Spatio-temporal variability of snow water equivalent in the extra-tropical Andes cordillera from a distributed energy balance modeling and remotely sensed snow cover

Abstract

Abstract. Seasonal snow cover is the primary water resource precursor for human use and environmental sustain along the extratropical Andes Cordillera. Despite its importance, relatively little research has been devoted to understanding the properties, distribution and variability of this natural resource. This research provides high-resolution distributed estimates of end-of-winter and spring snow water equivalent over a 152 000 km2 domain that includes the mountainous reaches of central Chile and Argentina. Remotely sensed fractional snow covered area and other relevant forcings are combined with extrapolated data from meteorological stations and a simplified physically-based energy balance model in order to obtain melt-season peak SWE. Estimates show an overall coefficient of determination R2 of 0.61 compared to observations at 12 automatic snow water equivalent sensors distributed across the model domain, with R2 values between 0.32 and 0.88. Regional estimates of peak SWE accumulation show differential patterns strongly modulated by elevation, latitude and position relative to the continental divide. Average peak SWE increases by nearly 500 mm for every 1000 m in elevation gain for the central and southern sub-regions of the model domain, but this effect is much less pronounced in the northern reaches. The 3000–4000 m a.s.l. elevation band is the most significant accumulation area for most of the northern and central reaches of the domain, although the 4000–5000 m a.s.l. band, despite a smaller contributing area, almost doubles the accumulation amounts estimated for the lower adjacent subdomain. Snow accumulation reaches an earlier peak in the western Andes, and the eastern side of the continental divide shows lower snow accumulation at all elevations except for the southern region represented by the Neuquén River Basin. The results presented here have the potential of informing applications such as seasonal forecast model assessment and improvement, regional climate model validation, as well as evaluation of observational networks and water resource infrastructure development.

The Andes Cordillera. Part II: Rio Olivares Basin snow conditions (1979–2014), central Chile

“…The Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra Snow Cover Daily L3 Global 500-m grid (MOD10A1) product (Hall et al, 1995(Hall et al, , 2006Hall and Riggs, 2007) was used from 2000/2001 through 2013/2014 for validation of maximum annual simulated snow cover extent for a rectangle between latitude 31.5 ∘ -40.0 ∘ S and longitude 69.2 ∘ -72.3 ∘ W. The MOD10A2 scenes was filtered so only scenes with <10% cloud cover (in the area of interest) was used, and the chosen scene was inspected to ensure minimum cloud cover interference. Also, snow depth observations covering the period from 2010 to 2014 in four intense-study basins were calculated into 4 × 4 km grid mean snow depth values (grid increments identical to the grids used in SnowModel), and used for verification (Ayala et al, 2014;Cornwell et al, 2016). Both independent validation methods showed significant results between observations and simulations , even though local and regional topographical influences, thermal instability in the troposphere can produce local to regional snow conditions that are much more complex than coarse-scale patterns (e.g.…”

Surface water quality in a sulfide mineral‐rich arid zone in North‐Central Chile: Learning from a complex past, addressing an uncertain future

“…This is due to the fact that key hydrological processes at should also be considered when trying to forecast their hydrological response to climate modifications (Souvignet et al, 2010;Vuille et al, 2008). As shown in Cornwell, Molotch, and McPhee (2016), even nearby watersheds exhibit differences in terms of snow water equivalent (associated to the snow accumulation)…”

Spatio-temporal variability of snow water equivalent over the Vestre Broggerbreen and Feiringbreen glaciers, Ny-Ålesund, Svalbard

“…The SWE is an important parameter for snow hydrology, snow climatology and avalanche formations (Egli et al 2009;Rice et al 2011;L opez-Moreno et al 2016). The SWE can be estimated by snow depth (h s ) and bulk density (q) (Jonas et al 2009), automatic methods and empirical models (Skaugen 2007;Egli et al 2009;Clark et al 2011;Clow et al 2012;Bavera et al 2014;Cornwell et al 2016), ground penetrating radar (P€ alli et al 2002;Godio and Rege 2016;Holbrook et al 2016) and remote sensing techniques (Foster et al 2005). The SWE was measured at several regions for the characterisations of snow cover and also for the validation of several models (Liston and Sturm 2002;Bocchiola and Rosso 2007;Sturm et al 2010;Rice et al 2011;L opez-Moreno et al 2013L opez-Moreno et al , 2016Cornwell et al 2016).…”