Study on the Relationship between Snowmelt Runoff for Different Latitudes and Vegetation Growth Based on an Improved SWAT Model in Xinjiang, China

Round 1

Reviewer 1 Report

The paper presents a modified SWAT version for better assessment of natural flows in mountainous environments with significant snowfall and snowmelt processes, as well as, for better vegetation growth prediction. The article is well written and explains in detail the methodological steps. Moreover, it justifies the results and supports adequately the conclusions with informative tables and graphs. I recommend publication and I add a single comment below.

The article presentes directly the SWAT-modified model in the Methodology. However, for a reader who is not very familiar with modelling, what is SWAT? The model is widely-known but every paper has to provide a short description. Therefore, despite some references in the introduction, a short paragraph in the methodology describing the SWAT model and its contribution in hydrologic/water quality and management modelling would be useful. I recommend to add it along with a few SWAT citations, preferably among the newsest ones found in the Sustainability journal. For example,  https://doi.org/10.3390/su12176761 and  https://doi.org/10.3390/su13010022.

Author Response

Response to Reviewer 1 Comments

 

Dear reviewer and editor:

Thank you very much for your valuable comments and kind suggestions on our submission. Your academic sense and scientific literacy definitely promoted this manuscript to a new level. We highly appreciate your time and effort. The manuscript has been improved according to your comments. The detailed responses are followed in below point by point.

 

Point 1: The article presents directly the SWAT-modified model in the Methodology. However, for a reader who is not very familiar with modelling, what is SWAT? The model is widely-known but every paper has to provide a short description. Therefore, despite some references in the introduction, a short paragraph in the methodology describing the SWAT model and its contribution in hydrologic/water quality and management modelling would be useful. I recommend to add it along with a few SWAT citations, preferably among the newest ones found in the Sustainability journal. For example,  https://doi.org/10.3390/su12176761 and  https://doi.org/10.3390/su13010022.


 

Response 1:

Thanks reviewer for the good suggestion. We have added the related comments and citations. (Page 5, Line 162-174)

 

The Soil and Water Assessment Tool (SWAT) model is a conceptual, physically based, semi-distributed model developed by the United States Department of Agriculture, Agricultural Research Service (USDA-ARS) [41]. The original purpose of model development was to predict the long-term effects of land management on water, sediment and chemicals under the complex and variable soil types, land use patterns and management practices in large watersheds [42]. There are six modules integrated into the model, including the snow melting, surface runoff, return flow, evapotranspiration, infiltration, underground runoff. The Digital Elevation Model （DEM） is used as the basic data to divide the river channel. The land use and land cover change data, soil type data, and meteorological data are the basic data-driven by the model. In the calculation of the model, the Hydrologic Response Units (HRUs) are divided according to the unique soil and land use types and slopes [32, 33]. Each HRU is calculated separately and finally collected into the sub-basin. In the snow melting module of the SWAT model, the algorithm used is the degree-day factor algorithm. The influence of temperature is fully applied to hydrological processes and melting processes[43]. The SWAT model are also commonly used for pollutant migration assessment, water quality assessment and water resource management[44].

32. Debele, Bekele, Raghavan Srinivasan, and AK Gosain. "Comparison of Process-Based and Temperature-Index Snowmelt Modeling in Swat." Water Resources Management 24, no. 6 (2010): 1065-88.
33. Fontaine, TA, TS Cruickshank, JG Arnold, and RH Hotchkiss. "Development of a Snowfall–Snowmelt Routine for Mountainous Terrain for the Soil Water Assessment Tool (Swat)." Journal of hydrology262, no. 1-4 (2002): 209-23.
34. Arnold, Jeffrey G, Raghavan Srinivasan, Ranjan S Muttiah, and Jimmy R Williams. Large Area Hydrologic Modeling and Assessment Part I: Model Development 1. Vol. 34, Jawra Journal of the American Water Resources Association: Springer, 1998.
35. Golmohammadi, Golmar, Shiv Prasher, Ali Madani, and Ramesh Rudra. "Evaluating Three Hydrological Distributed Watershed Models: Mike-She, Apex, Swat." Hydrology 1, no. 1 (2014): 20-39.
36. Pan, Tianshi, Lijun Zuo, Zengxiang Zhang, Xiaoli Zhao, and Yingchun Liu. "Impact of Land Use Change on Water Conservation: A Case Study of Zhangjiakou in Yongding River." Sustainability 13, no. 1 (2020): 22.
37. Panagopoulos, Yiannis, and Elias Dimitriou. "A Large-Scale Nature-Based Solution in Agriculture for Sustainable Water Management: The Lake Karla Case." Sustainability 12, no. 17 (2020): 6761.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Paper Title: Study on the Relationship between Snowmelt Runoff for Different Latitudes and Vegetation Growth Based on an Improved SWAT Model in Xinjiang, China

Figure 1 could be redone. It is difficult to read legends. The elevation gradient legend and other cannot be read.  There is almost 2400 m elevation gradient and therefore the impact of elevation on snow depth and melt is important. The early works on importance of elevation bands on SWAT even for simple snow model such as SNTEMP need to be included.

Ln 102 ; 30 m written twice

The impact of aspect and elevation gradient is not considered in this new model.  This is something that needs to be addressed, as snowpack formation is largely dependent on the elevation and aspect.

 

The performance statistics of NDVI is poor. How is the NDVI from MODIS applied to the model? If the leaf area index is used as the surrogate, then what is the basis for converting the NDVI to the LAI?  Detailed method need to be written.

 

 

Few additional papers that need to be included in the literature are as below.

Rahman, K., Maringanti, C., Beniston, M., Widmer, F., Abbaspour, K. and Lehmann, A., 2013. Streamflow modeling in a highly managed mountainous glacier watershed using SWAT: the Upper Rhone River watershed case in Switzerland. Water resources management, 27(2), pp.323-339.

Pradhanang, S.M., Anandhi, A., Mukundan, R., Zion, M.S., Pierson, D.C., Schneiderman, E.M., Matonse, A. and Frei, A., 2011. Application of SWAT model to assess snowpack development and streamflow in the Cannonsville watershed, New York, USA. Hydrological Processes, 25(21), pp.3268-3277.

Abiodun, O.O., Guan, H., Post, V.E.A. and Batelaan, O., 2018. Comparison of MODIS and SWAT evapotranspiration over a complex terrain at different spatial scales.

Schneiderman, E.M., Matonse, A.H., Zion, M.S., Lounsbury, D.G., Mukundan, R., Pradhanang, S.M. and Pierson, D.C., 2013. Comparison of approaches for snowpack estimation in New York City watersheds. Hydrological Processes, 27(21), pp.3050-3060.

Omani, N., Srinivasan, R., Smith, P.K. and Karthikeyan, R., 2017. Glacier mass balance simulation using SWAT distributed snow algorithm. Hydrological Sciences Journal, 62(4), pp.546-560.

Author Response

Response to Reviewer 2 Comments

 

Dear reviewer and editor:

Thank you very much for your valuable comments and kind suggestions on our submission. Your academic sense and scientific literacy definitely promoted this manuscript to a new level. We highly appreciate your time and effort. The manuscript has been improved according to your comments. The detailed responses are followed in below point by point.

 

Point 1: Figure 1 could be redone. It is difficult to read legends. The elevation gradient legend and other cannot be read.  There is almost 2400 m elevation gradient and therefore the impact of elevation on snow depth and melt is important. The early works on importance of elevation bands on SWAT even for simple snow model such as SNTEMP need to be included.


 

Response 1:

Thanks reviewer for the good suggestion. We have redone the Figure.1, the details are clearer. We also added some contents about the application of elevation bands in SWAT model (Page 3, Line 123; Page 2, Line 71-91)

Thanks to the opinions of experts, we strongly agree that elevation bands and slope have great influence on snowmelt runoff. First of all, the algorithm of the snow melt module of SWAT model has been modified in this paper, which takes into account the influence of accumulated temperature on snow melt. Secondly, the elevation bands have been taken into account in the modified SWAT model, we have carried out elevation bands processing for each river basin when building the watershed model, and the improved snow melt algorithm was also applied to the model with elevation bands. Thirdly, at present, it cannot directly input slope data and parameters in SWAT model, but the influence of slope aspect has been considered when dividing basins and sub-basins based on DEM data and confluence calculation. Therefore, the modified SWAT model does not ignore the influence of elevation bands on snow cover and snow melt, but the direct influence of slope direction was not considered in the modified model. We will appropriately introduce slope aspect related parameters into SWAT model in subsequent studies.

Kazi et al. have evaluated the stream flow modeling in a highly managed mountainous glacier watershed using temperature index approach of SWAT without elevation bands, the results of implementing elevation bands were better than without elevation bands[34]. The results showed slight improvement in runoff simulation and significant improvement in simulated mass balance when considering elevation in the research of glacier mass balance simulation using SWAT distributed snow algorithm[35]. Soni M et al. compared and accessed model simulated snowpack and snowmelt at different elevation bands with snow survey data available for the Cannonsville reservoir watershed, SWAT indicated more precipitation falling as rain, increased and earlier snowmelt, and a reduced snowpack leading to a change in the pattern of streamflow, particularly during winter and early spring[36]. Three snowpack estimation approaches, the lumped-parameter temperature index approach from the Generalized Watershed Loading Function (GWLF) watershed model, a spatially distributed temperature index (SDTI) model, and the spatially distributed NOAA Snow Data Assimilation System (SNODAS) product were compared and tested by Elliot M et al[37]. All three snowpack estimation approaches, performed well in estimating basin-wide snow water equivalent for most of the basins studied. In order to assess the climate-change effects on streamflow, the simulated streamflows from the calibrated Ground-water/Surface-water FLOW (GSFLOW) model and other basin characteristics were used as input to the one-dimensional Stream-Network TEMPerature (SNTEMP) model to simulate daily stream temperature in selected tributaries in the watershed[38].

However, few studies have been conducted on the differences between the calculated snowmelt runoff in different latitude alpine regions and its relationship to the basin ecology Olanrewaju et al. compared the evapotranspiration (ET) from the MODIS remotely sensed ET dataset (MOD16) with the ET estimates from a SWAT hydrological model on graduated spatial scales at different spatial scales. Based on the results of the study, the scale of 4 km2 for catchment-scale evapotranspiration is suggested in complex terrain[39].

Point 2: Ln 102 ; 30 m written twice

Response 2:

I am sorry for this error and we have corrected it in the manuscript. (Page 4, Line 130)

 

The 30 m DEM data were obtained from the Shuttle Radar Topography Mission’s (SRTM) official website and were applied to building the model.

Point 3: The impact of aspect and elevation gradient is not considered in this new model.  This is something that needs to be addressed, as snowpack formation is largely dependent on the elevation and aspect. Implementing elevation bands generates better results than without elevation bands.

Response 3:

Thanks for posing this question. We apologize for unclear description for the elevation bands. We have added related descriptions.

In the model, the elevation was divided into 10 bands, and the calculation of snowpack and snowmelt in each band was based on Precipitation Lapse Rate (PLAPS) and Temperature Lapse Rate (TLAPS). The improvement of elevation bands in this paper was that the influence of accumulated temperature on snowmelt calculation was increased, and a new accumulated temperature threshold was added in different band to judge snowmelt, which optimized the original model to judge snowmelt by only the maximum temperature, and fully considered the influence of accumulated temperature. Finally, the snowmelt amount of each band was summarized to get the total snowmelt amount. At present, it cannot directly input slope data and parameters in SWAT model, but the influence of slope aspect has been considered when dividing basins and sub-basins based on DEM data and confluence calculation. Therefore, the modified SWAT model does not ignore the influence of elevation bands on snow cover and snow melt, but the direct influence of slope direction was not considered in the modified model. We will appropriately introduce slope aspect related parameters into SWAT model in subsequent studies.

  Point 4:

 The performance statistics of NDVI is poor. How is the NDVI from MODIS applied to the model? If the leaf area index is used as the surrogate, then what is the basis for converting the NDVI to the LAI?  Detailed method need to be written.

Response 4:

Thanks for posing this question. We apologize for unclear description. (Page 12, Line 348-353)

First of all, MODIS NDVI reflects the ability of vegetation growth over a long period of time. In the mountainous areas with less human disturbance, the more water there is, the better the vegetation growth will be [8, 9].Secondly, MODIS NDVI was not applied to the model in this study. Here, it reflects the response degree of vegetation change in spring brought by the change of snowmelt runoff in the basin. Thirdly, although there are LAI related parameters in the SWAT model, they are not modified due to the lack of measured data. Therefore, the conversion of LAI and NDVI was not considered, and NDVI is only used to represent the response of watershed vegetation to snowmelt runoff changes. In arid and semi-arid high-altitude mountainous areas, vegetation types were mainly alpine meadow with sparse canopies [57-60]. Compared with Leaf Area Index (LAI), NDVI is more accurate and more suitable for reflecting vegetation growth and verifying the accuracy of model calculation in this area.

Therefore, we apologize for any misunderstanding caused to the experts due to our lack of clarity. In this study, we found that the larger the snowmelt runoff, the better the vegetation growth, and the improved model showed a more obvious pattern.

Point 5:

Few additional papers that need to be included in the literature are as below.

Rahman, K., Maringanti, C., Beniston, M., Widmer, F., Abbaspour, K. and Lehmann, A., 2013. Streamflow modeling in a highly managed mountainous glacier watershed using SWAT: the Upper Rhone River watershed case in Switzerland. Water resources management, 27(2), pp.323-339.

Pradhanang, S.M., Anandhi, A., Mukundan, R., Zion, M.S., Pierson, D.C., Schneiderman, E.M., Matonse, A. and Frei, A., 2011. Application of SWAT model to assess snowpack development and streamflow in the Cannonsville watershed, New York, USA. Hydrological Processes, 25(21), pp.3268-3277.

Abiodun, O.O., Guan, H., Post, V.E.A. and Batelaan, O., 2018. Comparison of MODIS and SWAT evapotranspiration over a complex terrain at different spatial scales.

Schneiderman, E.M., Matonse, A.H., Zion, M.S., Lounsbury, D.G., Mukundan, R., Pradhanang, S.M. and Pierson, D.C., 2013. Comparison of approaches for snowpack estimation in New York City watersheds. Hydrological Processes, 27(21), pp.3050-3060.

Omani, N., Srinivasan, R., Smith, P.K. and Karthikeyan, R., 2017. Glacier mass balance simulation using SWAT distributed snow algorithm. Hydrological Sciences Journal, 62(4), pp.546-560.

Response 4:

Thanks for good suggestions. The several high-quality literatures you recommended are of great help to improve the quality of this article, and also fully reflect your professionalism. The comments about the additional papers have been included in this paper. (Page 2, Line 71-91)

 

Kazi et al. have evaluated the stream flow modeling in a highly managed mountainous glacier watershed using temperature index approach of SWAT without elevation bands, the results of implementing elevation bands were better than without elevation bands[34]. The results showed slight improvement in runoff simulation and significant improvement in simulated mass balance when considering elevation in the research of glacier mass balance simulation using SWAT distributed snow algorithm[35]. Soni M et al. compared and accessed model simulated snowpack and snowmelt at different elevation bands with snow survey data available for the Cannonsville reservoir watershed, SWAT indicated more precipitation falling as rain, increased and earlier snowmelt, and a reduced snowpack leading to a change in the pattern of streamflow, particularly during winter and early spring[36]. Three snowpack estimation approaches, the lumped-parameter temperature index approach from the Generalized Watershed Loading Function (GWLF) watershed model, a spatially distributed temperature index (SDTI) model, and the spatially distributed NOAA Snow Data Assimilation System (SNODAS) product were compared and tested by Elliot M et al[37]. All three snowpack estimation approaches, performed well in estimating basin-wide snow water equivalent for most of the basins studied.

However, few studies have been conducted on the differences between the calculated snowmelt runoff in different latitude alpine regions and its relationship to the basin ecology Olanrewaju et al. compared the evapotranspiration (ET) from the MODIS remotely sensed ET dataset (MOD16) with the ET estimates from a SWAT hydrological model on graduated spatial scales at different spatial scales. Based on the results of the study, the scale of 4 km² for catchment-scale evapotranspiration is suggested in complex terrain[39].

We have polished the language of the article through a professional retouching company.

 

34. Rahman, Kazi, Chetan Maringanti, Martin Beniston, Florian Widmer, Karim Abbaspour, and Anthony Lehmann. "Streamflow Modeling in a Highly Managed Mountainous Glacier Watershed Using Swat: The Upper Rhone River Watershed Case in Switzerland." Water Resources Management 27, no. 2 (2012): 323-39.
35. Omani, Nina, Raghavan Srinivasan, Patricia K. Smith, and Raghupathy Karthikeyan. "Glacier Mass Balance Simulation Using Swat Distributed Snow Algorithm." Hydrological Sciences Journal 62, no. 4 (2016): 546-60.
36. Pradhanang, Soni M., Aavudai Anandhi, Rajith Mukundan, Mark S. Zion, Donald C. Pierson, Eliot M. Schneiderman, Adao Matonse, and Allan Frei. "Application of Swat Model to Assess Snowpack Development and Streamflow in the Cannonsville Watershed, New York, USA." Hydrological Processes 25, no. 21 (2011): 3268-77.
37. Schneiderman, Elliot M., Adao H. Matonse, Mark S. Zion, David G. Lounsbury, Rajith Mukundan, Soni M. Pradhanang, and and Donald C. Pierson. "Comparison of Approaches for Snowpack Estimation in New York City Watersheds." Hydrobiological Processes 27, no. 21 (2014): 3050-60.
38. Hunt, Randall J., Stephen M. Westenbroek, John F. Walker, William R. Selbig, R. Steven Regan, Andrew T. Leaf, and David A. Saad. "Simulation of Climate Change Effects on Streamflow, Groundwater, and Stream Temperature Using Gsflow and Sntemp in the Black Earth Creek Watershed, Wisconsin." (2016): 1-117.
39. Abiodun, Olanrewaju O., Huade Guan, Vincent E. A. Post, and Okke Batelaan. "Comparison of Modis and Swat Evapotranspiration over a Complex Terrain at Different Spatial Scales." Hydrology and Earth System Sciences 22, no. 5 (2018): 2775-94
40. Cohen, Juval, Jouni Pulliainen, Cécile B. Ménard, Bernt Johansen, Lauri Oksanen, Kari Luojus, and Jaakko Ikonen. "Effect of Reindeer Grazing on Snowmelt, Albedo and Energy Balance Based on Satellite Data Analyses." Remote Sensing of Environment 135, no. Complete (2013): 107-17.
41. Gómez-Giráldez, P. J., C. Aguilar, M. J. Polo, Christopher M. U. Neale, and Antonino Maltese. "Ndvi Sensitivity to the Hydrological Regime in Semiarid Mountainous Environments." Proceedings of SPIE - The International Society for Optical Engineering 8531 (2012): 103-04.
42. Zhao, X., K. Tan, S. Zhao, and J. Fang. "Changing Climate Affects Vegetation Growth in the Arid Region of the Northwestern China." Journal of Arid Environments 75, no. 10 (2011): 946-52.
43. Xin, Zhong Bao, Jiong Xin Xu, and Wei Zheng. "Spatiotemporal Variations of Vegetation Cover on the Chinese Loess Plateau (1981–2006): Impacts of Climate Changes and Human Activities." Science China (English) (2008).
44. Grippa, M., L. Kergoat, T. Le Toan, N. M. Mognard, N. Delbart, J. L'Hermitte, and S. M. Vicente‐Serrano. "The Impact of Snow Depth and Snowmelt on the Vegetation Variability over Central Siberia." Geophysical Research Letters 32, no. 21 (2005).
45. Molotch, N. P, B Guan, and E Trujillo. "Elevation-Dependent Controls on Snowmelt Partitioning and Vegetation Response Inferred from Satellite Observations (Invited)." 2012.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

The manuscript entitled “Study on the Relationship between Snowmelt Runoff for Different Latitudes and Vegetation Growth Based on an Improved 3 SWAT Model in Xinjiang, China” investigated runoff simulations in four basins in Xinjiang, China using SWAT model. The authors proposed a method to decompose precipitation into rainfall, snowfall with snowmelt, and snowfall without snowmelt to improve the accuracy of SWAT simulations. Results show that the improvement by using this new method is clear when compared to the default model. The study is well designed, although the authors will need to add or clarify some details in the revision. Here are my comments:

Lines 46-47: “Ecosystems in arid and semi-arid regions are relatively fragile, and the sparse vegetation in the mountains and numerous rocks demonstrate the sensitivity and vulnerability of the ecological environment in these areas [22].” Please also point out that these ecosystems in Xinjiang are also sensitive to recent climate change, as observed in recent studies, see e.g., Zhang et al. (2020). Response of Natural Vegetation to Climate in Dryland Ecosystems: A Comparative Study between Xinjiang and Arizona. Remote Sensing, 12(21), 3567. https://doi.org/10.3390/rs12213567

Line 55: Not clear what the “temperature-index temperature-day model” is. Please rephrase or cite the original paper.

Line 57: “Fernandez et al. established a snow melt model based on the energy balance theory.” – missing reference.

Line 70: Please elaborate on how to add “a precipitation form” or what the “precipitation form” is even in the Introduction.

Lines 99-106: Please considering adding proper references for all datasets described in this paragraph.

Section 2.3: Please add a description of simulation details, such as the selection of validation and calibration periods (why only two years when the actual measurement period is much longer), the temporal resolution of simulations, and a table of tuned parameters for different basins.

Lines 144-158: I understand that simplifying the diurnal cycle of temperature as a sinusoidal function is common, but what are the potential uncertainties induced by such assumptions?

Line 204: “It was increased to 7.94” – should be “it increased to 7.94”.

Figure 3: The improvement of simulation is clear when using the modified SWAT, but what are the possible reasons for the persistent underestimation in Sep-Nov in 2011 (see e.g., Fig. 3a-2 and d-2)?

Figure 5: It might not be necessary to use different colors in subplots for regression curves.

Lines 327-328: “Compared with the other three basins, the vegetation regeneration time in the PSR obviously lagged behind” because? I would also point out connections of analysis to the modified SWAT model in this paragraph.

Section 4.3: It might be meaningful to add a brief discussion on how climate change in this region might influence snowmelt and vegetation growth.

Lines 455-466: It would be better to present these values in a table.

Author Response

Response to Reviewer 3 Comments

 

Dear reviewer and editor:

Thank you very much for your valuable comments and kind suggestions on our submission. Your academic sense and scientific literacy definitely promoted this manuscript to a new level. We highly appreciate your time and effort. The manuscript has been improved according to your comments. The detailed responses are followed in below point by point.

 

Point 1: Lines 46-47: “Ecosystems in arid and semi-arid regions are relatively fragile, and the sparse vegetation in the mountains and numerous rocks demonstrate the sensitivity and vulnerability of the ecological environment in these areas [22].” Please also point out that these ecosystems in Xinjiang are also sensitive to recent climate change, as observed in recent studies, see e.g., Zhang et al. (2020). Response of Natural Vegetation to Climate in Dryland Ecosystems: A Comparative Study between Xinjiang and Arizona. Remote Sensing, 12(21), 3567. https://doi.org/10.3390/rs12213567


 

Response 1:

Thanks reviewer for the good suggestion. We have added related contents. (Page 5, Line 47-51)

As one of the representative arid regions, the sensitive response of ecosystem in Xinjiang to climate change has also attracted the attention of scholars. Zhang et al. compared the changing climate and corresponding responses of major natural vegetation cover types in Xinjiang and Arizona,  found that much of Xinjiang experienced warming and wetting trends (although not co-located) over the past 18 years[23].

Point 2: Line 55: Not clear what the “temperature-index temperature-day model” is. Please rephrase or cite the original paper.

Response 2:

I am sorry for this unclear description; we have corrected that. (Page 2, Line 57-58)

At present, in order to simulate snowmelt runoff, many snowmelt models have been developed, ranging from a simple temperature-index model such as degree-day factor to a complete energy balance model [25-27].

  Point 3:

 Line 57: “Fernandez et al. established a snow melt model based on the energy balance theory.” – missing reference.

Response 3:

Thanks for posing this question. We apologize for missing reference, and we have added the reference. (Page 2, Line 62)

Fernandez et al. established a snow melt model based on the energy balance theory[29].

Point 4:

Line 70: Please elaborate on how to add “a precipitation form” or what the “precipitation form” is even in the Introduction.

Response 4:

Thanks for your good suggestion, and we have added related explanation. (Page 2, Line 92-95)

During the input of meteorological data in the SWAT model, the precipitation type was only input as one type, and there was no distinction between rainfall and snowfall, which affected the precision of snowmelt calculation and model simulation. Therefore, by increasing the temperature judgment conditions, the form discrimination of rainfall and snowfall was carried out first when the precipitation data was input.

Point 5:

Lines 99-106: Please considering adding proper references for all datasets described in this paragraph.

Response 5:

Thanks for your good suggestion, and we have added related contents. (Page 4, Line 131-136)

The data collection and preparation were the first steps in the model-driven process. In addition, the types of model data were mainly divided into basic data and observation data. The basic data mainly include a digital elevation model (DEM), land use and land cover data (LUCC), and soil type data, while the observation data mainly came from the me-teorological stations and hydrological stations in the basin. The 30 m 30 m DEM data were obtained from the Shuttle Radar Topography Mission’s (SRTM) official website (http://srtm.csi.cgiar.org/) and were applied to building the model. The spatial resolution of the LUCC data  and the soil type data  is both 30 m. The data were obtained from websites (https://www.usgs.gov/products/data-and-tools/real-time-data/remote-land-sensing-and-landsat), the visual interpretation of imagery, and the China Soil Category Data Network (https://geodata.pku.edu.cn/). The NDVI reflects the growth of vegetation. The NDVI data were calculated using 8-day MOD09Q1 data (https://modis.gsfc.nasa.gov/data/dataprod/mod09.php).

Point 6:

Section 2.3: Please add a description of simulation details, such as the selection of validation and calibration periods (why only two years when the actual measurement period is much longer), the temporal resolution of simulations, and a table of tuned parameters for different basins.

Response 6:

Thanks for your good suggestion, and we have added related contents. (Page 7, Line 210-217)

The most important and difficult problem in the study of runoff in high altitude mountainous areas was verification, because the runoff observation in mountainous areas was not perfect, and the available runoff observation data was difficult to obtain. In this study, 2008-2011 was selected as the research period, 2008 and 2009 were the warm-up periods of the model, 2010 as the calibration period, and 2011 as the validation period. The daily runoff simulation and comparative study were carried out on the four rivers at the same periods. Some parameters which have great influence on snow melt and runoff simulation are selected as key calibration targets shown in the Table 2.

File extension	Parameter	Description	Range of values
.bsn(New)	SFTMP_A	Snowfall accumulated temperature	0–40
.bsn(New)	SMTMP_A	Snowmelt accumulated temperature	0–40
.bsn	SFTMP	Snowfall temperature	-20 to 20
.bsn	SMTMP	Snow-melt base temperature	-20 to 20
.bsn	SMFMX	Maximum melt rate for snow during the year	0–20
.bsn	SMFMN	Minimum melt rate for snow during the year	0–20
.bsn	TIMP	Snowpack temperature lag factor	0–1
.bsn	SNOCOVMX	Minimum snow water content corresponding to 100% snow cover	0–500
.bsn	SFTMP	Snowfall temperature	-40
.bsn	SURLAG	Surface runoff lag time	0.05–24
.gw	ALPHA_BF	Base flow alpha factor (days)	0–1
.gw	GW_DELAY	Groundwater delay (days)	0–500
.gw	GWQMN	Threshold water depth in the shallow aquifer required for return flow to occur (mm)	0–5000
.gw	SHALLST	Initial water depth in the shallow aquifer (mm)	0–50,000
.gw	GW_REVAP	Groundwater “revamp” coefficient	0.02–0.2
.mgt	CN2	SCS runoff curve number	35–98
.ohru	OV_N	Manning’s “n” value for overland flow	0.01–30
.ohru	ESCO	Soil evaporation compensation factor	0–1
.ohru	EPCO	Plant uptake compensation factor	0–1
.rte	CH_N2	Manning’s “n” value for the main channel	-0.01 to 0.3
.rte	CH_K2	Effective hydraulic conductivity in main channel alluvium	-0.01 to 500
.sol	SOL_K	Saturated hydraulic conductivity	0–2000
.sol	SOL_AWC	Available water capacity of the soil layer	0–1
.sub	PLAPS	Precipitation lapse rate	-20 to 20
.sub	TLAPS	Temperature lapse rate	-10 to 10
.sub	CH_N1	Manning’s “n” value for the tributary channels	0.01–30
.sub	CH_K1	Effective hydraulic conductivity in tributary channel alluvium	0–300
.sub	SNO_SUB	Initial snow water content	0–150

Point 7:

Lines 144-158: I understand that simplifying the diurnal cycle of temperature as a sinusoidal function is common, but what are the potential uncertainties induced by such assumptions?

Response 7:

Thanks for your good suggestion, and we have added related contents. (Page 19, Line 553-557)

As a conventional algorithm to calculate daily accumulated temperature, there was also some uncertainty. The change of daily temperature was considered to be relatively smooth, and abrupt changes in temperature did not take into account. In this way, there were some uncertainty in the daily accumulated temperature calculation, which is also one of the key points to be improved in future work.

Point 8:

Line 204: “It was increased to 7.94” – should be “it increased to 7.94”.

Response 8:

We apologize for mistake, and we have corrected that. (Page 8, Line 255)

Point 9:

Figure 3: The improvement of simulation is clear when using the modified SWAT, but what are the possible reasons for the persistent underestimation in Sep-Nov in 2011 (see e.g., Fig. 3a-2 and d-2)?

Response 9:

We apologize for the unclear descriptions, and we have added related contents. (Page 17, Line 493-499)

In September, snowfall gradually increased in high altitude mountainous areas, but the temperature was not stable, and the temperature difference between day and night was very large[110, 111]. The daily average temperature was only used to determine the conditions of snowmelt in the original model. At high altitude mountainous area, ground vegetation was scarce, mainly composed of rocks and sand, when the temperature rise at noon, the snowpack began to melt and supply channel flow received the influence of the accumulated temperature [14,112,113]. When daily average temperature was lower but not the accumulated temperature, there were still a month of snowmelt replenishing river runoff[114]. This can lead to an underestimate of river runoff over a period of time, such as in September and October.

Point 10:

Figure 5: It might not be necessary to use different colours in subplots for regression curves.

Response 10:

First of all, thank you for your good suggestion. However, there was so much information and multiple pictures in Figure 5, including the calibration and validation periods of these basins. The use of different colour settings can enable readers to better and faster distinguish pictures of different basins and carry out accurate positioning without reading explanations.

Point 11:

Lines 327-328: “Compared with the other three basins, the vegetation regeneration time in the PSR obviously lagged behind” because? I would also point out connections of analysis to the modified SWAT model in this paragraph.

Response 11:

Thank you for your good suggestion, and we have added related contents. (Page 14, Line 389-398)

The Pishan River, located in Kunlun Mountains, with least precipitation among the four basins and was a typical representative of the rivers in the arid region. There was less rainfall in spring and the water needed for vegetation greening was mainly from melting snow in high altitude mountain areas [22,61,62]. The altitude was so high and meteorological conditions were so complicated that the temperature began to rise, the snow began to melt, and the river runoff increased until in April [63, 64]. The vegetation regeneration was started when water source was plentiful. Due to the altitude and complex meteorological conditions, vegetation regeneration in Pishan River Basin was late and obviously lagged behind[65]. The time of snow melt in each basin can be accurately located by studying the time of vegetation regeneration in each basin. As the main water supply, the amount of snow melt affects the vegetation regeneration and growth, and there is a significant relationship between them. Therefore, the accuracy and rationality of the model modification can be verified by establishing the correlation between NDVI and snow melt.

Point 12:

Section 4.3: It might be meaningful to add a brief discussion on how climate change in this region might influence snowmelt and vegetation growth.

Response 12:

We apologize for the unclear descriptions, and we have added related contents. (Page 19, Line 557-565)

The influence of climate change on vegetation NDVI in Xinjiang has been studied by some scholars. The study shows that the climate in much of Xinjiang experienced warming and wetting trends, and rainfall increased in mountainous areas in summer [23, 128, 129]. Through field investigation, it was also found that in the mountainous area of arid area, especially in the Kunlun Mountains, rainfall increased in summer and vegetation area increased obviously. NDVI was introduced in this paper as the verification of model modification. Therefore, in order to more accurately reflect the relationship between vegetation growth and snow melt, the vegetation NDVI in Spring was selected as the focus, which effectively excluded the influence of summer rainfall and climate change. The relationship between vegetation NDVI and snow melt calculated by the model can accurately reflect the accuracy of the model.

Point 13:

Lines 455-466: It would be better to present these values in a table.

Response 13:

Thanks for your good suggestion, and we have added a new table in the manuscript (page 18, Line 529-530).

Table 4. Parameter SFTMP_A and SMTMP_A information for global sensitivity

	SFTMP_A			SMTMP_A
Basins	Calibration value (°C)	T-states	p-Value	Calibration value (°C)	T-states	p-Value
DQR	24.24	3.34	0.06	18.35	8.34	0.01
KKR	22.09	4.05	0.08	19.46	8.86	0.01
HZR	20.18	5.27	0.04	17.5	9.05	0.01
PSR	25.62	3.28	0.07	20.18	7.55	0.02

We have polished the language of the article through a professional retouching company.

 

14. Zhang, Yulan, Shichang Kang, Tanguang Gao, Julia Schmale, Yajun Liu, Wei Zhang, Junming Guo, Wentao Du, Zhaofu Hu, and Xiaoqing Cui. "Dissolved Organic Carbon in Snow Cover of the Chinese Altai Mountains, Central Asia: Concentrations, Sources and Light-Absorption Properties." Science of the Total Environment 647 (2019): 1385-97.
15. Gamon, John A, K Fred Huemmrich, Robert S Stone, and Craig Tweedie. "Spatial and Temporal Variation in Primary Productivity (Ndvi) of Coastal Alaskan Tundra: Decreased Vegetation Growth Following Earlier Snowmelt." Remote Sensing of Environment 129 (2013): 144-53.
16. Zhang, Fang, Chenghao Wang, and Zhi-Hua Wang. "Response of Natural Vegetation to Climate in Dryland Ecosystems: A Comparative Study between Xinjiang and Arizona." Remote Sensing 12, no. 21 (2020).
17. Bekele, Debele, Raghavan, SrinivasanA., K., and Gosain. "Comparison of Process-Based and Temperature-Index Snowmelt Modeling in Swat." Water Resources Management (2010).
18. Debele, Bekele, rnRaghavan Srinivasan, and rnA. K. Gosain. "Comparison of Process-Based and Temperature-Index Snowmelt Modeling in Swat." Water Resources Management 24, no. 6 (2010): 1065-88.
19. Wenwu, Qing, Rensheng Chen, Shiyin Liu, Haidong Han, and Wang Jian. "Research and Application of Two Kinds of Temperature-Index Model on the Koxkar Glacier." (2011).
20. Fernández, Alberto. "An Energy Balance Model of Seasonal Snow Evolution." Physics Chemistry of the Earth 23, no. 5 (1998): 661-66.
21. Li, Baofu, Yaning Chen, Zhongsheng Chen, Weihong Li, and Baohuan Zhang. "Variations of Temperature and Precipitation of Snowmelt Period and Its Effect on Runoff in the Mountainous Areas of Northwest China." Journal of Geographical Sciences 23, no. 1 (2013): 17-30.
22. Ma, Y. G., Y. Huang, X. Chen, Y. P. Li, and A. M. Bao. "Modelling Snowmelt Runoff under Climate Change Scenarios in an Ungauged Mountainous Watershed, Northwest China." Mathematical Problems in Engineering (2013).
23. Duan, Yongchao, Fanhao Meng, Tie Liu, Yue Huang, Min Luo, Wei Xing, and Philippe De Maeyer. "Sub-Daily Simulation of Mountain Flood Processes Based on the Modified Soil Water Assessment Tool (Swat) Model." International journal of environmental research and public health 16, no. 17 (2019).
24. Liu, Jinping, Wanchang Zhang, and Ning Nie. "Spatial Downscaling of Trmm Precipitation Data Using an Optimal Subset Regression Model with Ndvi and Terrain Factors in the Yarlung Zangbo River Basin, China." Advances in Meteorology (2018).
25. Adosi, Juliana J. "Seasonal Variation of Carbon Dioxide, Rainfall, Ndvi and It’s Association to Land Degradation in Tanzania." In Climate and Land Degradation, 373-89: Springer, 2007.
26. Duan, Yongchao, Tie Liu, Fanhao Meng, Ye Yuan, Min Luo, Yue Huang, Wei Xing, Vincent Nzabarinda, and Philippe De Maeyer. "Accurate Simulation of Ice and Snow Runoff for the Mountainous Terrain of the Kunlun Mountains, China." Remote Sensing12, no. 1 (2020): 179.
27. Hong, Ma, Liu Zongchao, and Liu %J Annals of Glaciology Yifeng. "Energy Balance of a Snow Cover and Simulation of Snowmelt in the Western Tien Shan Mountains, China." 16 (1992): 73-78.
28. Chen, Y. N., H. J. Deng, B. F. Li, Z. Li, and C. C. Xu. "Abrupt Change of Temperature and Precipitation Extremes in the Arid Region of Northwest China." Quaternary International 336 (2014): 35-43.
29. Grusson, Youen, Xiaoling Sun, Simon Gascoin, Sabine Sauvage, Srinivasan Raghavan, François Anctil, and José-Miguel Sáchez-Pérez. "Assessing the Capability of the Swat Model to Simulate Snow, Snow Melt and Streamflow Dynamics over an Alpine Watershed." Journal of Hydrology531 (2015): 574-88.
30. Le, Trung. "An Application of Soil and Water Analysis Tool (Swat) for Water Quality of Upper Cong Watershed, Vietnam." (2005).
31. Luo, Nana, Dehua Mao, Bolong Wen, and Xingtu Liu. "Climate Change Affected Vegetation Dynamics in the Northern Xinjiang of China: Evaluation by Spei and Ndvi." Land 9 (2020).
32. Xu, Yufeng, Jing Yang, and Yaning Chen. "Ndvi-Based Vegetation Responses to Climate Change in an Arid Area of China." Theoretical & Applied Climatology 126, no. 1-2 (2016): 213-22.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Accept - ensure that the references and spellings are correct .

Author Response

Response to Reviewer 2 Comments

 

Dear reviewer and editor:

Thank you for your approval of the previous revision. We highly appreciate your time and effort. The manuscript has been improved according to your comments. The detailed responses are followed in below point by point.

 

Point 1: Accept - ensure that the references and spellings are correct.


 

Response 1:

Thanks reviewer for the good suggestion. References and spellings have been checked and revised.

“The snowmelt replenishes the soil moisture and provides sufficient moisture for the spring vegetation.”(Page 2, Line 52)

“At present, in order to simulate snow melting runoff, many snowmelt models have been developed, ranging from a simple temperature-index model such as degree-day factor to a complete energy balance model [25-27].” (Page 2, Line 57-58)

“The energy balance of the snow surface and the entire snow layer was used to predict the snow surface temperature and the freezing depth and to accurately reproduce the development and melting process of the seasonal snow cover [29, 30].” (Page 2, Line 62-64)

“Fontaine modified the SWAT model by improving the hydrological and atmospheric processes and applied it to the simulation of snow melting runoff in mountain areas with high elevations [33].” (Page 2, Line 70-71)

“Soni M et al. compared and accessed model simulated snowpack and snowmelt at different elevation bands with snow survey data available for the Canyonville reservoir watershed, SWAT indicated more precipitation falling as rain, increased and earlier snowmelt, and a reduced snowpack leading to a change in the pattern of stream flow, particularly during winter and early spring[36].” (Page 2, Line 76-79)

“All three snowpack estimation approaches, performed well in estimating basin-wide snow water equivalent for most of the basins studied. In order to assess the climate-change effects on stream flow, the simulated stream flow from the calibrated Ground-water/Surface-water FLOW (GSFLOW) model and other basin characteristics were used as input to the one-dimensional Stream-Network Temperature (SNTEMP) model to simulate daily stream temperature in selected tributaries in the watershed[38] ”(Page 2, Line 88-91)

“The SWAT model is a conceptual, physically based, semi-distributed model developed by the United States Department of Agriculture, Agricultural Research Service (USDA-ARS)[41].” (Page 5, Line 162-163)

“However, in general, the model modification was excellent and achieved the expected goal [94].” (Page 17, Line 456)

“At high altitude mountain areas, ground vegetation was scarce, mainly composed of rocks and sand, when the temperature rise at noon, the snowpack began to melt and supply channel flow received the influence of the accumulated temperature [14, 112, 113].” (Page 17, Line 494-497)

“Combined with the relationship between the amount of snowmelt and the NDVI, it was found that the vegetation in the northern part of the Tianshan Mountains and the high-altitude mountain areas of the Altai Mountains rejuvenates earlier than that in the southern part of the Tianshan Mountains and the Kunlun Mountains, and the vegetation growth in the spring was also more significant.” (Page 19, Line 585-588)

“24. Mu, Zhen-xia, Hui-fang Jiang, Feng Liu, and Geocryology.”Spatial and Temporal Variations of Snow Cover Area and NDVI in the West of Tianshan Mountains." Journal of Glaciology 32, no. 5 (2010): 875-82.” (Page 21, Line 664-665)

“32. Debele, Bekele, Raghavan Srinivasan, and AK Gosain.”Comparison of Process-Based and Temperature-Index Snowmelt Modeling in Swat." Water Resources Management 24, no. 6 (2010): 1065-88.

33. Fontaine, TA, TS Cruickshank, JG Arnold, and RH Hotchkiss. "Development of a Snowfall–Snowmelt Routine for Mountainous Terrain for the Soil Water Assessment Tool (Swat)." Journal of hydrology262, no. 1-4 (2002): 209-23.” (Page 21, Line 681-685)

“40. Luo, Y, J Arnold, P Allen, X Chen, and Earth System Sciences Discussions.”Baseflow Simulation of Swat Model in an Inland River Basin in Tianshan Mountains, Northwest China."Journal of Hydrology 8, no. 6 (2011).” (Page 22, Line 703-704)

“47. Choi, Heung Sik.”Parameter Estimation of Swat Model Using Swat-Cup in Seom-River Experimental Watershed." Journal of the Korean Society of Civil Engineers 33, no. 2 (2013): 529-36.” (Page 22, Line 719-720)

“51. Khalid, Khairi, Mohd Fozi Ali, Nor Faiza Abd Rahman, Muhamad Radzali Mispan, Siti Humaira Haron, Zulhafizal Othman, and Mohd Fairuz Bachok. "Sensitivity Analysis in Watershed Model Using Sufi-2 Algorithm." Procedia engineering 162 (2016): 441-47.” (Page 22, Line 727-729)

“61. Li, Baofu, Yaning Chen, Zhongsheng Chen, Weihong Li, and Baohuan Zhang.”Variations of Temperature and Precipitation of Snowmelt Period and Its Effect on Runoff in the Mountainous Areas of Northwest China." Journal of Geographical Sciences 23, no. 1 (2013): 17-30.

” (Page 22, Line 727-729)

“110.      Duan, Yongchao, Tie Liu, Fanhao Meng, Ye Yuan, Min Luo, Yue Huang, Wei Xing, Vincent Nzabarinda, and Philippe  De Maeyer. "Accurate Simulation of Ice and Snow Runoff for the Mountainous Terrain of the Kunlun Mountains, China." Remote Sensing12, no. 1 (2020): 179.” (Page 26, Line 859-861)

“113.      Grusson, Youen, Xiaoling Sun, Simon Gascoin, Sabine Sauvage, Srinivasan Raghavan, François Anctil, and José-Miguel Sáchez-Pérez. "Assessing the Capability of the Swat Model to Simulate Snow, Snow Melt and Streamflow Dynamics over an Alpine Watershed." Journal of Hydrology531 (2015): 574-88.” (Page 26, Line 866-868)

“124.      Cao, Yang, Jing Zhang, Mingxiang Yang, Xiaohui Lei, Binbin Guo, Liu Yang, Zhiqiang Zeng, and Jiashen Qu. "Application of Swat Model with Cmads Data to Estimate Hydrological Elements and Parameter Uncertainty Based on Sufi-2 Algorithm in the Lijiang River Basin, China." Water10, no. 6 (2018): 742.” (Page 26, Line 894-896)

 

Reviewer 3 Report

The manuscript has been substantially improved in this round of revision. Thanks for the efforts. I only have one additional comment that the authors will need to address before it can be accepted for publication:

Line 50: please check this line. It is not a complete sentence and different from that in the authors’ response 1.

Author Response

Response to Reviewer 3 Comments

 

Dear reviewer and editor:

Thank you for your approval of the previous revision. We highly appreciate your time and effort. The manuscript has been improved according to your comments. The detailed responses are followed in below point by point.

 

Point 1: The manuscript has been substantially improved in this round of revision. Thanks for the efforts. I only have one additional comment that the authors will need to address before it can be accepted for publication:

Line 50: please check this line. It is not a complete sentence and different from that in the authors’ response 1.


 

Response 1:

I am sorry for this unclear description; we have reorganized the language and corrected that. (Page 2, Line 49-50)

“As one of the representative arid regions, the sensitive response of ecosystem in Xinjiang to climate change has also attracted the attention of scholars. Zhang et al. found that major natural vegetation cover types in Xinjiang were sensitive to the climate change over the past 18 years. Xinjiang has experienced the warming and wetting trends (although not co-located) [23].”