Developments for a semi-distributed runoff modelling system and its application in the drainage basin Ötztal. Diplomarbeit

Transcription

1 Developments for a semi-distributed runoff modelling system and its application in the drainage basin Ötztal Diplomarbeit Zur Erlangung des akademischen Grades Magister der Naturwissenschaften an der Leopold-Franzens-Universität Innsbruck eingereicht von Markus Heidinger Innsbruck, Dezember 2004

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3 Abstract A hydrological modelling system was further developed and applied to the Alpine drainage basin Ötztal (Austria). The system consists of basic modules for hydrological 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 hydrological 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 meteorological 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 meteorological and hydrological data as well as pre-processed meteorological, snow cover and satellite data were supported by an object-relational database system. Simulation runs for daily runoff were carried out for three spring and summer seasons ( ) in the Alpine valley Ötztal 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

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5 Contents Abstract Contents i ii 1 Introduction and Outline 1 2 Hydrological Modelling System Introduction Basic Concept of Snowmelt Runoff Modelling Data Processing in the Hydrological Modelling System The Snowmelt Runoff Model (SRM) Overview Structure of SRM Evolution of SRM Programmes Codes SRM Extensions Pre-Processing of Meteorological Data Introduction Spatial Interpolation Elevation Adjustment of Meteorological Data Two-Dimensional Interpolation Methods Examples and Quality Assessment of Interpolation in the Ötztal Test Basin Hydrological Basin Setup Overview of the Ötztal Test Basin Delineation of Basins and Sub-basins Hydrological Response Units Hydro-meteorological Stations Remote Sensing Data Analysis 47 iii

6 iv CONTENTS 6.1 Land-cover Classification Snow Cover Mapping Temporal SCA Interpolation Accumulated Melt Depth Method SCA Interpolation in the Ötztal Test Basin SRM Parameter Setup in the Ötztal Basin Overview SRM Parameter Derivation Recession Coefficient Time Lag Runoff Coefficient Critical Temperature Degree-day Factor Rain Contributing Area Parameterization of Severe Precipitation Listing of SRM-Parameters for the Ötztal Sub-basins Runoff Simulations Quality Assessment Runoff Simulations Runoff Simulations Runoff Simulations Summary and Conclusions 77 A Tables 87 A.1 Information from MODIS Classifications A.2 Temporal Assignment of Degree-day Factors Acknowledgments 101

7 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 ( ) 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 Ötztal. 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

8 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 Ötztal (Austria). The drainage basin Ötztal 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 Ötztal test basin is shortly explained in Chapter 8. The results of the runoff simulations in the Ötztal basin, which were carried out from 1 April to 30 September for the years are presented in Chapter 9. Finally in Chapter 10 a summary and the conclusions of this work are given.

9 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 Ötztal 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

10 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 sophisticated 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 für 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)

11 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 extrapolated 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., modified). Air temperature is used to estimate snow melt and to decide whether precipitation

12 6 Hydrological Modelling System falls as snow or rain. In SRM areal averages of rainfall are required to determine the rainfall contribution to runoff. Extrapolation of precipitation from point measurements 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 Modelling 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

13 2.3 Data Processing in the Hydrological Modelling System 7 Hydrological System Setup Meteorological Stations Runoff Gauges Basin Setup Model Setup Remote Sensing Module HydroMet Database (PostgreSQL) Satellite based information (snow covered area) IRSL Software Tools (processing of RS Data) Meteo Pre-Processing Grids of Interpolated data Pre-processing of meteorological data Snow cover interpolation HydroMet Database (PostgreSQL) Hydrological Modelling Post-processing Input (File) generation for hydrological Modelling Quality Assessment Computed Runoff Runoff Modelling (SRM) Graphic data output Figure 2.2: Schematic process diagram of the hydrological modelling system.

14 8 Hydrological Modelling System elevation model and land cover information, which are derived from remote sensing data or other sources. Furthermore, specific hydrological parameters of the basins (such as the recession coefficient) are derived from archived time series of the runoff (see Chapter 8). Remote Sensing module The remote sensing module includes presently an automatic snow mapping procedure using MODIS data (Chapter 6.2), which was developed in the project EnviSnow. The automatic snow mapping procedure downloads MODIS Level 1B data via Internet and generates spatially detailed snow maps. For use with the SRM, snow cover is aggregated for each HRU. On days without snow maps the snow extent is estimated using a degree day model (Chapter 7). Snow classification procedures are also available for other satellite sensors, in particular synthetic aperture radar (SAR). Meteorological Pre-processing The meteorological pre-processor is a versatile tool for activities requiring spatially distributed information. It carries out temporal integration of meteorological measurements and prepares gridded temperature and precipitation data by spatial interpolation. Depending on the hydrological model, whether it is fully distributed or not, the rastered data are integrated for each zone. In Chapter 4 pre-processing of meteorological data is described in more detail. Also the interpolation of the snow covered area (Chapter 7), which is necessary on daily basis for the SRM, is done in this module. Hydrological Modelling Finally, when all necessary input data for runoff modelling are generated and stored in the database (DB), these data have to be transformed for use in the individual runoff simulation program. For this purpose an assisting file builder was created to derive the input files for the runoff model. Some models are able to get the necessary input data directly from databases. As this option is not as flexible and the controlling of input data in this case would be more error prone, the usage of separate files was chosen. For runoff simulation in this work the snowmelt runoff model (SRM) is used (Chapter 3). Post-processing The post-processing module includes quality assessment of the simulated runoff and graphical data output generation. For numerical quality description of the simulations the relative volume deviation and the goodness of fit measure after

15 2.3 Data Processing in the Hydrological Modelling System 9 Nash and Sutcliffe (1970) are implemented (Chapter 9.1). The graphs of the simulated runoff are generated automatically with gnuplot after runoff calculation. The freely distributed plotting tool gnuplot ( 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., modified). 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

16 10 Hydrological Modelling System records. Each record is stored as separate row in the table and each column contains the same type of information. Using the structured query languages this information can be prompted from the database. The advantage of PostgreSQL is the availability of the PostGIS extension which allows GIS (Geographic Information Systems) objects to be stored in the database (Ramsey, 2004). In detail it is used to select the meteorological stations in the area of interest from the database. As the coordinates of the stations are in the DB, the area can be defined for example by creating a box, needing only the coordinates of the upper left and lower right corner. An other way would be to create a circle using a center point and the radius. The queries for selecting the meteorological stations and data is embedded in the C code of the pre-processor (Chapter 4.1). For writing this client application the PostgreSQL libpq interface is used. libpq is a collection of C libraries, which allow client programs to send queries to the PostgreSQL server and to retrieve their results (Eisentraut, 2003). A hierarchical scheme of the database used is shown in Figure 2.4. The single tables are referenced by key indices to another. The DB consists of several tables for station measurements, the results of the pre-processor, including temperature, 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.

17 2.3 Data Processing in the Hydrological Modelling System 11 Figure 2.4: Hierarchical scheme of the hydro-meteorological database tables (with keys).

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19 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 degree north and 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

20 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

21 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) where M Q n+1 = Q n k n+1 + }{{} (c s,i,na i,n T i,n + SCA i,na i + c r,i,n P i,n A i ) i=1 }{{}}{{} (1 k n+1)f (3.1) (a) (b) (c) n... index, representing the sequence of days during the modelling period M... total number of HRUs i... index for HRU f = conversion factor from [mmkm2 d 1 ] to [m 3 s 1 ] A... area of HRU in km 2 Measurement variables: Q... mean daily runoff [m 3 s 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: c r... runoff coefficient for rain c s... runoff coefficient for snowmelt k... recession coefficient a... degree-day factor [mm C 1 d 1 ] Temperature (T ), precipitation (P ) and snow covered area (SCA) are the variables to be measured or determined each day. The runoff coefficients c r and c s are HRU wide estimated, in general dependent on the surface type and condition. As conditions 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

22 16 The Snowmelt Runoff Model (SRM) discharge during undisturbed conditions, without neither snowmelt nor rainfall. Snowmelt and/or glacier melt in each unit is calculated from air temperature using the degree-day method (term (b) of Equation 3.1). The degree-day factor a is used to convert the positive degree days T + [ C] into daily melt rates (M) of snow water equivalent by M i,n = a i,n T + 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 calculated snowmelt has to be multiplied with the recession coefficient for snow (c s ), which parameterizes different loss factors. To the snowmelt runoff the rainfall, multiplied with the recession coefficient for rain (c r ), 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, c s, c r the SRM needs three further parameters, 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.

23 3.3 Evolution of SRM Programmes Codes 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 ( 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.

24 18 The Snowmelt Runoff Model (SRM) Figure 3.2: Snapshot of the SRM-C GUI. 3.4 SRM Extensions To use the SRM in extended basins with several sub-basins, the hydrological network has to be analyzed. This network can be divided into further elements. In the literature several denotations for this elements are given, in this work these (gauged) elements are in common named sub-basins. To get the discharge of the whole basin, runoff of the sub-basins has to be summed up. As water needs a certain time to travel down the sub-basins this could not be done by simple addition. For this reason a routing routine was introduced to the SRM. This routing describes the time-lag of the water flowing down the channel. If water from one sub-basin flows into another, not the whole runoff of the actual day is added on, but only the part which reaches the second sub-basin on this day. This inflow is calculated by

25 3.4 SRM Extensions 19 R in,t = R pout,t (24 routing) 24 + R pout,t 1 (routing) 24 (3.3) where R in,t is the inflow in a sub-basin at time t, R pout the (calculated) outflow 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.

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27 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 meteorological 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

28 22 Pre-Processing of Meteorological Data Figure 4.1: Flow diagram of the main processing steps carried out within the meteorological 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 Ötztal 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 meteorological 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

29 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 conceptional 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 vertical 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

30 24 Pre-Processing of Meteorological Data vertical gradient of the measured parameter height of the reference levels Spatial interpolation of meteorological data within the pre-processor includes the following steps (Figure 4.2) (after Malcher et al. (2004)): The reduction of the measurements from different elevations to a reference level using a linear polynomial. The coefficients for the polynomial, describing the vertical dependence of the parameters, are specified by the user. Spatial interpolation of point measurements at the reference level to a regular grid (which usually has the same raster interval as the digital elevation model). Interpolated values at each grid point are transformed from the reference level to the surface elevation taken from the digital elevation model. Figure 4.2: Meteorological interpolation and adjustment scheme.

31 4.2 Spatial Interpolation 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 meteorological 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 meteorological stations, which are located at different elevations. determination of the gradient from temperature measurements by radiosondes. 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.

32 26 Pre-Processing of Meteorological Data Precipitation Because precipitation is spatially more variable than temperature, the derivation of a vertical gradient is more problematic. In addition, the precipitation measurements at high altitudes are less accurate, because higher wind velocities increase the deficit of catch of precipitation gauges (Lang, 1985). For calculation of the altitude relation of precipitation, data from several adjoining meteorological stations located at different elevations would be necessary. Analysis of Kuhn and Batlogg (1997, 1999) showed that the vertical gradient within the Alps is dependent on the meteorological situation and the location within the Alps. For advective precipitation in most areas a strong vertical gradient was found, whereas for convective events the increase of precipitation with altitude is usually small 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

33 4.2 Spatial Interpolation 27 these lines are constructed so that the area around each station is enclosed by the bisectors or the area boundary. The enclosed areas around the rain gauges are the Thiessen polygons. The area within this polygon is closer to the rain gauge in that polygon than to any other rain gauge and the measured rainfall is assumed to be representative for the total polygon area (Ward and Trimble, 2003). Isohyetal Method With the isohyetal method, lines of equal rainfall (isohyets) are drawn (Fig. 4.4). From the resulting map a weighted average based on the area within each of the contour lines can be calculated. This method may have some benefit in mountainous regions, where isohyets should be able to better represent the rainfall distribution than Thiessen polygons, because orographic effects show up with isohyets. Different algorithms for taken into account the elevation using this method exist (e.g. Dawdy and Langbein (1960)). Figure 4.4: Isohyetal Method. Kriging Kringing, a common geostatistical method, is based on theoretical variogramms to estimate the spatial distribution of point data. The advantage of geostatistical methods is that not only interpolated values are calculated, but also a bias (Barcelo, 2001). Data of unknown points are estimated with the aid of weighted means of neighbor-

34 28 Pre-Processing of Meteorological Data ing values. The weighting factors are optimized by a geostatistical model and a variogram, which describes the spatial dependences. A disadvantage of this method is the need of a dense station network. Also the automatic computation of the best fitting variogram is complicated and time-consuming. Details of kriging and other geostatistical methods are explained for example in Schafmeister (1999) or Griffith and Layne (1999). Inverse Distance Weighting A method which is used in numerous hydrological modelling systems for spatial interpolation of point measurements is inverse distance weighting (IDW). It is a purely statistic method and does not take the vertical dependences into account. The inverse distance interpolated value F (x, y) for the point (x, y) is specified by (Bonham-Carter, 1994): F (x, y) = Nk=1 w k (x, y)f k Nk=1 w k (x, y) (4.1) with and d m k = w k (x, y) = d m k (4.2) (x x k ) 2 + (y y k ) 2 (4.3) The weighting factor w k (x, y) depends on the distance d to the measure point k, where for the exponent m = 2 is used. f k 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 Examples and Quality Assessment of Interpolation in the Ötztal 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.

35 4.2 Spatial Interpolation 29 For reducing the measured data to the reference level where the IDW interpolation is done, simple linear gradients were chosen. For temperature calculation a linear lapse rate, T z = 0.6 [ C/100m], found by Hoinkes and Steinacker (1974) for the area of Vent in the southern part of the test basin, is used and assigned to the whole valley. This value is kept constant during the whole simulation period. Especially during cold conditions (spring, autumn) when temperature inversions are well developed in mountainous regions, this temperature lapse rate may not describe the true condition of the atmosphere. For precipitation a vertical increase of 8 percent per 100 m is assumed up to a height of 3100 m. At higher levels no further increase of precipitation is assumed. The value for the precipitation increase with height was adopted from analyses of Kuhn and Batlogg (1997) in several Alpine areas. This value is valid for advective events, for convective precipitation very little increase of the precipitation amount with altitude was found in general. For this work, aimed at automatic procedures, no discrimination between advective and convective precipitation events was done. Therefore this value was used for all events. This may lead to some errors especially in the summer-months. The spatial interpolation in the area of the Ötztal basin uses a digital elevation 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 To get some information of the quality of the applied interpolation method, calculated 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.

36 30 Pre-Processing of Meteorological Data Figure 4.5: Precipitation linear corrected to reference level (top) and to elevation of DEM (bottom) on 27 June, Grid-resolution 25 m.

37 4.2 Spatial Interpolation 31 Figure 4.6: Time series of mean daily precipitation grids of the Grid-resolution 25 m. Ötztal area, 1-30 June

38 32 Pre-Processing of Meteorological Data Figure 4.7: Time series of daily temperature grids of the Grid-resolution 25 m. Ötztal area, 1-30 June 2002.

39 4.2 Spatial Interpolation 33 Figure 4.8: Comparison of measured precipitation versus interpolated at station Vent from 1 June to 31 July Figure 4.9: Comparison of measured temperature versus interpolated at station Vent from 1 June to 31 July 2002.

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41 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 Ötztal, a valley within the Austrian Alps, was chosen. The tasks refer to the model SRM that was used. 5.1 Overview of the Ötztal Test Basin The Ötztal watershed is located north of the main ridge of the Eastern Alps of Austria. The basin (Figure 5.1) used for modelling (Ötztal 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 km 2 whereof 106 km 2 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 resolution 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

42 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 (Rofenache) and Obergurgl were selected. With these informations the routines generate the borders of the basin and sub-basins.

43 5.2 Delineation of Basins and Sub-basins 37 Sub-basins are spatially related to one another, which means that the outflow 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 (H g ) at the runoff gauges are listed. The sub-basin Horlachbach, with an area of about 26 km 2 has not been taken into account, as it is influenced by abstraction to the Finstertal reservoir, which drains directly to the river Inn. Basin H g [m] A[km 2 ] Vent Obergurgl Huben Tumpen Ötztal Table 5.1: Area (A) of Ötztal sub-basins and elevation (H g ) at runoff gauges. The area of the Horlachbach sub-basin is not included. Characterizing the basin for hydrological purposes needs thematic information, in particular the land-cover classes and their area-elevation-distribution. For the Ötztal 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.

44 38 Hydrological Basin Setup Figure 5.2: Cumulative area elevation curve for the Ötztal 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 km 2. 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 information. Basin name Elevation [m] Size [km 2 ] Glacier [%] Forest [%] Vent Obergurgl Huben Tumpen Ötztal Table 5.2: Overview of all basins defined for the Ötztal.

45 5.3 Hydrological Response Units 39 Figure 5.3: Cumulative area elevation curve for the Ötztal 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 Ötztal, which were derived by land-cover discrimination and sub-division into elevation zones, are listed in Tables These Tables list the sub-basin name, the land-cover

46 40 Hydrological Basin Setup Figure 5.4: Hydrological response units of the four the HRUs for each sub-basin are listed in Table Ötztal sub-basins. Information of 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 Ötztal 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 für Meteorologie und Geodynamik (ZAMG), Hydrographisches Amt Bozen (HA) and Institut für Meteorologie und Geophysik Innsbruck (IMGI), hydrological data by Hydrographischer Dienst Tirol and Tiroler Wasserkraft AG

47 5.4 Hydro-meteorological Stations 41 Basin Land-cover class Elevation range [m] Mean elevation [m] Id Vent glacier < Vent glacier Vent glacier 3200 < Vent other Vent other Vent other Vent other Vent other Vent other Vent other Vent other 3200 < Table 5.3: Hydrological Response Units of the sub-basin Vent (Rovenache). Basin Land-cover class Elevation range [m] Mean elevation [m] Id Obergurgl glacier < Obergurgl glacier Obergurgl glacier 3200 < Obergurgl other Obergurgl other Obergurgl other Obergurgl other Obergurgl other Obergurgl other Obergurgl other 3000 < Table 5.4: Hydrological Response Units of the sub-basin Obergurgl. (TIWAG). The available data are listed in Table 5.7 and 5.8.

48 42 Hydrological Basin Setup Basin Land-cover class Elevation range [m] Mean elevation [m] Id Huben glacier < Huben glacier Huben glacier 3000 < Huben other Huben other Huben other Huben other Huben other Huben other Huben other Huben other Huben other Huben other 3000 < Huben forest Huben forest Huben forest Huben forest 1800 < Table 5.5: Hydrological Response Units of the sub-basin Huben.

49 5.4 Hydro-meteorological Stations 43 Basin Land-cover class Elevation range [m] Mean elevation[m] Id Tumpen glacier < Tumpen glacier 3000 < Tumpen other < Tumpen other Tumpen other Tumpen other Tumpen other Tumpen other Tumpen other Tumpen other Tumpen other Tumpen other 3000 < Tumpen forest < Tumpen forest Tumpen forest Tumpen forest Tumpen forest 1800 < Table 5.6: Hydrological Response Units of the sub-basin Tumpen.

50 Station name Country Operator Location Hight [m] Online Time series T P 44 Hydrological Basin Setup Brenner AT ZAMG E N 1449 X X X Brenner AT ZAMG E N X X Brunnenkogel AT ZAMG E N 3440 X X Galtuer AT ZAMG E N 1587 X X X Gries i. Sellrain HD ZAMG E N X X Haiming AT ZAMG E N X X Imst AT HD E N 860 X X X Innsbruck (airport) AT ZAMG E N 579 X X X Ischgl - Idalpe AT ZAMG E N 2323 X X X Kurzras IT HA E N X X Landeck AT ZAMG E N 798 X X X Längenfeld AT HD E N X X Meran IT HA E N X X Nauders AT ZAMG E N X X Obergurgl AT ZAMG E N 1938 X X X Pitztaler Gletscher AT ZAMG E N 2850 X X X Prutz AT ZAMG E N X X Seefeld AT ZAMG E N 1182 X X X St.Leonhard-Neurur AT ZAMG E N X X Steinach-Plon AT ZAMG E N X X Sölden AT HD E N 1380 X X Umhausen AT ZAMG E N 1041 X X X Vent AT IMGI E N X X Ötz AT HD E N X X Table 5.7: Available meteorological stations. Data are provided by Zentralanstalt für Meteorologie and Geodynamik (ZAMG), Hydrographischer Dienst Tirol (HD), Hydrographisches Amt Bozen (HA) and Institut für Meteorologie and Geophysik Innsbruck (IMGI).

51 Gauge Operator River Location Hight [m] Time series Vent (Rofenache) HD Rofenache E N Obergurgl TIWAG Gurgler Ache E N a Huben HD Ötztaler Ache E N Tumpen HD Ötztaler Ache E N Table 5.8: Available hydrological data. Data are provided by Hydrographischer Dienst Tirol (HD) and Tiroler Wasserkraft AG (TIWAG). a 2003 data are provisional values. Not all consistency checks were accomplished by the provider up to now. 5.4 Hydro-meteorological Stations 45

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53 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 Ötztal three surface classes were discriminated: glacier forest 47

54 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 (Fig.5.1). Mapping of forested areas was based on an image acquired on , 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] Table 6.1: Landsat 7 ETM+ bands ( status: November 2004). The algorithms that where used for land-cover classification are based on the planetary albedo (R p ) of the individual bands. R p is given by (Epema, 1990) R p = πd2 L(λ) S 0 (λ)cosφ 0 (6.1) where d [AU] is the Earth-Sun distance, S 0 (λ) [W m 2 µm 1 ] is the exo-atmospheric solar irradiance, L(λ) [W m 2 sr 1 µm 1 ] is the spectral radiance measured by the sensor in band λ and φ 0 [ ] 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,

55 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 discriminated 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 = R p(4) R p (3) R p (4) + R p (3) (6.2) Figure 6.1 shows a map of the resulting land-cover classes for the Ötztal. Figure 6.1: Land-cover classes for Ötztal. Red - low vegetation and bare surfaces, green - forest, blue - glacier.

56 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 images, 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 Spectroradiometer (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 für 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 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.

57 6.2 Snow Cover Mapping 51 Figure 6.2: MODIS image and derived SCA map of the wider Ötztal 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

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59 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(t 1) SCA(t 2 ) M(t 1, t A ) + M(t 2, t E ) (7.1) where d is the gradient of the SCA per mm melting [mm]. The accumulated melt depth M is a function of the positive degree-days T + and the degree-day factor a and is given by M(t 1, t 2 ) = (at + ) (7.2) The SCA of the seasonal snow cover at the time t x is calculated as SCA(t x ) = SCA(t x 1 ) d M(t x 1, t x ) (7.3) 53

60 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 (t a ) occurs. Until the new temporary snow cover is melted completely (t e ) 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 unchanged. 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 Ötztal 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

61 7.2 SCA Interpolation in the Ötztal 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 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 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 The red triangles indicate days with satellite images. Figure 7.2 and 7.5 shows an increase of the temporal snow cover during April

62 56 Temporal SCA Interpolation 2001 in all hydrological response units. Whereas the temporal snow pack starts to decrease at the end of the month in lower level zones, in higher zones the snowpack still increases until mid of May. Thereafter melting proceeds in all HRUs until begin of June, where in above 2400 meter height temporal snowpack increases. At end of June the temporal snowpack has disappeared in all zones. In the lowest levels even the winter snow pack has already melted completely. In July and August two small new-snow events occurred in the highest elevation zones. In September the melting period ended and the temporal snow cover increased again. Figure 7.3: (Top) Temperature and precipitation at station Vent. The red dashed line is the temperature at Obergurgl (dashed) while temperature Vent was out of order. SWE of the temporal snowpack for un-glaciated HRUs (centre) and (bottom) SCA fraction for un-glaciated HRUs of sub-basin Vent during the simulation period The red triangles indicate days with satellite images.

63 7.2 SCA Interpolation in the Ötztal Test Basin 57 In the year 2002 (Fig.7.3 and 7.6) the snowpack increased in the highest zone up to begin of June. In the lowest zones melting starts at the end of April. In these zones no significant increase of the temporal snow-cover occurred up to end of September. Figure 7.4: (Top) Temperature and precipitation at station Vent, (centre) SWE of the temporal snowpack for un-glaciated HRUs and (bottom) SCA fraction for un-glaciated HRUs of sub-basin Vent during the simulation period 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.

64 58 Temporal SCA Interpolation An inter-comparison of the different sub-basins shows similar temporal snow cover and depletion of the SCA in similar zones. Some local differences are caused by the spatial variability of precipitation. Figure 7.5: (Top) Temperature and precipitation at station Längenfeld, (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 The red triangles indicate days with satellite images.

65 7.2 SCA Interpolation in the Ötztal Test Basin 59 Figure 7.6: (Top) Temperature and precipitation at station Längenfeld, (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 The red triangles indicate days with satellite images.

66 60 Temporal SCA Interpolation Figure 7.7: (Top) Temperature at station Vent and precipitation at station Längenfeld, (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 The red triangles indicate days with satellite images.

67 Chapter 8 SRM Parameter Setup in the Ötztal Basin 8.1 Overview The SRM calculates the water runoff produced from snowmelt and rainfall, superimposed 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 required 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 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 corresponds to the ratio of the runoff of two successive days. k is estimated from archived runoff data. Values of Q n and Q n+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 k n+1 = xq y n (8.1) 61

68 62 SRM Parameter Setup in the Ötztal Basin Figure 8.1: Determination of recession coefficients for the sub-basins of the Ötztal. which means that k is not constant but increases with decreasing Q. In the Ötztal a five-year time period ( ) 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 = and y = 0.097, for Huben x = 0.89 and y = 0.1 and for Tumpen x = and y = (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 Time Lag The time lag, t lag, 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 t lag. The SRM already assumes a default time lag of 6 hours. For the four Ötztal sub-basins runoff data are available in 15 minutes time intervals for the years 1998 and These data were used to determine the time lag, which were between 2 hours for Vent

69 8.2 SRM Parameter Derivation 63 (Rofenache) and 5 hours for Huben, in addition to the default lag of 6 hours Runoff Coefficient The runoff coefficient, c r, describes the portion of snowmelt (index s) or precipitation (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: c r = precipitation runoff precipitation (8.2) In case of snowmelt the runoff coefficient corresponds to the ratio: c s = drained meltwater produced meltwater (8.3) The runoff coefficients include losses like evapotranspiration, interflow and subsurface 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. c r and c s were determined by means of runoff simulation. Table 8.1 lists the coefficients derived for the sub-basins of the Ötztal. Due to increased surface flow when precipitation 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 c r 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. c s 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 Critical Temperature The critical temperature, T crit, defines the temperature threshold below which precipitation 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 precipitation as rain contributes directly to the discharge. New snow is stored temporarily

70 64 SRM Parameter Setup in the Ötztal Basin Basin c r c s Vent(Rofen) Obergurgl Huben Tumpen Table 8.1: SRM runoff coefficients for the Ötztal basins. The runoff coefficients for rain (c r ) were raised 0.05 every 10 mm of precipitation. The runoff coefficients for snow (c r ) were kept constant. in the snowpack. For the Ötztal this threshold was kept constant at 1 C during the whole simulation 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 Degree-day Factor For calculating meltwater production the SRM uses the degree-day method. The amount of meltwater Q melt is given by Q melt = a i,n T + 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 1 d 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 Ötztal basin. The values and thresholds for the the degree-day factor (Table 8.2 and 8.3) are adopted from the runoff simulation ( ) and runoff forecasting activities (1999, 2000) of Rott et al. (2000) in the Zillertal basins.

71 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 Ötztal. 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 Ötztal 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 snowfree 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 Ötztal the rain contributing area was interrelated to the degree-day factor. So RCA becomes 1 as soon as T + > 100 C 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 k n+1 = x(fq n ) 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

72 66 SRM Parameter Setup in the Ötztal Basin parameters were also adopted from Rott et al. (2000) and are listed in Table 8.4. These values were assigned to the whole basin, although individual specification for HRUs would possible. Also no adjustment of those parameters during the season was made. Daily precipitation did not reach this threshold in the Ötztal during the study period. f 4 Rthr 60 [mmd 1 ] Rdur 5 [d] Table 8.4: Parameters for heavy rainfall in the Ötztal Listing of SRM-Parameters for the Ötztal 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 y T lag c s c r T crit Table 8.5: Summary of SRM parameters for the Ötztal sub-basins.

73 Chapter 9 Runoff Simulations In Chapter 2 the modular hydrological modelling system was introduced. system contains components for This 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 Ötztal were carried out for the period 1 April to 30 September for the years The Ötztal 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 Ötztal 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 Ötztal 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

74 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 Ötztal. 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, R 2, and the deviations of runoff volumes D v (Nash and Sutcliffe, 1970). Where R 2 is given by R 2 = 1 (Qt Q t )2 (Qt Q) 2 (9.1) where Q t is observed discharge at time t, Q t is simulated discharge, and Q is the observed mean discharge. R 2 increases from towards 1, as the root-mean-square prediction error decreases towards zero. The volume deviation of discharge, which represents the percentage difference between observed and simulated mean or total discharge is given by D v = V R V R V R 100[%] (9.2) where V R is the measured runoff volume and V R is the simulated runoff volume. A negative D v value means an over-estimation of the discharge. R 2 and D v for the cumulated runoff at the the gauges Vent (Rofenache), Obergurgl, Huben and Tumpen during the simulation periods are listed in Table 9.1.

75 9.2 Runoff Simulations Sub-basin R 2 D v R 2 D v R 2 D v Vent Obergurgl Huben Tumpen Table 9.1: Nash-Sutcliffe correlation coefficients, R 2, and deviations of runoff volumes, D v, for the routed sub-basin of the Ötztal, in the years 2001, 2002 and 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 für 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 The cold weather in the beginning of the simulation period resulted in low, even decreasing simulated runoff up to end of April in the Ötztal basin. The

76 70 Runoff Simulations decrease of the simulated discharge in April, which does not agree with the observation at the runoff gauge Vent (Rofenache), is caused by overestimation of the recession of baseflow. Because no separate storage was used for base flow, the rapidity of recession is overestimated. This is also the cause in September, and similar in all basins. With increase of temperature in early May, discharge started to increase. Therefore the first significant peak around May 30 was mainly caused by snowmelt. Advection of colder air masses during June led to lower runoff. The two peaks in the middle of this month were mainly caused by higher precipitation events. At the end of the month, snowmelt increased again. The peaks in July were caused by strong precipitation superimposed to high melting rates. Also in August discharge was characterized by such processes. In September, as air temperature was lower, runoff amounts decreased rapidly. Figure 9.3: Runoff Vent (Rofenache) Figure 9.4: Runoff Obergurgl The quality of the simulation runs for the year 2001 in general are good (Table 9.1). Especially for the sub-basins Vent, with R 2 = 0.93 and Obergurgl with R 2 = The volume of runoff for the basin Obergurgl is slightly overestimated

77 9.2 Runoff Simulations Figure 9.5: Runoff Huben Figure 9.6: Runoff Tumpen (-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 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 subbasins 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 ), 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.

78 72 Runoff Simulations 9.3 Runoff Simulations 2002 In April 2002 the air temperature and the amount of precipitation in the Ötztal area were around the longterm mean ( 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). R 2 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

79 9.3 Runoff Simulations Figure 9.8: Runoff Vent (Rofenache) Figure 9.9: Runoff Obergurgl Figure 9.10: Runoff Huben 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 assumption 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.

80 74 Runoff Simulations Figure 9.11: Runoff Tumpen Runoff Simulations 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 C higher then the longterm mean, the precipitation amount was about 70% - 90% of the normal sums ( 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.

81 9.4 Runoff Simulations Figure 9.13: Runoff Vent (Rofenache) Figure 9.14: Runoff Obergurgl 2003 (measured data are provisional values). Figure 9.15: Runoff Huben Figure 9.16: Runoff Tumpen 2003.