Intercomparison of Sentinel-2 and modelled snow cover maps in a high-elevation Alpine catchment

Hofmeister, Florentin; Arias-Rodriguez, Leonardo F.; Premier, Valentina; Marín, Carlo; Notarnicola, Claudia; Disse, Markus; Chiogna, Gabriele

doi:10.1016/j.hydroa.2022.100123

Cited by 13 publications

(11 citation statements)

References 64 publications

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“…Sentinel band 3 was available at 10 m resolution while band 11 was at 20 m resolution. The NDSI calculation was carried out by resampling band 11 at 10 m resolution (Hofmeister et al, 2022). The Sentinel-2 snow pixels were selected with NDSI > 0.4 (Riggs et al, 1994).…”

Section: Sentinel-2 Snow Covermentioning

confidence: 99%

Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment

Gupta

Reverdy

Cohard

et al. 2023

Hydrol. Earth Syst. Sci.

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Abstract. From the micro- to the mesoscale, water and energy budgets of mountainous catchments are largely driven by topographic features such as terrain orientation, slope, steepness, and elevation, together with associated meteorological forcings such as precipitation, solar radiation, and wind speed. Those topographic features govern the snow deposition, melting, and transport, which further impacts the overall water cycle. However, this microscale variability is not well represented in Earth system models due to coarse resolutions. This study explores the impact of precipitation, shortwave radiation, and wind speed on the water budget distribution over a 15.28 ha small, mid-elevation (2000–2200 m) alpine catchment at Col du Lautaret (France). The grass-dominated catchment remains covered with snow for 5 to 6 months per year. The surface–subsurface coupled distributed hydrological model ParFlow-CLM is used at a very high resolution (10 m) to simulate the impacts on the water cycle of meteorological variability at very small spatial and temporal scales. These include 3D simulations of hydrological fluxes with spatially distributed forcing of precipitation, shortwave radiation, and wind speed compared to 3D simulations of hydrological fluxes with non-distributed forcing. Our precipitation distribution method encapsulates the spatial snow distribution along with snow transport. The model simulates the dynamics and spatial variability of snow cover using the Common Land Model (CLM) energy balance module and under different combinations of distributed forcing. The resulting subsurface and surface water transfers are computed by the ParFlow module. Distributed forcing leads to spatially heterogeneous snow cover simulation, which becomes patchy at the end of the melt season and shows a good agreement with the remote sensing images (mean bias error (MBE) = 0.22). This asynchronous melting results in a longer melting period compared to the non-distributed forcing, which does not generate any patchiness. Among the distributed meteorological forcings tested, precipitation distribution, including snow transport, has the greatest impact on spatial snow cover (MBE = 0.06) and runoff. Shortwave radiation distribution has an important impact, reducing evapotranspiration as a function of the slope orientation (decreasing the slope between observed and simulated evapotranspiration from 1.55 to 1.18). For the primarily east-facing catchment studied here, distributing shortwave radiation helps generate realistic timing and spatial heterogeneity in the snowmelt at the expense of an increase in the mean bias error (from 0.06 to 0.22) for all distributed forcing simulations compared to the simulation with only distributed precipitation. Distributing wind speed in the energy balance calculation has a more complex impact on our catchment, as it accelerates snowmelt when meteorological conditions are favorable but does not generate snow patches at the end of our test case. This shows that slope- and aspect-based meteorological distribution can improve the spatio-temporal representation of snow cover and evapotranspiration in complex mountain terrain.

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Section: Sentinel-2 Snow Covermentioning

confidence: 99%

Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment

Gupta

Reverdy

Cohard

et al. 2023

Hydrol. Earth Syst. Sci.

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“…Precipitation and solar radiation were interpolated with an inverse divergence weighting (IDW) method. The model setup and parameterisation published by Hofmeister et al (2022) was used. The beginning and the end of the growing season were defined, following Bishop and Bishop (2014), by the mean temperature of 0 °C for four consecutive days (above 0 °C: start of the season; below 0 °C: end of the season) and the simultaneous absence of snow cover.…”

Section: Atmospherementioning

confidence: 99%

“…The parameter 'distance to the glacier front' was determined as the shortest distance from every single study plot to the glacier tongue using the 'near' function in ArcGIS 10.6®. Information on the number of days with snow cover was derived from WaSiM (Schulla and Jasper, 2019) for the years 2018/19 by Hofmeister et al (2022). Snowmelt is simulated with the energy balance method, which computes the energy fluxes on the top of the snowpack.…”

Section: Cryospherementioning

confidence: 99%

Primary succession and its driving variables – a holistic approach applied in three proglacial areas in the upper Martell Valley (Eastern Italian Alps)

Ramskogler

Knoflach

Elsner³

et al. 2023

Preprint

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Abstract. Climate change and the associated glacier retreat lead to considerable enlargement and alterations of the proglacial systems. The colonisation of plants in this ecosystem was found to be highly depending on terrain age, initial site conditions and geomorphic disturbances. Although the explanatory variables are generally well understood, there is little knowledge on their collinearities and resulting influence on proglacial primary succession. To develop a holistic understanding of vegetation development, a more interdisciplinary approach was adopted. In the proglacial area of Fürkele-, Zufall-, and Langenferner (Martell Valley/Eastern Italian Alps), totally 65 plots of 5 × 2 m were installed to perform the vegetation analysis on vegetation cover, species number, and species composition. For each of those, 30 potential explanatory variables were collected, selected through an extensive literature review. To analyse and further avoid multicollinearities, 26 of the explanatory variables were clustered via Principal Component Analysis (PCA) to five components. Subsequently, generalised additive models (GAM) were used to analyse the potential explanatory factors of primary succession. The results showed that primary succession patterns were highly related to the first component (‘elevation and time’), the second component (‘solar radiation’), and the third component (‘south-eastness’) as well as snow free freeze-thaw days, and landforms. In summary, the analysis of all explanatory variables together provides an overview of the most important influencing variables and their interactions, and thus a basis for the debate on future vegetation development in a changing climate.

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“…Temporally, this relatively recent satellite has 250 increased the visitation frequency to more than weekly and increased the spatial resolution to 20 m for snow cover (Gascoin et al, 2019). High temporal resolution makes Sentinel-2 images preferable to Landsat images, which are available only once every 16 days and whose total number is often further reduced because of cloudiness (Hofmeister et al, 2022). For each image available in the PoI, we calculate the Normalized Difference Snow Index (NDSI) as suggested in the work of Dozier (1989): 255…”

Section: Average Fractional Snow Cover Area (Fsca) Computationmentioning

confidence: 99%

“…We classify as snowy pixels those with a NDSI value > 0.4 (Dozier, 1989). Based on the pixel-wise snow classification, we compute fSCA as in the works of Di Marco et al (2020) and Hofmeister et al (2022):…”

Section: Average Fractional Snow Cover Area (Fsca) Computationmentioning

confidence: 99%

What drives F_yw variations with elevation in Alpine catchments?

Gentile¹,

Canone²,

Ceperley

et al. 2022

Preprint

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Abstract. The young water fraction (Fyw), defined as the fraction of catchment outflow with transit times of less than about 2–3 months, is increasingly used in hydrological studies, replacing the widely used Mean Transit Time (MTT), which is subject to aggregation error. The use of this new metric in catchment intercomparison studies is helpful to understand and conceptualize the relevant processes controlling catchment’s hydrological function. Past work has shown the remarkable and counterintuitive evidence that steep (and generally high elevation) catchments worldwide reveal small Fyw values. However, the topographic slope only partially explains the observed Fyw variance, and the mechanisms hidden behind this lowering with slope remain basically unclear. The main aim of this paper is to investigate what drives Fyw variations with elevation in Alpine catchments clarifying why Fyw is low at high altitudes. In this regard, we use a dataset composed of 27 study catchments, located both in Switzerland and in Italy, that we categorize as rainfall-dominated, hybrid or snow-dominated according to a proposed formal classification scheme that considers both a common-used monthly streamflow ratio and the snow cover regime. We analyze three not previously investigated variables that could potentially explain the Fyw elevation gradients: the fractional snow cover area (FSCA), the fraction of quaternary deposits (Fqd), and the fraction of baseflow (Fbf). We also consider a fourth variable, namely the Winter Flow Index (WFI), for comparing our results about the groundwater contribution to streamflow with those of previous scientific publications. Our results suggest that unconsolidated sediments could play a role in modulating Fyw elevation gradients via their capacity to store groundwater, but further geological information, such as the portion of fractured bedrocks, would be desirable for a complete picture of the role of geology. Based on our analysis concerning the FSCA, we develop a perceptual model that explains how the increasing duration of the snowpack promotes a progressive emptying of the groundwater storage during winter, thereby increasing the streamwater age, while ephemeral snowpack generally favors rapid flow paths that increase Fyw. Finally, our work highlights that Fbf, considered as a proxy for groundwater flow, is roughly the one’s complement of Fyw. In harmony with the model, we find high Fbf during all low-flow periods, which underlines that streamflow is mainly sustained by groundwater in such flow conditions. For catchments where the winter low-flow period is long compared to the summer high-flow period, this results in low Fyw. In conclusion, our data set suggests that Fbf is the best explanatory variable of Fyw elevation gradients in Alpine catchments, implying the key-role of major groundwater storages that, with the increasing snowpack duration, are actively involved in streamflow generation processes.

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Intercomparison of Sentinel-2 and modelled snow cover maps in a high-elevation Alpine catchment

Cited by 13 publications

References 64 publications

Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment

Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment

Primary succession and its driving variables – a holistic approach applied in three proglacial areas in the upper Martell Valley (Eastern Italian Alps)

What drives F_yw variations with elevation in Alpine catchments?

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Intercomparison of Sentinel-2 and modelled snow cover maps in a high-elevation Alpine catchment

Cited by 13 publications

References 64 publications

Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment

Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment

Primary succession and its driving variables – a holistic approach applied in three proglacial areas in the upper Martell Valley (Eastern Italian Alps)

What drives Fyw variations with elevation in Alpine catchments?

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What drives F_yw variations with elevation in Alpine catchments?