Integrated Flood Risk Management

Institut für Wasserwirtschaft, Hydrologie und Konstruktiven Wasserbau Vorstand: Prof. H.P. Nachtnebel Universität für Bodenkultur Wien 816.336 Integ...
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Institut für Wasserwirtschaft, Hydrologie und Konstruktiven Wasserbau Vorstand: Prof. H.P. Nachtnebel

Universität für Bodenkultur Wien

816.336 Integrated Flood Risk Management 2nd Unit H.P. Nachtnebel, H. Habersack, H. Holzmann

Content

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Content Date 27. 11. 07 29. 11. 07

Time 9 – 11 h 9 – 11 h

Lecturer Habersack Holzmann

4. 12. 07

9 – 11 h

Habersack

6. 12. 07

9 – 11 h

Habersack

11. 12. 07 13. 12. 07 18. 12. 07 8. 1. 08 10. 1. 08 15. 1. 08 17. 1. 08 22. 1. 08 24. 1. 08 29. 1 08 31. 1. 08

9 – 11 h 9 – 11 h 9 – 11 h 9 – 11 h 9 – 11 h 9 – 11 h 9 – 11 h 9 – 10 h 9 – 10 h 9 – 10 h 9 – 10 h

Holzmann Holzmann Nachtnebel Nachtnebel Nachtnebel Nachtnebel

Content Hazard mapping, flood properties (depth, velocity) Flood forecast techniques (meteorological forecasts) Flood damages (sediment, debris) and mitigation measures Flood management (public participation, security measures) Rainfall runoff models, statistical models Updating procedures, operational data demands Risk, Integrated Flood Management Loss Analysis River related management and Hazard reduction Flood protection measures (dams, retention basins) Reservetermin Prüfungstermin (optional) Prüfungstermin (optional) Prüfungstermin (optional) Prüfungstermin (optional)

1

Introduction Aim of course Providing an overview of the relevant themes and processes related to flood formation, flood mitigation and flood management. The course introduces methods of meteo-hydrological modeling and refers to computational methods for the modelling of floods and their mitigation measures and the estimation of flood related risks.

Course Material by Internet: http://www.boku.ac.at/iwhw/integratedflood/

International Glossary of Hydrology (from UNESCO) http://webworld.unesco.org/water/ihp/db/glossary/glu/aglu.htm

Elements of Risk Management

From ISDR, 2005

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Structural Mitigation Measures Structural mitigation reduces the impact of hazards on people and buildings via engineering measures. Examples include designing infrastructure, such as electrical power and transportation systems, to withstand damage. Levees, dams, and channel diversions are all examples of structural flood mitigation. Structural mitigation projects can be very successful from a cost/benefit perspective. Argentina’s Flood Rehabilitation Project invested US$153 million in structural improvements that spared an estimated US$187 million (in 1993 dollars) in damages during the 1997 floods, generating a 35 percent return on investment to date (World Bank, 2000). However, structural mitigation projects have the potential to provide short-term protection at the cost of long-term problems. In areas in Vietnam, flood control systems have exacerbated rather than reduced the extent of flooding; sediment deposit in river channels has raised the height of river channels and strained dike systems. Now when floods occur, they tend to be of greater depth and more damaging than in the past (Benson, 1997b). Furthermore, structural mitigation projects have the potential to provide people with a false sense of security. The damages from the 1993 flooding of the Mississippi river in the United States were magnified because of misplaced confidence in structural mitigation measures that had encouraged development in high-risk areas (Mileti, 1999; Platt, 1999; Linnerooth-Bayer and others, 2000).

Non-structural Mitigation Measures Nonstructural mitigation measures are nonengineered activities that reduce the intensity of hazards or vulnerability to hazards. Examples of nonstructural mitigation measures include land use and management, zoning ordinances and building codes, public education and training, and reforestation in coastal, upstream, and mountain areas. Nonstructural measures can be encouraged by government and private industry incentives, such as preferential tax codes and deductibles, or adjusted insurance premiums that reward private loss-reducing measures. Nonstructural mitigation measures can be implemented by central authorities through legislating and enforcing building codes and zoning requirements, by NGOs initiating neighborhood loss-prevention programs, or by the private sector in providing incentives to take loss-reducing measures. Nonstructural mitigation measures are particularly appropriate for developing countries because they usually require fewer financial resources. A drawback to such measures, however, is that even when they exist, there is a tendency on the part of the private and public sectors not to enforce the regulations or standards on the books. The best practices in nonstructural mitigation are those that directly combine with development goals. An innovative model recently developed in the Grau region of Peru identifies hazards, assesses regional development objectives, and integrates a nonstructural approach to disaster mitigation into the overall development program. This “microzonation” approach focuses on land-use planning and infrastructure (Kuroiwa, 1991).

Additional Sources: http://www.fema.gov/plan/prevent/howto/index.shtm#4 Protect Your Property from Flooding Build With Flood-Resistant Materials (PDF 87 KB) Dry Floodproof Your Building (PDF 56 KB) Add Waterproof Veneer to Exterior Walls (PDF 75 KB) Raise Electrical System Components (PDF 65 KB) Anchor Fuel Tanks (PDF 68 KB) Raise or Floodproof HVAC Equipment (PDF 60 KB) Install Sewer Backflow Valves (PDF 75 KB) Protect Wells From Contamination by Flooding (PDF 94 KB)

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Disaster Risk Reduction

Hydrological forecasting and flood risk management

From ISDR, 2005

Institut für Wasserwirtschaft, Hydrologie und Konstruktiven Wasserbau Vorstand: Prof. H.P. Nachtnebel

Universität für Bodenkultur Wien

Runoff forecasts and early warning systems Ao.Univ.Prof. Dipl.Ing. Dr. Hubert Holzmann (Email: [email protected])

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Risikomanagement und Naturgefahren

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Situation

• Increasing Number of Floods Oder, Weichsel, Rhein, Donau, Traisen, Machland, Tessin, etc.

• Significant increasing Flood Losses • Potential Causes - Cyclic behaviour of meteorological forces - Climatic Change - Decrease of retention areas - Increasing settlements and constructional activities - Inaccurate design of flood protection measures

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Risikomanagement und Naturgefahren

Loss development of the last 50 years

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Flood Damages

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Risikomanagement und Naturgefahren

Flood Warning Principles Runoff Q (m3/s)

Threshold

Time t

1h - days

1h - 12h

Upstream Gauge: - Flood Routing - Statistical Methods

Rainfall : - Rainfall-Runoff Modelling - Snow Melt Modelling - Flood Routing

Weather Forecasts: 3h - 3 days

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- Weather Models - Rainfall-Runoff Modelling - Snow Melt Modelling - Flood Routing Risikomanagement und Naturgefahren

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Forecast Methods Statistical Methods:

•(Multiple) Regression •Cross Correlation •Markov Processes •Bayesian Methods •Kalman Filter Techniques

Predictors are upstream runoff data, rainfall, air temperature or soil moisture data Data are available online.

Rainfall-Runoff Models:

•Event based models •Continuous Models •Deterministic Models •Conceptual Models •Snowmelt and Snow accumulation Models

Rainfall data are used as online model input. The lead time corresponds to the runoff formation and translation time)

Meteorological Forecasts:

•ECMWF (Reading) •ALADIN (LAM) •+ RR-Modelling

Distribution of continental Air Temperature, Humidity and Air pressure.

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Risikomanagement und Naturgefahren

Snowmelt and Runoff Niederschlag Schneeschmelze Verdunstung Schneeschmelzmodell Schneeakkumulation Tiroler Inn 1990 - 1991 Schneeakkumulation Tiroler Inn 1990 - 1991 500

Hoehenzone 0-500 m.Sh Hoehenzone 500-1000 m.Sh Hoehenzone 1000-1500 m.Sh Hoehenzone 1500-2000 m.Sh Hoehenzone 2000-2500 m.Sh Hoehenzone 2500-3000 m.Sh

bw1

0

Versickerung

400 200 100 0

10

f(bw1, h2, k2)

200 f(bw1, h2, k3)

6

wobei qi den aktuellen, akkumulierten Schneespeicher nicht überschreiten kann.

. 2

bw2

400

4

Zwischenabfluss h2

Abfluss (m3/s)

8

100 0 PWP

Zeit (d)

o Q zukuenftig If Ti > O C Versickerung h2, (Grad-Tag-verfahren) k3) Ti qi = fak*f(bw1, f(bw, h1, k1)

h1

FK Pflanzenverfügbares Bodenwasser

f(bw1, h2, k2)

Q beobachtet Q Echtzeitsimulation Q Prognose

Oberflächenabfluss bw1

Zwischenabfluss

Schneeschmelze:

PWP

Niederschlag Schneeschmelze

Oberflächenspeicher

Freies Bodenwasser

wobei Ti ... mittl. Tageslufttemperatur der Höhenstufe i (gemäß Temperaturgradient)

Durch die Schneeakkumulation reduziert sich der abflußwirksame Niederschlag 0 200 400 600 gemäß dem flächengewichteten Anteil des Neuschnees.

h2

Pflanzenverfügbares Bodenwasser Niederschlags-Abfluss

Modell

Oberflächenabfluss f(bw, h1, k1)

h1 If Ti < O oC FK

Verdunstung

300

Akk. Schnee in mmWaequ.

500 400 300

Freies Bodenwasser

Schneeschmelze und Schneeakkumulation

Schneeakkumulation:

200

Akk. Schnee in mmWaequ.

Oberflächenspeicher

Hoehenzone 0-500 m.Sh Hoehenzone 500-1000 m.Sh Hoehenzone 1000-1500 m.Sh Hoehenzone 1500-2000 m.Sh Hoehenzone 2000-2500 m.Sh Hoehenzone 2500-3000 m.Sh

600

Zeit (d)

Basisabfluss Basisabfluss

f(bw2, k4)

f(bw2, k4)

0

bw2

0

20

40

60

Zeit (d)

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Flood Warning Systems •Lead time must be sufficient for protection measures - Reliable results achievable for bigger catchments with longer response time - For smaller catchments the combination with retention basins is recommended

•Protection Measures: Active Measures: - Mobile Flood Protection - (operable) retention basin - sand bags Passive Measures: - Evacuation of victims - Polders (pumping)

The effectiveness increases with the length of the lead time !!! BOKU Kongress - Wien, November 2001

Risikomanagement und Naturgefahren

Data Management Real time observation Rainfall, Temperature, Runoff (incl. Forecasts)

Data Transmission to computer center Radio- and telephone transmission

Data Processing Time Series, Preprocessing, Regionalisation

Improving of forecasts by means of estimation error

Runoff Computation

No Flood

Updating:

Models

Flood

Transmission of results to the civil services Actions and Master Plans due to runoff categories

Short term protection actions Mobile flood protectors, warnings, evacuations, etc.

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Conclusions • Flood Warning Systems are important instruments of civil protection. • Short term measures are efficiently applicable if - online data , - efficient forecast models, - appropriate protection measures and - sufficient master plans are available. • Permanent protection level (dams, runoff capacity) varies within 30 and 100 years frequency. Additional warning systems decrease the remaining risk for big flood events. • Flood warning systems do not substitute the necessity of a reliable urban and rural planning system with adopted land utilisation due to hazards and risks. • Runoff forecasts can be used for other objectives (e.g. forecasts of hydro-electrical potential, river navigation, etc.)

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Risikomanagement und Naturgefahren

Requirements for flood forecasting systems An operational real time flood forecasting system can be a complex system according to the actual needs of forecasting lead time and to the size and complexity of the system to be monitored and controlled. In order to analyse the actual requirements of a real time operational flood forecasting system one must consider all the following components: - a precipitation forecasting model (deterministic and/or stochastic); - a catchment model (deterministic and/or stochastic); - a flood routing model; - a flood plain model; - a Geographical Information System (GIS); - a geo-referenced Data Bank; - an Expert System shell.

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Rainfall as input for flood forecasts Observed data: - Rain gauges - Radar images - Visible spectra of satellites

Forecast data: - Mesoscale / global atmospheric models - Limited Area Models (LAM) - Model Output Statistics (MOS) - Ensemble Modelling (stochastic modelling)

Rainfall Gauges in Austria

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Rainfall Gauges in Austria by ZAMG

Meteosat Infrarot Satellitenbild vom 7.8.2002, 00 Uhr UTC (Quelle: Berliner Wetter-karte, FU Berlin, 2002). Nach Steinacker (2002).

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Räuml. Niederschlagsstruktur im Niederschlagsradar-Bild vom 6.8.2002, 17 UTC (18 MEZ, 19 MESZ). Dargestellt ist der Maximalwert jeder vertikalen Säule, bzw. der Maximalwert projiziert auf die x-z und die y-z Ebene. Die Grenze von grün-gelb liegt bei 2,7 mm/h, die von braun-violett bei 27,5 mm/h. Quelle: Österreichischer Radarverbund, Flugwetterdienst der Austrocontrol GesmbH.

Meteorological Forecast Models

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ECMWF

ALADINLACE

ALADINVIENNA

Operat. centre

Reading, UK

Prague, CZ

Vienna, Aut

Model domain

global

Europe

Central Europe

Grid space

60 km

12 km

10 km

Layers

50

31

31

Boundaries

-

ARPEGE

ALADIN-LACE

Lead Time

10 days

48 hours

48 hours

Temp. resolution

6h

3h

1h

Runs per day

2

2

2

In operation since

1979

1996

1999

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Physical-meteorological Processes • Radiation • Vertical Diffusion • Cloudiness • Precipitation (stratiform / convective) • Orographic forcing • Surface processes

The European Centre for Medium-Range Weather Forecasts (ECMWF, the Centre) is an international organisation supported by 25 European States. Its Member States are: Belgium, Denmark, Germany, Spain, France, Greece, Ireland, Italy, Luxembourg, the Netherlands, Norway, Austria, Portugal, Switzerland, Finland, Sweden, Turkey, United Kingdom.

The objectives of the centre The principal objectives of the Centre are: •the development of numerical methods for medium-range weather forecasting; •the preparation, on a regular basis, of medium-range weather forecasts for distribution to the meteorological services of the Member States; •scientific and technical research directed to the improvement of these forecasts; •collection and storage of appropriate meteorological data.

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ECMWF Images: 500 mb heights (in color contours) and sea level pressure (in white line contours)

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Vom ECMWF-Modell vorhergesagte Niederschlagsverteilung in Österreich und Umgebung für den 6-StundenZeitraum 6.8.02/18-24 UTC, für Ausgangslagen vom 2.8. bis 6.8.02, jeweils 12 UTC. Die erste Vorhersagekarte war also am 3.8. morgens verfügbar, die letzte am 7.8. morgens, also knapp nach dem Vorhersagetermin. Aus Haiden (2002).

Rainfall Forecast efficiency

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Basin area small

Basin area big

Rainfall area small (Konvection)

little

mean

Rainfall area big (Front)

mean

good

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Sources of Errors • Initial conditions (Observation errors, missing data …) • Parameterisation (lack of detailed process knowledge) • Mathematical Iterations (Nonlinearities, numerical solutions, …)

ECMWF enables deterministic and stochastic ensemble forecasts (model confidence).

Air Temperature Forecast Air temperature forecasting is relevant for snowmelt forecasting. In general air temperature is spatially interpolated by means of constant elevation gradients. Temperature is decreasing with increasing elevations e.g.

∆t ≅ 0.7 o C / 100m

Air temperature exhibits a certain range of persistence.

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Process-oriented approach Cloudiness

Advection

Wind speed

T2m

Soil • • • • • •

1-d model: radiation fluxes, turbulent fluxes, surface exchange Run every hour, use adapted model sounding as initial condition Cloudiness: extrapolate observed trend (+ trajectories) Advection: apply trajectories to observed temperature distribution Wind speed: weighted combination of model and observation Soil: use observed near-surface temperatures, soil conditions

! perform separate verification of individual modules From HAIDEN (2003)

T2m nowcasting error 4,0

Persistence

Mean absolute error (K)

3,5

Climatology, adjusted ALADIN DMO

3,0

ALADIN, adjusted 2,5

ALADIN, adjusted + cloud corr 2,0 1,5 1,0 0,5 0,0 0

1

2

3

4

5

6

7

8

9

10

11

12

Forecast time (h)

Adjusted LAM skill > Climatology skill > LAM DMO skill

From HAIDEN (2003)

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T2m error distribution during the first forecast hours 70 AVI5 +1h AVI5 +2h AVI5 +3h AVI5 +4h

60

Frequency (%)

50 40 30 20 10

-9 .5 -8 .5 -7 .5 -6 .5 -5 .5 -4 .5 -3 .5 -2 .5 -1 .5 -0 .5 +0 .5 +1 .5 +2 .5 +3 .5 +4 .5 +5 .5 +6 .5 +7 .5 +8 .5 +9 .5 >+ 10

Tmelt,k oC qi = fakk* Ti (Day Degree Method) where qi ... specific discharge fakk ... snowmelt factor of day k qi .cannot exceed the accumulated snow water equivalent.

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Statistical forecast model: Multiple linear regression type model with nonlinear predictors (snowmelt, soil moisture accounting)

• • • •

Pros: Good online data availability of precipitation and runoff. High online computation efficiency for the 13 forecast gages. Seasonal and discharge dependant classification. Easy estimation of model output confidence.

• • •

Contras: Averaging effect of regression type models. No event based analysis (too short observation periods) No physical meaning of the regression coefficients.

Regression confidence Value of expectation:

Yˆ = ∑ Ci ⋅ dQ A + ∑ C j ⋅ dQB + ∑ C k ⋅ GN Model variance:

()

var Yˆ

M

(

= MSE ⋅ 1 + X 0′ ( X ′X ) X 0 −1

)

Input variance:

( ) = ∑C

var Yˆ

D

2 j1

⋅ var (QB ) + ∑ C 2j1 ⋅ var (QB prog ) + ∑ C k22 ⋅ var (GN prog )

Total confidence limits: α⎞ ⎛ ∆Yˆ = t ⎜ FG ,100 − ⎟ ⋅ 2⎠ ⎝

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(var (Yˆ )

M

())

+ var Yˆ

D

24

Performance of meteorological forecasts Table 1: Statistical analysis of the residuals of the forecasted air temperature data. 250 m Sl. Mean

-3.01 -2.38 -2.42

1-day forecast 2-day forecast 3-day forecast

750 m Sl.

Stadev Correl

1.92 2.01 2.11

0.97 0.97 0.96

Mean

-2.75 -2.39 -2.42

1500 m Sl.

Stadev Correl

2.4 2.39 2.46

Mean

0.95 0.95 0.95

-0.11 -0.05 -0.07

2500 m Sl.

Stadev Correl

1.22 1.19 1.39

0.99 0.99 0.98

Mean

-0.68 -0.71 -0.73

Stadev Correl

1.62 1.46 1.61

0.97 0.98 0.98

Table 2: Statistical analysis of observed and forecasted rainfall data. Maximum

Mean

Stand.Dev.

Skew

Correlation

Sum Error (mm)

Sum Error (%)

42.3 38.5 61.2 39.6

4.06 2.96 4.06 3.81

6.24 4.3 5.85 5.09

2.33 3.36 3.97 2.72

0.67 0.62 0.49

-400.12 0.08 -90.92

-27.13 0.01 -6.16

Observed 1-day forecast 2-day forecast 3-day forecast

Precipitation Forecasts – Saalach 1999

0

100

200

60 20 -20

500

observed forecasted

Observed

-60

daily Precipitation (mm)

1000 1500

1 day- Forecast

0

accum. Rain (mm)

1 day- Forecast

300

Forecasted 0

Correlation of daily data: 0.67 100

Time (d)

200

60 -20

20

Observed

-60

daily Precipitation (mm)

1000 1500 500 0

accum. Rain (mm)

2 day- Forecast

observed forecasted

100

300

Forecasted 0

Correlation of daily data: 0.62 100

Time (d)

Time (d)

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300

60 -20

20

Observed

-60

daily Precipitation (mm)

1000 1500 500

accum. Rain (mm)

0

200

300

3 day- Forecast

observed forecasted

100

200 Time (d)

3 day- Forecast

0

300

Time (d)

2 day- Forecast

0

200

Forecasted 0

Correlation of daily data: 0.49 100

200

300

Time (d)

25

20 30 40 50 60

10

15

q observed q simulated Surface Runoff Interflow Baseflow Accum. Evapotranspiration Precip. + Snowmelt

0

5

Spec. Discharge [mm]

20

400 200 100 0

Accum. Evapotranspiration

25

0 10

Precip. + Snowmelt [mm/d]

Saalach 1999

0

100

200

300

Time [d]

5

10

15

20

25

q observed real time computation forecast tail

0

Spec. Discharge (mm)

30

Precip. and Temp. forecasts of ECMWF

0

50

100

150

200

Time (d)

25 5

10

15

20

q observed real time computation forecast tail

0

Spec. Discharge (mm)

30

No use of meteorol. forecasts

0

50

100

150

200

Time (d)

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4500

Prognosepegel Greifenstein Prognose mit Standardabweichung

3000 1500

2000

2500

Abfluss

3500

4000

Prognose Konf.grenze

06/30/99

07/06/99

07/12/99

07/18/99

07/24/99

07/30/99

Tage

Regression model: Forecasts (red) and 75%-confidence limits (blue).

Conclusions and Résumé Selected Methods: • For mean term predictions (4 days) no alternatives to meteorological forecasts exist. • Extreme meteorological situations need a strong emphasis on physically based concepts. • Some model improvements by spatio - temporal error models. Organisational perspective: • Interdisciplinary approach (hydrology, meteorology, economy). • Expert decisions still recommended (for extreme events) to evaluate and weighing different model results. • High pressure of customer and immediate response (feed back).

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7.1.1 Wettervorhersagen / Sources of weather forecasts

7.1.1.1 Quellen Wettervorhersagen werden in Österreich von einer Anzahl staatlicher und privater Stellen erstellt und verbreitet. In diesem Bericht wird das Hauptaugenmerk auf die Prognosen des nationalen Wetterdienstes, der ZAMG, gelegt. Zentralanstalt für Meteorologie und Geo-dynamik (ZAMG): Die ZAMG ist der natio-nale Wetterdienst Österreichs, der für Vorhersagen für die Allgemeinheit zuständig ist. Die Vorher-sagen der ZAMG werden daher in diesem Bericht noch genauer diskutiert. Die ZAMG betreibt ein umfangreiches Stationsnetz. Davon sind mehr als 130 Stationen online und melden im 10-Minuten-Abstand alle rele-van-ten meteorologischen Daten an die ZAMG - Zentrale in Wien. Die ZAMG ist der öster-reichische Vertreter beim ECMWF (s.u.) und besitzt die Infrastruktur zur Aufbereitung der ECMWF-Daten. Diese aufbereiteten Er-geb-nisse werden der ACG (s.u.) und dem Militär-wetterdienst sowie den Universitäts-instituten auf Basis von Kooperations-ab-kom-men zur Verfügung gestellt. Die ZAMG be-treibt im Rahmen einer internationalen Koope-ration (ALADIN LACE) ein eigenes meso-skaliges Vorhersagemodell, ALADIN Vienna. Online-Informationen sind für die Öffentlichkeit auf der Homepage der ZAMG (http://www.zamg.ac.at/) verfügbar. Zur Abrundung der Information werden auch die anderen möglichen Quellen für Wetter-vorhersagen in Österreich kurz beschrieben: – Flugwetterdienst der Austrocontrol GesmbH (ACG, ehem. Bundesamt für Zivilluftfahrt): Der Flugwetterdienst ist ein aus der Bundesverwaltung ausgeglie-derter, staatlicher Wetterdienst, dessen Zu-stän-digkeit aber auf die Zivilluftfahrt be-schränkt ist. Er arbeitet mit der ZAMG zusam-men, und es gibt eine Aufgaben-tei-lung in manchen Bereichen. Der Flug-wetter-dienst betreibt auch eigene Wetter-sta-tionen (METAR) sowie das Wetter-radar-Netz Österreichs (der praktische Betrieb und die Datenarchivierung wurden aller-dings an das Institut für Nachrichten-technik und Wellenausbreitung an der TU Graz ver-ge-ben). Einige online - Infor-ma-tio-nen wer-den der allgemeinen Öffentlich-keit unter http://www.austrocontrol.co.at/main.php zur Verfü-gung gestellt.

7.1.1

Militärwetterdienst: Der Wetterdienst des Bundesheeres betreut primär den militä-rischen Flugbetrieb.



Wetterredaktionen des ORF: Sowohl Radio als auch Fernsehen haben eine eige-ne Wetterredaktion in Wien. Teil-weise be-schäftigen auch die Landes-studios Mete-orologen für die Wetter-sendungen im Rah-men von "Bundesland heute". Die Wet-ter-redaktionen sind teils mit ausge-bildeten MeteorologInnen, teils mit Journa-listInnen besetzt; auch Studien-abbrecher-Innen sind dort tätig. Ihre Aufgabe ist es, auf der Basis der Prognosen und Vorhersageunterlagen (Wetterkarten, Wettermeldungen, Satel-li-ten-bilder, etc.) der ZAMG eine journalis-tisch aufbereitete Darstellung des gegen-wärtigen und zukünftig erwarteten Wetters für die Präsentation im Rundfunk, Fern-sehen und in ORF - online (http://wetter.orf.at) vorzubereiten, und diese zu präsentieren.



Private Wetterfirmen: In Österreich sind auch private Firmen tätig, die an Kunden (elektronische und Printmedien, sowie auch andere Nutzer ähnlich denen der ZAMG) Wetterinformationen einschließ-lich Vor-hersagen abgeben. In der Regel be-schäf-tigen sie auch MeteorologInnen. Ihre Daten-grundlagen unterscheiden sich von jener der ZAMG, und sie erstellen ihre Prog-nosen unabhängig von den staatlichen Wetter-diensten. Daher kön-nen diese auch von-einander abweichen. Der Sitz dieser Firmen kann im Inland, aber auch im Ausland liegen.



Medien: Wie bereits ausgeführt, lassen sich Privatmedien (Zeitungen, Privat-radios, OnlinePortale) von der ZAMG oder priva-ten Wetterfirmen Produkte (Wetter-meldun-gen und vorhersagen, Satelliten-bilder etc.) liefern, die sie dann – in der Regel ohne eigene Bearbeitung – veröffentlichen.



WorldWideWeb: Die Menge an meteo-rolo-gischer Information, die allen Interessierten im WWW zugänglich ist, ist kaum mehr überschaubar. http://www.meteorologie.at/oegmlinks.html findet sich eine Zusam-menstellung der wichtigsten Links für Österreich sowie von Linksammlungen im deutsch-sprachigen Bereich.

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