Snowpack sensitivity to temperature, precipitation, and solar radiation variability over an elevational gradient in the Iberian mountains

Alonso‐González, Esteban; López‐Moreno, Juan I.; Navarro-Serrano, Francisco; Sanmiguel‐Vallelado, Alba; Aznarez-Balta, Marina; Revuelto, Jesús; Ceballos, Antonio

doi:10.1016/j.atmosres.2020.104973

Cited by 16 publications

(24 citation statements)

References 52 publications

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“…Each elevation represents the aggregated pixels of the elevation, with a range of ±50 m a.s.l. For reference, they show on average a peak SWE of 306 mm at the elevation band of 2000 m a.s.l., which is comparable to those found in the Iberian Peninsula mountain ranges (Alonso-González et al, 2020b). More specifically, the peak…”

Section: Snowpack Dynamics Over the Lebanese Mountainssupporting

confidence: 85%

“…However, the length of the reanalyses constitutes a reasonably representative sample of the main snowpack dynamics over the region. The snowpack over Lebanon has exhibited the high temporal variability that is characteristic of the Mediterranean snowpacks (Fayad et al, 2017b), with similar values of the coefficient of variation to those observed in other Mediterranean mountain ranges (Alonso-González et al, 2020b). The maximum accumulations reach 2000 mm of SWE and are located at the higher elevations of Mount Lebanon where there is a plateau over the elevation of the winter zero isotherm (Fayad and Gascoin, 2020).…”

Section: Snowpack Dynamics Over the Lebanese Mountainssupporting

confidence: 54%

“…These results suggest new challenges for the water management of Lebanon in the future as a consequence of warming climate. The snowpack at low-elevation areas is more sensitive to warming (Jefferson, 2011;Marty et al, 2017;Sproles et al, 2013), particularly over areas with mild winter conditions, as has been shown in other Mediterranean regions (Alonso-González et al, 2020a).…”

Section: Swe and Duration Values Show Intermediate Values Between The Central Iberian Andmentioning

confidence: 58%

“…The hydrological processes related to mountain areas are essential for the water supplies for a large part of humanity (Viviroli et al, 2007). Despite the relatively mild temperature of the Mediterranean climates, mountains there often exhibit deep and long-lasting snowpacks accumulating more than 3 m and an average snow season of 5 months at the summit areas (Alonso-González et al, 2020b;Fayad et al, 2017b). Thus, as most of the annual precipitations falls during the winter season (García-Ruiz et al, 2011) the mountain snowpack strongly reshapes the hydrographs to sustain high flows until the end of the spring, permitting better syn-E. Alonso-González et al: Snowpack dynamics in the Lebanese mountains chronization of water demand and availability during the dry season (García-Ruiz et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, high sublimation rates are associated with Mediterranean snowpacks (Fayad and Gascoin, 2020;Herrero et al, 2016;Schulz and de Jong, 2004). The fact that snowpack conditions are close to isothermal during most of the snow season makes them highly sensitive to the current climate warming (Alonso-González et al, 2020a;López-Moreno et al, 2017;Yilmaz et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area

Alonso‐González

Gutmann²,

Aalstad³

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. The snowpack over the Mediterranean mountains constitutes a key water resource for the downstream populations. However, its dynamics have not been studied in detail yet in many areas, mostly because of the scarcity of snowpack observations. In this work, we present a characterization of the snowpack over the two mountain ranges of Lebanon. To obtain the necessary snowpack information, we have developed a 1 km regional-scale snow reanalysis (ICAR_assim) covering the period 2010–2017. ICAR_assim was developed by means of an ensemble-based data assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) fractional snow-covered area (fSCA) through an energy and mass snow balance model, the Flexible Snow Model (FSM2), using the particle batch smoother (PBS). The meteorological forcing data were obtained by a regional atmospheric simulation from the Intermediate Complexity Atmospheric Research model (ICAR) nested inside a coarser regional simulation from the Weather Research and Forecasting model (WRF). The boundary and initial conditions of WRF were provided by the ERA5 atmospheric reanalysis. ICAR_assim showed very good agreement with MODIS gap-filled snow products, with a spatial correlation of R=0.98 in the snow probability (P(snow)) and a temporal correlation of R=0.88 on the day of peak snow water equivalent (SWE). Similarly, ICAR_assim has shown a correlation with the seasonal mean SWE of R=0.75 compared with in situ observations from automatic weather stations (AWSs). The results highlight the high temporal variability in the snowpack in the Lebanese mountain ranges, with the differences between Mount Lebanon and the Anti-Lebanon Mountains that cannot only be explained by hypsography as the Anti-Lebanon Mountains are in the rain shadow of Mount Lebanon. The maximum fresh water stored in the snowpack is in the middle elevations, approximately between 2200 and 2500 m a.s.l. (above sea level). Thus, the resilience to further warming is low for the snow water resources of Lebanon due to the proximity of the snowpack to the zero isotherm.

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Section: Snowpack Dynamics Over the Lebanese Mountainssupporting

confidence: 85%

Section: Snowpack Dynamics Over the Lebanese Mountainssupporting

confidence: 54%

Section: Swe and Duration Values Show Intermediate Values Between The Central Iberian Andmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area

Alonso‐González

Gutmann²,

Aalstad³

et al. 2021

Hydrol. Earth Syst. Sci.

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Comparison of linear, generalized additive models and machine learning algorithms for spatial climate interpolation

Bonsoms,

Ninyerola

2023

Theor Appl Climatol

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Geospatial atmospheric data is the input variable of a wide range of hydrological and ecological spatial models, many of which are oriented towards improving the socioeconomic and environmental sustainability. Here, we provide an evaluation of machine learning (ML) methods for the spatial interpolation of annual precipitation, minimum and maximum temperatures for a mountain range, in this case, the Pyrenees. To this end, this work compares the performance and accuracy of multiple linear regressions (MLR) and generalized additive models (GAM) against five ML methods (K-Nearest Neighbors, Supported Vector Machines, Neural Networks, Stochastic Gradient Boosting and Random Forest). The ML algorithms outperformed the MLR and GAM independently of the predictor variables used, the geographical sector analyzed or the elevation range. Overall, the differences between ML algorithms are negligible. Random Forest shows a slightly higher than average accuracy for the spatial interpolation of precipitation (R2 = 0.93; MAE = 70.44 mm), whereas Stochastic Gradient Boosting is the best ML method for the spatial interpolation of the mean maximum annual temperature (R2 = 0.96, MAE = 0.43 ºC). Stochastic Gradient Boosting, Neural Networks and Random Forest have similar performances for the spatial interpolation of the mean minimum annual temperature (R2 = 0.98, MAE = 0.19 ºC). Results presented here can be valuable for the past and future climate spatial analysis, environmental niche modelling, hydrological projections, and water management.

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Snow dynamics influence tree growth by controlling soil temperature in mountain pine forests

Sanmiguel‐Vallelado¹,

Camarero²,

Morán-Tejéda

et al. 2021

Agricultural and Forest Meteorology

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Snowpack sensitivity to temperature, precipitation, and solar radiation variability over an elevational gradient in the Iberian mountains

Cited by 16 publications

References 52 publications

Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area

Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area

Comparison of linear, generalized additive models and machine learning algorithms for spatial climate interpolation

Snow dynamics influence tree growth by controlling soil temperature in mountain pine forests

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