816.336 Integrated Flood Risk Management - iwhw.boku.ac.at fileBOKU Kongress 2 RR-Models Rainfall...

BOKU Kongress 1

Institut für Wasserwirtschaft, Hydrologieund Konstruktiven Wasserbau

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

816.336 Integrated Flood Risk Management

5th UnitRainfall Runoff Models for Forecasts

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 Holzmann Rainfall runoff models, statistical models 11. 12. 07 9 – 11 h Holzmann Updating procedures, operational data demands 13. 12. 07 9 – 11 h Habersack Flood management (public participation, security

measures) 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)

BOKU Kongress 2

RR-Models

Rainfall Runoff ModelsRR-Models use (areal) rainfall data and transform them to discharge rates at specific river locations (gauge). The model considers the relevant processes like

- rainfall retention (loss),

- runoff formation and

- runoff propagation.

Some RR-models also deal with snowmelt processes and integrate the snowmelt rates into the model input.

Rainfall - Runoff

(Aus KOVAR, 2001)

Niederschlag - Wasserstand Mittereggbach

August bis Oktober 1995

0

10

20

30

40

50

01.08. 06.08. 11.08. 16.08. 21.08. 26.08. 31.08. 05.09. 10.09. 15.09. 20.09. 25.09. 30.09. 05.10. 10.10. 15.10. 20.10. 25.10. 30.10.

Niederschlag [mm/h]

0

50

100

150

200

250

300

350

400

450

500

01.08. 06.08. 11.08. 16.08. 21.08. 26.08. 31.08. 05.09. 10.09. 15.09. 20.09. 25.09. 30.09. 05.10. 10.10. 15.10. 20.10. 25.10. 30.10.

Pegel [mm]

NS-Freifläche Pegel

Abfluss Mittereggbach :

BOKU Kongress 3

Water BalancePhysical Processes involved in Runoff

Generation

From http://snobear.colorado.edu/IntroHydro/geog_hydro.html

Runoff Formation

Hillslope

Rainfall R

Infiltration INF

Surface Runoff Q

Temporal sequenceof infiltration and surface runoffZeitliche Abfolgevon Infiltration und Oberflächenabfluss

INF ≥ R:- Initial phase of rainfall event- mean to high conductivity- high rate of subsurface drainage

R ≥ INF ≥ Q:- mean phase of rainfall event- mean conductivity- mean rate of subsurface drainage

R ≥ Q ≥ INF:- Final phase of rainfall event- mean to low conductivity- mean rate of subsurface drainage

R ≥ Q :- Final phase of rainfall event- low conductivity- restricted subsurface drainage- saturation of soils

BOKU Kongress 4

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

]

Classification of RR-Models

Black Box Model Physical based Model

Lumped Model Distributed Model

Stochastic Model Deterministic Model

Event based Model Continuous Model

Statistical Model Conceptual Model

BOKU Kongress 5

Description of RR-Models

(a) Black Box Models:These type of models contain mathematical (not physically based) transfer functions to relate input to output. Successful approaches are the unit hydrograph, extreme frequency analysis and regression analysis. Shortcoming are due to the exterpolation of extreme events and in the assumption of linear processes.

(b) Conceptual Models:These models use simple representations of the dominant processes by means of linear or nonlinear reservoirs. The source of the assumed non-linearity are the soil moisture conditions. The parameters of conceptual models can be physically based (e.g. TOPMODEL).

(c) Deterministic Models:These models are based on physical theory and generally have huge demand of data. Therefore much effort has to be done for model development, calibration and operation. They can be sufficiently applied for estimation of human impact assessment to the hydrological system response.

(d) Stochastic Models:These models use parameters, which are random variables and are defined in terms of probability distribution functions. This means, that the simulated results are also distributed. The randomness can be related to temporal and spatial variability.

Description of RR-Models

(e) Hybrid Models:These type of models include components of (c) and (d). They use a system approach to the basic basin response and combine it with stochastic time series parameterisation.

(f) Distributed Models:This type of model is physically based on spatially distributed input and system parameters. They require the specification of descriptive equations for the hydrological sub-processes to be considered. The solutions for the physical equations are made numerically. This requires spatio-temporal discretisation of the domain.

(g) Lumped Models.Lumped models do not use spatial distributions of parameters but aggregates them to mean representative values.

(h) Statistical Models.They use statistical relations between input and output. E.g. (Multiple) regression models, ARIMA models, Markov chain models, etc. The basic components of such models are trend, periodicity and persistence (autocorrelative term). Some stochastic terms can be included.

BOKU Kongress 6

Real time runoff simulation8 10 2002 - 8 19 2002

Julian. Tag

Nie

ders

chla

g (m

m)

15562 15564 15566 15568 15570

02

46

810

Julian. Tag

Abf

luss

(m3/

s)

15562 15564 15566 15568 15570

020

4060

8010

0

WORKSHOP ON DISASTER PREVENTION AND REDUCTION Prague, 21. – 29. 6. 2006

8 10 2002 - 8 19 2002

Julian. Tag

Nie

ders

chla

g (m

m)

15562 15564 15566 15568 15570

02

46

810

Julian. Tag

Abf

luss

(m3/

s)

15562 15564 15566 15568 15570

020

4060

8010

0


Real time runoff simulation

BOKU Kongress 7

WORKSHOP ON DISASTER PREVENTION AND REDUCTION Prague, 27.6. – 2.7 2005WORKSHOP ON DISASTER PREVENTION AND REDUCTION Prague, 21. – 29. 6. 2006

8 10 2002 - 8 19 2002

Julian. Tag

Nie

ders

chla

g (m

m)

15562 15564 15566 15568 15570

02

46

810

Julian. Tag

Abf

luss

(m3/

s)

15562 15564 15566 15568 15570

020

4060

8010

0

Real time runoff simulation

Forecast domainPrecipitation

Runoff

Realtime Nowcast LA-Model (ALADIN)


BOKU Kongress 8

EnsemblemodellPrecipitation

Runoff

Realtime Nowcast LA-Model (ALADIN)


Differences between real time rainfall runoff modelling and runoff forecasting

Real time RR-modellingPast and present data availability of the storm (precipitation, discharge)

Runoff response due to recent rain

Lead time is the runoff formation time (time of concentration).

Estimation error of spatial rain distribution patterns (interpolation error)

Flood forecastingEstimation of precipitation and discharge for the total storm event

Runoff response considers total event

Lead time is the forecast period

Error in forecast data


BOKU Kongress 9

Methods

Statistical Models

Predicted Values e.g.:Peak discharge, peak occurrence, Water level,

Predictors are e.g.:Observed rainfall, forecasted rainfall, accumulated and temporalrainfall, rainfall intensities, upstream discharge, observed or modelled soil moisture, wetness index, snowmelt, …

Methods:(Multiple) linear / nonlinear regressionsMixed models (statistical and deterministical model)


Methods

Statistical Models

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

MULTIPLE LINEAR REGRESSION:

∑∑∑∑∑∑∑∑ −⋅+−⋅+−⋅+−⋅=∆+n j

nnm j

mmk j

kki j

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


BOKU Kongress 10

Methods

Regression model withAPI (Antecedent precipitation Index)as predictor

RainfallRain IndexRunoff

∆−⋅+= ii APIbaqwhere q ... specific discharge

i ... time indexAPI ... antecedent precipitation indexa, b ... regression coefficients


Methods

)(1i

nt

ti

int

ti

ii Pa

aARI ⋅⋅= ∑

∑

−

=−

=

(1)

were i … Time index (in days) a … coefficient (=0.88) P … Precipitation (plus snowmelt

… optional) in mm/d n … memory length in days (=28)

Antecedent Precipitation Index API

time

t

t-i

t-j

P1

P2

2 Parametersn ... Memory lengtha ... Recession coefficient


BOKU Kongress 11

Methods

1.Jan 1.Mar 1.Mai 1.Jul 1.Sep 1.Nov 31.Dec

0

500

1000

1500

Enns (ARI Rain) - Validation 1996D

isch

arge

(m3/

s)

Discharge (observed)Discharge (computed)Antecedent Rain Index

40

30

20

10

0

Ant

eced

ent R

ain

Inde

x [m

m/d

]


0

500

1000

1500

Enns (ARI Rain+Snowmelt) - Validation 1996

Dis

char

ge (m

3/s)

Discharge (observed)Discharge (computed)Antecedent Rain Index

40

30

20

10

0

Ant

eced

ent R

ain

Inde

x [m

m/d

]

Example 1: Regression model with antecedent rain index as predictor

RAINiicomp ARIQ ,, 33.4296.53 ⋅+=

SNOWMELTRAINiicomp ARIQ +⋅+= ,, 80.4744.36


Methods

Example 2: Regression model with accumulated rainfall and wetness index as predictor

tttt

ttqlent

RIdARcAPIbaqlen

ARcAPIbaq

⋅+⋅+⋅+=

⋅+⋅+=+

where q ... peak dischargeqlen ... transformation timet ... time indexAPI ... anecedent precip indexAR ... accumulated rainRI ... rainfall intensitya,b,c ... regression coefficients


BOKU Kongress 12

8 10 2002 - 8 19 2002

Julian. Tag

Nie

ders

chla

g (m

m)

15562 15564 15566 15568 15570

02

46

810

Julian. Tag

Abflu

ss (m

3/s)

15562 15564 15566 15568 15570

020

4060

8010

0

Statistisches Modell

Warnstufe 1

Warnstufe 2Warnstufe 3Warnstufe 4

tp

Qp


MethodsModel performance

Flood peaksFahrafeld / Triesting

Q observed (m3/s)

Q c

ompu

ted

(m3/

s)

0 50 100 150

050

100

150 Korrelation: 0.89

Jan - FebMar - AprMai - JunJul - AugSep - OktNov - Dez

Time

Peak

disc

harg

e(m

3/s)

050

100

150

200

250

03/17/1993 03/17/1994 03/17/1995 03/17/1996 03/17/1997 03/17/1998 03/17/1999 03/17/2000 03/17/2001

Warning Level 1

Warning Level 2

Warning Level 3

Warning Level 4

Q beobachtetQ berechnet


BOKU Kongress 13

Event based ModelsThis type of models considers single rainfall runoff events. The main task is the proper estimation of the initial condition of the system, which affect the separation of direct runoff and rainfall losses.

Examples are Unit Hydrograph Models or Statistical Models.

Start of RainfallTimeTime DiscretisationStart and end of Direct RunoffHydrographDirect RunoffBaseflowIntensity of Areal PrecipitationLoss RateRainfall ExcessVolume of direct runoffVolume of rainfall excessBasin area

Niederschlag 1 mm Fläche 1 km2 1 mm = 1 L/m2 = 106 L/ km2 = 1.000 m3/ km2

BOKU Kongress 14

Areal Precipitation

Thiessen Polygon Method

Inverse Distance Method

Coupling Spatial and Temporal Data

1 hour Nexrad Rainfallon the Guadalupe Basin

October 13, 2001

Source: Arc Hydro: GIS for Water Resources (modified)David R. MaidmentUniversity of Texas at Austin

0123456

Station Radar

Nie

ders

chla

g [m

m]

0123456

Station Radar

Nie

ders

chla

g [m

m]

0123456

Station Radar

Nie

ders

chla

g [m

m]

0123456

Station Radar

Nie

ders

chla

g [m

m]

0123456

Station Radar

Nie

ders

chla

g [m

m]

0123456

Station Radar

Nie

ders

chla

g [m

m]

0123456

Station Radar

Nie

ders

chla

g [m

m]

17,5 – 22,5 mm

12,5 – 17,5 mm

7,5 – 12,5 mm

2,5 – 7,5 mm

0 mm

BOKU Kongress 15

Rainfall Excess

(aus Baumgartner & Liebscher, 1989)

Separation of Rainfall Excess(Loss) and Direct Runoff

Rainfall Excess (Surface storage, interception, soil moisture, groundwater recharge

Direct (Effective) Runoff

Einheitsganglinienverfahren (Unit-Hydrograph)

Black Box Model

Principles of UH-Method- Linearity

- Superposition

- Time invariance

BOKU Kongress 16

Einheitsganglinienverfahren (Unit-Hydrograph)Estimation of UH ordinates

Unit Hydrograph

The transformation of unit rainfall to unit hydrograph is as follows :

where B … base of unit hydrograph (UH) in hoursStretch … stretching factorTc … time of concentrationQmax … peak discharge in m3/sArea … basin area in km2

1 + T stretch) + (1 B c⋅=

1 + T stretch) + (1 B c⋅= 3600) * 1) - area)/((B * (2000 Qmax =

BOKU Kongress 17

Rainfall and Excess

Julian day

Pre

cipi

tatio

n (m

m)

15234 15235 15236 15237 15238 15239

01

23

16.9.2001

UH-Discharge

Julian day

Dis

char

ge (m

3/s)

15234 15235 15236 15237 15238 15239

010

2030

4050

60

PrognoseTime to Peak 30.96 hh

Rainfall and Excess

Julian day

Pre

cipi

tatio

n (m

m)

15234 15235 15236 15237 15238 15239

01

23

16.9.2001

UH-Discharge

Julian day

Dis

char

ge (m

3/s)

15234 15235 15236 15237 15238 15239

010

2030

4050

60

PrognoseTime to Peak 24 hh

BOKU Kongress 18

Rainfall and Excess

Julian day

Pre

cipi

tatio

n (m

m)

15234 15235 15236 15237 15238 15239

01

23

16.9.2001

UH-Discharge

Julian day

Dis

char

ge (m

3/s)

15234 15235 15236 15237 15238 15239

010

2030

4050

60


Rainfall and Excess

Julian day

Pre

cipi

tatio

n (m

m)

15234 15235 15236 15237 15238 15239

01

23

16.9.2001

UH-Discharge

Julian day

Dis

char

ge (m

3/s)

15234 15235 15236 15237 15238 15239

010

2030

4050

60


BOKU Kongress 19

Conceptual (semidistributed) ModelsThis type of models comprises a series of linear reservoirs, which are related to surface, soil and groundwater storage. The parameters have partially relations to physical properties of soils and geology. For semidistributed models the spatial aggregation is due to the hydrological response conditions of sub-areas (Hydrological Response Units HRU).

Examples are HBV or COSERO (Nachtnebel).

Pros: - Specific consideration of the runoff formation process due to the Hydrological response unit HRU. (Differentiation in parameterisation)

- Utilisation of automatic GIS tools (overlay of information layers).- Simple parameterisation

Contra: - Lateral interactions between HRUs are not considered- Difficult calibration and model verification of the contributions of HRUs- Simplification of model concept will not provide good results for extremes

COSERO ModelNachnebel et al.

Climate Change

Land use change

Change of retention capacity

Aus NACHTNEBEL (2003)

BOKU Kongress 20

Single Linear Storage

Sub Basins


BOKU Kongress 21

Vegetation Zones and Land Utilisation or Land Cover


Elevation Zones


BOKU Kongress 22

Intersection of Subareas (subbasins, land utilisation, elevation) leads to hydrologic similar units HSU.


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.

.

Snowmelt and Runoff

Schneeschmelzmodell

BOKU Kongress 23

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

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

iEMSs 2002, Integrated Assessment and Decision Support Lugano, 24.. – 27. June 2002

Considering Elevation Zones

Figure 7: Runoff performance of Model 4 without consideration of elevation distribution.


0

500

1000

1500

Without Elevation Distribution - Validation 1996

Dis

char

ge(m

3/s)

Discharge (observed)Discharge (computed)Precipitation + Snowmelt

403020100

Prec

ipita

tion

+Sn

owm

elt

[mm

/d]


0

5

10

15

Sno

wm

elt (

mm

) Snowmelt with Elevation Discretisation


0

5

10

15

Sno

wm

elt (

mm

) Snowmelt without Elevation Discretisation

0 1000 2000 3000 4000

020

4060

8010

0

Enns - 6861 km2

Elevation [m.a.sl.]

Area

con

tribu

tion

[%]

BOKU Kongress 24

River basin modelsThis type of models divides the total catchment into sub-areas and interlinks the specific processes of runoff formation and runoff propagation by superposing the hydrographs response of the subbasins.

Examples are HEC-HMS and deviations.

River Basin Models

Beispiel: HEC-1, HEC-WMSThe modelling process comprises • Runoff formation (Neff)• runoff transformation• Flood Routing• Aggregation of subbasin discharge

Pros: Lumped Model and Black Box Modell (simple methods) are applied for homogeneous subbasinsand are accumulated. This enables a particular considerarion of specific characteristics of the local subareas.

Contras: Demand of input data (precipitation ) and output (discharges at the subbasin outlets)for each subbasin.

BOKU Kongress 25

Home Page of the US Army Corps of Engineers :

http://www.hec.usace.army.milHEC-HMS Software includes different unit hydrograph methods.

A direct opportunity for application and for testing of UH-methods is given in the seminary hydrologisches seminar – oberflächenhydrologie (summer semester).

BOKU Kongress 26

Flood RoutingThe computation of the flood propagation considers the storage processes and the friction resistances along the river branch. Important key parameters are

- Attenuation of the peak discharge,

- Time Lag (Travel Time),

- Conservation of Mass

Compuation of Flood Routing

General Storage Equation

BOKU Kongress 27

Muskingum Flood Routing Method

For the application of Muskingum Method Input- and Output-discharges of the river branch have to be available. The required parameters are

- Storage coefficient (or retention constant) K,

- form value x (is between 0 and 0.5),

- Number of Iteration steps n (optional).

Muskingum Method with spatial iterations

Stability criterion:

816.336 Integrated Flood Risk Management - iwhw.boku.ac.at fileBOKU Kongress 2 RR-Models Rainfall...

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