Forest impacts on snow accumulation and ablation across an elevation gradient in a temperate montane environment

Roth, Travis R.; Nolin, Anne W.

doi:https://doi.org/10.5194/hess-21-5427-2017

Articles | Volume 21, issue 11

https://doi.org/10.5194/hess-21-5427-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-21-5427-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 21, issue 11

Research article

|

06 Nov 2017

Research article |

| 06 Nov 2017

Forest impacts on snow accumulation and ablation across an elevation gradient in a temperate montane environment

Travis R. Roth and Anne W. Nolin

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Final revised paper (published on 06 Nov 2017)
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Preprint (discussion started on 01 Nov 2016)
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Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Review', Anonymous Referee #1, 02 Dec 2016
- AC1: 'Author response to Reviewer #1 comments', Travis R. Roth, 14 Feb 2017
RC2: 'Referee comment', Anonymous Referee #2, 16 Jan 2017
- AC2: 'Author response to Reviewer #2 comments', Travis R. Roth, 14 Feb 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to revisions (further review by Editor and Referees) (14 Feb 2017) by Jan Seibert

AR by Travis R. Roth on behalf of the Authors (29 Mar 2017) Author's response Manuscript

ED: Referee Nomination & Report Request started (04 Apr 2017) by Jan Seibert

RR by Anonymous Referee #3 (26 Jun 2017)

Suggestions for revision or reasons for rejection

SUMMARY AND RECOMMENDATION

This manuscript describes the effect of forest cover on snow with elevation in a Maritime basin in Oregon, USA. The ForEST network is described in a previous HESS paper by the authors (Gleason et al., 2017), and includes paired forest-open stations at low, middle, and high elevations. The study examines SWE from snow surveys (with a federal sampler) made across multiple years at the six sites, showing greater springtime SWE and later snow persistence in open areas (vs. forests) at low and mid elevations, but in forested areas at higher elevations. They examine canopy interception efficiency at the different elevations, general finding greater efficiency and the lower and middle elevations vs. the high elevation. They attempt to connect the changes in snowpack to the daily energy balance (forest vs. open) using observations and estimates (e.g., longwave, sensible, latent), finding that the radiative fluxes dominate at all sites, but the importance of longwave vs. shortwave radiation is the result of forest canopy presence/absence. The role of wind speeds in redistributing the snow is discussed, with a greater perceived impact at the high elevations only (which may redistribute snow from open areas to the forest).

This paper makes a valuable contribution to the literature on a topic that has attracted much interest recently. The unique observations from the ForEST network are summarized well and contribute to recent hypotheses posed on snow persistence with respect to forest and temperature/climate. I have found some potential issues in the energy balance estimation that require additional attention, but I do not expect the conclusions to change after making these corrections. I recommend publishing this paper in HESS after all these aspects are addressed. I have also suggested a number of technical edits for clarity, etc.

GENERAL COMMENTS

1. The snow surface temperature is approximated here with the dewpoint temperature (section 2.4). In other words, the vapor pressure at the snow surface (e_0) is assumed equal to the vapor pressure of air at measurement height (e_a). As discussed in Raleigh et al. (2013), one limitation of this simplification is that it results in an elimination of the vapor pressure gradient, thereby muting latent energy exchange (i.e., e_a should equal e_0 in equation 11, so latent flux should be zero). However, it appears this is not the case in Figure 4, as the latent energy is non-zero. This has a bearing on the relative contribution of turbulent vs. radiative fluxes (section 3.3). Can you please clarify this discrepancy?

2. The formulae used to calculate incoming longwave radiation (equations 8 and 9) may have errors. Two errors related to this method were published in Table 1 of Flerchinger et al. (2009) for the Dilley and O’Brien (1998) method (their model B). Specifically, the parameter in the denominator at the end should be 2.5, not 25. Additionally, the precipitable water equation should have “465” instead of “4650” for w in centimeters, e_0 in kPa and T_0 in Kelvin. You should confirm with the original Dilley and O’Brien and Prata papers to ensure the errors from the Flerchinger table are not repeated here. Please check and revise calculations as needed. Careful description of all variables and units in the longwave equations will help others discern their accuracy, so please include.

TECHNICAL CORRECTIONS

- P1, L27: You might also note here that melt rates may be decreasing with earlier snowmelt timing, based on the recent Musselman et al. (2017) paper.

- P2, L09: I would avoid use of contraction “don’t”. Suggest replacing with “do not”.

- P3, L11: Add “on” after “focusing”.

- P3, L25: I would replace the phrase “principal components” with another one, so as to not confuse this with principal component analysis, a statistic analysis method. Perhaps “principal drivers of melt” is an appropriate replacement?

- P4, L09: Add “to” after “sensitivity”.

- P4, L11: Remove “for” after “MRB”.

- P4, L29: Perhaps state why the High sites were not included before WY2014? Were these installed after the Low and Mid sites?

- P5, L02-03: Please consider estimating the approximate uncertainty for the depth measurements, in terms of representing conditions with an interval of point observations. You could readily estimate using the graphs of Trujillo and Lehning (2015), assuming their errors estimates (made in continental zone) are valid in a maritime zone.

- P05, L04: In your estimation of SDD, please state whether there were any late season snow storms after the final snow courses.

- P05, L07: Please check whether the journal allows a single sub-section (i.e., 2.2.1 is presented, but not 2.2.2, etc.). Is this sub-section even necessary, or could it just be another paragraph in section 2.2?

- P05, L13: Why was NLCD 2001 selected, when a more recent canopy cover product is available (i.e., NLCD 2011)? Please clarify to what degree did the forest change between 2001 and the study period.

- P05, L19: Should Table 2 be cited here instead? Table 2 shows the tree characteristics. You might consider reordering the tables (swapping 1 and 2), such that they are introduced sequentially based on the narrative.

- P05, L26: After “surface”, consider adding a semicolon or starting a new sentence.

- P05, L30: Consider citing Friesen et al. (2014) here for a review of interception measurements and their limitations.

- P06, L02: Please clarify how you define an event. This may be tricky when there is intermittent snowfall through a day, for example.

- P06, L17: Remove “is” before “cannot”. Also, I would note that this can be measured using different sensors (e.g., IR temperature sensor), so you might want to change the phrasing of this sentence.

- P07, L17: Remove “on” after “subsequently”.

- P07, L18: Rephrase to say “Incorporating a sky-view factor (SVF) into the longwave radiation calculations allowed us…”

- P09, L29: Please state which significance test was used.

- P10, L10-11: As stated, this is a bit misleading. The topic of this paragraph is snowmelt rates, but it does not make sense that the melt rate is the reason why snow disappears earlier in Low-Forest relative to Low-Open. In fact, the melt rate is higher in the low-open than the low-forest. Hence, the longer lasting snow in low-open is likely related to snow accumulation dynamics (i.e., more interception losses in low-forest), something that is recognized in the discussion section (P13, L24-26). Please consider restating to avoid confusion.

- P10, L12: Presumably, high-forest is being compared to high-open here, but that is not explicitly stated. Please consider including this.

- P10, L16: “duration” of what? Snow in the canopy?

- P10, L22-30: These reported R2 values do not match what is shown in Figure 3. Should they?

- P11, L10-16: This is more appropriate for the discussion section, rather than the results section. Also, some useful context would be the frequency and severity of rain on snow events at the study sites during the study period.

- P13, L25: “it is” instead of “it’s”

- P14, L30: Replace “effects” (noun) with “affects”( verb).

- P14, L30: Remove “truly” (avoid adverbs in science writing).

- P15, L03: Remove “the” before “emerged”.

- Fig. 1: The inset map is of low quality. Text and markers are too small to read, and this may not be comprehensible to someone unfamiliar with the region. Can this be improved?

- Table 2: Consider including variability in the tree characteristics (e.g., report the standard deviation as a plus/minus next to each average value). This could be useful context.

- Tables 1 and 2: I do not understand why C_IE at high-forest is 31% for the full study duration, when only one year of C_IE at high-forest is shown in Table 1 (39%). Either this is an error (and both should be 39%), or the study duration also includes another year (WY2015, likely C_IE~23%)? If the latter, then why is WY2015 not included in Table 1? Please check and clarify.

- Figures 4, 7: Can you please clarify whether the range in the energy balance is across the water years?

- Figure 5: The colors for longwave and sensible heat are difficult to distinguish. Also, the narrow widths of the bars make this figure difficult to inspect. Consider highlighting specific, shorter periods rather than the entire water year.

BIBLIOGRAPHY

Friesen, J., J. Lundquist, and J. T. Van Stan (2014), Evolution of forest precipitation water storage measurement methods, Hydrol. Process., doi:10.1002/hyp.10376.

Musselman, K. N., M. P. Clark, C. Liu, K. Ikeda, and R. Rasmussen (2017), Slower snowmelt in a warmer world, Nat. Clim. Chang., 7(February), 214–220, doi:10.1038/NCLIMATE3225.

Trujillo, E., and M. Lehning (2015), Theoretical analysis of errors when estimating snow distribution through point measurements, Cryosph., 9(3), 1249–1264, doi:10.5194/tc-9-1249-2015.

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ED: Publish subject to revisions (further review by Editor and Referees) (12 Jul 2017) by Jan Seibert

AR by Travis R. Roth on behalf of the Authors (25 Aug 2017) Manuscript

ED: Publish subject to minor revisions (further review by Editor) (03 Sep 2017) by Jan Seibert

AR by Travis R. Roth on behalf of the Authors (05 Sep 2017)

ED: Publish subject to technical corrections (09 Sep 2017) by Jan Seibert

AR by Travis R. Roth on behalf of the Authors (11 Sep 2017) Manuscript

Short summary

Maritime snowpacks are temperature sensitive and experience disproportionate effects of climate warming and changing forest cover. We studied the combined effects of forest cover, climate variability, and elevation on snow in a maritime montane environment. The dense, relatively warm forests at Low and Mid sites impede snow accumulation through increased canopy snow interception and increased energy inputs to the snowpack. These results are needed for improved forest cover model representation.