the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assimilation of Transformed Water Surface Elevation to Improve River Discharge Estimation in a Continental-Scale River
- 1Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
- 2Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, 152-8550, Japan
- 1Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
- 2Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, 152-8550, Japan
Abstract. Quantifying continental-scale river discharge is essential to understanding the terrestrial water cycle but is susceptible to errors caused by a lack of observations and the limitations of hydrodynamic modeling. Data assimilation (DA) methods are increasingly used to estimate river discharge in combination with emerging river-related remote sensing products (e.g., water surface elevation [WSE], water surface slope, river width, and flood extent). However, directly comparing simulated WSE to satellite altimetry data remains challenging (e.g., because of large biases between simulations and observations or uncertainties in parameters), and large errors can be introduced when satellite observations are assimilated into hydrodynamic models. In this study we performed direct, anomaly, and normalized value assimilation experiments to investigate the capacity of DA to improve river discharge within the current limitations of hydrodynamic modeling. We performed hydrological DA using a physically-based empirical localization method applied to the Amazon Basin. We used satellite altimetry data from ENVISAT, Jason 1, and Jason 2. Direct DA was the baseline assimilation method and was subject to errors due to biases in the simulated WSE. To overcome these errors, we used anomaly DA as an alternative to direct DA. We found that the modeled and observed WSE distributions differed considerably (e.g., differences in amplitude, seasonal flow variation, and a skewed distribution due to limitations of the hydrodynamic models). Therefore, normalized value DA was performed to improve discharge estimation. River discharge estimates were improved at 24 %, 38 %, and 62 % of stream gauges in the direct, anomaly, and normalized value assimilations relative to simulations without DA. Normalized value assimilation performed best for estimating river discharge given the current limitations of hydrodynamic models. Most gauges within the river reaches covered by satellite observations accurately estimated river discharge, with Nash-Sutcliffe efficiency (NSE) > 0.6. The amplitudes of WSE variation were improved in the normalized DA experiment. Furthermore, in the Amazon Basin, normalized assimilation (median NSE = 0.50) improved river discharge estimation compared to open-loop simulation with the global hydrodynamic model (median NSE = 0.42). River discharge estimation using direct DA methods was improved by 7 % with calibration of river bathymetry based on NSE. The direct DA approach outperformed the other DA approaches when runoff was considerably biased, but anomaly DA performed best when the river bathymetry was erroneous. The uncertainties in hydrodynamic modeling (e.g., river bottom elevation, river width, simplified floodplain dynamics, and the rectangular cross-section assumption) should be improved to fully realize the advantages of river discharge DA through the assimilation of satellite altimetry. This study contributes to the development of a global river discharge reanalysis product that is consistent spatially and temporally.
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Menaka Revel et al.
Status: open (until 31 Aug 2022)
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RC1: 'Comment on egusphere-2022-412', Anonymous Referee #1, 20 Jul 2022
reply
General comment:
In this research, the authors performed data assimilation (DA) experiments to explore the capacity to improve daily river discharge within current limitations of global hydrodynamic modeling. For this purpose, the water surface elevation (WSE) from satellite altimetry was assimilated in a configuration of three experiments, the direct (absolute values), the anomalies and the normalized anomalies. The authors also evaluated the capability of these DA experiments in some scenarios, for instance when some parameters/forcing (river bathymetry and runoff) of the hydrodynamic model are biased, as well as conditions when river bathymetry is calibrated. The results showed that, in general, the normalized DA performance was the best, improving the daily discharge estimates in up to almost 60% of the stations evaluated, compared to the simulation without DA. These results considering the current limited conditions of the global hydrodynamic models (e.g. without calibration).
The major contribution of this research is the evaluation of these experiments and scenarios, providing adequate knowledge and insights in terms of how DA techniques could be used to improve discharge estimates, which fits with the perspectives of SWOT missions for example. In general, this work is worth publishing in the Hydrology and Earth System Sciences journal, however, it needs “moderate revisions”. Some suggestions for revisions are as follows.
Moderate comments:
- The article is well written and follows a logical order. Although it is a bit long, an average reader can follow the reading, however at a certain point there are more experiments than initially described. For example, the evaluation of DA under biased runoff and river bathymetry conditions; DA under calibrated river bathymetry conditions; DA using the runoff forcing of a bias-corrected model. That is why I recommend the authors to describe more explicitly these experiments in section 2.6.
- Regarding the selection of virtual stations (VSs) for assimilation or validation, the justification is a bit vague, even though this may be important for the performance of the experiments, so I recommend improving this point.
- In sections 3.1.1, 3.1.2 and 3.1.3 take care with the description of the time series in figures 4, 5 and 6, respectively. There is a confusion between the description of the gauges results. For example, line 315 describes the Santos Dumont station on the Purus river, however the series in Figure 4d are from a gauge on the Juruá river. This confusion occurs for the stations Gaviao (Juruá) and Manacapurú (Amazon) in Figure 5, and in all the gauges in Figure 6. This was probably an involuntary error in the preparation of the figures, please correct.
- Experiments that assimilate absolute (direct) values, anomalies and normalized anomalies are referred to by the acronyms Exp. 1, Exp. 2 and Exp. 3 respectively, however throughout the manuscript both nomenclatures are used. I suggest that only one be adopted to improve the readability of the text. Even so that the information can be quickly abstracted by the reader these experiments could be called DIR_DA, ANOM_DA and NORM_DA for example.
- Since the authors have used a localization method in the DA scheme, I suggest reinforcing the discussion on how this might affect discharge estimates due to assimilation of WSE within or outside the influence coverage of the VSs.
- Could you discuss a bit about to what do you attribute the lower efficiency of flow estimates in the upper Solimoes River ? efficiency of the CaMa-Flood model ? selection of VSs ? localization ? large uncertainties in the VSs data in that region ?
Specific comments (Line-by-line comments):
Introduction:
- 35: It is more accurate to say, "River discharge records can be used...".
- 42: I would say that also these simulations (of GHMs) have been used to complement observed records.
- 48: If we go deeper we could say that these forcing factors can also be rainfall and climatic variables.
- 50: I would say: "Given the current limitations of GHMs and in-situ measurements, ...".
- 57: This sentence mentioning the SWOT mission seems a bit loose, you should rework it to integrate it with what you want to mention above.
- 62: Typo: "combining" instead of "combing".
- 83: This statement describes information repeated in the previous one, perhaps you could combine them.
Methodology:
- 124: In this sentence you can already start reporting on the period of DA experiments (2009-2014).
- 215: Why wasn't the SURFEX-TRIP model outputs used since it also belongs to WRR2?
- 236: To reference these annual average rainfall values you can cite Builes-Jaramillo & Poveda, 2018; Espinoza et al., 2009. (https://doi.org/10.1029/2017WR021338 and https://doi.org/10.1002/joc.1791)
- 237: Please specify what you mean by large number of observations, perhaps this is valid for remote sensing observations because it is a large basin with strong hydrological signals, hence the citation of Fassoni-Andrade et al. 2021.
- 249: You could elaborate a little more on this sentence. Why these virtual stations could affect the estimates using assimilation? this exclusion of 3% was by a visual analysis of the series only? these stations are located in some particular place in the Amazon, maybe rivers with a small width?
- Sections 2.7.1 and 2.7.2 could be merged, as it could confuse the reader. The main objective of this research is to evaluate the performance in simulating daily discharge but here also the performance of WSE will be evaluated. This merged section could be called "observational data" since the altimetry data has also been used for validation.
Results:
- 294-296: This sentence seems to be repetitive with the previous one, you could merge them.
- 302: It would be appropriate to refer to Figure 4b in this sentence.
- 309: The time series for the Santos Dumont station is not shown in Figure 4d. Instead, a station on the Juruá River is shown. See my major comments above.
- 325: The information in parentheses should go in the methodology section.
- 330: “WSE performance decreased…” instead of “WSE decreased…”.
- 332: The Gavião and Manacapuru gauges do not correspond to Figures 5c and d.
- 351-352: None of these described gauges correspond to figures 6c, d and e.
- 412: I think there is a typo, please delete "3.2.1.".
- 8: It is not possible to distinguish gauges inside or outside the coverage area of the altimetric satellites. Could you differentiate them somehow?
- Table 3. I have noticed that some values in this table do not correspond exactly to those described. For instance, in the first column (All and r) in the table, the values are 0.74, 0.85 and 0.84 for experiments 1, 2 and 3 respectively. While in the description the values are 0.73, 0.84 and 0.83 (L. 431, L. 415 and L. 439 respectively).
- 452: As shown in Figure 9, the BIAS values are only positive, so I recommend describing somewhere (probably methodology) that the index is an absolute value of BIAS.
- Figure 9: It is a bit difficult to differentiate the VSs that were used for assimilation and validation. Perhaps it could be improved by changing the symbology from "o" to "*", increasing the size of the maps by reducing the space between them and decreasing a little the size of the station symbols so that they do not overlap too much. This is just a suggestion.
- Section 3.3: Please detail how in this experiment you have generated the realizations of the set for assimilation.Was it with the same perturbation as for the WRR2 models?
- 476-478: The end of this sentence sounds strange, I suggest to redo it or delete this last part from "...,direct DA (Exp 1)...."
Conclusions:
- 624: Typo, it's HTESSEL not HTEESSEL (same for L.541, 542 and 544).
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AC1: 'Reply on RC1', Menaka Revel, 18 Aug 2022
reply
Referee #1
General comment:
In this research, the authors performed data assimilation (DA) experiments to explore the capacity to improve daily river discharge within current limitations of global hydrodynamic modeling. For this purpose, the water surface elevation (WSE) from satellite altimetry was assimilated in a configuration of three experiments, the direct (absolute values), the anomalies and the normalized anomalies. The authors also evaluated the capability of these DA experiments in some scenarios, for instance when some parameters/forcing (river bathymetry and runoff) of the hydrodynamic model are biased, as well as conditions when river bathymetry is calibrated. The results showed that, in general, the normalized DA performance was the best, improving the daily discharge estimates in up to almost 60% of the stations evaluated, compared to the simulation without DA. These results considering the current limited conditions of the global hydrodynamic models (e.g. without calibration).
The major contribution of this research is the evaluation of these experiments and scenarios, providing adequate knowledge and insights in terms of how DA techniques could be used to improve discharge estimates, which fits with the perspectives of SWOT missions for example. In general, this work is worth publishing in the Hydrology and Earth System Sciences journal, however, it needs “moderate revisions”. Some suggestions for revisions are as follows.
Reply:
We would like to thank the referee for his kind remarks. We will address all the comments in the revised manuscript, and comprehensive explanations are provided below.
Moderate comments:
1. The article is well written and follows a logical order. Although it is a bit long, an average reader can follow the reading, however at a certain point there are more experiments than initially described. For example, the evaluation of DA under biased runoff and river bathymetry conditions; DA under calibrated river bathymetry conditions; DA using the runoff forcing of a bias-corrected model. That is why I recommend the authors to describe more explicitly these experiments in section 2.6.
Reply:
Thank you very much for the recommendation. We will include brief descriptions of the additional experiments in section 2.6 considering the length of the manuscript.
2. Regarding the selection of virtual stations (VSs) for assimilation or validation, the justification is a bit vague, even though this may be important for the performance of the experiments, so I recommend improving this point.
Reply:
We would like to express our gratitude to referee #1. We simply separated VSs to assimilation and validation 80%, and 20%, respectively. We will revise the description to reflect the selection of the VS for assimilation and validation
3. In sections 3.1.1, 3.1.2 and 3.1.3 take care with the description of the time series in figures 4, 5 and 6, respectively. There is a confusion between the description of the gauges results. For example, line 315 describes the Santos Dumont station on the Purus river, however the series in Figure 4d are from a gauge on the Juruá river. This confusion occurs for the stations Gaviao (Juruá) and Manacapurú (Amazon) in Figure 5, and in all the gauges in Figure 6. This was probably an involuntary error in the preparation of the figures, please correct.
Reply:
We thank referee #1 for recognizing the mistake. It was indeed an involuntary error in preparing the figures. We will correct the cross-reference with the corresponding figure and the text. Furthermore, we will correct the manuscript according to the referee's comment. Basically, we will replace Figures 4, 5, and 6 to match the descriptions in Sect. 3.1.1, 3.1.2, and 3.1.3, respectively.
4. Experiments that assimilate absolute (direct) values, anomalies and normalized anomalies are referred to by the acronyms Exp. 1, Exp. 2 and Exp. 3 respectively, however throughout the manuscript both nomenclatures are used. I suggest that only one be adopted to improve the readability of the text. Even so that the information can be quickly abstracted by the reader these experiments could be called DIR_DA, ANOM_DA and NORM_DA for example.
Reply:
We appreciate the suggestion from referee #1. We will adopt a better naming convention for the experiments in the revised manuscript such as DIR, ANO, and NOM.
5. Since the authors have used a localization method in the DA scheme, I suggest reinforcing the discussion on how this might affect discharge estimates due to assimilation of WSE within or outside the influence coverage of the VSs.
Reply:
We would like to thank the great suggestion by the referee. The localization method we used in this study is an adaptive localization method (Revel et al., 2019) which is far different from conventional localization methods which use fixed square-shaped local patches. The comparison between those methods can be found in Revel et al., (2019). The adaptive localization method recognizes the highly correlated areas and removes less correlated areas (e.g., small downstream tributaries). We will highlight the effect of adaptive localization in the revised manuscript.
6. Could you discuss a bit about to what do you attribute the lower efficiency of flow estimates in the upper Solimoes River ? efficiency of the CaMa-Flood model ? selection of VSs ? localization ? large uncertainties in the VSs data in that region ?
Reply:We would like to thank referee #1 for raising the question. The WSE simulations using CaMa-Food of these two locations show a reasonable agreement with the satellite altimetry observations, therefore direct DA method performed better. On the other hand, anomaly and normalized value DA have a limitation that the assimilated WSE is dependent on the statistics (i.e., mean and standard deviation) of open loop simulation. If statistics may not represent the actual values of mean WSE and the standard deviation of WSE, the assimilated WSE can be also different from the actual values in the anomaly and normalized assimilation method. When the relationship between the WSE and discharge (i.e., rating curve) is well represented in the model, the anomaly and normalized DA may not correctly estimate the river discharge if the statistics are biased. We will add some explanation to the manuscript regarding this.
Specific comments (Line-by-line comments):
Introduction:
1. 35: It is more accurate to say, "River discharge records can be used...".
Reply: We will revise the sentence as per referee #1’s comment.
2. 42: I would say that also these simulations (of GHMs) have been used to complement observed records.
Reply: We agree with referee #1 that the simulations of GHSs have been used to complement observations rather than compensate. Hence, we will revise the text to reflect the referee's comment.
3. 48: If we go deeper we could say that these forcing factors can also be rainfall and climatic variables.
Reply: We concur with referee #1 that the forcing factors are primarily rainfall and climatic variables. Therefore, we will modify the text to include rainfall and climatic variables.
4. 50: I would say: “Given the current limitations of GHMs and in-situ measurements, …”.
Reply: We admit referee #1’s suggestion that both GHMS and in-situ measurements have their own limitations. It will be updated in the revised manuscript.
5. 57: This sentence mentioning the SWOT mission seems a bit loose, you should rework it to integrate it with what you want to mention above.
Reply: The sentence will be revised to reflect the referee #1’s comment.
6. 62: Typo: "combining" instead of "combing".
Reply: We will correct it.
7. 83: This statement describes information repeated in the previous one, perhaps you could combine them.
Reply: By thanking referee #1, the paragraph will be modified.
Methodology:
1. 124: In this sentence you can already start reporting on the period of DA experiments (2009-2014).
Reply: We would like to thank Referee #1 for the nice suggestion. But we try to dedicate this section (2.1) solely to describing the assimilation framework. So, we will introduce the period of the experiments in section 2.6.
2. 215: Why wasn't the SURFEX-TRIP model outputs used since it also belongs to WRR2?
Reply: We would like to thank referee #1 for the question. We did not consider outputs from the SURFEX-TRIP model because those are not compatible with the CaMa-Flood model. The SURFEX-TRIP model consists of the capillary rise in the runoff variable where CaMa-Flood is not capable of dealing with such runoff data. Therefore, we will add some descriptions to the text.
3. 236: To reference these annual average rainfall values you can cite Builes-Jaramillo & Poveda, 2018; Espinoza et al., 2009. (https://doi.org/10.1029/2017WR021338 and https://doi.org/10.1002/joc.1791)
Reply: We would like to express our gratitude to referee #1. We will use the above studies when describing the annual average rainfall in the Amazon basin and the text will be modified considering the above studies.
4. 237: Please specify what you mean by large number of observations, perhaps this is valid for remote sensing observations because it is a large basin with strong hydrological signals, hence the citation of Fassoni-Andrade et al. 2021.
Reply: We thank referee #1 for raising this issue. Yes, indeed we refer to remote sensing data in the Amazon basin. So, we will revise the text of the manuscript.
5. 249: You could elaborate a little more on this sentence. Why these virtual stations could affect the estimates using assimilation? this exclusion of 3% was by a visual analysis of the series only? these stations are located in some particular place in the Amazon, maybe rivers with a small width?
Reply: We would like to express our gratitude to referee #1 for the important question. We did some extensive analysis of these largely biased VS which cannot be compared with the MERIT DEM (Yamazaki et al., 2017). This exclusion was done by comparing the mean WSE of satellite altimetry observation with the MERIT DEM, not on visual analysis. Our analysis revealed that most of the “biased VS” were in narrow rivers at relatively high elevations. We will include some descriptions of these biased VS in the revised manuscript.
6. Sections 2.7.1 and 2.7.2 could be merged, as it could confuse the reader. The main objective of this research is to evaluate the performance in simulating daily discharge but here also the performance of WSE will be evaluated. This merged section could be called "observational data" since the altimetry data has also been used for validation.
Reply: We appreciate the comment on merging Sections 2.7.1 and 2.7.2. We will revise it in the revised manuscript.
Results:
1. 294-296: This sentence seems to be repetitive with the previous one, you could merge them.
Reply: We would like to thank referee #1 for the suggestion and we will revise the text.
2. 302: It would be appropriate to refer to Figure 4b in this sentence.
Reply: Thank you very much for pointing out this. We will refer to figure 4d in the sentence.
3. 309: The time series for the Santos Dumont station is not shown in Figure 4d. Instead, a station on the Juruá River is shown. See my major comments above.
Reply: Thank you very much for the comment. We will revise Figure 4d to be compatible with the description in the text.
4. 325: The information in parentheses should go in the methodology section.
Reply: Thank you for the kind suggestion. We will include that in the methodology section.
5. 330: “WSE performance decreased…” instead of “WSE decreased…”.
Reply: Thank you very much. We will revise it in the revised manuscript.
6. 332: The Gavião and Manacapuru gauges do not correspond to Figures 5c and d.
Reply: Thank you. We will revise figures 5c and d to match the description in Section 3.1.2.
7. 351-352: None of these described gauges correspond to figures 6c, d and e.
Reply: Thank you for raising this error. We will replace the correct figures for figures 6c, d, and e and the description will be modified to correspond to figures 6c, d, and e in Section 3.1.3.
8. 412: I think there is a typo, please delete "3.2.1.".
Reply: Thank you for pointing out it. We will delete "3.2.1.".
9. 8: It is not possible to distinguish gauges inside or outside the coverage area of the altimetric satellites. Could you differentiate them somehow?
Reply: Thank you for the great suggestion. We will modify figure 8 to distinguish the gauges inside and outside satellite altimetry coverage.
10. Table 3. I have noticed that some values in this table do not correspond exactly to those described. For instance, in the first column (All and r) in the table, the values are 0.74, 0.85 and 0.84 for experiments 1, 2 and 3 respectively. While in the description the values are 0.73, 0.84 and 0.83 (L. 431, L. 415 and L. 439 respectively).
Reply: Thank you for cross-checking the description with the data provided in the tables. The values in the table are correct values so we will revise the text according to Table 3. We will revise the L. 431, L. 435, and L. 439 with the values of 0.74, 0.85, and 0.84.
11. 452: As shown in Figure 9, the BIAS values are only positive, so I recommend describing somewhere (probably methodology) that the index is an absolute value of BIAS.
Reply: Thank you for pointing out this. We will add “absolute bias” to the methodology section when introducing “BIAS” term as follows:
12. Figure 9: It is a bit difficult to differentiate the VSs that were used for assimilation and validation. Perhaps it could be improved by changing the symbology from "o" to "*", increasing the size of the maps by reducing the space between them and decreasing a little the size of the station symbols so that they do not overlap too much. This is just a suggestion.
Reply: Thank you very much for your precious suggestion. We will revise figure 9 and other similar figures to better represent symbols.
13. Section 3.3: Please detail how in this experiment you have generated the realizations of the set for assimilation.Was it with the same perturbation as for the WRR2 models?
Reply: Thank you for asking for clarification. This section used the same assimilation outputs from the direct, anomaly, and normalized DA experiments. The perturbations are similar to WRR2 model outputs.
14. 476-478: The end of this sentence sounds strange, I suggest to redo it or delete this last part from "...,direct DA (Exp 1)...."
Reply: Thank you. We will revise the sentence in the revised manuscript.
Conclusions:1. 624: Typo, it's HTESSEL not HTEESSEL (same for L.541, 542 and 544).
Reply: Thank you, We will correct them to HTESSEL.
Reference:1. Revel, Ikeshima, Yamazaki and Kanae: A Physically Based Empirical Localization Method for Assimilating Synthetic SWOT Observations of a Continental-Scale River: A Case Study in the Congo Basin, Water, 11(4), 829, doi:10.3390/w11040829, 2019.
2. Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S. and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44(11), 5844–5853, doi:10.1002/2017GL072874, 2017.
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RC2: 'Comment on egusphere-2022-412', Anonymous Referee #2, 30 Jul 2022
reply
The authors investigated the assimilation of satellite altimetry to improve discharge. The article is excellent. The authors presented 3 types of altimetry assimilation, which are 1. direct, 2. anomaly, and 3. normalized assimilation, and concluded that to improve discharge it is better to just assimilate the water surface dynamics (3.) given that the model is relatively accurate. If the model is completely corrupted, the authors concluded that anomaly and/or direct assimilation are more effective.
My main complaint about this manuscript is in section 3.1 (specifically 3.1.1, 3.1.2, and 3.1.3) where the figures (fig 4, 5 and 6) showed hydrographs that were different from the locations discussed in the text. Besides that, I have just some small comments that are specified below:
Line 40. I think it is better to change GHM definition only to Global Hydrodynamic Models instead of Hydrological due to some features the authors discuss further such as "runoff as a forcing factor", "discretized river", "surface water dynamics", etc.. Line 77 and 519 could be GHM instead of global hydrodynamic models.
Line 52. The authors could also mention Laser altimetry. The ICESat missions are also very used in academic research. Maybe instead of a radar pulse, can be a radar/light pulse or even an electromagnetic pulse.
As an alternative to the semi-variogram analysis to determine the spatial dependency weights, the authors could have used "backwater lengths in rivers" studied by Samuel (1989). It can give an idea of which river reaches are affected by WSE variations at the VS locations. It would be a good idea to compare both approaches in future studies (not now).
Samuels, P. G. (1989). Backwater lengths in rivers. Proceedings of the Institution of Civil Engineers, 87(4), 571–582. https://doi.org/10.1680/iicep.1989.3779
Line 156. using a power law dependent on what? Width? Upstream drainage area? Is it the same power law parameters for the whole basin?
Line 199. Something went wrong with the font size of some words. Line 351. Line 438. Line 637.
Line 229. So, the mean and standard deviation were calculated based on the open loop simulation?
Line 231 to 239. Some of your readers might be unfamiliar with the Amazon Basin. It would be interesting to write a short and objective section about this basin, presenting a DEM map at least (a mean Precipitation map would be nice too).
Line 275. How do you measure the relative sharpness and the difference in reliability? Line 383 should be here.
Several wrong references in section 3.1.
Line 309. The authors said that the Santos Dumont gauge is in the Purus River, but in Figure 4 it says Jurua River.
Line 332. "Figure 5c–e displays hydrographs of the Jurua (Gaviao), Amazon (Manacapuru), and Negro (Serrinha) rivers" but in Figure 5 it says Manicore on the Madeira River, Aruma on the Purus River, and Sao Felipe on the Negro River.
Line 351. "The lower panels of Figure 6 illustrate flow dynamics along the Amazon mainstem (Sao Paulo De Olivenca; Figure 6c) and Japura (Vila Bittencourt; Figure 6d) and Negro (Curicuriari; Figure 6e) rivers." but in Figure 6 it is written "Hydrographs recorded at Humaita on the Madeira River, Santos Dumont on the Jurua River, and Canutama on the Purus River are presented on panels c, d, and e, respectively."
Line 321. Saying that the "direct DA generally improved flow dynamics" is very optimistic. Based on these results, I'd probably say that the direct DA maintained or even degraded the general performance, at least for discharge.
Line 340. Once again, I think it is an optimistic conclusion. In the last sentence, the authors just said: "although NSE and ISS values worsened slightly." So how can the authors say afterward that "discharge estimates improved moderately"? I don't think that improvements in the correlation coefficient are enough for such a statement given that the NSE has become worst. But I reckon that seasonality got better as correlation got higher. Maybe the authors should clarify what they try to achieve with DA assimilation.
Figure S2 should be in the main manuscript. It could be together with Figure 7 as 7c, 7d, and 7e. Figures 7a and 7b don't need to be so large.
Line 427. "However, the direct DA experiments efficiently improved sharpness, thereby increasing confidence in the assimilated river discharge." I would say "FALSELY increasing confidence" as the authors just observed that the reliability drops more than 50% for direct DA experiments. What is the point of being narrower if the observation falls out of the confidence interval? I think the authors should be careful with that.
On tables 3 and 4 it would be nice to see the Open Loop and the CaMa VIC BC performances for comparison.
Line 541. HTESSEL not HTEESSEL.
Line 624. Which experiment is that? The one in section 4.2.? Or the one with VIC BC (section 3.3)?
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AC2: 'Reply on RC2', Menaka Revel, 18 Aug 2022
reply
Referee #2
The authors investigated the assimilation of satellite altimetry to improve discharge. The article is excellent. The authors presented 3 types of altimetry assimilation, which are 1. direct, 2. anomaly, and 3. normalized assimilation, and concluded that to improve discharge it is better to just assimilate the water surface dynamics (3.) given that the model is relatively accurate. If the model is completely corrupted, the authors concluded that anomaly and/or direct assimilation are more effective.
Reply:
We would like to express our gratitude to referee #2 for his thoughtful comments. We will address all the comments in the revised manuscript and detailed responses to the comments are given below.
1. My main complaint about this manuscript is in section 3.1 (specifically 3.1.1, 3.1.2, and 3.1.3) where the figures (fig 4, 5 and 6) showed hydrographs that were different from the locations discussed in the text. Besides that, I have just some small comments that are specified below:
Reply:
We would like to thank referee #2 for identifying this issue. We found that some panels of figures 4, 5, and 6 are not matching with the description. Therefore, we will revise figures 4, 5, and 6 to be compatible with the description in Sections 3.1.1, 3.1.2, and 3.1.3.
2. Line 40. I think it is better to change GHM definition only to Global Hydrodynamic Models instead of Hydrological due to some features the authors discuss further such as "runoff as a forcing factor", "discretized river", "surface water dynamics", etc.. Line 77 and 519 could be GHM instead of global hydrodynamic models.
Reply:
We would like to express our gratitude to referee #2 for his valuable suggestion. We agree with referee #2 that our introduction should be more focused on global hydrodynamic models. We will revise the manuscript according to the suggestion.
3. Line 52. The authors could also mention Laser altimetry. The ICESat missions are also very used in academic research. Maybe instead of a radar pulse, can be a radar/light pulse or even an electromagnetic pulse.
Reply: We would like to thank referee #2 for suggesting introducing laser missions. So, we will revise the text in the revised manuscript to address the referee's comments.
4. As an alternative to the semi-variogram analysis to determine the spatial dependency weights, the authors could have used "backwater lengths in rivers" studied by Samuel (1989). It can give an idea of which river reaches are affected by WSE variations at the VS locations. It would be a good idea to compare both approaches in future studies (not now).
Samuels, P. G. (1989). Backwater lengths in rivers. Proceedings of the Institution of Civil Engineers, 87(4), 571–582. https://doi.org/10.1680/iicep.1989.3779
Reply: We would like to express our gratitude to referee #2 for the suggestion. We will consider the suggestion in our future studies.
5. Line 156. using a power law dependent on what? Width? Upstream drainage area? Is it the same power law parameters for the whole basin?
Reply: We appreciate referee #2 for raising this question. The power law depends on a prior annual average river discharge and uses a single set of parameters (i.e., a and b) for the whole basin (a=0.1, b=0.5). The power law is shown in Equation 1 in the supplementary document. We will revise the text according to this comment.
6. Line 199. Something went wrong with the font size of some words. Line 351. Line 438. Line 637.
Reply: We would like to thank referee #2 for identifying those mistakes. We will correct all those kinds of font errors.
7. Line 229. So, the mean and standard deviation were calculated based on the open loop simulation?Reply: Yes, the mean and standard deviation were calculated based on the long-term open loop simulation (2000-2014).
8. Line 231 to 239. Some of your readers might be unfamiliar with the Amazon Basin. It would be interesting to write a short and objective section about this basin, presenting a DEM map at least (a mean Precipitation map would be nice too).Reply: We like to thank referee #2 for the great suggestion. We will be happy to include a description and a figure of the Amazon basin but considering the length of and the number of figures in the manuscript, we will add them to the supplementary material.
9. Line 275. How do you measure the relative sharpness and the difference in reliability? Line 383 should be here.Reply: We believe Line 279-283 describes the same ideas as Line 383. We will modify the text to enhance the understanding of the Interval Skill Score (ISS).
10. Several wrong references in section 3.1.Line 309. The authors said that the Santos Dumont gauge is in the Purus River, but in Figure 4 it says Jurua River.
Line 332. "Figure 5c–e displays hydrographs of the Jurua (Gaviao), Amazon (Manacapuru), and Negro (Serrinha) rivers" but in Figure 5 it says Manicore on the Madeira River, Aruma on the Purus River, and Sao Felipe on the Negro River.
Line 351. "The lower panels of Figure 6 illustrate flow dynamics along the Amazon mainstem (Sao Paulo De Olivenca; Figure 6c) and Japura (Vila Bittencourt; Figure 6d) and Negro (Curicuriari; Figure 6e) rivers." but in Figure 6 it is written "Hydrographs recorded at Humaita on the Madeira River, Santos Dumont on the Jurua River, and Canutama on the Purus River are presented on panels c, d, and e, respectively."
Reply: We would be grateful to the referee for identifying the mistake. We revised Figures 4, 5, and 6 corresponding to the description of sections 3.1.1., 3.1.2., and 3.1.3.
11. Line 321. Saying that the "direct DA generally improved flow dynamics" is very optimistic. Based on these results, I'd probably say that the direct DA maintained or even degraded the general performance, at least for discharge.
Reply: We would be thankful to the referee for the detailed comments on the text. We agree with the referee that the Direct DA method somewhat degraded the performance of discharge estimations in some locations. But the discharge was improved in some gauge locations in the Amazon basin such as Santo Antonio Do Ica and Sao Paulo de Olivenca. Therefore, we will revise the text to deliver the ideas suggested by referee #2.
12. Line 340. Once again, I think it is an optimistic conclusion. In the last sentence, the authors just said: "although NSE and ISS values worsened slightly." So how can the authors say afterward that "discharge estimates improved moderately"? I don't think that improvements in the correlation coefficient are enough for such a statement given that the NSE has become worst. But I reckon that seasonality got better as correlation got higher. Maybe the authors should clarify what they try to achieve with DA assimilation.
Reply: We would like to express our gratitude to referee #2. Our goal is to improve the overall performance of the river discharge hence higher NSE the better discharge estimated would be. But we try to look at the positives of each method and come to a broader conclusion as none of the methods performed perfectly in all the scenarios. Ideally, it is better if the direct DA performs better than others as assimilation will not depend on prior statistics of the open loop simulation. We basically discuss the median performance in the discussion of NSE and ISS here. But there is a large variation in the statistics as shown in figure 7, table 2 and figure S2. A reasonable number of gauges improved their performance by anomaly DA. An overall moderate number of gauges improved their discharge estimations. Therefore, we will modify the text to highlight the percentage of improved gauges.
13. Figure S2 should be in the main manuscript. It could be together with Figure 7 as 7c, 7d, and 7e. Figures 7a and 7b don't need to be so large.
Reply: We would like to appreciate the suggestion by referee #2. We will include all or some of the panels of Figure S2 in Figure 7.
14. Line 427. "However, the direct DA experiments efficiently improved sharpness, thereby increasing confidence in the assimilated river discharge." I would say "FALSELY increasing confidence" as the authors just observed that the reliability drops more than 50% for direct DA experiments. What is the point of being narrower if the observation falls out of the confidence interval? I think the authors should be careful with that.
Reply: We would like to thank referee #2 for pointing out this. We agree with the referee that “falsely” increasing the confidence will not be beneficial. In this sentence, we mean that when data assimilation is performed in direct values, the spread of final assimilated values will be narrower than in other methods because the assimilation was performed in direct DA. But not in anomaly or normalized value DA. Of course, the reliability should be higher. We will revise the sentence to convey the idea more clearly.
15. On tables 3 and 4 it would be nice to see the Open Loop and the CaMa VIC BC performances for comparison.Reply: Thank you very much for the nice suggestion. We will include the median statistics of the Open Loop and the CaMa VIC BC in tables 3 and 4 even though CaMa VIC BC cannot be assessed for sharpness.
16. Line 541. HTESSEL not HTEESSEL.Reply: Thank you for recognizing the mistake. We modified all the instances which have HTESSEL in the revised manuscript.
17. Line 624. Which experiment is that? The one in section 4.2.? Or the one with VIC BC (section 3.3)?
Reply: Thank you for recognizing the mistake. It should be VIC BC. We will correct the text in the revised manuscript.
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AC2: 'Reply on RC2', Menaka Revel, 18 Aug 2022
reply
Menaka Revel et al.
Data sets
HydroDA v1.0 Revel, M., X. Zhou, S. Kanae, D. Yamazaki https://doi.org/10.4211/hs.08e1b18aa9f240758dd13d9ac875621f
Model code and software
HydroDA 1.0 Revel, M. https://github.com/MenakaRevel/HydroDA/releases
Menaka Revel et al.
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