CC effects in the Mediterranean:

CIRCE project

Workshop on

“Climate Change Impacts on Groundwater”

M. Vurro, E. Bruno, I. Portoghese,

Istituto di Ricerca Sulle Acque – CNR,

Bari, Italy

michele.vurro@ba.irsa.cnr.it

Mediterranean context: The IPCC 4AR, the CIRCE-IP and the RACCM

The IPCC 4AR:

many semi-arid and arid areas (e.g. the Mediterranean basin) are particularly exposed

to the impacts of climate change and are projected (with high confidence) to suffer a

decrease of water resources due to climate change

there is an urgent need to understand and quantify the impact of projected climatechange on hydrological processes including vegetation and crops (feedbacks)

linkages between models for climate change and hydrological processes is crude,with models’ scales not relevant for decision making

Water-related

issues

after Milly et al., 2005. Nature, 438(7066), 347–350 [in IPCC 4AR]

Mediterranean context: The CIRCE-IP and the RACCM

T106 INGV-IPCC run resolution (~ 120Km)

T42 IPCC standard resolution (~ 300Km)

Mediterranean context: The CIRCE-IP and the RACCM

T106 INGV-IPCC run resolution (~ 120Km)

Next INGV-CMCC model resolution (~ 60Km)

Mediterranean context: The CIRCE-IP and the RACCM

CIRCEClimate Change and Impact ResearCh:

the Mediterranean Environment

An FP6 Project of the European Union

Chair: Antonio Navarra and Laurence Tubiana

The project (FP6) aims at developing an assessment of the climate change

impacts in the Mediterranean area. Project’s objectives are:

to predict and to quantify physical impacts of climate change in the

Mediterranean area

to evaluate the consequences of climate change for the society and the

economy of the populations located in the Mediterranean area

to identify adaptation and mitigation strategies in collaboration with regional

stakeholders

Mediterranean context: The CIRCE-IP and the RACCM

The RACCM (Regional Assessment of Climate Change in Mediterranean Area) –

Part II will elaborate an assessment of

CC impacts on freshwater bodies (surface waters, groundwater, lakes)

adaptation strategies to minimize impacts

Impacts

Climate

Social

Case study

Policy

measures

GLOBAL High-Res MODELS

INGV-CMCC model

composed by ECHAM5.4 (Roeckner et al. 2003) as

atmospheric component and OPA8.2 (Madec et al.

1998) as oceanic component

METEO-FRANCE model

based on three models, one global stretched-grid

atmosphere model (ARPEGE-Climate 4.6) and two

ocean models (NEMO-ORCA2 and NEMO-MED8)

CNRS-IPSL model

an interactive coupled system among four individual

models: LMDZ-global, LMDZ-med, OPA-ORCA2 and

NEMO-MED8

REGIONAL MODELS

ENEA model

the PROTHEUS system is composed of the RegCM

atmospheric regional model and the MITgcm ocean

model, coupled through the OASIS3 flux coupler

MPI-HH model

Developed by the Max Planck Institute for

Meteorology consists of the REgional atmosphere

MOdel (REMO), the MPI ocean model (MPI-OM) and

the Hydrological Discharge Model (HD Model)

Mediterranean context: Climate projections and consequent impact (RACCM)

Courtesy of D. Hemming – MetOffice (UK)

8

Socio-economic

assumptions

Emission

Scenarios (SRES)

Radiative forcing

projections

GCM

simulations

Downscaling

Impact models

Adaptation

strategy

OB

JE

CT

IVE

S

Climate change and hydrological impact Catchment-scale investigation scheme

Predictive performance of RCMs designed for the

Mediterranean area

Bias correction and statistical downscaling of

projected climate

Impact analysis on water resources at basin scale

Stochastic weather generator approach

Quantile mapping approach

U N

C E

R T

A I N

T Y

Mediterranean context: Groundwater regimes

Substantial preponderance of

Calcareous soil (Cretaceus)

Arenaceus soil (Oligocene – Miocene )

with smaller areas especially of

Water resources projectionsThe karst spring of Cassano Irpino

Water resources projectionsStudy case: Non-linear hydrological model

11

Calibration period: 1981-1986

Validation period: 1970-1999

K 0.005

α 1.148

A 110 km2

1000

1500

2000

2500

3000

3500

4000

4500

5000

5500

6000

6500

7000

1/1/81 1/1/82 1/1/83 1/1/84 1/1/85 1/1/86P

ort

ata

(l/

s)

Observazione Simulazione

a

1000150020002500300035004000450050005500600065007000

1/1/7

0

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9

Por

tata

(l/s

)

Observazione Simulazione

a

Monthly mean error of hydrological model

related to validation period

1000

1500

2000

2500

3000

3500

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Po

rta

ta (

l/s)

0

50

100

150

200

250

300

350

400

Pre

cip

ita

zio

ne

(m

m)

Precipitazione efficace Pecipitazione totale

Portata-osservazioni Portata-simulazioni

q (t) = k Vα(t-1)

Climate change scenarios Variable correction method (Dèquè, 2006)

Quantile mapping transformation

R*(t)=f (R (t) |O) S*(t)=f( S (t)| O)

R: reference series; S: scenario series; O: observed series; t: time; *: bias-corrected series

f(x(d) |O): based on the inverse of cumulative density function (cdf) aligns along the diagonal the Q-Q plots

0 10 20 30 40 50 60 70 80 90 10010

-2

10-1

100

101

102

Curve di distribuzione della precipitazione-Primavera

[%]

pre

cip

itazio

ne [

mm

/day]

osservazioni

modello-20c3m

0 10 20 30 40 50 60 70 80 90 10010

-2

10-1

100

101

102

Curve di distribuzione della precipitazione-Estate

[%]

pre

cip

itazio

ne [

mm

/day]

osservazione

modello-20c3m

0 10 20 30 40 50 60 70 80 90 10010

-2

10-1

100

101

102

Curve di distribuzione della precipitazione-Autunno

[%]

pre

cip

itazio

ne [

mm

/day]

osservazione

modello-20c3m

0 10 20 30 40 50 60 70 80 90 10010

-2

10-1

100

101

102

Curve di distribuzione delle precipitazioni-Inverno

[%]

pre

cip

itazio

ne [

mm

/day]

precipitazione osservata

precipitazione simulata 20c3m

0 10 20 30 40 50 60 70 80 90 10010

-2

10-1

100

101

102

Curve di distribuzione della precipitazione-Primavera

[%]

pre

cip

itazio

ne [

mm

/day]

osservazioni

modello-20c3m

0 10 20 30 40 50 60 70 80 90 10010

-2

10-1

100

101

102

Curve di distribuzione della precipitazione-Estate

[%]

pre

cip

itazio

ne [

mm

/day]

osservazione

modello-20c3m

0 10 20 30 40 50 60 70 80 90 10010

-2

10-1

100

101

102

Curve di distribuzione della precipitazione-Autunno

[%]

pre

cip

itazio

ne [

mm

/day]

osservazione

modello-20c3m

0 10 20 30 40 50 60 70 80 90 10010

-2

10-1

100

101

102

Curve di distribuzione delle precipitazioni-Inverno

[%]

pre

cip

itazio

ne [

mm

/day]

precipitazione osservata

precipitazione simulata 20c3m

0 10 20 30 40 50 600

5

10

15

20

25

30

35

40

45

50

osservazioni

mo

de

llo

co

rre

tto

-20

c3

mQ-Qplot -Inverno

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

35

40

45

50

osservazioni

modello c

orr

ett

o-2

0c3m

Q-Qplot-Primavera

0 5 10 15 20 25 30 35 400

5

10

15

20

25

30

35

40

osservazione

modello c

orr

ett

o

Q-Qplot-Estate

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

osservazione

modello c

orr

ett

o-2

0c3m

Q-Qplot-Autunno

0 10 20 30 40 50 600

5

10

15

20

25

30

35

40

45

50

osservazioni

mo

de

llo

co

rre

tto

-20

c3

mQ-Qplot -Inverno

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

35

40

45

50

osservazioni

modello c

orr

ett

o-2

0c3m

Q-Qplot-Primavera

0 5 10 15 20 25 30 35 400

5

10

15

20

25

30

35

40

osservazione

modello c

orr

ett

o

Q-Qplot-Estate

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

osservazione

modello c

orr

ett

o-2

0c3m

Q-Qplot-Autunno

RCM data

OUTPUT from DSC

0 10 20 30 40 50 600

5

10

15

20

25

30

35

40

Osservazioni

Modello

Q-Q plot -Inverno

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

35

osservazioni

modello

Q-Qplot-Primavera

0 5 10 15 20 25 30 35 400

2

4

6

8

10

12

14

16

osservazione

modello

Q-Qplot-Estate

0 10 20 30 40 50 60 700

5

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30

35

40

45

50

osservazione

modello-2

0c3m

Q-Qplot-Autunno

0 10 20 30 40 50 600

5

10

15

20

25

30

35

40

Osservazioni

Modello

Q-Q plot -Inverno

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

35

osservazioni

modello

Q-Qplot-Primavera

0 5 10 15 20 25 30 35 400

2

4

6

8

10

12

14

16

osservazione

modello

Q-Qplot-Estate

0 10 20 30 40 50 60 700

5

10

15

20

25

30

35

40

45

50

osservazione

modello-2

0c3m

Q-Qplot-Autunno

a)

b) c)

Land reference

RCM simulation

Q-Q plots

Definition of local climate scenarios

UNDERESTIMETION OF OBSERVED PRECIPITATION

RCM performance

OVERESTIMATION OF OBSERVED TEMPERATURE

0

50

100

150

200

250

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

mm

/mo

nth

observation RCM 20c-raw RCM 21c-raw

0

5

10

15

20

25

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

°C

observation RCM 20c-raw RCM 21c-raw

Definition of local climate scenariosDownscaling validation and local scenario

0

50

100

150

200

250

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

mm

/mo

nth

observation RCM 20c-corrected RCM 21c-corrected

0

50

100

150

200

250

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

mm

/mo

nth

observation RCM 20c-raw RCM 21c-raw

Definition of local climate scenarios:

Validation downscaling

y(21c_corr) = 0,0427x + 1379y(21c_raw) = 1,2349x + 986,5

0

500

1000

1500

2000

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

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2022

2023

2024

2025

2026

2027

2028

2029

mm

/ye

ar

RCM21c-raw RCM21c-corr Lineare (RCM21c-corr) Lineare (RCM21c-raw)

y (20c_raw)= -4,6612x + 1070,6 y(20c_corr) = -4,1506x + 1406,8

0

500

1000

1500

2000

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

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mm

/ye

ar

RCM20c-raw RCM20c-corr Trend (RCM20c-raw) Trend (RCM20c-corr)

Rainfall

Downscaling do not modify the rainfall trend

Definition of local climate scenarios:

Validation downscaling

Temperature

Downscaling do not modify the temperature trend

y(20c_raw) = 0,0466x + 10,714 y (21c_corr)= 0,0469x + 7,62016

7

8

9

10

11

12

13

141970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

°C/y

ea

r

RCM 20c-raw RCM 20c-corrected Lineare (RCM 20c-raw) Lineare (RCM 20c-corrected)

y (21c_raw)= 0,0182x + 11,575 y(21c_corr) = 0,0102x + 8,67296

7

8

9

10

11

12

13

14

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

°C/y

ea

r

RCM 21c- raw RCM 21c-corrected Lineare (RCM 21c- raw ) Lineare (RCM 21c-corrected)

Water resources projectionsStudy case: climate change impact on spring regime

17 Spring hydrograph comparison between 20th century (Observed and downscaled RCM run) and

21th century (downscaled RCM run)

y(20c) = -16,774x + 36005y (obs) = -44,371x + 90847 y(21c) = 4,5964x - 65041000

1500

2000

2500

3000

3500

4000

4500

5000

1970 1980 1990 2000 2010 2020 2030

Sp

rin

g d

isch

arg

e (l

/s)

Observation RCM20c-corr RCM21c-corr Trend (RCM20c-corr) Trend (Observation) Trend (RCM21c-corr)

y(obs) = -0,4446x + 947,53 y (20c) = -0,153x + 367,45 y (21c) = 0,2284x - 395,140

200

400

600

800

1000

1200

1400

1970 1980 1990 2000 2010 2020 2030Eff

ecti

ve

pre

cip

ita

tio

n (

mm

/yea

r)

Observation RCM20c-corr RCM21c-corr Trend (Observation) Trend (RCM20c-corr) Trend (RCM21c-corr)

Water resources projectionsStudy case: climate change impact on spring regime

18

Spring hydrograph comparison between 20th century (Observed and downscaled RCM run) and 21th century

(downscaled RCM run)

2000

2200

2400

2600

2800

3000

3200

3400

3600

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Sp

rin

g d

isch

arg

e (

l/s)

Spring discharge-observation Spring discharge-RCM21c-corr Spring discharge-RCM21c-corr

Water resources projectionsStudy case: Adaptation

19

Scenario Hydrolgy Population Social

1 future current current

2 future future current

3 future future future

1010000

1015000

1020000

1025000

1030000

1035000

1040000

1045000

1050000

1055000

1060000

1065000

gen-0

0

gen-0

2

gen-0

4

gen-0

6

gen-0

8

gen-1

0

gen-1

2

gen-1

4

gen-1

6

gen-1

8

gen-2

0

gen-2

2

gen-2

4

gen-2

6

gen-2

8

Po

po

lazio

ne

Human supply

Current plan

(P.R.G.A.)

Future plan

(PA 2002)

l/ab/d 120-350 198-258

l/ab/d 235 228

106mc/month 7,05 6,84

y(20c) = -16,774x + 36005y (obs) = -44,371x + 90847 y(21c) = 4,5964x - 65041000

1500

2000

2500

3000

3500

4000

4500

5000

1970 1980 1990 2000 2010 2020 2030

Sp

rin

g d

isch

arg

e (l

/s)

Observation RCM20c-corr RCM21c-corr Trend (RCM20c-corr) Trend (Observation) Trend (RCM21c-corr)

Water resources adaptationStudy case: Demand/Supply

20

3000000

5000000

7000000

9000000

11000000

13000000

15000000

gen-7

0

gen-7

2

gen-7

4

gen-7

6

gen-7

8

gen-8

0

gen-8

2

gen-8

4

gen-8

6

gen-8

8

gen-9

0

gen-9

2

gen-9

4

gen-9

6

gen-9

8

gen-0

0

gen-0

2

gen-0

4

gen-0

6

gen-0

8

gen-1

0

gen-1

2

gen-1

4

gen-1

6

gen-1

8

gen-2

0

gen-2

2

gen-2

4

gen-2

6

gen-2

8

Fab

bis

og

no

/dis

po

nib

ilit

à (

mc/m

ese)

scenario 0 scenario 1 scenario 2 scenario 3 Disponibilità

0

2.000.000

4.000.000

6.000.000

8.000.000

10.000.000

12.000.000

14.000.000

1/1

/00

1/1

/02

1/1

/04

1/1

/06

1/1

/08

1/1

/10

1/1

/12

1/1

/14

1/1

/16

1/1

/18

1/1

/20

1/1

/22

1/1

/24

1/1

/26

1/1

/28

Dis

po

nib

ilit

a-F

ab

bis

og

no

(mc/m

ese)

Fabbisogno - Scenario 2

58%

42%

Deficit Surplus

SCENARIO 2: Future climate and population (2000-2029)

Water resources adaptationStudy case: Water Deficit

21

57,8

53,1

55,3

52,5

49,0

50,0

51,0

52,0

53,0

54,0

55,0

56,0

57,0

58,0

59,0

scenario 0 scenario 1 scenario 2 scenario 3

Defi

cit

id

rici

(%)

0

10

20

30

40

50

60

70

80

90

100

Gen Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dic

Defi

cit

id

rici(

%)

scenario 0 scenario 1 scenario 2 scenario 3

SCENARIO 3

Efficient Strategy

Water resources managementStudy case: Hydrological model

22

Semi-distributed hydrological model G-MAT(Portoghese et al, 2005).

Recharge-1.5 - -1.0 Std. Dev.-1.0 - -0.5 Std. Dev.-0.5 - 0.0 Std. Dev.Mean0.0 - 0.5 Std. Dev.0.5 - 1.0 Std. Dev.1.0 - 1.5 Std. Dev.1.5 - 2.0 Std. Dev.2.0 - 2.5 Std. Dev.2.5 - 3.0 Std. Dev.> 3 Std. Dev.

Irrigation (mm/yr)0 - 1010 - 60110 - 170170 - 220220 - 280280 - 340340 - 390390 - 450450 - 50060 - 110

Runoff (mm/yr)0 - 10

10 - 60120 - 180180 - 250250 - 310

310 - 370370 - 430430 - 490490 - 55060 - 120

1960 1970 1980 1990 20000

100

200

300

400

500

600

700

800

mm

/anno

Simulazione di bilancio acquifero Tavoliere Basso Pressione

Precipitazioni

Deflusso

Ricarica

Irrigazione

WB simulation, 1950-2000.

Res. 1,000x1,000 m

20,000 km2

domain

Puglia

y = - 2.94 x

y = - 1.46 x

y = - 1.27 x y = - 0.79 x

y = - 1.23 xy = - 0.61 x

y = - 0.38 x y = 0.13 x

0

200

400

600

800

1000

19

50

19

60

19

70

19

80

19

90

20

00

20

10

20

20

20

30

20

40

20

50

mm

/ y

r

Rainfall Runoff Recharge Irrigation ET

0 50 100 150 200 250 300 350 4000

1

2

3

4

5

6

7

8

9x 10

-3

mm/year

p

Lognormal PDF (Puglia)

Recharge 1951-2002

Recharge 2002-2050

Water resources projectionsStudy case: Considerations

23

The presented case study is an example of the uncertainty and controversial interpretation

of CC impacts assessment at the local scale for which further developments in climate

research are needed

After bias correction possible alterations of spring regime can be inferred by the

comparative analysis of 20c and 21c simulations

Hydrogeologic systems by the combination of complex hydrological processes are good

indicators of CC

RCM bias correction and downscaling is a crucial step in the CC impact assessment

A common language is needed between climatologist and hydrologist

Impacts are universally accepted

Mitigation and Adaptation

Are necessary….

Dutch cows, after adaptation measures

Thank you for your attention…

MEDITERRANEAN CONTEXT

Climate projections and consequent impact

(IPCC 4AR, CIRCE and RACCM)

Groundwater regimes

CLIMATE CHANGE and HYDROLOGICAL IMPACT

Downscaling issues

Climate change scenarios

Catchment-scale investigation scheme

WATER RESOURCES PROJECTIONS

Projection of discharge into a karst spring

Apulia region: a significant case study

Outline of presentation

Water resources projectionsStudy case: Non-linear hydrological model

26

Non-linear modelling

of spring discharge

q (t) = k Vα(t-1)

Mass balance equation Thornthwaite PotentialEvapoTraspiration

b(j) corrective coefficient depending

on considered month

t(j) monthly mean temperature [ C]

I thermic annual index (sum of

monthly index : ij=(tj /5)1.514

a=0.016* I+0.5 depending on

latitude

ETp = 16b(j) (10t(j) / I)αV(t+1)= V(t)+ A * Peff (t+1)-q (t)*Δt

V(t+1) and q (t+1) volume and discharge of

month following analyzed month

V(t) and q (t) volume and discharge of

analyzed month

A recharge area

Peff(t+1) effective precipitation (P-ETp) of

month following analyzed month

Development of a downscaling approach based on a Poissonian scheme of the rainfall process

Impacts on Ground Water Balance Regimes

SPACE-TIME DOWNSCALING METHODS

Physical D. (dynamic D.)

• RCM run with boundary c. from GCM.

Empirical D.

• Transfer functions

• Weather-typing

• Stochastic Weather Generators

Models calibrated on observed properties from real observations

Model parameters can de derived from GCM’s output

Suitable for cascade application of impact models for hydrology, ecology, agricultural projections

Often used in combination with transfer functions for the spatial downscaling

Water Research Institute, National Research Council

www.irsa.cnr.it

Caratterizzazione del regime della sorgente:

Curva di esaurimento

Master recession curve (1970-1999)

y = 3,5371e-0,0586x

1

10

1 2 3 4 5 6 7 8 9 10 11

n° di mesi del periodo di esaurimento

log

Q (

m3/s

)

portata mensile portata media mensile regres esponentiale (portata mensile)

Curve esaurimento (1970-1999)

1

10

1 2 3 4 5 6 7 8 9 10 11

n° di mesi del periodo di esaurimento

log

Q(m

3/s

)

Il periodo di esaurimento

dalla fine del periodo di alimentazione

dell’acquifero, quando cioè gli afflussi meteorici

divengono quantitativamente trascurabili,

all’inizio del successivo periodo di alimentazione

Maillet (1905)

α = [0.0284 - 0.1603]

range molto ampio

Indice di eterogeneità delle caratteristiche

idrologiche dell’acquifero a monte della

sorgente

tα0t eQQ

α: coefficiente di esaurimento

Dipende dalle caratteristiche geologiche e geomorfologiche

della sorgente