The Characteristics and Trend Prediction of Water and Sediment Evolution at the Toudaoguai Station on the Yellow River from 1960 to 2019
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis study investigates the characteristics and trends of water and sediment evolution using flow and sediment (solid matter) transport data from 1960 to 2019 at the Toudaoguai hydrological station in the Inner Mongolia branch of the Yellow River in China. Mann–Kendall test, cumulative anomaly and Morlet wavelet analyses were applied for trend analyses. Then, flow and sediment trends were modeled with ARIMA, Artificial Neural Network (BP) and ARIMA-BP combined models and projections were made for the years 2020–2029. The results show that both water flow and sediment transport have decreased significantly; the combined model (ARIMA-BP) offers higher accuracy (R² > 0.86) compared to the models used separately.
Using a long time series of 60 years in the study increases the reliability of the results. It provides technical contributions to water and sediment management, which is of critical importance for the region. It is important to perform systematic verification with three different methods. The accuracy of the ARIMA-BP combined model supports its applicability. Model evaluation criteria (R², RMSE, MAE) are clearly reported.
My criticisms and suggestions:
• The BP model and ARIMA alone show poor performance (e.g. the BP model gave 160% error in sediment load predictions).
• Prediction success is limited to the 7-year test period only. This reduces the generalizability of the model.
• Although the data sources are stated to be publicly available, the verification mechanism is not specified.
• Quantitative analysis of human impacts is lacking: factors such as reservoir impact, land use are only mentioned in the description, not included in the model. Activities carried out on the stream will undoubtedly affect the results.
• There are some resolution problems and grammatical errors in the graphs.
The justification for the station selection is limited, more explanations should be added (why only Toudaoguai?).
• The long-term performance of the model is not evaluated.
• No additional explanations to support the reliability of the data.
• The integration of human impacts into the model is limited.
• Some tables, graphs and language errors require attention.
Author Response
June. 26 2025
Dear Editor and Reviewers:
Thank you for your letter and comments concerning our manuscript entitled “Characteristics and Trend Prediction of Water and Sediment Evolution at the Toudaoguai Station on the Yellow River from 1960 to 2019” (Manuscript ID. hydrology-3695582). We have found your comments to be very valuable in revising and improving our paper, as well as an important guidance for our future research. We have studied your comments and recommendations carefully and we have made based on them revisions to our paper that we hope to meet your approval. As you will see when examining our revisions, the reviewers’ comments and recommendations have been considered seriously and thoroughly addressed in our revised paper. Please note: red is addition/modification; green is original sentence.
We hope that this manuscript is now acceptable for publication in Hydrology.
If you have any additional comments and/or concerns, please do not hesitate to contact us directly.
Sincerely,
Chao Li, Jing Guo*, Xinlei Guo*, And Hui Fu
Response to reviewers’ and editor’s comments
General comments by Reviewer 1
Question:This study investigates the characteristics and trends of water and sediment evolution using flow and sediment (solid matter) transport data from 1960 to 2019 at the Toudaoguai hydrological station in the Inner Mongolia branch of the Yellow River in China. Mann–Kendall test, cumulative anomaly and Morlet wavelet analyses were applied for trend analyses. Then, flow and sediment trends were modeled with ARIMA, Artificial Neural Network (BP) and ARIMA-BP combined models and projections were made for the years 2020–2029. The results show that both water flow and sediment transport have decreased significantly; the combined model (ARIMA-BP) offers higher accuracy (R² > 0.86) compared to the models used separately.
Using a long time series of 60 years in the study increases the reliability of the results. It provides technical contributions to water and sediment management, which is of critical importance for the region. It is important to perform systematic verification with three different methods. The accuracy of the ARIMA-BP combined model supports its applicability. Model evaluation criteria (R², RMSE, MAE) are clearly reported.
Response: Thank you very much for your positive comments on the overall layout and structure of this article, the model and experimental approach, and the perspective and writing of the selected topic. We have carefully considered your comments and suggestions and will make serious revisions below.
Specific comments by Reviewer 1
- Question: The BP model and ARIMA alone show poor performance (e.g. the BP model gave 160% error in sediment load predictions).
Response: Thank you for your suggestion. The reason is that the BP neural network model finds the global optimal weight parameters through optimization algorithms during the simulation process to minimize the network's error function. However, since its error function is usually a highly nonlinear and complex function with many local minima, the network is prone to getting trapped in these local minima during training and fails to find the global optimal solution. Moreover, the limited nature of sediment load data and the complexity of sediment transport mechanisms make the BP neural network model susceptible to overfitting. The network may learn the noise in the training data, resulting in large errors on the test data.Therefore, it is necessary to further optimize and improve the existing models to enhance the simulation accuracy. At the same time, ARIMA model is essentially a linear time - series model. The annual runoff and sediment load of the river cross - section are affected by natural conditions and human factors such as rainfall, water withdrawal and use, upstream water and sediment, and comprehensive basin management, and there are complex nonlinear relationships among them. The ARIMA model cannot effectively capture these nonlinear relationships, which leads to an increase in prediction errors. Meanwhile, the ARIMA model requires that the time - series data be stationary, that is, the mean, variance, and covariance of the data remain unchanged over time. However, the heterogeneous characteristics of the Yellow River's water and sediment often lead to significant non - stationarity and seasonal differences in the time-series data. When applying this model to simulate annual runoff and sediment load, it will result in inaccurate estimation of model parameters, thereby affecting the accuracy of the prediction results.
- Question: Prediction success is limited to the 7-year test period only. This reduces the generalizability of the model.
Response: Thank you for your suggestion. The dataset contains the annual runoff and sediment load from 1960 to 2019. For machine learning models such as BP neural network and ARIMA, due to the limited number of samples, this study appropriately increased the length of the training period to ensure that the models can fully learn the patterns in the data. Meanwhile, according to the previous analysis, there is a certain periodicity in the annual runoff and sediment load. The length of the training period and the validation period should preferably be multiples of an integer number of years. In the runoff and sediment load data after 2012, there are extreme events, such as the extremely low value in 2016 and the extremely high value in 2019. To ensure that these events are reflected in the validation period, the data from 2013 to 2019 were selected as the validation period.
- Question: Although the data sources are stated to be publicly available, the verification mechanism is not specified.
Response: Thank you for your suggestion. The verification mechanism has been explained in the revised version.
Modification: The ARIMA-BP coupled model can integrate the strengths of both models, better handle the linear and nonlinear relationships existing in the time series of runoff and sediment discharge, thereby enhancing the prediction accuracy and interpretability of the model [31]. Meanwhile, as runoff and sediment discharge are influenced by various factors such as meteorological conditions and basin characteristics, their time series often exhibit complex dynamic change characteristics. The ARIMA-BP coupled model can better adapt to these complex changes and maintain good prediction performance even in the presence of noise, outliers, or missing values in the data, thereby enhancing the robustness of the model[32].
[31] Li, L.; Gong, H. L.; Guo, L.; etc. Research advances on hydrologic time series analysis methods. Journal of Geo-informati-on Science, 2024, 26(4):927–945, doi: 10.12082/dqxxkx.2024.230336.
[32] Du, Y.; Ma, R. Y. Application of Different Improved ARIMA Models in the Prediction of Hydrological Time Series. Water Power, 2018, 44(04):12–14+28.
- Question:Quantitative analysis of human impacts is lacking: factors such as reservoir impact, land use are only mentioned in the description, not included in the model. Activities carried out on the stream will undoubtedly affect the results.
Response: Thank you for your suggestion. In the revised version, the impacts of natural factors and human activities on the evolution process and trend of runoff and sediment transport at the Tou daoguai hydrological station were discussed. Moreover, an improved method of comparing the rate of change of cumulative quantity slopes was employed to discuss the contributions of natural factors and human activities to runoff and sediment transport.
- Question: There are some resolution problems and grammatical errors in the graphs.
Response: Thank you for your suggestion. In the revised version, the resolution of the figures and tables and the grammatical errors have been corrected.
- Question: The justification for the station selection is limited, more explanations should be added (why only Toudaoguai?).
Response: Thank you for your suggestion. In the revised version, The author has elaborated on the reasons for selecting the Toudaoguai Hydrological Station as the study area, with the specific statements as follows:The Touaoguai Hydrological Station is the exit hydrological station of the Yellow River in Inner Mongolia and also the hydrological station at the boundary between the upper and middle reaches of the Yellow River. The annual runoff and sediment load of this hydrological station can reflect the characteristics and patterns of water and sediment from the upper reaches of the Yellow River. It has important reference value for the water and sediment regulation of the upper reaches and the basin governance. Meanwhile, it can also guide the water and sediment regulation and operation of the Wanjiazhai Water Conservancy Hub in the middle reaches of the Yellow River. Therefore, this study takes the cross - section of the Toudaoguai Hydrological Station as the research site.
- Question: The long-term performance of the model is not evaluated.
Response: Thank you for your suggestion. In the revised version, the author explains as follows: This study used the ARIMA-BP coupled model to predict the evolution trend of runoff and sediment load at the Toudaoguai hydrological station from 2020 to 2029. By comparing with the existing published data of runoff and sediment load, the runoff at the Toudaoguai hydrological station decreased from 36.98 billion m³ in 2020 to 17.34 billion m³ in 2023, and the sediment load decreased from 141 million tons in 2020 to 23.9 million tons in 2023. The predicted trend basically coincides with the measured results, which verifies the reliability of the model.
- Question: No additional explanations to support the reliability of the data.
Response: Thank you for your suggestion. In the revised version, the predicted values were compared with the measured runoff and sediment transport at the Toudaoguai Hydrological Station from 2020 to 2023, which supports the reliability of the prediction results.
- Question: The integration of human impacts into the model is limited.
Response: Thank you for your suggestion. In the revised version, the impacts of natural factors and human activities on runoff and sediment transport were discussed. Moreover, an improved method of comparing the rate of change of cumulative quantity slopes was employed to calculate the contribution of human activities to runoff and sediment transport.
- Question: Some tables, graphs and language errors require attention.
Response: Thank you for your suggestion. In the revised version, the errors in tables, figures, and language have been corrected.
Author Response File: Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsDear authors, I was tasked with reviewing your paper entitled "Characteristics and Trend Prediction of Water and Sediment Evolution at the Toudaoguai Station on the Yellow River from 1960 to 2019". The life of people along rivers has always been full of challenges, and modern challenges require adequate solutions that should make life and work along river flows easier. It is indeed true that the Yellow River is a renowned sediment-laden river, and analysing its water sediment evolution characteristics and trends is critical for rational water resource utilisation and water security.
The research is well designed, the methods used are adequate and modern, and the results obtained are interesting and provide opportunities for further research and improvement in this field. I notice that you have already changed your manuscript and probably acted on the suggestions of an earlier review. The quality of the manuscript is certainly present, and the additional changes have increased it. As a reviewer, I would also have a few suggestions that I believe would affect the quality of the work.
Figure 1. "Geographic location map of the study area" is good but I think it is too colorful which reduces its readability. For example, it would be nice if you could show the topography of the terrain in one color with shading.
In several places in the text you have a notice that has been left that says: "Error! Reference source not found." You can see this in the lines: 135, 137, 144, 156 and 178.
Your research, which in my opinion is really well done, deserves a better and more substantial conclusion. I suggest that you design it better, that you highlight all the peculiarities of your research as well as the specific results that you consider to be the most important. Also, make suggestions for further activities in this field.
Best regards.
Author Response
June. 26 2025
Dear Editor and Reviewers:
Thank you for your letter and comments concerning our manuscript entitled “Characteristics and Trend Prediction of Water and Sediment Evolution at the Toudaoguai Station on the Yellow River from 1960 to 2019” (Manuscript ID. hydrology-3695582). We have found your comments to be very valuable in revising and improving our paper, as well as an important guidance for our future research. We have studied your comments and recommendations carefully and we have made based on them revisions to our paper that we hope to meet your approval. As you will see when examining our revisions, the reviewers’ comments and recommendations have been considered seriously and thoroughly addressed in our revised paper. Please note: red is addition/modification; green is original sentence.
We hope that this manuscript is now acceptable for publication in Hydrology.
If you have any additional comments and/or concerns, please do not hesitate to contact us directly.
Sincerely,
Chao Li, Jing Guo*, Xinlei Guo*, And Hui Fu
General comments by Reviewer 2
Question: Dear authors, I was tasked with reviewing your paper entitled "Characteristics and Trend Prediction of Water and Sediment Evolution at the Toudaoguai Station on the Yellow River from 1960 to 2019". The life of people along rivers has always been full of challenges, and modern challenges require adequate solutions that should make life and work along river flows easier. It is indeed true that the Yellow River is a renowned sediment-laden river, and analysing its water sediment evolution characteristics and trends is critical for rational water resource utilisation and water security.
The research is well designed, the methods used are adequate and modern, and the results obtained are interesting and provide opportunities for further research and improvement in this field. I notice that you have already changed your manuscript and probably acted on the suggestions of an earlier review. The quality of the manuscript is certainly present, and the additional changes have increased it. As a reviewer, I would also have a few suggestions that I believe would affect the quality of the work.
Response: Thank you very much for your positive comments on the overall layout and structure of this article, the model and experimental approach, and the perspective and writing of the selected topic. We have carefully considered your comments and suggestions and will make serious revisions below.
Specific comments by Reviewer 2
- Question: Figure 1. "Geographic location map of the study area" is good but I think it is too colorful which reduces its readability. For example, it would be nice if you could show the topography of the terrain in one color with shading.
Response: Thank you for your suggestion. We have revised the Figure 1. The new figure shown as
Figure1. Geographic location map of the study area
- Question: In several places in the text you have a notice that has been left that says: "Error! Reference source not found." You can see this in the lines: 135, 137, 144, 156 and 178.
Response: Sorry, this is due to an omission in our writing, We have corrected the errors in the reference citations.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsThe authors performed a robust and high-quality analysis on the long-term change in runoff and sediment transport. I suggest a minor revision before publication.
The authors used the observation data to perform the various statistical analyses. The developed auto-correlation-based model showed a robust and high accuracy in predicting the target variable.
Overall, the findings are robust and based on high-quality analysis. However, I would like to suggest some issues be solved before its publication. Such issues include the missing link of the references and not enough discussion of the results.
Please check the following comments.
Section 2
- Please check the references not linked.
Line 245:
Reference is missing for Chen.
Overall:
Although the analysis is quite comprehensive and high quality, there is not so much discussion of the findings. The authors are encouraged to interpret the results better in the context of climate change and anthropogenic effects. Several recent studies show the increase in extreme rainfall intensity, such as:
- Hiraga, Y., Tahara, R., & Meza, J. (2025). A methodology to estimate Probable Maximum Precipitation (PMP) under climate change using a numerical weather model. Journal of Hydrology, 652, 132659.
Meanwhile, several studies showed the decline of mean/high percentile of rainfall, such as:
- Lagos-Zúñiga, M., Mendoza, P. A., Campos, D., & Rondanelli, R. (2024). Trends in seasonal precipitation extremes and associated temperatures along continental Chile. Climate Dynamics, 62(5), 4205-4222.
The declining trend that the authors found was as a result of combination of such changes?
Or anthropogenic effects are more dominant?
I appreciate the authors providing the discussion of the results more.
Author Response
June. 26 2025
Dear Editor and Reviewers:
Thank you for your letter and comments concerning our manuscript entitled “Characteristics and Trend Prediction of Water and Sediment Evolution at the Toudaoguai Station on the Yellow River from 1960 to 2019” (Manuscript ID. hydrology-3695582). We have found your comments to be very valuable in revising and improving our paper, as well as an important guidance for our future research. We have studied your comments and recommendations carefully and we have made based on them revisions to our paper that we hope to meet your approval. As you will see when examining our revisions, the reviewers’ comments and recommendations have been considered seriously and thoroughly addressed in our revised paper. Please note: red is addition/modification; green is original sentence.
We hope that this manuscript is now acceptable for publication in Hydrology.
If you have any additional comments and/or concerns, please do not hesitate to contact us directly.
Sincerely,
Chao Li, Jing Guo*, Xinlei Guo*, And Hui Fu
General comments by Reviewer 3
Question: The authors performed a robust and high-quality analysis on the long-term change in runoff and sediment transport. I suggest a minor revision before publication.
The authors used the observation data to perform the various statistical analyses. The developed auto-correlation-based model showed a robust and high accuracy in predicting the target variable.
Overall, the findings are robust and based on high-quality analysis. However, I would like to suggest some issues be solved before its publication. Such issues include the missing link of the references and not enough discussion of the results.
Please check the following comments.
Response: Thank you very much for your positive comments on the overall layout and structure of this article, the model and experimental approach, and the perspective and writing of the selected topic. We have carefully considered your comments and suggestions and will make serious revisions below.
Specific comments by Reviewer 3
- Question: Section 2Please check the references not linked.
Response: Thank you for your suggestion. The author has revised the references throughout the text to ensure the rationality of the citations and the validity of the links.
- Question: Line 245:Reference is missing for Chen.
Response: Thank you for your suggestion. In the revised version, the reference has been perfected.
- Question: The declining trend that the authors found was as a result of combination of such changes?Or anthropogenic effects are more dominant?
Response: Thank you for your suggestion. The impacts of natural factors and human activities on the evolution process and trend of runoff and sediment transport at the Tou daoguai hydrological station were discussed. Moreover, an improved method of comparing the rate of change of cumulative quantity slopes was employed to discuss the contributions of natural factors and human activities to runoff and sediment transport.The contribution rate of natural factors to the runoff at the Toudaoguai hydrological station is 75.14%, while the impact of human activities on the sediment load is 75.59%.
Author Response File: Author Response.pdf