Snow Area Research Articles

ネパール，ゴルカ地震によるヒマラヤ雪氷域の雪崩・崩壊イベント

ネパール，ゴルカ地震によるヒマラヤ雪氷域の雪崩・崩壊イベント

Comparison between Snow Albedo Obtained from Landsat TM, ETM+ Imagery and the SPOT VEGETATION Albedo Product in a Mediterranean Mountainous Site

Albedo plays an important role in snow evolution modeling quantifying the amount of solar radiation absorbed and reflected by the snowpack, especially in mid-latitude regions with semiarid conditions. Satellite remote sensing is the most extensive technique to determine the variability of snow albedo over medium to large areas; however, scale effects from the pixel size of the sensor source may affect the results of snow models, with different impacts depending on the spatial resolution. This work presents the evaluation of snow albedo values retrieved from (1) Landsat images, L (16-day frequency with 30 × 30 m pixel size) and (2) SPOT VEGETATION albedo products, SV (10-day frequency with 1 × 1 km pixel size) in the Sierra Nevada mountain range in South Spain, a Mediterranean site representative of highly heterogeneous conditions. Daily snow albedo map series were derived from both sources, and used as input for the snow module in the WiMMed (Watershed Integrated Management in Mediterranean Environment) hydrological model, which was operational at the study area for snow monitoring for two hydrological years, 2011–2012 and 2012–2013, in the Guadalfeo river basin in Sierra Nevada. The results showed similar albedo trends in both data sources, but with different values, the shift between both sources being distributed in space according to the altitude. This difference resulted in lower snow cover fraction values in the SV-simulations that affected the rest of snow variables included in the simulation. This underestimation, mainly due to the effects of mixed pixels composed by both snow and snow-free areas, produced higher divergences from both sources during the melting periods when the evapo-sublimation and melting fluxes are more relevant. Therefore, the selection of the albedo data source in these areas, where snow evapo-sublimation plays a very important role and the presence of snow-free patches is very frequent, can condition the final accuracy of the simulations of operational models; Landsat is the recommended source if the monitoring of the snowpack is the final goal of the modeling, whereas the SV product may be advantageous when water resource planning in the medium and long term is intended. Applications of large pixel size albedo sources need further assessment for short-term operational objectives.

Snow cover mapping using IRS-P6 AWiFS data and the relationships between some climatic factors with snowpack in the northwest of Iran

In this study, we explored the potential of the multispectral and multi-temporal IRS Advanced Wide Field Sensor (AWiFS) data for mapping of the snow cover in the northwest regions of Iran. The AWiFS snow cover maps, based on the unsupervised classification method, were compared with the estimates of snow cover area derived from the moderate resolution imaging spectroradiometer (MODIS) images based on the normalized difference snow index. Good concurrence was observed with respect to the snow area between the AWiFS features and the MODIS features; however, the snow spatial distribution of the AWiFS features differed from those of the MODIS based on the nonentity of the temporal accordance between two types of features. Also, we explored the relationships between some climatic and topographic factors with the snowpack in the northwest part of Iran. Relationships between some climatic factors with snowpack specifications were obtained, which showed significant correlation only between the components of daily temperature and snow density. The other results showed that the amounts of snowpack depth have significant correlations with the height of the stations and the height classes in 1% surface and snowpack depths showed significant differences together within the different height classes.

An In-Depth Evaluation of Heritage Algorithms for Snow Cover and Snow Depth Using AMSR-E and AMSR2 Measurements

AbstractThe Advanced Microwave Scanning Radiometer 2 (AMSR2) was launched in 2012 on board the Global Change Observation Mission 1st–Water (GCOM-W1) satellite. This study presents a robust evaluation of AMSR2 algorithms for the retrieval of snow-covered area (SCA) and snow depth (SD) that will be used operationally by the National Oceanic and Atmospheric Administration (NOAA). Quantitative assessment of the algorithms was performed for a 10-yr period with AMSR-E and a 2-yr period with AMSR2 data using the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and in situ SD data as references. AMSR-E SCA showed a monthly overall accuracy rate of about 80% except in May. Accuracy improves significantly to over 90% when wet snow cases are excluded, and accuracy differences between ascending and descending portions of orbits also decrease. Microwave-derived SCA over dry snow areas can therefore be obtained with accuracy close to optically derived SCA. An evaluation of the results for AMSR-E SD showed a low overall bias of 1 cm and a root-mean-square error of 20 cm. Results for AMSR2-based SCA and SD are similar to those from AMSR-E. Biases and root-mean-square errors show dependencies on elevation, forest fraction, the magnitude of snow depth, and snow cover class.

Detection of Day Time Fog Over India Using INSAT-3D Data

A simple and new fog detection algorithm using any geostationary satellite data that is having at least one visible and one thermal channel is developed. The visible channel observation is used to detect fog, whereas the thermal infrared channel data are used to eliminate the medium/high clouds and snow area. This method is based on temporal differencing technique. Thresholds used in this technique are derived dynamically for each data. It also includes the spatial homogeneity test, which is a unique characteristic of fog. The algorithm is tested with half hourly data of INSAT-3D imager from 0400 to 0930 UTC of 2013–2014 winter months. The fog maps were validated with visibility data available at hourly interval for five different locations over the Indo-Gangetic plains of India for January 2014. The main advantage of this algorithm is its capability to detect day time fog without using the $3.9\hbox{-}\upmu {\text {m}}$ channel, which is having both emissive and reflective components during day time. This technique is found to have potential for operational use.

Temporal and spatial variations of grassland desertification monitoring in Tibet of China

Tibet, the largest region of the Qinghai–Tibet Plateau, is undergoing extensive grassland deterioration and desertification due to both human and natural factors. Alpine meadow and grassland restoration is difficult after degradation; consequently, the desertification of the Tibetan grassland has attracted substantial social attention. This article considered Amdo, Baingoin, Coqên, and Zhongba counties in Tibet as the study areas, employed remote-sensing data, and developed Tibetan grassland desertification classification indices based on field surveys. Moreover, this study used spectral mixture analysis (SMA) methods to interpret remote-sensing image data from the study areas during three periods (1990, 2000, and 2009) and considered the bare sand (gravel) area proportion as the main basis for the evaluation of grassland desertification. The results of this study demonstrate that the slightly, moderately, and severely desertified grasslands of the monitoring zone covered a total area of 114,113.16 km2 in 1990, accounting for 82.12% of the study area. The area exhibited no change in 2000 and decreased by 4472.31 km2 in 2009. The severely desertified grassland area declined from 1990 to 2009. The degree of grassland desertification in these four Tibetan counties diminished from 1990 to 2009, and the grassland desertification area exhibited a gradual reduction during the same period. Regarding other soil coverage types, the ice and snow area markedly changed and declined to approximately one-third of its original extent during these 20 years, and most of the ice and snow area was converted to bare land and various types of desertified grassland.

Downscaling Snow Cover Fraction Data in Mountainous Regions Based on Simulated Inhomogeneous Snow Ablation

High-resolution snow distributions are essential for studying cold regions. However, the temporal and spatial resolutions of current remote sensing snow maps remain limited. Remotely sensed snow cover fraction (SCF) data only provide quantitative descriptions of snow area proportions and do not provide information on subgrid-scale snow locations. We present a downscaling method based on simulated inhomogeneous snow ablation capacities that are driven by air temperature and solar radiation data. This method employs a single parameter to adjust potential snow ablation capacities. Using this method, SCF data with a resolution of 500 m are downscaled to a resolution of 30 m. Then, 18 remotely sensed TM, CHRIS and EO-1 snow maps are used to verify the downscaled results. The mean overall accuracy is 0.69, the average root-mean-square error (RMSE) of snow-covered slopes between the downscaled snow map and the real snow map is 3.9°, and the average RMSE of the sine of the snow covered aspects between the downscaled snow map and the real snow map is 0.34, which is equivalent to 19.9°. This method can be applied to high-resolution snow mapping in similar mountainous regions.

An Analysis of Trajectory Data for Quasi Tsunami Evacuation in a Heavy Snow and Cold Area

An Analysis of Trajectory Data for Quasi Tsunami Evacuation in a Heavy Snow and Cold Area

Toward Improved Daily Cloud-Free Fractional Snow Cover Mapping with Multi-Source Remote Sensing Data in China

With the high resolution of optical data and the lack of weather effects of passive microwave data, we developed an algorithm to map daily cloud-free fractional snow cover (FSC) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard daily FSC product, the Advanced Microwave Scanning Radiometer (AMSR2) snow water equivalent (SWE) product and digital elevation data. We then used the algorithm to produce a daily cloud-free FSC product with a resolution of 500 m for regions in China. In addition, we produced a high-resolution FSC map using a Landsat 8 Operational Land Imager (OLI) image as a true value to test the accuracy of the cloud-free FSC product developed in this study. The analysis results show that the daily cloud-free FSC product developed in this study can completely remove clouds and effectively improve the accuracy of snow area monitoring. Compared to the true value, the mean absolute error of our product is 0.20, and its root mean square error is 0.29. Thus, the synthesized product in this study can improve the accuracy of snow area monitoring, and the obtained snow area data can be used as reliable input parameters for hydrological and climate models. The land cover type and terrain factors are the main factors that limit the accuracy of the daily cloud-free FSC product developed in this study. These limitations can be further improved by improving the accuracy of the MODIS standard snow product for complicated underlying surfaces.

Examination of snowmelt over Western Himalayas using remote sensing data

Snowmelt variability in the Western Himalayas has been examined using remotely sensed snow water equivalent (SWE) and snow-covered area (SCA) datasets. It is seen that climatological snowfall and snowmelt amount varies in the Himalayan region from west to east and from month to month. Maximum snowmelt occurs at the elevation zone between 4500 and 5000 m. As the spring and summer approach and snowmelt begins, a large amount of snow melts in May. Strength and weaknesses of temperature-based snowmelt models have been analyzed for this region by computing the snowmelt factor or the degree-day factor (DDF). It is seen that average DDF in the Himalayas is more in April and less in July. During spring and summer months, melting rate is higher in the areas that have height above 2500 m. The region that lies between 4500 and 5000 m elevation zones contributes toward more snowmelt with higher melting rate. Snowmelt models have been developed to estimate interannual variations of monthly snowmelt amount using the DDF, observed SWE, and surface air temperature from reanalysis datasets. In order to further improve the estimate snowmelt, regression between observed and modeled snowmelt has been carried out and revised DDF values have been computed. It is found that both the models do not capture the interannual variability of snowmelt in April. The skill of the model is moderate in May and June, but the skill is relatively better in July. In order to explain this skill, interannual variability (IAV) of surface air temperature has been examined. Compared to July, in April, the IAV of temperature is large indicating that a climatological value of DDF is not sufficient to explain the snowmelt rate in April. Snow area and snow amount depletion curves over Himalayas indicate that in a small area at high altitude, snow is still observed with large SWE whereas over most of the region, all the snow has melted.

Snow evolution in a semi-arid mountainous area combining snow modelling and Landsat spectral mixture analysis

Abstract. This study proposes the use of both physically-distributed hydrological modelling in combination with satellite remote sensing images, to study the evolution of the snowpack in the Sierra Nevada mountains, in southern Spain. The snowmelt-accumulation module inside WiMMed (Watershed Integrated Management in Mediterranean Environment) hydrological model was employed, which includes the use of depletion curves to expand the energy and water balance equations over a grid representation. Snow maps obtained from spectral mixture analysis of Landsat images were used to evaluate this model at the study site. The results show a significant agreement between observed and simulated snow pixels in the area, which allows production of sequences of snow maps with greater resolution than the remote sensing images employed. However, some mismatches do appear at the boundaries of the snow area, mainly related to: (a) the great number of mixed pixels; and (b) the influence of the snow transport by wind.

DETAILED AND HIGHLY ACCURATE 3D MODELS OF HIGH MOUNTAIN AREAS BY THE MACS-HIMALAYA AERIAL CAMERA PLATFORM

Abstract. Remote sensing in areas with extreme altitude differences is particularly challenging. In high mountain areas specifically, steep slopes result in reduced ground pixel resolution and degraded quality in the DEM. Exceptionally high brightness differences can in part no longer be imaged by the sensors. Nevertheless, detailed information about mountainous regions is highly relevant: time and again glacier lake outburst floods (GLOFs) and debris avalanches claim dozens of victims. Glaciers are sensitive to climate change and must be carefully monitored. Very detailed and accurate 3D maps provide a basic tool for the analysis of natural hazards and the monitoring of glacier surfaces in high mountain areas. There is a gap here, because the desired accuracies are often not achieved. It is for this reason that the DLR Institute of Optical Sensor Systems has developed a new aerial camera, the MACS-Himalaya. The measuring unit comprises four camera modules with an overall aperture angle of 116° perpendicular to the direction of flight. A High Dynamic Range (HDR) mode was introduced so that within a scene, bright areas such as sun-flooded snow and dark areas such as shaded stone can be imaged. In 2014, a measuring survey was performed on the Nepalese side of the Himalayas. The remote sensing system was carried by a Stemme S10 motor glider. Amongst other targets, the Seti Valley, Kali-Gandaki Valley and the Mt. Everest/Khumbu Region were imaged at heights up to 9,200 m. Products such as dense point clouds, DSMs and true orthomosaics with a ground pixel resolution of up to 15 cm were produced. Special challenges and gaps in the investigation of high mountain areas, approaches for resolution of these problems, the camera system and the state of evaluation are presented with examples.

Modeled intermittency risk for small streams in the Upper Colorado River Basin under climate change

• We analyzed historic flow records from streams in the Upper Colorado River Basin. • Dry years with high temperatures and low precipitation had more zero-flow days. • Mean flow was positively related to minimum flows. • Important predictors of low flow were: precipitation, PET, soils, drainage area. • Streams with high variability and low flows will be prone to more intermittency. Longer, drier summers projected for arid and semi-arid regions of western North America under climate change are likely to have enormous consequences for water resources and river-dependent ecosystems. Many climate change scenarios for this region involve decreases in mean annual streamflow, late-summer precipitation and late-summer streamflow in the coming decades. Intermittent streams are already common in this region, and it is likely that minimum flows will decrease and some perennial streams will shift to intermittent flow under climate-driven changes in timing and magnitude of precipitation and runoff, combined with increases in temperature. To understand current intermittency among streams and analyze the potential for streams to shift from perennial to intermittent under a warmer climate, we analyzed historic flow records from streams in the Upper Colorado River Basin (UCRB). Approximately two-thirds of 115 gaged stream reaches included in our analysis are currently perennial and the rest have some degree of intermittency. Dry years with combinations of high temperatures and low precipitation were associated with more zero-flow days. Mean annual flow was positively related to minimum flows, suggesting that potential future declines in mean annual flows will correspond with declines in minimum flows. The most important landscape variables for predicting low flow metrics were precipitation, percent snow, potential evapotranspiration, soils, and drainage area. Perennial streams in the UCRB that have high minimum-flow variability and low mean flows are likely to be most susceptible to increasing streamflow intermittency in the future.

Application of Remote Sensing and GIS in Forest Cover Change in Tehsil Barawal, District Dir, Pakistan

The forests of Pakistan replicate plentiful climatic, physiographic and edaphic differences in the country and these forests face a serious problem of deforestation. Geographic information system (GIS) techniques and remote sensing (RS) from satellite platforms offer a best way to identify those areas of deforestation, and thus a GIS and RS based study was conducted in tehsil Barawal, district Dir (U) to analyze forest cover change. The main objectives of the study were to: 1) identify different classes of land use and land cover, and its spatial distribution in the study area; 2) determine the trend, nature, location and magnitude of forest cover change; and 3) prepare maps of forest-cover change in different time periods in the study area. To assess the objectives remote sensing and GIS techniques were utilized. A supervised image classification technique was applied on Landsat 5 satellite images of 2000 and 2012. Five main classes such as agriculture, forest, barren land, snow and water were identified. The results showed that the area of forest, barren land, agriculture, water and snow in year 2000 was 49.54%, 43.38%, 5.19%, 1.40% and 0.49% and the area in 2012 was 37.17%, 41.36%, 12.69%, 5.05% and 3.72% respectively. Furthermore 2.02% decrease in barren land, 12.37% decrease in forest and 7.5% increase in agriculture land were identified. Due to high deforestation rate and increased agricultural activities, it is recommended that awareness campaign should be launched in the study area to protect and conserve this forest from further deforestation.

Comparison of traditional and optical grain-size field measurements with SNOWPACK simulations in a taiga snowpack

AbstractKnowledge of snow microstructure is relevant for modelling the physical properties of snow cover and for simulating the propagation of electromagnetic waves in remote-sensing applications. Characterization of the microstructure in field conditions is, however, a challenging task due to the complex, sintered and variable nature of natural snow cover. A traditional measure applied as a proxy of snow microstructure, which can also be determined in field conditions, is the visually estimated snow grain size. Developing techniques also allow measurement, for example, of the specific surface area (SSA) of snow, from which the optical-equivalent grain size can be derived. The physical snow model SNOWPACK simulates evolution of snow parameters from meteorological forcing data. In this study we compare an extensive experimental dataset of measurements of traditional grain size and SSA-derived optical grain size with SNOWPACK simulations of grain-size parameters. On average, a scaling factor of 1.2 is required to match traditional grain-size observations with the corresponding SNOWPACK simulation; a scaling factor of 2.1 was required for the optical equivalent grain size. Standard deviations of scaling factors for the winters of 2011/12 and 2012/13 were 0.36 and 0.42, respectively. The largest scaling factor was needed in early winter and under melting conditions.

Effect of climate change on the snow disappearance date in mountainous areas of central Japan

We investigated climate change in the annual snow cover period in mountainous areas of central Japan by downscaling simulations of four climate change projections based on a Coupled Model Intercomparison Project phase 3 (CMIP3) Special Report on Emission Scenarios (SRES) A1B emission scenario using a regional climate model. Our numerical simulation reproduced well the observed snow depths and areas of snow cover. The projected snow disappearance date in all areas occurred earlier in the future climate due to global warming and were substantially earlier in areas of both light and heavy snowpacks in the present climate. The time shift was smaller at areas where the present-day maximum snow depth is around 100 cm and the snow disappearance date is in mid-April. These projected changes in the duration of snow cover were associated with decreasing snowfall and accelerated snowmelt due to increasing surface air temperatures. The effect was interpreted using an idealized model of temporal variation in surface air temperature. Earlier snowmelt causes local enhancement of surface air temperature increases that will have considerable impact on mountain ecosystems.

Evolution of the specific surface area of snow during high‐temperature gradient metamorphism

AbstractThe structural evolution of low‐density snow under a high‐temperature gradient over a short period usually takes place in the surface layers on clear, cold nights. In this paper, X‐ray computed microtomography (microCT) was combined with numerical simulations to investigate the temperature gradient metamorphism (TGM) on different types of snow. Precipitation particles (PP), small rounded particles (RGsr), and large rounded particles (RGlr) were each observed in high‐temperature gradients (100–500 K m−1) at a mean temperature of −4°C. The specific surface area (SSA) was used to characterize the TGM, which were influenced by both the magnitude of the temperature gradient and the initial snow structures. PP samples experienced a logarithmic decrease of SSA with time, and the depth hoar structures created under high TGM (500 K m−1) have higher SSA compared to those under lower TGM. Unlike previous observations, for initial rounded and connected structures, like RGlr samples, the SSA increased during TGM. Simulated normal vapor flux distributions for different snow types were used to help understand the structural evolution under TGM. Understanding the SSA increase is important in order to predict the enhanced uptake of chemical species by snow or increase in snow albedo.

Impact of Orbital Parameters and Greenhouse Gas on the Climate of MIS 7 and MIS 5 Glacial Inceptions

Abstract This work explores the impact of orbital parameters and greenhouse gas concentrations on the climate of marine isotope stage (MIS) 7 glacial inception and compares it to that of MIS 5. The authors use a coupled atmosphere–ocean general circulation model to simulate the mean climate state of six time slices at 115, 122, 125, 229, 236, and 239 kyr, representative of a climate evolution from interglacial to glacial inception conditions. The simulations are designed to separate the effects of orbital parameters from those of greenhouse gas (GHG). Their results show that, in all the time slices considered, MIS 7 boreal lands mean annual climate is colder than the MIS 5 one. This difference is explained at 70% by the impact of the MIS 7 GHG. While the impact of GHG over Northern Hemisphere is homogeneous, the difference in temperature between MIS 7 and MIS 5 due to orbital parameters differs regionally and is linked with the Arctic Oscillation. The perennial snow cover is larger in all the MIS 7 experiments compared to MIS 5, as a result of MIS 7 orbital parameters, strengthened by GHG. At regional scale, Eurasia exhibits the strongest response to MIS 7 cold climate with a perennial snow area 3 times larger than in MIS 5 experiments. This suggests that MIS 7 glacial inception is more favorable over this area than over North America. Furthermore, at 239 kyr, the perennial snow covers an area equivalent to that of MIS 5 glacial inception (115 kyr). The authors suggest that MIS 7 glacial inception is more extensive than MIS 5 glacial inception over the high latitudes.

Application of MODIS snow cover products: wildfire impacts on snow and melt in the Sierra Nevada

Abstract. The current work evaluates the spatial and temporal variability in snow after a large forest fire in northern California using Moderate Resolution Imaging Spectroradiometer (MODIS) snow-covered area and grain size (MODSCAG). MODIS MOD10A1 fractional snow-covered area and MODSCAG fractional snow cover products are utilized to detect spatial and temporal changes in snowpack after the 2007 Moonlight Fire and an unburned basin, Grizzly Ridge, for water years (WY) 2002–2012. Estimates of canopy-adjusted and non-adjusted MODSCAG fractional snow-covered area (fSCA) are smoothed and interpolated to provide a continuous time series of average daily snow extent over the two basins. The removal of overstory canopy by wildfire exposes more snow cover; however, elemental pixel comparisons and statistical analysis show that the MOD10A1 product has a tendency to overestimate snow coverage pre-fire, muting the observed effects of wildfire. The MODSCAG algorithm better distinguishes subpixel snow coverage in forested areas and is highly correlated to soil burn severity after the fire. Annual MODSCAG fSCA estimates show statistically significant increased fSCA in the Moonlight Fire study area after the fire (P < 0.01 for WY 2008–2011) compared to pre-fire averages and the control basin. After the fire, the number of days exceeding a pre-fire high snow-cover threshold increased by 81%. Canopy reduction increases exposed viewable snow area and the amount of solar radiation that reaches the snowpack, leading to earlier basin average melt-out dates compared to the nearby unburned basin. There is also a significant increase in MODSCAG fSCA post-fire regardless of slope or burn severity. Regional snow cover change has significant implications for both short- and long-term water supply for impacted ecosystems, downstream communities, and resource managers.

Snow cover variations in Gansu, China, from 2002 to 2013

Gansu is an inland province located in the northwest of China with an arid to semi-arid climate and a developed animal husbandry. Snowmelt in Gansu is an important source of water for rivers and plays an important role in ecological environment and social-economic activities. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day composite snow products MOD10A2 and MYD10A2 are combined to analyse snow cover variations during the snow season (October to March) period from 2002 to 2013. We define the snow area percentage (SAP) and snow cover occurrence percentage (SCOP) to analyse the spatial and temporal characteristics of the snow cover variation in Gansu. In addition, we apply the Mann-Kendall test to verify the SAP inter-annual variation. The results indicate that the SAP in Gansu remained above 5 % with three peaks in November, December and January. SAP varies a lot in the four sub-regions of Gansu, with the highest in the Gannan Plateau sub-region and the lowest in the Longzhong Loess Plateau sub-region in most of the snow seasons examined. The SCOP is high in the southwest mountains and low in the northeast Gobi and desert. The SCOP is highly related to elevation in most of Gansu, with an exception in the high mountains. In the Hexi Desert and oasis region, the SAP significantly decreases during the snow season, particularly in February and March. We find that there are a significantly negative correlation between SCOP and temperature during the snow season and a significantly positive correlation between SCOP and precipitation in December.