Estimation of snow cover parameters on the earth’s surface with flat and hilly-mountainous terrain using radar interferometry

Исследуется зондирование снежного покрова методом радарной интерферометрии. Предложена приближенная модель интерферометрического зондирования, основанная на методе малых возмущений. Поле обратного рассеяния рассматривается как когерентная сумма волн, рассеянных на неровностях границ раздела “воздух - снег” и “снег - почва”. Представлены результаты определения водного эквивалента снега с помощью радарной интерферометрии на ровной земной поверхности (равнина) и поверхности с уклонами рельефа (холмисто-гористая местность). Проведена оценка вклада рассеяния от поверхности снега в значения амплитуды и интерферометрической фазы. Выполнен анализ влияния уклонов рельефа на оценку параметров снежного покрова. Приведены результаты численных оценок. Показано, что для местности с уклоном рельефа около 45изменения в интерферометрической фазе относительно ровной поверхности достигают 40%. Однако если склоны сравнительно ровные (менее 10), эти изменения не превышают 10% The main characteristics of the snow cover determining its impact on the environment are its thickness and the snow water equivalent (SWE). SWE assesses the water content in the snow cover. Radar interferometry is one of the methods for determining SWE. The paper presents the results of snow cover sensing by radar interferometry on both flat Earth’s surface and terrain with relief. A backscattering model taking into account backscattering from the snow surface is proposed in contrast to the existing methods. The backscattering field is considered as a coherent sum of waves scattered on the irregularities of the air - snow and snow - soil interfaces. These interfaces are statistically rough surfaces with random irregularities, whose heights are described by uncorrelated stationary random functions with their mean values, standard deviations, and correlation radii. It is assumed that the irregularities are small compared to the wavelength, their slopes are small, and the conditions for the applicability of the method of small perturbations are satisfied. It is also supposed that roughness does not affect the coherent field according to the Born approximation. The incident and scattered waves are assumed to follow Snell’s law. The coherent waves reflection and transmission coefficients are determined by Fresnel formulas for a flat interface. The contribution of backscattering from the snow surface to the values of the amplitude and interferometric phase is estimated using small perturbation method. It is shown that the relative error of interferometric phase determination due to the influence of the wave scattered by the air - snow boundary does not exceed 8% for the angles of incidence of 20-45and the density of snow 0.2-0.3 g/cm. The approximate relations show the linear dependence between the interferometric phase and SWE. The model is extended to the general case of backscattering from snow cover on the Earth’s surface with relief. The influence of terrain slopes on the interferometric phase is estimated. It is shown that for hilly terrain with slopes of about 45, the relative changes in the interferometric phase could reach 40%. However, if the slopes are relatively flat (less than 10), these changes do not exceed 10%.

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The Spatiotemporal Patterns and Interrelationships of Snow Cover and Climate Change in Tianshan Mountains

Water ◽

10.3390/w13040404 ◽

2021 ◽

Vol 13 (4) ◽

pp. 404

Author(s):

Tong Heng ◽

Xinlin He ◽

Lili Yang ◽

Jiawen Yu ◽

Yulin Yang ◽

...

Keyword(s):

Snow Cover ◽

Snow Water Equivalent ◽

Warming Trend ◽

Tianshan Mountains ◽

Spatiotemporal Patterns ◽

Tianshan Mountain ◽

Mountainous Areas ◽

The Future ◽

Snow Water ◽

Nighttime Warming

To reveal the spatiotemporal patterns of the asymmetry in the Tianshan mountains’ climatic warming, in this study, we analyzed climate and MODIS snow cover data (2001–2019). The change trends of asymmetrical warming, snow depth (SD), snow coverage percentage (SCP), snow cover days (SCD) and snow water equivalent (SWE) in the Tianshan mountains were quantitatively determined, and the influence of asymmetrical warming on the snow cover activity of the Tianshan mountains were discussed. The results showed that the nighttime warming rate (0.10 °C per decade) was greater than the daytime, and that the asymmetrical warming trend may accelerate in the future. The SCP of Tianshan mountain has reduced by 0.9%. This means that for each 0.1 °C increase in temperature, the area of snow cover will reduce by 5.9 km2. About 60% of the region’s daytime warming was positively related to SD and SWE, and about 48% of the region’s nighttime warming was negatively related to SD and SWE. Temperature increases were concentrated mainly in the Pamir Plateau southwest of Tianshan at high altitudes and in the Turpan and Hami basins in the east. In the future, the western and eastern mountainous areas of the Tianshan will continue to show a warming trend, while the central mountainous areas of the Tianshan mountains will mainly show a cooling trend.

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Seasonal and Ephemeral Snowpacks of the Conterminous United States

Hydrology ◽

10.3390/hydrology8010032 ◽

2021 ◽

Vol 8 (1) ◽

pp. 32

Author(s):

Benjamin J. Hatchett

Keyword(s):

United States ◽

At Risk ◽

New England ◽

Snow Cover ◽

Snow Water Equivalent ◽

Atlantic Coast ◽

Seasonal Snow ◽

Temperature And Precipitation ◽

Lower Elevation ◽

Precipitation Estimates

Snowpack seasonality in the conterminous United States (U.S.) is examined using a recently-released daily, 4 km spatial resolution gridded snow water equivalent and snow depth product developed by assimilating station-based observations and gridded temperature and precipitation estimates from PRISM. Seasonal snowpacks for the period spanning water years 1982–2017 were calculated using two established methods: (1) the classic Sturm approach that requires 60 days of snow cover with a peak depth >50 cm and (2) the snow seasonality metric (SSM) that only requires 60 days of continuous snow cover to define seasonal snow. The latter approach yields continuous values from −1 to +1, where −1 (+1) indicates an ephemeral (seasonal) snowpack. The SSM approach is novel in its ability to identify both seasonal and ephemeral snowpacks. Both approaches identify seasonal snowpacks in western U.S. mountains and the northern central and eastern U.S. The SSM approach identifies greater areas of seasonal snowpacks compared to the Sturm method, particularly in the Upper Midwest, New England, and the Intermountain West. This is a result of the relaxed depth constraint compared to the Sturm approach. Ephemeral snowpacks exist throughout lower elevation regions of the western U.S. and across a broad longitudinal swath centered near 35° N spanning the lee of the Rocky Mountains to the Atlantic coast. Because it lacks a depth constraint, the SSM approach may inform the location of shallow but long-duration snowpacks at risk of transitioning to ephemeral snowpacks with climatic change. A case study in Oregon during an extreme snow drought year (2014/2015) highlights seasonal to ephemeral snowpack transitions. Aggregating seasonal and ephemeral snowpacks to the HUC-8 watershed level in the western U.S. demonstrates the majority of watersheds are at risk of losing seasonal snow.

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Nimbus-7 SMMR Derived Global Snow Cover Parameters

Annals of Glaciology ◽

10.1017/s0260305500200736 ◽

1987 ◽

Vol 9 ◽

pp. 39-44 ◽

Cited By ~ 200

Author(s):

A.T.C. Chang ◽

J.L. Foster ◽

D.K. Hall

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Water Equivalent ◽

Theoretical Calculations ◽

Winter Season ◽

Western China ◽

High Plains ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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Cloud obstruction and snow cover in Alpine areas from MODIS products

Hydrology and Earth System Sciences ◽

10.5194/hess-18-4579-2014 ◽

2014 ◽

Vol 18 (11) ◽

pp. 4579-4600 ◽

Cited By ~ 16

Author(s):

P. Da Ronco ◽

C. De Michele

Keyword(s):

Snow Cover ◽

River Basin ◽

Cloud Cover ◽

Large Scale ◽

Snow Water Equivalent ◽

Practical Interest ◽

Computational Effort ◽

Po River ◽

Average Value ◽

Po River Basin

Abstract. Snow cover maps provide information of great practical interest for hydrologic purposes: when combined with point values of snow water equivalent (SWE), they enable estimation of the regional snow resource. In this context, Earth observation satellites are an interesting tool for evaluating large scale snow distribution and extension. MODIS (MODerate resolution Imaging Spectroradiometer on board Terra and Aqua satellites) daily Snow Covered Area product has been widely tested and proved to be appropriate for hydrologic applications. However, within a daily map the presence of cloud cover can hide the ground, thus obstructing snow detection. Here, we consider MODIS binary products for daily snow mapping over the Po River basin. Ten years (2003–2012) of MOD10A1 and MYD10A1 snow maps have been analysed and processed with the support of a 500 m resolution Digital Elevation Model (DEM). We first investigate the issue of cloud obstruction, highlighting its dependence on altitude and season. Snow maps seem to suffer the influence of overcast conditions mainly in mountain and during the melting period. Thus, cloud cover highly influences those areas where snow detection is regarded with more interest. In spring, the average percentages of area lying beneath clouds are in the order of 70%, for altitudes over 1000 m a.s.l. Then, starting from previous studies, we propose a cloud removal procedure and we apply it to a wide area, characterized by high geomorphological heterogeneity such as the Po River basin. In conceiving the new procedure, our first target was to preserve the daily temporal resolution of the product. Regional snow and land lines were estimated for detecting snow cover dependence on elevation. In cases when there was not enough information on the same day within the cloud-free areas, we used temporal filters with the aim of reproducing the micro-cycles which characterize the transition altitudes, where snow does not stand continually over the entire winter. In the validation stage, the proposed procedure was compared against others, showing improvements in the performance for our case study. The accuracy is assessed by applying the procedure to clear-sky maps masked with additional cloud cover. The average value is higher than 95% considering 40 days chosen over all seasons. The procedure also has advantages in terms of input data and computational effort requirements.

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Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

The Cryosphere ◽

10.5194/tc-11-1647-2017 ◽

2017 ◽

Vol 11 (4) ◽

pp. 1647-1664 ◽

Cited By ~ 29

Author(s):

Emmy E. Stigter ◽

Niko Wanders ◽

Tuomo M. Saloranta ◽

Joseph M. Shea ◽

Marc F. P. Bierkens ◽

...

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Snow Cover ◽

Climate Sensitivity ◽

Snow Depth ◽

Snow Water Equivalent ◽

Snowmelt Runoff ◽

Snow Water ◽

Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

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Nimbus-7 SMMR Derived Global Snow Cover Parameters

Annals of Glaciology ◽

10.3189/s0260305500200736 ◽

1987 ◽

Vol 9 ◽

pp. 39-44 ◽

Cited By ~ 85

Author(s):

A.T.C. Chang ◽

J.L. Foster ◽

D.K. Hall

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Water Equivalent ◽

Theoretical Calculations ◽

Winter Season ◽

Western China ◽

High Plains ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Snow covers about 40 million km2 of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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Variation of snow cover in the Nyang River basin of southeastern Tibetan Plateau, China

Journal of Water and Climate Change ◽

10.2166/wcc.2021.037 ◽

2021 ◽

Author(s):

Rui Zhang ◽

Zongxue Xu ◽

Depeng Zuo ◽

Chunguang Ban

Keyword(s):

Snow Cover ◽

River Basin ◽

Snow Water Equivalent ◽

Global Climate ◽

Remote Sensing Data ◽

Significant Negative Correlation ◽

Spatial And Temporal Variation ◽

Snow Cover Area ◽

Northern Edge ◽

Slight Upward Trend

Abstract Snow cover is highly sensitive to global climate change and strongly influences the climate at global and regional scales. Because of limited in situ observations, snow cover dynamics in the Nyang River basin (NRB) have been examined in few studies. Five snow cover indices derived from observation and remote sensing data from 2000 to 2018 were used to investigate the spatial and temporal variation of snow cover in the NRB. There was clear seasonality in the snow cover throughout the entire basin. The maximum snow-covered area was 8,751.35 km2, about 50% of the total basin area, and occurred in March. The maximum snow depth (SD) was 5.35 cm and was found at the northern edge of the middle reaches of the basin. Snow cover frequency, SD, and fraction of snow cover area increased with elevation. The decrease in SD was the most marked in the elevation range of 5,000–6,000 m. Above 6,000 m, the snow water equivalent showed a slight upward trend. There was a significant negative correlation between snow cover and temperature. The results of this study could improve our understanding of changes in snow cover in the NRB from multivariate perspectives. It is better for water resources management.

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Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

Applied Sciences ◽

10.3390/app11188365 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8365

Author(s):

Liming Gao ◽

Lele Zhang ◽

Yongping Shen ◽

Yaonan Zhang ◽

Minghao Ai ◽

...

Keyword(s):

Snow Cover ◽

River Basin ◽

Snow Depth ◽

Snow Water Equivalent ◽

Snow Accumulation ◽

Implicit Scheme ◽

Snow Water ◽

Irtysh River ◽

Simulation Results ◽

The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

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Modeling the snow surface temperature with a one-layer energy balance snowmelt model

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-15071-2013 ◽

2013 ◽

Vol 10 (12) ◽

pp. 15071-15118 ◽

Cited By ~ 2

Author(s):

J. You ◽

D. G. Tarboton ◽

C. H. Luce

Keyword(s):

Energy Balance ◽

Surface Temperature ◽

Snow Water Equivalent ◽

Effective Conductivity ◽

Energy Content ◽

Single Layer ◽

Conductive Heat ◽

Snow Surface ◽

Snow Surface Temperature ◽

Snowmelt Model

Abstract. \\label{sec:abstract} Snow surface temperature is a key control on energy exchanges at the snow surface, particularly net longwave radiation and turbulent energy fluxes. The snow surface temperature is in turn controlled by the balance between various external fluxes and the conductive heat flux, internal to the snowpack. Because of the strong insulating properties of snow, thermal gradients in snow packs are large and nonlinear, a fact that has led many to advocate multiple layer snowmelt models over single layer models. In an effort to keep snowmelt modeling simple and parsimonious, the Utah Energy Balance (UEB) snowmelt model used only one layer but allowed the snow surface temperature to be different from the snow average temperature by using an equilibrium gradient parameterization based on the surface energy balance. Although this procedure was considered an improvement over the ordinary single layer snowmelt models, it still resulted in discrepancies between modeled and measured snowpack energy contents. In this paper we examine the parameterization of snow surface temperature in single layer snowmelt models from the perspective of heat conduction into a semi-infinite medium. We evaluate the equilibrium gradient approach, the force-restore approach, and a modified force-restore approach. In addition, we evaluate a scheme for representing the penetration of a refreezing front in cold periods following melt. We also introduce a method to adjust effective conductivity to account for the presence of ground near to a shallow snow surface. These parameterizations were tested against data from the Central Sierra Snow Laboratory, CA, Utah State University experimental farm, UT, and Subnivean snow laboratory at Niwot Ridge, CO. These tests compare modeled and measured snow surface temperature, snow energy content, snow water equivalent, and snowmelt outflow. We found that with these refinements the model is able to better represent the snowpack energy balance and internal energy content while still retaining a parsimonious one layer format.

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Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-060.1 ◽

2014 ◽

Vol 15 (2) ◽

pp. 551-562 ◽

Cited By ~ 7

Author(s):

Jiarui Dong ◽

Mike Ek ◽

Dorothy Hall ◽

Christa Peters-Lidard ◽

Brian Cosgrove ◽

...

Keyword(s):

United States ◽

Snow Cover ◽

Air Temperature ◽

Snow Water Equivalent ◽

Probability Of Detection ◽

Joint Analysis ◽

Snow Cover Area ◽

Fractional Snow Cover ◽

Retrieval Errors

Abstract Understanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in Nevada [30% increase in probability of detection (POD)], especially in the early and late snow seasons; some improvement over California (10% POD increase); and a relatively small improvement over Colorado (2% POD increase). However, significant spatial and temporal variations in accuracy still exist, and a proxy is required to adequately predict the expected errors in MODIS C6 FSC retrievals. A relationship is demonstrated between the MODIS FSC retrieval errors and temperature over the CONUS domain, captured by a cumulative double exponential distribution function. This relationship is shown to hold for both in situ and modeled daily mean air temperature. Both of them are useful indices in filtering out the misclassification of MODIS snow cover pixels and in quantifying the errors in the MODIS C6 product for various hydrological applications.

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