|Review of the revised version of the paper (Referee 1) “Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment” by E. Stigter et al. submitted to The Cryosphere. |
The authors have proposed a clear and detailed answer to all my points and to the points raised by the two other referees. The quality of the manuscript has been increased during the reviewing process.
I have a remaining concern about the choice made by the authors to change the title of the paper. Indeed, the authors have decided to insist more on the core of the study : the assimilation of snow depth and snow cover in a snowpack model to improve the quantification of SWE and snowmelt runoff. The climate section is now presented as a sensitivity experiment and the authors explicitly stated in their answer that this paper is not a climate change impact study. This is supposed to justify the choice of the simple delta method used in the climate sensitivity tests.
If the core of the study is the assimilation of snow depth and snow cover in a snowpack model, the authors should insist more on this aspect in their paper. For example, it should be more emphasized in the abstract and in the conclusion. So far, the conclusion has only been slightly changed compared to the initial version of the paper and it seems to me that the main conclusions of the paper still concern the climate sensitivity experiment.
Since the main focus of the paper is the data assimilation of snowpack data, I also recommend the authors to add the following additional information:
- The impact of data assimilation on SWE and snow melt simulation could be also evaluated using the surface temperature data as an indirect evaluation. This would allow the authors to compare the presence of snow in the simulation and in the observation. The two transects for surface temperature cover a large altitudinal range and two different slope aspects (north and south). This would be an interesting complement to the spatial evaluation that has been carried out with the satellites data (MODIS and LandSat).
- The assimilation of snow depth data is carried out at two stations including Kyangjin station where almost no snow is found in 2014. Snow depth observations are used to optimize the parameter C6 governing snow settling in the model. Therefore, the impact of assimilation of snow depth from Kyangjin station is expected to be quite low in 2014. Time series of observed snow depth on Figure 5 suggest that Yala Pluvio or Yala BC could be used to carry out the assimilation of snow depth measurements. Since snow is present at these two stations in 2014, it could bring interesting improvements in model results. At least, the authors should comment the impact of the choice of stations for the assimilation of snow depth data.
- A discussion part on the benefits and challenges of assimilation of snow data (both spatial and punctual) would be very valuable. It could include a discussion around the choice of the EnKF framework compared to the particle filter that has been recently used in several studies on data assimilation for snowpack model (Charrois et al. 2016, Magnusson et al. 2017). The fact that the EnKF required continuous values (and not binary values) should be mentioned in the paper. It illustrates a limitation of the EnKF for the assimilation of spatially distributed snow data. Ideally, the assimilation of snow cover would be done on a pixel to pixel basis as mentioned by the authors in their answer.
Charrois, L., Cosme, E., Dumont, M., Lafaysse, M., Morin, S., Libois, Q., & Picard, G. (2016). On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model. The Cryosphere, 10, 1021-1038.
Magnusson, J., A. Winstral, A. S. Stordal, R. Essery, and T. Jonas (2017), Improving physically based snow simulations by assimilating snow depths using the particle filter, Water Resour. Res., 53, 1125–1143, doi:10.1002/2016WR019092.