Articles | Volume 26, issue 12
https://doi.org/10.5194/hess-26-3055-2022
https://doi.org/10.5194/hess-26-3055-2022
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

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Cited articles

Anwar, F.: Robust Parameter Optimization (ROPE) routine, GitHub [code], https://github.com/faizan90/depth_funcs (last access: 12 December 2021), 2020. a
Bárdossy, A. and Pegram, G.: Interpolation of precipitation under topographic influence at different time scales, Water Resour. Res., 49, 4545–4565, https://doi.org/10.1002/wrcr.20307, 2013. a
Bárdossy, A. and Singh, S. K.: Robust estimation of hydrological model parameters, Hydrology and Earth System Sciences, 12, 1273–1283, https://doi.org/10.5194/hess-12-1273-2008, 2008. a
Bárdossy, A. and Singh, S. K.: Robust estimation of hydrological model parameters, Hydrol. Earth Syst. Sci., 12, 1273–1283, https://doi.org/10.5194/hess-12-1273-2008, 2008. a
Bennett, K. E., Cherry, J. E., Balk, B., and Lindsey, S.: Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska, Hydrol. Earth Syst. Sci., 23, 2439–2459, https://doi.org/10.5194/hess-23-2439-2019, 2019. a
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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.
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