Open AccessStudy of Snow Dynamics at Subgrid Scale in Semiarid Environments Combining Terrestrial Photography and Data Assimilation Techniques
Author(s) -
R. Pimental,
Javier Herrero,
Yijian Zeng,
Zhongbo Su,
María José Polo
Publication year - 2015
Publication title -
journal of hydrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.733
H-Index - 123
eISSN - 1525-755X
pISSN - 1525-7541
DOI - 10.1175/jhm-d-14-0046.1
Subject(s) - snow , snowmelt , environmental science , data assimilation , scale (ratio) , snow cover , meteorology , remote sensing , ensemble kalman filter , climatology , kalman filter , geology , mathematics , geography , statistics , extended kalman filter , cartography
Snow cover simulation is a complex task in mountain regions because of its highly irregular distribution. GIS-based calculations of snowmelt–accumulation models must deal with nonnegligible scale effects below cell size, which may result in unsatisfactory predictions depending on the study scale. Terrestrial photography, whose scales can be adapted to the study problem, is a cost-effective technique, capable of reproducing snow dynamics at subgrid scale. A series of high-frequency images were combined with a mass and energy model to reproduce snow evolution at cell scale (30 m × 30 m) by means of the assimilation of the snow cover fraction observation dataset obtained from terrestrial photography in the Sierra Nevada, southern Spain. The ensemble transform Kalman filter technique is employed. The results show the convenience of adopting a selective depletion curve parameterization depending on the succession of accumulation–melting cycles in the snow season in these highly variable environments. A reduction in the error for snow depth to 50% (from 463.87 to 261.21 mm and from 238.22 to 128.50 mm) is achieved if the appropriate curve is selected.