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.

i

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

iii

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



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

1

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).

3

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


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.


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


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


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.


Figure 2.4: Hierarchical scheme of the hydro-meteorological database tables (with keys).

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

13

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


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.


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.

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


• 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.


4.2.1 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.


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


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-


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.


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.


Figure 4.5: Precipitation linear corrected to reference level (top) and to elevation of

DEM (bottom) on 27 June, 2002. Grid-resolution 25 m.


Figure 4.6: Time series of mean daily precipitation grids of the Otztal area, 1-30 June

2002. Grid-resolution 25 m.


Figure 4.7: Time series of daily temperature grids of the Otztal area, 1-30 June 2002.

Grid-resolution 25 m.


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.

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.


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


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).


Obergurgl glacier < 3000 2863 2001

Obergurgl glacier 3000-3200 3101 2301

Obergurgl glacier 3200 < 3278 2321

Obergurgl other 1801-2000 1955 2180






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.



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.

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.

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


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.


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.


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



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



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


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


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


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


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.



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



Figure 9.9: Runoff Obergurgl 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.




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.



Figure 9.14: Runoff Obergurgl 2003 (measured data are provisional values).




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

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).


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


3 28.07.2002 41.229 1.033 0 302.078

4 28.07.2002 17.124 0 0 226.557


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).


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


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


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


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


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

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

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