Evaluation of Evaporation from Water Reservoirs in Local Conditions at Czech Republic

Round 1

Reviewer 1 Report

The authors describe an approach to evaluating evaporation using statistical methods. The approach is based on measured evaporation data from 24 evaporation gauging stations (as dependent variable) and the data derived from ERA5-Land climate reanalysis (as independent variable). I think that the goal is achieved and the questions the authors wanted to answer have been answered (which model achieves better results, how important elevation and geographical location are to estimate evaporation, how many variables are needed to calculate evaporation). Still, some explanations are vague and need more clarification.

Line 45: Something is missing in the sentence: “Depending on the geographical location, the attribution of the is estimated…”

Lines 47-51: There is a strong difference between potential and reference evapotranspiration, perhaps that should be taken into consideration here. Also, I am confused with the statement ”…reference evapotranspiration is similar as water surface evaporation.” In terms of using Penman eq. that is often used to estimate open water evaporation?

Line 94: Is it average annual precipitation?

Line 99: At which elevation are the station? Additional information would be a graph showing the vertical distribution of the station (as an addition to the conclusion that the topography influence evaporation).

Line 102: It would be useful to show the climatic regions on the map in Figure 1.

Figure 1: The legend is missing from the map. Although it can be assumed that blue stands for reservoir and brown for the gauging stations, please indicate in the legend what is reservoir and what is the station. Lines, I suppose, represent watercourses. Also, consider adding climatic regions.

Line 107: Please consider giving more information on the observed data, e.g. min, max, average…

Lines 138-140: Instead of (.) use (∙) to indicate multiplying i.e. instead of J.m^-2 use J∙m^-2 or just J m^-2

Line 152: I think there should be a colon instead of comma: “…goodness-of-fit (GOF): mean absolute error (MAE), Root Mean Squared Error RMSE…”

Line 262: “…applied to the calculated evaporation…"?

Figure 2: Please consider adding short description what the lines represents, apart from the explanation what are the black and grey lines. There were 18 LM models and 14 RFM, according to the Table 3 in Appendix A.1.

Line 279: How did you detect outliers? Which method did you use?

Figure 4: Perhaps it would be interesting to see the comparison between the trend in the estimates (presented in the figure) and the trend in the observed data from the evaporation stations.

Line 333: The authors state that no observed data from the water bodies exists. Considering the observed data from 24 evaporation stations that you used, what do you mean by this sentence?

Lines 365-367: This is not clear. “…LM1 was used for the evaluation. Because the model is input-intensive, LM12 was used…” Please consider re-formulating this sentence.

Line 377: Suggestion: if the previous paragraph is re-formulated, this sentence is not necessary.

Table 3: Different set of abbreviations are used here than in the previous text. Please consider adding a short explanation.

Table 4: Units are missing. Also, it is not clear how much these values deviate from the observed values.

Author Response

Dear Reviewer,

 

We would like to thank you for all your constructive and enriching comments on the manuscript. We hope that the changes made according to your suggestions are acceptable. I am sending the document with changes from all reviewers. A full list of your suggested changes follows below.

 

On behalf of all co-authors,

Sincerely,

Eva Melišová

 

• Reviewer

Line 45: Something is missing in the sentence: “Depending on the geographical location, the attribution of the is estimated…”

• Authors: the sentence is rephrased.

Line 45: The study \cite{rodell2015observed} illustrates the impacts of climate change on the water cycle, which may impact from total evaporation, precipitation, atmospheric humidity, and horizontal moisture transport at the global scale.

• Reviewer

Lines 47-51: There is a strong difference between potential and reference evapotranspiration, perhaps that should be taken into consideration here. Also, I am confused with the statement ”…reference evapotranspiration is similar as water surface evaporation.” In terms of using Penman eq. that is often used to estimate open water evaporation?

• Authors: the sentence is rephrased.

Lines 47-51: There are many methods to calculate evaporation, which can be calculated from free water, from the soil surface or from vegetation over a period of time. The evaluation of evaporation can be done by direct methods namely measurement or by indirect methods: empirical methods, remote sensing of the Earth on regional or global scales \cite{glenn2007integrating, hersbach2020era5}, the use of models that are classified as fully physically-based combination models, semi-physically based models or black-box models \cite{srivastava2017modis}.

• Reviewer

Line 94: Is it average annual precipitation?

• Authors

Line 94: Yes, i tis average annual precipitation.

• Reviewer

Line 99: At which elevation are the station? Additional information would be a graph showing the vertical distribution of the station (as an addition to the conclusion that the topography influence evaporation).

• Authors

Line 99: Elevation is associated with evaporation stations and water reservoirs, the change is shown in Figure 2.

• Reviewer

Line 102: It would be useful to show the climatic regions on the map in Figure 2.

• Authors

Line 102: The climatic region are introduced in Figure 2.

• Reviewer

Figure 1: The legend is missing from the map. Although it can be assumed that blue stands for reservoir and brown for the gauging stations, please indicate in the legend what is reservoir and what is the station. Lines, I suppose, represent watercourses. Also, consider adding climatic regions.

• Authors

Figure 1: The legend is added in Figure 2.

• Reviewer

Line 107: Please consider giving more information on the observed data, e.g. min, max, average…

• Authors

Line 107: Figure 1 has been added, showing the Hlasivo evaporation station. This station is characterized by a long time series, 58 years. Further information about the observed data is described in the lines 145-149.

• Reviewer

Lines 138-140: Instead of (.) use (∙) to indicate multiplying i.e. instead of J.m^-2 use J∙m^-2 or just J m^-2

• Authors

Lines 138-140: Throughout the article the designation (∙), J∙m^-2  

• Reviewer

Line 152: I think there should be a colon instead of comma: “…goodness-of-fit (GOF): mean absolute error (MAE), Root Mean Squared Error RMSE…”

• Authors

Line 152: It is corrected: The resulting linear and non-linear models were evaluated based on cross validation and Goodness-Of-Fit (GOF): Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R²) and Relative Error (RERR).

• Reviewer

Line 262: “…applied to the calculated evaporation…"?

• Authors: the sentence is rephrased.

Line 262: Selected models were used to calculate evaporation from the water reservoirs.

• Reviewer

Figure 2: Please consider adding short description what the lines represents, apart from the explanation what are the black and grey lines. There were 18 LM models and 14 RFM, according to the Table 3 in Appendix A.1.

• Authors

Figure 2: The changes are corrected in Figure 3. Model evaluation using GOF (R², RMSE, MAE, RERR). The lines in the plot represent the LM and RFM models. Part (a) linear regression models (18 models) is divided into two parts: orange line: models created manually (8 models), grey part: stepwise regression was used (10 models). Part (b) random forest regression models (15 models).

• Reviewer

Line 279: How did you detect outliers? Which method did you use?

• Authors

Line 279: Outliers (the worst 10 % GOF values) are present in all LM models, which also happens in RF models, but on a smaller scale. Added to the text.

• Reviewer

Figure 4: Perhaps it would be interesting to see the comparison between the trend in the estimates (presented in the figure) and the trend in the observed data from the evaporation stations.

• Authors

Figure 4: The changes are corrected in Figure 5. Figure 5 shows the linear and random forest models for the period 1981 and 2020, and the observed data for the period 2005 and 2019. The average values of the LM and RFM models and the observed data are mentioned in the text.

• Reviewer

Line 333: The authors state that no observed data from the water bodies exists. Considering the observed data from 24 evaporation stations that you used, what do you mean by this sentence?

• Authors

Line 333: The estimation of evaporation from water reservoirs is complicated because a large number of water reservoirs do not have observed evaporation data. In this work, Quitt's climate classification was used to assign a evaporimeter station that is not near a reservoir to a given reservoir based on climate region and elevation. Within the Czech Republic, the evaporation value from water reservoirs is determined on the basis of a handling order, which is established according to a Czech technical standard which is based on old climatic data and does not deal with climate change. For this reason, the determination of the evaporation from water reservoirs is based on estimation using statistical methods.

• Reviewer

Lines 365-367: This is not clear. “…LM1 was used for the evaluation. Because the model is input-intensive, LM12 was used…” Please consider re-formulating this sentence.

• Authors

Lines 365-367: For the evaluation of evaporation, models from LM and RFM models were used. Among the best models that were evaluated by linear regression, models LM1 from the manual linear regression group and LM12 from the stepwise regression group were used.  Model LM1 was selected as the best model among the six predictors. The LM1 model can be replaced by an alternative model LM12 with which also performed satisfactorily with four predictors.

• Reviewer

Line 377: Suggestion: if the previous paragraph is re-formulated, this sentence is not necessary.

• Authors

Line 377: \conflictsofinterest{The~authors declare no conflict of interest.}, - I would like to apologise, but I did not understand the proposal.

• Reviewer

Table 3: Different set of abbreviations are used here than in the previous text. Please consider adding a short explanation.

• Authors

Table 3: Abbreviations are consistent throughout and explanations of single variables added.

• Reviewer

Table 4: Units are missing. Also, it is not clear how much these values deviate from the observed values.

• Authors

Table 4: Units were added to the table. Observed evaporation from water reservoir dont exist..Comparison is made on Figrue 5.

 

 

On behalf of all co-authors,

Sincerely,

Eva Melišová

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Reviewer’s Report on the manuscript entitled:

Evaluation of Evaporation from Water Reservoirs in Local Conditions

The authors apply the statistical methods of linear and random forest regressions to calculate evaporation. They use the observed data from 24 evaporation stations and ERA5-Land climate reanalysis data to generate the regression models and test the proposed regression formulas on 33 water reservoirs. The paper is very well-written, well-organized, and suitable for publications in Hydrology. I have some minor comments for further improvement.

Line 71. There are other popular methods for meteorological data and streamflow analysis that can be added here (with the given references below), such as Mann-Kendall tests, season-trend fit models, wavelet, and cross-wavelet analyses:

https://doi.org/10.3390/w8030077

https://doi.org/10.1016/j.ejrh.2021.100847

Please join the sentence in Line 85 to Line 86. Also, join Lines 133 and 134. Please also join lines 365, 366, and 368.

Please express the scalebar values in km shown in the bottom left of Figure 1.

Lines 152 and 153. The first letters of the words being abbreviated must be capitalized. Please keep this consistency throughout the paper where an abbreviation is defined.

Figure 5. The series shown in gray can be a bit darker for better visibility.

Thank you for your interesting contribution

Regards,

Author Response

Dear Reviewer,

 

We would like to thank you for all your constructive and enriching comments on the manuscript. We hope that the changes made according to your suggestions are acceptable. I am sending the document with changes from all reviewers. A full list of your suggested changes follows below.

 

On behalf of all co-authors,

Sincerely,

Eva Melišová

 

• Reviewer

Line 71. There are other popular methods for meteorological data and streamflow analysis that can be added here (with the given references below), such as Mann-Kendall tests, season-trend fit models, wavelet, and cross-wavelet analyses:

https://doi.org/10.3390/w8030077

https://doi.org/10.1016/j.ejrh.2021.100847

• Authors: The literature is supplemented.

Line 71 The assessment of long-term climate variables can be based on time series. The time series is a sequence of measurements recorded over time, that can be analysed using, e.g. Least-Squares Spectral Analysis, Least-Squares Wavelet Analysis, Least-Squares Cross Wavelet Analysis \citep{ghaderpour2021LSW}. Other methods for evaluation may include parametric and non-parametric trend tests, which are used in machine learning \cite{shadmani2012MKS,chen2016MK}. Parametric method (Logistic Regression, Linear Discriminant Analysis, and Simple Neural Network) use a fixed number of parameters to build models, require fewer variables, and the result may be affected by outliers. Non-paramtric method (The Mann-Kendall, Spearman’s Rho, and k-Nearest Neighbors) use a flexible number of parameters, both variable and attribute can be used in the models, the result is not affected by outliers.

• Reviewer

Please join the sentence in Line 85 to Line 86. Also, join Lines 133 and 134. Please also join lines 365, 366, and 368.

• Authors: All the recommended connections are made, the rephrasing of the sentences is shown below:

Line 85-86: Statistical method for evaluation evaporation with respect to Goodness-Of-Fit (GOF) is evaluated in the R environment \citep{manR} and described Section 3. Results and discussion are sented in Section 4 along with a detailed evaluation of the Goodness-Of-Fit (GOF) regression for evaporation stations and subsequently for water reservoirs.

Line 133-134: The data was downloaded in NetCDF format, which can be used for drought or flood forecasting \citep{sabater20191981}.

Line 365-368: For the evaluation of evaporation, models from LM and RFM models were used. Among the best models that were evaluated by linear regression, models LM1 from the manual linear regression group and LM12 from the stepwise regression group were used.  Model LM1 was selected as the best model among the six predictors. The LM1 model can be replaced by an alternative model LM12 with which also performed satisfactorily with four predictors.

• Reviewer

Figure 1: Please express the scalebar values in km shown in the bottom left of Figure 1.

• Authors

Figure 1: Figure 1 is corrected, currently it is Figure 2

• Reviewer

Line 152 and 153: The first letters of the words being abbreviated must be capitalized. Please keep this consistency throughout the paper where an abbreviation is defined.

• Authors: Abbreviations are unified.

Line 152 and 153: The resulting linear and non-linear models were evaluated based on cross validation and Goodness-Of-Fit (GOF): Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R\textsuperscript{2}) and Relative Error (RERR). This section introduced building linear and non-linear models and their evaluating.

• Reviewer

Figure 5: The series shown in gray can be a bit darker for better visibility.

• Authors

Figure 5: Figure 5 is now Figure 6. Figure 6 was the impetus for the change by the other reviewers as well.

Author Response File: Author Response.pdf

Reviewer 3 Report

The authors have done great work and analysis, however, there are few suggestions to include to improve the quality of the manuscript. 

Evaluation of Evaporation from Water Reservoirs in Local Conditions 
 
 
The authors have done a good amount of work to justify the estimation of ET with a machine learning  approach,  the  amount  of  work  conducted  in  this  work  is  enormous  and  further,  I believe that there are some problems after addressing those can be considered for publication.  
 
In the current version of the manuscript, the literature review is very narrow. Authors may like to  find  studies  in  line  with  their  statements  to  add  scientific  weight  to  their  observations.  I believe  that  after  duly  addressing  the  comments  authors  can  improve  the  quality  of  the 
manuscript  substantially  to  make  it  more  insightful.  The  discussion  does  not  provide  clear support from previous studies. I recommend them to compare their study with a very recent paper described later in this field. In the introduction, research gaps should be identified better. 
 
In the title section please mention the name of the study area. 
 
In the abstract section please provide some key results stating the GOE. 

Though  authors  have  mentioned  the  classification  from  Line  51-54  but  some  important references  are  missing  in  the  Introduction;  the  authors  have  missed  providing  detailed discussion on the important aspect of different classification of ET estimation methods and its associated factors. It is important to mention about the classification of ET methods and factors 
as the basis of this study lies in the evaporation. Several previous studies have been done in this area and I would like to suggest few lines following this which author should add is “The ETo estimation models available in the literature can be broadly classified as fully physically-based;  semi-physically  based  models,  black-box models”. I would recommend adding these 
recent references to add more scientific weight in their Introduction 
(https://doi.org/10.1061/(ASCE)IR.1943-4774.0001199;  https://doi.org/10.1007/s00704-016-1996-2). Not limited to this, authors can add a paragraph.  

Also, it would be clear if the authors can describe the novelty of their ML technique than the previous studies.  
 
In  the  entire  introduction  the  paragraph  is  not  uniformly  distributed  merge  few  paragraphs which are very short. For example – Line 85 
Provide  a  flowchart  describing  the  preprocessing  of  raw  datasets  obtained  from  different sources followed by their application. In addition, a detailed table should be given detailing all hydrometeorological information with their sources and duration. 

Add the time series plot of rainfall and temperature of this study area. 

Please provide the latitude and longitude of the study area – Figure 1 

Figure 4 monthly evaporation is shown for different reservoirs with different models, however, my concern is what the authors are trying to say from this plot is not clear. If seasonality, then it's ok but that should be commented in the text. If not then please state. In addition, it would be beneficial to take advantage of satellite based remote sensing products to compare the results of your findings. As the validation is not possible, therefore I would recommend authors utilize it atleast for few stations.  Authors are required to add a plot showing the sensitivity of the PM equation to climatic variables. 

Figure 5 authors have shown and stated that the evaporation increase with the elevation above sea level however it is not obvious from the plot that this pattern is observed. Evaporation is highest at a lower elevation, thereafter decreases, and then a slight increase. Therefore, I would 
recommend  authors  to  look  into  it  and  present  the  figure  in  a  scatter plot  (1:1).  Further,  the discussion of the figure is lacking in the text. 
 
  
 

 

Author Response

Dear Reviewer,

We would like to thank you for all your constructive and enriching comments on the manuscript. We hope that the changes made according to your suggestions are acceptable. I am sending the document with changes from all reviewers. A full list of your suggested changes follows below.

 

On behalf of all co-authors,

Sincerely,

Eva Melišová

 

• Reviewer

Title: In the title section please mention the name of the study area. 

• Authors: The name is changed.

Title: Evaluation of Evaporation from Water Reservoirs in Local Conditions at Czech Republic.

• Reviewer

Abstract: In the abstract section please provide some key results stating the GOE. 

• Authors: Is added.

Abstract: The study introduced that the use of reanalyzed data, ERA5-Land products using the random forest regression method is suitable for the calculation of evaporation from water reservoirs in the conditions of the Czech Republic.

• Reviewer

Introduction

Though  authors  have  mentioned  the  classification  from  Line  51-54  but  some  important references  are  missing  in  the  Introduction;  the  authors  have  missed  providing  detailed discussion on the important aspect of different classification of ET estimation methods and its associated factors. It is important to mention about the classification of ET methods and factors 
as the basis of this study lies in the evaporation. Several previous studies have been done in this area and I would like to suggest few lines following this which author should add is “The ETo estimation models available in the literature can be broadly classified as fully physically-based;  semi-physically  based  models,  black-box models”. I would recommend adding these 
recent references to add more scientific weight in their Introduction 
(https://doi.org/10.1061/(ASCE)IR.1943-4774.0001199;  https://doi.org/10.1007/s00704-016-1996-2). Not limited to this, authors can add a paragraph.  

Also, it would be clear if the authors can describe the novelty of their ML technique than the previous studies.  
 
In  the  entire  introduction  the  paragraph  is  not  uniformly  distributed  merge  few  paragraphs which are very short. For example – Line 85.

• Authors: The Introduction has been edited based on suggestions from other reviewers and recommended readings have been added. The short paragraphs are linked.

Introduction: The estimation of reference evapotranspiration was used in the study \cite{almorox2018worldwide}, where the Penman-Monteith temperature-based equation achieved the best rating for the evaluation of reference evapotranspiration because it preserves the physical philosophy of the Penman-Monteith equation method. The method was applied at a global scale using the Köppen climate classification system with respect world dataset under different climate conditions. Calculation of reference evapotranspiration based on indirect methods can provide acceptable results when direct measurements of are not available \citep{srivastava2017modis}.

• Reviewer

Study Area and Data: Add the time series plot of rainfall and temperature of this study area. Please provide the latitude and longitude of the study area – Figure 1.

• Authors:

Study Area and Data: Figure 1 has been added, which describes the evaporimeter station Hlasivo with a long time series 58 years.

Figure 1 describe long--term temperature, evaporation trend at evaporation station Hlasivo. The Hlasivo evaporation measuring station provides a consistent time series of 58 years, the evaporation values are measured by a 20 [m^-2] benchmark evaporator. Other observed variables are: air temperature at 2 m [°C], water surface temperature in the evaporimeter [°C], relative humidity [%], global solar radiation [W m^-2] and wind speed at 2 m [m s^-1] \citep{suhajkova2019aktualizace}.

 Figure 1 is now the Figure 2 to which a map of Europe has been added and the longitudes and latitudes are showen in the text.

• Reviewer

Figure 4: monthly evaporation is shown for different reservoirs with different models, however, my concern is what the authors are trying to say from this plot is not clear. If seasonality, then it's ok but that should be commented in the text. If not then please state. In addition, it would be beneficial to take advantage of satellite based remote sensing products to compare the results of your findings. As the validation is not possible, therefore I would recommend authors utilize it atleast for few stations.  Authors are required to add a plot showing the sensitivity of the PM equation to climatic variables. 

• Authors

Figure 4: Figure 4 is now the Figure 5. We would like to present seasonality and variance, Figure was modified, we added observed data from evaporimeters.

• Reviewer

Figure 5: authors have shown and stated that the evaporation increase with the elevation above sea level however it is not obvious from the plot that this pattern is observed. Evaporation is highest at a lower elevation, thereafter decreases, and then a slight increase. Therefore, I would 
recommend  authors  to  look  into  it  and  present  the  figure  in  a  scatter plot  (1:1).  Further,  the discussion of the figure is lacking in the text. 

• Authors

Figure 5: Figure 5 is now the Figure 6. Thank you for your note, for sure that evaporation decrease with the elevation, which ilustrate the figure. Figure was modified.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

I want to thank the authors for addressing previous comments and for their constructive work. I found all replies satisfactory and the changes made to the manuscript significantly improve the quality of the paper. Overall, I think the resubmitted manuscript is almost ready for acceptance.