the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Jan 2025
Identifying controlling climate factors conducive to water and nitrogen export from agricultural watershed during snowmelt runoff period by using the SWAT model
Abstract. Temperature and precipitation variations during the freezing-thawing period affect snowmelt and accompanying nitrogen export in a complex manner. These influences can be long-lasting, superimposed, and strengthened. Daily discharge and nitrate nitrogen NO3--N concentrations were monitored during the snowmelt periods of 2015 and 2016 in an agricultural watershed in northeastern China. The SWAT model was used to simulate the water and NO3--N export during the snowmelt period of 1951–2014 to identify the controlling climate factors and confirm their suitable combination that facilitates snowmelt water and NO3--N export. Our results show that the SWAT model performs well for Re values in simulating the daily snowmelt runoff and NO3--N export, but poorly for NSE and R2 values in simulating NO3--N export. This is attributed to the absence of snowmelt water refrozen and hysteresis modules. The number of days and precipitation of the stable freezing period and the stating day of snowmelt period are controlling factors of daily snowmelt runoff, while daily NO3--N export are mostly affected by precipitation during the snowmelt period. The combinations of climatic factors favored by snowmelt runoff and NO3--N export were different. Years with longer stable freezing periods, later snowmelt period starting days, and higher rainfall during snowmelt, more readily generated high snowmelt runoff. Correspondingly, later appeared, higher and concentrated rainfall events, and higher temperatures between these rainfall and snowmelt events favored the NO3--N export. Finally, this study is of great importance for the prevention of spring floods and water pollution during snowmelt periods.
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RC1: 'Comment on egusphere-2024-3984', Anonymous Referee #1, 20 Feb 2025
The manuscript ‘Identifying controlling climate factors conducive to water and nitrogen export from agricultural watershed during snowmelt runoff period by using the SWAT model’ proposes to elucidate the drivers behind the hydrology and nutrient export during the winter in cold regions using a modelling approach. Although the topic is meritorious, the methodological approach used in the analysis is not robust. Specifically, the study only used two winter seasons to collect observations, each used to calibrate and validate the model (i.e., one year each). This approach is not robust enough and conflicts with sound modelling practices which use several years of data for calibration and validation, respectively. This standard practice ensures several weather patterns (e.g, wet, dry, and average years) are represented during the model fine tuning and validation. Using a single year of data will likely bias the model parameterization and may influence the results. The poor model performance reported in the abstract could be a result of the short records used to calibrate and validate the model. Moreover, the study area is very small (i.e., < 7km2), which results in Maize being the dominant land use (>80% of the study area). These conditions are very specific and reduce the relevance of the findings to other jurisdictions. That is, the hydrological and nutrient export response is largely influenced by the dominant land cover, physiography (e.g. ,soil type) and weather prevailing during the study. To achieve the claim of the manuscript to assess watershed-scale hydrology and water quality response, a larger area that integrates several land uses and management practices, as well as a longer record that captures a representative variation of weather should be used.
Citation: https://doi.org/10.5194/egusphere-2024-3984-RC1 -
AC1: 'Reply on RC1', Qiang Zhao, 05 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2024-3984/egusphere-2024-3984-AC1-supplement.pdf
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AC1: 'Reply on RC1', Qiang Zhao, 05 Mar 2025
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RC2: 'Comment on egusphere-2024-3984', Dan Myers, 20 May 2025
This study examined watershed modeling of water and nutrient exports during complex freeze-thaw periods. I believe this research is novel and would be pertinent to an international readership. I thought the introduction did a good job summarizing prior research, identifying knowledge gaps, stating the contribution, and setting the stage for the modeling. The discussion was also invigorating. For the methods, I have some concerns with the clarity and robustness of the calibration approach, the clarity of the potential influence of rain-on-snow melt on the simulations, and the poor quality of the NO3-N simulation at the daily time step. I provide some suggestions to hopefully help alleviate these concerns.
Major Comments:
Page 2, lines 37-38. Rain-on-snow melt events are mentioned here (and in the discussion) as being pertinent to snowmelt and nitrogen exports from the watershed. I could see this being important when simulating at the daily time step. However, I believe the SWAT model used in this study simulates snowpack temperature-based melt only, omitting the energy transfer to snowpack during rain events and rain-on-snow melt simulation. This was confusing for me, so I suggest clarifying the snowmelt simulation in this study, for instance after the SWAT model is introduced on Line 67 and/or in Section 2.3. Alternately, if rain-on-snow melt events are common in the watershed, you could consider using a model that simulates rain-on-snow melt to solidify this (e.g., Zare et al. 2022).
Zare, M., Azam, S., and Sauchyn, D: A modified SWAT model to simulate soil water content and soil temperature in cold regions: a case study of the south saskatchewan river basin in Canada. Sustainability 14.17 (2022): 10804. https://doi.org/10.3390/su141710804.
Page 7, line 158. I think more clarity is needed here. If the simulations were only evaluated during the snowmelt period, do you need to account for changes during the rest of the year to variables dependent on states, such as groundwater levels, that could affect runoff model performance at the start of each annual snowmelt period? Was there a model warm-up period to help with this? I may simply not be understanding this right, so more clarity here would be helpful.
Page 7, lines 158-164. I think you should add a few sentences to explain the reproducibility of the manual calibration approach used here. Are there other similar studies using manual calibrations you could cite to document the reasons for choosing this approach? Or, could you provide more details about the parameter combinations that were attempted and how optimal values were reached? I feel that calibrating 15 parameters for both discharge and NO3-N export would have a very large number of potential “optimal” parameter combinations to consider, so it would be helpful to expand on how this was achieved with this calibration approach, and what the limitations are. Also, an automated approach such as using R-SWAT (Nguyen et al., 2022) may help make the calibration more reproduceable.
Nguyen, T. V, Dietrich, J., Dang, T. D., Tran, D. A., Van Doan, B., Sarrazin, F. J., Abbaspour, K., and Srinivasan, R.: An interactive graphical interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization for the Soil and Water Assessment Tool, Environ. Modell. Softw., 156, 105497, https://doi.org/10.1016/j.envsoft.2022.105497, 2022.
Page 8, line 168. I think more details about the principal components regression would be useful here. For instance, were data transformed? Was multicollinearity considered? Are there any references for the method you used?
Page 8, line 182. Simulating nitrate nitrogen export at the daily time step can certainly be difficult, but the NSE of -0.19 and Fig. 4 c-d suggest it is often not being simulated well, with lags in particular. However, it appears that the simulation could be alright at the weekly time step. Would it be better to aggregate results to a weekly time step for nitrate-nitrogen instead of daily, note that the higher temporal resolution was not achievable, and then continue using the weekly time step for nitrate-nitrogen model throughout the results section (e.g., the long term 1951-2014 model)? This could potentially lead to a better performing NO3-N model and also alleviate the need for the “NSE and R2 values when simulated values during the initial snowmelt period were put off a day” results and footnote of Table 2.
Page 15, lines 261-265. This text basically states that the model did not simulate nitrate-nitrogen well, but in a hypothetical situation with better lags, in would have. I don’t believe that supports the statement “Hence, the SWAT model is considered suitable for simulating daily NO3--N export during the snowmelt period.” If the model is not simulating NO3-N well at the daily time step, I suggest as an alternative, aggregating to the weekly time step for NO3-N may potentially help alleviate this and lead to better performance.
Page 15, line 286 to page 16, line 287. This statement could use clarification. Minorly, the “runoff and runoff” may be a typo? But more importantly, I believe that clarity is needed because the rain-on-snow melt was not simulated in the SWAT model of this study, only snowpack temperature-based melt, so it is confusing to me. Since rain-on-snow melt is mentioned as pertinent to the findings, I think it would be beneficial to clarify that it wasn’t simulated here and mention any potential limitations. You may also discuss how simulating rain-on-snow melt (such as by using an energy balance approach; e.g., Zare et al., 2022) could provide further insights about these relationships.
Minor Comments:
Page 4, lines 101-102. For reproducibility, could you give a more specific description of the fertilizer and manure application rates you used based on the survey with farmers? Perhaps a couple sentences describing the application timings and rates that were used would help clarify this.
Fig. 1. The font size for labels on the legend and scale bar is small and difficult for me to read. I suggest making it larger.
Fig. 7. I really like the insights gained from this visualization.
Page 17, line 319 to page 18, line 329. For the conclusions section, I think it would benefit readers to not use the acronyms (ND-SFP, etc.), or at least redefine them, so readers can understand the summary of the findings without having to find the all the definitions in the paper.
I wish the authors the best with this manuscript and their future endeavors.
Citation: https://doi.org/10.5194/egusphere-2024-3984-RC2 - AC2: 'Reply on RC2', Qiang Zhao, 04 Jun 2025
Status: closed
-
RC1: 'Comment on egusphere-2024-3984', Anonymous Referee #1, 20 Feb 2025
The manuscript ‘Identifying controlling climate factors conducive to water and nitrogen export from agricultural watershed during snowmelt runoff period by using the SWAT model’ proposes to elucidate the drivers behind the hydrology and nutrient export during the winter in cold regions using a modelling approach. Although the topic is meritorious, the methodological approach used in the analysis is not robust. Specifically, the study only used two winter seasons to collect observations, each used to calibrate and validate the model (i.e., one year each). This approach is not robust enough and conflicts with sound modelling practices which use several years of data for calibration and validation, respectively. This standard practice ensures several weather patterns (e.g, wet, dry, and average years) are represented during the model fine tuning and validation. Using a single year of data will likely bias the model parameterization and may influence the results. The poor model performance reported in the abstract could be a result of the short records used to calibrate and validate the model. Moreover, the study area is very small (i.e., < 7km2), which results in Maize being the dominant land use (>80% of the study area). These conditions are very specific and reduce the relevance of the findings to other jurisdictions. That is, the hydrological and nutrient export response is largely influenced by the dominant land cover, physiography (e.g. ,soil type) and weather prevailing during the study. To achieve the claim of the manuscript to assess watershed-scale hydrology and water quality response, a larger area that integrates several land uses and management practices, as well as a longer record that captures a representative variation of weather should be used.
Citation: https://doi.org/10.5194/egusphere-2024-3984-RC1 -
AC1: 'Reply on RC1', Qiang Zhao, 05 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2024-3984/egusphere-2024-3984-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Qiang Zhao, 05 Mar 2025
-
RC2: 'Comment on egusphere-2024-3984', Dan Myers, 20 May 2025
This study examined watershed modeling of water and nutrient exports during complex freeze-thaw periods. I believe this research is novel and would be pertinent to an international readership. I thought the introduction did a good job summarizing prior research, identifying knowledge gaps, stating the contribution, and setting the stage for the modeling. The discussion was also invigorating. For the methods, I have some concerns with the clarity and robustness of the calibration approach, the clarity of the potential influence of rain-on-snow melt on the simulations, and the poor quality of the NO3-N simulation at the daily time step. I provide some suggestions to hopefully help alleviate these concerns.
Major Comments:
Page 2, lines 37-38. Rain-on-snow melt events are mentioned here (and in the discussion) as being pertinent to snowmelt and nitrogen exports from the watershed. I could see this being important when simulating at the daily time step. However, I believe the SWAT model used in this study simulates snowpack temperature-based melt only, omitting the energy transfer to snowpack during rain events and rain-on-snow melt simulation. This was confusing for me, so I suggest clarifying the snowmelt simulation in this study, for instance after the SWAT model is introduced on Line 67 and/or in Section 2.3. Alternately, if rain-on-snow melt events are common in the watershed, you could consider using a model that simulates rain-on-snow melt to solidify this (e.g., Zare et al. 2022).
Zare, M., Azam, S., and Sauchyn, D: A modified SWAT model to simulate soil water content and soil temperature in cold regions: a case study of the south saskatchewan river basin in Canada. Sustainability 14.17 (2022): 10804. https://doi.org/10.3390/su141710804.
Page 7, line 158. I think more clarity is needed here. If the simulations were only evaluated during the snowmelt period, do you need to account for changes during the rest of the year to variables dependent on states, such as groundwater levels, that could affect runoff model performance at the start of each annual snowmelt period? Was there a model warm-up period to help with this? I may simply not be understanding this right, so more clarity here would be helpful.
Page 7, lines 158-164. I think you should add a few sentences to explain the reproducibility of the manual calibration approach used here. Are there other similar studies using manual calibrations you could cite to document the reasons for choosing this approach? Or, could you provide more details about the parameter combinations that were attempted and how optimal values were reached? I feel that calibrating 15 parameters for both discharge and NO3-N export would have a very large number of potential “optimal” parameter combinations to consider, so it would be helpful to expand on how this was achieved with this calibration approach, and what the limitations are. Also, an automated approach such as using R-SWAT (Nguyen et al., 2022) may help make the calibration more reproduceable.
Nguyen, T. V, Dietrich, J., Dang, T. D., Tran, D. A., Van Doan, B., Sarrazin, F. J., Abbaspour, K., and Srinivasan, R.: An interactive graphical interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization for the Soil and Water Assessment Tool, Environ. Modell. Softw., 156, 105497, https://doi.org/10.1016/j.envsoft.2022.105497, 2022.
Page 8, line 168. I think more details about the principal components regression would be useful here. For instance, were data transformed? Was multicollinearity considered? Are there any references for the method you used?
Page 8, line 182. Simulating nitrate nitrogen export at the daily time step can certainly be difficult, but the NSE of -0.19 and Fig. 4 c-d suggest it is often not being simulated well, with lags in particular. However, it appears that the simulation could be alright at the weekly time step. Would it be better to aggregate results to a weekly time step for nitrate-nitrogen instead of daily, note that the higher temporal resolution was not achievable, and then continue using the weekly time step for nitrate-nitrogen model throughout the results section (e.g., the long term 1951-2014 model)? This could potentially lead to a better performing NO3-N model and also alleviate the need for the “NSE and R2 values when simulated values during the initial snowmelt period were put off a day” results and footnote of Table 2.
Page 15, lines 261-265. This text basically states that the model did not simulate nitrate-nitrogen well, but in a hypothetical situation with better lags, in would have. I don’t believe that supports the statement “Hence, the SWAT model is considered suitable for simulating daily NO3--N export during the snowmelt period.” If the model is not simulating NO3-N well at the daily time step, I suggest as an alternative, aggregating to the weekly time step for NO3-N may potentially help alleviate this and lead to better performance.
Page 15, line 286 to page 16, line 287. This statement could use clarification. Minorly, the “runoff and runoff” may be a typo? But more importantly, I believe that clarity is needed because the rain-on-snow melt was not simulated in the SWAT model of this study, only snowpack temperature-based melt, so it is confusing to me. Since rain-on-snow melt is mentioned as pertinent to the findings, I think it would be beneficial to clarify that it wasn’t simulated here and mention any potential limitations. You may also discuss how simulating rain-on-snow melt (such as by using an energy balance approach; e.g., Zare et al., 2022) could provide further insights about these relationships.
Minor Comments:
Page 4, lines 101-102. For reproducibility, could you give a more specific description of the fertilizer and manure application rates you used based on the survey with farmers? Perhaps a couple sentences describing the application timings and rates that were used would help clarify this.
Fig. 1. The font size for labels on the legend and scale bar is small and difficult for me to read. I suggest making it larger.
Fig. 7. I really like the insights gained from this visualization.
Page 17, line 319 to page 18, line 329. For the conclusions section, I think it would benefit readers to not use the acronyms (ND-SFP, etc.), or at least redefine them, so readers can understand the summary of the findings without having to find the all the definitions in the paper.
I wish the authors the best with this manuscript and their future endeavors.
Citation: https://doi.org/10.5194/egusphere-2024-3984-RC2 - AC2: 'Reply on RC2', Qiang Zhao, 04 Jun 2025
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