Snow Depth Distribution Research Articles

Modeling snowdrift habitat for polar bear dens

Throughout the Arctic most pregnant polar bears (Ursus maritimus) construct maternity dens in seasonal snowdrifts that form in wind-shadowed areas. We developed and verified a spatial snowdrift polar bear den habitat model (SnowDens-3D) that predicts snowdrift locations and depths along Alaska's Beaufort Sea coast. SnowDens-3D integrated snow physics, weather data, and a high-resolution digital elevation model (DEM) to produce predictions of the timing, distribution, and growth of snowdrifts suitable for polar bear dens. SnowDens-3D assimilated 18 winters (1995 through 2012) of observed daily meteorological data and a 2.5m grid-increment DEM covering 337.5km2 of the Beaufort Sea coast, and described the snowdrift depth distributions on 30 November of each winter to approximate the timing of polar bear den entrance. In this region of Alaska, winds that transport snow come from two dominant directions: approximately NE to E (40–110°T) and SW to W (210–280°T). These wind directions control the formation and location of snowdrifts. In this area, the terrestrial, coastal mainland and barrier island banks where polar bear dens are found average approximately 3m high. These banks create snowdrifts that are roughly 2m deep, which historical den analyses suggest is approximately the minimum snow depth required for a polar bear den. We compared observed den locations (n=55) with model-simulated snow-depth distributions for these 18 winters. For the 31 den locations where position accuracy estimates were available in the original field notes, 29 locations (97%) had a simulated snowdrift suitable for denning within that distance. In addition, the model replicated the observed inter-annual variability in snowdrift size and location at historical den sites, suggesting it simulates interactions between the terrain and annual weather factors that produce the snowdrifts polar bears use for dens. The area of viable den habitat ranged from 0.0ha to 7.6ha (0.00–0.02% of the 337.5km2 simulation domain), depending on the winter. SnowDens-3D is available to help management agencies and industry improve their prediction of current polar bear den sites in order to reduce disturbance of denning bears by winter recreational and industrial activities.

Mapping starting zone snow depth with a ground-based lidar to assist avalanche control and forecasting

The distribution of snow depth in avalanche starting zones exerts a strong influence on avalanche potential and character. Extreme depth changes over short distances are common, especially in wind-affected, above-treeline environments. Snow depth also affects the ease of avalanche triggering. Experience shows that avalanche reduction efforts are often more successful when targeting shallow trigger point areas near deeper slabs with explosives or ski cutting. Our paper explores the use of high-resolution (cm scale) snow depth and snow depth change maps from terrestrial laser scanning (TLS) data to quantify loading patterns for use in both pre-control planning and in post-control assessment.We present results from a pilot study in three study areas at the Arapahoe Basin ski area in Colorado, USA. A snow-free reference data set was collected in a summer TLS survey. Mapping multiple times during the snow season allowed us to produce time series maps of snow depth and snow depth change at high resolution to explore depth and slab thickness variations due to wind redistribution. We conducted surveys before and after loading events and control work, allowing the exploration of loading patterns, slab thickness, shot and ski cut locations, bed surfaces, entrainment, and avalanche characteristics. We also evaluate the state of TLS for use in operational avalanche control settings.

Seasonal evolution of melt ponds on Arctic sea ice

AbstractThe seasonal evolution of melt ponds has been well documented on multiyear and landfast first‐year sea ice, but is critically lacking on drifting, first‐year sea ice, which is becoming increasingly prevalent in the Arctic. Using 1 m resolution panchromatic satellite imagery paired with airborne and in situ data, we evaluated melt pond evolution for an entire melt season on drifting first‐year and multiyear sea ice near the 2011 Applied Physics Laboratory Ice Station (APLIS) site in the Beaufort and Chukchi seas. A new algorithm was developed to classify the imagery into sea ice, thin ice, melt pond, and open water classes on two contrasting ice types: first‐year and multiyear sea ice. Surprisingly, melt ponds formed ∼3 weeks earlier on multiyear ice. Both ice types had comparable mean snow depths, but multiyear ice had 0–5 cm deep snow covering ∼37% of its surveyed area, which may have facilitated earlier melt due to its low surface albedo compared to thicker snow. Maximum pond fractions were 53 ± 3% and 38 ± 3% on first‐year and multiyear ice, respectively. APLIS pond fractions were compared with those from the Surface Heat Budget of the Arctic Ocean (SHEBA) field campaign. APLIS exhibited earlier melt and double the maximum pond fraction, which was in part due to the greater presence of thin snow and first‐year ice at APLIS. These results reveal considerable differences in pond formation between ice types, and underscore the importance of snow depth distributions in the timing and progression of melt pond formation.

Relationship between the spatial and temporal distribution of snow depth and the terrain over the Tibetan Plateau

Relationship between the spatial and temporal distribution of snow depth and the terrain over the Tibetan Plateau

Sea Ice Enhancements to Polar WRF*

Abstract The Polar Weather Research and Forecasting Model (Polar WRF), a polar-optimized version of the WRF Model, is developed and made available to the community by Ohio State University’s Polar Meteorology Group (PMG) as a code supplement to the WRF release from the National Center for Atmospheric Research (NCAR). While annual NCAR official releases contain polar modifications, the PMG provides very recent updates to users. PMG supplement versions up to WRF version 3.4 include modified Noah land surface model sea ice representation, allowing the specification of variable sea ice thickness and snow depth over sea ice rather than the default 3-m thickness and 0.05-m snow depth. Starting with WRF V3.5, these options are implemented by NCAR into the standard WRF release. Gridded distributions of Arctic ice thickness and snow depth over sea ice have recently become available. Their impacts are tested with PMG’s WRF V3.5-based Polar WRF in two case studies. First, 20-km-resolution model results for January 1998 are compared with observations during the Surface Heat Budget of the Arctic Ocean project. Polar WRF using analyzed thickness and snow depth fields appears to simulate January 1998 slightly better than WRF without polar settings selected. Sensitivity tests show that the simulated impacts of realistic variability in sea ice thickness and snow depth on near-surface temperature is several degrees. The 40-km resolution simulations of a second case study covering Europe and the Arctic Ocean demonstrate remote impacts of Arctic sea ice thickness on midlatitude synoptic meteorology that develop within 2 weeks during a winter 2012 blocking event.

Canopy influence on snow depth distribution in a pine stand determined from terrestrial laser data

AbstractIn this study, we analyzed the effects of the forest canopy and trunks of a pine stand in the central Spanish Pyrenees on the snow depth (SD) distribution. Using LiDAR technology with a terrestrial laser scanner (TLS), high‐resolution data on the SD distribution were acquired during the 2011–2012 and 2012–2013 snow seasons, which were 2 years having very contrasting climatic and snow accumulation conditions. Average SD evolution in open and canopy areas was characterized. Principal component analysis was applied to identify days having similar spatial patterns of SD distribution. There was a clear contrast in the temporal variability of the snowpack in different areas of the forest stand, corresponding generally to beneath the canopy, and in open sites. The canopy and openings showed markedly different accumulation and melting, with higher snow accumulation found in openings. Differences ranged from 14 to 80% reduction (average 49%) in the SD beneath the canopy relative to open sites. The difference in SD between open and canopy areas increased throughout the snow season. The surveyed days were classified in terms of SD distribution, and included days associated with: high SD, low SD, intense melting conditions and periods when the SD distribution was driven by wind conditions. The SD increased with distance from the trunks to a distance of 3.5–4.5 m, coinciding with the average size of the crown of individual trees.

Characteristics of snow-depth distribution around Mt. Iizuna in the northern part of Nagano prefecture

Characteristics of snow-depth distribution around Mt. Iizuna in the northern part of Nagano prefecture

Terrestrial laser scanner for spatial snow depth and density measurements in mountain environment

Climate change is the main factor that induces alterations in the hydrological cycle. In this context, mountains represent the first indicators of climate change, because they respond rapidly and intensely to climatic and environmental modifications. Obtaining reliable scenarios on water resources availability is a prerequisite to planning management measures as a matter of fact snowfall and the resulting seasonal snow cover represent an important source of water, including surface and subsurface flows.The applicability of terrestrial laser scanning (TLS) techniques to measure snow depth and snow cover was investigated in an area located in the Mascognaz Valley (Ayas municipality, Regione Autonoma Valle d'Aosta, Italy). First aim of this job is recognize the accumulation areas from melting areas through the generation of high dense digital snow elevation model. In this way is possible better understand the snowmelt process in mountain areas that contributes widely to the groundwater recharge. Secondly, the TLS was also used to analyze a snow cross section in order to define a correlation between the reflectance of laser signal and the density of snowpack. In fact, the snow density measure represents a crucial information for the snow water equivalent (S.W.E.) evaluation. Finally, analyzing the spatial snow-depth distribution is important to individuate the potential avalanche-starting zones.

Extraction of Heavy Snowfall Vulnerable Area for 3 Representative Facilities Using GIS and Remote Sensing Techniques

This study is to analyze the heavy snowfall vulnerable area of snow load design criteria for greenhouse, cattle shed and building using ground measured snow depth data and Terra MODIS snow cover area(SCA). To analyze the heavy snowfall vulnerable area, Terra MODIS satellite images for 12 years(2001–2012) were used to obtain the 2014년 10월 20일 접수 Received on October 20, 2014 / 2014년 12월 4일 수정 Revised on December 4, 2014 / 2014년 12월 31일 심사완료 Accepted on December 31, 2014 * 이 논문은 2010년도 정부(미래창조과학부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(NRF2010-0029194). 1 건국대학교 사회환경시스템공학과 Dept. of Civil & Environmental Systems Engineering, Konkuk University 2 한국수자원공사 K-water 연구원 수자원연구소 Water Resources Research Center, K-water Institute, Korea Water Resources Corporation ※ Corresponding Author E-mail : kimsj@konkuk.ac.kr Extraction of Heavy Snowfall Vulnerable Area for 3 Representative Facilities Using GIS and Remote Sensing Techniques 2 characteristics of snow depth and snow cover areas respectively. By comparing the snow load design criteria for greenhouse(cm), cattle shed(kg/m2), and building structure(kN/m2) with the snow depth distribution results by Terra MODIS satellite images, the facilities located in Jeolla-do, Chungcheong-do, and Gangwon-do areas were more vulnerable to exceed the current design criteria.

Inductive machine learning for improved estimation of catchment-scale snow water equivalent

Summary Infrastructure for the automatic collection of single-point measurements of snow water equivalent (SWE) is well-established. However, because SWE varies significantly over space, the estimation of SWE at the catchment scale based on a single-point measurement is error-prone. We propose low-cost, lightweight methods for near-real-time estimation of mean catchment-wide SWE using existing infrastructure, wireless sensor networks, and machine learning algorithms. Because snowpack distribution is highly nonlinear, we focus on Genetic Programming (GP), a nonlinear, white-box, inductive machine learning algorithm. Because we did not have access to near-real-time catchment-scale SWE data, we used available data as ground truth for machine learning in a set of experiments that are successive approximations of our goal of catchment-wide SWE estimation. First, we used a history of maritime snowpack data collected by manual snow courses. Second, we used distributed snow depth (HS) data collected automatically by wireless sensor networks. We compared the performance of GP against linear regression (LR), binary regression trees (BT), and a widely used basic method (BM) that naively assumes non-variable snowpack. In the first experiment set, GP and LR models predicted SWE with lower error than BM. In the second experiment set, GP had lower error than LR, but outperformed BT only when we applied a technique that specifically mitigated the possibility of over-fitting.

Snow depth mapping in high-alpine catchments using digital photogrammetry

Abstract. Information on snow depth and its spatial distribution is crucial for numerous applications in snow and avalanche research as well as in hydrology and ecology. Today, snow depth distributions are usually estimated using point measurements performed by automated weather stations and observers in the field combined with interpolation algorithms. However, these methodologies are not able to capture the high spatial variability of the snow depth distribution present in alpine terrain. Continuous and accurate snow depth mapping has been successfully performed using laser scanning but this method can only cover limited areas and is expensive. We use the airborne ADS80 optoelectronic scanner, acquiring stereo imagery with 0.25 m spatial resolution to derive digital surface models (DSMs) of winter and summer terrains in the neighborhood of Davos, Switzerland. The DSMs are generated using photogrammetric image correlation techniques based on the multispectral nadir and backward-looking sensor data. In order to assess the accuracy of the photogrammetric products, we compare these products with the following independent data sets acquired simultaneously: (a) manually measured snow depth plots; (b) differential Global Navigation Satellite System (dGNSS) points; (c) terrestrial laser scanning (TLS); and (d) ground-penetrating radar (GPR) data sets. We demonstrate that the method presented can be used to map snow depth at 2 m resolution with a vertical depth accuracy of ±30 cm (root mean square error) in the complex topography of the Alps. The snow depth maps presented have an average accuracy that is better than 15 % compared to the average snow depth of 2.2 m over the entire test site.

Effects of radar side-lobes on snow depth retrievals from Operation IceBridge

AbstractArctic snow depth data products from four years (2009–12) of Operation IceBridge (OIB) surveys are examined. In our analysis, we found spurious spikes in the snow depth distributions of both the multi-year and seasonal ice covers. These spikes are artifacts that stem from the incorrect identification of side lobes and main lobes of the impulse response of the snow radar as returns from the air–snow interface. The current OIB snow depth retrieval algorithm does not explicitly account for the presence of these side lobes and main lobes. As a result, overall accuracy of snow depth returns and related statistics is negatively affected. Although the range locations of these side lobes are predictable for each radar installation, they vary with individual airborne campaigns. Comparisons with limited in situ snow surveys show significant differences of >20 cm between OIB and in situ snow surveys. These artifacts affect OIB ice thickness estimates because they rely on estimates of sea-ice freeboard, which are calculated as the differences between coincident snow freeboard from lidar elevations and the retrieved snow depth estimates discussed here. Since these products are widely distributed to the scientific community, our results suggest that earlier geophysical studies based on these products may need to be re-examined.

Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence

Abstract. In this study we analyzed the relations between terrain characteristics and snow depth distribution in a small alpine catchment located in the central Spanish Pyrenees. Twelve field campaigns were conducted during 2012 and 2013, which were years characterized by very different climatic conditions. Snow depth was measured using a long range terrestrial laser scanner and analyses were performed at a spatial resolution of 5 m. Pearson's r correlation, multiple linear regressions (MLRs) and binary regression trees (BRTs) were used to analyze the influence of topography on the snow depth distribution. The analyses were used to identify the topographic variables that best explain the snow distribution in this catchment, and to assess whether their contributions were variable over intra- and interannual timescales. The topographic position index (index that compares the relative elevation of each cell in a digital elevation model to the mean elevation of a specified neighborhood around that cell with a specific shape and searching distance), which has rarely been used in these types of studies, most accurately explained the distribution of snow. The good capability of the topographic position index (TPI) to predict snow distribution has been observed in both, MLRs and BRTs for all analyzed days. Other variables affecting the snow depth distribution included the maximum upwind slope, elevation and northing. The models developed to predict snow distribution in the basin for each of the 12 survey days were similar in terms of the explanatory variables. However, the variance explained by the overall model and by each topographic variable, especially those making a lesser contribution, differed markedly between a year in which snow was abundant (2013) and a year when snow was scarce (2012), and also differed between surveys in which snow accumulation or melting conditions dominated in the preceding days. The total variance explained by the models clearly decreased for those days on which the snowpack was thinner and more patchily. Despite the differences in climatic conditions in the 2012 and 2013 snow seasons, similarities in snow distributions patterns were observed which are directly related to terrain topographic characteristics.

Are flat‐field snow depth measurements representative? A comparison of selected index sites with areal snow depth measurements at the small catchment scale

AbstractSingle or a limited number of point observations, such as from index stations, are commonly assumed to be representative for the snow cover of larger areas in many applications. This study presents a systematic investigation of the relationship between point observations and areal mean snow depths ranging from the scale of tens of metres to entire catchments. We analyse aerial snow depth information from four mountain regions in the European Alps, one in the Spanish Pyrenees and one in the Canadian Rocky Mountains, obtained from airborne laser scanning surveys. This rich data set allowed to compare point values with snow cover statistics, reflecting the real snow depth distribution of the investigation areas. We present two contrasting approaches in order to assess the representativeness of typical flat‐field snow depth measurements. In the first approach, we define potential index stations based on topographic characteristics as commonly applied for snow cover monitoring stations. The point observations of these index stations are then compared with the mean values in their vicinities. We show that most of the index stations strongly overestimate the snow depth of the catchment and of their surrounding area at distances of several hundreds of metres. Results confirm the expectation that the larger the support area, the smaller the difference to the mean of the complete catchment. The second approach was to analyse topographic characteristics of all cells with snow depths that deviated less than 10% from the catchment mean. It appears that these representative cells are rather randomly distributed and cannot be identified a priori. In summary, our results show large potential biases of index stations with respect to snow distribution and therefore also snow water equivalent. Copyright © 2014 John Wiley & Sons, Ltd.

Interdecadal changes in snow depth on Arctic sea ice

AbstractSnow plays a key role in the growth and decay of Arctic sea ice. In winter, it insulates sea ice from cold air temperatures, slowing sea ice growth. From spring to summer, the albedo of snow determines how much insolation is absorbed by the sea ice and underlying ocean, impacting ice melt processes. Knowledge of the contemporary snow depth distribution is essential for estimating sea ice thickness and volume, and for understanding and modeling sea ice thermodynamics in the changing Arctic. This study assesses spring snow depth distribution on Arctic sea ice using airborne radar observations from Operation IceBridge for 2009–2013. Data were validated using coordinated in situ measurements taken in March 2012 during the Bromine, Ozone, and Mercury Experiment (BROMEX) field campaign. We find a correlation of 0.59 and root‐mean‐square error of 5.8 cm between the airborne and in situ data. Using this relationship and IceBridge snow thickness products, we compared the recent results with data from the 1937, 1954–1991 Soviet drifting ice stations. The comparison shows thinning of the snowpack, from 35.1 ± 9.4 to 22.2 ± 1.9 cm in the western Arctic, and from 32.8 ± 9.4 to 14.5 ± 1.9 cm in the Beaufort and Chukchi seas. These changes suggest a snow depth decline of 37 ± 29% in the western Arctic and 56 ± 33% in the Beaufort and Chukchi seas. Thinning is negatively correlated with the delayed onset of sea ice freezeup during autumn.

Inter‐annual variation in the topographic controls on catchment‐scale snow distribution in a maritime alpine catchment, New Zealand

AbstractSeasonal snow is a globally important water resource that contributes substantially to upland and lowland water resources. As such, there is a need to understand the controls on the spatial and temporal variation in snow distribution. This study meets this research need by investigating the topographic controls on snow depth distribution in the upper Jollie catchment in the Southern Alps of New Zealand. Furthermore, inter‐annual variation in the importance of the topographic controls is examined and linked to variation in the dominant synoptic‐scale weather patterns over a 4‐year period (2007–2010). Through the use of regression trees, the relative importance of the topographic controls on snow depth was shown to vary between the four study years. In particular, elevation explained the greatest amount of variance in 2007 and 2008 and east‐exposure explained the greatest variance in 2009 and 2010. The other wind exposure variables also had a large effect on the snow depth distribution in 2009 and 2010. Differences in the frequency and duration of synoptic weather patterns were physically consistent with the changing importance of these variables. In particular, a higher frequency of troughing events in 2009 and 2010 is thought to be associated with a reduced importance of elevation and greater influence of wind exposure on snow depth in these years. These findings demonstrate the importance of using multi‐year data sets, and of considering topographic and climatic influences, when attempting to model alpine snow distribution. Copyright © 2014 John Wiley & Sons, Ltd.

Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach

Abstract. In alpine terrain, the snow-covered winter surface deviates from its underlying summer terrain due to the progressive smoothing caused by snow accumulation. Terrain smoothing is believed to be an important factor in avalanche formation and avalanche dynamics, and it affects surface heat transfer, energy balance as well as snow depth distribution. To assess the effect of snow on terrain, we use an adequate roughness definition. We developed a method to quantify terrain smoothing by combining roughness calculations of snow surfaces and their corresponding underlying terrain with snow depth measurements. To this end, elevation models of winter and summer terrain in three selected alpine basins in the Swiss Alps characterized by low, medium and high terrain roughness were derived from high-resolution measurements performed by airborne and terrestrial lidar. The preliminary results in the selected basins reveal that, at basin scale, terrain smoothing depends not only on mean snow depth in the basin but also on its variability. The multi-temporal analysis over three winter seasons in one basin suggests that terrain smoothing can be modelled as a function of mean snow depth and its standard deviation using a power law. However, a relationship between terrain smoothing and snow depth was not found at pixel scale. Further, we show that snow surface roughness is to some extent persistent, even in-between winter seasons. Those persistent patterns might be very useful to improve the representation of a winter terrain without modelling of the snow cover distribution. This can for example improve avalanche release area definition and, in the long term, natural hazard management strategies.

Mapping the annual evolution of snow depth in a small catchment in the Pyrenees using the long-range terrestrial laser scanning

This paper presents the methodology used to develop snow depth distribution maps for a small catchment in the Central Spanish Pyrenees covering 55 ha in a 1:10,000 scale. The Main Map was obtained using LiDAR (light detection and ranging) technology from a long-range Terrestrial Laser Scanner (TLS) in six field surveys undertaken during the 2012 winter–spring period. This technique enabled the acquisition of information at a very high resolution concerning the spatial variability of snow cover, providing snow depth information for remote areas where data acquisition is complex and hazardous. We describe the methodological steps and the quality assessment applied in developing the maps. Comparison with manual measurements confirmed the reliability of the snow depth maps, including areas located at large distances from the scanner (800 m). This method provides a promising tool for future investigations of snow dynamics in mountainous environments.

Lidar snow cover studies on glaciers in the Ötztal Alps (Austria): comparison with snow depths calculated from GPR measurements

Abstract. The storage of water within the seasonal snow cover is a substantial source of runoff in high mountain catchments. Information about the spatial distribution of snow accumulation is necessary for calibration and validation of hydro-meteorological models. Generally, only a small number of precipitation measurements deliver precipitation input for modelling in mountain areas. The spatial interpolation and extrapolation of measurements of precipitation is still difficult. Multi-temporal application of lidar techniques from aircraft, so-called airborne laser scanning (ALS), provides surface elevations changes even in inaccessible terrain. These ALS surface elevation changes can be used to derive changes in snow depths of the mountain snow cover for seasonal or subseasonal time periods. However, since glacier surfaces are not static over time, ablation, densification of snow, densification of firn and ice flow contribute to surface elevation changes. ALS-derived surface elevation changes were compared to snow depths derived from 35.4 km of ground penetrating radar (GPR) profiles on four glaciers. With this combination of two different data acquisitions, it is possible to evaluate the effect of the summation of these processes on ALS-derived snow depth maps in the high alpine region of the Ötztal Alps (Austria). A Landsat 5 Thematic Mapper image was used to distinguish between snow covered area and bare ice areas of the glaciers at the end of the ablation season. In typical accumulation areas, ALS surface elevation changes differ from snow depths calculated from GPR measurements by −0.4 m on average with a mean standard deviation of 0.34 m. Differences between ALS surface elevation changes and GPR derived snow depths are small along the profiles conducted in areas of bare ice. In these areas, the mean absolute difference of ALS surface elevation changes and GPR snow depths is 0.004 m with a standard deviation of 0.27 m. This study presents a systematic approach to analyze deviations from ALS generated snow depth maps to ground truth measurements on four different glaciers. We could show that ALS can be an important and reliable data source for the spatial distribution of snow depths for most parts of the here investigated glaciers. However, within accumulation areas, just utilizing ALS data may lead to systematic underestimation of total snow depth distribution.

Assessment of Snowmelt Impact on Chungju Dam Watershed Inflow Using Terra MODIS Data and SWAT Model

본 연구에서는 충주댐 유역(<TEX>$6,642.0km^2$</TEX>)을 대상으로 Terra MODIS (Moderate-Resolution Imaging Spectroradiometer) 위성영상과 SWAT (Soil and Water Assessment Tool) 모형을 이용하여 융설이 댐유입량에 미치는 영향을 분석하였다. SWAT 모형의 융설 매개변수인 적설면적 감소곡선(Snow Cover Depletion Curve; SCDC)을 결정하기 위해 10 sets (2000-2010)의 Terra MODIS 위성영상으로부터 추출한 적설분포도와 지상 강설자료를 이용하여 구축한 적설심분포도(snow depth distribution; SDD)를 도출한 결과, 50% 적설면적일 때의 적설면적감소계수(sno50cov)는 0.4~0.7의 범위를 가지는 것으로 분석되었다. 매년의 융설 매개변수를 적용하여 융설기간 (11~4월)동안의 유출을 보정한 결과 평균 <TEX>$R^2$</TEX>는 0.54이었다. 10년 동안 융설기간의 유출량은 평균 104.3 mm로 연 유출량의 12.0%를 차지하는 것으로 분석되었다. This study is to evaluate the snowmelt impact on dam inflow for the Chungju Dam watershed <TEX>$6,642.0km^2$</TEX> using Terra MODIS (Moderate-Resolution Imaging Spectroradiometer) and Soil and Water Assessment Tool (SWAT). To determine the SWAT snowmelt parameter; snow cover depletion curve (SCDC) the snow depth distribution (SDD) using Terra MODIS was used, the snow depth was spatially interpolated using snowfall data of ground meteorological stations. For 10 sets (2000-2010) data during snowmelt period (November-April), the sno50cov parameter, that is, the 50% coverage at a fraction of SCDC which determines the shape of snow depletion process, showed the values of 0.4 to 0.7. The SWAT model was calibrated with average <TEX>$R^2$</TEX> of 0.54 using the sno50cov of each year. The 10 years average streamflow during snowmelt period was 104.3 mm which covers 12.0% of the annual streamflow.