Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS

Gyawali, Dhiraj Raj; Bárdossy, András

doi:https://doi.org/10.5194/hess-26-3055-2022

Articles | Volume 26, issue 12

https://doi.org/10.5194/hess-26-3055-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-26-3055-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 12

Research article

|

17 Jun 2022

Research article |

| 17 Jun 2022

Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS

Dhiraj Raj Gyawali and András Bárdossy

Data sets

Index of /climate_environment/CDC/ Climate Data Center of the German Weather Service (DWD) https://opendata.dwd.de/climate_environment/CDC

Model code and software

MODIS based snow-cover distribution calibration Dhiraj Raj Gyawali https://doi.org/10.5281/zenodo.6549342

Robust Parameter Optimization (ROPE) routine F. Anwar https://github.com/faizan90/depth_funcs

Short summary

In this study, different extensions of the degree-day model were calibrated on snow-cover distribution against freely available satellite snow-cover images. The calibrated models simulated the distribution very well in Baden-Württemberg (Germany) and Switzerland. In addition to reliable identification of snow cover, the melt outputs from the calibrated models were able to improve the flow simulations in different catchments in the study region.