Incorporation of satellite-derived snow-cover area in spatial snowmelt modeling for a large area: determination of a gridded degree-day factor

Abstract

Spatial 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.

Land abandonment and changes in snow cover period accelerate range expansions of sika deer

“…Firstly, a daily mean temperature and daily precipitation were calculated in 1‐km grid cells by distance‐weighted mean of three nearest Automated Meteorological Data Acquisition System (AMeDAS) station managed by Japan Meteorological Agency (data were derived from Agriculture, Forestry, and Fisheries Basic Numeric DataBase of AFFRIT, MAFF, Japan). Snow melt coefficient was optimized using satellite data (SPOT Vegetation) (Asaoka & Kominami, , ). Daily SWE (snow water equivalent) and then existence of snow cover and snow depth were calculated in 1‐km grid cells; we calculated the mean value in 5‐km grid cells for the analysis, and the mean values between either 1973 and 1977 or 1998 and 2002 were used.…”

Analysis of Regional Difference in Altitude Dependence of Snow Depth Using High Resolve Numerical Experiments

“…In recent years, ADSD has been used to estimate the distribution of snow depth on a regional scale as well as on a larger scale. ADSD is determined by the surface observation, remote sensing, and wind profiler data (Asaoka and Kominami 2013;Molotch et al 2005;Lundquist et al 2010). However, the estimated snow depth using these ADSD had significant errors because these studies had applied ADSD to several slopes and mountains.…”

Soil temperature and soil moisture dynamics in winter and spring under heavy snowfall conditions in North‐Eastern Japan

“…Snow depth in the Sierra Nevada varied between 15 cm and 1 m (Bales et al, 2011). In north-eastern Japan air temperature in winter is around 0 C (Kominami, Tanaka, Endo, & Niwano, 2005) and the Japanese Sea coastline receives heavy snowfall (Asaoka & Kominami, 2013) from the cold Siberian winds, which hit the Asahi Mountains and produce around 1,500 mm snowfall in winter (Dorman et al, 2004;Yamaguchi, Abe, Nakai, & Sato, 2011). Such high precipitation results in snow depths of more than three meters and snow coverage lasting until May.…”