the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Apr 2021
Operational and experimental snow observation systems in the upper Rofental: data from 2017–2020
Abstract. According to the living data process in ESSD, this publication presents extensions of a comprehensive hydrometeorological and glaciological data set for several research sites in the Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria). Whereas the original dataset has been published in a first original version in 2018 (https://doi.org/10.5194/essd-10-151-2018), the new time series presented here originate from meteorological and snow-hydrological recordings that have been collected from 2017 to 2020. Some data sets represent continuations of time series at existing locations, others come from new installations complementing the scientific monitoring infrastructure in the research catchment. Main extensions are a fully equipped automatic weather and snow monitoring station, as well as extensive additional installations to enable continuous observation of snow cover properties. Installed at three high Alpine locations in the catchment, these include automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water content, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow redistribution. They are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor.
The data sets represent a unique time series of high-altitude mountain snow and meteorology observations. We present three years of data for temperature, precipitation, humidity, wind speed, and radiation fluxes from three meteorological stations. The continuous snow measurements are explored by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations are demonstrated with examples of measured wind speeds, snow drift rates and redistributed snow amounts in December 2019 when a tragic avalanche accident occurred in the vicinity of the station. All new data sets are provided to the scientific community according to the Creative Commons Attribution License by means of the PANGAEA repository (https://www.pangaea.de/?q=%40ref104365).
- Preprint
(41848 KB) - Metadata XML
- BibTeX
- EndNote
Status: closed
-
RC1: 'Comment on essd-2021-68', Anonymous Referee #1, 28 Apr 2021
Review
The current manuscript draft on « Operational and experimental snow observation systems in the upper Rofental: data from 2017 - 2020” describes new weather and snow sensors installed and corresponding data collected in the Rofental catchment.
General comments:
Unfortunately, with the exception of the snow drift sensor, the authors miss the opportunity to introduce the new sensors in detail and to describe the applied data curation. Moreover, the available data are sometimes carelessly interpreted without any critical plausibility assessment or references to other studies. The possibility of wrong measurements, especially regarding SPA and SGG, was not considered. If really no manual control measurements were performed during the three years, it should at least be mentioned and explained. Nevertheless, the new measurement infrastructure and the corresponding data are worth to be published as soon as the following points have been addressed:
- An overview is missing about what has already been documented in Paper I and what is now newly documented in this paper. Has anything been abandoned?
- There is no information about any quality assurance or quality control procedures applied to the data. There is no information about the frequency data are downloaded and screened, if at all?
- The snow drift sensor is explained and referenced in detail. In contrast, e.g. information and literature about the SPA and its measured quantities is missing entirely. For example, the difference in data series S1 and S2 listed in the data set is not explained at all.
- Several times snow fall or snow accumulation is mentioned (e.g. L198-199) without including information of the concurrent precip data. For example, the case mentioned in L198-199 is contradicting the precip signal! Additionally, the case in L228-229 can’t be true because the clearly negative temperatures demonstrate that the reason for the SWE increase can’t be rain! Finally, what was the precip for the case explained in L316-317?
- There are several situations where the pressure measured SWE is wrong. For example, see the described case in the paragraph above or the SWE increase and concurrent stable snow depth during the second half of March in Fig. 8. Please elaborate. I suggest to also check the plausibility of the calculated density as provided in 9b. The reason of the difference described in L246-247 is probably also a such wrong SWE measurement and not the difference in measurement location.
Specific comments:
L34: Matiu et al. 2021
L55: Since the paper will not been published before summer 2021 I’d recommend to also include the winter season 2020/21.
L63: (same special issue)?
L67: The Rofenache river
Fig. 1: Very bad map. Not even valleys or ridges are easy recognizable. Many geographical locations described in the text are missing in the map.
L95: “..the existing weather stations…” how many?
L96: “..at several locations..” What do you want to say?
L113: 1.5 m does not make sense for high alpine AWS? What is the reason. Add the exact height above ground for each sensor Table 1-3. This is important for many applications. Moreover, it is in contradiction to the min/max height of 2 m written e.g. here: https://doi.pangaea.de/10.1594/PANGAEA.918096
L123: 10 min mean values?
L124; I suggest to use HS instead of SD, because it is the official abbreviation.
L126: …by two European Avalanche…
L134: Why do you mention Sommer SSG-2 and not also accordingly the same for the snow depth and snow temperature sensors?
L136: The new instruments complement…
L142: .. installed at the main station
Fig2: The red arrow marks the main “exposed” AWSS. The blue…
L155: Why Sommer is mentioned for the SIR sensor, but not the SCA and the SPA-2 sensors? Be consistent!
L163: time resolution, raw data , quality controlled?
L164: I’d recommend to provide PDFs about the instruments used instead of manufacturer URLs, which can change any time.
L171: time zone?
L180: How do you manage to have enough power for heating?
L190: In 4.2.1, there is only snow depth described despite the SWE mentioned in the title.
Fig 7: Please provide the same figure for SWE.
L323: the technical details of the instruments are not all described!
L324: It’s hard to believe that no manual measurements were performed during the three years to check the plausibility and representativity of the automatic snow measurements?
L335: Can you tell anything about funding?
Table 1: EE08 instead of E08.
Is the air temperature ventilated?
Is the radiation sensor ventilated? What is the source of the given accuracy? It should rather be given in percentage.Table4: The calculated snow drift values are wrong!
Citation: https://doi.org/10.5194/essd-2021-68-RC1 - AC1: 'Reply on RC1', Michael Warscher, 17 Jul 2021
-
RC2: 'Comment on essd-2021-68', Anonymous Referee #2, 07 May 2021
1 General comments
Warscher et al. present a data set of non-standard measurements of snow properties together with meteorological variables at three alpine stations in the Austrian Alps. However, the unique part (i.e. non-standard measurements at three sites) of the dataset is only available for the beginning of this year and partially for the 2019/2020 snow season. I do not see that this short period is particularly useful compared to other published multi-year datasets (e.g., Morin et al., 2012; Ménard et al., 2019). Therefore, I suggest rejecting the manuscript at this stage and waiting a few more years until more data are available. In addition to the short time frame, I also agree with the first reviewer that the data quality checking is unclear and that conclusions from the data are sometimes incorrect (which I will show in the next section). In addition, I would like to point out that data gaps are a major problem with this dataset. These three issues should be considered before submitting a new manuscript in a few years.
2 Specific comments
2.1 Short time period
I can identify useful and unique parts of this data set, but in too short a time period. These are the spatial distribution (three measurements) in a high alpine environment, with non-standard snow measurements such as SWE from different gauges, liquid and solid fractions of snow, snow temperatures, and snowdrift sensors combined with standard measurements such as snow depth, precipitation, and meteorological variables. However, there is only one complete snow season (2019/20) in which more than one SWE measurement is available, but at leasot one of those is exposed to wind erosion, and the usefulness of this location will not become apparent until the start of the 2020/21 winter season, when a nearby wind-sheltered station was established. Similarly, the non-standard drift sensor and Snow Pack Analyzer (SPA) measurements are not available before early 2020. Therefore, I don't see much use for this data set described here. However, I can very well imagine one developing in a few years.
2.2 Data quality example and quality checks
Since this dataset is not standard and prone to errors, I propose to address typical measurement errors and possible automatic quality check routines in a next manuscript version. Here I would like to describe an erroneous SWE measurement that has already been identified by reviewer 1 as a misinterpretation by the authors. In lines 225ff, the authors described the stage at which the snowpack at Latschbloder becomes isothermal (Figure 8) and explained the subsequent SWE values. This is a typical time when pressure sensors measuring SWE exhibit errors (Johnson and Schaefer, 2002). I do not claim to provide the correct interpretation, but the authors certainly missed something. This description should serve as an example of how future quality control can be designed or how errors in the data set can be described in a future manuscript, especially when more instances of redundant SWE measurements will be available (SPA and snow scale). The authors claim snowmelt starting at midnight on 11 April 2020, explaining the loss of SWE of ~110 mm in less than 18 hours. It is questionable whether this is melt, as air temperatures were well below 0 °C and the snow depth sensor only indicated a constant decrease similar to the days before and after. It appears to be more a measurement error which is typical when the isothermal front reaches the snow-ground boundary as described by Johnson and Schaefer (2002), which was detected based on the snow temperature measurements two days earlier. A decrease in SWE could be explained by snow shear strength being able to bridge the sensor (Johnson and Marks, 2004), which could happen when meltwater near the ground refreezes. The authors describe the later increase in SWE as rain percolating into the snow. However, reviewer 1 correctly pointed out the negative air temperatures during this time (colder than -5 °C). In addition, the rain gauge measured only <3 mm of precipitation, while the SWE sensor increases by more than 150 mm during the same period through April 18. This discrepancy cannot be explained by undercatch of the rain gauge, especially since the snow depth sensor shows the same continuous decrease as in the days before, without any indication of a major snowfall. Thus, it seems more likely that the previously mentioned snow bridges have been continuously weakened as snow temperatures are around the melting temperature. Future availability of redundant SWE data, snow depths, air and snow temperatures will provide more examples in a few years from which the authors can select examples of faulty and good situations. The methods of Johnson and Marks (2004) or others may be included to tag poor quality data.
2.3 Data gaps
The use of this dataset is also limited due to data gaps, which is partially visible in Figure 5. For example, for Bella Vista in 2018, over 33% of all data are missing with gap sizes of 49, 27, 20, ... days. Precipitation is missing 75% of the time, with another gap of 199 days. In 2019, this station typically has 7% data gaps, wind over 11%, with gap sizes of 12, 7, 3, <1 days. Such multi-day data gaps are difficult to fill.
2.4 Other
- A measurement height of 1.50 m does not seem sufficient in alpine terrain. Please provide the exact height for each sensor in the tables. Please provide time periods when a sensor is buried or consider larger masts (if possible).
- Literature describing the quality of the snow pack analyzer should be added. For example, Staehli et al. (2004) and Egli et al. (2009).
- The fact that each year and station is in individual files makes it difficult to use the data. Please consider consolidating the data into one or a few file(s) as much as possible.
References
Egli, L., Jonas, T., and Meister, R. (2009). Comparison of different automatic methods for estimating snow water equivalent. Cold Regions Science and Technology, 57(2-3), 107-115.
Johnson, J. B., and Schaefer, G. L. (2002). The influence of thermal, hydrologic, and snow deformation mechanisms on snow water equivalent pressure sensor accuracy. Hydrological Processes, 16(18), 3529-3542.
Johnson, J. B., and Marks, D. (2004). The detection and correction of snow water equivalent pressure sensor errors. Hydrological Processes, 18(18), 3513-3525.
Ménard, C. B., Essery, R., Barr, A., Bartlett, P., Derry, J., Dumont, M., Fierz, C., Kim, H., Kontu, A., Lejeune, Y., Marks, D., Niwano, M., Raleigh, M., Wang, L., and Wever, N. (2019). Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data, Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, 2019.
Morin, S., Lejeune, Y., Lesaffre, B., Panel, J.-M., Poncet, D., David, P., and Sudul, M. (2012). An 18-yr long (1993-–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models, Earth Syst. Sci. Data, 4, 13–21, https://doi.org/10.5194/essd-4-13-2012.
Stähli, M., Stacheder, M., Gustafsson, D., Schlaeger, S., Schneebeli, M., and Brandelik, A. (2004). A new in situ sensor for large-scale snow-cover monitoring. Annals of Glaciology, 38, 273-278.
Citation: https://doi.org/10.5194/essd-2021-68-RC2 - AC2: 'Reply on RC2', Michael Warscher, 17 Jul 2021
-
RC3: 'Comment on essd-2021-68', Anonymous Referee #3, 18 May 2021
General comments
The authors present an extension of their previous ESSD publication that focuses on automated meteorological and snowpack observations collected in an alpine environment in Rofental, Austria. The authors followed the ESSD living data process to guide this manuscript, and accordingly nicely focus on extensions of the time series, instrumentation upgrades, and descriptions of some new instrument installations that offer additional insights into snow cover processes. I found the article easy to follow and was able to download and plot some of the data relatively easily, suggesting this data is readily accessible for future research applications. However, I did find some of the data incomplete and lacking a proper description of errors and uncertainties (see comments below).
Specific comments
- Based on Fig. 6 it looks like some of the snow depth and SWE measurements have some missing values. Please specify why in the text. Overall the text is light on descriptions of the uncertainties in the measurements, with only the instrument resolution listed in the tables. The paper would benefit from better discussion of sources of error.
- Some of the time series for the new sensors are relatively short in duration, such as the snow measurements at Proviantdepot. It would make sense to include data from the entire 2020-2021 winter season now that it is mostly completed.
- When downloading the tab-delimited data I found it difficult to work with the column headers because the variable name, units, and method/device details were all in the same cell. If working with a scripting language like R or Python it is much easier when the columns can be indexed with short concise name, in which case the units and descriptions could be on their own rows. That being said, I am not familiar with the standards and limitations of the PANGEA data platform.
Technical comments
- Line 118: Please define the acronym “GSM”
- Lines 155-157: Please provide more description of the SPA instrument, and how it can be used to calculate density and SWE as later shown in Fig. 9.
- Line 167: By “daily values” I assume this means daily averages.
- 5 and 6: These plots are missing the subplot labels (a-f)
- Fig 9: Would it be more logical to move snow depth from subplot (d) to (a) since it is the first plot discussed in the text?
- Line 301: Can you provide an actual distance instead of “in close proximity”
- Table 4: I don’t understand the need for the final 2 rows in this table since they simply repeat the same values. It’s also unclear why some values are in italics. Perhaps the 6 unique values presented in this table could simply be stated in the text, along with an explanation of how they relate to each other.
- Fig 13: The caption description of the avalanche should use proper avalanche terminology. The edges are called ‘fracture lines’ with the one along the top called a ‘crown’ and the ones along the sides called ‘flanks’.
Citation: https://doi.org/10.5194/essd-2021-68-RC3 - AC3: 'Reply on RC3', Michael Warscher, 17 Jul 2021
-
RC4: 'Comment on essd-2021-68', Anonymous Referee #4, 21 May 2021
General comments:
The manuscript ‘Operational and experimental snow observation systems in the upper Rofental: data from 2017-2020’ by Warscher et al. provides a description of different types of continuously recorded snow and meteorological datasets - using standard as well as experimental sensors - collected at three sites in the Rofental in the European Alps. The manuscript is an extension of the ESSD paper ‘The Rofental: a high Alpine research basin (1890–3770ma.s.l.) in the Oetztal Alps (Austria) with over 150 years of hydrometeorological and glaciological observations’ by Strasser et al. 2018.
Although the title and the abstract imply that all data has been available since 2017, a closer look reveals that some datasets do not start before 2019 or even 2020. In addition, data gaps are an issue that has not been discussed in detail. I agree with Reviewer 2 that the covered time period for some recordings (especially for the unique experimental snow measurement setups) is too short for publication at current state. Therefore, I also recommend waiting some more years and collecting a longer time period of data before publication. In general, I agree with the general and specific issues raised by Reviewer 1 and 2 as well as the specific/technical comments raised by Reviewer 3 and will not repeat them here again. In particular, information on assessment data quality should be included.
However, I see good potential for publication in a few years (i.e. after extending the dataset for approx. two more years: 1) There is a great need for standard and experimental continuous snow monitoring datasets that cover longer periods in high-alpine regions, as such datasets are still very sparse. 2) The Rofental research catchment seems to be an ideal site for glacier, snowpack and hydrological model applications and developments, especially since the basin is not influenced by hydropower structures.
As the authors are focusing on datasets for snow observation, it would be wise to include and describe also the other snow measurement sites in the Rofental research basin (stations Hintereisferner and Vernagtbach) in this manuscript, although they were already introduced in Strasser et al. 2018. Adding these two sites in the manuscript would make the multi-station dataset even more valuable. I agree with Reviewer 3 that the data provided on the PANGEA platform was easily accessible and, except for the data gaps, was complete as described in the manuscript.
Specific comments:
- L. 2: The altitude of the research basin might be of interest for the reader; however, as you describe the data sets of specific measurement sites, the altitude of these sites would be at least as interesting to mention.
- 3: The expression ‘original’ (which is written twice in this line) seems strange in this context and implicates your work is somehow not original. Better change to: ‘The dataset of our first study published in 2018 (https://doi.org/10.5194/essd-10-151-2018) contains... The time series presented here…’
- Section 1: Please add some information on similar sites and studies (i.e. Ménard et al. 2019, https://essd.copernicus.org/articles/11/865/2019/).
- 58-60: As you are describing snow drift measurements in detail (Section 4.2.4), I would recommend to introduce this point already here, i.e. extending point I to : I) Improved process understanding of snow drift, accumulation and melt dynamics in high mountain regions.
- 92-93: Information on topography and meteorological conditions of the research site should be moved to Section 2.
- Section 3.1 and 3.2: Several statements (especially the site descriptions, coordinates) are repetitive. I would suggest merging these two subsections and describing each site individually introducing their meteorological and snow sensors together in one subsection.
- Section 4.2.4: This section is very long compared to the other subsections of 4.2. I would suggest to describe the snow drift measurements in general in this section and move the explicit case study to a new section (i.e. Section 5: Case study - Application of the dataset for an improved assessment of avalanche-critical blowing snow situations).
Citation: https://doi.org/10.5194/essd-2021-68-RC4 - AC4: 'Reply on RC4', Michael Warscher, 17 Jul 2021
Status: closed
-
RC1: 'Comment on essd-2021-68', Anonymous Referee #1, 28 Apr 2021
Review
The current manuscript draft on « Operational and experimental snow observation systems in the upper Rofental: data from 2017 - 2020” describes new weather and snow sensors installed and corresponding data collected in the Rofental catchment.
General comments:
Unfortunately, with the exception of the snow drift sensor, the authors miss the opportunity to introduce the new sensors in detail and to describe the applied data curation. Moreover, the available data are sometimes carelessly interpreted without any critical plausibility assessment or references to other studies. The possibility of wrong measurements, especially regarding SPA and SGG, was not considered. If really no manual control measurements were performed during the three years, it should at least be mentioned and explained. Nevertheless, the new measurement infrastructure and the corresponding data are worth to be published as soon as the following points have been addressed:
- An overview is missing about what has already been documented in Paper I and what is now newly documented in this paper. Has anything been abandoned?
- There is no information about any quality assurance or quality control procedures applied to the data. There is no information about the frequency data are downloaded and screened, if at all?
- The snow drift sensor is explained and referenced in detail. In contrast, e.g. information and literature about the SPA and its measured quantities is missing entirely. For example, the difference in data series S1 and S2 listed in the data set is not explained at all.
- Several times snow fall or snow accumulation is mentioned (e.g. L198-199) without including information of the concurrent precip data. For example, the case mentioned in L198-199 is contradicting the precip signal! Additionally, the case in L228-229 can’t be true because the clearly negative temperatures demonstrate that the reason for the SWE increase can’t be rain! Finally, what was the precip for the case explained in L316-317?
- There are several situations where the pressure measured SWE is wrong. For example, see the described case in the paragraph above or the SWE increase and concurrent stable snow depth during the second half of March in Fig. 8. Please elaborate. I suggest to also check the plausibility of the calculated density as provided in 9b. The reason of the difference described in L246-247 is probably also a such wrong SWE measurement and not the difference in measurement location.
Specific comments:
L34: Matiu et al. 2021
L55: Since the paper will not been published before summer 2021 I’d recommend to also include the winter season 2020/21.
L63: (same special issue)?
L67: The Rofenache river
Fig. 1: Very bad map. Not even valleys or ridges are easy recognizable. Many geographical locations described in the text are missing in the map.
L95: “..the existing weather stations…” how many?
L96: “..at several locations..” What do you want to say?
L113: 1.5 m does not make sense for high alpine AWS? What is the reason. Add the exact height above ground for each sensor Table 1-3. This is important for many applications. Moreover, it is in contradiction to the min/max height of 2 m written e.g. here: https://doi.pangaea.de/10.1594/PANGAEA.918096
L123: 10 min mean values?
L124; I suggest to use HS instead of SD, because it is the official abbreviation.
L126: …by two European Avalanche…
L134: Why do you mention Sommer SSG-2 and not also accordingly the same for the snow depth and snow temperature sensors?
L136: The new instruments complement…
L142: .. installed at the main station
Fig2: The red arrow marks the main “exposed” AWSS. The blue…
L155: Why Sommer is mentioned for the SIR sensor, but not the SCA and the SPA-2 sensors? Be consistent!
L163: time resolution, raw data , quality controlled?
L164: I’d recommend to provide PDFs about the instruments used instead of manufacturer URLs, which can change any time.
L171: time zone?
L180: How do you manage to have enough power for heating?
L190: In 4.2.1, there is only snow depth described despite the SWE mentioned in the title.
Fig 7: Please provide the same figure for SWE.
L323: the technical details of the instruments are not all described!
L324: It’s hard to believe that no manual measurements were performed during the three years to check the plausibility and representativity of the automatic snow measurements?
L335: Can you tell anything about funding?
Table 1: EE08 instead of E08.
Is the air temperature ventilated?
Is the radiation sensor ventilated? What is the source of the given accuracy? It should rather be given in percentage.Table4: The calculated snow drift values are wrong!
Citation: https://doi.org/10.5194/essd-2021-68-RC1 - AC1: 'Reply on RC1', Michael Warscher, 17 Jul 2021
-
RC2: 'Comment on essd-2021-68', Anonymous Referee #2, 07 May 2021
1 General comments
Warscher et al. present a data set of non-standard measurements of snow properties together with meteorological variables at three alpine stations in the Austrian Alps. However, the unique part (i.e. non-standard measurements at three sites) of the dataset is only available for the beginning of this year and partially for the 2019/2020 snow season. I do not see that this short period is particularly useful compared to other published multi-year datasets (e.g., Morin et al., 2012; Ménard et al., 2019). Therefore, I suggest rejecting the manuscript at this stage and waiting a few more years until more data are available. In addition to the short time frame, I also agree with the first reviewer that the data quality checking is unclear and that conclusions from the data are sometimes incorrect (which I will show in the next section). In addition, I would like to point out that data gaps are a major problem with this dataset. These three issues should be considered before submitting a new manuscript in a few years.
2 Specific comments
2.1 Short time period
I can identify useful and unique parts of this data set, but in too short a time period. These are the spatial distribution (three measurements) in a high alpine environment, with non-standard snow measurements such as SWE from different gauges, liquid and solid fractions of snow, snow temperatures, and snowdrift sensors combined with standard measurements such as snow depth, precipitation, and meteorological variables. However, there is only one complete snow season (2019/20) in which more than one SWE measurement is available, but at leasot one of those is exposed to wind erosion, and the usefulness of this location will not become apparent until the start of the 2020/21 winter season, when a nearby wind-sheltered station was established. Similarly, the non-standard drift sensor and Snow Pack Analyzer (SPA) measurements are not available before early 2020. Therefore, I don't see much use for this data set described here. However, I can very well imagine one developing in a few years.
2.2 Data quality example and quality checks
Since this dataset is not standard and prone to errors, I propose to address typical measurement errors and possible automatic quality check routines in a next manuscript version. Here I would like to describe an erroneous SWE measurement that has already been identified by reviewer 1 as a misinterpretation by the authors. In lines 225ff, the authors described the stage at which the snowpack at Latschbloder becomes isothermal (Figure 8) and explained the subsequent SWE values. This is a typical time when pressure sensors measuring SWE exhibit errors (Johnson and Schaefer, 2002). I do not claim to provide the correct interpretation, but the authors certainly missed something. This description should serve as an example of how future quality control can be designed or how errors in the data set can be described in a future manuscript, especially when more instances of redundant SWE measurements will be available (SPA and snow scale). The authors claim snowmelt starting at midnight on 11 April 2020, explaining the loss of SWE of ~110 mm in less than 18 hours. It is questionable whether this is melt, as air temperatures were well below 0 °C and the snow depth sensor only indicated a constant decrease similar to the days before and after. It appears to be more a measurement error which is typical when the isothermal front reaches the snow-ground boundary as described by Johnson and Schaefer (2002), which was detected based on the snow temperature measurements two days earlier. A decrease in SWE could be explained by snow shear strength being able to bridge the sensor (Johnson and Marks, 2004), which could happen when meltwater near the ground refreezes. The authors describe the later increase in SWE as rain percolating into the snow. However, reviewer 1 correctly pointed out the negative air temperatures during this time (colder than -5 °C). In addition, the rain gauge measured only <3 mm of precipitation, while the SWE sensor increases by more than 150 mm during the same period through April 18. This discrepancy cannot be explained by undercatch of the rain gauge, especially since the snow depth sensor shows the same continuous decrease as in the days before, without any indication of a major snowfall. Thus, it seems more likely that the previously mentioned snow bridges have been continuously weakened as snow temperatures are around the melting temperature. Future availability of redundant SWE data, snow depths, air and snow temperatures will provide more examples in a few years from which the authors can select examples of faulty and good situations. The methods of Johnson and Marks (2004) or others may be included to tag poor quality data.
2.3 Data gaps
The use of this dataset is also limited due to data gaps, which is partially visible in Figure 5. For example, for Bella Vista in 2018, over 33% of all data are missing with gap sizes of 49, 27, 20, ... days. Precipitation is missing 75% of the time, with another gap of 199 days. In 2019, this station typically has 7% data gaps, wind over 11%, with gap sizes of 12, 7, 3, <1 days. Such multi-day data gaps are difficult to fill.
2.4 Other
- A measurement height of 1.50 m does not seem sufficient in alpine terrain. Please provide the exact height for each sensor in the tables. Please provide time periods when a sensor is buried or consider larger masts (if possible).
- Literature describing the quality of the snow pack analyzer should be added. For example, Staehli et al. (2004) and Egli et al. (2009).
- The fact that each year and station is in individual files makes it difficult to use the data. Please consider consolidating the data into one or a few file(s) as much as possible.
References
Egli, L., Jonas, T., and Meister, R. (2009). Comparison of different automatic methods for estimating snow water equivalent. Cold Regions Science and Technology, 57(2-3), 107-115.
Johnson, J. B., and Schaefer, G. L. (2002). The influence of thermal, hydrologic, and snow deformation mechanisms on snow water equivalent pressure sensor accuracy. Hydrological Processes, 16(18), 3529-3542.
Johnson, J. B., and Marks, D. (2004). The detection and correction of snow water equivalent pressure sensor errors. Hydrological Processes, 18(18), 3513-3525.
Ménard, C. B., Essery, R., Barr, A., Bartlett, P., Derry, J., Dumont, M., Fierz, C., Kim, H., Kontu, A., Lejeune, Y., Marks, D., Niwano, M., Raleigh, M., Wang, L., and Wever, N. (2019). Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data, Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, 2019.
Morin, S., Lejeune, Y., Lesaffre, B., Panel, J.-M., Poncet, D., David, P., and Sudul, M. (2012). An 18-yr long (1993-–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models, Earth Syst. Sci. Data, 4, 13–21, https://doi.org/10.5194/essd-4-13-2012.
Stähli, M., Stacheder, M., Gustafsson, D., Schlaeger, S., Schneebeli, M., and Brandelik, A. (2004). A new in situ sensor for large-scale snow-cover monitoring. Annals of Glaciology, 38, 273-278.
Citation: https://doi.org/10.5194/essd-2021-68-RC2 - AC2: 'Reply on RC2', Michael Warscher, 17 Jul 2021
-
RC3: 'Comment on essd-2021-68', Anonymous Referee #3, 18 May 2021
General comments
The authors present an extension of their previous ESSD publication that focuses on automated meteorological and snowpack observations collected in an alpine environment in Rofental, Austria. The authors followed the ESSD living data process to guide this manuscript, and accordingly nicely focus on extensions of the time series, instrumentation upgrades, and descriptions of some new instrument installations that offer additional insights into snow cover processes. I found the article easy to follow and was able to download and plot some of the data relatively easily, suggesting this data is readily accessible for future research applications. However, I did find some of the data incomplete and lacking a proper description of errors and uncertainties (see comments below).
Specific comments
- Based on Fig. 6 it looks like some of the snow depth and SWE measurements have some missing values. Please specify why in the text. Overall the text is light on descriptions of the uncertainties in the measurements, with only the instrument resolution listed in the tables. The paper would benefit from better discussion of sources of error.
- Some of the time series for the new sensors are relatively short in duration, such as the snow measurements at Proviantdepot. It would make sense to include data from the entire 2020-2021 winter season now that it is mostly completed.
- When downloading the tab-delimited data I found it difficult to work with the column headers because the variable name, units, and method/device details were all in the same cell. If working with a scripting language like R or Python it is much easier when the columns can be indexed with short concise name, in which case the units and descriptions could be on their own rows. That being said, I am not familiar with the standards and limitations of the PANGEA data platform.
Technical comments
- Line 118: Please define the acronym “GSM”
- Lines 155-157: Please provide more description of the SPA instrument, and how it can be used to calculate density and SWE as later shown in Fig. 9.
- Line 167: By “daily values” I assume this means daily averages.
- 5 and 6: These plots are missing the subplot labels (a-f)
- Fig 9: Would it be more logical to move snow depth from subplot (d) to (a) since it is the first plot discussed in the text?
- Line 301: Can you provide an actual distance instead of “in close proximity”
- Table 4: I don’t understand the need for the final 2 rows in this table since they simply repeat the same values. It’s also unclear why some values are in italics. Perhaps the 6 unique values presented in this table could simply be stated in the text, along with an explanation of how they relate to each other.
- Fig 13: The caption description of the avalanche should use proper avalanche terminology. The edges are called ‘fracture lines’ with the one along the top called a ‘crown’ and the ones along the sides called ‘flanks’.
Citation: https://doi.org/10.5194/essd-2021-68-RC3 - AC3: 'Reply on RC3', Michael Warscher, 17 Jul 2021
-
RC4: 'Comment on essd-2021-68', Anonymous Referee #4, 21 May 2021
General comments:
The manuscript ‘Operational and experimental snow observation systems in the upper Rofental: data from 2017-2020’ by Warscher et al. provides a description of different types of continuously recorded snow and meteorological datasets - using standard as well as experimental sensors - collected at three sites in the Rofental in the European Alps. The manuscript is an extension of the ESSD paper ‘The Rofental: a high Alpine research basin (1890–3770ma.s.l.) in the Oetztal Alps (Austria) with over 150 years of hydrometeorological and glaciological observations’ by Strasser et al. 2018.
Although the title and the abstract imply that all data has been available since 2017, a closer look reveals that some datasets do not start before 2019 or even 2020. In addition, data gaps are an issue that has not been discussed in detail. I agree with Reviewer 2 that the covered time period for some recordings (especially for the unique experimental snow measurement setups) is too short for publication at current state. Therefore, I also recommend waiting some more years and collecting a longer time period of data before publication. In general, I agree with the general and specific issues raised by Reviewer 1 and 2 as well as the specific/technical comments raised by Reviewer 3 and will not repeat them here again. In particular, information on assessment data quality should be included.
However, I see good potential for publication in a few years (i.e. after extending the dataset for approx. two more years: 1) There is a great need for standard and experimental continuous snow monitoring datasets that cover longer periods in high-alpine regions, as such datasets are still very sparse. 2) The Rofental research catchment seems to be an ideal site for glacier, snowpack and hydrological model applications and developments, especially since the basin is not influenced by hydropower structures.
As the authors are focusing on datasets for snow observation, it would be wise to include and describe also the other snow measurement sites in the Rofental research basin (stations Hintereisferner and Vernagtbach) in this manuscript, although they were already introduced in Strasser et al. 2018. Adding these two sites in the manuscript would make the multi-station dataset even more valuable. I agree with Reviewer 3 that the data provided on the PANGEA platform was easily accessible and, except for the data gaps, was complete as described in the manuscript.
Specific comments:
- L. 2: The altitude of the research basin might be of interest for the reader; however, as you describe the data sets of specific measurement sites, the altitude of these sites would be at least as interesting to mention.
- 3: The expression ‘original’ (which is written twice in this line) seems strange in this context and implicates your work is somehow not original. Better change to: ‘The dataset of our first study published in 2018 (https://doi.org/10.5194/essd-10-151-2018) contains... The time series presented here…’
- Section 1: Please add some information on similar sites and studies (i.e. Ménard et al. 2019, https://essd.copernicus.org/articles/11/865/2019/).
- 58-60: As you are describing snow drift measurements in detail (Section 4.2.4), I would recommend to introduce this point already here, i.e. extending point I to : I) Improved process understanding of snow drift, accumulation and melt dynamics in high mountain regions.
- 92-93: Information on topography and meteorological conditions of the research site should be moved to Section 2.
- Section 3.1 and 3.2: Several statements (especially the site descriptions, coordinates) are repetitive. I would suggest merging these two subsections and describing each site individually introducing their meteorological and snow sensors together in one subsection.
- Section 4.2.4: This section is very long compared to the other subsections of 4.2. I would suggest to describe the snow drift measurements in general in this section and move the explicit case study to a new section (i.e. Section 5: Case study - Application of the dataset for an improved assessment of avalanche-critical blowing snow situations).
Citation: https://doi.org/10.5194/essd-2021-68-RC4 - AC4: 'Reply on RC4', Michael Warscher, 17 Jul 2021
Data sets
Meteorological and snow measurements at station Proviantdepot in 2019 Marke, Thomas; Strasser, Ulrich; Warscher, Michael https://doi.pangaea.de/10.1594/PANGAEA.919324
Continuous meteorological observations and snow measurements from Latschbloder 2020 Warscher, Michael; Strasser, Ulrich https://doi.pangaea.de/10.1594/PANGAEA.928649
Continuous meteorological observations and snow measurements from Latschbloder 2019 Warscher, Michael; Strasser, Ulrich https://doi.pangaea.de/10.1594/PANGAEA.918097
Continuous meteorological observations and snow measurements from Latschbloder 2018 Warscher, Michael; Strasser, Ulrich https://doi.pangaea.de/10.1594/PANGAEA.918096
Continuous meteorological observations and snow measurements from Latschbloder 2017 Warscher, Michael; Strasser, Ulrich https://doi.pangaea.de/10.1594/PANGAEA.918094
Continuous meteorological observations and snow measurements at station Bella Vista in 2020 Strasser, Ulrich; Warscher, Michael https://doi.pangaea.de/10.1594/PANGAEA.928599
Continuous meteorological observations and snow measurements at station Bella Vista in 2019 Strasser, Ulrich; Warscher, Michael https://doi.pangaea.de/10.1594/PANGAEA.918690
Continuous meteorological observations and snow measurements at station Bella Vista in 2018 Strasser, Ulrich; Warscher, Michael https://doi.pangaea.de/10.1594/PANGAEA.918688
Continuous meteorological observations and snow measurements at station Bella Vista in 2017 Strasser, Ulrich; Warscher, Michael https://doi.pangaea.de/10.1594/PANGAEA.918687
Meteorological and snow measurements at station Proviantdepot in 2020 Warscher, Michael; Strasser, Ulrich https://doi.pangaea.de/10.1594/PANGAEA.928595
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,015 | 537 | 56 | 1,608 | 46 | 44 |
- HTML: 1,015
- PDF: 537
- XML: 56
- Total: 1,608
- BibTeX: 46
- EndNote: 44
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
