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

Baba, Mohamed Wassim; Gascoin, Simon; Kinnard, Christophe; Marchane, Ahmed; Hanich, Lahoucine

doi:10.31219/osf.io/9zxqg

Cited by 8 publications

(10 citation statements)

References 10 publications

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“…Mountainous areas also experience substantial precipitation during summer, due to small-scale convection (Born et al, 2008). As a consequence, vegetation outside the lower elevation valleys is sparse, essentially lim- ited to bare soil, grass and occasional shrubs (Baba et al, 2019). Snowfall is common between November and March above 1500 m elevation, and it is frequent to observe snow cover persisting for several months above 2500 m (Marchane et al, 2015).…”

Section: Study Areamentioning

confidence: 99%

Future projections of High Atlas snowpack and runoff under climate change

Tuel

Moçayd

Hasnaoui³

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. The High Atlas, culminating at more than 4000 m, is the water tower of Morocco. While plains receive less than 400 mm of precipitation in an average year, the mountains can get twice as much, often in the form of snow between November and March. Snowmelt thus accounts for a large fraction of the river discharge in the region, particularly during spring. In parallel, future climate change projections point towards a significant decline in precipitation and enhanced warming of temperature for the area. Here, we build on previous research results on snow and climate modelling in the High Atlas to make detailed projections of snowpack and river flow response to climate change in this region. We develop end-of-century snowpack projections using a distributed energy balance snow model based on SNOW-17 and high-resolution climate simulations over Morocco with the MIT Regional Climate Model (MRCM) under a mitigation (RCP4.5) and a business-as-usual (RCP8.5) scenario. Snowpack water content is projected to decline by up to 60 % under RCP4.5 and 80 % under RCP8.5 as a consequence of strong warming and drying in the region. We also implement a panel regression framework to relate runoff ratios to regional meteorological conditions in seven small sub-catchments in the High Atlas. Relative humidity and the fraction of solid-to-total precipitation are found to explain about 30 % of the inter-annual variability in runoff ratios. Due to projected future atmospheric drying and the associated decline in snow-to-precipitation ratio, a 5 %–30 % decrease in runoff ratios and 10 %–60 % decrease in precipitation are expected to lead to severe (20 %–70 %) declines in river discharge. Our results have important implications for water resources planning and sustainability of agriculture in this already water-stressed region.

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Section: Study Areamentioning

confidence: 99%

Future projections of High Atlas snowpack and runoff under climate change

Tuel

Moçayd

Hasnaoui³

et al. 2022

Hydrol. Earth Syst. Sci.

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“…Within a topographical class, observations are affected by (1) natural variability, (2) retrieval errors and (3) classification errors. In particular, DEM errors and resolution have a strong impact in satellite retrievals via shadows and subgrid topography (Baba et al, 2019;Davaze et al, 2018), leading to about ±10% errors in broadband albedo for MODIS data (Dumont et al, 2012). Moreover, S2 data are particularly affected by the three sources, since the retrieval DEM (SRTM90) in the MAJA processor is too coarse to capture the topographic variability at the scale of the pixels (10-20 m) and because the classification is done to an even much coarser scale (IGN250).…”

Section: On the Relevance Of The Comparison In The Semi-distributed Fmentioning

confidence: 99%

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations

Cluzet

Revuelto

Lafaysse

et al. 2020

Cold Regions Science and Technology

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Uncertainties of snowpack models and of their meteorological forcings limit their use by avalanche hazard forecasters, or for glaciological and hydrological studies. The spatialized simulations currently available for avalanche hazard forecasting are only assimilating sparse meteorological observations. As suggested by recent studies, their forecasting skills could be significantly improved by assimilating satellite data such as snow reflectances from satellites in the visible and the near-infrared spectra. Indeed, these data can help constrain the microstructural properties of surface snow and light absorbing impurities content, which in turn affect the surface energy and mass budgets. This paper investigates the prerequisites of satellite data assimilation into a detailed snowpack model. An ensemble version ofMétéo-France operational snowpack forecasting system (named S2M) was built for this study. This operational system runs on topographic classes instead of grid points, so-called 'semi-distributed' approach. Each class corresponds to one of the 23 mountain massifs of the French Alps (about 1000km 2 each), an altitudinal range (by step of 300m) and aspect (by step of 45 o ). We assess the feasability of satellite data assimilation in such a semi-distributed geometry. Ensemble simulations are compared with satellite observations from MODIS and Sentinel-2, and with in-situ reflectance observations. The study focuses on the 2013-2014 and 2016-2017 winters in the Grandes-Rousses massif. Substantial Pearson R 2 correlations (0.75-0.90) of MODIS observations with simulations are found over the domain. This suggests that assimilating it could have an impact on the spatialized snowpack forecasting system. However, observations contain significant biases (0.1-0.2 in reflectance) which prevent their direct assimilation. MODIS spectral band ratios seem to be much less biased. This may open the way to an operational assimilation of MODIS reflectances into the Météo-France snowpack modelling system. Highlights -Ensemble simulations of the snowpack are compared with satellite reflectances -Spatial aggregation into the semi-distributed geometry filters the observation noises -Satellite reflectances carry useful information worth to assimilate -MODIS reflectances can not be directly assimilated because they are biased -Ratios of MODIS reflectances show no evidence of bias and could be assimilated

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“…The 90 m Shuttle Radar Topography Mission (SRTM) digital elevation model was used after resampling it to 200 m spatial resolution by using cubic interpolation. The choice of the spatial resolution is based on a previous study in the Rheraya catchment [36], where we analyzed the trade-off between spatial resolution and the computation time. This study suggested that a distributed snow model with a grid coarser than 250 m is not able to capture the high spatial variability in the snow cover patterns due to the importance of the incoming solar radiation in the snowpack energy budget.…”

Section: Topography and Land Covermentioning

confidence: 99%

“…We chose the 2008-2009 snow season for which we dispose of an accurate time series of high resolution SCA maps from Formosat-2 data [8]. [36]. These intervals were chosen following the recommendations of Froidurot et al [48].…”

Section: Snowpack Modelmentioning

confidence: 99%

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

2018

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

Cited by 8 publications

References 10 publications

Future projections of High Atlas snowpack and runoff under climate change

Future projections of High Atlas snowpack and runoff under climate change

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations

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

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