Estimating degree-day factors from MODIS for snowmelt runoff modeling

Abstract

Abstract. Degree-day factors are widely used to estimate snowmelt runoff in operational hydrological models. Usually, they are calibrated on observed runoff, and sometimes on satellite snow cover data. In this paper, we propose a new method for estimating the snowmelt degree-day factor (DDF S ) directly from MODIS snow covered area (SCA) and ground-based snow depth data without calibration. Subcatchment snow volume is estimated by combining SCA and snow depths. Snow density is estimated to be the ratio between observed precipitation and changes in the snow volume for days with snow accumulation. Finally, DDF S values are estimated to be the ratio between changes in the snow water equivalent and difference between the daily temperature and the melt threshold value for days with snow melt. We compare simulations of basin runoff and snow cover patterns using spatially variable DDF S estimated from snow data with those using spatially uniform DDF S calibrated on runoff. The runoff performances using estimated DDF S are slightly improved, and the simulated snow cover patterns are significantly more plausible. The new method may help reduce some of the runoff model parameter uncertainty by reducing the total number of calibration parameters. This method is applied to the Lienz catchment in East Tyrol, Austria, which covers an area of 1198 km 2 . Approximately 70 % of the basin is covered by snow in the early spring season.

On the Direct Calculation of Snow Water Balances Using Snow Cover Information

“…The values of the snowmelt coefficients are also about a half of typical literature values [11,28,46]: out of 16 sites, six yield snowmelt coefficients below 1 mm/ • C day, and only three above 2 mm/ • C day. The snowmelt coefficients are negatively and significantly correlated to the cumulative temperature (r = −0.73, p = 1.2E − 03).…”

Backward snow depth reconstruction at high spatial resolution based on time‐lapse photography

“…The use of satellite imagery combined with groundbased snow depth data also enables to spatially estimate DDFs for simulating melt dynamics over extended areas (He et al, 2014). The improvement obtained with such combination of data sources can reduce uncertainty by taking advantage of additional data as part of the calibration procedure (Parajka and Blöschl, 2008).…”

“…Hassan (2012) used a similar approach for a 18 132 km 2 study area in northern Alberta (Canada) using moderate resolution imaging spectroradiometer (MODIS) data. Besides, other researches have used MODIS data for estimating DDF (He et al, 2014) and calibrating hydrological models (Parajka and Blöschl, 2008;Kahl, 2013). Other methods have also been used for snowpack reconstruction, including a Bayesian approach combined with remote sensing data at a similar spatial scale (Durand et al, 2008a(Durand et al, ,2008b.…”

Canopy structure and topography effects on snow distribution at a catchment scale: Application of multivariate approaches

“…Therefore, the use of methods applying one or more independent variables leads to applicable results as shown in our study. Promising results have been also reported using remote sensing approaches such as the use of MODIS satellite data (Duchacek, 2014;He et al, 2014;Krajčí et al, 2016;Parajka et al, 2012), aerial or terrestrial laser scanning (Grünewald et al, 2013;López-Moreno et al, 2015) and unmanned aerial systems (UAV) (De Michele et al, 2016;Lendzioch et al, 2016). We are now testing camera placed on UAV to monitor the snow depth (Lendzioch et al, 2016).…”