Physically based approach to modelling distributed snowmelt in a small alpine catchment (original) (raw)
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This paper presents an attempt at deterministically modeling spatially distributed snowmelt in an alpine cfitchment. The basin is 9.4 km 2 in area and elevations range from 1900 to 3050 m above sea level. The model makes use of digital terrain data with 25 m grid spacing. Energy balance components are calculated for ea6h grid element taking topographic. variations of solar radiation into account. For each grid element albedo and snow surface temperatures are simulated. Model performance is evaluated on the basis of snow cover depletion patterns as derived from weekly air photographs. The use of spatially distributed data allows for addressing individual model components. Results indicate that the basic model assumptions are realistic. Model inadequacies are shown to arise from processes not included in the model such as avalanching and long wave emission fxom surrounding terrain as well as inaccurate model parameters. Numerous papers have been published on distributed model components su:Ch as radiation [e.g., Dozier, 1980; Olyphant, 1986] and some papers on the distribution of water equivalent [Woo et al? 1983a; Elder et al., 1989]. However, no more than a few studies deal with spatially distributed sn0wmelt models. Charbonneau et al. [1981] presented a model which accounted for variations in shortwave radiation and snow surface temperature at slopes of different aspect.
Incorporating remotely-sensed snow albedo into a spatially-distributed snowmelt model
Geophysical Research Letters, 2004
Basin-average albedo estimated from remotely-sensed Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS) data specific to the catchment typically differed by 20% from albedo estimated using a common snow-agebased empirical relation. In some parts of the basin, differences were as large as 0.31. Using the AVIRIS albedo estimates in a distributed snowmelt model that explicitly includes net solar radiation resulted in a much more accurate estimate of the timing and magnitude of snowmelt as compared to the same model with the empirical albedo (R 2 of 0.73 versus 0.59 and magnitude error of 2% versus 36%). Model improvement was most significant in areas and at times where incident solar radiation was relatively high and temperatures low.
An energy balance method and remote-sensing data were used to simulate snow distribution and melt in an alpine watershed in northwestern China within a complete snow accumulation-melt period. The spatial energy budgets were simulated using meteorological observations and a digital elevation model of the watershed. A linear interpolation method was used to estimate the daily snow cover area under cloudy conditions, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Hourly snow distribution and melt, snow cover extent and daily discharge were included in the simulated results. The root mean square error between the measured snow-water equivalent samplings and the simulated results is 3.2 cm. The Nash and Sutcliffe efficiency statistic (NSE) between the measured and simulated discharges is 0.673, and the volume difference (Dv) is 3.9 %. Using the method introduced in this article, modelling spatial snow distribution and melt runoff will become relatively convenient.
2022
From the micro to mesoscale, water and energy budgets of mountainous catchments are largely driven by topographic features such as terrain orientation, slope, steepness, elevation together with associated meteorological forcings such as precipitation, solar radiation and wind. This impact the snow deposition, melting and transport, which further impact the overall water cycle. However, this microscale variability is not well represented in Earth System Models due to coarse resolutions, and impacts of such resolution assumptions on simulated water and energy budget lack quantification. This study aims at exploring these effects on a 15.28 ha small mid-elevation (2000-2200 m) alpine catchment at Col du Lautaret (France). This grass-dominated catchment remains covered with snow for 5 to 6 months per year. The surface-subsurface coupled hyperresolution (10 m) distributed hydrological model ParFLOW-CLM is used to simulate the impacts of meteorological variability at spatio-temporal micro-scale on the water cycle. These include 3D simulations with spatially distributed forcing of precipitation, solar radiation and wind compared to 3D simulations with non-distributed forcing simulation. Our precipitation distribution method encapsulates the spatial snow distribution along with snow transport. The model simulates the snow cover dynamics and spatial variability through the CLM energy balance module and under the different combinations of distributed forcing. The resulting subsurface and surface water transfers are solved by the ParFLOW module. Distributed forcing induce a snowpack with a more spatially heterogeneous thickness, which becomes patchy during the melt season and shows a good agreement with the remote sensing images. This asynchronous melting results in a longer melting period and smoother hydrological response than the non-distributed forcing, which does not generate any patchiness. Amongst the tested distributed meteorological forcing that impacts the hydrology, precipitation distribution, including snow transportation, is the most important. Solar insolation distribution has an important impact in reducing evapotranspiration depending on the slope orientation. For the studied catchment mainly facing east, it adds small differential melting effect. Wind distribution in the energy budget calculation has a more complicated impact on our catchment as it participate to accelerate the melting when meteorological conditions are favourable but does not generate patchiness at the end in our test case. 1 Introduction Mountains are natural water reservoirs, which mitigate seasonal precipitation variability through snowpack accumulation, whose progressive melting helps meet the fresh water and energy demand all year long. Climate projections for warmer climate in the near and far future for these regions will impact this mitigation process. Earth System Models (ESMs) are then challenged to simulate water fluxes in mountainous catchments where highly variable topographic features and vegetation, soils and geological structures affect water transfers at different scales. In particular topography controls snow/rain precipitation estimation and partition uncertainties, snow redistribution, slope/aspect effect and hill-shading that leads to spatial differential melting (
A distributed grid-based model is used (1) to analyze the importance of selected model parameters, (2) to simulate spatial distributions of snow cover properties in a small basin and (3) for a comparison with less sophisticated models as typically used in operational applications. Results indicate that variations of water equivalent with slope and local relief are of utmost importance for realistic distributed simulations but more moderately influence mean basin melt. Snow cover variables of which spatial distributions are simulated include the thermal and hydraulic state of the pack and hourly melt water release. All variables exhibit substantial variations in space and time. They are primarily controlled by topography and the delay of melt water in deep packs. The grid model is compared with a snow band model and a parametric model. The latter estimates the snowpack's areal extent from water equivalent. Simulated snow-covered areas suggest the grid model to be the most realistic. Differences in terms of mean basin melt derive from different assumptions associated with model structure.
Snowmelt Evolution Mapping Using an Energy Balance Approach over an Alpine Terrain
Arctic, Antarctic, and Alpine Research, 2002
A computer model simulating snowmelt evolution and the spatial snowmelt pattern using an energy balance approach over an alpine terrain was developed. With a digital elevation model (DEM), surface characteristics information and meteorological data as input, all radiation balance components, turbulent fluxes, precipitation, and finally snowmelt were modeled on a daily basis. Special emphasis was given to snow redistribution. The model was applied to an area of 35 km 2 in the Schilthorn Massif (Bernese Oberland, Switzerland) for 1996-97. The model calculations are compared with a snowmelt evolution map, which was produced by combining seven scenes of aerial photographs taken in the Bernese Alps during the melting season 1997 (March-September). Both the temporal comparison of the snowmelt evolution and the spatial comparison of simulated and observed snowmelt patterns show a good accordance: at any of the compared dates, spatial coincidence is equal to or better than 78%. It can therefore be concluded that the model supplies a quite realistic reproduction of the energy exchange processes taking place at the ground snow-cover/atmosphere interface during winter and spring.
Extrapolating meteorological data to the basin scale represents a major problem of spatial snowmelt modelling in alpine terrain. Within this study errors in air temperature introduced by regionalization are analyzed for the Sellrain region in the Austrian Alps. Albedo is simulated using a range of model parameters representing different snow cover conditions. The influence on snowmelt is assessed by simulating water equivalent at the site scale using estimated air temperatures and albedoes. Simulation results indicate that a bias in measured temperatures as produced by local effects may be significantly more important than interpolation errors. Uncertainty in albedo appears to affect snowmelt to a higher degree than air temperature.
Hydrological Processes, 2002
In mountainous regions, wind plays a prominent role in determining snow accumulation patterns and turbulent heat exchanges strongly affecting the timing and magnitude of snowmelt runoff. In this study, digital terrain analysis was employed to quantify aspects of the upwind topography related to wind shelter and exposure, to efficiently develop a distributed timeseries of snow accumulation rates and wind speeds to force a distributed snow model. Parameters are presented that determined each grid cell's topographic exposure and potential for drift development relative to observed winds. Using meteorological data taken from both an exposed and a sheltered site in the Reynolds Mountain East watershed (0.38 km 2 ) in southwestern Idaho, the terrain parameters were used to distribute rates of snow accumulation and wind speeds at an hourly time-step for input to ISNOBAL, an energy and mass-balance snow model. Model runs were initiated prior to the development of the seasonal snow cover and continued through complete meltout for the 1986 (precipitation 128% of average), 1987 (66%), and 1989 (108%) water years. A comprehensive dataset consisting of a time-series of aerial photographs taken during meltout, measured runoff, and snow data from the sheltered meteorological site were used to validate the simulations. ISNOBAL forced with accumulation rates and wind fields generated from the applied terrain parameterizations accurately modeled the observed snow distribution including the formation of drifts and scoured wind-exposed ridges, and snowmelt runoff for all three years of study. By contrast, ISNOBAL forced with spatially constant accumulation rates and wind speeds taken from the sheltered meteorological site, a typical snow-monitoring site, overestimated peak snowmelt inputs and tended to underestimate snowmelt inputs prior to the runoff peak.