Tree canopy and snow depth relationships at fine scales with
terrestrial laser scanning

Hojatimalekshah, Ahmad; Uhlmann, Zach; Glenn, Nancy F.; Hiemstra, Christopher A.; Tennant, Christopher J.; Graham, Jake D.; Spaete, L.; Gelvin, Art; Marshall, Hans‐Peter; McNamara, J. P.; Enterkine, Josh

doi:10.5194/tc-2020-277

Cited by 6 publications

(12 citation statements)

References 10 publications

(14 reference statements)

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“…This advocates the use of spatial field observations at high temporal and spatial resolution for patchy snow covers. Ground-based methods fulfilling these requirements include, amongst others, terrestrial laser scanning (Egli et al, 2012;Grünewald et al, 2010;Hojatimalekshah et al, 2021;Schlögl et al, 2018b) or georectification of oblique time-lapse photography (Härer et al, 2013). Additionally, aerial platforms such as aerial laser scanning, either manned (e.g., Deems et al, 2013;Painter et al, 2016) or unmanned (e.g., Harder et al, 2020;Jacobs et al, 2021), can be used.…”

Section: Introductionmentioning

confidence: 99%

Understanding wind-driven melt of patchy snow cover

et al. 2022

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Abstract. The representation of snow processes in most large-scale hydrological and climate models is known to introduce considerable uncertainty into the predictions and projections of water availability. During the critical snowmelt period, the main challenge in snow modeling is that net radiation is spatially highly variable for a patchy snow cover, resulting in large horizontal differences in temperatures and heat fluxes. When a wind blows over such a system, these differences can drive advection of sensible and latent heat from the snow-free areas to the snow patches, potentially enhancing the melt rates at the leading edge and increasing the variability of subgrid melt rates. To get more insight into these processes, we examine the melt along the upwind and downwind edges of a 50 m long snow patch in the Finseelvi catchment, Norway, and try to explain the observed behavior with idealized simulations of heat fluxes and air movement over patchy snow. The melt of the snow patch was monitored from 11 June until 15 June 2019 by making use of height maps obtained through the photogrammetric structure-from-motion principle. A vertical melt of 23 ± 2.0 cm was observed at the upwind edge over the course of the field campaign, whereas the downwind edge melted only 3 ± 0.4 cm. When comparing this with meteorological measurements, we estimate the turbulent heat fluxes to be responsible for 60 % to 80 % of the upwind melt, of which a significant part is caused by the latent heat flux. The melt at the downwind edge approximately matches the melt occurring due to net radiation. To better understand the dominant processes, we represented this behavior in idealized direct numerical simulations, which are based on the measurements on a single snow patch by Harder et al. (2017) and resemble a flat, patchy snow cover with typical snow patch sizes of 15, 30, and 60 m. Using these simulations, we found that the reduction of the vertical temperature gradient over the snow patch was the main cause of the reductions in vertical sensible heat flux over distance from the leading edge, independent of the typical snow patch size. Moreover, we observed that the sensible heat fluxes at the leading edge and the decay over distance were independent of snow patch size as well, which resulted in a 15 % and 25 % reduction in average snowmelt for, respectively, a doubling and quadrupling of the typical snow patch size. These findings lay out pathways to include the effect of highly variable turbulent heat fluxes based on the typical snow patch size in large-scale hydrological and climate models to improve snowmelt modeling.

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Section: Introductionmentioning

confidence: 99%

Understanding wind-driven melt of patchy snow cover

et al. 2022

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“…10), which is a commonly observed phenomenon over mid-elevation mountainous catchments (Revuelto et al, 2020). The lidar-based snow distribution map is particularly effective due to its accurate prediction of distributed snow depth in mountain and forest landscapes, as recently suggested (Painter et al, 2016;Hojatimalekshah et al, 2021;Jacobs et al, 2021). We moved one step ahead in using the lidar map to distribute snow precipitation over the catchment in hydrological models.…”

Section: Discussionmentioning

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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“…Spatial variation of snow depth and temporal consistency of snow cover diversified due to snow freezing temperature, slope, aspect, and radiation. Hojatimalekshah et al (2020) examined the relationship between tree canopy and snow depth and Kantzas et al (2014) investigated the change of the snow regime according to vegetation dynamics through a ground model; these studies show that consideration of the vegetation effect is necessary for the snow parameterization method.…”

Section: Snow Density Parameterization In Lsmsmentioning

confidence: 99%

Review article: Parameterizations of snow-related physical processes in land surface models

Lee

Gim

Park

2021

Preprint

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Abstract. Snow on land surface plays a vital role in the interaction between land and atmosphere in the state-of-the-art land surface models (LSMs) and the real world. Since the snow cover affects the snow albedo and the ground and soil heat fluxes, it is crucial to detect snow cover changes accurately. It is challenging to acquire observation data for snow cover, snow albedo, and snow depth; thus, an excellent alternative is to use the simulation data produced by the LSMs that calculate the snow-related physical processes. The LSMs show significant differences in the complexities of the snow parameterizations in terms of variables and processes considered. Thus, the synthetic intercomparisons of the snow physics in the LSMs will help the improvement of each LSM. This study revealed and discussed the differences in the parameterizations among LSMs related to snow cover fraction, snow albedo, and snow density. We selected the most popular and well-documented LSMs embedded in the Earth System Model or operational forecasting systems. We examined single layer schemes, including the Unified Noah Land Surface Model (Noah LSM), the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the Biosphere-Atmosphere Transfer Scheme (BATS), the Canadian Land Surface Scheme (CLASS), and multilayer schemes of intermediate complexity including the Community Noah Land Surface Model with Multi-Parameterization Options (Noah-MP), the Community Land Model version 5 (CLM 5), the Joint UK Land Environment Simulator (JULES), and the Interaction Soil-Biosphere-Atmosphere (ISBA). First, we identified that BATS, Noah-MP, JULES, and ISBA reflect the snow depth and roughness length to parameterize snow cover fraction, and CLM 5 accounts for the standard deviation of the elevation value for the snow cover decay function. Second, CLM 5 and BATS are relatively complex, so that they explicitly take into account the solar zenith angle, black carbon, mineral dust, organic carbon, and ice grain size for the determinations of snow albedo. Besides, JULES and ISBA are also complicated model which concerns ice grain size, solar zenith angle, new snow depth, fresh snowfall rate, and surface temperature for the albedo scheme. Third, HTESSEL, CLM 5, and ISBA considered the effects of both wind and temperature in the determinations of the new snow density. Especially, ISBA and JULES considered internal snow characteristics such as snow viscosity, snow temperature, and vertical stress for parameterizing new snow density. The future outlook discussed geomorphic and vegetation-related variables for the further improvement of the LSMs. Previous studies clearly show that spatio-temporal variation of snow is due to the influence of altitude, slope, and vegetation condition. Therefore, we recommended applying geomorphic and vegetation factors such as elevation, slope, time-varying roughness length, vegetation indexes, or optimized parameters according to the land surface type to parameterize snow-related physical processes.

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Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning

Cited by 6 publications

References 10 publications

Understanding wind-driven melt of patchy snow cover

Understanding wind-driven melt of patchy snow cover

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

Review article: Parameterizations of snow-related physical processes in land surface models

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