Effect of digital elevation model resolution on the simulation of the snow cover evolution in the High Atlas

The snow melt from the High Atlas represents a crucial water resource for crop irrigation in the semi-arid regions of Morocco. Recent studies have used assimilation of snow cover area (SCA) data from high resolution optical sensors to compute the snow water equivalent (SWE) and snow melt in other mountain regions. These techniques require large model ensembles and therefore a challenge is to determine the adequate model resolution, which yields accurate results with reasonable computation time. Here we study the sensitivity of an energy-balance model to the resolution of the model grid for a pilot catchment in the High Atlas. We used a time series of 8 m resolution SCA maps with an average revisit time of 7.5 days to evaluate the model results. The DEM was generated from Pléiades stereo-images and resampled from 8 m to 30 m, 90 m, 250 m, 500 m and 1000 m. The results indicate that the model performs well from 8 m to 250 m but the agreement with observations drops at 500 m. This is because significant features of the topography were too smoothed out to properly characterize the spatial variability of meteorological forcing, including solar radiation. We conclude that a resolution of 250 m might be sufficient in this area. This result is consistent with the shape of the semivariogram of the topographic slope, suggesting that this semivariogram analysis could be used to transpose our conclusion to other study regions.

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Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco

10.20944/preprints201810.0264.v1 ◽

2018 ◽

Author(s):

Mohamed Wassim Baba ◽

Simon Gascoin ◽

Lahoucine Hanich

Keyword(s):

Data Assimilation ◽

Snow Cover ◽

Snow Water Equivalent ◽

Snow Cover Area ◽

High Atlas ◽

Data Assimilation Scheme ◽

Snow Cover Extent ◽

Assimilation Scheme ◽

Sentinel 2

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.

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Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco

Remote Sensing ◽

10.3390/rs10121982 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1982 ◽

Cited By ~ 9

Author(s):

Mohamed Baba ◽

Simon Gascoin ◽

Lahoucine Hanich

Keyword(s):

Data Assimilation ◽

Snow Cover ◽

Snow Water Equivalent ◽

Snow Cover Area ◽

High Atlas ◽

Data Assimilation Scheme ◽

Snow Cover Extent ◽

Assimilation Scheme ◽

Sentinel 2

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements of MERRA-2 reanalysis and the full revisit capacity of Sentinel-2.

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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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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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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.

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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.

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

Geografie ◽

10.37040/geografie2014119020145 ◽

2014 ◽

Vol 119 (2) ◽

pp. 145-160

Author(s):

Libor Ducháček

Keyword(s):

Snow Cover ◽

Large Scale ◽

Snow Water Equivalent ◽

Field Measurements ◽

Mountainous Areas ◽

Snow Cover Area ◽

Interconnected System ◽

Central Highland ◽

Snow Water ◽

Source Of Information

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.

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Simulation of snow distribution and melt under cloudy conditions in an alpine watershed

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-3189-2010 ◽

2010 ◽

Vol 7 (3) ◽

pp. 3189-3211 ◽

Cited By ~ 1

Author(s):

H.-Y. Li ◽

J. Wang

Keyword(s):

Snow Cover ◽

Snow Water Equivalent ◽

Interpolation Method ◽

Snow Accumulation ◽

Energy Budgets ◽

Bias Error ◽

Snow Cover Area ◽

Snow Cover Extent ◽

Snow Distribution ◽

Alpine Watershed

Abstract. An energy balance method and remote sensing data were used to simulate snow distribution and melt in an alpine watershed in Northwestern China within a complete snow accumulation-melt period. Spatial energy budgets were simulated using the meteorological observations and digital elevation model of the watershed. A linear interpolation method was used to discriminate daily snow cover area under cloudy conditions, using Moderate Resolution Imaging Spectroradiometer data. Hourly snow distribution and melt, snow cover extent, and daily discharge were included in the simulated results. The bias error between field snow water equivalent samplings and simulated results is −2.1 cm, and Root Mean Square Error is 33.9 cm. The Nash and Sutcliffe efficiency statistic (R2) between measured and simulated discharges is 0.673, and the volume difference (Dv) is 3.9%. Using the method introduced in this article, modeling spatial snow distribution and melt runoff will become relatively convenient.

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Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS

10.5194/hess-2021-310 ◽

2021 ◽

Author(s):

Dhiraj Raj Gyawali ◽

András Bárdossy

Keyword(s):

Snow Cover ◽

Snow Water Equivalent ◽

Mean Squared Error ◽

Snow Melt ◽

Data Intensive ◽

Degree Day ◽

Snow Water ◽

Snow Distribution ◽

Wide Scale ◽

The Time Domain

Abstract. Given the importance of snow on different land and atmospheric processes, accurate representation of seasonal snow evolution including distribution and melt volume, is highly imperative to any water resources development trajectories. The limitation of reliable snow-melt estimation in these regions is however, further exacerbated with data scarcity. This study attempts to develop relatively simpler extended degree-day snow-models driven by freely available snow cover images in snow-dominated regions. This approach offers relative simplicity and plausible alternative to data intensive models as well as in-situ measurements and have a wide scale applicability, allowing immediate verification with point measurements. The methodology employs readily available MODIS composite images to calibrate the snow-melt models on snow-distribution in contrast to the traditional snow-water equivalent based calibration. The spatial distribution of snow cover is simulated using different extended degree-day models calibrated against MODIS snow-cover images for cloud-free days or a set of images representing a period within the snow season. The study was carried out in Baden-Württemberg in Germany, and in Switzerland. The simulated snow cover show very good agreement with MODIS snow cover distribution and the calibrated parameters exhibit relative stability across the time domain. The snow-melt from these calibrated models were further used as standalone inputs to a “truncated” HBV without the snow component in Reuss (Switzerland), and Horb and Neckar (Baden-Wuerttemberg) catchments, to assess the performance of the melt outputs in comparison to a calibrated standard HBV model. The results show slight increase in overall NSE performance and a better NSE performance during the winter. Furthermore, 3–15 % decrease in mean squared error was observed for the catchments in comparison to the results from standard HBV. The increased NSE performance, albeit less, can be attributed to the added reliability of snow-distribution coming from the MODIS calibrated outputs. This paper highlights that the calibration using readily available images used in this method allows a flexible regional calibration of snow cover distribution in mountainous areas across a wide geographical extent with reasonably accurate precipitation and temperature data. Likewise, the study concludes that simpler specific alterations to processes contributing to snow-melt can contribute to identifying the snow-distribution and to some extent the flows in snow-dominated regimes.

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How is snow cover in global mountain area changing? Detection of snow cover and snow phenology changes by using MODIS imagery over 2000 - 2018

10.5194/egusphere-egu2020-20482 ◽

2020 ◽

Author(s):

Claudia Notarnicola

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Air Temperature ◽

Lower Latitude ◽

Snow Melt ◽

Snow Cover Area ◽

Mountain Areas ◽

Snow Albedo ◽

Modis Imagery ◽

Snow Cover Duration

Mountain areas have raised a lot of attention in the past years, as they are considered sentinel of climate changes. Quantification of snow cover changes and related phenology in global mountain areas can have multiple implications on water resources, ecosystem services, tourism, and energy production [1]. Up to now, several studies have investigated snow cover changes at continental scale and there are several indications of snow cover decline over the Northern Hemisphere [2, 3], despite no study has analyzed snow behavior specifically in mountain areas at global level. In this context, this study investigates the changes in the main snow cover parameters (snow cover area, snow cover duration, snow onset and snow melt) over global mountain areas from 2000 to 2018.To proper monitor the evolution of snow changes at global mountain areas and interlinkages with meteorological drivers (air temperature, snowfall), automatic procedures were developed based on MODIS imagery in global mountain areas over the period 2000-2018 by exploiting Google Earth Engine where the whole time series of MODIS is available at a global scale. MODIS snow cover products have the highest resolution available, 500 m, and with daily global acquisitions. From MODIS snow cover areas (MOD10v6), snow phenology parameters were derived, namely snow cover duration, snow onset and snow melt. Together with snow cover and phenology changes, snow albedo changes were assessed, especially in relation to snow onset and melt variability.The results of the trend analysis carried with Man-Kendall statistics indicate that around 78% of the global mountain areas present a snow decline. In average, snow cover duration has decreased up to 43 days, and a snow cover area up to 13%. Significant snow cover duration changes can be linked in 58% of the areas to both delayed snow onset, and advanced melt. Few areas show positive changes, mainly during winter time and located in the Northern Hemisphere.Considering the relationship with meteorological parameters and albedo, air temperature is detected as the main driver for snow onset and melt, while a mixed effect of air temperature and precipitation dominates the winter season. Moreover, snowmelt timing is strongly related to significant changes in snow albedo during March and April in the Northern Hemisphere. Regarding snow onset changes, it has been detected a latitude amplification for the dependency con air temperature, indicating that the sensitivity of snow onset on temperature changes is amplified going from higher to lower latitude.&#160;References[1] Barnett, T.P., Adam J.C., Lettenmaier D.P. Potential impact of a warming climate on water availability in snow-dominated regions, Nature 438 (2005).[2] Bormann, K. J., Brown, R. D., Derksen, C., Painter, T. H. Estimating snow-cover trends from space, Nat. Clim. Change&#160;8,&#160;924&#8211;928 (2018).[3] Ye. K. H., & Wu, R. G. Autumn snow cover variability over northern Eurasia and roles of atmospheric circulation. Adv. Atmos. Sci. 34(7), 847&#8211;858 (2017) doi: 10.1007/s00376-017-6287-z.

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