the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Dec 2021
Extraction of roughness parameters from remotely-sensed products for hydrology applications
Abstract. Along rivers, where local insitu gauges are unavailable, estimation of river discharge are undirectly derived from the Manning formula that relate discharge to geomorphological characteristics of the rivers and flow conditions. Most components of the Manning formula can currently be derived from spaceborne products except for two features: the unobserved always-wet bathymetry and the roughness coefficient. Global-scale applications use simplified equivalent riverbed shapes and empirical parameters while local-scale applications rely on finer model dynamics, field survey and expert knowledge. Within the framework of the incoming Surface Water and Ocean Topography (SWOT) mission, scheduled for a launch in 2022, and more particularly, the development of the SWOT-based discharge product, fine-resolution but global discharge estimates should be produced. Currently implemented SWOT-based discharge algorithms require prior information on bathymetry and roughness and their performances highly depend on the quality of such priors. Here we introduce an automatic and spaceborne-data-based-only methodology to derive physically-based roughness coefficients to use in one-dimensional hydrological models. The evaluation of those friction coefficients showed that they allow model performances comparable to calibrated models. Finally, we illutrate two cases of application where our roughness coefficients are used as-is to initiate the experiment: a data assimilation experiment designed to correct the roughness parameters and an application around the HiVDI SWOT-based discharge algorithm. In both cases, the roughness coefficients showed promising perspectives by reproducing, for the data assimilation application, and sometimes improving, in the SWOT discharge algorithm case, the calibrated-parameter-based performances.
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RC1: 'Comment on hess-2021-551', Anonymous Referee #1, 03 Jan 2022
General comments
The intention of the paper is to show how Manning river roughness coefficients derived from a geomorphological space borne data base methodology can be of use, for the estimation of river discharge and related to the framework of the SWOT mission.
In fact three different studies are described where the roughness coefficients obtained through the procedure are applied.
- Deriving water levels in the Po and Garonne river through a 1 dimensional Mascarat hydraulic model
- A data assimilation (DA) application based on observations at a number of gauging sites in the Po river, using the roughness coefficients obtained through the “new method” as a starting point.
- A SWOT application, modelling discharges for a particular year in the Garonne, Po and Brahmaputra river.
The results of study 1 are discussed in the chapter 2 “method and materials” and in the same chapter the “new method” that in fact applies to all three studies is extensively explained. Chapter 3 describes the background of the other two studies and in a very condensed way background, results and discussion for the DA application (chapter 3.1) and SWOT discharge algorithm (chapter 3.2).
A more logic structure would be to have the general applicable explanations in a method and materials chapter, including the area descriptions. The chapters 3.1 and 3.2 are so condensed that required background and explanations are sometimes missing. Results could be more extensively discussed.The conclusions provided in this paper are fare in stating the severe limitations of the studies. This aligns with the aim of the research to “ introduce an automatic methodology to derive physically based, model prior values of friction coefficients”. Nevertheless, modelling results are presented and performance of the “new method” is evaluated against the performance of the “reference model”. It would be fair to state the limitations from the approach as mentioned in the conclusions also in the abstract, and not claim that the friction coefficients were evaluated (as stated in the abstract)..
In fact, it would have been valuable to provide more attention to the obtained roughness coefficients in e.g. the Data Assimilation procedure.The paper is compiled by contributions from different authors. It would become more readable if they team up and standardize in wordings, expressions, and handling captions of tables and figures.
Specific comments (per chapter)
Abstract
The abstract mentions “that the evaluation of those (i.e. physically based) roughness coefficients showed that they allow model performance comparable to calibrated models”, line 11-12. It are not the roughness coefficients that have been evaluated, what indeed would have been a valuable addition to the research conducted. When this sentence is rephrased, please be aware that evaluation took place not against “calibrated models” in general, but to the model as applied in this research.Information of applicability to types / sizes of rivers is missing as well as any information to the geographical setting of this research.
Introduction:
Line 32: to give a complete overview of recent developments, measurements based on LSPIV (large scale particle image velocimetry) with camera monitoring and hence options for validation of discharge and verification of the roughness coefficients could be mentioned as well. The same applies to line 70 where LSPIV can be mentioned. Examples of publications referring to discharge camera monitoring are:-) Tauro, F., Olivieri, G., Petroselli, A., Porfiri, M., & Grimaldi, S. (2016). Flow monitoring with a camera: a case study on a flood event in the Tiber river. Environmental Monitoring and Assessment, 188(2), 1-11. --
-) Fujita, I., Watanabe, H., & Tsubaki, R. (2007). Development of a nonâintrusive and efficient flow monitoring technique: The spaceâtime image velocimetry (STIV). International Journal of River Basin Management, 5(2), 105-114.
Methods and materials
The chapter combines too much. It gives theory, method, explanation of deriving at geomorphological river roughness coefficients, definition of the reference model, comparison of performance results for water levels at gauging sites (and conclusions) for the Po and Garonne in one the same chapter. It was already mentioned to separately provide the general applicable theory and method. Then next the specific background information, performance results and discussions for the three different studies can be handled (waterlevel modelling PO and Garonne , the DA experiment and SWOT application).The explanation and theory behind the Manning formula equation (1), line 92, in this chapter is very limited, and is never referred to in the paper. This theory would be a handle to explain the definition of A0, as used in the introduction. It also shows that the hydraulic head loss I is a factor in hydraulic models, surprisingly it is not described in the paper how this is determined and applied in the models.
The performance results for the Garonne and Po river are based on comparison of water levels at gauging stations. The study would be more complete if the performance was also evaluated on discharges. Deriving at discharges is a justification for this research, as the abstract and introduction mentions.
In addition, in the Po river, with several gauging stations, the reference model could have been set up with more than one uniform roughness (distinguishing between main channel and floodplain) for the whole 100km of river. It would give insight in the sensitivity of performance results to different ways of applying roughness coefficients in the reference model.
Data Assimilation experiment
The DA experiment is interesting as it updates model parameters in the DA procedure. It is applied on data at the different gauging stations over 7 zones in the Po river. It would have been useful to see the result of the updating process for the roughness coefficients, and also other parameters that were updated for both reference model and the “new method” modelling. Unfortunately this information is not provided and only information is given on improvement of the performance on water level for each of the modelling methods.
SWOT Discharge Algorithm
The SWOT discharge algorithm compares results of discharge modelling on the basis of roughness according to reference model and the “new method”. The background and results are provided so condensed that too often it remains guessing what is demonstrated or meant. E.g. although roughness coefficients from the reference method and “new method” are applied, the discharges are only estimated in the main channel. There is no explanation how this is compared to observations. In addition, the presented graphs of estimated discharges should have clear legends.
To summarize, the part describing the SWOT discharge hardly good conclusions can be drawn from this exercise to proof that good enough roughness coefficients are obtained from the new method. This part needs lot of attention in providing background information, wording, figures and tables..
Technical corrections (per chapter)
Introduction
Line 36: It is not clear in the sentence “5 current algorithms being developed” where this refers to. Correct “relies” to “rely”.
Line 37: “more or less” is a soft statement, should be clear and concise.
Line 39: ‘unobserved bathymetry, denoted A0” is “the underwater cross sectional area A0”.
Line 48: missing word “A0 as it a physically …” should read “A0 as it is a physically …”
Line 71: “no acceptable methodology to estimate flow velocities” can be rephrased in mentioning the perspectives of applying LSVIP at gauging sites.
Methods and materials
Line 90: the present study aims at automatically derive deriving the friction parameters such as etc. “Such as” should be removed as the study only deals with the Manning coefficient.
Line 92/93: provide units for the terms used in the equation (1), “A is the wetted cross sectional area”
Line 111: “on the field” should read “in the field”
Caption of tables (and graphs) should be self-explaining, to avoid the reader has to search in the text what is meant
Table 1: Better e.g. Availability of global scale datasets used to derive river roughness coefficients.
Line 122: Avoid use of spoken language, better e.g. The following global scale products have been used in the approach to derive river roughness coefficients, also see table 1.
Table 2: Better e.g. Aggregated land cover classes considered to derive river roughness coefficients.
Table 3: Indicators and values of nb (bed material) for both the main channel and the floodplain, by Acrement and Schneider (1989).
Table 4: same as above, include reference
Line 167,168: “Note that currently the threshold values were picked to match our knowledge of the study domains”. Comment: strange sentence, which threshold values and does this mean the process is not fully automatic ?
Line 178, 179: same remark applies as for line 167,168
Line 182-184: “Since the scope of the proposed method is framed by the SWOT mission, its goal is to estimate the value of n for large rivers, for which obstructions within the channel are rare and can be neglected. The value of n3 is therefore assumed to be equal to 0.0 s.m1/3 in the channel.” As this is not necessarily true in general, better refer to the cases in this paper that are examined.
Line 185, Table 7: n3 for the floodplain: The conversion of obstructions into a n value can be disputed. The values are quite high and therefore contribute a lot to the final n. E.g. artificial surfaces according to IOTA (column 1) are always considered appreciable obstructions(column 2) and have n3 of 0.025. However it would make a difference whether it is just a paved surface, or a building.
Line 189 effect of vegetation for the main channel is n4=0. Can this claim be substantiated.
Line 198: “This method aims at …”, Better: “Our method to obtain river roughness parameter n based on a geo morphological classification aims at being applicable globally.”
Table 10: is the classification specific for the Garonne, as it is under 2.2.1. Garonne domain.
Line 232: Table C2 of appendix C (C2 and C1 have the same caption, there seems a mix up of C1, C2, C3)
Line 264-265: Its results are compared to the same observed data and the method is deemed validated if the estimation performance is close to the reference performance. Comment: “close” is very subjective, better to mention only that the results are compared in terms of RMSE etc.
Line 268-269: This procedure needs further clarification.
Figure 4 and figure 8 are of the wrong river. Figure 4 belongs to the Po river and Figure 8 the Garonne.
Line 291-292: “It also showed that the model was far less sensitive to the floodplain roughness coefficient value than the main channel one”. Comment: This claim is not supported by any evidence.
Table 11. For clarity, the table could also compare MRE and SD. These values are shown in figure 10 but not in table 11. Note that RMSE has a unit, which is not provided.
Table 12: same applies as comment for table 11
Data Assimilation experiment
Each of the application chapters 3.1 and 3.2 give background, method, results and discussion in one and the same chapter, and very condensed for both applications. It is clearly written by (a) different author(s) than the one(s) for the previous chapters. The advice is to team up with all other authors to standardize wordings. Example is table 14. where Exp#1 calibrated roughness was called the reference model previously and in table 13 Ksmin and Ksmaj are headers for the main channel and floodplain respectively. Also terms like former method, reference method, calibrated roughness to indicate the same method could be standardized throughout the document.
Table 13: in addition to above, Ksmin and Ksmaj units have been omitted
Table 14: in addition to the above, the caption gives two different explanations for what is written in front and after a slash sign.
SWOT Discharge Algorithm
Table 15: In the column “the former value” the values of the roughness coefficient in the Garonne and Po deviate from what is written in the text in chapter 2.3.2. line 281 and line 291, without explanation.
Figure 16: The caption does not inform to which gauging site in the Po river the results refer to.
Citation: https://doi.org/10.5194/hess-2021-551-RC1 -
RC2: 'Comment on hess-2021-551', Anonymous Referee #2, 06 Jan 2022
Review Emery et al. 2021
This paper aims to deliver a physically based approach to estimate roughness coefficients for main channels and floodplains, using readily available Earth Observation data. There are major issues with this paper, both in the proposed methodology, and the experimental design. A very major revision is needed to consider this paper for publication. I have listed my major concerns below. These are followed by more detailed comments.
General larger comments:
- The quality of English is insufficient. For example, the first sentence in the abstract is grammatically not correct, and also may confuse a reader whether “estimation of river discharge is indirectly derived from …” by the authors as the subject of this paper (which I assumed), or whether this is a generically sensible approach. Please make sure a near native English speaker reviews the manuscript thoroughly.
- The abstract does not at all discuss the limitations of the approach. I see the possibly large amounts of noise introduced by the enormous amounts of introduced degrees of freedom, as a major obstacle for the use of this method. This has not been tested, and therefore, I would judge that simply choosing two roughness coefficients from text books may perform within the same limits of acceptability, at a much lower level of uncertainty.
- The basis for the approach is Arcement and Schneider (1989), which is not a scientific publication. Since the suggested approach is entirely dependent on this source, I cannot judge if the approach is scientifically grounded. Reference is needed to a scientific (peer reviewed) article, or that article should first be written before this publication can pass. At the very least, the original method should be more elaborately described and shown to be scientifically sound in the first place.
- The general (and largest) problem I foresee with the suggested approach is the level of noise using so many degrees of freedom (6 parameters whereas most hydrological applications use 1 or maybe 2, and a very large allowed spatial distribution!) with such noisy data as soilgrids, and a simple land cover classification with lookup tables, with unknown uncertainties. The authors do not demonstrate that the amount of degrees of freedom are warranted and contain any predictive value. Moreover, the classification of the original approach also contains very large ranges. If these ranges would be explored in a sensitivity analysis, I can already predict that the level of uncertainty in outcomes Manning’s coefficients will be very large. The argument that using this approach yields better values than non-calibrated models is not demonstrated because no proper benchmark experiment has been established and uncertainty of the estimation method has not been considered at all.
- A strong reliance on the “IOTA2” approach is suggested but it is not clear why particularly the IOTA2 method to land cover classification should be used. Why not any other land cover method based on medium resolution optical imagery?
- The DA experiment (3.1) is not clearly described. What do you expect to improve in data assimilation with your method? And how do you test this exactly? It looks like you are comparing one uniform manning roughness against 14 different manning roughnesses (i.e. many more degrees of freedom). That makes it highly logical that a DA experiment (or any calibration experiment) leads to a better fit of observed values, regardless of the method used to set a prior estimate on the roughness values. I don’t see the added value of the proposed method as one does not need such a method to impose more degrees of freedom on a 1D simulation model.
- The SWOT experiment in 3.2 is also not clear. How are your observations introduced in the experiment? And what is the experiment exactly? Also here, is the better result not merely a result of the fact that you introduce more degrees of freedom? This would render the experiment invalid as it is not a fair comparison.
- Conclusions: given the likely invalidity of all experiments I have seen in this paper, if this paper is considered for improvement, all conclusions will alter as well. There is no discussion on the limitations of the method and certainly the fact that one introduces many additional degrees of freedom (both due to multiple manning roughness components and due to the spatial distribution). No proof is given that introduced noise because of so many degrees of freedom is at satisfactory levels. The uncertainty must be investigated and discussed.
Detailed comments:
Abstract
- there is no information about the EO methods used to estimate the roughness in the abstract. I would add at least one sentence on this method.
- Introduction
- I would introduce the Manning-strickler relationship earlier (around l.40) and decide whether to use Manning (n) or Strickler (k) for the remainder of the paper, as n is simply 1/k.
- 70 applyable → applicable.
- 104. What does the meandering ratio mean? And does it apply under all circumstances? If a natural river is at low levels, it follows the stronger meanders of the permanent bed, whilst at higher flows it will follow a shorter path, i.e. between the natural levees.
2. Method
- How does this work where soilgrids is not respresentative for the river bed? For instance, in smaller mountaineous streams, sol grids does not at all constitute a representative database. Even for larger alluvial streams, the river bed sediment may already be very different from the floodplain sediment where finer grain sizes will be dumped during floods. Are you now assuming these are always equal?
- 152. “There are a few locations where SoilGrids provide no data. In this case n b is computed as the average of the three adopted values of n b values, equal to 0.0245 s.m − 3”. This is not clear. Which “adopted values?”. And it looks like for no data areas, you do the same as for with data values.
- 163: cross-sectionnal should be cross-sectional
- computation of n1. It is not clear where the cross-sectional profiles should come from if this method should be entirely remote sensing based. Another problem is that the suggested ratio is strongly dependent on the resolution of the profile observations AND the level of smoothing, which also seems arbitrary (it was supposed to be described in Appendix B but it is not, only examples are shown). If a surveyor measures a profile point every 1.0 meters, you’ll get a very different measure compared to when an observation is taken every 0.2 meters, which I find very problematic for a suggested generic approach.
- Computation of n2. Similar to n1, the choice of distance between sampled widths is not elaborated upon, does impact strongly (of course!) on the variability of the derivative of width in space, and it is also not clear how the authors then can justify the relationship between this sampled width and a contribution to Manning’s n (i.e. the “adopted value” in Table 6.
- Same story as with n2 and n1, there is no support for the mapping of the parameters to the land cover maps.
- Meandering coefficient. I think it makes sense that sinuosity can be assumed to have no impact on the floodplain, but please then describe why. And also here, a relationship is made between the indicator and the table by Arcement and Schneider, without any reasoning.
2.3 validation
- 264-265 “Its results are compared to the same observed data and the method is deemed validated if the estimation performance is close to the reference performance.”. This experiment is not valid for 2 reasons:
- there is no properly defined benchmark, i.e. a different method to sample roughness without calibration than presented in this paper. A valid experiment would be to generate several models with a priori sampled roughness coefficients, following typical lookup tables, as one would do without having the presented approach.
- Second no notion has been taken of noise in the sampled estimates. Given that there is a strong variance in all coefficients, and additional noise in the relation between the lookup tables and the presented components of n, and there is no reason to suggest that any of the presented coefficients covaries, I expect that the noise in your n estimates will be very large and that the noise in river flow will hence we disproportionately large to render a method with so many degrees of freedom useful.
- 278 – 292. It is not clear how far the boundary conditions are from the evaluation station. If these are too close, then this will affect the results (e.g. backwater from downstream boundary condition). Has this been checked?
- Results of the validation are presented in the methods section
Section 3.1. The experiment with Data Assimilation is not properly described. What hypothesis is exactly tested? And what is the experimental design leading to that test?
Section 3.2. Same problem as 3.1. Experiment is not clear and I fear that the experiment is invalid.
- 15-17. It is not clear what the different labels mean. I guess “target” is the “observation”, but what does “prior VDA” and “real-time” mean?
- Fig 15: The blue and black line are nearly on top of each other, which makes the suggestion that a “47% reduction in errors” rather superfluous. My impression of the results is that there is overall no real difference (comparing all 3 locations) and the improvements over the Po could easily be explained by the fact that your method introduces more degrees of freedom.
- 449. “serenely”? Should this be “securely”?
Citation: https://doi.org/10.5194/hess-2021-551-RC2 -
EC1: 'Editor Comment on hess-2021-551', Hubert H.G. Savenije, 06 Jan 2022
Dear Authors,
You have received two very detailed and extensive reviews, which I am sure will benefit your research very much if you follow-up on these comments. I am afraid that the paper in the present state has to be rejected. I cannot see how a revised paper would become acceptable within a reasonable period of time. After you have fully revised your work and benefited from the comments provided, you may submit it as a new paper, or submit it somewhere else. But make sure you have made the necessary revision before doing so.
Citation: https://doi.org/10.5194/hess-2021-551-EC1
Interactive discussion
Status: closed
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RC1: 'Comment on hess-2021-551', Anonymous Referee #1, 03 Jan 2022
General comments
The intention of the paper is to show how Manning river roughness coefficients derived from a geomorphological space borne data base methodology can be of use, for the estimation of river discharge and related to the framework of the SWOT mission.
In fact three different studies are described where the roughness coefficients obtained through the procedure are applied.
- Deriving water levels in the Po and Garonne river through a 1 dimensional Mascarat hydraulic model
- A data assimilation (DA) application based on observations at a number of gauging sites in the Po river, using the roughness coefficients obtained through the “new method” as a starting point.
- A SWOT application, modelling discharges for a particular year in the Garonne, Po and Brahmaputra river.
The results of study 1 are discussed in the chapter 2 “method and materials” and in the same chapter the “new method” that in fact applies to all three studies is extensively explained. Chapter 3 describes the background of the other two studies and in a very condensed way background, results and discussion for the DA application (chapter 3.1) and SWOT discharge algorithm (chapter 3.2).
A more logic structure would be to have the general applicable explanations in a method and materials chapter, including the area descriptions. The chapters 3.1 and 3.2 are so condensed that required background and explanations are sometimes missing. Results could be more extensively discussed.The conclusions provided in this paper are fare in stating the severe limitations of the studies. This aligns with the aim of the research to “ introduce an automatic methodology to derive physically based, model prior values of friction coefficients”. Nevertheless, modelling results are presented and performance of the “new method” is evaluated against the performance of the “reference model”. It would be fair to state the limitations from the approach as mentioned in the conclusions also in the abstract, and not claim that the friction coefficients were evaluated (as stated in the abstract)..
In fact, it would have been valuable to provide more attention to the obtained roughness coefficients in e.g. the Data Assimilation procedure.The paper is compiled by contributions from different authors. It would become more readable if they team up and standardize in wordings, expressions, and handling captions of tables and figures.
Specific comments (per chapter)
Abstract
The abstract mentions “that the evaluation of those (i.e. physically based) roughness coefficients showed that they allow model performance comparable to calibrated models”, line 11-12. It are not the roughness coefficients that have been evaluated, what indeed would have been a valuable addition to the research conducted. When this sentence is rephrased, please be aware that evaluation took place not against “calibrated models” in general, but to the model as applied in this research.Information of applicability to types / sizes of rivers is missing as well as any information to the geographical setting of this research.
Introduction:
Line 32: to give a complete overview of recent developments, measurements based on LSPIV (large scale particle image velocimetry) with camera monitoring and hence options for validation of discharge and verification of the roughness coefficients could be mentioned as well. The same applies to line 70 where LSPIV can be mentioned. Examples of publications referring to discharge camera monitoring are:-) Tauro, F., Olivieri, G., Petroselli, A., Porfiri, M., & Grimaldi, S. (2016). Flow monitoring with a camera: a case study on a flood event in the Tiber river. Environmental Monitoring and Assessment, 188(2), 1-11. --
-) Fujita, I., Watanabe, H., & Tsubaki, R. (2007). Development of a nonâintrusive and efficient flow monitoring technique: The spaceâtime image velocimetry (STIV). International Journal of River Basin Management, 5(2), 105-114.
Methods and materials
The chapter combines too much. It gives theory, method, explanation of deriving at geomorphological river roughness coefficients, definition of the reference model, comparison of performance results for water levels at gauging sites (and conclusions) for the Po and Garonne in one the same chapter. It was already mentioned to separately provide the general applicable theory and method. Then next the specific background information, performance results and discussions for the three different studies can be handled (waterlevel modelling PO and Garonne , the DA experiment and SWOT application).The explanation and theory behind the Manning formula equation (1), line 92, in this chapter is very limited, and is never referred to in the paper. This theory would be a handle to explain the definition of A0, as used in the introduction. It also shows that the hydraulic head loss I is a factor in hydraulic models, surprisingly it is not described in the paper how this is determined and applied in the models.
The performance results for the Garonne and Po river are based on comparison of water levels at gauging stations. The study would be more complete if the performance was also evaluated on discharges. Deriving at discharges is a justification for this research, as the abstract and introduction mentions.
In addition, in the Po river, with several gauging stations, the reference model could have been set up with more than one uniform roughness (distinguishing between main channel and floodplain) for the whole 100km of river. It would give insight in the sensitivity of performance results to different ways of applying roughness coefficients in the reference model.
Data Assimilation experiment
The DA experiment is interesting as it updates model parameters in the DA procedure. It is applied on data at the different gauging stations over 7 zones in the Po river. It would have been useful to see the result of the updating process for the roughness coefficients, and also other parameters that were updated for both reference model and the “new method” modelling. Unfortunately this information is not provided and only information is given on improvement of the performance on water level for each of the modelling methods.
SWOT Discharge Algorithm
The SWOT discharge algorithm compares results of discharge modelling on the basis of roughness according to reference model and the “new method”. The background and results are provided so condensed that too often it remains guessing what is demonstrated or meant. E.g. although roughness coefficients from the reference method and “new method” are applied, the discharges are only estimated in the main channel. There is no explanation how this is compared to observations. In addition, the presented graphs of estimated discharges should have clear legends.
To summarize, the part describing the SWOT discharge hardly good conclusions can be drawn from this exercise to proof that good enough roughness coefficients are obtained from the new method. This part needs lot of attention in providing background information, wording, figures and tables..
Technical corrections (per chapter)
Introduction
Line 36: It is not clear in the sentence “5 current algorithms being developed” where this refers to. Correct “relies” to “rely”.
Line 37: “more or less” is a soft statement, should be clear and concise.
Line 39: ‘unobserved bathymetry, denoted A0” is “the underwater cross sectional area A0”.
Line 48: missing word “A0 as it a physically …” should read “A0 as it is a physically …”
Line 71: “no acceptable methodology to estimate flow velocities” can be rephrased in mentioning the perspectives of applying LSVIP at gauging sites.
Methods and materials
Line 90: the present study aims at automatically derive deriving the friction parameters such as etc. “Such as” should be removed as the study only deals with the Manning coefficient.
Line 92/93: provide units for the terms used in the equation (1), “A is the wetted cross sectional area”
Line 111: “on the field” should read “in the field”
Caption of tables (and graphs) should be self-explaining, to avoid the reader has to search in the text what is meant
Table 1: Better e.g. Availability of global scale datasets used to derive river roughness coefficients.
Line 122: Avoid use of spoken language, better e.g. The following global scale products have been used in the approach to derive river roughness coefficients, also see table 1.
Table 2: Better e.g. Aggregated land cover classes considered to derive river roughness coefficients.
Table 3: Indicators and values of nb (bed material) for both the main channel and the floodplain, by Acrement and Schneider (1989).
Table 4: same as above, include reference
Line 167,168: “Note that currently the threshold values were picked to match our knowledge of the study domains”. Comment: strange sentence, which threshold values and does this mean the process is not fully automatic ?
Line 178, 179: same remark applies as for line 167,168
Line 182-184: “Since the scope of the proposed method is framed by the SWOT mission, its goal is to estimate the value of n for large rivers, for which obstructions within the channel are rare and can be neglected. The value of n3 is therefore assumed to be equal to 0.0 s.m1/3 in the channel.” As this is not necessarily true in general, better refer to the cases in this paper that are examined.
Line 185, Table 7: n3 for the floodplain: The conversion of obstructions into a n value can be disputed. The values are quite high and therefore contribute a lot to the final n. E.g. artificial surfaces according to IOTA (column 1) are always considered appreciable obstructions(column 2) and have n3 of 0.025. However it would make a difference whether it is just a paved surface, or a building.
Line 189 effect of vegetation for the main channel is n4=0. Can this claim be substantiated.
Line 198: “This method aims at …”, Better: “Our method to obtain river roughness parameter n based on a geo morphological classification aims at being applicable globally.”
Table 10: is the classification specific for the Garonne, as it is under 2.2.1. Garonne domain.
Line 232: Table C2 of appendix C (C2 and C1 have the same caption, there seems a mix up of C1, C2, C3)
Line 264-265: Its results are compared to the same observed data and the method is deemed validated if the estimation performance is close to the reference performance. Comment: “close” is very subjective, better to mention only that the results are compared in terms of RMSE etc.
Line 268-269: This procedure needs further clarification.
Figure 4 and figure 8 are of the wrong river. Figure 4 belongs to the Po river and Figure 8 the Garonne.
Line 291-292: “It also showed that the model was far less sensitive to the floodplain roughness coefficient value than the main channel one”. Comment: This claim is not supported by any evidence.
Table 11. For clarity, the table could also compare MRE and SD. These values are shown in figure 10 but not in table 11. Note that RMSE has a unit, which is not provided.
Table 12: same applies as comment for table 11
Data Assimilation experiment
Each of the application chapters 3.1 and 3.2 give background, method, results and discussion in one and the same chapter, and very condensed for both applications. It is clearly written by (a) different author(s) than the one(s) for the previous chapters. The advice is to team up with all other authors to standardize wordings. Example is table 14. where Exp#1 calibrated roughness was called the reference model previously and in table 13 Ksmin and Ksmaj are headers for the main channel and floodplain respectively. Also terms like former method, reference method, calibrated roughness to indicate the same method could be standardized throughout the document.
Table 13: in addition to above, Ksmin and Ksmaj units have been omitted
Table 14: in addition to the above, the caption gives two different explanations for what is written in front and after a slash sign.
SWOT Discharge Algorithm
Table 15: In the column “the former value” the values of the roughness coefficient in the Garonne and Po deviate from what is written in the text in chapter 2.3.2. line 281 and line 291, without explanation.
Figure 16: The caption does not inform to which gauging site in the Po river the results refer to.
Citation: https://doi.org/10.5194/hess-2021-551-RC1 -
RC2: 'Comment on hess-2021-551', Anonymous Referee #2, 06 Jan 2022
Review Emery et al. 2021
This paper aims to deliver a physically based approach to estimate roughness coefficients for main channels and floodplains, using readily available Earth Observation data. There are major issues with this paper, both in the proposed methodology, and the experimental design. A very major revision is needed to consider this paper for publication. I have listed my major concerns below. These are followed by more detailed comments.
General larger comments:
- The quality of English is insufficient. For example, the first sentence in the abstract is grammatically not correct, and also may confuse a reader whether “estimation of river discharge is indirectly derived from …” by the authors as the subject of this paper (which I assumed), or whether this is a generically sensible approach. Please make sure a near native English speaker reviews the manuscript thoroughly.
- The abstract does not at all discuss the limitations of the approach. I see the possibly large amounts of noise introduced by the enormous amounts of introduced degrees of freedom, as a major obstacle for the use of this method. This has not been tested, and therefore, I would judge that simply choosing two roughness coefficients from text books may perform within the same limits of acceptability, at a much lower level of uncertainty.
- The basis for the approach is Arcement and Schneider (1989), which is not a scientific publication. Since the suggested approach is entirely dependent on this source, I cannot judge if the approach is scientifically grounded. Reference is needed to a scientific (peer reviewed) article, or that article should first be written before this publication can pass. At the very least, the original method should be more elaborately described and shown to be scientifically sound in the first place.
- The general (and largest) problem I foresee with the suggested approach is the level of noise using so many degrees of freedom (6 parameters whereas most hydrological applications use 1 or maybe 2, and a very large allowed spatial distribution!) with such noisy data as soilgrids, and a simple land cover classification with lookup tables, with unknown uncertainties. The authors do not demonstrate that the amount of degrees of freedom are warranted and contain any predictive value. Moreover, the classification of the original approach also contains very large ranges. If these ranges would be explored in a sensitivity analysis, I can already predict that the level of uncertainty in outcomes Manning’s coefficients will be very large. The argument that using this approach yields better values than non-calibrated models is not demonstrated because no proper benchmark experiment has been established and uncertainty of the estimation method has not been considered at all.
- A strong reliance on the “IOTA2” approach is suggested but it is not clear why particularly the IOTA2 method to land cover classification should be used. Why not any other land cover method based on medium resolution optical imagery?
- The DA experiment (3.1) is not clearly described. What do you expect to improve in data assimilation with your method? And how do you test this exactly? It looks like you are comparing one uniform manning roughness against 14 different manning roughnesses (i.e. many more degrees of freedom). That makes it highly logical that a DA experiment (or any calibration experiment) leads to a better fit of observed values, regardless of the method used to set a prior estimate on the roughness values. I don’t see the added value of the proposed method as one does not need such a method to impose more degrees of freedom on a 1D simulation model.
- The SWOT experiment in 3.2 is also not clear. How are your observations introduced in the experiment? And what is the experiment exactly? Also here, is the better result not merely a result of the fact that you introduce more degrees of freedom? This would render the experiment invalid as it is not a fair comparison.
- Conclusions: given the likely invalidity of all experiments I have seen in this paper, if this paper is considered for improvement, all conclusions will alter as well. There is no discussion on the limitations of the method and certainly the fact that one introduces many additional degrees of freedom (both due to multiple manning roughness components and due to the spatial distribution). No proof is given that introduced noise because of so many degrees of freedom is at satisfactory levels. The uncertainty must be investigated and discussed.
Detailed comments:
Abstract
- there is no information about the EO methods used to estimate the roughness in the abstract. I would add at least one sentence on this method.
- Introduction
- I would introduce the Manning-strickler relationship earlier (around l.40) and decide whether to use Manning (n) or Strickler (k) for the remainder of the paper, as n is simply 1/k.
- 70 applyable → applicable.
- 104. What does the meandering ratio mean? And does it apply under all circumstances? If a natural river is at low levels, it follows the stronger meanders of the permanent bed, whilst at higher flows it will follow a shorter path, i.e. between the natural levees.
2. Method
- How does this work where soilgrids is not respresentative for the river bed? For instance, in smaller mountaineous streams, sol grids does not at all constitute a representative database. Even for larger alluvial streams, the river bed sediment may already be very different from the floodplain sediment where finer grain sizes will be dumped during floods. Are you now assuming these are always equal?
- 152. “There are a few locations where SoilGrids provide no data. In this case n b is computed as the average of the three adopted values of n b values, equal to 0.0245 s.m − 3”. This is not clear. Which “adopted values?”. And it looks like for no data areas, you do the same as for with data values.
- 163: cross-sectionnal should be cross-sectional
- computation of n1. It is not clear where the cross-sectional profiles should come from if this method should be entirely remote sensing based. Another problem is that the suggested ratio is strongly dependent on the resolution of the profile observations AND the level of smoothing, which also seems arbitrary (it was supposed to be described in Appendix B but it is not, only examples are shown). If a surveyor measures a profile point every 1.0 meters, you’ll get a very different measure compared to when an observation is taken every 0.2 meters, which I find very problematic for a suggested generic approach.
- Computation of n2. Similar to n1, the choice of distance between sampled widths is not elaborated upon, does impact strongly (of course!) on the variability of the derivative of width in space, and it is also not clear how the authors then can justify the relationship between this sampled width and a contribution to Manning’s n (i.e. the “adopted value” in Table 6.
- Same story as with n2 and n1, there is no support for the mapping of the parameters to the land cover maps.
- Meandering coefficient. I think it makes sense that sinuosity can be assumed to have no impact on the floodplain, but please then describe why. And also here, a relationship is made between the indicator and the table by Arcement and Schneider, without any reasoning.
2.3 validation
- 264-265 “Its results are compared to the same observed data and the method is deemed validated if the estimation performance is close to the reference performance.”. This experiment is not valid for 2 reasons:
- there is no properly defined benchmark, i.e. a different method to sample roughness without calibration than presented in this paper. A valid experiment would be to generate several models with a priori sampled roughness coefficients, following typical lookup tables, as one would do without having the presented approach.
- Second no notion has been taken of noise in the sampled estimates. Given that there is a strong variance in all coefficients, and additional noise in the relation between the lookup tables and the presented components of n, and there is no reason to suggest that any of the presented coefficients covaries, I expect that the noise in your n estimates will be very large and that the noise in river flow will hence we disproportionately large to render a method with so many degrees of freedom useful.
- 278 – 292. It is not clear how far the boundary conditions are from the evaluation station. If these are too close, then this will affect the results (e.g. backwater from downstream boundary condition). Has this been checked?
- Results of the validation are presented in the methods section
Section 3.1. The experiment with Data Assimilation is not properly described. What hypothesis is exactly tested? And what is the experimental design leading to that test?
Section 3.2. Same problem as 3.1. Experiment is not clear and I fear that the experiment is invalid.
- 15-17. It is not clear what the different labels mean. I guess “target” is the “observation”, but what does “prior VDA” and “real-time” mean?
- Fig 15: The blue and black line are nearly on top of each other, which makes the suggestion that a “47% reduction in errors” rather superfluous. My impression of the results is that there is overall no real difference (comparing all 3 locations) and the improvements over the Po could easily be explained by the fact that your method introduces more degrees of freedom.
- 449. “serenely”? Should this be “securely”?
Citation: https://doi.org/10.5194/hess-2021-551-RC2 -
EC1: 'Editor Comment on hess-2021-551', Hubert H.G. Savenije, 06 Jan 2022
Dear Authors,
You have received two very detailed and extensive reviews, which I am sure will benefit your research very much if you follow-up on these comments. I am afraid that the paper in the present state has to be rejected. I cannot see how a revised paper would become acceptable within a reasonable period of time. After you have fully revised your work and benefited from the comments provided, you may submit it as a new paper, or submit it somewhere else. But make sure you have made the necessary revision before doing so.
Citation: https://doi.org/10.5194/hess-2021-551-EC1
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