816.336 integrated flood risk management · • flood warning systems are important instruments of...

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Institut für Wasserwirtschaft, Hydrologieund Konstruktiven Wasserbau

Vorstand: Prof. H.P. Nachtnebel Universität für Bodenkultur Wien

816.336 Integrated Flood Risk Management

2nd UnitH.P. Nachtnebel, H. Habersack, H. Holzmann

Content

Content Date Time Lecturer Content 27. 11. 07 9 – 11 h Habersack Hazard mapping, flood properties (depth, velocity) 29. 11. 07 9 – 11 h Holzmann Flood forecast techniques (meteorological

forecasts) 4. 12. 07 9 – 11 h Habersack Flood damages (sediment, debris) and mitigation

measures 6. 12. 07 9 – 11 h Habersack Flood management (public participation, security

measures) 11. 12. 07 9 – 11 h Holzmann Rainfall runoff models, statistical models 13. 12. 07 9 – 11 h Holzmann Updating procedures, operational data demands 18. 12. 07 9 – 11 h Nachtnebel Risk, Integrated Flood Management 8. 1. 08 9 – 11 h Nachtnebel Loss Analysis 10. 1. 08 9 – 11 h Nachtnebel River related management and Hazard reduction 15. 1. 08 9 – 11 h Nachtnebel Flood protection measures (dams, retention basins) 17. 1. 08 9 – 11 h Reservetermin 22. 1. 08 9 – 10 h Prüfungstermin (optional) 24. 1. 08 9 – 10 h Prüfungstermin (optional) 29. 1 08 9 – 10 h Prüfungstermin (optional) 31. 1. 08 9 – 10 h Prüfungstermin (optional)

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Introduction

Aim of courseProviding 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.

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

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

From ISDR, 2005

Elements of Risk Management

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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. Nonstructuralmitigation 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#4Protect Your Property from FloodingBuild 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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From ISDR, 2005

Disaster Risk Reduction

Hydrological forecastingand flood riskmanagement

Institut für Wasserwirtschaft, Hydrologieund 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])

Risikomanagement und NaturgefahrenBOKU Kongress - Wien, November 2001

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Situation

• Increasing Number of FloodsOder, 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

Loss development of the last 50 years

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Flood Damages

Flood Warning Principles

Upstream Gauge:- Flood Routing- Statistical Methods

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

Weather Forecasts:- Weather Models- Rainfall-Runoff Modelling- Snow Melt Modelling- Flood Routing

1h - days

1h - 12h

3h - 3 days

Time t

Runoff Q (m3/s)

Threshold


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Statistical Methods:Predictors are upstream runoff data, rainfall, air temperature or soil moisture dataData are available online.

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

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

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

Meteorological Forecasts:Distribution of continental Air Temperature, Humidity and Air pressure.

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


Forecast Methods

Schneeschmelze undSchneeakkumulation

Schneeakkumulation:

If Ti < O oC wobei Ti ... mittl. Tageslufttemperatur der Höhenstufe i(gemäß Temperaturgradient)

Durch die Schneeakkumulation reduziert sich der abflußwirksame Niederschlaggemäß dem flächengewichteten Anteil des Neuschnees.

Schneeschmelze:

If Ti > O oC qi = fak* Ti (Grad-Tag-verfahren)wobei qi den aktuellen, akkumulierten Schneespeicher nicht überschreitenkann.

.

bw1

Oberflächenabfluss f(bw, h1, k1)

NiederschlagSchneeschmelze

Zwischenabfluss f(bw1, h2, k2)

Versickerung f(bw1, h2, k3)

h1

h2

bw2Basisabfluss f(bw2, k4)

Oberflächenspeicher

Freies Bodenwasser

Pflanzenverfügbares Bodenwasser

Verdunstung

FK

PWP

Niederschlags-Abfluss Modell

Schneeakkumulation Tiroler Inn 1990 - 1991

Zeit (d)

Akk

. Sch

nee

in m

mW

aequ

.

0 200 400 600

010

020

030

040

050

0

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

Zeit (d)

Abflu

ss (

m3/

s)

0 20 40 60

02

46

810

Q beobachtetQ EchtzeitsimulationQ PrognoseQ zukuenftig

Snowmelt and Runoff

SchneeschmelzmodellSchneeakkumulation Tiroler Inn 1990 - 1991

Zeit (d)

Akk.

Sch

nee

in m

mW

aequ

.

0 200 400 600

010

020

030

040

050

0

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

bw1

Oberflächenabfluss f(bw, h1, k1)

NiederschlagSchneeschmelze

Zwischenabfluss f(bw1, h2, k2)

Versickerung f(bw1, h2, k3)

h1

h2

bw2Basisabfluss f(bw2, k4)

Oberflächenspeicher

Freies Bodenwasser

Pflanzenverfügbares Bodenwasser

Verdunstung

FK

PWP


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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 !!!


Data Management

Real time observationRainfall, Temperature, Runoff (incl. Forecasts)

Data Transmission to computer centerRadio- and telephone transmission

Data ProcessingTime Series, Preprocessing, Regionalisation

Runoff ComputationModels

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

Short term protection actionsMobile flood protectors, warnings, evacuations, etc.

Updating:Improving of forecasts by means of estimation error

No Flood

Flood


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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 within30 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.)


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 forecastsObserved 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

199919961979In operation since

222Runs per day

1h3h6 hTemp. resolution

48 hours48 hours10 daysLead Time

ALADIN-LACEARPEGE-Boundaries

313150Layers

10 km12 km 60 kmGrid space

Central EuropeEuropeglobalModel domain

Vienna, AutPrague, CZReading, UKOperat. centre

ALADIN-VIENNA

ALADIN-LACE

ECMWF

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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-Stunden-Zeitraum 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

goodmeanRainfall area big

(Front)

meanlittleRainfall area

small(Konvection)

Basin areabig

Basin areasmall

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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 ForecastAir 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.

Air temperature exhibits a certain range of persistence.

mCt o 100/7.0≅∆

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Process-oriented approach

• 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

T2m

Cloudiness

Soil

Advection Wind speed

From HAIDEN (2003)

T2m nowcasting error

Adjusted LAM skill > Climatology skill > LAM DMO skill

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

0 1 2 3 4 5 6 7 8 9 10 11 12Forecast time (h)

Mea

n ab

solu

te e

rror

(K)

Persistence

Climatology, adjusted

ALADIN DMO

ALADIN, adjusted

ALADIN, adjusted + cloud corr

From HAIDEN (2003)

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T2m error distribution during the first forecast hours

• Error mostly between –2 and +2 K• Occasional outliers with error of 3-6 K (non-Gaussian)

0

10

20

30

40

50

60

70

<-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 >+1

0

Forecast error (K)

Freq

uenc

y (%

)AVI5 +1hAVI5 +2hAVI5 +3hAVI5 +4h

From HAIDEN (2003)

Error characteristics

• Air mass change (frontal passage): timing problem• Amount/speed of evening cooling overestimated

11-20 March 2003

-10-8-6-4-202468

101214161820

11.03.2003 8:00

12.03.2003 8:00

13.03.2003 8:00

14.03.2003 8:00

15.03.2003 8:00

16.03.2003 9:00

17.03.2003 9:00

18.03.200310:00

19.03.200310:00

20.03.200310:00

Date

Tem

pera

ture

(C)

Observed4 hr forecastError

From HAIDEN (2003)

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Reduced leeside cooling

3-d high resolution (1 km) model necessary?Statistical correction?

From HAIDEN (2003)

Low stratus

• Temperature inversion too smooth• Inversion base too warm → cloudiness underestimated• Underestimated cloudiness → PBL cooling too weak

MODELOBS

From HAIDEN (2003)

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Low stratus 1-d experiments

Experiment I: Vertical diffusion + subsidence throughout PBL

00 UTC obs12 UTC obs12 UTC forecast

From HAIDEN (2003)

ZAMGPrognosemodul

KAMP (Vorversion)

Meteorologischer Teil des Prognosesystems - Status

Wetter-Radar (ACG)

Meteorologische Modelle

Stationsdaten ZAMG (TAWES-Messnetz)

Stationsdaten LAND NÖ / EVN

ALADIN ECMWF5 min

10 min

12 h12 h

Minicomputer

Minicomputer

Arbeitsstation

Prognoserechner:Hochwasserprognose-Programm

Rasterdaten: Niederschlag, Temperatur

Stationsdaten ZAMG

15 min

15 min

Minicomputer

/ 1 h

From HAIDEN (2004)

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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 subcatchments and runoff forecast gages.

Simulated Domain

Forecast Gages

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meteorological data: measurements and forecasts of rainfall and temperature at four altitudesZA

MG

BOKU Snowmelt and soil

moisture model

BOKU Linear Regression model

output:discharge forecasts at 13 stationstime step: one daycalculated 4 times/day4 days ahead

TU W

ien HYSIM - flood routing and

rainfall-runoff model

output: discharge forecasts at 23 stationstime step: one hour24 (30) hours ahead

BOKU Rainfall-runoff

model

combination of different model results to one single forecast

TU W

MULTIPLE LINEAR REGRESSION:

LEGEND:

Forecast Gauge FG

Reference Gauge RGPrecipitation PSoil Moisture Accounting SMA

Snow Melt SM

RG1RG2

RG3

RG4

RG5

P1

P2

P3

P4

FG

SUBBASIN 1

SUBBASIN 2

SMA1SM1

SMA2

∑∑∑∑∑∑∑∑ −⋅+−⋅+−⋅+−⋅=∆+n j

nnm j

mmk j

kki j

iRGiFG jtdSMdjtdSMAcjtPbjtdQattdQ )()()()()( ,

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Scheme of the soil moisture accounting module.

bw1

Surfac Runoff f(bw1, h1, k1)

Rainfall

Interflow f(bw1, h2, k2)

Percolation f(bw1, h2, k3)

h1

h2

bw2Baseflow f(bw2, k4)

Surface Storage

Mobile Soil Water

Plant Available Soil Water

Evapotranspiration

FC

PV

WPResidual Soil Water

ReferenceTemperature

T4

T3

T2

T1

A1 A2 A3 A4

Snow Melt and Snow Accumulation

Snow Accumulation:

If Ti < Tmelt,koC

where Ti ... mean, daily air temperature of layer i.Tmelt,k ... threshold temperature of day k

Snow accumulation reduces the net rainfall proportional to the wheigted area of layer contribution.

Snow Melt:

If Ti > Tmelt,koC

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:

Model variance:

Input variance:

Total confidence limits:

∑∑∑ ⋅+⋅+⋅= GNCdQBCAdQCY kjiˆ

( ) ( )( )01

01ˆvar XXXXMSEY M−′′+⋅=

( ) ( ) ( ) ( )∑∑∑ ⋅+⋅+⋅= progkprogjjD GNCQBCQBCY varvarvarˆvar 22

21

21

( ) ( )( )DM YYFGtY ˆvarˆvar2

100,ˆ +⋅⎟⎠⎞

⎜⎝⎛ −=∆

α

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Performance of meteorological forecasts

Table 1: Statistical analysis of the residuals of the forecasted air temperature data.250 m Sl. 750 m Sl. 1500 m Sl. 2500 m Sl.

Mean Stadev Correl Mean Stadev Correl Mean Stadev Correl Mean Stadev Correl

1-day forecast -3.01 1.92 0.97 -2.75 2.4 0.95 -0.11 1.22 0.99 -0.68 1.62 0.972-day forecast -2.38 2.01 0.97 -2.39 2.39 0.95 -0.05 1.19 0.99 -0.71 1.46 0.983-day forecast -2.42 2.11 0.96 -2.42 2.46 0.95 -0.07 1.39 0.98 -0.73 1.61 0.98

Table 2: Statistical analysis of observed and forecasted rainfall data.Maximum Mean Stand.Dev. Skew Correlation Sum Error

(mm)Sum Error

(%)Observed 42.3 4.06 6.24 2.33

1-day forecast 38.5 2.96 4.3 3.36 0.67 -400.12 -27.132-day forecast 61.2 4.06 5.85 3.97 0.62 0.08 0.013-day forecast 39.6 3.81 5.09 2.72 0.49 -90.92 -6.16

Precipitation Forecasts – Saalach 19991 day- Forecast

Time (d)

accu

m. R

ain

(mm

)

0 100 200 300

050

010

0015

00

observedforecasted

1 day- Forecast

Time (d)

daily

Pre

cipi

tatio

n (m

m)

0 100 200 300

-60

-20

2060

Observed

Forecasted Correlation of daily data: 0.67

2 day- Forecast

Time (d)

accu

m. R

ain

(mm

)

0 100 200 300

050

010

0015

00

observedforecasted

2 day- Forecast

Time (d)

daily

Pre

cipi

tatio

n (m

m)

0 100 200 300

-60

-20

2060

Observed


3 day- Forecast

Time (d)

accu

m. R

ain

(mm

)

0 100 200 300

050

010

0015

00

observedforecasted

3 day- Forecast

Time (d)

daily

Pre

cipi

tatio

n (m

m)

0 100 200 300

-60

-20

2060

Observed


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Saalach 1999

Time [d]

Spe

c. D

isch

arge

[m

m]

0 100 200 300

05

1015

2025

q observedq simulatedSurface RunoffInterflowBaseflowAccum. EvapotranspirationPrecip. + Snowmelt

010

020

040

0

Acc

um. E

vapo

trans

pira

tion

60

50

40

30

20

10

0

Pre

cip.

+ S

now

mel

t [m

m/d

]

Precip. and Temp. forecasts of ECMWF

Time (d)

Spec

. Dis

char

ge (

mm

)

0 50 100 150 200

05

1015

2025

30

q observedreal time computationforecast tail

No use of meteorol. forecasts

Time (d)

Spec

. Dis

char

ge (

mm

)

0 50 100 150 200

05

1015

2025

30

q observedreal time computationforecast tail

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Tage

Abf

luss

1500

2000

2500

3000

3500

4000

4500

06/30/99 07/06/99 07/12/99 07/18/99 07/24/99 07/30/99

PrognoseKonf.grenze

Prognosepegel Greifenstein Prognose mit Standardabweichung

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

Conclusions and RésuméSelected 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-wetterdienstsowie 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-rischenFlugbetrieb.

– 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-tischaufbereitete 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-nosenunabhängig 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. http://www.meteorologie.at/oegmlinks.html findetsich eine Zusam-menstellung der wichtigsten Links für Österreich sowie von Linksammlungen im deutsch-sprachigen Bereich.

816.336 integrated flood risk management · • flood warning systems are important instruments of...

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