Integration of Remote Sensing Techniques
into the Snowmelt Component of Hydrological Models
Owen Turpin1, Rob Ferguson1, and Barbro Johansson2
Models of daily runoff from seasonal snowpacks and glaciers require knowledge or assumptions
about the decline in the snow-covered area (SCA). Some semi-distributed models (e.g., SRM) rely
on satellite data as an input in addition to meteorological data, but general-purpose hydrological
models with a snow component (e.g., HBV) do not normally use earth observation (EO) data. EO
data have the potential to verify or update SCA predictions generated by these models. Integration of
EO data into general-purpose hydrological models poses an initial problem, though, as they tend to
assume that energy input reduces the snowwater equivalent (SWE), but that the SCA remains equal
to that of the sub-area until snowmelt within the sub-area is complete. Two possible solutions are
either to allow a stepped SWE distribution within a sub-area, or to assume uniform melt over a
nonuniform snowpack within a sub-area. In both approaches melt is then converted into a reduction
in SCA as well as SWE , thereby allowing snowpack depletion to be compared directly with EO data.
Two examples are given in which the gradual reduction of SCA is verified using EO data. The HBV
model is applied to a basin in arctic Sweden and a recently developed glacier runoff model is applied
to a basin in the Swiss Alps. Landsat TM data of both basins revealed considerably less snow than
that predicted by the models. TM data for the Swedish basin show that only glaciated zones were
100% snow covered. Despite over-predicting the SCA, both models achieved very good observed
discharge fits. It is argued that runoff models should correctly simulate the hydrological system if
they are to be transferred to different environments or new climate scenarios with confi-dence, and
that EO data can play a valuable role in this.
1 Sheffield Centre for Earth Observation Science and Department of Geography, University of Sheffield, Winter
Street, Sheffield S10 2TN, UK
2
Research and Development Department, Swedish Meteorological and Hydrological Institute, S-601 76 Nor-
rkping, Sweden
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