Distributed Snowmelt Simulations in an Alpine Catchment: 1. Model Evaluation on the Basis of Snow Cover Patterns

Blöschl, Günter; Kirnbauer, R.; Gutknecht, Dieter

doi:10.1029/91wr02250

Cited by 184 publications

(126 citation statements)

References 23 publications

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“…Even though some results of snow simulations and observations have discussed the heterogeneity of snow distributions, they have mainly focused on small or local areas of about 150 km 2 or less (e.g., Blöschl et al, 1991;Déry et al, 2004). However, those methods are not necessarily suitable for a larger domain such as the QRB.…”

Section: Concluding Discussionmentioning

confidence: 99%

Topographic control of snow distribution in an alpine watershed of western Canada inferred from spatially-filtered MODIS snow products

Tong

Déry

Jackson

2009

Hydrol. Earth Syst. Sci.

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Abstract. A spatial filter (SF) is used to reduce cloud coverage in Moderate ResolutionImaging Spectroradiometer (MODIS) 8-day maximum snow cover extent products (MOD10A2) from 2000-2007, which are obtained from MODIS daily snow cover extent products (MOD10A1), to assess the topographic control on snow cover fraction (SCF) and snow cover duration (SCD) in the Quesnel River Basin (QRB) of British Columbia, Canada. Results show that the SF reduces cloud coverage and improves by 2% the accuracy of snow mapping in the QRB. The new product developed using the SF method shows larger SCF and longer SCD than MOD10A2, with higher altitudes experiencing longer snow cover and perennial snow above 2500 m. The gradient of SCF with elevation (d(SCF)/dz) during the snowmelt season is 8% (100 m) −1 . The average ablation rates of SCF are similar for different 100 m elevation bands at about 5.5% (8 days) −1 for altitudes <1500 m with decreasing values with elevation to near 0% (8 days) −1 for altitudes >2500 m. Different combinations of slopes and aspects also affect the SCF with a maximum difference of 20.9% at a given time. Correlation coefficients between SCD and elevation attain 0.96 (p<0.001). Mean gradients of SCD with elevation are 3.8, 4.3, and 11.6 days (100 m) −1 for the snow onset season, snowmelt season, and entire year, respectively. The SF decreases the standard deviations of SCDs compared to MOD10A2 with a maximum difference near 0.6 day, 0.9 day, and 1.0 day for the snow onset season, snowmelt season, and entire year, respectively.

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Section: Concluding Discussionmentioning

confidence: 99%

Topographic control of snow distribution in an alpine watershed of western Canada inferred from spatially-filtered MODIS snow products

Tong

Déry

Jackson

2009

Hydrol. Earth Syst. Sci.

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“…2), but likely explanations are snow redistribution by wind or orographic precipitation effects. Snow erosion and redeposition may be parameterized based on surface curvature, which is a good indicator of regions with wind-induced snow redistribution (Blöschl et al, 1991;Huss et al, 2008). Giesen (2009) tested a surfacecurvature approach for Hardangerjøkulen, but the plateau was too flat for snow redistribution to occur in the model.…”

Section: Mass Balance Parameterizationmentioning

confidence: 99%

Simulating the evolution of Hardangerjøkulen ice cap in southern Norway since the mid-Holocene and its sensitivity to climate change

et al. 2017

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Abstract. Understanding of long-term dynamics of glaciers and ice caps is vital to assess their recent and future changes, yet few long-term reconstructions using ice flow models exist. Here we present simulations of the maritime Hardangerjøkulen ice cap in Norway from the mid-Holocene through the Little Ice Age (LIA) to the present day, using a numerical ice flow model combined with glacier and climate reconstructions.In our simulation, under a linear climate forcing, we find that Hardangerjøkulen grows from ice-free conditions in the mid-Holocene to its maximum extent during the LIA in a nonlinear, spatially asynchronous fashion. During its fastest stage of growth (2300-1300 BP), the ice cap triples its volume in less than 1000 years. The modeled ice cap extent and outlet glacier length changes from the LIA until today agree well with available observations. Volume and area for Hardangerjøkulen and several of its outlet glaciers vary out-of-phase for several centuries during the Holocene. This volume-area disequilibrium varies in time and from one outlet glacier to the next, illustrating that linear relations between ice extent, volume and glacier proxy records, as generally used in paleoclimatic reconstructions, have only limited validity.We also show that the present-day ice cap is highly sensitive to surface mass balance changes and that the effect of the ice cap hypsometry on the mass balancealtitude feedback is essential to this sensitivity. A mass balance shift by +0.5 m w.e. relative to the mass balance from the last decades almost doubles ice volume, while a decrease of 0.2 m w.e. or more induces a strong mass balance-altitude feedback and makes Hardangerjøkulen disappear entirely. Furthermore, once disappeared, an additional +0.1 m w.e. relative to the present mass balance is needed to regrow the ice cap to its present-day extent. We expect that other ice caps with comparable geometry in, for example, Norway, Iceland, Patagonia and peripheral Greenland may behave similarly, making them particularly vulnerable to climate change.

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“…Terrain curvature, referred to as curvature in this study, is a combination of profile and planform curvature. This variable represents the local relief of terrain (i.e., concavity or convexity) in all directions, which, in terms of snow accumulation, primarily accounts for wind drifting from high-exposure areas with steep slopes to lowlying gullies (Blöschl et al, 1991;Lapen and Martz, 1996). Curvature was calculated using both 30 m DEM resolution (90 m footprint) and 100 m DEM resolution (300 m footprint) and will be referred to as curvature 30 and curvature 100, respectively.…”

Section: Basin Scale Swe Variabilitymentioning

confidence: 99%

What drives basin scale spatial variability of snowpack properties in northern Colorado?

Sexstone

Fassnacht

2014

The Cryosphere

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Abstract. This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow density and snow water equivalent (SWE) variability at the basin scale (100s to 1000s km 2 ). Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE near peak snow accumulation. Bivariate relations and multiple linear regression models were developed to understand the relation of snow density and SWE with terrain variables (derived using a geographic information system (GIS)). Snow density variability was best explained by day of year, snow depth, UTM Easting, and elevation. Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance, and model validation suggests the model is transferable to independent data within the bounds of the original data set. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many timeconsuming measurements of SWE are often not feasible. A comparison with a previously developed snow density model shows that calibrating a snow density model to a specific basin can provide improvement of SWE estimation at this scale, and should be considered for future basin scale analyses. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and/or UTM Northing) were the most important SWE model variables, suggesting that orographic precipitation and storm track patterns are likely driving basin scale SWE variability. Terrain curvature was also shown to be an important variable, but to a lesser extent at the scale of interest.

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Distributed Snowmelt Simulations in an Alpine Catchment: 1. Model Evaluation on the Basis of Snow Cover Patterns

Cited by 184 publications

References 23 publications

Topographic control of snow distribution in an alpine watershed of western Canada inferred from spatially-filtered MODIS snow products

Topographic control of snow distribution in an alpine watershed of western Canada inferred from spatially-filtered MODIS snow products

Simulating the evolution of Hardangerjøkulen ice cap in southern Norway since the mid-Holocene and its sensitivity to climate change

What drives basin scale spatial variability of snowpack properties in northern Colorado?

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