ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
3.3.9.2 Modelling of water run-off
Methods for the extraction of information from remote sensing data as well as data layers needed to
run ASGI have been delivered to the customer. The project work offered the opportunity to investigate
the specific limitations but also chances given by the alpine environment and to discuss the problem of
parameter extraction from remote sensing data with different potential.
Furthermore a review on the existing administrative data layers including accuracy assessment of this
data have been performed and recommendations for the appropriated method for the divers preprocessing
and processing steps have been elaborated.
The Sulm test site study demonstrates the full evaluation chain with the ASGI water run of model. To
operate the model time series of precipitation, air temperature and runoff were prepared. Besides the
time series data, spatial data on various parameters are required for the model. Following spatial
source data sets were derived in a first step: Landcover from Landsat TM data, DEM from the 1:50.000
scale national map series, and soil type information estimated from the digital geological map 1:50.000
and already available point-wise analysed soil profiles.
Meteorological time series data for precipitation and temperature were delivered from the Section of
Water Resources. Shell script programming was done to check for various errors (e.g. negative
precipitation values, revised time order) in the data. The remaining plausible time series data were then
pre-processed in the ASGI environment. As only 7 precipitation stations remained with reliable data,
IDW-Interpolation was selected as interpolation method. Also temperature time series data were
interpolated with IDW.
The potential evaporation was calculated after Hamon (Federer & Lash, 1983). This model includes in
addition to the temperature (IDW-interpolated as described above), an empirical monthly correction
factor fi.
Estimation of the interception storage capacity is based on leaf area index, degree of vegetation
coverage and height of water stored on the surface. Leaf area index LAI (based on Thompson et al.,
1981) and degree of vegetation coverage were derived from the landcover classification results.
For the Sulm catchment (and Austria in general), soils of agricultural areas are mapped with high level
of detail in a scale of 1:2.500. Soils in forests and alpine regions, however, are in general only sampled
in 4km by 4km grid. Up to now, no full area covering soil information is available which would be an
appropriate basis for hydrological modelling in meso-scale catchments. As the basis for the estimation
of soil-types, the soil-type map of Styria 1:1.000.000 (Krainer et al., 1998), the geological map of Styria
1:50.000 in digital format, and point-wise available soil analysis data (Krainer et al., 1998) were used.
The result is an “estimated soil type distribution” of the catchment area, with the 1:50.000 scale of the
geological map and the soil type information of the 1:1.000.000 scale soil type map. The so derived
parameters can only give a rough estimate of the actual soil parameters. However, major
improvements on the soil type distribution information, e.g. detailed delineation of valley bottoms,
alluvial cones etc. result from this procedure.
The applied TOPMODEL approach is based on the saturated area approach using the topographic
index after Beven and Kirkby (1979). This statistically based model differs from earlier soil-vegetation
process models by including spatial variations in the main driving processes and uses topographic
HRU’s to calculate lateral soil water transport. The recession parameter m was determined by slope
hydrograph analysis. The main parameter of the TOPMODEL approach, the soil-topographical index,
has been derived from the DEM.
All these methods delivered the necessary parameters for running the ASGI model properly.
Distributed models such as ASGI require land cover data for parameterisation. The use of earth
observation data can be seen as an important pre-requisite for such models, as the spatial data
demand for large area applications could hardly be satisfied without earth observation derived data
sets (compare e.g. cost-estimates for land-cover mapping given by Konecny, 1995) . This leads to the
conclusion that earth observation data support the application of distributed models, thus extending the
toolbox of hydrologists.
3.3.9.3 Avalanche risk
Different types of avalanche risk models are currently developed at the Austrian Institute for Avalanche
and Torrent Research, who was the national customer of this feasibility study. All models need similar
parameters from remote sensing and DEM sources. Among these, the actual information on land
JR, RSDE, ALU, LMU, Seibersdorf, WSL 81