Snow Depth Data Research Articles

Snow cover trends in Finland over 1961–2014 based on gridded snow depth observations

Snow conditions in high‐latitude regions are changing in response to climate warming, and these changes are likely to accelerate as the warming proceeds. Here, we analyse daily gridded snow depth, temperature and precipitation data from Finland over the period 1961–2014 to discover the ongoing changes in monthly average snow depths (SN) and several snow‐related indices. Our results indicate that regional differences of changes in snow conditions can be relatively large, even within such a small district as Finland. Moreover, the interannual variation of the various snow indices was found to be larger in southern Finland than in northern Finland. The largest decrease in snow depth occurred in the southern, western and central parts of Finland in late winter and early spring. This decrease was driven by increasing mixed and liquid precipitation and, especially in spring, increasing temperature. In northern Finland, the decreasing trend of snow depth was most evident in spring, but no change occurred during winter months, although the amount of solid precipitation was found to increase in December–February. In the same months, temperature and the amount of mixed and liquid precipitation increased, likely counteracting the effects of the increasing solid precipitation on snow depth. The annual maximum snow depth that typically occurs in March was found to decrease in over 85% of Finland's area, most strongly in western coastal areas. In almost half of Finland's area, this decrease occurred despite increasing solid precipitation. Our findings highlight the complexity of the responses of snow conditions to climatic variability in northern Europe.

Particle Filter Data Assimilation of Monthly Snow Depth Observations Improves Estimation of Snow Density and SWE

AbstractSnow depth observations and modeled snow density can be combined to calculate snow water equivalent (SWE). In this approach, SWE uncertainty is dominated by snow density uncertainty, which depends on meteorological data quality and process representation (e.g., compaction) in models. We test whether assimilating snow depth observations with the particle filter can improve modeled snow density, thus improving SWE estimated from intermittent depth observations. We model snowpack at Mammoth Mountain (California) over water years 2013–2016, assuming monthly snow depth data (e.g., sampling intervals relevant to lidar or manual surveys) for assimilation, and validate against observed SWE and density. The particle filter reduced density and SWE root‐mean‐square error by 27% and 28% relative to open loop simulations when using high‐quality, point location forcing. Assimilation gains were greater (35% and 51% reduction in density and SWE root‐mean‐square error) when using coarse‐resolution North American Land Data Assimilation System phase 2 meteorology. Ensembles created with both meteorological and compaction perturbations led to the greatest model improvements. Because modeled depth and density were both generally lower than observations, assimilation favored particles with higher precipitation and thus more overburden compaction. This moved depth and density (therefore SWE) closer to observations. In contrast, ensemble generation that varied only compaction parameters degraded performance. These results were supported by synthetic experiments with prescribed error sources. Thus, assimilation of snow depth data from lidar or other techniques can likely improve snow density and SWE derived at the basin scale. However, supplementary in situ observations are valuable to identify primary error sources in simulated snow depth and density.

Comparing Coincident Elevation and Freeboard From IceBridge and Five Different CryoSat-2 Retrackers

The airborne Operation IceBridge and spaceborne CryoSat-2 missions observe polar sea ice at different spatial and temporal scales as well as with different sensor suites. Comparison of data products from IceBridge and CryoSat-2 is complicated by the fact that they use different geophysical corrections: reference ellipsoid, geoid model, tide model, and atmospheric corrections to derive surface elevation and sea-ice freeboard. In this paper, we compare sea-ice surface elevation and freeboard using eight coincident CryoSat-2, Airborne Topographic Mapper (ATM), and Land, Vegetation, and Ice Sensor (LVIS) observations with direct IceBridge underflights of CryoSat-2 ground tracks. We apply identical geophysical corrections to CryoSat-2 and IceBridge data to eliminate elevation biases due to these differences and focus on differences due to retracker performance. The IceBridge ATM and LVIS elevation and freeboard and Snow Radar snow depth data sets are averaged to each CryoSat-2 footprint for comparison. With snow depth measurements, we are able to compare elevations and freeboards at the snow/ice interface for five different CryoSat-2 retrackers (ESA, GSFCv1, AWI, JPL, and GSFCv2) and IceBridge. The overall mean of freeboard differences between GSFCv2, ESA, AWI, JPL retrackers, and ATM are in agreement within 0.05 m. However, the five different CryoSat-2 retrackers show distinct differences in mean elevation over leads and over floes. This suggests that the physical interpretation of the different retrackers needs to be considered depending on usage, for example, elevations from CryoSat-2 retrackers need to be carefully calibrated before comparing with elevation from other satellites for long-term surface elevation trends.

Sensitivity of active-layer freezing process to snow cover in Arctic Alaska

Abstract. The contribution of cold-season soil respiration to the Arctic–boreal carbon cycle and its potential feedback to the global climate remain poorly quantified, partly due to a poor understanding of changes in the soil thermal regime and liquid water content during the soil-freezing process. Here, we characterized the processes controlling active-layer freezing in Arctic Alaska using an integrated approach combining in situ soil measurements, local-scale (∼50 m) longwave radar retrievals from NASA airborne P-band polarimetric SAR (PolSAR) and a remote-sensing-driven permafrost model. To better capture landscape variability in snow cover and its influence on the soil thermal regime, we downscaled global coarse-resolution (∼0.5∘) MERRA-2 reanalysis snow depth data using finer-scale (500 m) MODIS snow cover extent (SCE) observations. The downscaled 1 km snow depth data were used as key inputs to the permafrost model, capturing finer-scale variability associated with local topography and with favorable accuracy relative to the SNOTEL site measurements in Arctic Alaska (mean RMSE=0.16 m, bias=-0.01 m). In situ tundra soil dielectric constant (ε) profile measurements were used for model parameterization of the soil organic layer and unfrozen-water content curve. The resulting model-simulated mean zero-curtain period was generally consistent with in situ observations spanning a 2∘ latitudinal transect along the Alaska North Slope (R: 0.6±0.2; RMSE: 19±6 days), with an estimated mean zero-curtain period ranging from 61±11 to 73±15 days at 0.25 to 0.45 m depths. Along the same transect, both the observed and model-simulated zero-curtain periods were positively correlated (R>0.55, p<0.01) with a MODIS-derived snow cover fraction (SCF) from September to October. We also examined the airborne P-band radar-retrieved ε profile along this transect in 2014 and 2015, which is sensitive to near-surface soil liquid water content and freeze–thaw status. The ε difference in radar retrievals for the surface (∼<0.1 m) soil between late August and early October was negatively correlated with SCF in September (R=-0.77, p<0.01); areas with lower SCF generally showed larger ε reductions, indicating earlier surface soil freezing. On regional scales, the simulated zero curtain in the upper (<0.4 m) soils showed large variability and was closely associated with variations in early cold-season snow cover. Areas with earlier snow onset generally showed a longer zero-curtain period; however, the soil freeze onset and zero-curtain period in deeper (>0.5 m) soils were more closely linked to maximum thaw depth. Our findings indicate that a deepening active layer associated with climate warming will lead to persistent unfrozen conditions in deeper soils, promoting greater cold-season soil carbon loss.

Freezing/thawing index variations over the circum-Arctic from 1901 to 2015 and the permafrost extent

Due to sparse data and discontinuous time observations in the circum-Arctic region, freezing index and thawing index, as useful indicators, are widely used in permafrost distribution, climate changes and cold-region engineering analysis. However, previous researches on freezing/thawing index over this region were estimated based on mean monthly air temperature. In this paper, we analyzed the spatial and temporal variations of the freezing/thawing index over the circum-Arctic from 1901 to 2015 based on the daily datasets, besides monthly datasets. The results showed that freezing index had a downward changing trend and thawing index had an upward trend during 1901–2015. More important, the change trend in freezing/thawing index after 1988 was more significant than before. Furthermore, different freezing/thawing index based on daily datasets and the monthly datasets were assessed and compared according to daily data from 17 meteorological stations, comprehensive relative errors evaluation implied that freezing/thawing based on daily datasets was more accurate generally, although both of other datasets were available in calculating the freezing/thawing index. As the daily datasets are better in calculating annual freezing/thawing index, therefore, the permafrost extent was estimated by a climate-based predictive model combined with snow depth data from Canadian Meteorological Centre (CMC). Finally, considering that the published permafrost map of the circum-Arctic only shows the past permafrost distribution, but it cannot reflect the permafrost distribution after 2000 under the climate warming. Hence, we simulated the current (mean from 2000 to 2015) permafrost area which is 19.96 × 106 km2, and the results showed some discrepancies between published and simulated permafrost extent mainly located in isolated permafrost regions.

How do westerly jet streams regulate the winter snow depth over the Tibetan Plateau?

The linkages between upper-level westerly jet streams and snow depth over the Tibetan Plateau (TP) in winter (from November to the following April) were investigated for the period 1979–2014 using satellite-borne passive microwave retrievals of snow depth data and ERA-Interim reanalysis data. Anomalies in atmospheric circulation, temperature, and precipitation corresponding to variation in westerly jets were examined to find the causes of variation in snow depth over the TP, using singular value decomposition, composite analysis and dynamical diagnosis. Results show that variation in intensity and meridional shifts of westerly jets, with particular attention to the North Tibetan Plateau jet (NTPJ) and the South Tibetan Plateau jet (STPJ), significantly influence the interannual variation of snow depth over the TP in late winter (February–April). For the conjunction of intense STPJ and weak NTPJ, an anomalous cold low-pressure vortex is observed over the TP. The vortex extends across the TP and spans from the ground surface to the upper troposphere. There is anomalous ascending motion above the TP due to secondary circulations immediately south and north of STPJ, with increased moisture flux from the southwest. These circulation structures cause significant cooling and increased precipitation, thus promoting snowfall and snow accumulation. Temperature is a more important influence than precipitation on snow accumulation. Cooling over the TP is caused by cold temperature advection due to intensely cold air and weakened descending adiabatic heating due to anomalous ascending motion. Local moisture is reduced, and anomalous ascending moisture advection leads to more net precipitation and snowfall over the TP.

Crowdsourcing Snow Depth Data with Citizen Scientists

A new project harnesses the power of the winter backcountry recreation community to gather data that are vital to understanding snow, from winter hazards to water resources.

Estimating snow depth over Arctic sea ice from calibrated dual-frequency radar freeboards

Abstract. Snow depth on sea ice remains one of the largest uncertainties in sea ice thickness retrievals from satellite altimetry. Here we outline an approach for deriving snow depth that can be applied to any coincident freeboard measurements after calibration with independent observations of snow and ice freeboard. Freeboard estimates from CryoSat-2 (Ku band) and AltiKa (Ka band) are calibrated against data from NASA's Operation IceBridge (OIB) to align AltiKa with the snow surface and CryoSat-2 with the ice–snow interface. Snow depth is found as the difference between the two calibrated freeboards, with a correction added for the slower speed of light propagation through snow. We perform an initial evaluation of our derived snow depth product against OIB snow depth data by excluding successive years of OIB data from the analysis. We find a root-mean-square deviation of 7.7, 5.3, 5.9, and 6.7 cm between our snow thickness product and OIB data from the springs of 2013, 2014, 2015, and 2016 respectively. We further demonstrate the applicability of the method to ICESat and Envisat, offering promising potential for the application to CryoSat-2 and ICESat-2, which launched in September 2018.

Using a Linear Unmixing Method to Improve Passive Microwave Snow Depth Retrievals

Satellite microwave radiometer measurements have been used to extract snow depth information for approximately three to four decades. However, the coarse spatial resolution of satellite microwave radiobrightness observations affects the snow depth retrieval accuracy and hinders the application of snow depth data in studies on water, energy, and carbon cycles. Therefore, snow depth measurements require better spatial resolution to improve the retrieval accuracy and enhance the resolution of the data. In this paper, an improved linear unmixing method is proposed to improve the accuracy of existing methods and downscale brightness temperatures from satellite observations to improve snow depth estimates. Tests conducted on a dataset consisting of both simulated and satellite data show the effectiveness of the proposed method. The unmixing method is then applied to FengYun-3B satellite/microwave radiation imager measurements for snow depth retrievals, and the resulting snow depths are compared with weather station observations from January to February 2011. The results show that the snow depth estimated from downscaled brightness temperatures performs better than the original data and presents higher correlations and lower root mean square errors.

Spatio-temporal variability of Antarctic sea-ice thickness and volume obtained from ICESat data using an innovative algorithm

We use total (sea ice plus snow) freeboard as estimated from Ice, Cloud and land Elevation Satellite (ICESat) Geophysical Laser Altimeter System (GLAS) observations to compute Antarctic sea-ice thickness and volume. In order to overcome assumptions made about the relationship between snow depth and total freeboard or biases in snow depth products from satellite microwave radiometry, we implement a new algorithm. We treat the sea ice-snow system as one layer with reduced density, which we approximate by means of a priori information about the snow depth to sea-ice thickness ratio. We derive this a priori information directly from ICESat total freeboard data using empirical equations relating in-situ measurements of total freeboard to snow depth or sea-ice thickness. We apply our new algorithm (one-layer method or OLM), which uses the buoyancy equation approach without the need for auxiliary snow depth data, to compute sea-ice thickness for every ICESat GLAS footprint from a valid total freeboard. An improved method for sea-ice volume retrieval is also used to derive ice volume at 6.25 km scale. Spatio-temporal variations of sea-ice thickness and volume are then analyzed in the circumpolar Antarctic as well as its six sea sectors: Pacific Ocean, Indian Ocean, Weddell East, Weddell West, Bell-Amund Sea, and Ross Sea, under both interannual and seasonal scales. Because the OLM algorithm relies on only one parameter, the total freeboard, and is independent of auxiliary snow depth information, it is believed to become a viable alternative sea-ice thickness retrieval method for satellite altimetry.

Direct Insertion of NASA Airborne Snow Observatory‐Derived Snow Depth Time Series Into the iSnobal Energy Balance Snow Model

AbstractAccurately simulating the spatiotemporal distribution of mountain snow water equivalent improves estimates of available meltwater and benefits the water resource management community. In this paper we present the first integration of lidar‐derived distributed snow depth data into a physics‐based snow model using direct insertion. Over four winter seasons (2013–2016) the National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) Airborne Snow Observatory (ASO) performed near‐weekly lidar surveys throughout the snowmelt season to measure snow depth at high resolution over the Tuolumne River Basin above Hetch Hetchy Reservoir in the Sierra Nevada Mountains of California. The modeling component of the ASO program implements the iSnobal model to estimate snow density for converting measured depths to snow water equivalent and to provide temporally complete snow cover mass and thermal states between flights. Over the four years considered in this study, snow depths from 36 individual lidar flights were directly inserted into the model to provide updates of snow depth and distribution. Considering all updates to the model, the correlation between ASO depths and modeled depths with and without previous updates was on average r2 = 0.899 (root‐mean‐square error = 12.5 cm) and r2 = 0.162 (root‐mean‐square error = 41.5 cm), respectively. The precise definition of the snow depth distribution integrated with the iSnobal model demonstrates how the ASO program represents a new paradigm for the measurement and modeling of mountain snowpacks and reveals the potential benefits for managing water in the region.

Measuring snow ablation rates in alpine terrain with a mobile multioffset ground‐penetrating radar system

AbstractGround‐penetrating radar (GPR) has become a promising technique in the field of snow hydrological research. It is commonly used to measure snow depth, density, and water equivalent over large distances or along gridded snow courses. Having built and tested a mobile lightweight set‐up, we demonstrate that GPR is capable of accurately measuring snow ablation rates in complex alpine terrain. Our set‐up was optimized for efficient measurements and consisted of a multioffset radar with four pairs of antennas mounted to a plastic sled, which was small enough to permit safe and convenient operations. Repeated measurements at intervals of 2 to 7 days were taken during the 2014/2015 winter season along 10 profiles of 50 to 200 m length within two valleys located in the eastern Swiss Alps. Resulting GPR‐based data of snow depth, density, and water equivalent, as well as their respective change over time, were in good agreement with concurrent manual measurements, in particular if accurate alignment between repeated overpasses could be achieved. Corresponding root‐mean‐square error (RMSE) values amounted to 4.2 cm for snow depth, 17 mm for snow water equivalent, and 22 kg/m3 for snow density, with similar RMSE values for corresponding differential data. With this performance, the presented radar set‐up has the potential to provide exciting new and extensive datasets to validate snowmelt models or to complement lidar‐based snow surveys.

Snow Depth Retrieval Based on a Multifrequency Passive Microwave Unmixing Method for Saline-Alkaline Land in the Western Jilin Province of China

Passive microwave data of a snow-covered land surface received by a passive microwave radiometer is composed of microwave radiation from the snow cover and the underlying surface. The western Jilin Province of China is an important area with seasonal snow. Here, the land cover types mainly include characteristically saline-alkaline farmland and grassland. The main aim of this paper is to obtain more accurate snow depth (SD) data in the western Jilin Province of China. First, a multifrequency passive microwave unmixing method is proposed for the study area. Second, based on Fengyun-3B microwave radiation imagery (MWRI) data and advanced microwave scanning radiometer 2 (AMSR2) data, the SD retrieval results are evaluated using four methods: using MWRI data with Foster's algorithm and with the standard MWRI algorithm, and using the AMSR2 data with Foster's algorithm and with the standard AMSR2 algorithm. The experimental results demonstrate that using MWRI data with the standard MWRI algorithm yields the highest accuracy for SD retrieval, with average bias and root-mean-square-error (RMSE) values of approximately 6.9 and 7.5 cm, respectively. Finally, we combined the unmixing method with the optimal SD retrieval method, and obtained more accurate SD data for the study area. Using MWRI data with the standard MWRI algorithm, the average bias and RMSE improved by approximately 9.7% and 7% when using the unmixed pixels compared with using the original mixed pixels.

Developing a composite daily snow cover extent record over the Tibetan Plateau from 1981 to 2016 using multisource data

Snow cover condition across the Tibetan Plateau (TP) is not only a significant indicator of climate change but also a vital variable in water availability because of its water storage function in high-mountain regions of Southwest China and the surrounding Asian countries. Limited by low spatial resolution, incomplete spatial coverage, and short time span of the current snow cover products, the long-term snow cover change across the TP under the climate change background remains unclear. To resolve this issue, a composite long-term gap-filled TP daily 5-km snow cover extent (SCE) record (TPSCE) is generated by integrating SCE from the Advanced Very High-Resolution Radiometer (AVHRR) surface reflectance climate data record (CDR) and several existing snow cover data sets, with the help of a decision tree snow cover mapping algorithm, for the period 1981–2016. A snow discrimination process was used to classify the land surface into snow (pre-TPSCE) and non-snow using AVHRR surface reflectance CDR. To fill gaps caused by invalid observations and cloud contamination in pre-TPSCE, several existing daily SCE products, including MOD10C1, MYD10C1, IMS, JASMES, and a passive microwave snow depth data set are employed in the composition process. The daily snow discrimination accuracy, tested by ground snow-depth observations during 2000–2014, shows that the TPSCE captures the distribution of snow duration days (R2 = 0.80, bias = 3.93 days) effectively. The comparison between the TPSCE and fine-resolution snow cover maps (MCD10A1-TP) indicates high comparability between the TPSCE and MCD10A1-TP. In addition, cross-comparisons with changes in temperature, precipitation, and land surface albedo indicate that the TPSCE is reliable in climate change studies. In summary, the TPSCE is spatially complete and covers the longest period among all current snow cover products from satellite observations. The TPSCE seamlessly records changes in snow cover across the TP over the past 36 years, thereby providing valuable snow information for climate change and hydrological studies.

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

Snow profile temperature measurements in spatiotemporal analysis of snowmelt in a subarctic forest-mire hillslope

Continuous data on spatial and temporal patterns of snowmelt rates are essential for hydrological studies, but are commonly not available, especially in the subarctic, mainly due to high monitoring costs. In this study, temperature loggers were used to measure local and microscale variations in snowpack temperature, in order to understand snowmelt processes and rates in subarctic northern Finland. The loggers were deployed on six test plots along a hillslope with varying topography (elevation and aspect) and vegetation (forest, transitional zone and mires, i.e. treeless peatlands) during two consecutive winters (2014 and 2015). At each test plot, the sensors were installed in five locations, at two heights in a snow profile. Algorithms were developed to analyse the snowmelt rates from high-resolution snowpack temperature data. The validity of the results was evaluated using snow depth and soil moisture data from adjacent reference sensors and the results were tested using an empirical degree-day snow model calibrated for each test plot. Snowmelt rates were relatively similar in mires (median 2.3 mm d−1 °C−1) and forests (median 2.6 mm d−1 °C−1) with apparent inter-annual variation. The observed melt rates were highest in the highest elevation plots, in transition zone in 2014 (median 4.6 mm d−1 °C−1) and southwest-facing forest line in 2015 (median 3.2 mm d−1 °C−1). The timing of the modelled meltwater outflow and snowpack ablation showed good agreement with the snowpack temperature-derived estimates and the soil moisture and snow depth measurements. The simple approach used represents a novel and cost-effective method to improve the spatial accuracy of in situ snow cover ablation measurements and melt rates and the precision of snowmelt models in the subarctic. An open-access R-based model is provided with this paper for analysis of high-frequency snow temperature data.

Support vector regression snow-depth retrieval algorithm using passive microwave remote sensing data

Snow cover is an informative indicator of climate change because it can affect local and regional surface energy and water balance, hydrological processes and climate, and ecosystem function. Passive microwave (PM) remote sensing data have long been used to retrieve snow depth and snow water equivalent with large uncertainties. The objective of this study is to develop snow-depth retrieval algorithm based on support vector regression (SVR) technique using PM remote sensing data and other auxiliary data. Ground-based daily snow depth data from 1223 stations across Eurasian continent were used to construct and validate the snow-depth retrieval algorithm. This SVR snow-depth retrieval algorithm partitioned three snow cover stages, and four land cover types then generated twelve snow-depth models for each phases. A non-linear regression method based on support vector regression (SVR) was used to retrieve snow depth with PM brightness temperatures, location (latitude and longitude), and terrain parameters (elevation) as input data and land cover as auxiliary data. In addition, we compared the performance of the SVR snow-depth retrieval algorithm with four alternative algorithms: the Chang algorithm, the Spectral Polarization Difference (SPD) algorithm, the Artificial/Neural Networks (ANN) and, an algorithm based on linear regression. Comparing results obtained from these five snow-depth retrieval algorithms against the ground-based daily snow depth data, the SVR snow-depth retrieval algorithm performs much superior with reduced uncertainties. We report the results aimed at evaluating the impact of the variation of snow cover stages and land cover types. The preliminary results suggest that the SVR snow-depth algorithm could detect deep snow with high accuracy and decrease the impact of saturation effects. These results suggest that the SVR snow-depth retrieval algorithm integrating PM remote sensing data and other auxiliary data (land cover types, location, terrain, snow cover stage with indirectly considering grain size variation) can be a more efficient and effective algorithm for retrieving snow depth and snow water equivalent over various scales.

Cluster Analysis of Snowfall Observatory Using K-means Algorithm

Because of global warming, the polar vortex become weak and the cold and low pressure air in pole move down to the middle latitude. The cold air causes the severely cold winter recently. In South Korea, the same phenomenon happens every year from 2014. Therefore, recording the amount of winter snowfall and preparing for a sudden disaster became another homework to cope with the global warming. Since Korea has traditionally not suffered by heavy snow damage, the number of snow observation stations is smaller than that of rainfall observation stations. Therefore, it is necessary to install additional snow observation stations, but before this, the current distribution of snow observation stations should be analyzed. Monthly maximum fresh daily snow depth and observational altitude data of January, February, and December were used as the four parameters of K-mean algorithm. Total 94 snowfall stations were classified by data retention period and cluster analysis was performed. As a result of the cluster analysis, a cluster was formed into four: the west coast area, the inland mountainous area along the Taebeek - Sobaek mountain vein, Gyeongsang and South coast, and Jeju island, Ulleungdo and Daegwallyeong. In addition, since the snow observation station in Jeju Island is only installed on the coastal area, additional snow observation stations should be considered in the mountainous area. Keywords: Clustering, K-means, Snowfall, Snowfall Observatory

A new method for establishing hydrologic fidelity of snow depth measurements based on snowmelt–runoff hydrographs

ABSTRACTFor snowmelt-driven flood studies, snow water equivalent (SWE) is frequently estimated using snow depth data. Accurate measurements of snow depth are important in providing data for continuous hydrologic simulations of such watersheds. A new hydrologic fidelity metric is proposed in this study to evaluate the potential contribution of particular snow depth datasets to flow characteristics using observed data and hydrologic modeling using the Variable Infiltration Capacity (VIC) model. Data-based hydrologic fidelity of snow depth measurements is defined as a categorical skill score between the snow depth in the watershed and the hydrograph peak or volume at the watershed outlet. Similarly, model-based hydrologic fidelity is defined as a categorical skill score between the model-simulated snow depth and the model-simulated hydrograph peak or volume. The proposed framework is illustrated using the Pecatonica River watershed in the USA, indicating which sites have a higher hydrologic fidelity, which is preferred in hydrologic studies.

Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow

Wind sometimes creates a hard, wind-packed layer at the surface of a snowpack. The formation of such wind crusts was observed during wind tunnel experiments with combined SnowMicroPen and Microsoft Kinect sensors. The former provides the hardness of new and wind-packed snow and the latter spatial snow depth data in the test section. Previous experiments showed that saltation is necessary but not sufficient for wind-packing. The combination of hardness and snow depth data now allows to study the case with saltation in more detail. The Kinect data requires complex processing but with the appropriate corrections, snow depth changes can be measured with an accuracy of about 1 mm. The Kinect is therefore well suited to quantify erosion and deposition. We found that no hardening occurred during erosion and that a wind crust may or may not form when snow is deposited. Deposition is more efficient at hardening snow in wind-exposed than in wind-sheltered areas. The snow hardness increased more on the windward side of artificial obstacles placed in the wind tunnel. Similarly, the snow was harder in positions with a low Sx parameter. Sx describes how wind-sheltered (high Sx) or wind-exposed (low Sx) a position is and was calculated based on the Kinect data. The correlation between Sx and snow hardness was -0.63. We also found a negative correlation of -0.4 between the snow hardness and the deposition rate. Slowly deposited snow is harder than a rapidly growing accumulation. Sx and the deposition rate together explain about half of the observed variability of snow hardness.