Abstract:We describe an approach for reconstructing snowfall that combines satellite observations of the snow disappearance date (SDD) with a snow model for two mountainous areas in Japan having distinct snow climatology. This approach allows assessment of the distribution of snow and topographical effects on snowfall within a catchment. We also evaluated how the reconstructed snowfall affects the catchment snow hydrology.Validation at observation sites demonstrated that a combination of the snow model and snowfall reconstructions successfully estimated the seasonal changes of snow water equivalent (SWE). In Japan, the dependence of snowfall on elevation is stronger in mountainous areas along the coast on the Sea of Japan side of Japan's central mountain spine (where snowfall triples with every 1000 m increase in elevation; C sf = 0.002 m −1 ) compared with inland locations of the same region (where snowfall doubles with every 1000 m increase; C sf = 0.001 m
−1). Moreover, the reconstructed snowfall improved the estimation of maximum catchment SWE. Maximum total SWE, estimated with reconstructed snowfall, was 3.8 × 10 8 m 3 in Kurobe catchment (along the coast on the Sea of Japan side of Japan's central mountain spine), while that, estimated by convectional method with the spatially-constant C sf = 0.001 m −1 , was 2.0 × 10 8 m 3 . As a result, estimations of the snow disappearance date and of the catchment snowmelt were also improved. These results suggest that it is useful to estimate the spatial snow distribution, especially where steep topography causes large gradients of snowfall amount with respect to elevation.
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.
A Snow Model (SM) using a temperature-index method was used to optimize the degree-day factor (DDF) and precipitation gradient (PG) for the different elevation zones of the Panjshir sub-basin for snowmelt runoff modelling. The values derived for DDF and PG were calibrated and validated by comparing observed snow cover area and snow cover area simulated by SM. The Snowmelt Runoff Model (SRM) was used to simulate daily runoff over the hydrological years 2009-2014 using the optimized values for SRM accuracy. The optimized DDF values were 0.3 to 0.9 (cm °C-1 d-1) for elevations from 1593 m to 5694 m. Meanwhile the PG was +0.002 m-1 for elevations 1593-4000 m and 0 m-1 above 4000 m. The simulated runoff by SRM during the entire data period correlated very well with a Nash-Sutcliffe coefficient NS = 0.93 utilizing both observed and simulated snow cover area. This method not only evaluates the characteristics of snowfall and snowmelt in different elevation zones to obtain the DDF and PG, but can also estimate the snowmelt runoff.
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