11 Jun 2021

11 Jun 2021

Review status: this preprint is currently under review for the journal HESS.

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

Dhiraj Raj Gyawali and András Bárdossy Dhiraj Raj Gyawali and András Bárdossy
  • Institute for Modelling Hydraulic and Environmental Systems (IWS), University of Stuttgart

Abstract. Given the importance of snow on different land and atmospheric processes, accurate representation of seasonal snow evolution including distribution and melt volume, is highly imperative to any water resources development trajectories. The limitation of reliable snow-melt estimation in these regions is however, further exacerbated with data scarcity. This study attempts to develop relatively simpler extended degree-day snow-models driven by freely available snow cover images in snow-dominated regions. This approach offers relative simplicity and plausible alternative to data intensive models as well as in-situ measurements and have a wide scale applicability, allowing immediate verification with point measurements.

The methodology employs readily available MODIS composite images to calibrate the snow-melt models on snow-distribution in contrast to the traditional snow-water equivalent based calibration. The spatial distribution of snow cover is simulated using different extended degree-day models calibrated against MODIS snow-cover images for cloud-free days or a set of images representing a period within the snow season. The study was carried out in Baden-Württemberg in Germany, and in Switzerland. The simulated snow cover show very good agreement with MODIS snow cover distribution and the calibrated parameters exhibit relative stability across the time domain.

The snow-melt from these calibrated models were further used as standalone inputs to a “truncated” HBV without the snow component in Reuss (Switzerland), and Horb and Neckar (Baden-Wuerttemberg) catchments, to assess the performance of the melt outputs in comparison to a calibrated standard HBV model. The results show slight increase in overall NSE performance and a better NSE performance during the winter. Furthermore, 3–15 % decrease in mean squared error was observed for the catchments in comparison to the results from standard HBV. The increased NSE performance, albeit less, can be attributed to the added reliability of snow-distribution coming from the MODIS calibrated outputs.

This paper highlights that the calibration using readily available images used in this method allows a flexible regional calibration of snow cover distribution in mountainous areas across a wide geographical extent with reasonably accurate precipitation and temperature data. Likewise, the study concludes that simpler specific alterations to processes contributing to snow-melt can contribute to identifying the snow-distribution and to some extent the flows in snow-dominated regimes.

Dhiraj Raj Gyawali and András Bárdossy

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-310', Juraj Parajka, 30 Jun 2021
    • AC1: 'Response to RC1', Dhiraj Raj Gyawali, 03 Aug 2021
  • RC2: 'Comment on hess-2021-310', Bettina Schaefli, 08 Jul 2021
    • AC2: 'Response to RC2', Dhiraj Raj Gyawali, 03 Aug 2021

Dhiraj Raj Gyawali and András Bárdossy

Dhiraj Raj Gyawali and András Bárdossy


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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-Wuerttemberg (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.