3rd SWITCH Scienti�c MeetingBelo Horizonte, Bresil
29 November � 4 December 2008
Predicting rainfall for the city of the future
Xavier Beuchat, Marc Soutter
Ecohydrology LaboratorySwiss Federal Institute of Technology (EPFL)
Lausanne, Switzerland
December 2008
Flood in western France10 Mars 2008
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Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Planning for the city of the future
- pollution- urbanisation- energy- ...
Economic crisis
Climate change
Strategies should be �exible or robust!
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Planning for the city of the future
- pollution- urbanisation- energy- ...
Economic crisis
Climate change
Strategies should be �exible or robust!
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Planning for the city of the future
- pollution- urbanisation- energy- ...
Economic crisis
Climate change
Strategies should be �exible or robust!
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Planning for the city of the future
- pollution- urbanisation- energy- ...
Economic crisis
Climate change
Strategies should be �exible or robust!
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Why climate change should be taken into account?
There is now evidence that climate change may perturb
global water cycle
Climate change prediction are uncertain
Classical stormwater managment may not be adequate
(neither �exible nor adaptable)
Alternative strategies BMP, SUDS may be more valuable
Objectives of the work
Modelling rainfall for the city of the futur, taking
uncertainties into account
Using predicted rainfall series as input in a rainfall-runo�
model
Testing di�erent strategies to mitigate impacts of climate
change
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Why climate change should be taken into account?
There is now evidence that climate change may perturb
global water cycle
Climate change prediction are uncertain
Classical stormwater managment may not be adequate
(neither �exible nor adaptable)
Alternative strategies BMP, SUDS may be more valuable
Objectives of the work
Modelling rainfall for the city of the futur, taking
uncertainties into account
Using predicted rainfall series as input in a rainfall-runo�
model
Testing di�erent strategies to mitigate impacts of climate
change
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Problem of scales!
Global Climate Models (GCMs)
prediction are coarse.
Typically:
2,5° latitude and 3,75°
longitude
Monthly time step
Urban hydrology
Spatial variability is especially important
Temporal resolution can be 10 minutes depending on the
phenomenon we want to model
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Downscaling
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Theoretical framework for producing probabilisticrainfall scenarios for the city of the future
Approach based on the pilot methodolgy developped within
the DEFRA project FD2113: �single and multi-site rainfall
generation with climate model uncertainty�(ended
september 2006)
Modelling and simulation of daily rainfall time series at a
single or multiple sites
Methods
Generalized Linear Models (GLMs) are used to downscale
rainfall at daily time step from GCMs outputs
Disagregation to hourly data is then achieved via methods
based on Poisson cluster models
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Using GLMs to downscale rainfall at a daily timestep (1)
Basically, GLMs derive relationships between a variable of
interest, Y = (Y1, . . . ,Yn)t , called the predictand and a set of
p temporally varying predictor variables, or covariates, whose
values can be arranged in a n × p matrix X. The relationships
between the predictand and the predictors are assumed to be
given by
g(µ) = Xβ ; µ = E(Y) (1)
Moreover, the distribution of each Yi is assumed to belong to
the exponential family. That is
fY (y ; θ, φ) = exp
[yθ − b(θ)
a(φ)+ c(y , φ)
](2)
where a, b and c are some speci�c functions and φ is called the
dispersion parameter.
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Using GLMs to downscale rainfall at a daily timestep (2)
Here,
Predictand ← rainfall series
Predictors ← terms relating to seasonality, terms relating
to the history of the series itself (autocorrelations, intensity
of previous rain events. . . ) and external covariates such as
coarse-scale atmospheric variables.
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Using GLMs to downscale rainfall at a daily timestep (3)
Models are �tted using historical data (rainfall and large
scale atmospheric variables derived from NCEP reanalysis)
to describe relationships.
Then, for future scenarios, one may simulate from the
�tted models driven by GCM-generated atmospheric
variables.
Actually, the modelling framework is composed of two
components: �rst, an occurrence model based on logistic
regression is used to model the pattern of wet and dry
days, and second, an amount model based on gamma
distributions allows the simulation of rainfall amounts on
wet days.
Simulating nonstationary rainfall sequences is achieved by
allowing some predictors to modulate the e�ect of other
predictors incorporated via interactions (alternative to,
e.g., �tting separate models in each month of the year).
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Case study: Geneva, Switzerland
Station Genève-Cointrin Genève-Aïre Croix-de-Rozon Jussy
Northings [km] 508 810 498 580 499 480 495 800Eastings [km] 120 310 122 320 111 080 116 900Altitude [m] 420 375 478 465Data availability 1900�. . . 1968�. . . 1974�2005 1900�. . .
Auto. since 1980;Type
TB beforeTB TB TB
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Evolution of surface air temperatures
−1
01
23
Low greenhouse gas emission scenario (SRESB1)
1950 1980 2010 2040 2070 2100
bccr_bcm2_0cnrm_cm3csiro_mk3_0csiro_mk3_5gfdl_cm2_0gfdl_cm2_1giss_model_e_riap_fgoals1_0_ginmcm3_0miroc3_2_hires
miroc3_2_medresmpi_echam5mri_cgcm2_3_2ancar_ccsm3_0ncar_pcm1ukmo_hadcm3
−1
01
23
4
Medium greenhouse gas emission scenario (SRESA1B)
1950 1980 2010 2040 2070 2100
bccr_bcm2_0cnrm_cm3csiro_mk3_0csiro_mk3_5gfdl_cm2_0gfdl_cm2_1giss_model_e_hgiss_model_e_riap_fgoals1_0_gingv_echam4
inmcm3_0miroc3_2_hiresmiroc3_2_medresmpi_echam5mri_cgcm2_3_2ancar_ccsm3_0ncar_pcm1ukmo_hadcm3ukmo_hadgem1
−1
01
23
4
High greenhouse gas emission scenario (SRESA2)
1950 1980 2010 2040 2070 2100
bccr_bcm2_0cnrm_cm3csiro_mk3_0csiro_mk3_5gfdl_cm2_0gfdl_cm2_1giss_model_e_ringv_echam4inmcm3_0miroc3_2_medres
mpi_echam5mri_cgcm2_3_2ancar_ccsm3_0ncar_pcm1ukmo_hadcm3ukmo_hadgem1
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Evolution of surface air temperatures
−1
01
23
Low greenhouse gas emission scenario (SRESB1)
1950 1980 2010 2040 2070 2100
bccr_bcm2_0cnrm_cm3csiro_mk3_0csiro_mk3_5gfdl_cm2_0gfdl_cm2_1giss_model_e_riap_fgoals1_0_ginmcm3_0miroc3_2_hires
miroc3_2_medresmpi_echam5mri_cgcm2_3_2ancar_ccsm3_0ncar_pcm1ukmo_hadcm3
−1
01
23
4
Medium greenhouse gas emission scenario (SRESA1B)
1950 1980 2010 2040 2070 2100
bccr_bcm2_0cnrm_cm3csiro_mk3_0csiro_mk3_5gfdl_cm2_0gfdl_cm2_1giss_model_e_hgiss_model_e_riap_fgoals1_0_gingv_echam4
inmcm3_0miroc3_2_hiresmiroc3_2_medresmpi_echam5mri_cgcm2_3_2ancar_ccsm3_0ncar_pcm1ukmo_hadcm3ukmo_hadgem1
−1
01
23
4
High greenhouse gas emission scenario (SRESA2)
1950 1980 2010 2040 2070 2100
bccr_bcm2_0cnrm_cm3csiro_mk3_0csiro_mk3_5gfdl_cm2_0gfdl_cm2_1giss_model_e_ringv_echam4inmcm3_0miroc3_2_medres
mpi_echam5mri_cgcm2_3_2ancar_ccsm3_0ncar_pcm1ukmo_hadcm3ukmo_hadgem1
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Summary
Models were �tted using observed rainfall series and NCEP
predictors during the period 1982-2007.
Fitting and simulations were carried out simultaneously for
the all the four sites.
Three SRES scenarios were considered: A2 (high), A1B
(medium), B1 (low)
Around 20 di�erent GCMs were considered per scenario.
The period 1956-1981 has been used for validation.
The future period used for simulation is 2072-2098.
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Summary monthly statistics of 200 simulations forthe site Genève-Cointrin during the �tting period
2 4 6 8 10 12
12
34
56
Month
mm
Mean
2 4 6 8 10 12
24
68
1216
Month
mm
SD
2 4 6 8 10 12
0.2
0.3
0.4
0.5
0.6
0.7
Month
Pro
port
ion
Pwet
2 4 6 8 10 12
24
68
1216
Month
mm
CMean
2 4 6 8 10 12
05
1015
2025
Month
mm
CSD
2 4 6 8 10 12
050
150
250
Month
mm
Max
2 4 6 8 10 12
0.0
0.1
0.2
0.3
0.4
Month
Cor
rela
tion
ACF1
2 4 6 8 10 12
−0.
10.
10.
20.
30.
4
Month
Cor
rela
tion
ACF2
2 4 6 8 10 12
−0.
10.
00.
10.
20.
3
Month
Cor
rela
tion
ACF3
2 4 6 8 10 12
510
2030
Month
days
Max Dry Spell
2 4 6 8 10 12
050
150
250
Month
mm
Max 5−days tot
2 4 6 8 10 12
24
68
1012
Month
Skewness
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Summary monthly statistics of 200 simulations forthe site Genève-Cointrin during the validation period
2 4 6 8 10 12
1.5
2.5
3.5
4.5
Month
mm
Mean
2 4 6 8 10 12
46
810
Month
mm
SD
2 4 6 8 10 12
0.2
0.3
0.4
0.5
0.6
0.7
Month
Pro
port
ion
Pwet
2 4 6 8 10 12
46
810
12
Month
mm
CMean
2 4 6 8 10 12
46
810
14
Month
mm
CSD
2 4 6 8 10 12
050
100
200
Month
mm
Max
2 4 6 8 10 12
0.00
0.10
0.20
0.30
Month
Cor
rela
tion
ACF1
2 4 6 8 10 12−0.
100.
000.
100.
20
Month
Cor
rela
tion
ACF2
2 4 6 8 10 12−0.
100.
000.
100.
20
Month
Cor
rela
tion
ACF3
2 4 6 8 10 12
510
2030
Month
days
Max Dry Spell
2 4 6 8 10 12
050
150
250
Month
mm
Max 5−days tot
2 4 6 8 10 12
24
68
1012
Month
Skewness
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Summary monthly statistics of 200 simulations forthe site Genève-Cointrin during the future period(scenario A2)
2 4 6 8 10 12
02
46
810
Month
mm
Mean
2 4 6 8 10 12
05
1015
2025
Month
mm
SD
2 4 6 8 10 12
0.2
0.4
0.6
0.8
Month
Pro
port
ion
Pwet
2 4 6 8 10 12
05
1015
2025
Month
mm
CMean
2 4 6 8 10 12
05
1020
30
Month
mm
CSD
2 4 6 8 10 12
010
030
050
0
Month
mm
Max
2 4 6 8 10 12
−0.
10.
10.
30.
5
Month
Cor
rela
tion
ACF1
2 4 6 8 10 12
−0.
10.
10.
20.
30.
4
Month
Cor
rela
tion
ACF2
2 4 6 8 10 12
−0.
10.
00.
10.
20.
3
Month
Cor
rela
tion
ACF3
2 4 6 8 10 12
510
2030
Month
days
Max Dry Spell
2 4 6 8 10 12
020
040
060
0
Month
mm
Max 5−days tot
2 4 6 8 10 12
05
1015
20
Month
Skewness
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Annual total rainfall simulated by the models
1985 1995 2005
050
015
0025
00
Year
mm
Total year rainfall conditionnal
on NCEP outputs
1985 1995 2005
020
060
0
Year
mm
Total summer (JJA) rainfall conditionnal
on NCEP outputs
1985 1995 2005
020
060
0
Year
mm
Total winter (DJF) rainfall conditionnal
on NCEP outputs
2075 2085 2095
050
015
0025
00
Year
mm
Total year rainfall conditionnal
on all SRESA1B outputs
2075 2085 2095
020
060
0
Year
mm
Total summer (JJA) rainfall conditionnal
on all SRESA1B outputs
2075 2085 2095
020
060
0
Year
mm
Total winter (DJF) rainfall conditionnal
on all SRESA1B outputs
2075 2085 2095
050
015
0025
00
Year
mm
Total year rainfall conditionnal
on all SRESA2 outputs
2075 2085 2095
020
060
0
Year
mm
Total summer (JJA) rainfall conditionnal
on all SRESA2 outputs
2075 2085 2095
020
060
0
Year
mm
Total winter (DJF) rainfall conditionnal
on all SRESA2 outputs
2075 2085 2095
050
015
0025
00
Year
mm
Total year rainfall conditionnal
on all SRESB1 outputs
2075 2085 2095
020
060
0
Year
mm
Total summer (JJA) rainfall conditionnal
on all SRESB1 outputs
2075 2085 2095
020
060
0
Year
mm
Total winter (DJF) rainfall conditionnal
on all SRESB1 outputs
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Extreme value analysis
Independently of the SRES scenario considered, the
average forecasted 100-year return level for the future
period is higher than for the �tting period.
The higher the forcing is, the higher the average 100-year
return level is (SRESB1: 117.71 mm, SRESA1B: 124.40
mm, SRESA2: 133.97 mm).
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Perpsectives
Futur tasks make the methodology more robust and easy
to use
go on with subdaily simulationsfor a given city:
build a detailed rainfall-runo� modelassess impacts of perturbed rainfallsceanrios on stormwater mangementquantify these impacts against otherpotential threads (population increase. . . )test di�erent strategies to mitigate theseimpacts.
Demo cities on demand, provide them with scenarios of
rainfall for the future
I need for that at least 20 years of observed
rainfall series at (sub)daily time step, for one
or more stations.
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Example: a model for Belo Horizonte (1961-1999)
Predictingrainfall forthe city ofthe future
Xavier
Beuchat,
Marc Soutter
Introduction
Climatechange:globalpredictionsanddownscaling
Modellingstrategy:theoreticalconsidera-tions
Case study:Geneva,Switzerland
Data
Results of thedownscalingat a dailytime step
Example: a model for Belo Horizonte (2061-2099)