Developments for a semi-distributed runoff modelling ...
Transcript of Developments for a semi-distributed runoff modelling ...
Developments for asemi-distributed runoff modellingsystem and its application in the
drainage basin Otztal
Diplomarbeit
Zur Erlangung des akademischen Grades
Magister der Naturwissenschaften
an der
Leopold-Franzens-Universitat
Innsbruck
eingereicht von
Markus Heidinger
Innsbruck, Dezember 2004
Abstract
A hydrological modelling system was further developed and applied to the Alpine
drainage basin Otztal (Austria). The system consists of basic modules for hydro-
logical system setup, remote sensing, pre-processing of meteorological data, runoff
modelling and post-processing. For runoff modelling an advanced version of the
snowmelt runoff model (SRM) from Martinec (1975) was used. Within the hydro-
logical setup, sub-basins and hydrological response unites (HRUs), describing areas
with similar runoff properties, were specified. HRUs were defined using topographic
data and land-cover information from Landsat 7 ETM+. Essential input data for the
SRM are temperature, precipitation and snow covered area (SCA). A meteorolog-
ical pre-processor was designed to extrapolate meteorological point measurements
to a grid. Inverse distance weighting (IDW) and altitude gradients were applied
to interpolate meteorological point measurements spatially. The spatially detailed
maps of meteorological data were aggregated HRU-wise for use in the runoff model.
Snow maps from high resolution optical data of MODIS, operating on-board NASA’s
TERRA satellite were used to provide HRU-wise SCA input for the SRM. On days
without satellite image acquisition SCA was interpolated using the accumulated
melt depth method (AMD). Data management and storage of un-processed meteo-
rological and hydrological data as well as pre-processed meteorological, snow cover
and satellite data were supported by an object-relational database system. Simu-
lation runs for daily runoff were carried out for three spring and summer seasons
(2001 - 2003) in the Alpine valley Otztal and its four sub-basins Vent (Rofenache),
Obergurgl, Huben and Tumpen. Observed and simulated runoff show good overall
agreement. During some periods runoff is over- or underestimated, mainly in the
two basins at lower altitude. Reasons for this are discussed and possibilities for
further improvement of the model are suggested.
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Contents
Abstract i
Contents ii
1 Introduction and Outline 1
2 Hydrological Modelling System 3
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Basic Concept of Snowmelt Runoff Modelling . . . . . . . . . . . . . 4
2.3 Data Processing in the Hydrological Modelling System . . . . . . . . 6
3 The Snowmelt Runoff Model (SRM) 13
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Structure of SRM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 Evolution of SRM Programmes Codes . . . . . . . . . . . . . . . . . . 17
3.4 SRM Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4 Pre-Processing of Meteorological Data 21
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 Spatial Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2.1 Elevation Adjustment of Meteorological Data . . . . . . . . . 25
4.2.2 Two-Dimensional Interpolation Methods . . . . . . . . . . . . 26
4.2.3 Examples and Quality Assessment of Interpolation in the
Otztal Test Basin . . . . . . . . . . . . . . . . . . . . . . . . . 28
5 Hydrological Basin Setup 35
5.1 Overview of the Otztal Test Basin . . . . . . . . . . . . . . . . . . . . 35
5.2 Delineation of Basins and Sub-basins . . . . . . . . . . . . . . . . . . 35
5.3 Hydrological Response Units . . . . . . . . . . . . . . . . . . . . . . 39
5.4 Hydro-meteorological Stations . . . . . . . . . . . . . . . . . . . . . . 40
6 Remote Sensing Data Analysis 47
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iv CONTENTS
6.1 Land-cover Classification . . . . . . . . . . . . . . . . . . . . . . . . . 47
6.2 Snow Cover Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
7 Temporal SCA Interpolation 53
7.1 Accumulated Melt Depth Method . . . . . . . . . . . . . . . . . . . . 53
7.2 SCA Interpolation in the Otztal Test Basin . . . . . . . . . . . . . . . 54
8 SRM Parameter Setup in the Otztal Basin 61
8.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
8.2 SRM Parameter Derivation . . . . . . . . . . . . . . . . . . . . . . . 61
8.2.1 Recession Coefficient . . . . . . . . . . . . . . . . . . . . . . . 61
8.2.2 Time Lag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
8.2.3 Runoff Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . 63
8.2.4 Critical Temperature . . . . . . . . . . . . . . . . . . . . . . . 63
8.2.5 Degree-day Factor . . . . . . . . . . . . . . . . . . . . . . . . 64
8.2.6 Rain Contributing Area . . . . . . . . . . . . . . . . . . . . . 65
8.2.7 Parameterization of Severe Precipitation . . . . . . . . . . . . 65
8.2.8 Listing of SRM-Parameters for the Otztal Sub-basins . . . . . 66
9 Runoff Simulations 67
9.1 Quality Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
9.2 Runoff Simulations 2001 . . . . . . . . . . . . . . . . . . . . . . . . . 69
9.3 Runoff Simulations 2002 . . . . . . . . . . . . . . . . . . . . . . . . . 72
9.4 Runoff Simulations 2003 . . . . . . . . . . . . . . . . . . . . . . . . . 74
10 Summary and Conclusions 77
A Tables 87
A.1 Information from MODIS Classifications . . . . . . . . . . . . . . . . 87
A.2 Temporal Assignment of Degree-day Factors . . . . . . . . . . . . . . 94
Acknowledgments 101
Chapter 1
Introduction and Outline
At the Institute for Meteorology and Geophysics Innsbruck several projects to
improve runoff modelling and forecasting with earth observation data were carried
out in the last years. The project MISSION (Rott et al., 1998) and especially
the HYDALP (Rott et al., 2000) project pointed out the importance of remote
sensing for hydrological modelling. Within those projects methods for remote
sensing (snow cover mapping), as well as methods for hydro-meteorological data
processing were advanced. This thesis was partly carried out within the EnviSnow
project (http://projects.itek.norut.no/EnviSnow/ ) supported by the European
Commission. The aims of that project include improvement of remote sensing
methods for snow parameter retrieval and assimilation of remotely sensed data into
hydrological models.
Runoff simulations based on the snowmelt runoff model (SRM) from Martinec
(1975) were carried out for the Alpine drainage basin Otztal. The SRM is one
of few models that uses snow area extent as input parameter. Remote sensing
enables to observe snow extent over wide areas. The model has been applied in
over 100 basins on all continents except Antarctica (Seidel and Martinec, 2004).
Although the principles of the SRM are quite simple, especially compared to fully
distributed physical models, the quality of the simulation results are good. To
improve runoff simulations with the SRM a hydrological modelling system was
further developed. The system was concepted in a modular and open way, so
that it can be applied to other models too. An important component of this
system are the pre-processing procedures for meteorological data, that prepare
meteorological station measurements for use in hydrological modelling. It caries out
spatial interpolation to take the spatial variability of the main input parameters,
temperature and precipitation, into account.
Thesis Outline
The hydrological modelling system is introduced in Chapter 2. The various
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2 Introduction and Outline
components of the system are explained in the following Chapters. In Chapter 3 the
concept of the snowmelt runoff model (SRM) and its extension and implementation
into the hydrological modelling system is explained. As the SRM needs areal
precipitation and temperature as input, spatial interpolation of these parameter is
required. In Chapter 4 it is explained how the meteorological pre-processor is doing
this interpolation.
The enhanced hydrological modelling system was tested in the Alpine basin Otztal
(Austria). The drainage basin Otztal covers 760 km2 in area and extends over an
elevation range of almost 3000 m. For use with the SRM it was divided into the four
sub-basins Vent (Rofenache), Obergurgl, Huben and Tumpen. The sub-basins were
further partitioned into hydrological response units (HRU). The tasks of the ’basin
setup’ are explained in Chapter 5. Remote sensing was used to determine different
land-cover types within the basin setup and to deliver the snow extent as essential
model input. The applied techniques are reviewed in Chapter 6. As the SRM
needs the snow covered area (SCA) daily, temporal interpolation of this parameter
is needed. For this purpose the accumulated melt depth method (AMD) was used
(Chapter 7). The derivation of the essential model parameters for the Otztal test
basin is shortly explained in Chapter 8. The results of the runoff simulations in the
Otztal basin, which were carried out from 1 April to 30 September for the years
2001 - 2003 are presented in Chapter 9. Finally in Chapter 10 a summary and the
conclusions of this work are given.
Chapter 2
Hydrological Modelling System
In this Chapter the hydrological modelling system is introduced which was used
and further developed within this thesis. First the basic concept of snowmelt runoff
modelling is explained. Then the applied data processing and assimilation chain of
the hydrological modelling system and the database system are described.
2.1 Introduction
Hydrological runoff modelling is concerned with the transformation of falling
precipitation and snowmelt over a basin into outgoing stream-flow. Runoff models
parameterize the pathways of the water flow through the basin, the lag times and
losses with various degrees of complexity, depending on the model and available
information. If precipitation falls as snow, the timing of runoff is shifted towards
periods of higher temperature when energy for melting is available (Malcher et al.,
2004).
A major weakness for predicting runoff from snowmelt with these models results
from estimating the snow storage over a basin from point measurements of
precipitation. The snowmelt runoff model (SRM) (Martinec, 1975), which was
developed specifically for calculating snowmelt runoff, circumvents this problem by
using spatially detailed data on snow extent derived from remote sensing sources.
Rainfall and temperature are spatially not uniform, but can show rapid changes in
intensity and volume over short distance, particularly in convective events (Newson,
1980; Smith et al., 1996; Goodrich et al., 1997). Comparisons of precipitation
sums, measured at the station Vent, which is located within the Otztal basin,
during the ablation period and at various points nearby, show significant increase of
precipitation with altitude and also some variability depending on the orientation
of mountain chains relative to the prevailing wind direction and on distance from
the main Alpine ridge (Kuhn and Batlogg, 1999).
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4 Hydrological Modelling System
The grade of modelled discharge is strongly dependent on the quality of its input
parameters. Therefore taking into account the spatial distribution and altitude
dependences of precipitation and temperature improves the quality of calculated
runoff.
Traditional methods for estimating the hydrological response using tables,
charts and graphs were replaced with computer models in the last decades. Since
the mid-1960s, engineers and scientists have been developing hydrological models
for computers (Ward and Trimble, 2003). A number of different approaches and
applications for hydrological computer simulations are used.
Most hydrological computer models consist of at least one input file (including
meteorological data), the hydrologic model itself and an output file. Input data
generation is not included in most hydrological computer models. More sophisti-
cated hydrological modelling systems include input data pre-processing. As the
goal, calculating runoff from precipitation over a basin, is the same, the essential
input data for all runoff models are similar.
Within this thesis a hydrological modelling system was further developed
and tested. It includes data pre-processing, runoff modelling and post-processing
of the results. To improve spatial representation of input data, a meteorological
data pre-processor was implemented. It is designed in modular form, open for
further developments. Therefore different input processing routines, or different
runoff models can be applied to this modelling platform, by a simple exchange of
the individual system component.
2.2 Basic Concept of Snowmelt Runoff Modelling
In high latitude and alpine basins snowmelt is an important source of discharge.
Runoff models for these regions include therefore a snowmelt routine. The SRM is
especially designed for snowmelt runoff modelling using the snow covered area as
input. This snow covered area is derived with the aid of remote sensing. Further
input data for the SRM are areal precipitation and mean temperature.
The snowmelt runoff modelling system available at IMGI (Institut fur Meteorologie
und Geophysik Innsbruck) is based on the SRM. It includes the following basic
operations:
• Spatial extrapolation of meteorological data
• Snow extent mapping with satellite data/interpolation of snow covered area
• Snow melt calculation for each hydrological response unit (HRU)
2.2 Basic Concept of Snowmelt Runoff Modelling 5
• Integration of melt over the snow-covered area
• Integration of rainfall runoff over the rain contributing area
• Runoff routing
Figure 2.1 shows a simplified flow chart of the main steps performed within the
modelling system . In the beginning meteorological point measurements are extrap-
olated to a grid. The areal snow coverage is derived from satellite data. These data
are then aggregated for each HRU and afterwards used for rain runoff calculation,
snowpack accounting and snowmelt generation. Finally, the discharge is computed
by routing the runoff volumes derived for the HRUs.
Figure 2.1: Basic operations for snowmelt runoff modelling, using meteorological data
from single stations and snow extent from satellite data as inputs (Rott et al., 2000 -
modified).
Air temperature is used to estimate snow melt and to decide whether precipitation
6 Hydrological Modelling System
falls as snow or rain. In SRM areal averages of rainfall are required to determine the
rainfall contribution to runoff. Extrapolation of precipitation from point measure-
ments at stations to zones or a whole basin is particularly problematic in mountains,
where strong altitude gradients and spatial variability of precipitation exist. Some
runoff models generate the snow storage from precipitation measured at stations,
which is even more problematic (Malcher et al., 2004). Extrapolation of station data
to a higher resolution grid and aggregation of these data afterwards should deliver
more representative model input.
2.3 Data Processing in the Hydrological Mod-
elling System
Runoff modelling with semi-distributed and distributed hydrological models needs
adequate tools for handling large amounts of hydro-meteorological data and
remote sensing products. For this reason a processing system for management
of satellite derived products and hydro-meteorological data obtained from station
measurements was developed. A simplified flowchart of the main processing steps
is shown in Figure 2.2. It has been designed in a modular and flexible way, in
order to be applicable for runoff simulations and runoff forecasts. Storage and
handling of meteorological and hydrological data, including basin and model setup,
is supported by a relational database management system, that is able to handle
geographic information. Several different projections and datums are supported,
so that the whole system can be applied easily to other areas. The five main
modules of the hydrological modelling system are the hydrological model setup, a
remote sensing module, a meteorological pre-processor, the runoff model itself and
a post-processor.
Hydrological System Setup
System setup is conveniently done as the first step for preparing hydrological
modelling. This includes collecting meteorological and discharge data of the study
area, basin setup and model setup. Meteorological and discharge data are provided
by different operators, which store data in different formats. For use in the IMGI
hydrological modelling platform these data were transformed to a specific format
and stored within the relational database.
The detailed steps of the basin setup (Chapter 5) depend on the hydrological runoff
model which is used and the users preferences. For runoff simulations with the
SRM it includes the delineation of the drainage basins and sub-basins using digital
elevation data. Hydrological response units (HRUs) can be defined using a digital
2.3 Data Processing in the Hydrological Modelling System 7
HydroMet Database
(PostgreSQL)
Meteorological StationsRunoff Gauges
Basin Setup
Satellite based information(snow covered area)
IRSL Software Tools
(processing ofRS Data)
Pre-processing ofmeteorological data
Snow coverinterpolation
Grids of Interpolated
data
Input (File) generation for hydrological Modelling
Runoff Modelling(SRM)
Remote Sensing Module
Meteo Pre-Processing
HydroMet Database
(PostgreSQL)
Hydrological Modelling
Computed Runoff
Graphic data output
Quality Assessment
Post-processing
Model Setup
Hydrological System Setup
Figure 2.2: Schematic process diagram of the hydrological modelling system.
8 Hydrological Modelling System
elevation model and land cover information, which are derived from remote sensing
data or other sources. Furthermore, specific hydrological parameters of the basins
(such as the recession coefficient) are derived from archived time series of the runoff
(see Chapter 8).
Remote Sensing module
The remote sensing module includes presently an automatic snow mapping
procedure using MODIS data (Chapter 6.2), which was developed in the project
EnviSnow. The automatic snow mapping procedure downloads MODIS Level 1B
data via Internet and generates spatially detailed snow maps. For use with the
SRM, snow cover is aggregated for each HRU. On days without snow maps the
snow extent is estimated using a degree day model (Chapter 7). Snow classification
procedures are also available for other satellite sensors, in particular synthetic
aperture radar (SAR).
Meteorological Pre-processing
The meteorological pre-processor is a versatile tool for activities requiring spatially
distributed information. It carries out temporal integration of meteorological
measurements and prepares gridded temperature and precipitation data by spatial
interpolation. Depending on the hydrological model, whether it is fully distributed
or not, the rastered data are integrated for each zone. In Chapter 4 pre-processing
of meteorological data is described in more detail. Also the interpolation of the
snow covered area (Chapter 7), which is necessary on daily basis for the SRM, is
done in this module.
Hydrological Modelling
Finally, when all necessary input data for runoff modelling are generated and stored
in the database (DB), these data have to be transformed for use in the individual
runoff simulation program. For this purpose an assisting file builder was created
to derive the input files for the runoff model. Some models are able to get the
necessary input data directly from databases. As this option is not as flexible and
the controlling of input data in this case would be more error prone, the usage of
separate files was chosen. For runoff simulation in this work the snowmelt runoff
model (SRM) is used (Chapter 3).
Post-processing
The post-processing module includes quality assessment of the simulated runoff
and graphical data output generation. For numerical quality description of the
simulations the relative volume deviation and the ’goodness of fit measure’ after
2.3 Data Processing in the Hydrological Modelling System 9
Nash and Sutcliffe (1970) are implemented (Chapter 9.1). The graphs of the
simulated runoff are generated automatically with gnuplot after runoff calculation.
The freely distributed plotting tool gnuplot (http://www.gnuplot.org) provides the
opportunity to store the graphs in several data formats such as scalable vector
graphics (SVG). SVG enables the viewer to zoom into the graph without loss of
quality.
The Database System
To simplify data handling within the different processing modules a PostgreSQL
database system was set up, allowing systematic and easy to use data storage and
retrieval. All input data and calculated results that are needed in other modules
are stored in this DB (see Figure2.3).
Figure 2.3: Contents of the hydro-meteorological database (Malcher et al., 2004 - modi-
fied).
PostgreSQL is an object-relational database system, using the Structured Query
Language (SQL) for accessing and manipulating database systems. Relational
databases consist of different objects, including tables. As these tables can be
related to another, the database is named relational. A table is a collection of
10 Hydrological Modelling System
records. Each record is stored as separate row in the table and each column
contains the same type of information. Using the structured query languages this
information can be prompted from the database.
The advantage of PostgreSQL is the availability of the PostGIS extension which
allows GIS (Geographic Information Systems) objects to be stored in the database
(Ramsey, 2004). In detail it is used to select the meteorological stations in the area
of interest from the database. As the coordinates of the stations are in the DB, the
area can be defined for example by creating a box, needing only the coordinates
of the upper left and lower right corner. An other way would be to create a circle
using a center point and the radius.
The queries for selecting the meteorological stations and data is embedded in the
C code of the pre-processor (Chapter 4.1). For writing this client application the
PostgreSQL libpq interface is used. libpq is a collection of C libraries, which allow
client programs to send queries to the PostgreSQL server and to retrieve their
results (Eisentraut, 2003).
A hierarchical scheme of the database used is shown in Figure 2.4. The sin-
gle tables are referenced by key indices to another. The DB consists of several
tables for station measurements, the results of the pre-processor, including temper-
ature, precipitation, snow-covered area (measured and interpolated), the degree-day
factor of each HRU and the (SRM) model setup, including area, land-cover type of
the HRU and the (SRM) model parameters.
2.3 Data Processing in the Hydrological Modelling System 11
Figure 2.4: Hierarchical scheme of the hydro-meteorological database tables (with keys).
12
Chapter 3
The Snowmelt Runoff Model
(SRM)
In this Chapter the snowmelt runoff model is introduced, which was used for runoff
simulation. After providing an overview of the model, different former software
developments of the SRM and the new SRM computer application within the IMGI
hydrological modelling system are presented.
3.1 Overview
The snowmelt runoff model (SRM) was developed by Martinec (1975) at the Swiss
Snow and Avalanche Research Institute. The model has been applied in more then
25 countries between 32 - 60 degree north and 33 - 54 degree south (Martinec et al.,
1998). SRM was developed specifically to predict runoff due to precipitation and
snowmelt using areal information about the snow extent derived by remote sensing.
Several software versions of the SRM have been developed by now. The
original version was limited to 8 HRUs, which corresponded to elevation zones.
Different land-cover types were not taken into account (Martinec et al., 1998). For
this work a recent version of SRM made available by the USGS on the programming
environment Modular Modelling System (MMS) (Leavesley et al., 1996) was
used in the beginning. Later on a new software version of the SRM was written
in the programming language C and embedded into the hydrological modelling
system. This version offers maximum flexibility in selection of sub-zones, including
different land-cover types, and enables the addition of own developments and
parameterization of hydrological processes. The number of zones is theoretically
only limited by computer capacity. As the necessary data and parameters for
the runoff model were stored in a PostgreSQL database, an input file builder was
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14 The Snowmelt Runoff Model (SRM)
developed to generate the input files for the new SRM version. To offer some
comfort to the user a graphical user interface was developed, which includes input
data and parameter file generation/editing and SRM simulation.
Necessary input data for the snowmelt runoff model are temperature, areal
precipitation and areal snow-coverage. From these data the runoff is calculated.
Further input data processing is not included in the SRM, which has the advantage
that meteorological input data generation and processing is strictly separated
from hydrological runoff modelling. So improvement of meteorological data
pre-processing can improve runoff modelling without changing anything within the
SRM.
3.2 Structure of SRM
The basic structure of SRM, is shown schematically in Figure 3.1 (Ferguson, 1999).
The basin is subdivided into several sub-units (HRUs). HRUs are supposed to
Figure 3.1: Structure of SRM after Ferguson (1999).
represent areas of similar hydrological and meteorological characteristics. Unlike
basins and sub-basins, HRUs are not essentially lumped geographically. For each
3.2 Structure of SRM 15
HRU the model Equation 3.1 is separately solved to calculate the runoff. In the
concept of the HRU it is assumed that there are no interactions between HRUs of
the several sub-basins (Neitsch et al., 2002). To obtain the runoff for the complete
watershed these results are linearly cumulated.
The runoff of day n + 1 is derived by (Martinec et al., 1998)
Qn+1 = Qnkn+1︸ ︷︷ ︸
(a)
+M∑
i=1
(cs,i,nai,nT+
i,nSCAi,nAi︸ ︷︷ ︸
(b)
+ cr,i,nPi,nAi)︸ ︷︷ ︸
(c)
(1 − kn+1)f (3.1)
where
n... index, representing the sequence of days during the modelling period
M... total number of HRUs
i... index for HRU
f = 100086400
conversion factor from [mmkm2d−1] to [m3s−1]
A... area of HRU in km2
Measurement variables:
Q... mean daily runoff [m3s−1]
T+... positive degree day sum [◦C]
P... precipitation [mm]
SCA... ratio of snow-covered area to the total area of a HRU
Hydrological parameters:
cr... runoff coefficient for rain
cs... runoff coefficient for snowmelt
k... recession coefficient
a... degree-day factor [mm◦C−1d−1]
Temperature (T ), precipitation (P ) and snow covered area (SCA) are the variables
to be measured or determined each day. The runoff coefficients cr and cs are HRU
wide estimated, in general dependent on the surface type and condition. As condi-
tions of a surface type may change during the season, these coefficients may change
either. The recession coefficient k is characteristic for a given basin and climate
(Martinec et al., 1998). It is determined with runoff time series of former years and
unchanged as long as mean climate conditions or land-cover do not change within
the basin.
Term (a) of Equation 3.1 delivers the recession runoff. This is equivalent to the
16 The Snowmelt Runoff Model (SRM)
discharge during undisturbed conditions, without neither snowmelt nor rainfall.
Snowmelt and/or glacier melt in each unit is calculated from air temperature using
the degree-day method (term (b) of Equation 3.1). The degree-day factor a is used
to convert the positive degree days T + [◦C] into daily melt rates (M) of snow water
equivalent by
Mi,n = ai,nT+i,n (3.2)
This method implicitly parameterizes the radiation balance at the surface. As the
albedo decreases during the snowmelt periods, the degree-day factor increases (Lang,
1986). To gain the part of snowmelt runoff which contributes to discharge, the cal-
culated snowmelt has to be multiplied with the recession coefficient for snow (cs),
which parameterizes different loss factors.
To the snowmelt runoff the rainfall, multiplied with the recession coefficient for rain
(cr), is added on. Runoff from all HRUs is added together before routing, which
means that location of a single HRU within the basin is not taken into account. The
total water from all sources is routed through a single store, which is described by
the recession coefficient k (Martinec et al., 1998).
In addition to the four parameters a, k , cs, cr the SRM needs three further param-
eters, which are not contained in the SRM equation. These are
• critical temperature
• rainfall contributing area
• time lag
The critical temperature determines whether the measured precipitation is snow or
rain. The rainfall contributing area describes if rain falling on a snow covered area
contributes to the runoff or not. In the beginning of the melting season rain falling
into a snowpack is retained by the snow. Later in the season, when the snowpack
is wetter, rainfall falling on snow covered areas contributes to discharge in the
same manner as rain falling on a snow-free area. The time lag parameter describes
the lag between temperature maximum and the maximum of snowmelt runoff. In
former SRM software versions also the temperature lapse rate was necessary as
temperature was calculated from single stations by the SRM. In the version of
this work temperature calculation is done within the meteorological pre-processing
and is not longer included in the SRM. The determination of the individual SRM
parameters in the test basin is explained in more detail in Chapter 8.
3.3 Evolution of SRM Programmes Codes 17
3.3 Evolution of SRM Programmes Codes
Since introduction of SRM in 1975 by Martinec (Martinec, 1975), a number
of enhancements and different computer versions of the ’Martinec-Model’ were
applied.
The very first programmed version was written for mainframe computers in Fortran
at NASAs Goddard Space Flight Center (Martinec et al., 1983). The first version
run-able on personal computers, the Micro-SRM (Micro=Microsoft), was developed
by R. Roberts at the US Department of Agriculture (USDA) using QuickBasic 4.5
(Martinec et al., 1998). Later on the Micro-SRM was enhanced for usage in climate
change modelling (Rango, 1992).
Within the HYDALP project (Rott et al., 2000) development of a Java version, the
SRM-Java, was started by H. Kleindienst. This version was able to execute runoff
forecasts and should be run-able on different operating systems.
At USDA-ARS Hydrology and Remote Sensing Laboratory, the WinSRM,
a version for Microsoft Windows was developed. The latest version Win-
SRM(beta) was released on December 23, 2000 and is still available via FTP
(ftp://hydrolab.arsusda.gov/pub/srm/winsrm install.exe, status: November 2004).
Based on the SRM-MMS software version, running in the environment of the
Modular Modelling System (Leavesley et al., 1996) of the United States Geological
Survey (USGS), an new software version was written in C within this thesis. The
original version of the SRM-MMS version was developed by Cajina et al. (1999).
Oberparleiter (2002) enhanced this version further and provided thus a basis of
the ’SRM-C’ development. This new version reduced the SRM-MMS code by a
factor 10, as the C code only contains the basic mathematical SRM equations. For
simulation runs the SRM-C needs
• a parameter file, containing model and basin setup
• an input data file, containing the pre-processed meteo data
For generation of the necessary input files for the SRM an assistant, using the
programming language Python (www.python.org), was developed. The ’input file
builder’ carries out the necessary database queries for the user to create the input
data and parameter files. After the SRM simulation run, in the post-processing,
an other Python module calculates the Nash-Sutcliffe correlation coefficient and
volumes deviations of the simulation run. Finally a plot of the calculated runoff
against the measured runoff is plotted using gnuplot. A snapshot of the graphical
user interface (GUI) which controls all these programs and modules is shown in
Figure 3.2.
18 The Snowmelt Runoff Model (SRM)
Figure 3.2: Snapshot of the SRM-C GUI.
3.4 SRM Extensions
To use the SRM in extended basins with several sub-basins, the hydrological
network has to be analyzed. This network can be divided into further elements.
In the literature several denotations for this elements are given, in this work these
(gauged) elements are in common named sub-basins. To get the discharge of the
whole basin, runoff of the sub-basins has to be summed up. As water needs a certain
time to travel down the sub-basins this could not be done by simple addition. For
this reason a routing routine was introduced to the SRM. This routing describes
the time-lag of the water flowing down the channel. If water from one sub-basin
flows into another, not the whole runoff of the actual day is added on, but only
the part which reaches the second sub-basin on this day. This inflow is calculated by
3.4 SRM Extensions 19
Rin,t = Rpout,t
(24 − routing)
24+ Rpout,t−1
(routing)
24(3.3)
where Rin,t is the inflow in a sub-basin at time t, Rpout the (calculated) out-
flow at the previous sub-basin and routing is the lag time in hours. As the time
step for runoff calculation with SRM is one day, the time-lag is divided by 24 hours.
20
Chapter 4
Pre-Processing of Meteorological
Data
4.1 Introduction
Hydrological models require meteorological data as input. To prepare measured
data for hydrological modelling a pre-processor was developed. This meteoro-
logical pre-processor checks the consistency of station data, carries out temporal
integration of meteorological measurements and prepares gridded temperature and
precipitation data by spatial interpolation.
The meteorological data pre-processor transforms observed station data and
grid point values of numerical weather prediction models into a given regular grid
covering the drainage basin (e.g. a digital elevation model with a certain grid
size) (Malcher et al., 2004). For extrapolating the point measurements from the
meteorological stations a distance weighted method was chosen. The output of the
pre-processor are grids of interpolated meteorological variables for every time step,
which for the SRM are aggregated for estimating the corresponding value for each
HRU as model input.
The pre-processor is designed in a modular way so that it allows to pre-process
observed meteorological data of stations (measured with different time intervals)
as well as the output from numerical weather prediction models. It can be applied
to temperature and precipitation time series, which are handled slightly differently
(Malcher et al., 2004). Figure 4.1 shows an overview of the processing steps for a
pre-processor running to provide input to runoff models.
In the first step meteorological data (temperature and precipitation) from
stations located within the drainage basins and its surrounding areas are extracted
21
22 Pre-Processing of Meteorological Data
Figure 4.1: Flow diagram of the main processing steps carried out within the meteoro-
logical data pre-processor as input for runoff models (Malcher et al., 2004).
from the database for a modelling time period. In addition to the time series of
measurements, station characteristics including location (latitude, longitude, height
above sea level) are taken from the database.
In the ”Temporal integration” module, the time steps of measurements from
each station are checked in respect to the calculation time step of the hydrological
model. Temporal integration is carried out separately station-by-station. In the
case of Otztal runoff simulations (Chapter 9) the calculation time step of the
hydrological model is 1 day. In this case the mean daily temperature and the
accumulated daily precipitation are calculated for each station (in dependence of
the station type) and stored in a temporary database table.
The ”Spatial interpolation” module performs the interpolation of meteorolog-
ical variables of the same time step measured at irregularly distributed stations
to a regular grid. It is designed as a three-step procedure and takes the elevation
(vertical dependence) and spatial dependence of the variables into account. In the
4.2 Spatial Interpolation 23
next Section more information on spatial interpolation is given.
Spatial aggregation of the interpolated data is optional, but required for con-
ceptional and semi-distributed runoff models like the SRM. In the spatial
integration module precipitation data of each pixel of a HRU are cumulated. For
temperature the arithmetic mean of the gridded values is calculated for each HRU.
The data for each HRU and time-step are then stored in the PostgreSQL database.
4.2 Spatial Interpolation
Various methods have been developed and tested for spatial interpolation of point
measurements. The arithmetic mean would be the simplest method, but the
accuracy of the arithmetic mean is in general insufficient. Singh and Chowdhury
(1986) compared 13 different methods of calculating and statistically evaluating
mean basin precipitation and came to the result that “there was no particular
basis to claim that one method was significantly better than the other, although
in a given situation one method might be preferable to another” (Black, 1991).
Thiessen polygons, isohyetals and geostatistical methods provide good facilities for
interpolating precipitation spatially.
The spatial variability of temperature is not as high as for precipitation. But
for generating grids of this meteorological parameter also spatial interpolation is
necessary. Many methods that are used for interpolating precipitation data are
able to handle temperature data too. The quality of the interpolated data strongly
depends on the number of available stations and their distribution in the area of
interest.
In addition to the horizontal interpolation of meteorological data also verti-
cal dependences have to be considered. The vertical dependence is modeled as a
piecewise linear polynomial. Next to the meteorological variables of the same time
step a digital elevation model covering the investigation area is required as input.
The output, interpolated meteorological data for each grid element, has the same
resolution as the digital elevation model.
To carry out interpolation of meteorological data to a grid the following input
parameters are needed:
• measurements at station
• location of the meteorological station
• a digital elevation model
24 Pre-Processing of Meteorological Data
• vertical gradient of the measured parameter
• height of the reference levels
Spatial interpolation of meteorological data within the pre-processor includes the
following steps (Figure 4.2) (after Malcher et al. (2004)):
• The reduction of the measurements from different elevations to a reference
level using a linear polynomial. The coefficients for the polynomial, describing
the vertical dependence of the parameters, are specified by the user.
• Spatial interpolation of point measurements at the reference level to a regular
grid (which usually has the same raster interval as the digital elevation model).
• Interpolated values at each grid point are transformed from the reference level
to the surface elevation taken from the digital elevation model.
Figure 4.2: Meteorological interpolation and adjustment scheme.
4.2 Spatial Interpolation 25
4.2.1 Elevation Adjustment of Meteorological Data
In common spatial interpolation methods altitude dependences usually are not
considered. But especially in mountainous regions this is of particular importance
for most meteorological parameters used for runoff modelling. Before the mete-
orological data are interpolated horizontally, the measured values are reduced to
a reference height. At this reference level two-dimensional spatial interpolation is
carried out. Afterwards these calculated values at each pixel are adjusted to its
altitude. The rule applied for vertical adjustment depends on the meteorological
parameter.
Temperature
The vertical temperature gradient varies with different weather conditions and
topographic properties. As both, different weather conditions and topographic
influences to temperature, are hard to analyze automatically, another way to
estimate the real temperature has to be used. Possible ways to determine a vertical
temperature gradient are
• using a standard temperature gradient of −0.65 [◦K/100gpm]. This gradient
corresponds to the US Standardatmosphere (1962, 1976), that describes the
mean annual atmosphere for 45◦ north. This linear temperature gradient is
approximately valid up to tropopause level (11000 gpm) (Pichler, 1997).
• calculation of a gradient with temperatures from several adjoining meteoro-
logical stations, which are located at different elevations.
• determination of the gradient from temperature measurements by radioson-
des.
Radiosonde data would be helpful for vertical temperature gradient assessment.
But as the temporal and spatial availability of radiosondes is limited, the use of
these data is rather un-practicable.
If data from near-by radiosondes or adjoining stations are available on a daily basis
(dependent on the used time step), a daily temperature gradient can be calculated.
If no continual time series over the whole simulation period is available, mean
values for the temperature gradient of historical time series can be used. As the
number of radiosondes or meteo stations is in common limited within a certain
area, the value of a certain location usually has to be assigned to the wider area in
the surrounding.
26 Pre-Processing of Meteorological Data
Precipitation
Because precipitation is spatially more variable than temperature, the derivation of
a vertical gradient is more problematic. In addition, the precipitation measurements
at high altitudes are less accurate, because higher wind velocities increase the
deficit of catch of precipitation gauges (Lang, 1985).
For calculation of the altitude relation of precipitation, data from several adjoining
meteorological stations located at different elevations would be necessary. Analysis
of Kuhn and Batlogg (1997, 1999) showed that the vertical gradient within the Alps
is dependent on the meteorological situation and the location within the Alps. For
advective precipitation in most areas a strong vertical gradient was found, whereas
for convective events the increase of precipitation with altitude is usually small.
4.2.2 Two-Dimensional Interpolation Methods
Four methods for horizontal interpolation of meteorological data are explained in
this subsection. The methods are explained for interpolation of precipitation, but
can be used for temperature either.
Thiessen Method
The use of the Thiessen Method is illustrated in Figure 4.3. Adjacent rain
Figure 4.3: Illustration of the Thiessen Method. The Thiessen polygons enclose the area
nearest to a rain gauge.
gauges are connected by straight lines (dashed). Perpendicular bisectors to
4.2 Spatial Interpolation 27
these lines are constructed so that the area around each station is enclosed by
the bisectors or the area boundary. The enclosed areas around the rain gauges
are the Thiessen polygons. The area within this polygon is closer to the rain
gauge in that polygon than to any other rain gauge and the measured rainfall
is assumed to be representative for the total polygon area (Ward and Trimble, 2003).
Isohyetal Method
With the isohyetal method, lines of equal rainfall (isohyets) are drawn (Fig. 4.4).
From the resulting map a weighted average based on the area within each of the
contour lines can be calculated. This method may have some benefit in mountainous
regions, where isohyets should be able to better represent the rainfall distribution
than Thiessen polygons, because orographic effects show up with isohyets. Different
algorithms for taken into account the elevation using this method exist (e.g. Dawdy
and Langbein (1960)).
Figure 4.4: Isohyetal Method.
Kriging
Kringing, a common geostatistical method, is based on theoretical variogramms
to estimate the spatial distribution of point data. The advantage of geostatistical
methods is that not only interpolated values are calculated, but also a bias (Barcelo,
2001).
Data of unknown points are estimated with the aid of weighted means of neighbor-
28 Pre-Processing of Meteorological Data
ing values. The weighting factors are optimized by a geostatistical model and a
variogram, which describes the spatial dependences. A disadvantage of this method
is the need of a dense station network. Also the automatic computation of the best
fitting variogram is complicated and time-consuming. Details of kriging and other
geostatistical methods are explained for example in Schafmeister (1999) or Griffith
and Layne (1999).
Inverse Distance Weighting
A method which is used in numerous hydrological modelling systems for spatial
interpolation of point measurements is inverse distance weighting (IDW). It is a
purely statistic method and does not take the vertical dependences into account.
The inverse distance interpolated value F (x, y) for the point (x, y) is specified by
(Bonham-Carter, 1994):
F (x, y) =
∑Nk=1 wk(x, y)fk
∑Nk=1 wk(x, y)
(4.1)
with
wk(x, y) = d−mk (4.2)
and
dmk =
√
(x − xk)2 + (y − yk)2 (4.3)
The weighting factor wk(x, y) depends on the distance d to the measure point k,
where for the exponent m = 2 is used. fk is the measured value at the station
(reduced to the reference height).
The advantage of this method is that it is very stable. It even works when only a
single meteorological station is available. A disadvantage is the high computational
cost, which increases significantly with the number of stations used for interpolation
and the resolution of the output grid.
4.2.3 Examples and Quality Assessment of Interpolation in
the Otztal Test Basin
For the meteorological pre-processor the inverse distance method was chosen to
interpolate meteorological point data horizontally. The IDW algorithm can be
applied to precipitation data as well as to temperature. The stability of this method
and the possibility to implement it in an automatic working system were the main
reasons for using IDW. The computing time, that is higher for this algorithm
compared to others, is of little relevance on modern computers.
4.2 Spatial Interpolation 29
For reducing the measured data to the reference level where the IDW interpolation
is done, simple linear gradients were chosen. For temperature calculation a linear
lapse rate, ∂T∂z
= −0.6 [◦C/100m] , found by Hoinkes and Steinacker (1974) for
the area of Vent in the southern part of the test basin, is used and assigned to
the whole valley. This value is kept constant during the whole simulation period.
Especially during cold conditions (spring, autumn) when temperature inversions
are well developed in mountainous regions, this temperature lapse rate may not
describe the true condition of the atmosphere . For precipitation a vertical increase
of 8 percent per 100 m is assumed up to a height of 3100 m. At higher levels no
further increase of precipitation is assumed. The value for the precipitation increase
with height was adopted from analyses of Kuhn and Batlogg (1997) in several
Alpine areas. This value is valid for advective events, for convective precipitation
very little increase of the precipitation amount with altitude was found in general.
For this work, aimed at automatic procedures, no discrimination between advective
and convective precipitation events was done. Therefore this value was used for all
events. This may lead to some errors especially in the summer-months.
The spatial interpolation in the area of the Otztal basin uses a digital eleva-
tion model with a pixel size of 25 m. Figure 4.5 shows an example of precipitation
after inverse distance interpolation on reference level (top) and after correction to
height of the digital elevation model grid point (bottom). At reference level so
called ’bull eyes’ appear, caused by the squared distance dependence from the point
measurements. As precipitation (and temperature as well) depends on elevation,
the elevation model strongly modulates the signal in the modeled grid at DEM
altitude. Figure 4.6 and Figure 4.7 show a time series of meteorological grids of
precipitation and temperature for June 2002.
To get some information of the quality of the applied interpolation method, calcu-
lated point values were compared with measured values at a meteorological station.
Figure 4.9 and 4.8 show plots of these data at the meteorological station Vent. For
calculation of precipitation and temperature at the station, the measured data from
Vent were not considered for the spatial interpolation. Especially for temperature
the calculated mean values are very similar to the measured. The precipitation
computation shows some deviations. Generally the calculated precipitation fits
very well, but some small precipitation events are calculated, but not measured and
vice versa. A reason for this is, that precipitation events are often locally bounded,
especially in mountainous regions. To get more accurate values at each point, the
meteorological station network should be more dense for this purpose.
30 Pre-Processing of Meteorological Data
Figure 4.5: Precipitation linear corrected to reference level (top) and to elevation of
DEM (bottom) on 27 June, 2002. Grid-resolution 25 m.
4.2 Spatial Interpolation 31
Figure 4.6: Time series of mean daily precipitation grids of the Otztal area, 1-30 June
2002. Grid-resolution 25 m.
32 Pre-Processing of Meteorological Data
Figure 4.7: Time series of daily temperature grids of the Otztal area, 1-30 June 2002.
Grid-resolution 25 m.
4.2 Spatial Interpolation 33
Figure 4.8: Comparison of measured precipitation versus interpolated at station Vent
from 1 June to 31 July 2002.
Figure 4.9: Comparison of measured temperature versus interpolated at station Vent
from 1 June to 31 July 2002.
34
Chapter 5
Hydrological Basin Setup
This Chapter describes the preparatory steps which are necessary for hydrological
modelling in an Alpine basin. The so called basin setup includes the delineation of
the basin and sub-basins and the definition of hydrological response units. Another
step is the selection of the meteorological stations and runoff gauges. As test basin
the Otztal, a valley within the Austrian Alps, was chosen. The tasks refer to the
model SRM that was used.
5.1 Overview of the Otztal Test Basin
The Otztal watershed is located north of the main ridge of the Eastern Alps of
Austria. The basin (Figure 5.1) used for modelling (Otztal above Tumpen) covers
an elevation range from 931 m at the runoff gauge Tumpen up to the highest peak,
Wildspitze at 3774 m. The land-cover is made up by cultivated meadows and a few
agricultural fields in the valleys and coniferous forests up to the timberline at about
2200 m. At higher elevation alpine tundra vegetation, bare soil, rocks, moraines and
glaciers are the dominating land-cover classes. The whole basin covers an area of
about 760 km2 whereof 106 km2 are glacierised.
5.2 Delineation of Basins and Sub-basins
The first step in hydrological modelling is to define the borders of the basin and
sub-basins.
For this purpose a digital elevation model (DEM) with 25 meter interpolated reso-
lution was used. The DEM was available in Transverse Mercator projection, Bessel
ellipsoid and the Austrian geodetic date. Basin and sub-basins were automatically
delineated from the elevation model using a method from Jenson and Domingue
(1988), which is included in the EASI/PACE software from PCI. The procedure
35
36 Hydrological Basin Setup
Figure 5.1: Landsat7 ETM+ image with the borders of the sub-basins. The sub-basins
are named after the gauging station.
includes
• generation of a depression-loss DEM
• generation of maps of flow direction
• generation of flow accumulation and change in flow accumulation maps
As outlet pixels the coordinates of the four gauges Tumpen, Huben, Vent (Rofen-
ache) and Obergurgl were selected. With these informations the routines generate
the borders of the basin and sub-basins.
5.2 Delineation of Basins and Sub-basins 37
Sub-basins are spatially related to one another, which means that the out-
flow of one sub-basin drains to an other sub-basin (Neitsch et al., 2002). How
the flow from on basin into another does take place is described by the routing
procedure (Section 3.4). Partition of a basin into several sub-basins should increase
the accuracy of the calculated discharge of the basin if runoff gauges are available
for the sub-basins (Braun and Lang, 1986).
In Table 5.1 the area (A) of the sub-basins and the elevation (Hg) at the
runoff gauges are listed. The sub-basin Horlachbach, with an area of about 26 km2
has not been taken into account, as it is influenced by abstraction to the Finstertal
reservoir, which drains directly to the river Inn.
Basin Hg[m] A[km2]
Vent 1890 98
Obergurgl 1878 72.8
Huben 1185 344.4
Tumpen 931 243.7
Otztal 931 758.9
Table 5.1: Area (A) of Otztal sub-basins and elevation (Hg) at runoff gauges. The area
of the Horlachbach sub-basin is not included.
Characterizing the basin for hydrological purposes needs thematic informa-
tion, in particular the land-cover classes and their area-elevation-distribution. For
the Otztal following land classes were discriminated in the model:
• glaciers
• forests
• other surfaces (meadows, low vegetation, bare soil, rock, ...)
Surface classification is carried out by means of high resolution optical data (Section
6.1). The area-elevation curve for these land-cover classes of the whole basin is
shown in Figure 5.2. In Figure 5.3 the area-elevation distributions of the land-cover
classes within the several sub-basins are plotted.
38 Hydrological Basin Setup
Figure 5.2: Cumulative area elevation curve for the Otztal basin.
Glaciers cover 38.2%, respectively 30%, of the two head sub-basins Vent and
Obergurgl with a total size of about 98 and 73 km2. 10% of the largest sub-basin
Huben are covered by glaciers, and about 6% of the sub-basin Tumpen.
Table 5.2 contains the elevation range of the basins and land-cover informa-
tion.
Basin name Elevation [m] Size [km2] Glacier [%] Forest [%]
Vent 1890 - 3770 98 38.2 0
Obergurgl 1878 - 3551 72.7 29.6 0
Huben 1185 - 3599 344.4 10.4 9
Tumpen 931 - 3287 243.7 5.6 23.4
Otztal 931 -3770 758.9 14.4 11.6
Table 5.2: Overview of all basins defined for the Otztal.
5.3 Hydrological Response Units 39
Figure 5.3: Cumulative area elevation curve for the Otztal sub-basins.
5.3 Hydrological Response Units
Hydrological response units (HRUs) are areas with similar hydrological runoff
characteristics. HRUs do not need necessarily to be contiguous and spatially related
(see Figure 5.4). For characterizing the runoff behavior of an area various features,
such as land-use, soil types, water management attributes or as in this thesis
land-cover type and elevation can be used. The use of hydrological response units
enable the user to take different characteristics of an area into account by using
various parameters to reflect differences of the characteristic features of the several
HRUs. The SRM, which is used for this work, was designed for characterizing such
zones. To calculate the discharge of a (sub-)basin, the model-equation for each
HRU is solved, runoff calculated and added up.
The hydrological response units of the four sub-basins of the Otztal, which
were derived by land-cover discrimination and sub-division into elevation zones,
are listed in Tables 5.3 - 5.6. These Tables list the sub-basin name, the land-cover
40 Hydrological Basin Setup
Figure 5.4: Hydrological response units of the four Otztal sub-basins. Information of
the HRUs for each sub-basin are listed in Table 5.3 - 5.6.
class, the elevation range, the mean elevation and the id of the HRU. The id itself
is a four digit number, where the first one denotes the sub-basin. The next two
specify the lower altitude limit of the HRU and the last one, the land-cover type
where 0 stands for low vegetation and other surfaces, 1 for glacier and 2 for forest.
5.4 Hydro-meteorological Stations
The hydrological and meteorological data for the Otztal and surrounding area are
stored in a PostgreSQL database. In this database the pre-processor (Chapter 4.1)
automatically searches for meteorological stations in the user defined area.
Meteorological data are provided by Hydrographischer Dienst Tirol (HD),
Zentralanstalt fur Meteorologie und Geodynamik (ZAMG), Hydrographisches
Amt Bozen (HA) and Institut fur Meteorologie und Geophysik Innsbruck (IMGI),
hydrological data by Hydrographischer Dienst Tirol and Tiroler Wasserkraft AG
5.4 Hydro-meteorological Stations 41
Basin Land-cover class Elevation range [m] Mean elevation [m] Id
Vent glacier < 3000 2874 1001
Vent glacier 3000-3200 3106 1301
Vent glacier 3200 < 3310 1321
Vent other 1801-2000 1965 1180
Vent other 2001-2200 2108 1200
Vent other 2201-2400 2310 1220
Vent other 2401-2600 2510 1240
Vent other 2601-2800 2708 1260
Vent other 2801-3000 2897 1280
Vent other 3001-3200 3084 1300
Vent other 3200 < 3308 1320
Table 5.3: Hydrological Response Units of the sub-basin Vent (Rovenache).
Basin Land-cover class Elevation range [m] Mean elevation [m] Id
Obergurgl glacier < 3000 2863 2001
Obergurgl glacier 3000-3200 3101 2301
Obergurgl glacier 3200 < 3278 2321
Obergurgl other 1801-2000 1955 2180
Obergurgl other 2001-2200 2144 2200
Obergurgl other 2201-2400 2308 2220
Obergurgl other 2401-2600 2507 2240
Obergurgl other 2601-2800 2702 2260
Obergurgl other 2801-3000 2895 2280
Obergurgl other 3000 < 3122 2300
Table 5.4: Hydrological Response Units of the sub-basin Obergurgl.
(TIWAG). The available data are listed in Table 5.7 and 5.8.
42 Hydrological Basin Setup
Basin Land-cover class Elevation range [m] Mean elevation [m] Id
Huben glacier < 3000 2891 3001
Huben glacier 3000-3200 3103 3301
Huben glacier 3000 < 3293 3321
Huben other 1201-1400 1328 3120
Huben other 1401-1600 1501 3140
Huben other 1601-1800 1724 3160
Huben other 1801-2000 1910 3180
Huben other 2001-2200 2108 3200
Huben other 2201-2400 2306 3220
Huben other 2401-2600 2506 3240
Huben other 2601-2800 2701 3260
Huben other 2801-3000 2892 3280
Huben other 3000 < 3128 3300
Huben forest 1201-1400 1323 3122
Huben forest 1401-1600 1512 3142
Huben forest 1601-1800 1703 3162
Huben forest 1800 < 1935 3182
Table 5.5: Hydrological Response Units of the sub-basin Huben.
5.4 Hydro-meteorological Stations 43
Basin Land-cover class Elevation range [m] Mean elevation[m] Id
Tumpen glacier < 3000 2875 4001
Tumpen glacier 3000 < 3101 4301
Tumpen other < 1000 961 4090
Tumpen other 1001-1600 1222 4100
Tumpen other 1601-1800 1710 4160
Tumpen other 1801-2000 1914 4180
Tumpen other 2001-2200 2109 4200
Tumpen other 2201-2400 2303 4220
Tumpen other 2401-2600 2503 4240
Tumpen other 2601-2800 2695 4260
Tumpen other 2801-3000 2887 4280
Tumpen other 3000 < 3107 4300
Tumpen forest < 1200 1118 4102
Tumpen forest 1201-1400 1303 4122
Tumpen forest 1401-1600 1501 4142
Tumpen forest 1601-1800 1697 4162
Tumpen forest 1800 < 1942 4182
Table 5.6: Hydrological Response Units of the sub-basin Tumpen.
44H
ydro
logi
calB
asin
Set
up
Station name Country Operator Location Hight [m] Online Time series T P
Brenner AT ZAMG 11.5078E 47.0033N 1449 X 16.12.1992 - 31.12.2003 X X
Brenner AT ZAMG 11.5133E 47.0056N 1450 01.01.2000 - 31.12.2003 X X
Brunnenkogel AT ZAMG 10.8577E 46.9077N 3440 X 22.01.2002 - 31.12.2003 X
Galtuer AT ZAMG 10.1848E 46.9703N 1587 X 13.02.1997 - 31.12.2003 X X
Gries i. Sellrain HD ZAMG 11.0239E 47.0708N 1577 02.01.1998 - 01.12.2003 X X
Haiming AT ZAMG 10.8792E 47.2586N 663 01.01.2000 - 31.12.2003 X X
Imst AT HD 10.7411E 47.2489N 860 X 02.01.1991 - 31.12.2003 X X
Innsbruck (airport) AT ZAMG 11.3500E 47.2666N 579 X 01.07.2002 - 31.12.2003 X X
Ischgl - Idalpe AT ZAMG 10.3170E 46.9758N 2323 X 17.02.1993 - 31.12.2003 X X
Kurzras IT HA 10.7814E 46.7581N 2070 01.01.1997 - 31.12.2003 X X
Landeck AT ZAMG 10.5585E 47.1365N 798 X 23.12.1993 - 31.12.2003 X X
Langenfeld AT HD 10.9700E 47.0764N 1880 01.01.1998 - 31.12.2003 X X
Meran IT HA 11.1667E 46.6734N 392 01.01.1997 - 31.12.2003 X X
Nauders AT ZAMG 10.5000E 46.8917N 1331 01.01.2000 - 31.12.2003 X X
Obergurgl AT ZAMG 10.0230E 46.8672N 1938 X 04.01.1999 - 31.12.2003 X X
Pitztaler Gletscher AT ZAMG 10.8750E 46.9242N 2850 X 09.12.1993 - 31.12.2003 X X
Prutz AT ZAMG 10.6667E 47.0667N 870 01.01.2000 - 31.12.2003 X X
Seefeld AT ZAMG 11.1720E 47.3218N 1182 X 11.06.1997 - 31.12.2003 X X
St.Leonhard-Neurur AT ZAMG 10.8644E 47.0222N 1462 01.01.2000 - 31.12.2003 X X
Steinach-Plon AT ZAMG 11.4700E 47.0794N 1204 01.01.2000 - 31.12.2003 X X
Solden AT HD 11.0111E 46.9667N 1380 X 02.01.1991 - 31.12.2003 X
Umhausen AT ZAMG 10.9337E 47.1358N 1041 X 27.05.2003 - 31.12.2003 X X
Vent AT IMGI 10.9130E 46.8594N 1906 01.01.1990 - 31.12.2003 X X
Otz AT HD 10.8869E 47.2058N 760 01.01.1991 - 31.12.2003 X X
Table 5.7: Available meteorological stations. Data are provided by Zentralanstalt fur Meteorologie and Geodynamik (ZAMG), Hydrographis-
cher Dienst Tirol (HD), Hydrographisches Amt Bozen (HA) and Institut fur Meteorologie and Geophysik Innsbruck (IMGI).
5.4H
ydro-m
eteorologicalStation
s45
Gauge Operator River Location Hight [m] Time series
Vent (Rofenache) HD Rofenache 10.9111E 46.8575N 1890 02.01.1991 - 31.12.2003
Obergurgl TIWAG Gurgler Ache 11.0211E 46.8683N 1879 02.01.1991 - 31.12.2003 a
Huben HD Otztaler Ache 10.9700E 47.0453N 1185 02.01.1991 - 31.12.2003
Tumpen HD Otztaler Ache 10.9111E 47.1636N 1890 02.01.1991 - 31.12.2003
Table 5.8: Available hydrological data. Data are provided by Hydrographischer Dienst Tirol (HD) and Tiroler Wasserkraft AG (TIWAG).
a2003 data are provisional values. Not all consistency checks were accomplished by the provider up to now.
46
Chapter 6
Remote Sensing Data Analysis
Earth observation can provide relevant data for runoff modelling with SRM in two
ways:
• for basin setup
– land-cover information for defining hydrological zones
• for runoff simulations and forecasts
– time series of snow-maps from high and medium resolution optical data
or SAR (synthetic aperture radar) data
– maps of surface albedo for estimation of degree-day factors
In this Chapter the methods of land-cover classification and snow cover mapping
are shortly reviewed. Estimation of the degree-day factor from the surface albedo
was not used within this thesis.
6.1 Land-cover Classification
Land-cover maps are essential input for delineation of areas with similar runoff
properties (HRU). Remote sensing has a long tradition in land surface classification
using high resolution (HROI) and medium resolution (MROI) optical sensors (Rott
et al., 2000). In mountainous terrains high resolution optical sensors like Landsat
5 TM and Landsat 7 ETM+ are in general preferred due to the high spatial
variability of surface classes.
For the Otztal three surface classes were discriminated:
• glacier
• forest
47
48 Remote Sensing Data Analysis
• other (low vegetation, meadows, bare soil, rocks,...)
Classification of those three classes was done with two cloud-free Landsat7 ETM+
scenes. Therefore no cloud detection was required. For detection of glaciated areas
a scene with minimum snow extent was used, acquired on 13.09.1999 (Fig.5.1).
Mapping of forested areas was based on an image acquired on 05.03.2000, where low
vegetation was covered by snow. The Enhanced Thematic Mapper Plus (ETM+)
of NASAs Landsat 7 satellite measures in 8 bands with 30 m resultion in bands 1-5
and band 7, 60 m in band 6 and 15 m in band 8. Band 6 operates in wavelengths
of terrestrial infrared, the other bands operate in the visible and in the short-wave
infrared bands (Tab. 6.1).
Band Bandwidth [µm]
1 0.45 - 0.52
2 0.53 - 0.61
3 0.63 - 0.69
4 0.78 - 0.90
5 1.55 - 1.75
6 10.4 - 12.5
7 2.09 - 2.35
8 0.52 - 0.90
Table 6.1: Landsat 7 ETM+ bands (http://ltpwww.gsfc.nasa.gov/, status: November
2004).
The algorithms that where used for land-cover classification are based on the
planetary albedo (Rp) of the individual bands. Rp is given by (Epema, 1990)
Rp =πd2L(λ)
S0(λ)cosφ0
(6.1)
where d [AU ] is the Earth-Sun distance, S0(λ) [Wm−2µm−1] is the exo-atmospheric
solar irradiance, L(λ) [Wm−2sr−1µm−1] is the spectral radiance measured by the
sensor in band λ and φ0 [◦] is the solar zenith angle. Effects of surface topography
are not considered in this equation.
Classification of glaciers
For the determination of glacier areas an algorithm based on the ratio of surface
albedo band 3 and band 5 (Rott and Markl, 1989; Sephton et al., 1994), was used.
Because the areas of alpine glaciers do not change significantly within a few years,
6.1 Land-cover Classification 49
it is sufficient to determine this parameter from one optical image acquired at end
of summer, when snow cover of ice-free areas is minimal.
Classification of forests
Forests and low vegetation have similar spectral characteristics but can be dis-
criminated using the normalized difference vegetation index (NDVI) with a winter
image, when low vegetation is covered by snow (Rott et al., 2000). The NDVI for
Landsats 7 ETM+ is given by
NDV I =Rp(4) − Rp(3)
Rp(4) + Rp(3)(6.2)
Figure 6.1 shows a map of the resulting land-cover classes for the Otztal.
Figure 6.1: Land-cover classes for Otztal. Red - low vegetation and bare surfaces, green
- forest, blue - glacier.
50 Remote Sensing Data Analysis
6.2 Snow Cover Mapping
Time series of snow covered area (SCA) are an important input for the SRM model.
SCA fraction is required for each HRU and timestep. On days without satellite im-
ages, interpolation of SCA is accomplished using a simple degree-day model (Chapter
7.1). To minimize possible errors of SCA interpolation, the time between satellite
image acquisition should be as short as possible, especially during periods with high
melting rates. Snow cover mapping for the SRM is mainly done with
• synthetic aperture radar sensors
• optical sensors
Radar sensors are independent of day/night and clouds. The disadvantage is
the information deficit caused by the radar geometries (Shading, Foreshortening,
Layover). More detailed information about snow cover mapping with SAR can be
found in Nagler (1996), Rott et al. (2000) and Nagler and Rott (2000).
For this thesis optical data from the Moderate Resolution Imaging Spectroradiome-
ter (MODIS) on-board of the NASA Terra satellite were used for SCA mapping.
SCA Mapping Using MODIS
MODIS measures the reflected and emitted radiation from the Earth’s surface and
atmosphere in 36 spectral bands at wavelengths between 0.40 µm and 14.4 µm
over a swath of 2330 km width. The spatial resolution is 250 m (bands 1 and
2), 500 m (bands 3-7) and 1000 m (bands 8-36) at nadir (Herring et al., 1998).
The snow maps used in this thesis were generated by a fully automated processing
scheme, developed at the Institut fur Meteorologie and Geophysik Innsbruck
(IMGI), using MODIS Level 1B data (Malcher et al., 2004). This processing
line uses a modified version of the SNOWMAP algorithm for global snow cover
mapping of the MODIS team (Hall et al., 2002). The applied modifications aim
to improve snow classification for Alpine zones, in particular in shadow slopes and
coniferous forests. Figure 6.2 shows an example of a classified MODIS picture,
derived on June 17, 2002, where snow is red, lakes are blue and clouds are white.
The dates of available MODIS images for the study area are shown in Figure 6.3.
The incapability of optical imagers to penetrate clouds further increases the gap
between SCA acquisitions from satellite data. In Table A.1 - A.3 (Appendix A)
the satellite derived informations (snow-, cloud-, invalid pixel- and snow free-area)
for the simulation periods 2001 - 2003 aggregated for the four sub-basins are
listed. MODIS data are supposed to slightly overestimate snow-extent as a pixel is
assumed as 100% snow covered when snow is classified.
6.2 Snow Cover Mapping 51
Figure 6.2: MODIS image and derived SCA map of the wider Otztal area on June 17.
The classification map shows snow cover (red), lakes (blue), clouds (white) and snow free
(brown).
Figure 6.3: Available MODIS data of the Alps for the simulation periods 2001 - 2003.
52
Chapter 7
Temporal SCA Interpolation
As the SRM requires the fraction of snow covered area every day, the SCA has
to be estimated for the days where no satellite information is acquired. In this
chapter the accumulated melt depth method (AMD), which was used for snow-cover
interpolation, and it’s applications in the study basin are described.
7.1 Accumulated Melt Depth Method
The ratio of snow covered area to total area (SCA), is derived from remote sensing
data (Chapter 6.2). As SCA is needed on daily basis it has to be calculated for
days without satellite data. For that aim the accumulated melt depth (AMD)
method was developed in the project HYDALP (Rott et al., 2000). This method
assumes a linear relationship between the SCA derived by earth observation and
the accumulated melt depth change ∆M :
d =SCA(t1) − SCA(t2)
∆M(t1, tA) + ∆M(t2, tE)(7.1)
where d is the gradient of the SCA per mm melting [mm]. The accumu-
lated melt depth M is a function of the positive degree-days T + and the degree-day
factor a and is given by
∆M(t1, t2) =∑
(aT+) (7.2)
The SCA of the seasonal snow cover at the time tx is calculated as
SCA(tx) = SCA(tx−1) − d ∗ ∆M(tx−1, tx) (7.3)
53
54 Temporal SCA Interpolation
Figure 7.1: Illustration of the accumulated melt depth method (Rott et al., 2000).
Figure 7.1 illustrates the interconnection of snow covered area and melt-depth
between two satellite images. SCA decreases until a snowfall event (ta) oc-
curs. Until the new temporary snow cover is melted completely (te) the snow
covered area is hold constant. Thereafter melting of the winter snow cover continues.
In case of snowfall during the melting season the seasonal SCA remains un-
changed. Based on precipitation a temporary snowpack is build up in the model.
Therefore this snowpack contains no information on the areal snow extent, but on
snow water equivalent. After the whole temporary snowpack has melted, melting of
the seasonal snowpack continues. Though an increase of snow covered area through
the season is possible, only satellite images, that observe the winter snowpack,
should be used (Martinec et al., 1998).
7.2 SCA Interpolation in the Otztal Test Basin
Within the hydrological modelling system of IMGI the remote sensing module
derives snow extent from MODIS data (Chapter 6.2). This information and
information about clouds, snow free areas and invalid pixels is HRU-wise stored
7.2 SCA Interpolation in the Otztal Test Basin 55
in the PostgreSQL database. The SCA-interpolation module of the pre-processor
checks this information end decides for each HRU and satellite image if it is usable
for AMD interpolation or not.
The results of snow cover interpolation using the accumulated melt depth method
for the sub-basins Vent and Huben are shown in Figure 7.2 - 7.7. Daily precipitation
and temperature of a nearby meteorological station, the snow water equivalent
(SWE) of the temporal snow for each HRU and the SCA fraction for each HRU
are plotted for the simulation periods 2001 - 2003. This illustrates the amount of
temporal snow cover changes and the melting of the winter snow cover.
Figure 7.2: (Top) Temperature and precipitation at station Vent, (centre) SWE of the
temporal snowpack for un-glaciated HRUs and (bottom) SCA for un-glaciated HRUs of
sub-basin Vent during the simulation period 2001. The red triangles indicate days with
satellite images.
Figure 7.2 and 7.5 shows an increase of the temporal snow cover during April
56 Temporal SCA Interpolation
2001 in all hydrological response units. Whereas the temporal snow pack starts to
decrease at the end of the month in lower level zones, in higher zones the snowpack
still increases until mid of May. Thereafter melting proceeds in all HRUs until begin
of June, where in above 2400 meter height temporal snowpack increases. At end of
June the temporal snowpack has disappeared in all zones. In the lowest levels even
the winter snow pack has already melted completely. In July and August two small
new-snow events occurred in the highest elevation zones. In September the melting
period ended and the temporal snow cover increased again.
Figure 7.3: (Top) Temperature and precipitation at station Vent. The red dashed line
is the temperature at Obergurgl (dashed) while temperature Vent was out of order. SWE
of the temporal snowpack for un-glaciated HRUs (centre) and (bottom) SCA fraction for
un-glaciated HRUs of sub-basin Vent during the simulation period 2002. The red triangles
indicate days with satellite images.
7.2 SCA Interpolation in the Otztal Test Basin 57
In the year 2002 (Fig.7.3 and 7.6) the snowpack increased in the highest zone up to
begin of June. In the lowest zones melting starts at the end of April. In these zones
no significant increase of the temporal snow-cover occurred up to end of September.
Figure 7.4: (Top) Temperature and precipitation at station Vent, (centre) SWE of the
temporal snowpack for un-glaciated HRUs and (bottom) SCA fraction for un-glaciated
HRUs of sub-basin Vent during the simulation period 2003. The red triangles indicate
days with satellite images.
In the year 2003 (Fig.7.4 and 7.7) melting started slowly, interrupted by three
events with temporal snow, in April, in mid-May and in mid-September. In May
the temporal snow was built up only in zones above 2400 meter. Overall, snowfall
and build-up of temporal snowpack was strongly reduced in the year 2003 compared
to the previous years.
58 Temporal SCA Interpolation
An inter-comparison of the different sub-basins shows similar temporal snow
cover and depletion of the SCA in similar zones. Some local differences are caused
by the spatial variability of precipitation.
Figure 7.5: (Top) Temperature and precipitation at station Langenfeld, (centre) SWE of
the temporal snowpack for un-glaciated HRUs and (bottom) SCA fraction for un-glaciated
HRUs of sub-basin Huben during the simulation period 2001. The red triangles indicate
days with satellite images.
7.2 SCA Interpolation in the Otztal Test Basin 59
Figure 7.6: (Top) Temperature and precipitation at station Langenfeld, (centre) SWE of
the temporal snowpack for un-glaciated HRUs and (bottom) SCA fraction for un-glaciated
HRUs of sub-basin Huben during the simulation period 2002. The red triangles indicate
days with satellite images.
60 Temporal SCA Interpolation
Figure 7.7: (Top) Temperature at station Vent and precipitation at station Langenfeld,
(centre) SWE of the temporal snowpack for un-glaciated HRUs and (bottom) SCA fraction
for un-glaciated HRUs of sub-basin Huben during the simulation period 2003. The red
triangles indicate days with satellite images.
Chapter 8
SRM Parameter Setup in the
Otztal Basin
8.1 Overview
The SRM calculates the water runoff produced from snowmelt and rainfall, super-
imposed on the calculated recession flow on a daily basis according to Equation 3.1
(Chapter 3). The variables temperature, precipitation and snow covered area are re-
quired as input for each day. The runoff coefficients for snow and rain, the recession
coefficient and the degree-day factor are parameters which are characteristic for a
basin or HRU. These parameters vary during the melting season but can in general
considered to be more or less constant over short time periods. In the following
sections the determination of these parameters is explained in detail.
8.2 SRM Parameter Derivation
8.2.1 Recession Coefficient
The recession coefficient k describes the portion of runoff that is hold back in the
single store of the SRM. In periods without precipitation and snow melt k corre-
sponds to the ratio of the runoff of two successive days. k is estimated from archived
runoff data. Values of Qn and Qn+1 are plotted against each other and the lower
envelope line of all points is considered to indicate the k-values (Martinec et al.,
1998).
k is derived using the runoff of the previous day by
kn+1 = xQyn (8.1)
61
62 SRM Parameter Setup in the Otztal Basin
Figure 8.1: Determination of recession coefficients for the sub-basins of the Otztal.
which means that k is not constant but increases with decreasing Q. In the Otztal
a five-year time period (1.1.1998 - 31.12.2002) was chosen to determine the two
parameters x and y which define k (see Figure 8.1). For Vent (Rofenache) x = 0.83
and y = 0.094, for Obergurgl x = 0.826 and y = 0.097, for Huben x = 0.89 and
y = 0.1 and for Tumpen x = 0.885 and y = 0.0016 (see Table 8.5) was derived with
the help of Figure 8.1. Sephton et al. (1994) report similar values for the sub-basin
Vent.
8.2.2 Time Lag
The time lag, tlag, corresponds to the time interval between temperature increase and
runoff increase. The lag is derived by superimposing a characteristic hourly runoff
volume curve over the temperature curve during a period without precipitation.
The time between maximum of temperature and runoff specifies the tlag. The SRM
already assumes a default time lag of 6 hours. For the four Otztal sub-basins runoff
data are available in 15 minutes time intervals for the years 1998 and 1999. These
data were used to determine the time lag, which were between 2 hours for Vent
8.2 SRM Parameter Derivation 63
(Rofenache) and 5 hours for Huben, in addition to the default lag of 6 hours.
8.2.3 Runoff Coefficient
The runoff coefficient, cr, describes the portion of snowmelt (index s) or precipita-
tion (index r), that contributes to the basin outflow. In case of rain it corresponds
to the ratio of precipitation runoff to the whole precipitation amount:
cr =precipitation runoff
precipitation(8.2)
In case of snowmelt the runoff coefficient corresponds to the ratio:
cs =drained meltwater
produced meltwater(8.3)
The runoff coefficients include losses like evapotranspiration, interflow and subsur-
face runoff. The change of those processes during the season (e.g. increased growth
of vegetation) leads to change in runoff volumes. Therefore the SRM allows to
change the runoff coefficients if required daily and independently for every zone. In
this work the same runoff coefficients were assumed for all zones of a basin. cr and
cs were determined by means of runoff simulation. Table 8.1 lists the coefficients
derived for the sub-basins of the Otztal. Due to increased surface flow when precipi-
tation amounts increase, the runoff coefficients of rain were raised by 0.05 per 10 mm
rain. The runoff coefficients of snow were not changed during simulations. Sephton
et al. (1994) derived cr values from 0.8 to 1.6. These values where so high as the
vertical precipitation gradient was not calculated directly but considered through an
increase of the runoff coefficients with height. cs with 0.9 and 0.95 on glaciered areas
respectively, were also higher in the simulations of Sephton et al. (1994). The cause
therefore are probably different degree-day factors. For this work the degree-day
factors were assigned using the sum of positive degree days (Section 8.2.5). Sephton
et al. (1994) used the surface reflectance measured by remote sensing to calculate
the degree-day factors.
8.2.4 Critical Temperature
The critical temperature, Tcrit, defines the temperature threshold below which pre-
cipitation is assumed to fall as snow. This parameter influences the timing of the
modelled runoff. For runoff generation the snow has to be melted, while precipita-
tion as rain contributes directly to the discharge. New snow is stored temporarily
64 SRM Parameter Setup in the Otztal Basin
Basin cr cs
Vent(Rofen) 0.8 0.8
Obergurgl 0.78 0.78
Huben 0.73 0.73
Tumpen 0.7 0.7
Table 8.1: SRM runoff coefficients for the Otztal basins. The runoff coefficients for rain
(cr) were raised 0.05 every 10 mm of precipitation. The runoff coefficients for snow (cr)
were kept constant.
in the snowpack.
For the Otztal this threshold was kept constant at 1 ◦C during the whole simula-
tion period. Effects like heavy precipitation and different humidity conditions were
neglected. In Rott et al. (2000) values between 0.75 ◦C and 3 ◦C where used for
basins located in the Swiss Alps.
8.2.5 Degree-day Factor
For calculating meltwater production the SRM uses the degree-day method. The
amount of meltwater Qmelt is given by
Qmelt = ai,nT+i,n (8.4)
The positive degree-day (T +) is defined as positive difference of the mean daily
temperature to a reference temperature (in common 0 ◦C). The degree-day factor
a assigns the amount of meltwater that is produced per degree-day. In generally
a varies between 3 and 9 mm◦C−1d−1 (Rott et al., 1998). The degree-day factor
depends strongly on the snow conditions (age, impurities, grain size, etc.) that
change the albedo of the snow-cover and therfore the energy budget for melting.
The values and thresholds for the degree-days are determined by the sum of positive
degree-days and the land-cover type. The dates for switching to a higher a-value
are determined individually for each HRU depending on the cumulative degree-days
in this zone. Table A.4 - A.6 in Appendix A lists these dates for all HRUs of the
Otztal basin. The values and thresholds for the the degree-day factor (Table 8.2
and 8.3) are adopted from the runoff simulation (1996 - 1998) and runoff forecasting
activities (1999, 2000) of Rott et al. (2000) in the Zillertal basins.
8.2 SRM Parameter Derivation 65
a = 3.0mm/(◦Cd) 0 <∑
T+ < 25(◦C)
a = 4.0mm/(◦Cd) 25 <∑
T+ < 100(◦C)
a = 4.5mm/(◦Cd) 100 <∑
T+ < 150(◦C)
a = 5.0mm/(◦Cd) 150 <∑
T+
Table 8.2: Degree-day factor, a, for non-glacier areas of the Otztal.
a = 3.0mm/(◦Cd) 0 <∑
T+ < 25(◦C)
a = 4.0mm/(◦Cd) 25 <∑
T+ < 100(◦C)
a = 5.0mm/(◦Cd) 100 <∑
T+ < 150(◦C)
a = 7.0mm/(◦Cd) 150 <∑
T+
Table 8.3: Degree-day factor, a, for glacier areas of the Otztal.
8.2.6 Rain Contributing Area
When rain falls on a snowpack two options of treating this event can be selected in
the SRM. In the beginning of the melting season, when the snow-cover is dry and
deep, the rain is retained by the snow. Only the precipitation which falls on snow-
free areas is immediately released to the runoff. Therefore the rain contributing
area (RCA) parameter is set to 0 in the beginning of the melting period. During the
season, when the snowpack becomes thinner and the liquid water content increases,
the same amount of water that falls on the snow is released from the snowpack and
added to the runoff. When this is the case RCA is set to 1. For modelling runoff in
the Otztal the rain contributing area was interrelated to the degree-day factor. So
RCA becomes 1 as soon as∑
T+ > 100 ◦C.
8.2.7 Parameterization of Severe Precipitation
Heavy rainfall change the storage behavior of the soil and therefore change runoff
properties of a HRU. In the case of such an event the recession coefficient increases
by the factor f as described by
kn+1 = x(fQn)y (8.5)
The threshold Rthr decides whether rainfall is classified as heavy or not. After a
severe rainfall event the changed runoff properties do not reshape immediately but
hold on a while. The duration of this is described by the parameter Rdur. These
66 SRM Parameter Setup in the Otztal Basin
parameters were also adopted from Rott et al. (2000) and are listed in Table 8.4.
These values were assigned to the whole basin, although individual specification for
HRUs would possible. Also no adjustment of those parameters during the season
was made. Daily precipitation did not reach this threshold in the Otztal during the
study period.
f 4
Rthr 60 [mmd−1]
Rdur 5 [d]
Table 8.4: Parameters for heavy rainfall in the Otztal.
8.2.8 Listing of SRM-Parameters for the Otztal Sub-basins
Table 8.5 lists the model parameter which were used for runoff simulation in this
thesis.
Parameter Vent (Rofenache) Obergurgl Huben Tumpen
recession coeff.k:
x 0.83 0.826 0.891 0.885
y 0.094 0.097 0.011 0.016
Tlag 3 3 4.5 4
cs 0.75 0.74 0.67 0.72
cr 0.75 0.74 0.67 0.72
Tcrit 1 1 1 1
Table 8.5: Summary of SRM parameters for the Otztal sub-basins.
Chapter 9
Runoff Simulations
In Chapter 2 the modular hydrological modelling system was introduced. This
system contains components for
• hydrological system setup, including basin setup (Chapter 5)
• meteorological data pre-processing (Chapter 4)
• snow cover (SCA) mapping by remote sensing (Chapter 6.2)
• SCA temporal interpolation with the AMD method (Chapter 7)
• runoff modelling with the SRM
The snowmelt runoff model (SRM) itself is explained in Chapter 3. Determination
of the SRM model parameters used for the following runoff simulations was
explained in the previous Chapter.
Model simulations of daily runoff of the alpine basin Otztal were carried out
for the period 1 April to 30 September for the years 2001 - 2003. The Otztal
basin (above runoff gauge Tumpen) was divided into the four sub-basins Vent
(Rofenache), Obergurgl, Huben and Tumpen. Discharge was calculated for the
sub-basins and routed afterwards. This means that for the test basin the runoff
calculated in the sub-basin Vent and Obergurgl was added to the runoff of the
sub-basin Huben with a time lag (routing) to obtain the runoff at the gauge Huben.
This runoff is then added to the runoff from the sub-basin Tumpen to derive the
total discharge of the Otztal basin at the gauge Tumpen (Figure 9.1). Discharge
from the Horlachbach basin is neglected as the main part of runoff is abstracted to
the Finstertal reservoir and no information about the residual runoff was available.
How the routing is treated by the SRM is explained in Section 3.4. The routing
lag time for the Otztal sub-basins was assumed to be two hours from gauge Huben
to Tumpen and three hours from the gauges Obergurgl and Vent to Huben. The
67
68 Runoff Simulations
following results for Huben and Tumpen are for the cumulated discharge, that
means that also the errors from the previous sub-basins are cumulated. But also
compensation of error can occur.
Figure 9.1: Sub-basin routing in the Otztal.
9.1 Quality Assessment
The success of hydrological models has generally been quantified by comparison of
observed and simulated values of daily discharge using the Nash-Sutcliffe correlation
coefficient, R2, and the deviations of runoff volumes Dv (Nash and Sutcliffe, 1970).
Where R2 is given by
R2 = 1 −
∑(Qt − Q
′
t)2
∑(Qt − Q)2
(9.1)
where Qt is observed discharge at time t, Q′
t is simulated discharge, and Q is the
observed mean discharge. R2 increases from −∞ towards 1, as the root-mean-square
prediction error decreases towards zero.
The volume deviation of discharge, which represents the percentage difference be-
tween observed and simulated mean or total discharge is given by
Dv =VR − V
′
R
VR
100[%] (9.2)
where VR is the measured runoff volume and V′
R is the simulated runoff volume.
A negative Dv value means an over-estimation of the discharge. R2 and Dv for
the cumulated runoff at the the gauges Vent (Rofenache), Obergurgl, Huben and
Tumpen during the simulation periods 2001 - 2003 are listed in Table 9.1.
9.2 Runoff Simulations 2001 69
2001 2002 2003
Sub-basin R2 Dv R2 Dv R2 Dv
Vent 0.93 2.58 0.93 -2.07 0.91 2.01
Obergurgl 0.91 -0.78 0.94 2.04 0.91 -0.17
Huben 0.91 4.53 0.93 8.84 0.85 -4.35
Tumpen 0.91 1.1 0.94 7.69 0.84 -6.85
Table 9.1: Nash-Sutcliffe correlation coefficients, R2, and deviations of runoff volumes,
Dv, for the routed sub-basin of the Otztal, in the years 2001, 2002 and 2003.
9.2 Runoff Simulations 2001
In Figure 9.2 the measured air temperature and precipitation during the simulation
period 2001 at the meteorological station Vent are plotted. The station is located
at 1906 m a.s.l. and is operated by the Institut fur Meteorologie und Geophysik
Innsbruck.
In April 2001 low temperatures and precipitation prevailed. The snow line was
below 1000 m (see Chapter 7.2). At the end of the month the temperatures
increased. In May the air temperature was relatively high for this region and
altitude, and the amount of precipitation was rather low. In June some heavy
precipitation events, caused by cold fronts, were observed. Up to end of August
high temperatures and a few precipitation events were dominating. In the end of
the simulation period the temperature decreased, which led to a strong decrease of
glacier melt.
Figure 9.2: Precipitation and temperature at station Vent 2001.
The cold weather in the beginning of the simulation period resulted in low,
even decreasing simulated runoff up to end of April in the Otztal basin. The
70 Runoff Simulations
decrease of the simulated discharge in April, which does not agree with the
observation at the runoff gauge Vent (Rofenache), is caused by overestimation of
the recession of baseflow. Because no separate storage was used for base flow, the
rapidity of recession is overestimated. This is also the cause in September, and
similar in all basins. With increase of temperature in early May, discharge started
to increase. Therefore the first significant peak around May 30 was mainly caused
by snowmelt. Advection of colder air masses during June led to lower runoff. The
two peaks in the middle of this month were mainly caused by higher precipitation
events. At the end of the month, snowmelt increased again. The peaks in July were
caused by strong precipitation superimposed to high melting rates. Also in August
discharge was characterized by such processes. In September, as air temperature
was lower, runoff amounts decreased rapidly.
Figure 9.3: Runoff Vent (Rofenache) 2001.
Figure 9.4: Runoff Obergurgl 2001.
The quality of the simulation runs for the year 2001 in general are good
(Table 9.1). Especially for the sub-basins Vent, with R2 = 0.93 and Obergurgl with
R2 = 0.91. The volume of runoff for the basin Obergurgl is slightly overestimated
9.2 Runoff Simulations 2001 71
Figure 9.5: Runoff Huben 2001.
Figure 9.6: Runoff Tumpen 2001.
(-0.78%) by the model, whereas it is underestimated for the other sub-basins. For
the basins Huben and Tumpen the values for the Nash-Sutcliffe correlation are
0.91. The volume deviation was about 4.53% for Huben and 1.1% for Tumpen.
Problematic is runoff simulation of high peaks, caused by high precipitation
superimposed to high snow or glacier melt rates. In particular in the two sub-
basins Huben and Tumpen, located at lower altitude, where rainfall is even more
important, this leads to some errors. The spatial and temporal variability of rain
intensities may also be a source of some errors. So some runoff peaks (e.g. June
7 and June 16 2001), mainly caused by cold fronts, were underestimated in the
simulation. On June 16 and August 10 a time shift of about one day is obvious.
The possible reason is that daily precipitation measurements cover 6 to 6 UTC
but, daily discharge values are calculated from 0 to 0 UTC. In order to use the
daily precipitation sums, they were assigned to the previous day before the 6 UTC
measurements, the time when the precipitation falls is not taken into account. If
the main precipitation falls in the second half of the night (like on the mentioned
dates) it is assigned to the previous day. End of August melting rates where slightly
underestimated in the beginning, later on they were overestimated.
72 Runoff Simulations
9.3 Runoff Simulations 2002
In April 2002 the air temperature and the amount of precipitation in the Otztal area
were around the longterm mean (www.zamg.ac.at). The temperature measurement
at the station Vent (Figure 9.7) was out of order at this time. The first little peak
in the runoff curves in early May was caused by an intense precipitation event
with a small snowmelt contribution. Towards mid-May the melting season started
and runoff increased. The strong precipitation on 5 and 6 June, superimposed
to snow melt, led to a runoff peak, which rapidly fell off on the following days
due to low temperatures. Strong rise of temperature after June 11 initiated
the start of the main period of snow and glacier melt, leading, in connection
with rainfall around June 24, to the runoff maximum of the year. In July and
August, until mid-September, the runoff in the basins Vent and Obergurgl was
dominated by glacier melt, superimposed by rainfall, whereas in the lower basins
rainfall was the main source. In this period rainfall was mainly convective. In
the end of the simulation period the albedo of snow increased due to new snow
falls. This would require lower degree-day factors to calculate the right melting rates.
Figure 9.7: Precipitation and temperature at station Vent 2002 (and Obergurgl - dashed
line).
The 2002 SRM runoff simulations fit well to the measured data (Figure 9.8 to
Figure 9.11). R2 over the simulation period was between 0.94 respectively 0.93
for the sub-basins, denoting the good timing of the simulated runoff. The overall
volumes deviated from -2.07% in Vent to 8.54% in Tumpen. Table 9.1 lists the
Nash-Sutcliffe correlation and the deviations of runoff volumes.
The first runoff peak in begin of May is underestimated in the simulation. This
peak is more pronounced in Huben and Tumpen than in the higher elevated basins
Vent and Obergurgl. The main reason for the difference is probably precipitation
connected with underestimation of the rain contributing area, which is still
9.3 Runoff Simulations 2002 73
Figure 9.8: Runoff Vent (Rofenache) 2002.
Figure 9.9: Runoff Obergurgl 2002.
Figure 9.10: Runoff Huben 2002.
important at this time. Slight overestimation of simulated runoff at the beginning
of the melting period in May in the sub-basins Vent and Obergurgl suggests that
the actual saturation of the snowpack was somewhat delayed compared to the as-
sumption introduced by the degree-day factor. Later on in the main melting period
measured and simulated runoff show good agreement. Some differences between
modeled and simulated runoff are caused by convective precipitation events, which
are quite variable in time and location. The registrations by meteorological stations
are not always representative. Effects of such events for runoff simulations are
especially obvious in the first half of July 2002.
74 Runoff Simulations
Figure 9.11: Runoff Tumpen 2002.
9.4 Runoff Simulations 2003
2003 was a year of unusual high air temperatures and low precipitation sums. In
general it was the warmest summer since meteorological measurements in Austria.
In the test basin the mean annual temperature was 0.6 ◦C - 1.0 ◦C higher then the
longterm mean, the precipitation amount was about 70% - 90% of the normal sums
(www.zamg.ac.at).
In April the air temperatures were similar to the years before. At the beginning
of May the temperature increased and snowmelt started. After two short cold
spells in mid-May, the temperature remained at a high level up to end of August.
During this period melting of the seasonal snowpack and glaciers dominated. Some
convective precipitation events increased the runoff peaks additionally. Short-term
advection of cool air at the beginning of July and end of July led to some decrease
of discharge. End-August the highest precipitation event of the whole period, with
about 20 mm at the station Vent, occurred.
Figure 9.12: Precipitation and temperature at station Vent 2003.
9.4 Runoff Simulations 2003 75
Figure 9.13: Runoff Vent (Rofenache) 2003.
Figure 9.14: Runoff Obergurgl 2003 (measured data are provisional values).
Figure 9.15: Runoff Huben 2003.
Figure 9.16: Runoff Tumpen 2003.
76 Runoff Simulations
The 2003 runoff simulation was less accurate than in the two other years
with R2 of 0.85-0.91, especially in the lower sub-basins Huben and Tumpen.
The start of the melting period and two events of advection of cold air were
simulated reasonably well, and also the increase of runoff due to snowmelt after
this period. In July and August the differences are larger, and also a time
shift is obvious. On one hand some errors may originate from slight under-
estimation of the increased glacier melt for this long melting period with low
glacier albedo, on the other hand, the warm summer probably resulted in higher
evapotranspiration losses that are not directly taken into account in the model.
This is especially important when air temperatures are high and precipitation is low.
Chapter 10
Summary and Conclusions
A hydrological modelling system was further developed and applied in the Alpine
drainage basin Otztal. It is designed for mountainous regions where snowmelt
and strong spatial variability of meteorological variables are of importance for
runoff modelling. The whole system is build up in a modular form to be open for
further developments. The hydrological modelling system contains components
for hydrological setup, including basin and model setup, meteorological data
pre-processing, remote sensing, runoff modelling and post-processing of the results.
As runoff model an advanced version of the snowmelt runoff model (SRM) from
Martinec (1975) was used. The main features of the model and the developments
carried out in this work are summarized below.
• The SRM is a simple conceptual runoff model for runoff simulations and fore-
casts on daily basis. For use in more extended basins with several sub-basin a
routing module was added to the standard SRM.
• The meteorological pre-processor was developed that prepares point measure-
ments from stations for use in the SRM. To take the spatial variability of
temperature, and in particular precipitation, into account the point data are
extrapolated to a grid using inverse distance weighting (IDW) and HRU-wise
aggregation afterwards. To consider the vertical dependences linear gradients
are used. Although IDW interpolation improves the quality of input data, the
high spatial and temporal variability of especially precipitation is not exactly
reproduced. The use of constant temperature lapse rates and vertical precipi-
tation gradients are critical points that should be reconsidered and might be
replaced by daily values or by monthly mean values, if available. Further-
more, discrimination of convective and advective precipitation might be useful
for vertical precipitation adjustment and horizontal variability.
77
78 Summary and Conclusions
• Remote sensing is used to provide information on several surface classes for the
basin setup, and on snow cover extent as data input for the SRM simulation
or forecast runs. With the aid of two Landsat 7 ETM+ scenes the three
land-cover classes glacier, forest and other surfaces were discriminated. For
snow cover observations optical data from MODIS on-board NASA’s Terra
satellite was used. For use in the SRM the gridded snow cover information
was aggregated HRU-wise.
To raise the number of snow cover observations, SAR data, which are able
to measure the SCA during cloudy conditions, but also other optical sensors
can be used. For applying snow cover data from several sensors, the snow
maps have to be homogenized to compensate for different spatial resolution
and sensitivity to wet/dry snow. Further work on this topic is needed.
• Within the basin setup the Otztal basin was divided into four sub-basins.
Using land-cover classification (gained by remote sensing) and an elevation
model, hydrological response units (HRUs) were defined. These units describe
areas with similar runoff properties.
• The accumulated melt depth method (AMD) was used to interpolate the snow
covered area (SCA) on days without satellite image acquisition.
This method calculates snow cover depletion curves at daily time steps. It does
not build up the snow covered area in case of a new snow fall, but a temporary
snowpack, by calculating snow water equivalent. Satellite images which show
an increase of the snow covered area should not be used during periods of new
snow. Therefore the development of a method that can simulate the new snow
covered area would be useful for further improvement of the model.
• Coefficients for runoff routing and recession were determined from runoff time
series, and other hydrological model parameters were adopted from previous
hydrological work carried out at the IMGI in other Alpine basins.
As the SRM allows to change several parameters, like the runoff coefficients,
throughout the simulation period, these parameters can be adjusted to the dif-
ferent weather and runoff conditions. Use of this capability can improve runoff
simulations in special situations, but was not applied to avoid complexity.
• Runoff simulations for the period April 1 to September 30 for the years 2001
- 2003 were carried out in the basin Otztal. The Otztal basin was further
divided into the four sub-basins Vent (Rofenache), Obergurgl, Huben and
Tumpen. For runoff modelling of Huben and Tumpen the runoff of the upper
basins was added to the discharge of the lower sub-basins. Measured and
simulated runoff in general agrees well in the sub-basins Vent (Rofenache)
79
and Obergurgl. The agreement of the runoff simulations for the two larger
sub-basins at lower elevation Huben and Tumpen are quite good for the years
2001 and 2002. For the year 2003, an unusual warm and dry year, problems
in timing of the discharge and slight overestimation are obvious.
Major reasons for differences between simulated and measured runoff, and
suggested model improvements include the following topics:
– Evapotranspiration (ET) is by now parameterized by the runoff coeffi-
cients cr and cs. Introduction of an own ET routine, in particular for
basins at lower elevations, would improve runoff simulations, especially
in warmer years like 2003.
– Runoff from the sub-basin Horlachbach was neglected because no infor-
mation was available. As not all runoff of that basin is abstracted by the
hydropower company, including the residual discharge should improve
runoff calculations.
– Before and after the melting period separate treatment of the baseflow
would improve the quality of the simulated runoff in this period.
– Although the runoff coefficient for rain was assumed to increase with the
precipitation amount, runoff peaks for heavier rainfall are still underesti-
mated. Further adjustment of cr is necessary for such situations to obtain
better estimates of the losses.
– The degree-day factor increases during the melting season due an decrease
of the albedo of snow. In the end of the simulation period, when new
snow falls, the degree-day factor should in principle decrease. This was
not taken into account so far, but might improve runoff calculations, in
particular in late summer and autumn.
”Working weeks come to its end, party time is here again”
(Come with me, Depeche Mode)
81
82
Bibliography
Barcelo, A. (2001). Meteorological applications for agriculture: report on rainfall,
Cost Action 718, EU.
Black, P. (1991). Watershed Hydrology, Prentice-Hall, Inc.
Bonham-Carter, G. (1994). Geographic Information Systems for Geoscientists. Mod-
eling with GIS, Geological Survey of Canada, Pergamon, Elsevier Science
Ltd., Kidlington, New York, Tokyo.
Braun, N. and H. Lang (1986). Simulating of snowmelt runoff in lowland and
lower alpine regions of switzerland, Modelling Snowmelt Induced Processes,
pp. 125–140.
Cajina, N., K. Bruebaker and A. Rango (1999). Implementig the Snowmelt Runoff
Model in the USGS Modular Modeling System, Proceeding of the 1999
annual meeting, Eastern Snow Conference, Frederiction, New Brunswick,
Canada, 2-4 June 1999, pp. 177–186.
Dawdy, D. and W. Langbein (1960). Mapping Mean Areal Precipitation, Bulletin
of the International Association of Scientific Hydrology 19, 16.
Eisentraut, P. (2003). PostgreSQL, Das offizielle Handbuch, PostgreSQL Global De-
velopment Group.
Epema, G. (1990). Determination of planetary reflectance for Landsat TM tapes
processed at Earthnet Italy, ESA Journal 14, 9 – 22.
Ferguson, R. (1999). Snowmelt runoff models, Progress in Physical Geography 23(2),
205–227.
Goodrich, D., L. Lane, R. Shillito, S. Miller, K. Syed and D. Woolhiser (1997).
Linearity of basin response as a function of scale in a semiarid watershed,
Water Resources Research 33, 2951–2965.
Griffith, D. and L. Layne (1999). A casebook for spatial statistical data analysis:
compilation of analyses of different thematic data sets, Oxford University
Press, New York.
83
84 BIBLIOGRAPHY
Hall, D., G. Riggs, N. D. V.V. Salomonson and K. Bayr (2002). MODIS Snow-Cover
Products, Remote Sens. Environ. 83, 181–194.
Herring, D., Y. Kaufmann, J. Collatz, F. Bordi and J. Randson (1998). The First
EOS Satellite. NASA’s Earth Observing System EOS AM-1., NP-1998-03-
018-GSFC.
Hoinkes, H. and R. Steinacker (1974). Zur Parametrisierung der Beziehung Klima
- Gletscher, Sonderdruck aus den Verhandlungen der 13.en Internationalen
Tagung fur Alpine Meteorologie, Saint - Vincent, 17. - 19. Sept. 1974.
Jenson, S. and J. Domingue (1988). Extracting Topographic Structure from Digital
Elevation Data for Geographic Information System Analysis, Photogram-
metric Engineering and Remote Sensing 54(11), 1593–1600.
Kuhn, M. and N. Batlogg (1997). Modellierung der Auswirkung von
Klimaanderungen auf verschiedene Einzugsgebiete in Osterreich,
Nichtveroffentlichte Studie im Auftrag des VERBUND Osterreich.
Kuhn, M. and N. Batlogg (1999). Modellierung der Auswirkung von
Klimaanderungen auf verschiedene Einzugsgebiete in Osterreich, Ver-
bund. Forschung im Verbund, Schriftenreihe Band 46.
Lang, H. (1985). Hohenabhangigkeit der Niederschlage, pp. 149–157.
Lang, H. (1986). Forecasting meltwater runoff from snow-covered areas and from
glacier basins, River Flow Modelling and Forecasing pp. 99–127.
Leavesley, G., V. Salomonson, N. DiGirolamo and K. Bayr (1996). The Modular
Modelling System (MMS): User’s Manual, U.S. Geological Survey.
Malcher, P., M. Heidinger, T. Nagler and H. Rott (2004). Processing and data
assimilation scheme for satellite snow cover products in the hydrological
model, Deliverable no.18, EnviSnow project, D3-WP5.
Martinec, J. (1975). Snowmelt runoff model for river flow forecasts, Nordic Hydrology
6, 145–154.
Martinec, J., A. Rango and E. Major (1983). Snowmelt Runoff Model (SRM) user’s
manual, NASA Reference Publication 1100, Washington, D.C., USA.
Martinec, J., A. Rango and R. Roberts (1998). Snowmelt Runoff model (SRM)
user’s manual, Geographica Bernesia, Department of Geography, University
of Bern.
Nagler, T. (1996). Methods and Analysis of Synthetic Aperature Radar Data from
ERS-1 and X-SAR for Snow and Glacier Application, PhD thesis, Univer-
sity of Innsbruck.
BIBLIOGRAPHY 85
Nagler, T. and H. Rott (2000). Retrieval of wet snow by means of multitemporal
sar data., IEEE Trans. Geosc. Rem. Sens. 38(2), 755–765.
Nash, J. and J. Sutcliffe (1970). River Flow Forecasting Trough Conceptual Models,
Part 1: A Discussion of the Principles, Journal of Hydrology 10, 282–290.
Neitsch, S., J. Arnold, J. Kiniry, R. Srinivasan and J. Williams (2002). Soil and
Water Assesment Tool User’s Manual, Version 2000.
Newson, M. (1980). The geomorphological effectiveness of floods - a contribution
stimulated by two recent events in mid-Wales, Earth Surface Processes 5,
1–16.
Oberparleiter, C. (2002). Einfluss von Eingabedaten, Datenaufbereitung und Mod-
ellparametern auf die Simulation und Vorhersage des Abflusses mit dem
Schneeschmelz-Abflussmodel SRM, Master’s thesis, Universitat Innsbruck.
Pichler, H. (1997). Dynamik der Atmosphare, 3. edition, Spektrum, Akademischer
Verlag, Heidelberg, Berlin, Oxford.
Ramsey, P. (2004). Postgis manual, Retrieved March 23, 2004 from the World Wide
Web http://www.postgis.org/docs.
Rango, A. (1992). Worldwide Testing of the Snowmelt Runoff Model with Appli-
cations for Predicting the Effects of Climate Change, Nordic Hydrology 23,
155–172.
Rott, H. and G. Markl (1989). Improved snow and glacier monitoring by the Landsat
Thematic Mapper, Proc. Of Earthnet Pilot Projetct on Landsat Thematic
Mapper Application, Frascati, Italy, Dec. 1987, ESA SP-1102, ESA, pp. 3–
12.
Rott, H. et al. (2000). HYDALP, Hydrology of Alpine and High Latitude Basins,
Final Report, Mitteilungen Nr. 4, Institut fur Meteorologie und Geophysik,
Universitat Innsbruck.
Rott, H., T. Nagler, W. Rack and O. Pirker (1998). Projekt Mission, Alpine Hy-
drologie, Forschung im Verbund, Band 41.
Schafmeister, M.-T. (1999). Geostatistik fur die hydrogeologische Praxis, Springer,
Berlin.
Seidel, K. and J. Martinec (2004). Remote Sensing in Snow Hydrology. Runoff Mod-
elling, Effect of Climate Change., Springer, Berlin, Heidelberg, New York,
Hong Kong, London, Milan, Paris, Tokyo.
Sephton, A. et al. (1994). Simultaneous Implementation of a Synthetic Apera-
ture Radar and a High Optical Imager, Vol. 3 of ESA/ESTEC Contract
10063/92/NL/SF, Final Report, chap. 9.1.1.
86 BIBLIOGRAPHY
Singh, V. and P. Chowdhury (1986). Comparing Some Methods of Estimating Mean
Areal Rainfall, Water Resources Bulletin 22(2), 275.
Smith, J., M. Baeck, M. Steiner and A. Miller (1996). Catastrophic rainfall from an
upslope thunderstorm in the central Appalachians: the Rapidan storm of
June 27th, 1995, Water Resources Research 32(10), 3099–3113.
US Standardatmosphere (1962, 1976). ESSA, NOAA, NASA, US-Airforce, Wash-
ington D.C.
Ward, A. and S. Trimble (2003). Environmental Hydrology, Second Edition, CRC
Press.
Appendix A
Tables
A.1 Information from MODIS Classifications
subbasin id date snow area cloud area invalid pixel area snow-free area
1 02.04.2001 97.788 0 0 0.187
2 02.04.2001 72.726 0 0 0
3 02.04.2001 327.876 0 0 16.463
4 02.04.2001 193.206 0 0 50.474
1 04.04.2001 51.91 45.901 0 0.164
2 04.04.2001 8.226 64.498 0 0
3 04.04.2001 91.254 246.398 0 6.687
4 04.04.2001 113.649 78.621 0 51.409
1 27.04.2001 85.895 11.125 0 0.953
2 27.04.2001 72.726 0 0 0
3 27.04.2001 323.438 0.25 0 20.653
4 27.04.2001 186.334 13.731 0 43.62
1 11.05.2001 87.868 6.126 0 3.984
2 11.05.2001 64.952 7.553 0 0.221
3 11.05.2001 214.051 94.16 0 36.132
4 11.05.2001 83.724 87.696 0 72.261
1 13.05.2001 90.865 0.312 0 6.797
2 13.05.2001 71.426 0 0 1.301
3 13.05.2001 261.727 0 0 82.613
4 13.05.2001 139.309 5 6.688 92.683
1 20.05.2001 7.162 90.812 0 0
2 20.05.2001 66.307 4.361 0 2.056
3 20.05.2001 63.875 276.336 0 4.133
4 20.05.2001 70.556 97.452 0 75.672
87
88 Tables
1 27.05.2001 84.375 0.25 0 13.351
2 27.05.2001 67.014 1.232 0 4.481
3 27.05.2001 212.435 10.48 0 121.426
4 27.05.2001 57.385 69.616 0 116.68
1 29.05.2001 81.527 1.161 0 15.291
2 29.05.2001 63.306 0 0 9.419
3 29.05.2001 202.69 1.564 0 140.087
4 29.05.2001 91.119 0.313 0 152.25
1 05.06.2001 81.311 0 0 16.666
2 05.06.2001 60.368 0 0 12.358
3 05.06.2001 198.435 0 0 145.905
4 05.06.2001 107.438 0 0 136.242
1 12.06.2001 97.788 0 0 0.187
2 12.06.2001 72.726 0 0 0
3 12.06.2001 286.232 0 0 58.108
4 12.06.2001 155.038 0 0 88.645
1 14.07.2001 11.815 60.813 0 25.35
2 14.07.2001 5.675 42.953 0 24.096
3 14.07.2001 22.611 169.416 0 152.314
4 14.07.2001 21.509 88.849 0 133.323
1 23.07.2001 58.946 0 0 39.029
2 23.07.2001 34.787 10.481 0 27.456
3 23.07.2001 82.81 33.881 0 227.649
4 23.07.2001 26.468 45.838 0 171.377
1 25.07.2001 7.192 90.136 0 0.648
2 25.07.2001 0.5 72.163 0 0.062
3 25.07.2001 6.767 294.513 0 43.06
4 25.07.2001 1.375 192.628 0 49.681
1 30.07.2001 8.345 78.68 0 10.948
2 30.07.2001 6.697 49.672 0 16.357
3 30.07.2001 18.701 157.25 0 168.39
4 30.07.2001 1.063 158.57 0 84.048
1 03.08.2001 0 97.976 0 0
2 03.08.2001 0.787 71.937 0 0.003
3 03.08.2001 1.902 341.899 0 0.54
4 03.08.2001 0 243.682 0 0
1 15.08.2001 42.311 0 0 55.664
2 15.08.2001 22.771 0.459 0 49.496
A.1 Information from MODIS Classifications 89
3 15.08.2001 40.436 0.88 0 303.022
4 15.08.2001 10.973 4.152 0 228.558
1 19.08.2001 16.245 42.165 0 39.567
2 19.08.2001 11.579 29.346 0 31.801
3 19.08.2001 13.305 133.473 0 197.563
4 19.08.2001 0.858 143.752 0 99.07
1 26.08.2001 26.481 16.107 0 55.388
2 26.08.2001 12.731 12.615 0 47.379
3 26.08.2001 22.787 32.927 0 288.63
4 26.08.2001 10.326 1.121 0 232.236
1 18.09.2001 89.141 2.824 0 6.01
2 18.09.2001 54.342 13.174 0 5.21
3 18.09.2001 117.894 169.375 0 57.07
4 18.09.2001 8.783 229.524 0 5.373
Table A.1: Satellite derived areas (in km2) for snow,
clouds, invalid pixels and snow free for the Otztal sub-
basins 2001. The sub-basins are Vent (Rofenache) (1),
Obergurgl (2), Huben (3), Tumpen (4).
subbasin id date snow area cloud area invalid pixel area snow-free area
1 04.03.2002 97.976 0 0 0
2 04.03.2002 72.726 0 0 0
3 04.03.2002 344.34 0 0 0
4 04.03.2002 243.682 0 0 0
1 05.04.2002 93.795 3.125 0 1.055
2 05.04.2002 63.943 8.472 0 0.313
3 05.04.2002 232.74 91.514 0 20.086
4 05.04.2002 101.239 82.093 0 60.347
1 23.04.2002 78.257 19.72 0 0
2 23.04.2002 41.198 31.526 0 0
3 23.04.2002 167.005 161.739 0 15.595
4 23.04.2002 114.924 70.78 0 57.979
90 Tables
1 30.04.2002 53.233 43.781 0 0.96
2 30.04.2002 69.104 3.123 0 0.498
3 30.04.2002 272.334 8.996 0 63.01
4 30.04.2002 159.826 0 0 83.856
1 07.05.2002 95.91 0 0 2.065
2 07.05.2002 71.685 0 0 1.041
3 07.05.2002 280.918 0 0 63.422
4 07.05.2002 150.149 11.937 0 81.596
1 16.05.2002 89.27 0.375 0 8.33
2 16.05.2002 69.621 0 0 3.105
3 16.05.2002 240.871 0 0 103.471
4 16.05.2002 120.758 0 0 122.923
1 30.05.2002 86.834 0 0 11.144
2 30.05.2002 65.875 0 0 6.85
3 30.05.2002 216.542 0 0 127.8
4 30.05.2002 98.906 0 0 144.777
1 01.06.2002 82.249 1.25 0 14.477
2 01.06.2002 60.11 0 0 12.615
3 01.06.2002 196.028 0 0 148.314
4 01.06.2002 85.597 0 0 158.084
1 15.06.2002 76.796 0.626 0 20.556
2 15.06.2002 52.656 0 0 20.068
3 15.06.2002 152.943 0 0 191.397
4 15.06.2002 61.211 19.372 0 163.099
1 17.06.2002 73.177 0 0 24.799
2 17.06.2002 48.455 0 0 24.27
3 17.06.2002 141.98 0.139 0 202.222
4 17.06.2002 51.354 9.633 0 182.695
1 01.07.2002 43.946 17.38 0 36.651
2 01.07.2002 24.448 11.907 0 36.371
3 01.07.2002 49.6 55.02 0 239.722
4 01.07.2002 28.231 5.901 0 209.549
1 05.07.2002 19.675 49.057 0 29.244
2 05.07.2002 21.732 9.279 0 41.716
3 05.07.2002 52.944 40.135 0 251.258
4 05.07.2002 29.637 0 0 214.046
1 28.07.2002 37.784 0.647 0 59.547
2 28.07.2002 20.574 1.573 0 50.576
A.1 Information from MODIS Classifications 91
3 28.07.2002 41.229 1.033 0 302.078
4 28.07.2002 17.124 0 0 226.557
Table A.2: Satellite derived areas (in km2) for snow,
clouds, invalid pixels and snow free for the Otztal sub-
basins 2002. The sub-basins are Vent (Rofenache) (1),
Obergurgl (2), Huben (3), Tumpen (4).
subbasin id date snow area cloud area invalid pixel area snow-free area
1 16.03.2003 84.61 12.926 0 0.437
2 16.03.2003 68.259 4.468 0 0
3 16.03.2003 287.29 32.338 0 24.712
4 16.03.2003 126.188 65.294 0 52.199
1 23.03.2003 97.289 0 0 0.686
2 23.03.2003 72.726 0 0 0
3 23.03.2003 313.32 0 0 31.019
4 23.03.2003 181.27 0 0 62.41
1 25.03.2003 96.852 0.062 0 1.06
2 25.03.2003 72.726 0 0 0
3 25.03.2003 306.758 0 0 37.582
4 25.03.2003 174.576 0 0 69.105
1 08.04.2003 97.59 0.307 0 0.08
2 08.04.2003 72.726 0 0 0
3 08.04.2003 323.574 0.594 0 20.172
4 08.04.2003 204.342 0.875 0 38.463
1 15.04.2003 95.469 0.293 0 2.213
2 15.04.2003 71.79 0.754 0 0.183
3 15.04.2003 289.779 5.307 0 49.256
4 15.04.2003 175.2 0 0 68.483
1 17.04.2003 94.349 0 0 3.628
2 17.04.2003 71.67 0.417 0 0.638
3 17.04.2003 274.844 6.69 0 62.805
4 17.04.2003 151.835 9.249 0 82.597
1 24.04.2003 93.512 0 0 4.465
2 24.04.2003 71.477 0.472 0 0.778
3 24.04.2003 269.524 0.028 0 74.787
92 Tables
4 24.04.2003 147.21 2.015 0 94.457
1 26.04.2003 50.397 42.1 0 5.48
2 26.04.2003 43.187 26.981 0 2.555
3 26.04.2003 168.357 108.105 0 67.88
4 26.04.2003 124.261 53.941 0 65.481
1 01.05.2003 9.587 87.62 0 0.771
2 01.05.2003 32.744 36.299 0 3.681
3 01.05.2003 102.435 188.954 0 52.948
4 01.05.2003 74.229 78.27 0 91.18
1 03.05.2003 85.637 0 0.046 12.295
2 03.05.2003 68.576 0.063 0 4.087
3 03.05.2003 235.088 1.189 0 108.061
4 03.05.2003 131.243 0 0 112.437
1 19.05.2003 49.285 27.989 0 20.703
2 19.05.2003 41.644 11.743 0 19.336
3 19.05.2003 133.714 37.5 0 173.125
4 19.05.2003 75.646 1.702 0 166.332
1 02.06.2003 48.338 30.265 0 19.372
2 02.06.2003 36.412 21.394 0 14.918
3 02.06.2003 66.297 125.263 0 152.782
4 02.06.2003 21.889 87.425 0 134.368
1 04.06.2003 25.954 44.045 0 27.976
2 04.06.2003 33.363 7.803 0 31.56
3 04.06.2003 46.987 123.992 0 173.363
4 04.06.2003 34.832 82.699 0 126.148
1 13.06.2003 14.696 79.88 0 3.397
2 13.06.2003 3.138 62.07 0 7.517
3 13.06.2003 7.624 255.603 0 81.115
4 13.06.2003 1.369 217.573 0 24.74
1 20.06.2003 33.508 14.211 0 50.257
2 20.06.2003 28.186 0.989 0 43.55
3 20.06.2003 36.11 59.406 0 248.823
4 20.06.2003 11.557 89.535 0 142.587
1 27.06.2003 6.508 89.282 0 2.186
2 27.06.2003 4.302 57.522 0 10.902
3 27.06.2003 5.371 262.612 0 76.354
4 27.06.2003 0 239.489 0 4.191
1 29.06.2003 21.029 50.327 0 26.618
A.1 Information from MODIS Classifications 93
2 29.06.2003 0.725 63.289 0 8.712
3 29.06.2003 10.846 212.475 0 121.021
4 29.06.2003 0.611 118.143 0 124.927
1 06.07.2003 44.054 2.431 0 51.49
2 06.07.2003 23.617 12.186 0 36.923
3 06.07.2003 38.36 42.151 0 263.832
4 06.07.2003 9.629 65.326 0 168.728
1 08.07.2003 27.545 21.678 0 48.754
2 08.07.2003 14.065 16.321 0 42.342
3 08.07.2003 28.391 97.056 0 218.896
4 08.07.2003 5.398 108.301 0 129.982
1 13.07.2003 33.442 0.375 0 64.16
2 13.07.2003 19.429 0 0 53.297
3 13.07.2003 23.046 7.795 0 313.5
4 13.07.2003 4.058 25.658 0 213.965
1 20.07.2003 30.827 0 0 67.149
2 20.07.2003 16.52 0 0 56.207
3 20.07.2003 21.81 1 0 321.531
4 20.07.2003 5.801 2.191 0 235.691
1 22.07.2003 25.046 9.309 0 63.622
2 22.07.2003 13.325 23.493 0 35.91
3 22.07.2003 14.085 106.954 0 223.302
4 22.07.2003 5.582 38.601 0 199.496
1 05.08.2003 26.834 10.315 0 60.825
2 05.08.2003 8.498 22.905 0 41.322
3 05.08.2003 14.407 38.498 0 291.435
4 05.08.2003 2.75 53.67 0 187.262
1 07.08.2003 23.176 1.663 0 73.136
2 07.08.2003 12.027 0 0 60.698
3 07.08.2003 16.056 1.302 0 326.983
4 07.08.2003 4.646 0.469 0 238.566
1 09.08.2003 4.198 90.996 0 2.782
2 09.08.2003 2.669 67.356 0 2.701
3 09.08.2003 0 344.34 0 0
4 09.08.2003 0.062 243.306 0 0.314
1 23.08.2003 23.179 3.268 0 71.528
2 23.08.2003 9.37 1.396 0 61.96
3 23.08.2003 12.239 9.187 0 322.915
94 Tables
4 23.08.2003 5.283 0 0 238.398
1 15.09.2003 52.818 0 0 45.161
2 15.09.2003 31.12 0 0 41.605
3 15.09.2003 84.562 0 0 259.778
4 15.09.2003 71.555 0 0 172.125
1 22.09.2003 29.859 7.512 0 60.608
2 22.09.2003 13.545 5.208 0 53.976
3 22.09.2003 23.591 20.22 0 300.531
4 22.09.2003 13.455 0 0 230.226
1 26.09.2003 31.98 0 0 65.997
2 26.09.2003 18.146 0 0 54.58
3 26.09.2003 33.27 0 0 311.071
4 26.09.2003 17.807 0 0 225.875
Table A.3: Satellite derived areas (in km2) for snow,
clouds,bad pixels and snow free for the Otztal sub-basins
2003. The sub-basins are Vent (Rofenache) (1), Ober-
gurgl (2), Huben (3), Tumpen (4).
A.2 Temporal Assignment of Degree-day Factors
The degree-day factors were increased with increasing sum of positive degree-days.
The minimum degree-day factor is 3 mm◦C−1d−1, the thresholds when this factor
increases are listed in Table 8.2 and 8.3. In the following tables the dates when the
degree-day factors change are listed.
HRUID a=4 a=4.5 a=5 a=7
1001 20.05.2001 16.06.2001 28.06.2001
1180 28.04.2001 10.05.2001 17.05.2001
1200 30.04.2001 13.05.2001 21.05.2001
1220 02.05.2001 19.05.2001 26.05.2001
1260 13.05.2001 30.05.2001 21.06.2001
1280 21.05.2001 20.06.2001 28.06.2001
1300 27.05.2001 27.06.2001 07.07.2001
1301 28.05.2001 29.06.2001 10.07.2001
A.2 Temporal Assignment of Degree-day Factors 95
1320 30.05.2001 08.07.2001 27.07.2001
1321 30.05.2001 10.07.2001 28.07.2001
2001 17.05.2001 12.06.2001 26.06.2001
2180 28.04.2001 10.05.2001 17.05.2001
2200 30.04.2001 13.05.2001 21.05.2001
2220 02.05.2001 19.05.2001 26.05.2001
2240 05.05.2001 24.05.2001 30.05.2001
2260 12.05.2001 29.05.2001 15.06.2001
2280 20.05.2001 14.06.2001 26.06.2001
2300 27.05.2001 27.06.2001 06.07.2001
2301 27.05.2001 27.06.2001 06.07.2001
2321 29.05.2001 05.07.2001 22.07.2001
3001 21.05.2001 21.06.2001 29.06.2001
3120 04.04.2001 01.05.2001 06.05.2001
3122 06.04.2001 01.05.2001 06.05.2001
3140 06.04.2001 03.05.2001 09.05.2001
3142 09.04.2001 03.05.2001 09.05.2001
3160 25.04.2001 07.05.2001 13.05.2001
3162 25.04.2001 07.05.2001 13.05.2001
3180 28.04.2001 10.05.2001 16.05.2001
3182 28.04.2001 10.05.2001 17.05.2001
3200 30.04.2001 14.05.2001 22.05.2001
3220 02.05.2001 20.05.2001 27.05.2001
3240 06.05.2001 26.05.2001 02.06.2001
3260 13.05.2001 30.05.2001 21.06.2001
3280 21.05.2001 20.06.2001 28.06.2001
3300 28.05.2001 29.06.2001 10.07.2001
3301 28.05.2001 29.06.2001 09.07.2001
3321 30.05.2001 06.07.2001 25.07.2001
4001 22.05.2001 24.06.2001 02.07.2001
4090 04.04.2001 24.04.2001 30.04.2001
4100 04.04.2001 27.04.2001 02.05.2001
4102 04.04.2001 24.04.2001 01.05.2001
4122 04.04.2001 30.04.2001 04.05.2001
4142 10.04.2001 03.05.2001 09.05.2001
4160 25.04.2001 06.05.2001 12.05.2001
4162 25.04.2001 06.05.2001 13.05.2001
96 Tables
4180 29.04.2001 11.05.2001 17.05.2001
4182 29.04.2001 11.05.2001 17.05.2001
4200 01.05.2001 15.05.2001 23.05.2001
4220 03.05.2001 21.05.2001 28.05.2001
4240 07.05.2001 27.05.2001 08.06.2001
4260 14.05.2001 05.06.2001 24.06.2001
4280 22.05.2001 24.06.2001 03.07.2001
4300 29.05.2001 04.07.2001 15.07.2001
4301 28.05.2001 04.07.2001 15.07.2001
Table A.4: Day of degree-day factor (a) change for all
HRUs 2001
HRUID a=4 a=4.5 a=5 a=7
1001 18.05.2002 14.06.2002 19.06.2002
1180 30.04.2002 15.05.2002 21.05.2002
1200 03.05.2002 18.05.2002 25.05.2002
1220 09.05.2002 23.05.2002 02.06.2002
1240 13.05.2002 31.05.2002 10.06.2002
1260 17.05.2002 07.06.2002 16.06.2002
1280 21.05.2002 15.06.2002 20.06.2002
1300 31.05.2002 19.06.2002 24.06.2002
1301 01.06.2002 19.06.2002 25.06.2002
1320 13.06.2002 23.06.2002 10.07.2002
1321 13.06.2002 23.06.2002 10.07.2002
2001 18.05.2002 14.06.2002 19.06.2002
2180 26.04.2002 12.05.2002 18.05.2002
2200 01.05.2002 16.05.2002 22.05.2002
2220 08.05.2002 21.05.2002 31.05.2002
2240 12.05.2002 29.05.2002 05.06.2002
2260 15.05.2002 04.06.2002 15.06.2002
2280 18.05.2002 14.06.2002 19.06.2002
2300 31.05.2002 18.06.2002 23.06.2002
2301 31.05.2002 18.06.2002 23.06.2002
2321 04.06.2002 21.06.2002 03.07.2002
3001 20.05.2002 15.06.2002 20.06.2002
A.2 Temporal Assignment of Degree-day Factors 97
3120 08.04.2002 27.04.2002 03.05.2002
3122 08.04.2002 27.04.2002 03.05.2002
3140 12.04.2002 01.05.2002 09.05.2002
3142 12.04.2002 02.05.2002 09.05.2002
3160 21.04.2002 08.05.2002 14.05.2002
3162 19.04.2002 07.05.2002 14.05.2002
3180 26.04.2002 13.05.2002 18.05.2002
3182 28.04.2002 13.05.2002 18.05.2002
3200 02.05.2002 17.05.2002 24.05.2002
3220 08.05.2002 22.05.2002 01.06.2002
3240 12.05.2002 30.05.2002 08.06.2002
3260 16.05.2002 05.06.2002 16.06.2002
3280 20.05.2002 15.06.2002 20.06.2002
3300 01.06.2002 19.06.2002 26.06.2002
3301 01.06.2002 19.06.2002 25.06.2002
3321 05.06.2002 22.06.2002 07.07.2002
4001 18.05.2002 16.06.2002 21.06.2002
4090 04.04.2002 17.04.2002 24.04.2002
4100 04.04.2002 21.04.2002 28.04.2002
4102 04.04.2002 21.04.2002 27.04.2002
4122 05.04.2002 25.04.2002 01.05.2002
4142 11.04.2002 30.04.2002 06.05.2002
4160 13.04.2002 03.05.2002 10.05.2002
4162 13.04.2002 03.05.2002 11.05.2002
4180 25.04.2002 10.05.2002 16.05.2002
4182 26.04.2002 10.05.2002 17.05.2002
4200 01.05.2002 15.05.2002 21.05.2002
4220 03.05.2002 19.05.2002 31.05.2002
4240 09.05.2002 26.05.2002 07.06.2002
4260 13.05.2002 04.06.2002 16.06.2002
4280 17.05.2002 15.06.2002 21.06.2002
4300 01.06.2002 20.06.2002 02.07.2002
4301 01.06.2002 20.06.2002 02.07.2002
Table A.5: Day of degree-day factor (a) change for all
HRUs 2002
98 Tables
HRUID a=4 a=4.5 a=5 a=7
1001 06.05.2003 30.05.2003 06.06.2003
1180 22.04.2003 05.05.2003 08.05.2003
1200 25.04.2003 06.05.2003 12.05.2003
1220 29.04.2003 09.05.2003 23.05.2003
1240 01.05.2003 17.05.2003 28.05.2003
1260 05.05.2003 25.05.2003 02.06.2003
1280 06.05.2003 31.05.2003 07.06.2003
1300 07.05.2003 05.06.2003 12.06.2003
1301 08.05.2003 05.06.2003 12.06.2003
1320 19.05.2003 11.06.2003 21.06.2003
1321 19.05.2003 11.06.2003 21.06.2003
2001 06.05.2003 28.05.2003 05.06.2003
2180 21.04.2003 04.05.2003 08.05.2003
2200 25.04.2003 06.05.2003 11.05.2003
2220 29.04.2003 08.05.2003 19.05.2003
2240 01.05.2003 13.05.2003 27.05.2003
2260 05.05.2003 24.05.2003 01.06.2003
2280 06.05.2003 29.05.2003 05.06.2003
2300 07.05.2003 04.06.2003 11.06.2003
2301 07.05.2003 04.06.2003 11.06.2003
2321 08.05.2003 08.06.2003 16.06.2003
3001 06.05.2003 30.05.2003 06.06.2003
3120 15.04.2003 25.04.2003 30.04.2003
3122 15.04.2003 25.04.2003 30.04.2003
3140 16.04.2003 28.04.2003 03.05.2003
3142 16.04.2003 28.04.2003 03.05.2003
3160 17.04.2003 30.04.2003 06.05.2003
3162 17.04.2003 30.04.2003 05.05.2003
3180 20.04.2003 04.05.2003 07.05.2003
3182 21.04.2003 04.05.2003 08.05.2003
3200 25.04.2003 06.05.2003 11.05.2003
3220 28.04.2003 08.05.2003 19.05.2003
3240 30.04.2003 13.05.2003 27.05.2003
3260 05.05.2003 25.05.2003 02.06.2003
3280 06.05.2003 30.05.2003 06.06.2003
3300 07.05.2003 05.06.2003 12.06.2003
A.2 Temporal Assignment of Degree-day Factors 99
3301 07.05.2003 05.06.2003 12.06.2003
3321 09.05.2003 10.06.2003 17.06.2003
4001 05.05.2003 28.05.2003 05.06.2003
4090 11.04.2003 20.04.2003 25.04.2003
4100 13.04.2003 22.04.2003 27.04.2003
4102 13.04.2003 22.04.2003 27.04.2003
4122 14.04.2003 24.04.2003 29.04.2003
4142 15.04.2003 26.04.2003 01.05.2003
4160 16.04.2003 28.04.2003 04.05.2003
4162 16.04.2003 29.04.2003 04.05.2003
4180 18.04.2003 01.05.2003 07.05.2003
4182 18.04.2003 01.05.2003 07.05.2003
4200 23.04.2003 05.05.2003 09.05.2003
4220 26.04.2003 07.05.2003 18.05.2003
4240 29.04.2003 11.05.2003 26.05.2003
4260 02.05.2003 24.05.2003 01.06.2003
4280 05.05.2003 29.05.2003 06.06.2003
4300 07.05.2003 04.06.2003 12.06.2003
4301 07.05.2003 04.06.2003 12.06.2003
Table A.6: Day of degree-day factor change for all HRUs
2003
100
Acknowledgments
At first I have to thank my advisor Professor Helmut Rott for his expert guidance
and mentorship. Special thanks go to Tommy and Petra, who supported me at
all levels with lots of inspiration, motivation and their programming knowledge.
Furthermore I have to mention Carmen, who introduced me to the ’secrets’ of
runoff modelling with the SRM, and Florian, who was always open for questions on
programming and for coffee-breaks. I will not mention by name all others from the
IMGI and the remote sensing group who contributed to the success of this thesis,
but I am also very grateful to them.
For providing data I am grateful to Hydrographischer Dienst Tirol, Tiroler
Wasserkraft AG (TIWAG), Zentralanstalt fur Meteorologie und Geodynamik
(ZAMG) Innsbruck, Hydrographisches Amt Bozen and the synoptik group of our
institute. The people who supplied me these data were: Martin Neuner, Martin
Linser, Christian Malaun, Helmut Spiss, Holga Starke and Christian Zenkl. Also
very helpful, especially in the beginning of this work, was a digital OK provided by
the Abteilung Raumordnung/tiris of the Tyrolean government.
Last but not least I thank my family and all my friends who supported me
during the last years. THANKS TO ALL OF YOU!
101