Climate change and impacts on

hydrological extremes

Patrick Willems

KU Leuven – Hydraulics division

Patrick.Willems@bwk.kuleuven.be

Global temperature rise 1880 – 2014:

Source: MIRA from Royal Meteorological Institute of Belgium

Saturation concentration

of air vapour increases

Temperature rise

Increase of greenhouse gasses in atmosphere

Clausius – Clapeyron relation:

7% increase in water holding capacity

per 1°C temperature increase

Impact temperature rise

Longer dry periods

: lower water availability

Increased peak rainfall intensities

: more floods?

Sewer floods!

River floods ?

Droughts / Low flows /

Water scarcity

More hydrological extremes …

Do we also observe increasing trends

in the occurrence of hydrological extremes?

Historical trend analysis

Historical trends are difficult to quantify because of:

limited length of time series

instrumental or environmental changes (site relocation, changes and

deficiencies in monitoring program, improvement of instrumentation,

urban heat island effect)

strong inter-annual variations (weak signal-to-noise ratio)

(multi-)decadal climate oscillations.

Historical trends in rainfall extremes Uccle rain gauge (Brussels): 10-minutes precip. intensities since 1898:

Testing of statistical significance:

-30

-20

-10

0

10

20

30

40

[%]

Quantile perturbations

Quantile perturbations, MA

95% Conf. interval limits

-30

-20

-10

0

10

20

30

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

An

om

aly

1910-1920s 1950-1960s

1990-2000s

: winter (DJF)

: summer (JJA)

1930-1940s

1970-1980s

Willems, P. (2013), ‘Multidecadal oscillatory behaviour of rainfall extremes in Europe’,

Climatic Change, 120(4), 931–944

Multidecadal climate oscillations

Willems, P. (2013), ‘Multidecadal oscillatory behaviour of rainfall extremes in Europe’,

Climatic Change, 120(4), 931–944

Also in neighbouring countries …

(Anti-)correlations of climate oscillations across Europe

Daily rainfall ECA&D database:

-40

-20

0

20

40

60

80

1880 1900 1920 1940 1960 1980 2000

An

om

aly

[%]

precipitation, Uccle

precipitation, Bologna

Multidecadal climate oscillations

Willems, P. (2013), ‘Multidecadal oscillatory behaviour of rainfall extremes in Europe’,

Climatic Change, 120(4), 931–944

Multidecadal climate oscillations

Willems, P. (2013), ‘Multidecadal oscillatory behaviour of rainfall extremes in Europe’,

Climatic Change, 120(4), 931–944

Meuse river high flows:

-15

-10

-5

0

5

10

15

20

25

30

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

An

om

aly

[%

]

Precipitation, Uccle

River flow, Meuse at Monsin

1910s-1920s 1950s-1960s

1990s-2000s

?

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

year [-]

-50

-40

-30

-20

-10

0

10

20

30

ano

maly

in

extr

em

es [

%]

winter, 10-year window

winter, 15-year window

long-term average

approximate cyclic variations

cyclic variations plus climate change

climate change effect

Future climate trends and oscillations??

Climate model projections

Nested approach: GCMs -> RCMs GCMs

RCMs

cumulus formation and small scale cloud processes leading to rain storms are not modelled

explicitly

cumulus parameterization to represent the collective influence of clouds (e.g. rainfall, radiation

budget) within a larger area (single grid)

primary purpose of cumulus parameterization is not to produce accurate rainfall, but to release

model instabilities ??

synoptic-scale RCMs therefore have poor accuracy in simulating precipitation extremes

Climate models make projections on the response of the

atmosphere to external forcing including GHG concentration

pathways. Such projections are inherently probabilistic and

it is important to treat them as such in further analyses.

GCMs: response of the global circulation to large scale forcing

(i.e. greenhouse gas concentrations)

RCMs:

account for finer scale forcing (topographic features),

convective precipitation due to the local convection effects

Climate model validation point observations

GCM results

areal reduction factor

Bias !

be careful with climate model rejections: cfr. influence natural variability, the limited length of the

available time series, difference in spatial scales, and influence of climate oscillations, AND poor

accuracy of rainfall extremes of short duration

RCM results

Global Climate Models

(GCMs)

Regional Climate Models

(RCMs) 50 -> 12 km

Local Area Models

(LAMs) -> 3 km

Nested approach: GCMs -> RCMs (synoptic scale) -> LAMs (city scale)

cumulus formation only represented explicitly by model physics at < 3km

BUT: computational times do not allow LAM simulations to be conducted for long periods (30 years

or more) for a set of models and simulations

Ensemble of

>30-year

LAM runs not

feasible yet

Global Climate Models

(GCMs)

Regional Climate Models

(RCMs) 50 -> 12 km

Local Area Models

(LAMs) -> 3 km

Hydrological

impact models

Need for bias correction and

statistical downscaling Ensemble of

>30-year

LAM runs not

feasible yet

Nested approach: GCMs -> RCMs (synoptic scale) -> LAMs (city scale)

Large Scale

Hydrological scale

Dynamical

downscaling

Statistical

downscaling

General

Circulation Models

(GCMs)

Regional

Climate Models

(RCMs)

150 – 300 km; seasonally – monthly

± 50 km; weekly - daily

± 25 km; daily

river catchment; hourly

Downscaling of climate model outputs

Climate system Hydrological system

GCMs 150 km

RCMs 25 km

RCMs 12 km

Large scale

“predictants”

Rainfall-runoff model

Local scale

“predictors”

Transfer function: [Local scale variable (Predictants)]

= F [Large scale variables (Predictors)]

Statistical properties representation

Conditional probability: stochastic model based on a conditional probability

between the predictant and predictors

Stochastic representation

Re-sampling: “Weather typing”, a historical look to the data

Time series representation

Statistical downscaling

Climate model projections

Uccle (Brussels), extreme daily rainfall (summer, 1961-1990 -> 2071-2100):

Return period [years]

Rain

fall

change

facto

r [-

]

Climate scenarios

Uccle (Brussels), extreme daily rainfall (summer, 1961-1990 -> 2071-2100):

High

Mean

Low

Return period [years]

Facto

r ra

infa

ll change [

-]

Return period [years]

Rain

fall

change

facto

r [-

]

Impact climate scenarios

Caution must be exercised when interpreting climate change scenarios

Order of magnitude of uncertainty assessed by ensemble approach with

several:

future greenhouse gas emission scenarios

GCMs (global scale climate model physics)

RCMs (regional scale climate model physics)

initial states of the climate models

statistical downscaling assumptions and methods

Whatever methods are adopted: resulting change should not be

interpreted as an exact number but only as indicative of the

expected magnitude of future change

+ real uncertainty is larger: models share the same level of process understanding and sometimes

even the same parameterization schemes and code

Impact climate scenarios:

how to deal with the high uncertainties?

The large uncertainties that currently exist should not be an argument for

delaying climate change impact investigations or adaptation actions !

? very high

for High “pessimistic” climate scenario

≥ high

Precautionary principle:

take pessimistic scenario into account

take “climate insurance”

Risk = Probability * Consequence

Impact climate scenarios:

Uncertainties should be accounted for!

Future designs:

• flexible and sustainable solutions

• avoid closing off options (reversibility)

• active learning, public debate

o <-> traditional engineering approach, which is rather static and is

often based on design rules set by engineering communities

o recognize that flexibility is required as understanding increases

Belgium, from 1961-1990 to 2071-2100:

• Winter:

rainfall increase: 0 -> +60%

temperature & evaporation increase:

+1.5 -> 4°C

• Summer:

rainfall decrease: 0 -> -70%

number of rainy days: 0 -> -50%

temperature & evaporation increase:

+2 -> 7°C

extreme intensities increase:

2-year event: 0 -> +30%

10-year event: 0 -> +50%

• Coastal – polder area:

rainfall change +10% higher

• Sea level rise Belgian Coast:

20cm -> 2m

Impact on river floods highly

uncertain

Drier conditions!!

(low flows, reduced water

availability)

More sewer floods

Impact on hydrological extremes:

Higher flood freq. coast &

Scheldt & IJzer

Impact climate scenarios

Flanders and Brussels: vulnerable to higher peak intensities in summer

Because of high urbanisation (high % pavements)

Impact climate scenarios

1976: 4 – 5% paved

PhD Lien Poelmans,

KU Leuven, 2010

2000: 9 – 10% paved

2000

1976

Flanders and Brussels: vulnerable to drier summers

Because of low water availability

Impact climate scenarios

0

5000

10000

15000

20000

25000

gemiddelde waterbeschikbaarheid

(m3 per jaar en per inwoner)

Mean annual water availability:

1480 m3/(person.year)

International standards:

<2000 “very low”

<1000 “severe water shortage”

Due to: High population density

Limited surface water inflow from rivers

IWA/IAHR Joint Committee on Urban Drainage

INTERNATIONAL GROUP ON URBAN RAINFALL (IGUR)

Provides an international review on the climate change

impacts on urban rainfall extremes and on urban

hydrology and hydraulics

Content:

1. Climate change simulations

2. Modeling of urban rainfall extremes in a stationary

context

3. Study of rainfall and urban runoff variability and

trends in a non-stationary context

4. Statistical downscaling of rainfall extremes

5. Climate factors and changes in IDF relationships

6. Climate change impacts on urban drainage: results

and regional differences

7. Needs for adaptation and flexible designs

Contact: Patrick.Willems@bwk.kuleuven.be

Climate change serves as a driver for

changes in urban drainage paradigm …

Urban design and planning processes incorporating

more sustainable approaches:

In many cities in the world, the rate of renewal of urban

infrastructure is currently low, but this may need to change in the

future, in order for communities to cope with deteriorating pipe

networks, population growth and climate change

At the same time the changes need to be consistent with an

increasing awareness of the environmental stress the end-of-pipe

solution puts on the ecosystem of the city

There will be a need for more natural urban drainage approaches

and installation of “blue-green” storm water infrastructure, all of

which requires a change in design philosophy

Sustainable (urban) water management:

More local-upstream storage and infiltration

Permeable pavements: Individual infiltration:

Stormwater

retention

and re-use:

Stormwater storage and infiltration in public spaces:

reduces sewer flood & overflow frequencies & feeds groundwater system

RIONED 2009

Sustainable (urban) water management:

More local-upstream storage and infiltration

before

after

Improved interfacing between urban water management and spatial

planning / urban design:

Multiple functions to

open spaces (e.g.

parks) in the city:

RIONED 2009

Sustainable (urban) water management:

More local-upstream storage and infiltration

Campus Park, Clichy sous Bois [Composante Urbaine, 2004]

IWA/IAHR Joint Committee on Urban Drainage

INTERNATIONAL GROUP ON URBAN RAINFALL (IGUR)

Provides an international review on the climate change

impacts on urban rainfall extremes and on urban

hydrology and hydraulics

Content:

1. Climate change simulations

2. Modeling of urban rainfall extremes in a stationary

context

3. Study of rainfall and urban runoff variability and

trends in a non-stationary context

4. Statistical downscaling of rainfall extremes

5. Climate factors and changes in IDF relationships

6. Climate change impacts on urban drainage: results

and regional differences

7. Needs for adaptation and flexible designs

Contact: Patrick.Willems@bwk.kuleuven.be