Application of CarboSOIL model to predict the effects of climate change on soil organic carbon...

Application of CarboSOIL model to predict the effects of climate change on soil organic

carbon stocks in agro-silvo-pastoral Mediterranean management systems

Miriam Muñoz-Rojas , Luca Doro, Luigi Ledda, and Rosa Francaviglia

M ED_SoilResearch Group

CarboSOIL, a new component of the Agroecological Decision Support System

MicroLEIS

MicroLEIS DSS

Land degradation risk

Land suitabili

ty

Land capabilit

y

Carbon sequestra

tion capacity

CarboSOIL: a land evaluation tool for soil C assessment

MicroLEIS DSS

Land degradation risk

Land suitabilit

y

Land capability

Carbon sequestrat

ion capacity

Climate regulation

Soil structure

Soil fertility

Water holding capacity

Infiltration capacity

Water use efficiency

Soil biological health



SoilN, pH, CECTexture

Bulk densityWater Holding

Capacity

Land use15 land use

types

ClimateWinter

TemperatureSummer

temperatureAnnual

Rainfall

SiteElevationSlope

CarboSOIL

CarboSOIL model diagram

SOIL ORGANIC CARBON0-25, 25-50, 50-75 cmOU

TPUT

S

Incorporation of residues

Mineralisation

Aereation

Nutrient availabili

tyBiological activity

Accumulation / loss Respiration

SOIL

PROCESS

ES

CLIMATETJJA PPT

LAND USELULC

SITEELEV

SLOP

SERO

DRAI

SOIL

FCAP SAND CLAY PHWA NITROCEXC BULKIN

PUT

FACTOR

S

TDJF

Source: Muñoz-Rojas, 2012


CarboSOIL structureVariables CarboSOIL 25

CarboSOIL 50

CarboSOIL 75

Coef Coef CoefIntercept 774.69 1085.65 1150.92

ClimatePRPT 0.003 0.000 0.000TDJF 1.430 0.615 0.637TJJA -0.930 -0.687 0.067

SiteELEV 0.000 0.003 0.001SLOP 0.004 0.005 0.001DRAIad - - -df -2.078 -1.502 -0.210ex 1.887 -2.391 -4.076SEROne - - -se -0.997 -0.883 -0.403re -0.159 0.449 -0.466ge -0.333 1.216 -0.879

SoilNITRO 1.934 26.309 6.063PHWA 0.837 0.072 1.039CEXC -0.009 0.000 0.029SAND 0.803 1.056 1.161CLAY -1.192 -1.597 -1.687BULK -493.990 -686.455 -746.993FCAP 0.018 -0.068 0.002

Variables

CarboSOIL 25

CarboSOIL 50

CarboSOIL 75

Coef Coef Coef

Intercept 774.69 1085.65 1150.92Land use

LULCot - - -nr 1.169 -0.527 -0.469pr -1.483 0.858 -0.580vn -0.210 -3.229 -1.816fr -1.009 -0.525 1.068ol 1.481 -0.232 -0.313cm -0.371 -1.068 -2.222af -0.051 -1.627 -0.063bf -0.335 -2.405 0.029cf 0.284 -1.367 0.697mf 6.329 -1.377 0.982gr 1.511 0.409 -0.526sc 1.358 -0.635 -2.021wd 1.396 -3.398 1.334sm -2.465 -3.064 -3.576

y = a+ b1x1+b2x2+b3x3+ ---+bnxn

y= SOC ; a= Intercept; b= variables; x= coefficients


Study area

Mean altitude -285 m

Climate- warm temperate with dry and hot summer.• Mean annual rainfall of 623 mm (range 367–811)

• Mean annual temperature 15.0°C (13.8–16.4)

Soil type - Haplic Endoleptic Cambisols, Dystric (WRB)

Potential native vegetation - Cork oak forest (Quercus suber L.), converted to managed land with pastures and vineyards in recent years

North-eastern Sardinia (Italy)

40°46’N, 9°10’E

Land use transformation

Study area

Six land uses with different levels of cropping intensification were compared:

Tilled vineyards (TV)

No-tilled grassed vineyards (GV)

Hay crop (HC): oats, Italian ryegrass and annual clovers or vetch for 5 years and intercropped by spontaneous herbaceous vegetation in the 6th year

Pasture (PA): 5 years of spontaneous herbaceous vegetation, and 1 year of intercropping with oats, Italian ryegrass and annual clovers or vetch cultivated as a hay crop

Cork oak forest (CO)

Semi-natural systems (SN): natural re-vegetation of former vineyards (scrublands, Mediterranean maquis and Helichrysum meadows)

Land use transformation

From left to right clockwise: no-tilled grassed vineyard (GV),

grazed hay crop under oats land cover (HC), semi-natural systems with scrubs and Mediterranean maquis (SN), grazed pasture with

spontaneous vegetation (PA).

Study area

CarboSOIL application in agro-silvo-pastoral Mediterranean management systems

Input data• Climate (actual and future)

• Site• Soil• Land use

Model test and validation• Measured values vs. Predicted values

Scenario analysis• Actual scenario• Climate change scenarios

Input data: variables description and sources

VARIABLE TYPE

VARIABLE NAME CODE UNIT SOURCE AND REFERENCE

Dependent variable

Soil Organic C SOCC Mg/ha University of SassariFrancaviglia et al.

(2012;2014)Climate Total

precipitationPRPT mm

CRA elaborations from baseline data and GCMs

Winter Temperature

TDJF oC

Summer Temperature

TJJA oC

Site Elevation ELEV m University of SassariSlope SLOP %Drainage DRAI -

Field surveys and lab analyses

Francaviglia et al. (2012;2014)

Soil Erosion SERO -Soil Nitrogen NITRO g/100g

pH PHWA -Cation Exchange Capacity

CEXC meq/100g

Sand SAND g/100gClay CLAY g/100gBulk density BULK g/ccField capacity FCAP g/100g

Land use Land use/land cover

LULC - Field surveys

Input dataModel

test and validati

on

Scenario analysis


Input data: climate change scenarios

Global Climate Models

• GISS (Goddard Institute of Space Studies, USA)

• HadCM3 (Met Office, Hadley Centre, UK)

Climate Change

Horizons• 2010-2039 (2020)

• 2040-2069 (2050)

• 2070-2099 (2080)

IPCC Models

• A2 (high population growth, slow economic and energetic development)

• B2 (more emphasis on sustainability and efficient technologies

Input dataModel

test and validati

on

Scenario analysis


Input dataModel

test and validati

on

Model applicat

ionAssessment of the model performance and comparison between measured and simulated values

n

iii SO

nRMSE

1

2)(1

n

1i

2i

n

1i

2ii

)OO(

)OS(1EF

where Oi and Si are observed and simulated SOC at ith value, Ō is the mean of the observed data and n is the number of the paired values. The lowest possible value of RMSE is zero, indicating that there is no difference between simulated and observed data.


EF compares simulations and observations on an average level, and can range from - to 1, with the best performance at EF=1.

Input dataModel

test and validati

on

Model applicat

iony = 0.777x + 5.4985

R² = 0.9774; RM SE = 8.42; EF = 0.63

0

20

40

60

80

100

0 20 40 60 80 100

predicted SO

C (Mg/ha)

m easured SOC (M g/ha)

y = 0.8215x + 3.7392R² = 0.9902; RM SE = 5.07; EF = 0.98

0

20

40

60

80

100

0 20 40 60 80 100

predicted SO

C (Mg/ha)


y = 0.9702x + 1.4942R² = 0.7621; RM SE = 5.88; EF = 0.93

0

20

40

60

80

100

0 20 40 60 80 100

predicted SO

C (Mg/ha)


0-25 cm measured SD=14.12



Regression coefficients are significant at p<0.001 and R2 are high, the standard deviation of the measured values is higher than RMSE, and EF is very close to the optimum value.


Input dataModel

test and validati

on

Model applicat

ion

0.0%1.0%2.0%3.0%4.0%

TV GV HC PA CO SN

GISS 2020 A250-7525-500-25

0.0%1.0%2.0%3.0%4.0%5.0%

TV GV HC PA CO SN

GISS 2020 B250-7525-500-25

0.0%

1.0%

2.0%

3.0%

TV GV HC PA CO SN

Hadley 2020 A250-7525-500-25

-1.0%

0.0%

1.0%

2.0%

3.0%

TV GV HC PA CO SN

Hadley 2020 B2 50-7525-500-25

CarboSOIL model predicted an overall increase of SOC stocks in the 2020 climate scenarios in all the soil sections, with the higher increases in the 50-75 cm section, and the smaller in the 25-50 cm soil section.


Input dataModel

test and validati

on

Model applicat

ion

A SOC decrease is instead expected in the 2050 and 2080 scenarios in the 25-50 cm soil section, more marked in the vineyards in comparison with the other land uses.

-1.0%0.0%1.0%2.0%3.0%4.0%

TV GV HC PA CO SN

GISS 2050 A250-7525-500-25

-2.0%-1.0%0.0%1.0%2.0%3.0%

TV GV HC PA CO SN

GISS 2050 B250-7525-500-25

-2.0%

0.0%

2.0%

4.0%

6.0%

TV GV HC PA CO SN

Hadley 2050 A250-7525-500-25

-2.0%

0.0%

2.0%

4.0%

6.0%

TV GV HC PA CO SN

Hadley 2050 B250-7525-500-25


Input dataModel

test and validati

on

Model applicat

ion

-4.0%-2.0%0.0%2.0%4.0%6.0%8.0%

TV GV HC PA CO SN

GISS 2080 A250-7525-500-25

-2.0%

0.0%

2.0%

4.0%

6.0%

TV GV HC PA CO SN

GISS 2080 B250-7525-500-25

-5.0%

0.0%

5.0%

10.0%

TV GV HC PA CO SN

Hadley 2080 A250-7525-500-25

-2.0%

0.0%

2.0%

4.0%

6.0%

TV GV HC PA CO SN

Hadley 2080 B250-7525-500-25

Oppositely, SOC increases are still expected in the 0-25 cm section and to a more extent in the 50-75 cm section, particularly evident in the vineyards.


• The model CarboSOIL has proved its ability to predict SOC stocks at different soil depths under different climate change scenarios.

• Climate change will have a negative impact on SOC stocks in the soil section 25-50 cm, in particular in a long term (2050 and 2080).

• Important decreases of SOC stocks were found in vineyards.

• The methodology developed in this research might be easily applied to other Mediterranean areas with available data on climate, site, soil and land use.

Conclusions

Miriam Muñoz-Rojas , Luca Doro, Luigi Ledda, and Rosa Francaviglia

M ED_SoilResearch Group

Thank you!

Application of CarboSOIL model to predict the effects of climate change on soil organic carbon...

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