Integrated Flood Risk Management

Transkript

1 Institut für Wasserwirtschaft, Hydrologie und Konstruktiven Wasserbau Vorstand: Prof. H.P. Nachtnebel Universität für Bodenkultur Wien Integrated Flood Risk Management 2 nd Unit H.P. Nachtnebel, H. Habersack, H. Holzmann Content Content Date Time Lecturer Content h Habersack Hazard mapping, flood properties (depth, velocity) h Holzmann Flood forecast techniques (meteorological forecasts) h Habersack Flood damages (sediment, debris) and mitigation measures h Habersack Flood management (public participation, security measures) h Holzmann Rainfall runoff models, statistical models h Holzmann Updating procedures, operational data demands h Nachtnebel Risk, Integrated Flood Management h Nachtnebel Loss Analysis h Nachtnebel River related management and Hazard reduction h Nachtnebel Flood protection measures (dams, retention basins) h Reservetermin h Prüfungstermin (optional) h Prüfungstermin (optional) h Prüfungstermin (optional) h Prüfungstermin (optional) BOKU Kongress 1

2 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: International Glossary of Hydrology (from UNESCO) Elements of Risk Management From ISDR, 2005 BOKU Kongress 2

3 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: 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) BOKU Kongress 3

4 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 ( BOKU Kongress - Wien, November 2001 Risikomanagement und Naturgefahren BOKU Kongress 4

5 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 BOKU Kongress - Wien, November 2001 Risikomanagement und Naturgefahren Loss development of the last 50 years BOKU Kongress 5

6 Flood Damages BOKU Kongress - Wien, November 2001 Risikomanagement und Naturgefahren Flood Warning Principles Runoff Q (m 3 /s) Threshold Time t 1h - days Upstream Gauge: - Flood Routing - Statistical Methods 1h - 12h Rainfall : - Rainfall-Runoff Modelling - Snow Melt Modelling - Flood Routing 3h - 3 days Weather Forecasts: - Weather Models - Rainfall-Runoff Modelling - Snow Melt Modelling - Flood Routing BOKU Kongress - Wien, November 2001 Risikomanagement und Naturgefahren BOKU Kongress 6

7 Forecast Methods Statistical Methods: Predictors are upstream runoff data, rainfall, air temperature or soil moisture data Data are available online. Rainfall-Runoff Models: Rainfall data are used as online model input. The lead time corresponds to the runoff formation and translation time) Meteorological Forecasts: Distribution of continental Air Temperature, Humidity and Air pressure. (Multiple) Regression Cross Correlation Markov Processes Bayesian Methods Kalman Filter Techniques Event based models Continuous Models Deterministic Models Conceptual Models Snowmelt and Snow accumulation Models ECMWF (Reading) ALADIN (LAM) + RR-Modelling BOKU Kongress - Wien, November 2001 Risikomanagement und Naturgefahren Snowmelt and Runoff Niederschlag Schneeschmelzmodell Verdunstung Schneeakkumulation Schneeschmelze Tiroler Inn Schneeakkumulation Tiroler Inn Oberflächenspeicher Niederschlags-Abfluss Verdunstung Modell Oberflächenspeicher Freies Bodenwasser Pflanzenverfügbares Bodenwasser bw1 Akk. Schnee in mmwaequ. Freies Bodenwasser bw Pflanzenverfügbares Bodenwasser FK PWP bw1 Niederschlag Schneeschmelze h1 h2 Versickerung f(bw1, h2, k3) Oberflächenabfluss f(bw, h1, k1) Zwischenabfluss f(bw1, h2, k2) Basisabfluss f(bw2, k4) Hoehenzone m.sh Hoehenzone m.sh Hoehenzone m.sh Hoehenzone m.sh Hoehenzone m.sh Hoehenzone m.sh Akk. Schnee in mmwaequ Hoehenzone m.sh Hoehenzone m.sh Hoehenzone m.sh Schneeschmelze und Schneeakkumulation Oberflächenabfluss f(bw, h1, k1) Schneeakkumulation: h1 If T i < O o C wobei Ti... mittl. Tageslufttemperatur der Höhenstufe i (gemäß Temperaturgradient) Zwischenabfluss f(bw1, h2, k2) Durch die Schneeakkumulation reduziert sich der abflußwirksame Niederschlag gemäß dem 0 flächengewichteten 200 Anteil des Neuschnees Zeit (d) h2 Schneeschmelze: Abfluss (m3/s) Q Prognose If T i > O o Q zukuenftig C Versickerung q i = fak* f(bw1, T i h2, (Grad-Tag-verfahren) k3) wobei qi den aktuellen, akkumulierten Schneespeicher nicht überschreiten kann.. Zeit (d) Q beobachtet Q Echtzeitsimulation Hoehenzone m.sh Hoehenzone m.sh Hoehenzone m.sh bw2 FK PWP Basisabfluss f(bw2, k4) Zeit (d) BOKU Kongress - Wien, November 2001 Risikomanagement und Naturgefahren BOKU Kongress 7

8 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 Real time observation Rainfall, Temperature, Runoff (incl. Forecasts) Data Transmission to computer center Radio- and telephone transmission Data Management No Flood Flood Data Processing Time Series, Preprocessing, Regionalisation Runoff Computation Models Updating: Improving of forecasts by means of estimation error 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. BOKU Kongress - Wien, November 2001 Risikomanagement und Naturgefahren BOKU Kongress 8

9 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.) BOKU Kongress - Wien, November 2001 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. BOKU Kongress 9

10 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 BOKU Kongress 10

11 Rainfall Gauges in Austria by ZAMG Meteosat Infrarot Satellitenbild vom , 00 Uhr UTC (Quelle: Berliner Wetter-karte, FU Berlin, 2002). Nach Steinacker (2002). BOKU Kongress 11

12 Räuml. Niederschlagsstruktur im Niederschlagsradar-Bild vom , 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 ECMWF ALADIN- LACE ALADIN- VIENNA Operat. centre Reading, UK Prague, CZ Vienna, Aut Model domain global Europe Central Europe Grid space 60 km 12 km 10 km Layers Boundaries - ARPEGE ALADIN-LACE Lead Time 10 days 48 hours 48 hours Temp. resolution 6 h 3h 1h Runs per day In operation since BOKU Kongress 12

13 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. BOKU Kongress 13

14 ECMWF Images: 500 mb heights (in color contours) and sea level pressure (in white line contours) BOKU Kongress 14

15 Vom ECMWF-Modell vorhergesagte Niederschlagsverteilung in Österreich und Umgebung für den 6-Stunden- Zeitraum /18-24 UTC, für Ausgangslagen vom 2.8. bis , 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 Rainfall area small (Konvection) Rainfall area big (Front) Basin area small little mean Basin area big mean good BOKU Kongress 15

16 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. BOKU Kongress 16

17 Process-oriented approach Cloudiness Advection T2m Wind speed 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 Mean absolute error (K) 4,0 3,5 3,0 2,5 2,0 1,5 1,0 Persistence Climatology, adjusted ALADIN DMO ALADIN, adjusted ALADIN, adjusted + cloud corr 0,5 0, Forecast time (h) Adjusted LAM skill > Climatology skill > LAM DMO skill From HAIDEN (2003) BOKU Kongress 17

18 T2m error distribution during the first forecast hours AVI5 +1h AVI5 +2h AVI5 +3h AVI5 +4h Frequency (%) < Forecast error (K) >+10 Error mostly between 2 and +2 K Occasional outliers with error of 3-6 K (non-gaussian) From HAIDEN (2003) Error characteristics Temperature (C) :00 8: :00 Observed 4 hr forecast Error : March :00 9:00 Date : : : :00 Air mass change (frontal passage): timing problem Amount/speed of evening cooling overestimated From HAIDEN (2003) BOKU Kongress 18

19 Reduced leeside cooling 3-d high resolution (1 km) model necessary? Statistical correction? From HAIDEN (2003) Low stratus MODEL OBS Temperature inversion too smooth Inversion base too warm cloudiness underestimated Underestimated cloudiness PBL cooling too weak From HAIDEN (2003) BOKU Kongress 19

20 Low stratus 1-d experiments Experiment I: Vertical diffusion + subsidence throughout PBL 00 UTC obs 12 UTC obs 12 UTC forecast From HAIDEN (2003) Meteorologischer Teil des Prognosesystems - Status Meteorologische Modelle Stationsdaten ZAMG (TAWES-Messnetz) Stationsdaten LAND NÖ / EVN 10 min 15 min 5 min ALADIN ECMWF Wetter-Radar (ACG) 12 h 12 h ZAMG Prognosemodul KAMP (Vorversion) Minicomputer 15 min / 1 h Arbeitsstation Stationsdaten ZAMG Minicomputer Minicomputer Prognoserechner: Hochwasserprognose-Programm Rasterdaten: Niederschlag, Temperatur From HAIDEN (2004) BOKU Kongress 20

21 Mean term runoff forecast modeling by means of meteorological forecast data H. Holzmann 1), H.P. Nachtnebel 1) and M. Bachhiesl 2) 1) Department for Water Management, Hydrology and Hydraulic Engineering 1) University for Agricultural Sciences BOKU Vienna 2) Austrian Verbund AG Simulated Domain Forecast Gages Simulated subcatchments and runoff forecast gages. BOKU Kongress 21

22 ZAMG meteorological data: measurements and forecasts of rainfall and temperature at four altitudes BOKU Snowmelt and soil moisture model BOKU Rainfall-runoff model BOKU Linear Regression model output: discharge forecasts at 13 stations time step: one day calculated 4 times/day 4 days ahead TU Wien HYSIM - flood routing and rainfall-runoff model output: discharge forecasts at 23 stations time step: one hour 24 (30) hours ahead TU W combination of different model results to one single forecast SUBBASIN 2 P4 SMA2 RG2 RG5 RG1 RG3 P1 FG SUBBASIN 1 P2 LEGEND: P3 RG4 SM1 SMA1 Forecast Gauge FG Reference Gauge RG Precipitation P Soil Moisture Accounting SMA Snow Melt SM MULTIPLE LINEAR REGRESSION: dqfg( t + t) = ai dqrg, i( t j) + i j k j b P ( t j) + k k m j c dsma ( t j) + m m n j d dsm ( t j) n n BOKU Kongress 22

23 Scheme of the soil moisture accounting module. Evapotranspiration Rainfall Surface Storage PV Surfac Runoff f(bw1, h1, k1) Mobile Soil Water bw1 FC h1 Interflow f(bw1, h2, k2) Plant Available Soil Water Residual Soil Water WP h2 Percolation f(bw1, h2, k3) bw2 Baseflow f(bw2, k4) T4 Snow Melt and Snow Accumulation Snow Accumulation: If T i < T melt,k o C T3 where T i... mean, daily air temperature of layer i. T melt,k... threshold temperature of day k Snow accumulation reduces the net rainfall proportional to the wheigted area of layer contribution. A1 A2 A3 A4 T2 Reference Temperature T1 Snow Melt: If T i > T melt,k o C q i = fak k * T i (Day Degree Method) where q i... specific discharge fak k... snowmelt factor of day k q i.cannot exceed the accumulated snow water equivalent. BOKU Kongress 23

24 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: Model variance: Input variance: Yˆ = C dqa + C dqb + C GN var var i ( ˆ 1 ) = MSE ( 1+ X ( X X ) X ) Y M Total confidence limits: Yˆ α = t FG,100 var Yˆ M + var Yˆ 2 j 0 0 ( Yˆ ) D = C j1 var( QB) + C j1 var( QB prog ) + Ck 2 var( GN prog ) ( ( ) ( ) ) k D BOKU Kongress 24

25 Performance of meteorological forecasts Table 1: Statistical analysis of the residuals of the forecasted air temperature data. 250 m Sl. 750 m Sl m Sl m Sl. Mean Stadev Correl Mean Stadev Correl Mean Stadev Correl Mean Stadev Correl 1-day forecast day forecast day forecast Table 2: Statistical analysis of observed and forecasted rainfall data. Maximum Mean Stand.Dev. Skew Correlation Sum Error (mm) Sum Error (%) Observed day forecast day forecast day forecast Precipitation Forecasts Saalach day- Forecast 1 day- Forecast accum. Rain (mm) observed forecasted daily Precipitation (mm) Observed Forecasted Correlation of daily data: Time (d) Time (d) 2 day- Forecast 2 day- Forecast accum. Rain (mm) observed forecasted daily Precipitation (mm) Observed Forecasted Correlation of daily data: Time (d) Time (d) 3 day- Forecast 3 day- Forecast accum. Rain (mm) observed forecasted daily Precipitation (mm) Observed Forecasted Correlation of daily data: Time (d) Time (d) BOKU Kongress 25

26 Saalach 1999 Accum. Evapotranspiration Spec. Discharge [mm] q observed q simulated Surface Runoff Interflow Baseflow Accum. Evapotranspiration Precip. + Snowmelt Precip. + Snowmelt [mm/d] Time [d] Precip. and Temp. forecasts of ECMWF Spec. Discharge (mm) q observed real time computation forecast tail Time (d) No use of meteorol. forecasts Spec. Discharge (mm) q observed real time computation forecast tail Time (d) BOKU Kongress 26

27 Prognosepegel Greifenstein Prognose mit Standardabweichung Abfluss 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). BOKU Kongress 27

28 7.1.1 Wettervorhersagen / Sources of weather forecasts 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 ( 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 zur Verfü-gung gestellt 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 ( 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, Online- Portale) 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. findet sich eine Zusam-menstellung der wichtigsten Links für Österreich sowie von Linksammlungen im deutsch-sprachigen Bereich. BOKU Kongress 28