Vertical variability in the position of the zero isochion in geomorphologic regions of Czechia

Knowledge of the volume of water retained in mountainous areas serves as an important source of information for the anticipation of spring floods, as well as for other purposes, such as those related to agricultural management. Similarly, the extent and distribution of snow coverage (snow cover area – SCA) in lowlands are factors influencing the threat of large-scale floods caused by the melting of even a thin layer of snow cover. Every week during the winter months, the Czech Hydrometeorological Institute (CHMI) provides up to date information on the snow water equivalent present in Czech regions and especially within important hydrological basins. This information comes predominantly from an observation of net and field measurements. The position of the zero isohione, determined through remote sensing, helps to increase the accuracy of the calculations of such spatial distribution in Czechia. As a consequence of this practical use, changes in the accumulation and distribution of snow cover can be readily observed via remote senzing. This is further made easier by Czechia’s orographic disposition, specifically its interconnected system of border mountains and a central highland. As a result, the position of the zero isohione can be determined with an accuracy of 50 m a.s.l. If we compare selected geomorphological regions, we can find statistically substantiated differences in the position of the zero isohione of more than 200 m.

Download Full-text

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.

Download Full-text

Incorporation of satellite-derived snow-cover area in spatial snowmelt modeling for a large area: determination of a gridded degree-day factor

Annals of Glaciology ◽

10.3189/2013aog62a218 ◽

2013 ◽

Vol 54 (62) ◽

pp. 205-213 ◽

Cited By ~ 11

Author(s):

Yoshihiro Asaoka ◽

Yuji Kominami

Keyword(s):

Snow Cover ◽

Snow Water Equivalent ◽

Sensible Heat Flux ◽

Spatial Uncertainty ◽

Large Area ◽

Snow Cover Area ◽

Degree Day ◽

Snow Water ◽

Snowmelt Modeling

AbstractSpatial degree-day factors (DDFs) are required for spatial snowmelt modeling over large areas by the degree-day method. We propose a method to obtain DDFs by incorporating snow disappearance dates (SDDs), derived from 10 day composites of Satellite Pour l’Observation de la Terre (SPOT)/VEGETATION data, into the degree-day method. This approach allowed determination of DDFs for each gridpoint so as to better reflect regional characteristics than use of spatially constant DDFs obtained from point measurements. Simulations at six observation sites successfully accounted for variations in snow water equivalent (SWE), even at elevations different from the closest measurement site. These results suggest that incorporating satellite-derived SDDs into the degree-day method decreases spatial uncertainty compared with the use of spatially constant DDFs. Application of our method to Japanese cold regions revealed that gridded DDFs were negatively correlated with accumulated positive degree-days (APDDs) and were high only when APDDs were low. These results imply that high DDFs resulted from the dominant contribution of solar radiation to snowmelt at low temperatures and that low DDFs resulted from a relatively high contribution of sensible heat flux at high temperatures. The proposed method seems to adequately account for the main energetic components of snowmelt during the snow-cover season over large areas.

Download Full-text

The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

10.5194/hess-2019-514 ◽

2019 ◽

Author(s):

Abbas Fayad ◽

Simon Gascoin

Keyword(s):

Snow Cover ◽

Liquid Water ◽

Snow Depth ◽

Snow Water Equivalent ◽

Mountain Regions ◽

Snow Cover Area ◽

Water Percolation ◽

Snow Water ◽

Snow Model ◽

Snow Mass

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially-distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the snow water equivalent (SWE) and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 m and 3000 m a.s.l.. The results show that the new percolation scheme yields better performance especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments with values ranging between 73 mm and 286 mm we (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snow melt events during the accumulation season, and the high-variability of snow depth patterns at the sub-pixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in non-monitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

Download Full-text

Assessment of a multiresolution snow reanalysis framework: a multidecadal reanalysis case over the upper Yampa River basin, Colorado

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3575-2018 ◽

2018 ◽

Vol 22 (7) ◽

pp. 3575-3587 ◽

Cited By ~ 4

Author(s):

Elisabeth Baldo ◽

Steven A. Margulis

Keyword(s):

Data Assimilation ◽

River Basin ◽

Large Scale ◽

Snow Water Equivalent ◽

Low Complexity ◽

Snow Accumulation ◽

Computationally Efficient ◽

Snow Cover Area ◽

Mountain Ranges ◽

Snow Water

Abstract. A multiresolution (MR) approach was successfully implemented in the context of a data assimilation (DA) framework to efficiently estimate snow water equivalent (SWE) over a large head water catchment in the Colorado River basin (CRB), while decreasing computational constraints by 60 %. A total of 31 years of fractional snow cover area (fSCA) images derived from Landsat TM, ETM+, and OLI sensor measurements were assimilated to generate two SWE reanalysis datasets, a baseline case at a uniform 90 m spatial resolution and another using the MR approach. A comparison of the two showed negligible differences in terms of snow accumulation, melt, and timing for the posterior estimates (in terms of both ensemble median and coefficient of variation). The MR approach underestimated the baseline peak SWE by less than 2 % and underestimated day of peak and duration of the accumulation season by a day on average. The largest differences were, by construct, limited primarily to areas of low complexity, where shallow snowpacks tend to exist. The MR approach should allow for more computationally efficient implementations of snow data assimilation applications over large-scale mountain ranges, with accuracies similar to those that would be obtained using ∼ 100 m simulations. Such uniform resolution applications are generally infeasible due to the computationally expensive nature of ensemble-based DA frameworks.

Download Full-text

A regional climate model hindcast for Siberia – assessing the added value of snow water equivalent using ESA GlobSnow and reanalyses

The Cryosphere Discussions ◽

10.5194/tcd-6-4637-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 4637-4671

Author(s):

K. Klehmet ◽

B. Geyer ◽

B. Rockel

Keyword(s):

Regional Climate Model ◽

Snow Cover ◽

Large Scale ◽

Snow Water Equivalent ◽

Climate Model ◽

Regional Climate ◽

Snow Accumulation ◽

Added Value ◽

Snow Cover Extent ◽

Snow Water

Abstract. This study analyzes the added value of a regional climate model hindcast of CCLM compared to global reanalyses in providing a reconstruction of recent past snow water equivalent (SWE) for Siberia. Consistent regional climate data in time and space is necessary due to lack of station data in that region. We focus on SWE since it represents an important snow cover parameter in a region where snow has the potential to feed back to the climate of the whole Northern Hemisphere. The simulation was performed in a 50 km grid spacing for the period 1948 to 2010 using NCEP Reanalysis 1 as boundary forcing. Daily observational reference data for the period of 1987–2010 was obtained by the satellite derived SWE product of ESA DUE GlobSnow that enables a large scale assessment. The analyses includes comparisons of the distribution of snow cover extent, example time series of monthly SWE for January and April, regional characteristics of long-term monthly mean, standard deviation and temporal correlation averaged over subregions. SWE of CCLM is compared against the SWE information of NCEP-R1 itself and three more reanalyses (NCEP-R2, NCEP-CFSR, ERA-Interim). We demonstrate a significant added value of the CCLM hindcast during snow accumulation period shown for January for many subregions compared to SWE of NCEP-R1. NCEP-R1 mostly underestimates SWE during whole snow season. CCLM overestimates SWE compared to the satellite-derived product during April – a month representing the beginning of snow melt in southern regions. We illustrate that SWE of the regional hindcast is more consistent in time than ERA-Interim and NCEP-R2 and thus add realistic detail.

Download Full-text

The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China

Remote Sensing ◽

10.3390/rs11161879 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1879

Author(s):

Jianwei Yang ◽

Lingmei Jiang ◽

Liyun Dai ◽

Jinmei Pan ◽

Shengli Wu ◽

...

Keyword(s):

Snow Cover ◽

Snow Depth ◽

Snow Water Equivalent ◽

Depth Estimation ◽

Snow Cover Area ◽

Dual Channel ◽

Defense Meteorological Satellite Program ◽

Snow Water ◽

Microwave Imager

The long-term variations in snow depth are important in hydrological, meteorological, and ecological implications and climatological studies. The series of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments onboard the Defense Meteorological Satellite Program (DMSP) platforms has provided a consistent 30+ year data record of global observations that is well-suited for the estimation of snow cover, snow depth, and snow water equivalent (SWE). To maximize the use of this continuous microwave observation dataset in long-term snow analysis and obtain an objective result, consistency among the SSM/I and SSMIS sensors is required. In this paper, we evaluated the consistency between the SSM/I and SSMIS concerning the observed brightness temperature (Tb) and the retrieved snow cover area and snow depth from January 2007 to December 2008, where the F13 SSM/I and the F17 SSMIS overlapped. Results showed that Tb bias at 19 GHz spans from −2 to −3 K in snow winter seasons, and from −4 to −5 K in non-snow seasons. There is a slight Tb bias at 37 GHz from −2 to 2 K, regardless of season. For 85 (91) GHz, the bias presents some uncertainty from the scattering effect of the snowpack and atmospheric emission. The overall consistency between SSM/I and SSMIS with respect to snow cover detection is between 80% and 100%, which will result in a maximum snow cover area difference of 25 × 104 km2 in China. The inconsistency in Tb between SSM/I and SSMIS can result in a −2 and −0.67 cm snow depth bias for the dual-channel and multichannel algorithms, respectively. SSMIS tends to yield lower snow depth estimates than SSM/I. Moreover, there are notable bias differences between SSM/I- and SSMIS-estimated snow depths in the tundra and taiga snow classes. Our results indicate the importance of considering the Tb bias in microwave snow cover detection and snow depth retrieval and point out that, due to the sensitivity of bias to seasons, it is better to do the intercalibration with a focus on snow-covered winter seasons. Otherwise, the bias in summer will disturb the calibration coefficients and introduce more error into the snow retrievals if the seasonal difference is not carefully evaluated and separated.

Download Full-text

The role of liquid water percolation representation in estimating snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

Hydrology and Earth System Sciences ◽

10.5194/hess-24-1527-2020 ◽

2020 ◽

Vol 24 (3) ◽

pp. 1527-1542

Author(s):

Abbas Fayad ◽

Simon Gascoin

Keyword(s):

Snow Cover ◽

Liquid Water ◽

Snow Depth ◽

Snow Water Equivalent ◽

Mountain Regions ◽

Snow Cover Area ◽

Water Percolation ◽

Snow Water ◽

Snow Model ◽

Snow Mass

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the SWE and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 and 3000 m a.s.l. The results show that the new percolation scheme yields better performance, especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments, with values ranging between 73 and 286 mm w.e. (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snowmelt events during the accumulation season, and the high variability of snow depth patterns at the subpixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in nonmonitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

Download Full-text

Cloudiness and snow cover in Alpine areas from MODIS products

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-3967-2014 ◽

2014 ◽

Vol 11 (4) ◽

pp. 3967-4015 ◽

Cited By ~ 4

Author(s):

P. Da Ronco ◽

C. De Michele

Keyword(s):

Snow Cover ◽

Large Scale ◽

Snow Water Equivalent ◽

Practical Interest ◽

Climate Change Impacts ◽

Computational Effort ◽

Po River ◽

Snow Water ◽

Earth Observation Satellites ◽

Area Product

Abstract. Snow cover maps provide an information of great practical interest for hydrologic purposes: when combined with point values of snow water equivalent (SWE), they allow to estimate the regional snow resource. Earth observation satellites are an interesting tool for evaluating large scale snow distribution and extension. In this context, MODIS (MODerate resolution Imaging Spectroradiometeron 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 cloudiness can hide the ground, thus preventing snow detection. Here, we considered MODIS binary products for daily snow mapping over Po river basin. Modeling the variability of snow cover duration, distribution and snow water equivalent is a first important step in investigating climate change impacts on the regime of the major Italian river. Ten years (2003–2012) of MOD10A1 and MYD10A1 snow maps have been analyzed and processed with the support of 500 m-resolution Digital Elevation Model (DEM). We firstly investigated the issue of cloudiness, highlighting its dependence on altitude and season. Snow maps seem to suffer the influence of overcast conditions mainly in mountain and during the melting season. Such a result is certainly related to satellite crossing times, since cloud coverage over mountains usually increases in the afternoon: however, in Aqua and Terra snow products it 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, on the basis of previous studies, we proposed a cloud removal procedure and its application to a wide area, characterized by high topographic and geomorphological heterogeneities such as northern Italy. While conceiving the new method, 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 were not enough information on the same day within the cloud-free areas, we improved a temporal filter 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 has been compared against others, showing improvements in the performance for our case study. At the same time it results quite handy both in terms of input data required and computational effort.

Download Full-text

High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area

Remote Sensing ◽

10.3390/rs12030460 ◽

2020 ◽

Vol 12 (3) ◽

pp. 460 ◽

Cited By ~ 1

Author(s):

Mingyu Liu ◽

Chuan Xiong ◽

Jinmei Pan ◽

Tianxing Wang ◽

Jiancheng Shi ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Snow Cover ◽

Spatial Resolution ◽

Snow Water Equivalent ◽

Remote Sensing Data ◽

Landsat 8 ◽

Mountainous Areas ◽

Snow Water ◽

Sentinel 2

Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017–2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017–2018 was 377.83 mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research.

Download Full-text

Monitoring snowpack evolution with meteorological reanalysis data in the Atlas Mountains

10.5194/egusphere-egu21-486 ◽

2021 ◽

Author(s):

Wassim Mohamed Baba ◽

Abdelghani Boudhar ◽

Simon Gascoin ◽

Lahoucine Hanich ◽

Ahmed Marchane ◽

...

Keyword(s):

Snow Cover ◽

Snow Water Equivalent ◽

Growing Season ◽

Reanalysis Data ◽

Water Runoff ◽

Snow Cover Area ◽

Seasonal Snow ◽

Atlas Mountains ◽

Snow Water ◽

Seasonal Snow Cover

The seasonal snow cover in the Altas mountains of Morocco is an important resource, mostly because it provides melt-water runoff for irrigation during the crop growing season. However, the knowledge on physical properties of the snowpack (e.g., snow water equivalent (SWE) and snowmelt) is still very limited due to the scarcity or the lack of ground measurements in the elevated area. In this study we suggest that the recent progresses of meteorological reanalysis data (e.g., MERRA-2 and ERA-5) open new perspectives to overcome this issue. We fed a distributed snowpack evolution model (SnowModel) with downscaled ERA-5 and MERRA-2 reanalyses and evaluate their performance to simulate snow cover. The modeling covers the period 1981 to 2019 (37 water years). SnowModel simulations were assessed using observations of river discharge, snow height and snow cover area derived from MODIS.For most of hydrological years, the results show a good performance for both MERRA-2 and ERA-5 with a slight superiority of ERA-5, to reproduce the snowpack state.Key words: snow, snow water equivalent, reanalysis , MERRA-2, ERA-5

Download Full-text