the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 May 2025
Dynamics of Snow and Glacier Cover in the Upper Karnali Basin, Nepal: An Analysis of Its Relationship with Climatic and Topographic Parameters
Abstract. Snow and glacier cover in the Upper Karnali Basin (UKB) are crucial freshwater reservoirs that support downstream ecosystems and human populations. This study uses remote sensing and GIS data from various sources, MODIS-derived land surface temperature, and ERA5 reanalysis climate datasets to analyze snow cover dynamics from 2002 to 2023/24. The results show a significant decrease in snow-covered area (SCA), with an annual decline of about 3.99 km². Seasonal variations indicate the most significant reductions during the monsoon period (July-September), where rising temperatures accelerate snowmelt. The analysis also establishes a strong negative correlation between snow cover and temperature (r = -0.59 to -0.77, p < 0.05), with warming trends disproportionately affecting mid-to-high elevation zones (3000–5000 masl). Glacier basins exhibit consistent retreat, with the mean glacier area declining from 119.046 hectares in 2000 to 100.472 hectares in 2023, highlighting the impact of climate change. Additionally, snowline analysis demonstrates an upward migration, with the 10th percentile snowline increasing at a rate of approximately 5.16 m/year, indicating progressive snow loss at lower elevations. These findings emphasize the vulnerability of UKB’s cryosphere to climate change, necessitating adaptive water resource management strategies to mitigate impacts on hydrology, agriculture, and regional water security.
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RC1: 'Comment on egusphere-2025-1303', Anonymous Referee #1, 27 May 2025
Comments for EGUsphere on –
Dynamics of Snow and Glacier Cover in the Upper Karnali 1 Basin, Nepal: An Analysis of Its Relationship with Climatic and Topographic Parameters. Motilal Ghimire et al.
GENERAL
A useful set of data on snow cover is presented for 22 years, and related to temperature, precipitation, elevation and time: data on glacier decline are also presented. A loss of cover is consistent but strongest at lower elevations. Inter-year variability is greatest in winter, and least for July-September. Temperature and precipitation is taken from reanalysis data, presumably based on sparse observations and with little control for higher elevations, so the results in section 4.2 should be accompanied by precautionary warnings.
A lot of clarification is needed, and the are some inconsistencies between text, Figures and Tables. It is not always clear what is being correlated with what. Perhaps ‘trend’ (temporal trend) should be used more often in the place of correlation, in some passages: e.g. ‘negative trends with correlations over time of …’..
Some numbers have too many decimal places. Given that some error is inevitable, more rounding should be employed.
The Discussion becomes repetitive of previous comments, and could be shortened.
SPECIFIC
Line 110 With such relief, surely precipitation must vary more than this?
136-143 What effect did the cloud removal have (in biasing coverage, both spatial and temporal)?
Section 4.1 text implies a graph for annual cover is necessary: only the 4 seasons are illustrated..
190 Sen’s slope is not defined. It seems to be the gradient of the regression line over time, so why is attribution to ‘Sen’ needed?
207 These Fig.2 graphs are initially puzzling in that Oct-Dec shows the steepest trend line but is insignificant, while July-Sept seems flatter but has the only significant trend. This seems to relate to the lower variability of July-Sept (SD 38 cf. 212-373, Table 1).
Why is the correlation positive below 2000 m (Fig.7: and below 2300 m), where the T is rising (Fig.8)? Are the data so limited below 2000 m that it should perhaps be dropped? Fig. 9 shows that warmer years have less snow cover, consistently across all elevations (although<2000 m is not shown).
DETAILED
88 ’above’
90 ‘within Nepal’
107 and 150 Ghimire is not in References.
118 This identifies 3 rivers , but not Kawari. Also the upper Himla is apparently labelled ‘China Karnali’ in Table 2, but that has not been specifically located.. There should be a closer relation between map and text (and Table).
132 delete ‘then’
134 ‘sub-basin’
162 Why central? not sub-glaciers. Perhaps ‘both glaciers and surrounding slopes’? Is ‘fed by’ appropriate ?
185 424?? Table 1 shows a July-Sept min of 169 and an annual min of 514.
186 640.32 does not appear in the 25% row in Table 1.
192 km2
192 & 202 Unfortunately, Fig. 2 does not show annual averages.
199-201 I am unsure what this sentence means and how it relates to Fig.2. Also it needs a verb.
204 Fig.2 The heading is unhelpful. I suggest the more precise ‘Annual and seasonal snow cover statistics (km2) with correlations of the trends, 2002-2024.’
204-5 Strange that Kendall’s tau does not show a negative trend like all the other correlations. Is tau appropriate here?
210-211 Fig.3 does not show negative dominating: it is close to balance, with April-June (more negative) balancing Oct-Dec (more positive)
212 Incorrect. Fig.4 shows positive trends (probably insignificant) except for Jan-Mar. Why ‘June-July’?
215-217 This explanation of the 204 sampled should precede 210-211.
219 should be ‘-0.59 to -0.77’
222-224 More concisely ‘Precipitation and temperature are negatively correlated in winter (Oct-March) and positively in the summer (April-September) half-year’.
225 Fig.3 How were the 19 correlations plotted here selected from the 206 (or 204) ? And perhaps the altitudes of these locations are important, explaining the wide variability / lack of spatial consistency?
Fig. 4 would be improved if annual average values were connected by straight lines rather than curves: or if dots were used.
Fig.5 Larger numbers (on the coloured backgrounds) would clarify.
235 Presumably ‘over the 22 years’.
240 November?? What happened to December?
254 ‘the variability is strongest’ is a duplication.
242-257 All this makes sense in terms of altitude: the lowest area (downstream) has the least and most variable snow cover, and a define decline with warming over the 22 years.
269 & 285 State what snow cover is being correlated WITH – i. e. time?
269 ‘in the lowest’
283 delete ‘elevation’
289 No: Figure 8, not 5.
316 delete ‘the’
320-=321 What a truism! Delete the sentence.
334 delete ‘(able’
335 Too many decimal places. Drop ‘.163’ - of the order of a thousandth of a percent of the total area: surely spurious accuracy.
337 Drop ‘, indicating a relative reduction in glacier coverage’ - another unnecessary truism.
342 Yes, but S shows the largest absolute loss. You might also consider the relative (%) loss for each direction class.
343 Delete ‘Northeast (NE),’ which is repeated.
Fig.10: the order is illogical, these should be in rank order e.g. NW N NE E SE S SW W.
358 “May , June & July” straddles two of the seasons in the Figures.
361 delete ‘(n=’
361 “84%” is not apparent in any part of Fig.11.
363 Fig. 11 Does the orange line relate to all basins, rather than just the selection whose IDs are given?
369 ‘in the remaining basins’
415 What does “although this precipitation does not appear to facilitate snow accumulation.” Mean? Where is the evidence?
417 “rain instead of snow” is temperature-dependent and thus elevation-dependent.
431 ? exhibits … ‘less’?
432-437 duplicates 423-428. Poor editing!
454 Yet Fig. 8 and line 293 suggest reduced warming high up.
460 ‘inter-annual snow cover variability ‘
461 3700 m ? from Fig.8.
462 4100 m? “
473 ‘reveal’
474-475 Too many decimal places.
528—529 Decimal places !
571 delete one 2014
589 give authors
595 delete “(last ….”
Citation: https://doi.org/10.5194/egusphere-2025-1303-RC1 -
AC1: 'Reply on RC1', Motilal Ghimire, 28 May 2025
Dear Reviewer,
Thank you for your valuable time and important feedback. We will thoroughly review your comments, integrate the suggestions, and rectify inconsistencies and errors as early as possible. Your insights are essential for enhancing the manuscript.
Best regards,
Motilal Ghimire
Citation: https://doi.org/10.5194/egusphere-2025-1303-AC1 -
AC2: 'Reply on RC1', Motilal Ghimire, 14 Aug 2025
Dear reviewer, Thank you so much for your thorough and detailed evaluation of the manuscript. Your careful, line-by-line examination identified several errors, omissions, and unaddressed aspects within the text, figures, and tables. I have carefully reviewed my manuscript and incorporated almost all of your comments and suggestions. Please find the response to all comments in the PDF document attached herewith.
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AC1: 'Reply on RC1', Motilal Ghimire, 28 May 2025
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RC2: 'Comment on egusphere-2025-1303', Anonymous Referee #2, 23 Jun 2025
The manuscript “Dynamics of snow and glacier cover in the Upper Karnali Basin, Nepal: An analysis of its relationship with climatic and topographic parameters” presents a significant and timely study of snow and glacier changes in the Upper Karnali Basin, Nepal. The authors have successfully analyzed snow covered area(SCA) change during 2002 and 2023, and the relationship between SCA and climate change. This work highlights SCA, as a critical, yet hitherto under-researched, component of the region’s water resource, especially when compared to glaciers. The study’s findings have important implications for understanding regional hydrology, assessing regional water security. The manuscript is well-written and the conclusions are well-supported by the data. I recommend acceptance of this manuscript after minor revisions.
General comments
- Data sources and method should be given much more detail. Authors mentioned that MOD10A1 was used to analyze the SCA, and also pointed out that cloud-masked snow cover data was classified into four seasons and calculated using a threshold-based binary mask. But Authors must analyze the uncertainty of SCA due to cloud-masked.
- There are two SCA, one is derived from the resolution of MODIS products (500m). Another is derived from Landsat (30m). However, authors did not describe how to combine both SCAs. In addition, The Higher resolution from Landsat could be used to evaluate the uncertainty of MODIS products. But I do not see there is uncertainty evaluation.
- For the land surface temperature (LST), there is difference between glacier surface temperature and other surface cover (Wu et al., 2015). I'm skeptical of the existing results.
Wu, Y., 2015. Estimating mountain glacier surface temperatures from Landsat-ETM+ thermal infrared data: A case study of Qiyi glacier, China. Remote sensing of environment v. 163, pp. 286-295-2015 v.2163.
Specific comments
Line45 therby-thereby
Line 194 P=0.00??
Figure 6 have to mark the sub-figure as a,b,c,d, those sub-figures are also be explained in title. The same as Figure 9,11
Figure 8, The temperatures for different elevation bins were shown in Figure 8. It is very nice to show the temperature rate along with elevation. However， I do not know where the data of temperature come?? Is it LST or ERA5?
Line 316 Snow cover the trend in Glacier Basins-> The snow cover trend in Glacier Basins
Figure 11 January-march_ “delete _”
Line 431 exhibits what???
Citation: https://doi.org/10.5194/egusphere-2025-1303-RC2 -
CC2: 'Reply on RC2', Motilal Ghimire, 01 Jul 2025
Dear Reviewer
Thank you for your positive and constructive comments. I shall address all comments and suggestions appropriately.
Best regards,
Motilal Ghimire
Citation: https://doi.org/10.5194/egusphere-2025-1303-CC2 -
AC3: 'Reply on CC2', Motilal Ghimire, 14 Aug 2025
Dear Reviewer,
Thanks for your valuable and insightful comments and suggestions. In the manuscript, while addressing the reviewers’ comments, I came across several mistakes and inconsistencies, and identified issues in text, tables, figures, and references. I have tried to address and incorporate the comments and suggestions with sincerity and care. Please find responses to all comments in the attached pdf file.
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AC3: 'Reply on CC2', Motilal Ghimire, 14 Aug 2025
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CC1: 'Comment on egusphere-2025-1303', Zui Tao, 01 Jul 2025
This study comprehensively investigates snow and glacier dynamics in the Upper Karnali Basin (UKB), integrating multi-source remote sensing data (e.g., MODIS and Landsat) to address critical knowledge gaps in the mid-western Himalayas. The methodology is rigorous, employing NDSI thresholds and Google Earth Engine to mitigate cloud cover challenges, ensuring robust results. Overall, the research reveals significant climate change impacts and provides key evidence for regional resource management, representing a valuable contribution to cryosphere science. However, several improvements are warranted:
- The study depends solely on remote sensing data (e.g., MODIS LST, ERA5) without incorporating ground observations such as weather stations or glacier mass balance measurements. This omission introduces uncertainty, and the absence of validation protocols (e.g., cross-referencing with DHM station data mentioned in Section 3) weakens methodological credibility.
- While the fusion of MODIS (500 m) and Landsat (30 m) data is mentioned (Section 3), the spatial scaling approach remains unclear. The paper fails to specify how resolution discrepancies were reconciled or the final output resolution of integrated analyses (e.g., SCA calculations in Section 4.1).
Insufficient Mechanistic Analysis
- Using MODIS to compensate for Landsat cloud gaps (Section 3.2) is noted but lacks uncertainty assessment. The impact of spatial resolution downgrading (30m→500m) on seasonal SCA trends (e.g., monsoon declines in Figure 2) remains unaddressed, directly affecting conclusion reliability.
Citation: https://doi.org/10.5194/egusphere-2025-1303-CC1 -
AC4: 'Reply on CC1', Motilal Ghimire, 14 Aug 2025
Dear Reviewer,
Thank you for your insightful and valuable comments, which I believe have contributed to making the manuscript stronger and more acceptable. We have addressed all comments in the revised manuscript, including adding a validation discussion supported by relevant literature, clarifying the spatial scaling between MODIS and Landsat, conducting a quantitative accuracy and bias assessment, and expanding the uncertainty analysis. Comparisons between MODIS LST and station data, along with their limitations, are now included. Please find the response to comments in the attached document.
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CC3: 'Comment on egusphere-2025-1303', Nitesh Khadka, 02 Jul 2025
I read the manuscript by Ghimire et al., since the topic aligns with my interests. The manuscript aims to study snow and glacier dynamics using remote sensing datasets and analyze its relationship with climatic and topographic factors in less studied Karnali basin. While the subject matter is important, I regret to say that the paper is poorly prepared and has several issues regarding the data, methods, analysis, and results that may not meet the standards required for publication. I have several major comments and suggestions about the paper, but please disregard any that overlap with previous reviewers’ feedback.
- The language of the manuscript must be copy edited and polished.
- Introduction section: The structuring of the introduction must be improved reducing the over emphasis on social aspects, reviewing the key regional, national and basin scale studies (focus on more recent ones) substantiating with appropriate citations, identifying the gaps and explicitly developing the objectives (currently not stated clearly). Many sentences and paragraph seem unconnected. For example, L52 and L54
- Study area: The study area is transboundary but is merely mentioned [see: (Shrestha et al., 2019; Khadka et al., 2024)]. Why is it focused only on upper Karnali part and not on other sub-basins of Karnali river system, such as Bheri and West Seti, as these basins also have glacier and snow cover. Although the paper addresses snow cover and glaciers, the description of the study area deviates from the main focus. It should concentrate on providing a clear overview of the key cryospheric components, including state and status of clean and debris-covered glaciers, glacial lakes, and snow. Additionally, while snow and glaciers are affected by weather systems such as the monsoon and westerlies, the paper does not mention the climatology, trends in precipitation and temperature for the region. It is also important to note that several citations are missing; the sources for annual precipitation and temperature data should be included to support the claims made in the paper.
- Data and method: This whole section is not explicit. Authors used Landsat 7, for which date? Landsat 7 have SLC failure after 2003, how were data gaps filled? How much was acceptable cloud cover %? Solely using NDSI and threshold >0.4 is questionable regarding the complexity of the landscape, particularly in mixed pixel scenarios where snow is interspersed with other land cover types and shadows. How were shadows removed or incorporated in optical images? Authors could have combined with other indices (such as NDVI), used automatic image thresholding etc. for improving classification accuracy.
Why is the month composition of seasons different in this study (L138)? Nepal has four main seasons: post-monsoon (October–November), winter (December–February), pre-monsoon (March–May), and monsoon (June–September) [see, (DHM, 2017; Karki et al., 2020; Sharma et al., 2020)]. This will lead to contrast with previous studies and results can’t be compared; should be justified otherwise recalculation must be done throughout the paper.
What is the resolution of LST data used? Was ERA5 Land data checked for bias and performance? These products are utilized for climate analysis that have different spatial resolution. Chen et al. (2021) reports that ERA5 data have limitations in performance in high elevation region. L211;L217: What are those 204 locations? ERA5LAND data is gridded data, why and how 204 sample location is selected? Why not for all areas with maximum snow cover selected for analysis or analysis in different elevation bins not considered? This will lead to incorrect data, analysis and misinterpretation.
Glacier mapping: Glacier mapping is a very rigorous task for ensuring correct delineation but it not provided in detail. Did author use seasonal snow free images to delineate glacier boundary? Was debris covered glacier included, if so, how was it delineated, especially for identifying terminus position. For the years 2000 and 2010, it can be obtained from ICIMOD inventories. L166: What’s the name of 12.5 m DEM?
- Results: The section is disorganized and makes it difficult to identify which results correspond to which dataset and anlalysis. Additionally, the presentation of the results is cumbersome and should be streamlined for better readability. These issue are also mentioned by earlier reviewers too. L185 in which year?
L194 Summer monsoon season is considered both accumulation and ablation season in Nepal (see (Wagnon et al., 2013)).
Figure 4: Where are trends for other months? Why are lines smoothned?
L245 China Karnali: its clear that authors want to mean Karnali originating from China. Since there is no such name it should be described first to clear confusion among readers.
L267: 100 m for Landsat or MODIS? Fig.7, Fig 8 drawn utilizing which data?
Section 4.5: the unit of area is presented in ha, suggestion to use consistently throughout the ms, either sq. km or ha. Glacier basins is unclear and not shown in map anywhere, supplementary can be used in such case.
- Discussion: L414—418: The Westerly wind system is more pronounced in the study area, which significantly brings snowfall during winter and pre -monsoon seasons. Though potential shift in precipitation (shift from snow to rain) is noted in Everest Nepal, more analysis is need for study area to claim this. The reduction to snow over in winter might be also due to weakening of westerly.
The elevation bins for analysis in the methods (L319), results and discussions does not match. For instance, < 3000 m is mentioned in method and somewhere analysis is done <2000 m.
L415: In winter, if the temperature is less than zero then precipitation facilitate snow accumulation.
Discussion could be strengthened by comparing and contrasting with other regions of Nepal and Himalayas. Some literature suggestions for improvement of discussion and paper (https://doi.org/10.1007/s10113-023-02142-y; https://doi.org/10.1007/s10584-011-0181-y; https://doi.org/10.1016/j.scitotenv.2021.148648; https://doi.org/10.3126/jist.v25i2.33729; https://doi.org/10.1016/j.earscirev.2019.103043)
(These references and suggested literature are examples from the review process and can be used to enhance the writing. They are not intended solely for citation purposes.)
References:
- Chen Y., Sharma S., Zhou X., Yang K., Li X., Niu X. et al, 2021. Spatial performance of multiple reanalysis precipitation datasets on the southern slope of central Himalaya. Atmospheric Research 250, 105365 https://doi.org/10.1016/j.atmosres.2020.105365
- DHM, 2017. Observed Climatic trend analysis in the districts and Physiographic zones of Nepal (1971-2014). Department of Hydrology and Meteorology, Kathmandu
- Karki R., ul Hasson S., Gerlitz L., Talchabhadel R., Schickhoff U., Scholten T.,Böhner J., 2020. Rising mean and extreme near‐surface air temperature across Nepal. International Journal of Climatology 40, 2445-2463
- Khadka N., Chen X., Liu W., Gouli M.R., Zhang C., Shrestha B.,Sharma S., 2024. Glacial lake outburst floods threaten China-Nepal connectivity: Synergistic study of remote sensing, GIS and hydrodynamic modeling with regional implications. Sci Total Environ 948, 174701 https://doi.org/10.1016/j.scitotenv.2024.174701
- Sharma S., Khadka N., Hamal K., Shrestha D., Talchabhadel R.,Chen Y., 2020. How accurately can satellite products (TMPA and IMERG) detect precipitation patterns, extremities, and drought across the Nepalese Himalaya? Earth and Space Science, 7(8), e2020EA001315
- Shrestha B., Ye Q.,Khadka N., 2019. Assessment of Ecosystem Services Value Based on Land Use and Land Cover Changes in the Transboundary Karnali River Basin, Central Himalayas. Sustainability 11, 3183 https://doi.org/10.3390/su11113183
- Wagnon P., Vincent C., Arnaud Y., Berthier E., Vuillermoz E., Gruber S. et al, 2013. Seasonal and annual mass balances of Mera and Pokalde glaciers (Nepal Himalaya) since 2007. The Cryosphere 7, 1769-1786 10.5194/tc-7-1769-2013
Citation: https://doi.org/10.5194/egusphere-2025-1303-CC3 -
AC5: 'Reply on CC3', Motilal Ghimire, 19 Aug 2025
Response to comments and suggestions from Reviewer-4
I read the manuscript by Ghimire et al., since the topic aligns with my interests. The manuscript aims to study snow and glacier dynamics using remote sensing datasets and analyze its relationship with climatic and topographic factors in less studied Karnali basin. While the subject matter is important, I regret to say that the paper is poorly prepared and has several issues regarding the data, methods, analysis, and results that may not meet the standards required for publication. I have several major comments and suggestions about the paper, but please disregard any that overlap with previous reviewers’ feedback.
Response :
We sincerely thank the reviewer for the candid and constructive comments on our manuscript “Dynamics of Snow and Glacier Cover in the Upper Karnali Basin, Nepal: An Analysis of Its Relationship with Climatic and Topographic Parameters.” We appreciate your detailed and critical feedback, which has helped us improve the manuscript. Below, we respond to every point, making sure that overlapping concerns already addressed in our responses to Reviewers 1–3 are not repeated unnecessarily. When relevant, we have revised the text, figures, and tables to improve clarity, methodological rigor, and scientific depth.The language of the manuscript must be copy edited and polished.
1. Introduction section: The structuring of the introduction must be improved reducing the over emphasis on social aspects, reviewing the key regional, national and basin scale studies (focus on more recent ones) substantiating with appropriate citations, identifying the gaps and explicitly developing the objectives (currently not stated clearly). Many sentences and paragraph seem unconnected. For example, L52 and L54
Response: We have carefully revised the manuscript to enhance language, grammar, and overall flow, ensuring greater clarity and conciseness. We have updated the Introduction to reduce unnecessary emphasis on social aspects without avoiding essential socio-economic relevance. Current studies (e.g., Shrestha et al., 2019; Khadka et al., 2024) have been incorporated to place the research in a contemporary scientific context. Existing knowledge gaps, particularly regarding snow glacier dynamics in the mid-western Himalayas, are clearly identified and described. The study objectives are now clearly stated in the final paragraph. Logical linkages between paragraphs have also been improved, removing abrupt transitions noted at L52–L54.
2. Study area: The study area is transboundary but is merely mentioned [see: (Shrestha et al., 2019; Khadka et al., 2024)]. Why is it focused only on upper Karnali part and not on other sub-basins of Karnali river system, such as Bheri and West Seti, as these basins also have glacier and snow cover. Although the paper addresses snow cover and glaciers, the description of the study area deviates from the main focus. It should concentrate on providing a clear overview of the key cryospheric components, including state and status of clean and debris-covered glaciers, glacial lakes, and snow. Additionally, while snow and glaciers are affected by weather systems such as the monsoon and westerlies, the paper does not mention the climatology, trends in precipitation and temperature for the region. It is also important to note that several citations are missing; the sources for annual precipitation and temperature data should be included to support the claims made in the paper.
Response: The description of the study area has been revised. The Upper Karnali Basin covers above 50% of the total basin area at Chisapani at 225 m a.s.l., and according to et al. Bajracharya et al. (2011) indicate that this area covers about 66% of the total glacier area in the whole basin. Geologically, the Upper Karnai Basin covers the Lesser Himalaya, Higher Himalaya, and Tethys Himalaya (https://dmgnepal.gov.np/en/pages/general-geology-4128). The Upper Karnai Basin extend across Middle Mountain, High Mountain, High Himalaya and Tibetan Plateau. The climate varies from Polar Tundra in the glacier region to subtropical, temperate, and cold climates below 4000 m, with mean annual temperatures ranging from 27 °C to <- 12 °C and precipitation from 250 to ~ 1900 mm annually. The cryosphere zone of the study area basin encompasses both rain-bearing and rainshadow areas, influencing the distribution of snow and glaciers. Hence, from all the above characteristics, the study area represents the entire basin, including those of other glacier sub-basins such as West Seti and Bheri Basin.
3. Data and method: This whole section is not explicit. Authors used Landsat 7, for which date? Landsat 7 have SLC failure after 2003, how were data gaps filled? How much was acceptable cloud cover %? Solely using NDSI and threshold >0.4 is questionable regarding the complexity of the landscape, particularly in mixed pixel scenarios where snow is interspersed with other land cover types and shadows. How were shadows removed or incorporated in optical images? Authors could have combined with other indices (such as NDVI), used automatic image thresholding etc. for improving classification accuracy.
Response: For the analysis of Landsat data, Landsat 7 ETM+ imagery was utilized exclusively for the period preceding the Scan Line Corrector (SLC) failure (2000–2003), with subsequent analyses relying on Landsat 5 TM and Landsat 8 OLI datasets. Only scenes exhibiting less than 20% cloud cover were selected to ensure data quality. To further reduce cloud contamination, the MODIS MOD10A2 8-day composite product was incorporated. Snow cover was delineated using a Normalized Difference Snow Index (NDSI) threshold greater than 0.4, supplemented by Normalized Difference Vegetation Index (NDVI) filtering to reduce misclassification with vegetation, and hillshade masks derived from Digital Elevation Models (DEM) to minimize shadow-related errors. Validation was conducted through visual cross-referencing with high-resolution imagery.
4. Why is the month composition of seasons different in this study (L138)? Nepal has four main seasons: post-monsoon (October–November), winter (December–February), pre-monsoon (March–May), and monsoon (June–September) [see, (DHM, 2017; Karki et al., 2020; Sharma et al., 2020)]. This will lead to contrast with previous studies and results can’t be compared; should be justified otherwise recalculation must be done throughout the paper.
Response: We acknowledge the reviewer’s concern regarding the definition of seasons. To improve clarity, we have explained that our grouping (Jan–Mar, Apr–Jun, Jul–Sep, Oct–Dec) reflects the hydrological phases of snow accumulation and ablation and was selected to minimize cloud contamination during the core monsoon period. These intervals largely overlap with the Department of Hydrology and Meteorology’s (DHM) four climatological seasons (post-monsoon, winter, pre-monsoon, monsoon). For example, our Jan–Mar period aligns with DHM’s Dec–Feb winter season, while Apr–Jun corresponds closely to DHM’s Mar–May pre-monsoon season. Thus, our results remain broadly comparable with previous studies while providing a more process-oriented representation of snow cover dynamics in the Upper Karnali Basin.
5. What is the resolution of LST data used? Was ERA5 Land data checked for bias and performance? These products are utilized for climate analysis that have different spatial resolution. Chen et al. (2021) reports that ERA5 data have limitations in performance in high elevation region. L211;L217: What are those 204 locations? ERA5LAND data is gridded data, why and how 204 sample location is selected? Why not for all areas with maximum snow cover selected for analysis or analysis in different elevation bins not considered? This will lead to incorrect data, analysis and misinterpretation.Response: Land Surface Temperature (LST) data with a spatial resolution of 1 km were acquired from MODIS Terra (MOD11A1) and Aqua (MYD11A1) via the NASA AppEEARS platform (Wan et al., 2015). Precipitation data were obtained from the ERA5-Land reanalysis dataset at approximately 9 km resolution (Hersbach et al., 2020). A total of 204 well-distributed grid points were chosen throughout the Upper Karnali Basin to represent various sub-basins and elevation zones. This sampling approach enabled us to capture spatial variability in climate trends while avoiding excessive redundancy, considering the coarse resolution of ERA5. It is important to note that ERA5 products have recognized limitations in high-altitude areas (Chen et al., 2021), so they were mainly used for analyzing trend correlations with snow cover rather than for direct validation at specific points.
To improve representativeness further, additional analyses of LST trends were conducted by grouping results into elevation categories (2000–6000 meters above sea level), which confirmed consistent negative correlations between snow cover and temperature across different altitude ranges.
6. Glacier mapping: Glacier mapping is a very rigorous task for ensuring correct delineation but it not provided in detail. Did author use seasonal snow free images to delineate glacier boundary? Was debris covered glacier included, if so, how was it delineated, especially for identifying terminus position. For the years 2000 and 2010, it can be obtained from ICIMOD inventories. L166: What’s the name of 12.5 m DEM?
Response: Glacie data from Ghimire et al. (2025), accepted for publication in the Journal for Earth System Science, was used in this study. Glacier boundaries were manually delineated using multi-sensor imagery to ensure accuracy across different time periods and spectral ranges. High-resolution images from Google Earth, Bing Maps, and RapidEye for the years 2000, 2010, and 2023 were combined with multispectral data from Landsat 7–9, Sentinel-2, and ASTER. Visual interpretation was improved by applying band ratios (Red/SWIR, NDSI), color composites (SWIR–NIR–Red), and thermal images to detect debris-covered ice. Topographic information from DEMs and geomorphological features such as moraines, meltwater channels, supraglacial ponds, and ice cliffs further aided the analysis. Glacier termini were digitized following established methods (Bajracharya & Shrestha, 2011; Kääb et al., 2012; Pfeffer et al., 2014), with multi-temporal verification to maintain consistency. Limited field data from the study area and the Khumbu region were used to validate the glacier boundaries. Accumulation zones and ridgelines were identified based on NDSI values above 0.7, spectral composites, and elevation thresholds derived from DEMs. Manual digitization allowed for clear differentiation between glaciers and adjacent snow or rock, resulting in precise glacier outlines.
7. Results: The section is disorganized and makes it difficult to identify which results correspond to which dataset and analysis. Additionally, the presentation of the results is cumbersome and should be streamlined for better readability. These issue are also mentioned by earlier reviewers too. L185 in which year?Response: The Result section has been organized and refined in response to the suggestions from the previous reviewer as well.
L194 Summer monsoon season is considered both accumulation and ablation season in Nepal (see (Wagnon et al., 2013)).
Response: Addressed in previous comments (Comment 4)
8. Figure 4: Where are trends for other months? Why are lines smoothed?
Response: Addressed in previous comments (Comment 4)
9. L245 China Karnali: its clear that authors want to mean Karnali originating from China. Since there is no such name it should be described first to clear confusion among readers.
Response: Corrected as Humla Karnali (China) and incorporated
10. L267: 100 m for Landsat or MODIS? Fig.7, Fig 8 drawn utilizing which data?
Response: We have used MODIS data
11. Section 4.5: the unit of area is presented in ha, suggestion to use consistently throughout the ms, either sq. km or ha. Glacier basins is unclear and not shown in map anywhere, supplementary can be used in such case.
Response : Corrected
12. Discussion: L414—418: The Westerly wind system is more pronounced in the study area, which significantly brings snowfall during winter and pre -monsoon seasons. Though potential shift in precipitation (shift from snow to rain) is noted in Everest Nepal, more analysis is need for study area to claim this. The reduction to snow over in winter might also be due to the weakening of the westerly.
Response: We appreciate the reviewer's comment. While the westerly system does influence winter and pre-monsoon snowfall, unlike Everest, a more detailed local analysis is required to confirm any snow-to-rain transition in our basin. We have clarified this point in the discussion.
13. L415: In winter, if the temperature is less than zero then precipitation facilitate snow accumulation.Response: We agree and have specified that winter precipitation adds to snow accumulation only if temperatures stay below 0 °C.
14. Discussion could be strengthened by comparing and contrasting with other regions of Nepal and Himalayas. Some literature suggestions for improvement of discussion and paper (https://doi.org/10.1007/s10113-023-02142-y; https://doi.org/10.1007/s10584-011-0181-y; https://doi.org/10.1016/j.scitotenv.2021.148648; https://doi.org/10.3126/jist.v25i2.33729; https://doi.org/10.1016/j.earscirev.2019.103043)
(These references and suggested literature are examples from the review process and can be used to enhance the writing. They are not intended solely for citation purposes.)
Response: Thank you for the suggestion. We agree and have added relevant literature to strengthen the discussion and place our findings in the broader cryosphere context of Nepal and the Himalayas.References
Bajracharya, S. R., & Shrestha, B. (2011). The status of glaciers in the Hindu Kush-Himalayan region (p. 127). International Centre for Integrated Mountain Development (ICIMOD).
Chen, Y., Sharma, S., Zhou, X., Yang, K., Li, X., Niu, X., ... & Khadka, N. (2021). Spatial performance of multiple reanalysis precipitation datasets on the southern slope of central Himalaya. Atmospheric Research, 250, 105365.
Ghimire M, Sharma TPP, Chauhan R, Gurung SB, Devkota S, Sharma KP, Shrestha D, Wei Z, Timalsina N (2025) Status and changes in glaciers in the Upper Karnali Basin, West Nepal: assessing topographic influences on area, fragmentation, and volume. J Earth Syst Sci (Accepted).
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., ... & Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly journal of the royal meteorological society, 146(730), 1999-2049.
Kääb, A., Berthier, E., Nuth, C., Gardelle, J., & Arnaud, Y. (2012). Contrasting patterns of early twenty-first-century glacier mass change in the Himalayas. Nature, 488(7412), 495-498.Karki, R., Hasson, S. U., Schickhoff, U., Scholten, T., Böhner, J., & Gerlitz, L. (2020). Near surface air temperature lapse rates over complex terrain: a WRF based analysis of controlling factors and processes for the central Himalayas. Climate Dynamics, 54(1), 329-349.
Khadka, N., Zheng, G., Chen, X., Zhong, Y., Allen, S. K., & Gouli, M. R. (2024). An ice-snow avalanche triggered small glacial lake outburst flood in Birendra Lake, Nepal Himalaya. Natural Hazards, 1-9.
Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., ... & Randolph Consortium. (2014). The Randolph Glacier Inventory: a globally complete inventory of glaciers. Journal of glaciology, 60(221), 537-552.
Wan, W., Xiao, P., Feng, X., Li, H., Ma, R., Duan, H., & Zhao, L. (2014). Monitoring lake changes of Qinghai-Tibetan Plateau over the past 30 years using satellite remote sensing data. Chinese Science Bulletin, 59(10), 1021-1035.
Citation: https://doi.org/10.5194/egusphere-2025-1303-AC5
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