Prediction of the Discharge Flow in a Small Hydropower Station without Hydrological Data Based on SWAT Model
Round 1
Reviewer 1 Report
The manuscript introduces an interesting topic of proposing a method of predicting the power generation flow of small hydropower plants due to the serious lack of hydrological information. However, the authors should address several issues before the manuscript can be considered for acceptance at Water-MDPI. Here they are:
- Abstract: This section needs to be rewritten to be more meaningful. The authors should add more details about the most significant findings in the abstract. The abstract also does not contain any abbreviations which make it more clear and more understandable for broader readers.
- Introduction: this section needs to be improved and supported by more references and just refer to a reference by its number without using the phrase " Paper 1, 2, etc."
- Materials and Methods. This section presented the simple analyses and equations implemented in this work. The section is well presented and organized. If any equation or Figure used in this section takes from an external reference, it should be cited by a reference.
- Results and discussion: This section needs to be improved by discussing the obtained results in a way that is more appropriate for the peer-review article style and understandable for readers.
- Conclusions: The section requires more improvements by adding the most significant results and some numeric results. Some future recommendations can be highlighted in this section.
Author Response
Dear reviewer:
We would like to thank you for your careful reading, helpful comments, and constructive suggestions, which has significantly improved the presentation of our manuscript. We have carefully considered all comments from the reviewers and revised our manuscript accordingly. The manuscript has also been double-checked, and the typos and grammar errors we found have been corrected. In the following section, we summarize our responses to each comment from you.
Point 1:
Abstract: This section needs to be rewritten to be more meaningful. The authors should add more details about the most significant findings in the abstract. The abstract also does not contain any abbreviations which make it more clear and more understandable for broader readers.
Response 1:
We gratefully appreciate for your valuable suggestion. We have re-written this part according to your suggestions. We have added more details about the most significant findings in this section based on your comments. The study has a total of three innovations: first, a method based on SWAT to predict the generation flow of small hydropower without hydrological information is proposed; second, three methods are used to calibrate the model: using data from hydrological stations, calculating generation flow by the output of small hydropower stations, and migrating generation flow of similar stations by similarity analysis; third, the results of predicting generation flow of small hydropower stations with and without reservoir case are compared.
Meanwhile, we have revised the abbreviations according to your comments to make them clearer. Thank you for pointing out this problem in manuscript.The abstract of this paper after changes is as follows:“The availability of hydrological data for small hydropower plants is an important prerequisite for reservoir scheduling, reservoir flood control and integrated water resources. To address the problem of lack of hydrological data in small hydropower plants, this paper proposes a method to predict the power generation flow of small hydropower stations without hydrological data using the Soil and Water Assessment Tool model when the traditional data-driven methods cannot study the problem of power generation flow prediction in small hydropower stations well. The method can use gridded meteorological data as the input of the model to solve the problem of small hydropower stations without meteorological data. The problem that small hydropower plants without hydrological data cannot calibrate the hydrological model is solved by calculating the generation flow through the output of small hydropower station and by using the similarity analysis method to migrate the generation flow of similar small hydropower station. The model was tested in a watershed in southwest China to demonstrate the effectiveness of the proposed method. The results show that the coefficient of determination between the predicted and measured values of small hydropower station without information is about 0.84, which achieves a better prediction.”
Point 2:
Introduction: this section needs to be improved and supported by more references and just refer to a reference by its number without using the phrase " Paper 1, 2, etc."
Response 2:
Thank you for pointing out this problem in manuscript. We have re-written this part according to your suggestions. After we made changes to the content of the introduction, we refer to 22 references by its number. We also checked all the literature as you suggested and eliminated those that were less relevant to this study.
The introduction of this paper after changes is as follows:“Facing the new changes in the global energy pattern, vigorously developing clean low-carbon energy is the main direction of energy development. From a global perspective, as a clean renewable energy source, small hydropower (a single hydropower station with an installed capacity of less than 50,000 kW) resources is almost the main force of hydropower resources, and it is the most important support and guarantee to achieve the goal of "carbon emissions peak and carbon neutrality" and realize the energy transformation of human society [1,2]. There has been a great deal of research and many excellent methods proposed in the field of streamflow prediction, such as Costa Silva, D. F. et al. [3] proposed an integrated Long-Short Term Memory (LSTM) model that tested streamflow prediction for five scenarios using runoff and rainfall data. Hu, Y. et al. [4] used flow data from a hy-drological station and precipitation data from 11 surrounding rainfall stations to build an LSTM model for flow prediction in small rivers. Zaini, N et al. [5] used ten years of historical rainfall, river flow data and various meteorological data to construct support vector machine (SVM) models and their coupled models with particle swarm optimization (PSO) models for daily river flow prediction. However, most of these studies are carried out under the premise of having complete or relatively complete hydrological information. According to the research, most of the small hydropower plants are located in remote areas, and the equipment and facilities of the power stations are relatively backward, and they have been in the situation of no hydrological information, which will seriously affect the optimal scheduling of small hydropower groups and the flood prevention of power stations [6]. How to make scientific and efficient prediction of small hydropower generation flow without hydrological information, so as to improve the safety and comprehensive water resource utilization of small hydropower has been an urgent problem.
Prediction of ungauged watersheds has always been one of the most important and challenging problems. In the field of hydrology, construction of hydrological models and regionalization based on hydrological similarity are often used to carry out studies of uninformed watersheds[7,8,9]. Hydrological models commonly used for flow simulation are: Variable Infiltration Capacity Macroscale Hydrologic Model (VIC) [10,11,12], Soil and Water Assessment Tool model (SWAT) [13,14,15,16], Hydrologiska Byråns Vattenbalansavdelning model (HBV) [17,18], etc. Among them, the SWAT model is a distributed hydrological model with strong physical mechanism [19], the model can be simulated from three different time scales of year, month and day for continuous long-term simulation, and can use geographic information data and remote sensing data to establish a hydro-logical model in ungauged watershed. The SWAT model is one of the most widely used basin hydrology models in the world and can be found in various research areas [20]. For example, Narula, K. et al. [21] used the SWAT model to simulate and calibrate streamflow in two montane forested watersheds. Kanishka, G. et al. [22] proposed a method to combine watershed classification with regionalization using a dimensionality reduction technique and used the SWAT model for streamflow prediction. Using the method of con-structing hydrological models to physically solve the problem of predicting power generation flow in small hydropower plants without hydrological information can effectively avoid the traditional data-driven inability to obtain data for prediction.
Against the above background, the main objective of this study is to carry out research on the problem of small hydropower flow prediction without hydrological information based on the SWAT hydrological model, and to solve the current problems of small hydropower (especially private small hydropower plants) due to the serious lack of hydrological information, which makes flow prediction difficult and scheduling work difficult, by combining similarity analysis, cluster analysis, and construction of basin hydrological model. This study can provide a novel and reliable idea for the future development of flow prediction of small hydropower plants without hydrological data, and provide data support for the unified scheduling and management of small hydropower and monitoring of ecological flow in the future.”
Point 3:
Materials and Methods. This section presented the simple analyses and equations implemented in this work. The section is well presented and organized. If any equation or Figure used in this section takes from an external reference, it should be cited by a reference.
Response 3:
We gratefully thank you for reading our paper carefully and giving the above positive comments. We have corrected this section based on your comments, such as references 25, 26, 27, 28, and 31. In the paper we have marked them in yellow.
Point 4:
Results and discussion: This section needs to be improved by discussing the obtained results in a way that is more appropriate for the peer-review article style and understandable for readers.
Response 4:
Thank you so much for your careful check. We have corrected this section based on your comments.
1. In section 3.2, we provide a more detailed description of the tools used to calibrate the model by citing references to make it easier for the reader to understand.The content of this section was modified as follows:”Calibration of hydrological models in the absence of hydrological information is one of the difficulties that make it possible to carry out studies of small hydropower stations without hydrological information. Without calibration of the hydrological model, the training effect will be very different from the measured value. The SWAT-CUP tool is one of the effective ways to calibrate the SWAT model, and this tool is suitable to support decision makers in conceptualizing sustainable watershed management, allowing decision makers to better calibrate the model [29,30].”
2. We have described the judgment performed on the simulation results in more detail so that peers can more easily review the accuracy of the results of this test.
3. We have rewritten the content of the prediction results about the NB small hydropower station (small hydropower station without hydrological information). We illustrate the process of how to obtain the prediction results for this small hydropower station by first setting the points representing the NB small hydropower station on the DEM in advance, and then calibrating the model by the three different methods proposed in this paper, and finally we can obtain the flow of the NB small hydropower plant.
4. We show the prediction results in a more easily understandable expression for the reader, and in this section we add Figure 8 to more easily understand the results in two different cases (small hydropower station with reservoir and small hydropower station without reservoir).The content of this section was modified as follows:” Small hydropower can be divided into dam type, diversion type and hybrid type. In order to conduct a more comprehensive study, this paper conducts an analysis of two cases of NB small hydropower station with and without reservoirs. According to the research, the flow curve of small hydropower station without reservoirs will be smoother and similar to the flow curve of natural rivers. As shown in the red-brown curve in Figure 6, because there is no artificial regulation of the reservoir, this curve is smoother and less accurate, with a correlation coefficient of only about 0.65. On the contrary, as shown in the green curve in Figure 6, this curve is closer to the measured value and more volatile, with a correlation coefficient of about 0.83. It is obvious that the NB small hydropower station without hydrological data in this study has a reservoir.”
Point 5:
Conclusions: The section requires more improvements by adding the most significant results and some numeric results. Some future recommendations can be highlighted in this section.
Response 5:
We gratefully thanks for the precious time you spent making constructive remarks. We have rewritten the conclusion based on your comments.
First, we summarized the innovations of this study with the following three points: In this paper, the SWAT model is used to predict the power generation flow of small hydropower plants without hydrological information, which innovatively solves the problem of small hydropower plants without hydrological information that makes it impossible to carry out flow prediction and other work; correlation analysis and cluster analysis are used to classify 78 small hydropower plants, and three methods are proposed to calibrate the model; a detailed analysis of small hydropower plants without infor-mation with and without reservoirs is also carried out.
Then we added the highlights of this study in the conclusion section and detailed them with some numerical results. The content of this section was modified as follows: “The results of the correlation analysis and cluster analysis of 78 small hydropower stations showed that the closer the station to the no-information small hydropower station, the stronger the correlation. After calibration of the model, the NSE values of MH and NW small hydropower stations were 0.74 and 0.73, and the coefficients of determination were 0.84 and 0.78. it can be seen that the SWAT model has good applicability in the study basin. The results of sensitivity analysis on the parameters of the model show that the CN2 parameter has the greatest influence on the accuracy of the model. The results of the uncertainty analysis on the parameters of the model show that the larger yellow area in the plots, as shown in Figure 9 and Figure 10, indicates that the parameters of the model have a greater influence on the uncertainty of the prediction results. The main objective of the research in this paper is to carry out the prediction of power generation flow of small hydropower plants without hydrological information. The monthly power generation flow prediction for small hydropower plants in NB has been better achieved to achieve the daily power generation flow prediction. And the prediction results are analyzed from two cases with and without reservoirs, and the prediction accuracy is expressed by the decision coefficient, which is 0.64 and 0.83.”
Finally, we summarized the remaining shortcomings of this study and expressed our views on the future research directions. We analyze the problem from a small hydroelectric power plant and point out that the daily generation flow prediction accuracy of this study is still relatively. Then we propose two prospects: first, we can improve the prediction accuracy from the direction of solving the model input by migrating the data of large hydropower plants in the same large basin to small hydropower plants where meteorological data are scarce through similarity analysis and migration learning; second, we can improve the prediction accuracy from the direction of solving the model parameters by modeling the basin of large hydropower plants in the same large basin and then determining the model parameters of small hydropower plants through the regionalization method. prediction accuracy. The content of this section was modified as follows:” Most of the small hydropower stations are in remote areas, where neither hydrological nor meteorological information is available, so the daily generation flow prediction accuracy of this study is low. The problem of missing meteorological data for small hydropower stations can be solved in the future by using correlation methods to migrate meteorological data from large hydropower stations located in the same large basin using transfer learning methods. Similarly, a hydrological model can be established in the watershed where the large hydropower stations are located first, and the model parameters for the watershed where the small hydropower stations are located can be determined using a regionalization approach.”
Thank you for your consideration!
Sincerely yours,
Shenghuo Xie
Author Response File: 
Author Response.docx
Reviewer 2 Report
The present paper entitled “Prediction of the discharge flow in small hydropower station
without hydrological data based on SWAT model” deals with the determination for some quantitative aspects on discharge flow across indirect measurements.
The paper is interesting, but some comments are proposed by this reviewer:
Abstract. In this reviewer opinion, abstract should clearly indicate the novelty of the work
1. Introduction. This section presents the actual problem and statement of the studied research. In this reviewer opinion, more references could be added, and the main objectives of the work should be stated in this moment.
2. Materials and Methods. This section is well presented, describing the main model and aspects of the correlations.
In this reviewer opinion, figure 4 has too small legend. It should have bigger letters to be understable.
3. Construction of SWAT Model. This is a descriptive section. It is well presented but in this reviewer opinion, this section should be part of the previous one, materials and methods
4. Results and Discussion. Sensitivity and calibration are adequate and model is well accured. Furthermore, uncertainty analysis is well presented, and the results present a very well calibrated model and good results.
Conclusions. This section is well presented, with clear statements of the main results. In this reviewer opinion, authors should clearly indicate the novelty of the research and the degree of accomplishment of the main objectives of the developed research
Author Response
Dear Reviewer:
We would like to thank you for your careful reading, helpful comments, and constructive suggestions, which has significantly improved the presentation of our manuscript. We have carefully considered all comments from the reviewers and revised our manuscript accordingly. The manuscript has also been double-checked, and the typos and grammar errors we found have been corrected. In the following section, we summarize our responses to each comment from you.
Point 1:
Abstract. In this reviewer opinion, abstract should clearly indicate the novelty of the work.
Response 1:
We gratefully appreciate for your valuable suggestion. We have re-written this part according to your suggestions. We have added more details about the most significant findings in this section based on your comments. The study has a total of three innovations: first, a method based on SWAT to predict the generation flow of small hydropower without hydrological information is proposed; second, three methods are used to calibrate the model: using data from hydrological stations, calculating generation flow by the output of small hydropower stations, and migrating generation flow of similar stations by similarity analysis; third, the results of predicting generation flow of small hydropower stations with and without reservoir case are compared.
The abstract of this paper after changes is as follows: “The availability of hydrological data for small hydropower plants is an important prerequisite for reservoir scheduling, reservoir flood control and integrated water resources. To address the problem of lack of hydrological data in small hydropower plants, this paper proposes a method to predict the power generation flow of small hydropower stations without hydrological data using the Soil and Water Assessment Tool model when the traditional data-driven methods cannot study the problem of power generation flow prediction in small hydropower stations well. The method can use gridded meteorological data as the input of the model to solve the problem of small hydropower stations without meteorological data. The problem that small hydropower plants without hydrological data cannot calibrate the hydrological model is solved by calculating the generation flow through the output of small hydropower station and by using the similarity analysis method to migrate the generation flow of similar small hydropower sta-tion. The model was tested in a watershed in southwest China to demonstrate the effectiveness of the proposed method. The results show that the coefficient of determination between the pre-dicted and measured values of small hydropower station without information is about 0.84, which achieves a better prediction.”
Point 2:
Introduction. This section presents the actual problem and statement of the studied research. In this reviewer opinion, more references could be added, and the main objectives of the work should be stated in this moment.
Response 2:
Thank you for pointing out this problem in manuscript. We have re-written this part according to your suggestions. After we made changes to the content of the introduction, we refer to 22 references by its number. We also checked all the literature as you suggested and eliminated those that were less relevant to this study.
The introduction of this paper after changes is as follows:“Facing the new changes in the global energy pattern, vigorously developing clean low-carbon energy is the main direction of energy development. From a global perspective, as a clean renewable energy source, small hydropower (a single hydropower station with an installed capacity of less than 50,000 kW) resources is almost the main force of hydro-power resources, and it is the most important support and guarantee to achieve the goal of "carbon emissions peak and carbon neutrality" and realize the energy transformation of human society [1,2]. There has been a great deal of research and many excellent methods proposed in the field of streamflow prediction, such as Costa Silva, D. F. et al. [3] proposed an integrated Long-Short Term Memory (LSTM) model that tested streamflow prediction for five scenarios using runoff and rainfall data. Hu, Y. et al. [4] used flow data from a hy-drological station and precipitation data from 11 surrounding rainfall stations to build an LSTM model for flow prediction in small rivers. Zaini, N et al. [5] used ten years of histor-ical rainfall, river flow data and various meteorological data to construct support vector machine (SVM) models and their coupled models with particle swarm optimization (PSO) models for daily river flow prediction. However, most of these studies are carried out un-der the premise of having complete or relatively complete hydrological information. Ac-cording to the research, most of the small hydropower plants are located in remote areas, and the equipment and facilities of the power stations are relatively backward, and they have been in the situation of no hydrological information, which will seriously affect the optimal scheduling of small hydropower groups and the flood prevention of power sta-tions [6]. How to make scientific and efficient prediction of small hydropower generation flow without hydrological information, so as to improve the safety and comprehensive water resource utilization of small hydropower has been an urgent problem.
Prediction of ungauged watersheds has always been one of the most important and challenging problems. In the field of hydrology, construction of hydrological models and regionalization based on hydrological similarity are often used to carry out studies of un-informed watersheds[7,8,9]. Hydrological models commonly used for flow simulation are: Variable Infiltration Capacity Macroscale Hydrologic Model (VIC) [10,11,12], Soil and Water Assessment Tool model (SWAT) [13,14,15,16], Hydrologiska Byråns Vattenbalan-savdelning model (HBV) [17,18], etc. Among them, the SWAT model is a distributed hy-drological model with strong physical mechanism [19], the model can be simulated from three different time scales of year, month and day for continuous long-term simulation, and can use geographic information data and remote sensing data to establish a hydro-logical model in ungauged watershed. The SWAT model is one of the most widely used basin hydrology models in the world and can be found in various research areas [20]. For example, Narula, K. et al. [21] used the SWAT model to simulate and calibrate streamflow in two montane forested watersheds. Kanishka, G. et al. [22] proposed a method to com-bine watershed classification with regionalization using a dimensionality reduction tech-nique and used the SWAT model for streamflow prediction. Using the method of con-structing hydrological models to physically solve the problem of predicting power genera-tion flow in small hydropower plants without hydrological information can effectively avoid the traditional data-driven inability to obtain data for prediction.
Against the above background, the main objective of this study is to carry out re-search on the problem of small hydropower flow prediction without hydrological infor-mation based on the SWAT hydrological model, and to solve the current problems of small hydropower (especially private small hydropower plants) due to the serious lack of hydrological information, which makes flow prediction difficult and scheduling work dif-ficult, by combining similarity analysis, cluster analysis, and construction of basin hy-drological model. This study can provide a novel and reliable idea for the future develop-ment of flow prediction of small hydropower plants without hydrological data, and pro-vide data support for the unified scheduling and management of small hydropower and monitoring of ecological flow in the future.”
Point 3:
Materials and Methods. This section is well presented, describing the main model and aspects of the correlations.
In this reviewer opinion, figure 4 has too small legend. It should have bigger letters to be understable.
Response 3:
We gratefully thank you for reading our paper carefully and giving the above positive comments. We have corrected this section based on your comments, Figure 4 has been modified.
Point 4:
Construction of SWAT Model. This is a descriptive section. It is well presented but in this reviewer opinion, this section should be part of the previous one, materials and methods
Response 4:
Thank you so much for your careful check. We have corrected this section based on your comments. We have moved this section to 2.6.
Point 5:
Results and Discussion. Sensitivity and calibration are adequate and model is well accured. Furthermore, uncertainty analysis is well presented, and the results present a very well calibrated model and good results.
Conclusions. This section is well presented, with clear statements of the main results. In this reviewer opinion, authors should clearly indicate the novelty of the research and the degree of accomplishment of the main objectives of the developed research.
Response 5:
We gratefully thank you for reading our paper carefully and giving the above positive comments. We have rewritten the conclusion based on your comments.
First, we summarized the innovations of this study with the following three points: In this paper, the SWAT model is used to predict the power generation flow of small hydropower plants without hydrological information, which innovatively solves the problem of small hydropower plants without hydrological information that makes it im-possible to carry out flow prediction and other work; correlation analysis and cluster analysis are used to classify 78 small hydropower plants, and three methods are proposed to calibrate the model; a detailed analysis of small hydropower plants without infor-mation with and without reservoirs is also carried out.
Then we added the highlights of this study in the conclusion section and detailed them with some numerical results. Here we show the extent to which the main objectives of the research developed in this study were accomplished. The content of this section was modified as follows: “The results of the correlation analysis and cluster analysis of 78 small hydropower stations showed that the closer the station to the no-information small hydropower station, the stronger the correlation. After calibration of the model, the NSE values of MH and NW small hydropower stations were 0.74 and 0.73, and the coefficients of determination were 0.84 and 0.78. it can be seen that the SWAT model has good applicability in the study basin. The results of sensitivity analysis on the parameters of the model show that the CN2 parameter has the greatest influence on the accuracy of the model. The results of the uncertainty analysis on the parameters of the model show that the larger yellow area in the plots, as shown in Figure 9 and Figure 10, indicates that the parameters of the model have a greater influence on the uncertainty of the prediction results. The main objective of the research in this paper is to carry out the prediction of power generation flow of small hydropower plants without hydrological information. The monthly power generation flow prediction for small hydropower plants in NB has been better achieved to achieve the daily power generation flow prediction. And the prediction results are analyzed from two cases with and without reservoirs, and the prediction ac-curacy is expressed by the decision coefficient, which is 0.64 and 0.83.”
Finally, we summarized the remaining shortcomings of this study and expressed our views on the future research directions. We analyze the problem from a small hydroelectric power plant and point out that the daily generation flow prediction accuracy of this study is still relatively. Then we propose two prospects: first, we can improve the prediction accuracy from the direction of solving the model input by migrating the data of large hydropower plants in the same large basin to small hydropower plants where meteorological data are scarce through similarity analysis and migration learning; second, we can improve the prediction accuracy from the direction of solving the model parameters by modeling the basin of large hydropower plants in the same large basin and then determining the model parameters of small hydropower plants through the regionalization method. prediction accuracy. The content of this section was modified as follows:” Most of the small hydropower stations are in remote areas, where neither hydrologi-cal nor meteorological information is available, so the daily generation flow prediction accuracy of this study is low. The problem of missing meteorological data for small hy-dropower stations can be solved in the future by using correlation methods to migrate meteorological data from large hydropower stations located in the same large basin using transfer learning methods. Similarly, a hydrological model can be established in the wa-tershed where the large hydropower stations are located first, and the model parameters for the watershed where the small hydropower stations are located can be determined using a regionalization approach.”
Thank you for your consideration!
Sincerely yours,
Shenghuo Xie
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
The manuscript has been revised properly based on the provided comments.
