the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating surface water availability in high mountain rock slopes using a numerical energy balance model
Matan Ben-Asher
Florence Magnin
Sebastian Westermann
Emmanuel Malet
Johan Berthet
Josué Bock
Ludovic Ravanel
Philip Deline
Abstract. Water takes part in most physical processes that shape the mountainous periglacial landscapes and initiation of mass wasting. An observed increase in rockfall activity in several mountainous regions was previously linked to permafrost degradation in high mountains, and water that infiltrates into rock fractures is one of the likely drivers of these processes. However, there is very little knowledge on the quantity and timing of water availability for infiltration in steep rock slopes. This knowledge gap originates from the complex meteorological, hydrological and thermal processes that control snowmelt, and also the challenging access and data acquisition in the extreme alpine environments. Here we use field measurement and numerical modeling to simulate the energy balance and hydrological fluxes in a steep high elevation permafrost affected rock slope at Aiguille du Midi (3842 m a.s.l), in the Mont-Blanc massif. Our results provide new information about water balance at the surface of steep rock slopes. Model results suggest that only ~25 % of the snowfall accumulates in our study site, the remaining ~75 % are redistributed by wind and gravity. Snow accumulation depth is inversely correlated with surface slopes between 40° to 70°. Snowmelt occurs between spring and late summer and most of it does not reach the rock surface due to the formation of an impermeable ice layer at the base of the snowpack. The annual effective snowmelt, that is available for infiltration, is highly variable and ranges over a factor of six with values between 0.05–0.28 m in the years 1959–2021. The onset of the effective snowmelt occurs between May and August, and ends before October. It precedes the first rainfall by one month on average. Sublimation is the main process of snowpack mass loss in our study site. Model simulations at varying elevations show that effective snowmelt is the main source of water for infiltration above 3600 m a.s.l.; below, direct rainfall is the dominant source. The change from snowmelt-dominated to rainfall-dominated water availability is nonlinear and characterized by a rapid increase in water availability for infiltration. We suggest that this elevation of water availability transition is highly sensitive to climate change, if snowmelt-dominated permafrost-affected slopes experience an abrupt increase in water input that can initiate rock slope failure.
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Matan Ben-Asher et al.
Status: final response (author comments only)
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RC1: 'Comment on esurf-2022-58', Anonymous Referee #1, 07 Dec 2022
In the paper “Estimating surface water availability in high mountain rock slopes using a numerical energy balance model” by Matan-Asher et al., the authors measured snow depth on a steep south-facing slope of the Aiguille du Midi (AdM) in the Mont Blanc Massif. They used their data to calibrate a gridded snow depth data set and used this data set to drive the energy balance model CryoGRID. The model results enabled the quantification of individual processes contributing to snowmelt and their temporal (seasonal) and spatial (elevation) variation. The author related the snowmelt to geomorphic processes as permafrost degradation and landslide activity (e.g. rockfall).
Studies on snow cover and snowmelt in high alpine environments are rare as these processes are very dynamic with high temporal and spatial variation, which makes these processes very difficult to assess. Therefore, the novelty of the approach is very high and the snow results are of interest to a scientific community working on hydrological and thermal research questions in Alpine environments. However, the manuscript has substantial shortcomings especially as it lacks to connect snowmelt to geomorphic processes, which is of major interest for readers of Earth Surface Dynamics. For example, the authors measured and modelled what happens on the surface but the manuscript fails to explain how snowmelt is related to thermal processes as permafrost or maybe better active-layer thaw or frost cracking. Furthermore, the link between thermal or hydrologic processes driven by meltwater and landslides is not clearly established in the introduction and later picked up in the discussion. The novelty of this paper is quantifying snow and snowmelt, and their influence on the energy balance, which makes the manuscript maybe more suited for “The Cryosphere”.
In addition, I got some major comments on (1) the structure, of the paper, (2) the inadequate presentation of the applied methods, (3) the presentation and discussion of results.
(1) The paper especially the introduction is poorly structured as it is separated into two sections. The first section focusses on water and rockwall instabilities and mixes up many terms (infiltration water, surface moisture) with different geomorphic (frost cracking, permafrost degradation) or mechanical processes (subcritical cracking) without explaining terms and processes sufficiently. Therefore, the links between hydrologic, thermal and geomorphic processes remain unclear. For example, as geomorphic processes occur at different rock mass depth, it remains unclear how permafrost degradation occurring on time scales > 2 years are linked to snowmelt occurring in spring or summer at the surface. The second section focusses on snow in steep rockwalls. These sections should be united in one introduction with one paragraph introducing clearly the objectives of the paper and the applied techniques to address the objectives.
The study site should include more information on the Mont Blanc Massif, permafrost distribution and rockfall that the authors collected and published in numerous papers. They use the Aiguille du Midi to calibrate their model but model the snowmelt for higher and lower elevations. How representative is the AdM for rockwalls within the Mont Blanc massif? Can the authors provide more information on slope angles, rockwall distribution and rockfall for elevation ranges? The upscaling of results to different elevations is a key result but currently the consequences for thermal and geomorphic processes at regional scale are difficult to assess for the reader.
(2) The method section raises more questions than providing answers. The authors produced a 3D point cloud and it is unclear how the data was collected (UAV)? If an UAV was used it would be interesting what kind of UAV? What kind of sensor was used (LIDAR, photo)? What kind of resolution have the point clouds? The authors seemed to calculate a difference model from the point clouds to quantify maximum snow cover and it would be of interest what the level of detection and the uncertainties are as the maximum snow cover is a key parameter for the modelling approach. More information on the data acquisition and processing is needed. How was the data georeferenced in a high alpine area with snow cover that prohibited the use of ground control points? What software was used for data processing? All the information is missing but necessary to understand the data set used to drive the energy balance model.
The authors measured snow depth using time-lapse cameras in combination with snow poles, however, it remains unclear where the poles are located on the S-face. A mosaic figure with time-lapse photos could help to understand how this technique worked and visualize the snow cover dynamics through the year, which would be a very good result on its own. It remains unclear how long the time series is, what are the intervals between measurements. Furthermore, there they used data from the E-face in 2012 and this set up is not described at all.
The energy balance is modelled using CryoGRID. The model is currently under review in a different journal and there is no information given how this model works. Currently it is a black box where you put data in and receive some results. The authors should provide much more information in the paper or supplementary on the physical basis of this model. Somehow this model uses forcing data and calibration data. The authors used the gridded S2M-SAFRAN dataset for a period 1958 to 2021 as “forcing data” but how do they used their own data (point clouds, time-lapse photos remains) remains unclear. Did they use it to calibrate the gridded data to the rockwall? What kind of surface resolution has the gridded dataset? How this dataset related to the measured data? The authors need to provide much more information how they link data to modelling and they should communicate clearly the uncertainties of their approach.
(3) The result section comprises several paragraphs and is much too short to represent the interesting results of the manuscript. The authors should focus more on the observed patterns that are clearly visible in the figures but not sufficiently described in the text. Furthermore, the results should be discussed in full detail. The discussion section on snow depth is too broad. The authors should provide more detail. How does their results compare to other studies? What is the key message of these studies and how they support your results? Section 5.2. on the gridded data set reads like an extended conclusion not like a discussion. The results are not compared to other studies or critically analyzed. In section 5.3, the authors claim that they fill a major knowledge gap without explaining what this gap is and what their add-on is on current knowledge. Again, they cite papers without providing the key message in the discussion or previously in the introduction.
The final section on implications on geomorphology should be the chapter of major interest for the readers of Earth Surface Dynamics, however, as the processes link is not established in the introduction (see major comment 1), the links still remain unclear in the discussion.
For minor comments, see attached pdf.
- RC2: 'Comment on esurf-2022-58', Anonymous Referee #2, 03 Mar 2023
Matan Ben-Asher et al.
Matan Ben-Asher et al.
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