NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series

Fontrodona-Bach, Adrià; Schaefli, Bettina; Woods, Ross; Teuling, Adriaan J.; Larsen, Joshua R.

doi:https://doi.org/10.5194/essd-2023-31

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https://doi.org/10.5194/essd-2023-31

Preprints

26 Jan 2023

| 26 Jan 2023

Status: this preprint is currently under review for the journal ESSD.

NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series

Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen

Abstract. Ground-based datasets of observed Snow Water Equivalent (SWE) are scarce, while gridded SWE estimates from remote-sensing and climate reanalysis are unable to resolve the high spatial variability of snow on the ground. Long-term ground observations of snow depth, in combination with models that can accurately convert snow depth to SWE, can fill this observational gap. Here, we provide a new SWE dataset (NH-SWE) that encompasses 11,071 stations in the Northern Hemisphere, and is available at https://doi.org/10.5281/zenodo.7515603 (Fontrodona-Bach et al., 2023). This new dataset provides daily time series of SWE, varying in length between one and seventy-three years, spanning the period 1950–2022 and covering a wide range of snow climates including many mountainous regions. At each station, observed snow depth was converted to SWE using an established snow-depth-to-SWE conversion model, with excellent model performance using regionalised parameters based on climate variables. The accuracy of the model after parameter regionalisation is comparable to that of the calibrated model. The key advantages and strengths of the regionalised model presented here are its transferability across climates and the high performance in modelling daily SWE dynamics in terms of peak SWE, total snowmelt and duration of the melt season, as assessed here against a comparison model. This dataset is particularly useful for studies that require accurate time series of SWE dynamics, timing of snowmelt onset, and snowmelt totals and duration. It can e.g. be used for climate change impact analyses, water resources assessment and management, validation of remote sensing of snow, hydrological modelling and snow data assimilation into climate models.

Received: 24 Jan 2023 – Discussion started: 26 Jan 2023

Adrià Fontrodona-Bach et al.

Status: open (until 21 Apr 2023)

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RC1:
'Comment on essd-2023-31', Chunyu Dong, 24 Feb 2023 reply
General comments:
Thank you for the opportunity to review the paper. This manuscript mainly presents a modeled SWE dataset based on in-situ snow depth observations for the Northern Hemisphere. Indeed, SWE is a critical hydrological variable while there are rare data for the snow-dominated areas. Hence, I think the newly generated SWE dataset is useful for the research community. The authors worked well in parameterizing the model and evaluating the data accuracy. It seems the data have a satisfactory quality for use. The methodology was described in detail. Overall, this is a good study and the paper was written well. In my opinion, the paper is publishable in the journal of ESSD. I provide some comments below, which may be helpful for the authors to further improve the paper.
Major points：
Fig. 2. Section 2. Technical comments: In my opinion, the grouping of the observation data is not quite reasonable, and I would suggest the authors further justify it. The authors only used the data from the SNOTEL stations in the western US for the model regionalization. They mainly applied the CanSWE station data from northwestern and northeastern Canada for model evaluation. This may make the model regionalization and evaluation easy technically. However, as the snow characteristics vary largely over different climate zones, vegetation bands, and terrains in the globe, it should be more reasonable to regionalize and evaluate the model using the data from more areas.

Fig. 3. It seems the study assumed an ideal and single snow accumulation and melt pattern as shown in Fig. 3. In other areas, the snow processes might be more complicated than this one. Besides, even in the same location, the snow accumulation and melt may change greatly in different years. How would these changes affect the results?

L191-194. The authors only calibrated the parameters of ρ₀ and ρ_max for the model. However, the other five parameters may also be sensitive to environmental and climate-type changes. Can you further evaluate the reliability of using fixed values of the five parameters of the entire Northern Hemisphere? Alternatively, please discuss the limitation and uncertainties.

Minor points:
The text in some figures and tables is too small and not clear, e.g., Fig. A3, Tab. C1

Reply
Citation: https://doi.org/10.5194/essd-2023-31-RC1
- AC1:
  'Reply on RC1', Adrià Fontrodona-Bach, 28 Feb 2023 reply
  Dear Chunyu Dong, we highly appreciate your time to review the paper and your positive report and suggestions to improve the manuscript. Find the responses to your comments with our planned actions below:
  Regarding the major points:
  We agree the grouping into regionalisation and evaluation of the model was simplified by using the SNOTEL dataset for regionalisation and the others for evaluation. The reason for this was to evaluate the model with fully independent datasets, rather than splitting the SNOTEL dataset in two groups. We thought this was the best option because we later apply the regionalised model to a fully independent snow depth dataset to generate the NH-SWE. Furthermore, we were not able to find data from more areas, considering the model evaluation requires continuous daily measurements of snow depth along with measurements (not necessarily continuous) of SWE or snow density. We will emphasize and further justify this in the revised manuscript.
  
  We agree with the reviewer that Figure 3 gives that impression. We will expand the figure by adding other snow seasons from the same location, as well as some other locations so that the reader can see the effect of interannual variability on the results.
  
  Indeed calibrating only ρ0 and ρmax is a limitation of our modelling approach. However the other parameters did not show any clear sensitivity to the climate variables explored. A comprehensive sensitivity analysis has already been completed by Winkler et al. 2021, which identified the importance of the two density parameters compared to the others, which is why we have focused on these. This reasoning is only briefly mentioned in the manuscript, so we will further justify it and discuss the limitations and uncertainties of this.
  
  Regarding the minor point: We will increase the font size of tables and figures for the revised manuscript, and make two-column figures for those that look too small.
  
  Reply
  
  Citation: https://doi.org/10.5194/essd-2023-31-AC1
CC1:
'Comment on essd-2023-31', Christoph Marty, 08 Mar 2023 reply
Thanks for the work. I liked reading the manuscript. I have two general comments and some specific suggestions.
General:
Two main uncertainties need to be added, probably in 6.2:
The calibration is heavily dependent on the quality of the snotel data. It is long known and described in many studies (e.g. doi.org/10.1016/j.advwatres.2014.06.011 or Hill et al. (2019)) that daily snotel SWE can suffer from over-/under-measurement. Such errors are hard to detect and may also be responsible for processes described in L365.

Some of the SWE data, especially those from the manual profiles (destructive method), may not have been taken at the exactly same spot as the HS data, which is usually read from a fixed installed stake, from a snow course or from an automatic snow depth measurements.

Specific:
Table 1: SWE_d for daily measurements.

Please, add either here or in 2.3 or in the reference, that snow data acquired from IDAWEB contains data from Meteoswiss and from the Institute of snow and avalanche research SLF (e.g. the station Kuehboden shown in Fig. A4 is an SLF station, please correct)
L 183/4: It is important to prominently note that Δsnow_orginal parameters were obtained for the European Alps only. The authors confirm in the Conclusions, that “after calibration, the Δsnow model is widely usable”
L 240/41: I do not see data from two sites in Fig. 4?
L 291: …these two data sets were among others used…
L 293: It should also be mentioned that according to Table C1 for daily SWE Δsnow_regio had a similar or slightly worse performance for other data sets (like CanSWE or Sodankyla) compared with Hill et al. (2019).
Table 4: SWE peak is not really measured by the bi-weekly measurements of the GCOS-SWE data set. So I don’t know how fair the calculated bias measures are?
L 336: Again Δsnow_orginal was obtained for the European Alps!
Figure 9: I’d suggest explaining in the text as illustrative example why there two dark blue dots in the middle of the yellow dots somewhere in DE or AT in Fig. 9a & 9b. I assume, these are 2 high elevation stations surrounded by low elevation stations.
L 455: Not gap filled, means also not use for modelling SWE?
L 494: What is the difference to the previous paragraphs. They were also about snow depth?

Reply
Citation: https://doi.org/10.5194/essd-2023-31-CC1
- AC2:
  'Reply on CC1', Adrià Fontrodona-Bach, 13 Mar 2023 reply
  Dear Dr. Christoph Marty, thank you for taking the time to read the manuscript and for your positive comments and suggestions to improve it. Please find our responses with planned actions below:
  Regarding the two main uncertainties:
  We acknowledge that SNOTEL is subject to errors as described in the literature. Avanzi et al. (2014) mainly describe issues with the hourly data, and Hill et al. (2019) also find biases and errors in daily data. We used daily data and applied the same quality control as described in Hill et al. (2019). The process described in our manuscript (L365) shows that the ∆SNOW model considers uncertainty in the snow depth measurements when the daily snow depth change is smaller than 2.4 cm. However, some measurement errors will be larger and not detected, so we will add a description of the uncertainty associated with this process in Section 6.2.
  
  We agree with the uncertainty associated with manual profiles that might not be taken at the exact same spot. We will discuss this and we will also specify which SWE data is from manual profiles in Section 2 (Data sources).
  
  Regarding the specific comments:
  Table 1: Thanks, we will correct.
  We will add the SLF acknowledgement along with the Meteoswiss data.
  L183 and L336: We will specify and add where appropriate that the original parameters were obtained for the European Alps only
  L240/41: This is perhaps oddly phrased. We intended to help the reader interpreting Fig. 4, by hypothetically taking two of the many points in Fig 4b with a similar maximum snow depth (x-axis), and realising that the warmer climate (color-axis) has a higher snow density (y-axis). We will make it clear.
  L291: We will correct it.
  L293: We will add this.
  Table 4: You are right, we forgot to specify how we estimated peak SWE for the datasets with biweekly measurements of SWE such as the GCOS-SWE dataset. We took the highest biweekly SWE measurement in the snow season and compared it with modelled SWE on that same date. This might be an unfair comparison for the model but the only way to compare the highest measured SWE value with modelled SWE for the datasets without daily measurements of SWE. This adds value to the evaluation and the biases found for those datasets are similar to the ones found in the datasets with daily SWE measurements. The timing of snowmelt onset was not estimated though, hence the gap in Table 4 for snowmelt onset for GCOS-CH. We will add this.
  Figure 9: We will check this and explain it in the text. Both peak SWE and snow cover duration are higher than the surrounding stations for those two dots, so they are indeed probably high elevation stations.
  L455: For stations that were not gap-filled, we only modelled SWE for those years with continuous daily measurements of snow depth. If there were none, the stations was not used for modelling SWE. We will add this.
  L494: The previous paragraphs were on the gap-filling method, but we also apply a quality control of the gap-filling after that, as explained in the paragraph. We will make this clearer.
  
  Reply
  
  Citation: https://doi.org/10.5194/essd-2023-31-AC2
RC2:
'Comment on essd-2023-31', Anonymous Referee #2, 09 Mar 2023 reply
This study is a valuable addition to the existing dataset of snow water equivalent (SWE) over the Northern Hemisphere. Overall, the paper is well-structured and the approach is clearly explained. I suggest the paper be published after the authors address the provided comments..
There are some recent papers that are highly relevant to this work, but the authors did not include them.

Sun et al. (2019), which describes the development of regionally coherent snow parameters for a mass and energy balance snow model over the Western U.S. SNOTEL stations. Importantly, this paper emphasizes the biases in the SNOTEL dataset, including undercatch of snowfall, warm bias, and others. To enhance the quality of this work, I recommend that the authors explore the potential of using a QAQC SNOTEL dataset. The dataset is available for download at: https://www.pnnl.gov/data-products

Sun et al. (2022), which introduces the gridded SWE dataset over the Continental U.S produced by a physics-based snow model. This work also introduces the regionalization of snow parameters based on climate variables.

Zeng et al. (2018), which describes a gridded (4-km) daily SWE data over the Continental U.S by assimilating in situ measurements of SWE from SNOTEL stations, snow depth from thousands of NSW COOP stations.

Dawson et al. (2017), which describes an approach to converting snow depth to SWE.

Is there a specific reason why the authors only used the SNOTEL dataset for regionalization? Since the SNOTEL stations only represent Western U.S. mountain ranges, I believe incorporating evaluation data that represent diverse geography and climate regimes into the regionalization process would improve the transferability of the results across the Northern Hemisphere. I recommend including all HS-SWE data in the regionalization.

The paper's evaluation lacks spatial context, despite the availability of extensive spatial data. The model performance evaluation figures (Figures 5-7) aggregate all data across sites and time periods, precluding bias evaluations between sites. To better understand the spatial variation in the SWE error measured by different metrics, I suggest the authors add figures that display each error metric for each location on a spatial map, similar to the maps in Figure 9. Furthermore, the authors should provide an interpretation of the results to enhance the reader's comprehension.

Please add the mean performance to Table 4.

References:
Dawson, N., Broxton, P., & Zeng, X. (2017). A New Snow Density Parameterization for Land Data Initialization. Journal of Hydrometeorology, 18(1), 197–207. https://doi.org/10.1175/JHM-D-16-0166.1

Sun, N., Yan, H., Wigmosta, M. S., Leung, L. R., Skaggs, R., & Hou, Z. (2019). Regional Snow Parameters Estimation for Large‐Domain Hydrological Applications in the Western United States. Journal of Geophysical Research: Atmospheres, 124(10), 5296–5313. https://doi.org/10.1029/2018JD030140

Sun, N., Yan, H., Wigmosta, M. S., Coleman, A. M., Leung, L. R., & Hou, Z. (2022). Datasets for characterizing extreme events relevant to hydrologic design over the conterminous United States. Scientific Data, 9(1), 154. https://doi.org/10.1038/s41597-022-01221-9

Zeng, X., Broxton, P., & Dawson, N. (2018). Snowpack Change From 1982 to 2016 Over Conterminous United States. Geophysical Research Letters, 45(23). https://doi.org/10.1029/2018GL079621

Reply
Citation: https://doi.org/10.5194/essd-2023-31-RC2
- AC3:
  'Reply on RC2', Adrià Fontrodona-Bach, 13 Mar 2023 reply
  Dear referee, we appreciate you find the study and the dataset valuable, and we thank you for the comments to improve the paper. Find below our answers with our planned actions:
  Thank you for pointing out these relevant references that we had missed, we will appropriately introduce them in the text. Regarding the use of the Bias Correction and Quality Control SNOTEL data (BCQC SNOTEL), we have explored if this would potentially improve the calibration and regionalisation of the model. We agree that the BCQC SNOTEL dataset provides an improved quality with respect to the raw SNOTEL dataset. However, we had already applied a quality control to the SNOTEL dataset (same as in Hill et al. 2019, see Line 87-88 in our manuscript), and we only used SNOTEL stations and years with a continuous record of daily snow depth. This control has already highly reduced the frequency of poorer quality data. Nevertheless, we have downloaded the BCQC SNOTEL dataset and we have compared it with the raw daily SNOTEL data that we used. In the figure below left panel, the x-axis is the filtered SWE data used in our manuscript, and in the y-axis the SWE data from BCQC SNOTEL. Indeed, there are many points that differ a lot, but the scatter density in the 1:1 line is over 10 000, thus the overall variation is very minor and the difference between the two is smaller than 1 mm for over 99% of the points, as can be seen in the cdf plot in the right panel. We therefore believe that using BCQC SNOTEL would not change our results or our model regionalisation significantly given our quality control version is already remarkably similar. We will discuss in the manuscript the uncertainty associated with the few potentially biased or erroneous SNOTEL data.
  
  (bis) The rationale for using the SNOTEL dataset only for regionalisation was to keep independent datasets for model regionalisation and model evaluation, since this is ultimately what we did to generate the NH-SWE dataset (i.e., we regionalised the model with SNOTEL, evaluated it with 8 independent datasets (Table 1), and applied it to our independent collection of Northern Hemisphere snow depth datasets (Table 1)). If we used all the HS-SWE data to regionalise the model, we would potentially lose confidence in the application to an independent dataset, as we would no longer have a technically independent evaluation. In addition, the SNOTEL dataset does contain a wide range of hydroclimates to allow generation of a regional parameter set that can be tested for broader applicability with the independent dataset. As stated by Sun et al. (2019): “The 246 SNOTEL sites represent diverse hydroclimate conditions in the western United States: the Pacific Northwest's relatively mild and wet winter; the Intermountain West's continental climate; the Rocky Mountains' cold, snowy winter; California's Mediterranean climate; and the Southwest's arid climate.” We also used the sites from high latitudes in Alaska. We will make this rationale clearer in the text.
  
  We agree that the evaluation lacks spatial context, and we are happy to improve that. The tables and figures only provide mean values for different datasets, so we will add maps with the error of the assessed metrics and add a discussion interpreting the results, as suggested.
  
  Sorry we realised the table caption says median while in reality we are providing the mean. We will correct it.
  
  Reply
  
  Citation: https://doi.org/10.5194/essd-2023-31-AC3

Adrià Fontrodona-Bach et al.

Data sets

NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model Fontrodona-Bach, Adrià; Schaefli, Bettina; Woods, Ross; Teuling, Adriaan J.; Larsen, Joshua R. https://zenodo.org/record/7515603

Adrià Fontrodona-Bach et al.

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Short summary

We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11,071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.


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