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Melt water runoff estimations from the
snowpack for improved flood risk forecasts
Nander Wever*, Charles Fierz, Christoph Mitterer, Michael Lehning
1
1
1
1,2
WSL Institute for Snow and Avalanche Research SLF, Davos
2
EPFL ENAC IIE Laboratory of Cryospheric Sciences CRYOS, Lausanne
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Introduction
The IRKIS project focuses on improving monitoring and modeling of hydrological
processes for the assessment of flood risk in the Canton Graubünden and Südtirol.
Accurate snowpack runoff estimations are essential for flood risk forecasts.
Examples:
- Spring snow melt (see photo)
- Rain on snow events
Water movement in a snowpack is complex due to:
- Strong layering of grain size, type and density, influencing water flow properties.
- The occurrence of phase changes and associated heat transport.
- Existence of capillary barriers at strong transitions in grain size, obstructing the flow.
- Formation of ice layers, blocking the flow.
Approach
Spring snow melt in Dischma, Davos.
This study compares modeled snowpack runoff using the physical based SNOWPACK model with measured snowpack runoff by a
lysimeter for the Weissfluhjoch (2540m altitude), Davos, Switzerland for the hydrological years 1999-2009.
The study compares 3 different water transport schemes for physical based snowpack models:
1) Bucket model approach: water transport rate defined by exceedance of residual water content (only downward motion possible).
2) Richards Equation (RE): explicitly solving downward water transport by gravity and upward transport by capillary forces.
For RE, 2 relationships are given in literature to relate pressure head to water content: Daanen (2009) and Yamaguchi (2010).
3) NIED: downward water transport rate defined by an approximation of the RE, using parameterization of Yamaguchi (only downward motion possible).
Results for daily time scale
- All models perform quite equal with some minor differences (see table).
- Richards Equation combined with the Yamaguchi parameterization yields
the most accurate snowpack runoff estimation (see table).
- Examples (right figures) show that the degree of agreement between modeled and
measured runoff varies from year to year.
Table: Nash-Sutcliffe coefficients (closer to 1.0 is better) for daily sum of runoff
Model type
Nash-Sutcliffe coefficient
Bucket model
0.64
NIED model
0.62
RE with Yamaguchi
0.69
RE with Daanen
0.42
Results for sub-daily time scale
- Only Richards Equation and the parameterization by Yamaguchi performs
sufficiently well to accurately estimate runoff on a sub-daily time scale
(see table).
- Other model approaches show very little accuracy (see table).
Table: Nash-Sutcliffe coefficients (closer to 1.0 is better) for hourly sum of runoff
Model type
Nash-Sutcliffe coefficient
Bucket model
-0.14
NIED model
-0.84
RE with Yamaguchi
0.43
RE with Daanen
0.06
Conclusions
- For daily time scales, all methods perform reasonably well
- For sub-daily time scales, only solving Richards Equation with the water
retention parameterization by Yamaguchi (2010) accurately estimates
runoff during the day. Other model approaches are found to be very
inaccurate.
For many hydrological applications, accurate modeling on daily time
scales may be sufficient. However, for flood risk assessment during rain
on snow events, or melt in spring, the sub-daily time scale is very
important.
*Contact: [email protected]
Outlook
Because solving full Richards Equation is very time consuming (on average 8.5
hrs/year vs 11 min/year for Bucket or NIED), the next step will be to use the
knowledge of snowpack behavior in melt conditions from solving the full
Richards Equation to improve the bucket model or NIED approximation.
References
Daanen, R. and Nieber, J. (2009) Model for Coupled Liquid Water Flow and Heat Transport with Phase Change in a Snowpack. Journal of Cold
Regions Engineering (23) 43-68.
Yamaguchi et al. (2010) Water retention curve of snow with different grain sizes. Cold Regions Science and Technology (64) 87-93.