Assessing the National Water Model’s Streamflow Estimates Using a Multi-Decade Retrospective Dataset across the Contiguous United States
Round 1
Reviewer 1 Report
The study investigates the performance of the National Water Model (NWM) in time and space across the Contiguous United States. Streamflow simulations are compared to records from USGS gauging stations, applying classical statistical analysis to overall time series and seasonal ones. The work sounds scientifically interesting, the study is conducted rigorously, the outcomes are deeply discussed and the results are clearly exposed. It is recommended for publication.
Author Response
The authors are grateful for the careful and constructive review of the manuscript. The attached file contains a detailed response.
Author Response File: 
Author Response.docx
Reviewer 2 Report
1- Introduction:
to my knowledge streamflow modeling methods are generally divided according to the purpose; streamflow estimation, b short-term streamflow forecasting, and c- long-term streamflow forecasting . streamflow estimation can be addressed as a short-term forecast as it was a physically based representation of the rainfall-runoff processes to estimate the short-term hydrograph, the other types of short-term forecasting models are based on some statistical-mathematical methods. These models should do forecasting for some little future streamflow values time steps after that their parameters should be updated using real future measured values. long-term forecasting models such as time series modeling or stochastic models should reproduce the general statistical properties of the historical records by modeling the deterministic part (jump, trend, and cyclic component) and the stochastic one.
saying all that in Your title you put the word "streamflow estimate", while in line 33 you mentioned "streamflow forecasting", line 55"estimate of streamflow", line 69"streamflow estimate", line 92 "streamflow forecast"
please be more specific, In lines 135-140 section 2.1 your explanation shows that the model is a conceptual one as I know about that, which means estimation is the best word to use.
- Fig.1: add the reference if any, it would be a good representation if you add the percentage of natural flow gage stations and that for the regulated flow for each area and the area/no. of stations ratio.
- line 185: how many gaging stations out of the 9000 has 10%> missing data? please mention.
- line 186: what do you mean by "forecast failure" please explain more
- 10% missing data is not high for a gage station to be ignored, please justify the selection of this 10%, why not 25% or 5%, is there any criteria for selecting this 10%? This is important because after you selected this you reduced the gaging stations from 9000 to 3260, which is almost 1/3 so it is important to justify.
- what is the percent reduction in the regulated gaging stations and that of the unregulated?
- sometimes you use unregulated and sometimes you use natural flow please unify.
- line 196: The database you used for calculating the AI extends from (1979-2020), a period with evident climate change, hence before estimating any parameter from these data they should be tested for homogeneity and non-stationarity and then the estimation should be done upon the results by either selecting the whole period data or just the future sub-sample. also, the equation is numbered 5, which should be 1
- equation 2 missing the summation from i=1:n
-lines 230-262: RMSE and NRMSE are usually used to compare the performance between more than one model when we have one model we cannot decide if the model performance is good or bad unless we are lucky to have a very small value. I know that you used it here for spatial performance so please mention this.
-line 292: 5 statistics, not 4
section 3.1: please justify why you considered KGE=0.5 as the acceptance criteria for this metric, same for CC as shown in lines 358-374.
I would suggest at the end of this section that you made a spatial calibration factor map>1 for underestimation and <1 for overestimation, one for each month.
discussion: lines 621-676 can be reduced to a smaller paragraph because they are general facts. What I want to see is what you had in section starts at line 677 with more elaboration of exactly what are the reasons for the underestimation and overestimation in chronological sequence from the most affecting one and follows, with more specific justifications.
- In lines 721-723 can you please add on which analysis you made to come to the finding that the soil moisture dynamics misrepresentation in the model is the reason for the differences in the streamflow estimation?
-
very small corrections in the numbering of equations and some wordings.
Author Response
The authors are grateful for the careful and constructive review of the manuscript. The attached file contains a detailed response.
Author Response File: Author Response.docx
Reviewer 3 Report
This paper is well written but many issues need to be solved:
Methodology: The study provides a comprehensive evaluation of the National Water Model (NWM) across the Contiguous United States using a large dataset of 3260 USGS gauging stations. The statistical metrics employed for assessing the agreement between observed and simulated streamflow are appropriate and widely used in hydrological studies. However, it would be beneficial to provide a brief justification for the selection of these specific metrics and their relevance to the evaluation of hydrological models.
Literature review: Must discuss these latest work to improve literature quality:
1. Zhang, G., Zhao, Z., Yin, X., & Zhu, Y. (2021). Impacts of biochars on bacterial community shifts and biodegradation of antibiotics in an agricultural soil during short-term incubation. Science of The Total Environment, 771, 144751. doi: https://doi.org/10.1016/j.scitotenv.2020.144751
2. Wan, Z., Zhang, T., Liu, Y., Liu, P., Zhang, J., Fang, L.,... Sun, D. (2022). Enhancement of desulfurization by hydroxyl ammonium ionic liquid supported on active carbon. Environmental Research, 213, 113637. doi: https://doi.org/10.1016/j.envres.2022.113637
3. Li, T., Xia, T., Wang, H., Tu, Z., Tarkoma, S., Han, Z.,... Hui, P. (2022). Smartphone App Usage Analysis: Datasets, Methods, and Applications. IEEE Communications Surveys & Tutorials, 24(2), 937-966. doi: 10.1109/COMST.2022.3163176
4. Xie, X., Xie, B., Cheng, J., Chu, Q., & Dooling, T. (2021). A simple Monte Carlo method for estimating the chance of a cyclone impact. Natural Hazards, 107(3), 2573-2582. doi: 10.1007/s11069-021-04505-2
5. Li, J., Wang, Z., Wu, X., Xu, C., Guo, S.,... Chen, X. (2020). Toward Monitoring Short-Term Droughts Using a Novel Daily Scale, Standardized Antecedent Precipitation Evapotranspiration Index. Journal of Hydrometeorology, 21(5), 891-908. doi: 10.1175/JHM-D-19-0298.1
6. Wu, X., Guo, S., Qian, S., Wang, Z., Lai, C., Li, J.,... Liu, P. (2022). Long-range precipitation forecast based on multipole and preceding fluctuations of sea surface temperature. International Journal of Climatology, 42(15), 8024-8039. doi: https://doi.org/10.1002/joc.7690
7. Zhou, J., Wang, L., Zhong, X., Yao, T., Qi, J., Wang, Y.,... Xue, Y. (2022). Quantifying the major drivers for the expanding lakes in the interior Tibetan Plateau. Science Bulletin, 67(5), 474-478. doi: https://doi.org/10.1016/j.scib.2021.11.010
8. Wu, B., Quan, Q., Yang, S., & Dong, Y. (2023). A social-ecological coupling model for evaluating the human-water relationship in basins within the Budyko framework. Journal of Hydrology, 619, 129361. doi: https://doi.org/10.1016/j.jhydrol.2023.129361
9. Yang, M., Zhao, A., Ke, H., & Chen, H. (2023). Geo-Environmental Factors’ Influence on the Prevalence and Distribution of Dental Fluorosis: Evidence from Dali County, Northwest China. Sustainability, 15(3). doi: 10.3390/su15031871
10. "Xu, D., Zhu, D., Deng, Y., Sun, Q., Ma, J.,... Liu, F. (2023). Evaluation and empirical study of Happy River on the basis
Results: The findings presented in the study are clear and well-supported by the analysis. The distinction made between regulated and natural flow conditions provides valuable insights into the performance of the NWM. However, it would be helpful to include more specific information on the spatial patterns of agreement/disagreement between the model and observed streamflow, particularly for the regions where a reduced degree of agreement was observed (e.g., the Great Plains region). Including maps or spatial plots illustrating these patterns would enhance the understanding of the results.
Comparison of historical trends: The study mentions a comparison of historical trends in daily flow data between the NWM and observed streamflow. It would be beneficial to provide more details on this comparison, such as the specific time period considered, the method used for trend analysis, and any notable differences or similarities observed between the model and observed trends. This additional information would provide further context and strengthen the utility assessment of the NWM retrospective dataset.
Limitations and recommendations for improvement: The study acknowledges the need for enhancements to model performance for regulated flow and highlights the importance of bias correction for utilizing the NWM retrospective streamflow dataset. It would be valuable to discuss potential reasons for the reduced degree of agreement in the Great Plains region and suggest possible avenues for improvement in simulating streamflow in this region. Additionally, it would be beneficial to provide specific recommendations for bias correction techniques or strategies that could be employed to improve the utility of the NWM retrospective dataset for future applications.
NextGen NWM framework: The study proposes that the model-agnostic NextGen NWM framework, which accounts for regional performance of the utilized model, could be more suitable for continental-scale hydrologic prediction. While this is an interesting proposition, it would be helpful to provide more background information on the NextGen NWM framework, including its key features and advantages over the existing NWM. Additionally, discussing potential challenges or considerations in implementing the NextGen NWM framework would add depth to the discussion.
Discussion: Discuss these studies
1. AHP: a case study of Shaoxing City (Zhejiang, China). Marine and Freshwater Research. doi: 10.1071/MF22196"
2. Gao, C., Hao, M., Chen, J., & Gu, C. (2021). Simulation and design of joint distribution of rainfall and tide level in Wuchengxiyu Region,China. Urban climate, 40, 101005. doi: 10.1016/j.uclim.2021.101005
3. Gao, C., Zhang, B., Shao, S., Hao, M., Zhang, Y., Xu, Y.,... Wang, Z. (2023). Risk assessment and zoning of flood disaster in Wuchengxiyu Region, China. Urban Climate, 49, 101562. doi: https://doi.org/10.1016/j.uclim.2023.101562
4. Yue, Z., Zhou, W., & Li, T. (2021). Impact of the Indian Ocean Dipole on Evolution of the Subsequent ENSO: Relative Roles of Dynamic and Thermodynamic Processes. Journal of Climate, 34(9), 3591-3607. doi: 10.1175/JCLI-D-20-0487.1
5. Tian, Y., Yang, Z., Yu, X., Jia, Z., Rosso, M., Dedman, S.,... Wang, J. (2022). Can we quantify the aquatic environmental plastic load from aquaculture? Water Research, 219, 118551. doi: https://doi.org/10.1016/j.watres.2022.118551
6. Yin, L., Wang, L., Tian, J., Yin, Z., Liu, M.,... Zheng, W. (2023). Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer. Applied Sciences, 13(6). doi: 10.3390/app13063610
7. Liu, Z., Xu, J., Liu, M., Yin, Z., Liu, X., Yin, L.,... Zheng, W. (2023). Remote sensing and geostatistics in urban water-resource monitoring: a review. Marine and Freshwater Research. doi: 10.1071/MF22167
8. Yin, L., Wang, L., Ge, L., Tian, J., Yin, Z., Liu, M.,... Zheng, W. (2023). Study on the Thermospheric Density Distribution Pattern during Geomagnetic Activity. Applied Sciences, 13(9). doi: 10.3390/app13095564
Conclusion: The study concludes with a concise summary of the findings and emphasizes the need for enhancements to model performance and bias correction. It would be beneficial to briefly discuss the broader implications of the study's findings for hydrological modeling and prediction at the continental scale. Furthermore, it would be helpful to mention any potential future research directions or areas of further investigation that could build upon the study's findings.
Overall clarity and organization: The manuscript is generally well-written and organized, making it easy to follow the study's objectives, methods, results, and conclusions. However, some sections could be further clarified to provide additional context or explanation. Additionally, it is recommended to carefully proofread the manuscript for grammar, punctuation, and typographical errors.
ok
Author Response
The authors are grateful for the careful and constructive review of the manuscript. The attached file contains a detailed response.
Author Response File: Author Response.docx
Round 2
Reviewer 3 Report
Author fail to address comments of last revision , need to carefully solve
Author fail to address comments of last revision , need to carefully solve
Author Response
Responses are in the attached file.
Author Response File: Author Response.docx
