3. Line 51: Change “do” into “to”.

4. Lines 61 to 64: This paragraph is a repetition from the previous paragraph. Please remove one.

5. Line 74: Change the sentence to “…, located in the South of Spain...” or “,… located in southern Spain…”.

6. It would be relevant to include the annual mean temperature and total precipitation in table 1 or in the study areas description section. These would help better understand the climate contrast among the different study sites.

7. Line 83: you could add “input to” and change “modelling” to “model” in the sentence. “.., hydrogeology information needed for input to the spatially distributed hydrological model…”.

8. Please use superscript for units throughout the text (e.g. cubic meters, square meters, etc.).

9. Consider changing the symbology for the Google Earth observations because they are not easily observed as they can be confused with the river network.

10. In Figure 3. It should be “Daily flow state”, a “t” is missing.

11. Line 147. A period is missing after (HRU).

12. Line 157. Not clear if the period that you mention is for all study sites or whether it is for the calibration or validation step. Please clarify.

13. Figure 4: I recommend that you add the definitions of MPS, DPS, LPS and all the abbreviations that you used in the figure. These could go into the caption of the figure.

14. Table 2. The Albarine and Lepsämänjoki sites have a continuous simulation period but the Genal has a gap between the calibration and validation period. Would it be relevant to explain why is that?

15. The metrics in table 3 could also include a metric similar to your POD and FAR but for the hydrological model. For instance, set a low-flow threshold and indicate how many times the model succeeds simulating when the river is below the threshold (POD) or when the model is not simulating it accurately (FAR). This would also give information on how good the hydrological model is to simulate low-flow periods, which I think would add relevant information for your study. Is the hydrological model skillful simulating low-flow periods or is the RF algorithm making most of the work?

16. Figure 5. Along the text you refer to Fig. 5a or Fig. 5b, but in figure 5 there is no division between sections a or b.

17. Lines 167 and 278: Is it possible to infer why the uncertainty for the Genal catchment is higher? Is it that the catchment is very complex to simulate or some other issue?

18. Line 279: Would it be relevant to also include some results for configuration 1 (perhaps in the supplement)? Just for comparison and to include something related to the uncertainty of the results.

19. Figure 11. The name of the Finnish river needs to be corrected.

20. Lines 356 to 260. It is not clear if this paragraph only refers to the Albarine catchment. Please clarify.

21. Line 382: Should it be “are not-known”?

22. Line 439. You could support this claim using previous expert elicitation studies that looked into expert perception uncertainty. For example: https://doi.org/10.5194/hess-26-5605-2022 or https://doi.org/10.1002/2015WR018461

23. Lines 479 to 483. As you state that you will use this approach to assess impacts of climate change, it might good to add a line about the implications of using it for such kind of studies. For instance, mentioning a bit on what you wrote in line 396.

24. Line 265. You repeated “which shows the” in the sentence. Please remove one.

Citation: https://doi.org/10.5194/egusphere-2023-1322-RC3
- AC3: 'Reply on RC3', Louise Mimeau, 29 Nov 2023
  
  Dear referee RC3,
  We thank you for your positive comments and for all your suggestions to improve our paper.
  Please find in supplement our responses to the main comments (all other comments relating to form will also be taken into account in the revised version).
  With kind regards,
  Louise Mimeau et al.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1322-AC3

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Publish subject to minor revisions (further review by editor) (15 Dec 2023) by Fabrizio Fenicia

AR by Louise Mimeau on behalf of the Authors (12 Jan 2024) Author's response Author's tracked changes Manuscript

ED: Publish as is (15 Jan 2024) by Fabrizio Fenicia

AR by Louise Mimeau on behalf of the Authors (15 Jan 2024)

Journal article(s) based on this preprint

23 Feb 2024

Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model

Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal

Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024,https://doi.org/10.5194/hess-28-851-2024, 2024

Short summary

Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal

Supplement

https://doi.org/10.5194/egusphere-2023-1322-supplement

Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal

Viewed

Total article views: 480 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	Supplement	BibTeX	EndNote
339	118	23	480	44	13	13

HTML: 339
PDF: 118
XML: 23
Total: 480
Supplement: 44
BibTeX: 13
EndNote: 13

Views and downloads (calculated since 28 Jul 2023)

Month	HTML	PDF	XML	Total
Jul 2023	97	26	4	127
Aug 2023	53	26	0	79
Sep 2023	34	20	4	58
Oct 2023	98	24	9	131
Nov 2023	11	3	0	14
Dec 2023	26	13	5	44
Jan 2024	9	0	9
Feb 2024	11	6	1	18

Cumulative views and downloads (calculated since 28 Jul 2023)

Month	HTML	PDF	XML	Total
Jul 2023	97	26	4	127
Aug 2023	53	26	0	79
Sep 2023	34	20	4	58
Oct 2023	98	24	9	131
Nov 2023	11	3	0	14
Dec 2023	26	13	5	44
Jan 2024	9	0	9
Feb 2024	11	6	1	18

Viewed (geographical distribution)

Total article views: 470 (including HTML, PDF, and XML) Thereof 470 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 24 Feb 2024

Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Preprint (5149 KB)
Metadata XML

Short summary

Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging and observed data on temporary rivers is scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.


Total:	0
HTML:	0
PDF:	0
XML:	0