Division on Impacts on Agriculture, Forest and Natural Ecosystems (IAFENT) Responsible Prof. Riccardo Valentini Lecce, 25 Marzo 2009

Division on Impacts on Agriculture, Forest and Natural Ecosystems

(IAFENT)

Responsible Prof. Riccardo Valentini

Lecce, 25 Marzo 2009

The research activity of the IAFENT Division is divided into three MacroActivities (MA):

MA1 - Impacts on land use, agriculture and natural ecosystems

MA2 - Climate change, carbon cycle and desertification

MA3 - Impacts on crop water requirements and water resources management in agriculture

IAFENT ACTIVITIES

The activities of MA2 can be divided into three sub-activities:

A2.1 - Evaluation of carbon sources and sinks allocation and feedbacks between the carbon cycle and the climatic system

A2.2 - Climate changes’ impacts on forest and natural ecosystems

A2.3 - Desertification risk evaluation through integration of biophysical parameters and socio-economic indicators

IAFENT MA2 SUB- ACTIVITIES

LPJ

-LPJ is a process-based model representing key ecosystem processes governing terrestrial biogeochemistry and biogeography

-LPJ simulates the water and carbon exchange between biosphere and atmosphere by means of a given set of parameters and input variables

-Vegetation is described in terms of the Fractional Percentage Coverage (FPC) of 9 different Plant Functional Types (PFTs) that are able to compete for space and resources

Seven PTFs are woody (three temperate, two tropical and two boreal) and two herbaceous

A2.1 LPJ parameter optimization

Data assimilation techniques are generally used to find an optimal combination of parameter values that minimise a cost-function which describes the misfits between observation and model results

LPJ-DA (Data Assimilation) model version


Function Code descriptionPhotosynthesis bc3 Leaf respiration as a fraction of Rubisco capacity in C3 plants

θ Co-limitation shape parameterαa Scaling parameter (leaf to canopy)αc3 Intrinsic quantum efficiency of CO2 uptake in C3 plants

λm Optimal ci/ca for C3 plants

Water balance αm Empirical evapotranspiration parametergm Maximum canopy conductancePT Priestley-Taylor coefficient

PFTtemperate needleleaved evergreen tree z1 the fraction of fine roots in the upper soil layer

temperate broadleaved evergreen tree emax maximum transpiration rate

temperate broadleaved summergreen tree gmin the minimum canopy conductance boreal needleaved evergreen tree PT Priestley-Taylor coefficientC3 grass (only gmin)

- The most important parameter controlling a given variable have been computed by means the Partial Correlation Coefficient (PCC)

- On the basis of PCC results and the knowledge of the model formulation, we chose to constrain 12 parameters controlling directly or indirectly gross primary production and evapotranspiration: 5 of these are used to compute photosynthesis, 3 to compute water stress and water balance and the other 4 are specific PFT parameters

- Optimization was made via a Monte Carlo Markov chain (MCMC) procedure, showing the best performances in dealing with non-linear problems.


Site Name Dominant species PFT

1 Hainich Fagus sylvatica TBS

2 Sorø Fagus sylvatica TBS

3 Hesse Fagus sylvatica TBS

4 Loobos Pinus sylvestris TNE

5 Tharandt Picea abies TNE

6 Yatir Pinus halepensis TNE

7 Hyytiälä Pinus sylvestris BNE

8 Sodankylä Pinus sylvestris BNE

9 Puechabon Quercus ilex TBE

10 Castelporziano Quercus ilex TBE

• Daily values of ET and GPP, measured with EC technique in 10 different CarboeuropeIP sites are compared with modeled data.

• The study sites were chosen in order to represent all European forest types


By using separately or averaged observed data for the years 2000-2001-2002, different optimization schemes have been tested. The best resulted the one in which Data assimilation is made year by year; the optimal values are estimated with the means computed using only the 2nd-half of n points for each sequence

GPP: Gross Primary Production ET: EvapotranspirationNEE: Net Ecosystem Exchange (NEE) TER: Total Ecosystem Respiration (TER).

Tmin = 0.05Tmean = 2.27


• Some parameters are close to priors• Uncertainty reduction (except gm)

Prior and posterior parameter values and uncertainties for the normalized parameters. The boxes show the prior parameter values and their associated standard deviations; circle indicates optimized-posterior values and the error bars denote the standard deviation associated to the posterior parameters.


The final-optimized parameters were then used to simulate water and carbon fluxes by LPJ for the year 2003 with the aim of methodology validation. Results show a decreasing both for R2 and for RMSE between observed and simulated data.

Further 2003 data were used to evaluae the LPJ-DA performance in simulating vegetation response to extreme events(e.g heat wave during the summer 2003).

Relative increments between prior and posterior (final-optimized) squared correlation coefficients (R2) and root mean squared error (RMSE).

European-wide anomaly of GPP during the summer 2003. The figure represents the difference, averaged over JJA, between prior and posterior, for the summer 2003.


CONCLUSIONS ON DATA ASSIMILATION

• This study has demonstrated how data assimilation provides a powerful tool for analyzing ecosystem processes and it might help to improve our understanding about carbon and water exchange between land ecosystem and atmosphere

• The optimization helps to retrieve a better knowledge about some LPJ ecophysiological parameters and uncertainties

• Final-optimized values are very close for almost all parameters to the prior values


LPJ è stato usato per investigare come i cambiamenti della copertura del suolo possono influire sul clima, utilizzando il modello RegCM3. Sono state fatte tre simulazioni:Controllo (CTL): utilizzando la copertura attualeDeforestazione (DEF): sostituzione delle foreste con le coltivazioni, soprattutto nell’Europa dell’EstAfforestazione (AFF): sostituzione delle coltivazioni con le foreste, in particolare in Europa Centrale.

RegCM-Test:

Resolution: 30 Km

Time Step: 100 s

Boundary Conditions: u, v, T, q, SST (6 hourly)

Vertical levels: 18 sigma levels

Simulation length: 1981-2000

Grid points: 150x160 in lon-lat direction respectively

A2.1 Feedbacks of vegetation on climate

• CTL present vegetation cover: the surface vegetation and landuse types are obtained from satellite information and interpolated into model grid; at each grid element is assigned a dominant type of land cover


• DEF: starting from GLCC dataset, we substituted all the forests and the trees below 800 meters with crops

CAUSES OF DEFORESTATION IN EUROPE

• Land use changes (e.g. Poland)

• Air pollution

• Drought effect

• Overgrazing


• AFF: considers a plausible evolution to a spontaneous afforestation situation caused by abandonment of crops and fields which lead to a natural recapture by forests of abandonment of arable land

CAUSES OF AFFORESTATION

• Poor quality of soil

• Difficult access of farm

• Steep slopes

• High labor requirements

• Farmers’ age and health


• The simulations reveal a substantial thermodynamically and dynamically impact of vegetation on climate

• Also in regions not directly affected by LCC have been found significant changes in temperature

• The effects of the LCC may be in the same direction as those from increasing GHG forcing

• Necessity to adopt an improved and fully dynamic vegetation in GCMs and RCMs to take into account vegetation-climate feedbacks


CONCLUSIONS ON VEGETATION-CLIMATE FEEDBACKS

The ORCHIDEE model was used to evaluate the importance of the downscaling of the climate forcings to run the model at finer spatial resolution and do not miss information over coastal areas. We compared 3 simulations: a) input at 0.5x0.5° and monthly time step; b) 30 km of resolution and hourly time step; c) 10 km of resolution at and hourly time step.

a)

b)

c)

A2.1 ORCHIDEE forcing downscaling

The activities of MA2 can be divided into three sub-activities:

A2.1 - Evaluation of carbon sources and sinks allocation and feedbacks between the carbon cycle and the climatic system

A2.2 - Climate changes’ impacts on forest and natural ecosystems

A2.3 - Desertification risk evaluation through integration of biophysical parameters and socio-economic indicators

IAFENT MA2 SUB- ACTIVITIES

LAND USE CHANGES

LAND USE CHANGE (LUC) modeling

LAND USESCENARIOS

(IAFENT)

CLIMATE SCENARIOS

(ANS)

Impacts and Modifications

(IAFENT, SCO)

SOCIO-ECONOMICSSCENARIOS

(CIP)

Resposes: Prevention, Mitigation

Adaptation


CMCC framework

PROGNOSTIC APPROACH

DIAGNOSTIC APPROACH

PAST/RECENT LAND USE

ACTUAL LAND USE

ANALYSIS OF CHANGES

ACTUAL LAND USE SIMULATIONMODELS

VALIDATION

FUTURE LAND USESIMULATION

CALIBRATION


Diagnostic Approach: trend analysis

Fifties-SixtiesLand use mapCNR-TCIScale 1:200000

1990Corine Land CoverScale 1:100000

2000Corine Land CoverScale 1:25000

Study area: Italy


•Spatially explicit•Simple and GIS-based•Different spatial scales•Every type of land use•Not constrained by specific input data•Not constrained by a given time step•Not constrained by limited study areas

Agarwal et al., 2001

Prognostic approach: future simulations

The CLUES model approach (Verburg et al., 2002) was chosen as it is a good compromise among the spatial scales, the temporal scales and the human influence it is able to take into account.


• Introduction of a more reliable algorithm to calculate area on lat/long grid, more appropriate over continental scales. • the grid can also consider different fraction of land use inside each pixel, more appropriate to work at coarser resolutions. • The logistic regression among input driving factors and binary maps of presence/absence of a given land use is performed at each time step, in order to account for likely adaptation. • We introduced a more objective criterion to evaluate the effects of neighboring land use on the land use changes in a given cell.

CLUES modification


Spatial resolution 500 mSimulation from 2000 to 2100

Land Use classesused in the simulation

Corine Descrizione classi nuove111 Continuous urban fabric112 Discontinuous urban fabric121 Industrial or commercial units122 Road and rail networks and associated land123 Port areas124 Airports131 Mineral extraction sites132 Dump sites133 Construction sites141 Green urban areas142 Sport and leisure facilities211 Non-irrigated arable land212 Permanently irrigated land213 Rice fields221 Vineyards222 Fruit trees and berry plantations223 Olive groves231 Pastures 3241 Annual crops associated with permanent crops 4242 Complex cultivation patterns 3243 Land principally occupied by agriculture, with significant areas of natural vegetation5244 Agro-forestry areas 6311 Broad-leaved forest312 Coniferous forest313 Mixed forest321 Natural grasslands322 Moors and heathland323 Sclerophyllous vegetation 9324 Transitional woodland-shrub 8331 Beaches, dunes, sands332 Bare rocks333 Sparsely vegetated areas334 Burnt areas335 Glaciers and perpetual snow 12411 Inland marshes412 Peat bogs421 Salt marshes422 Salines423 Intertidal flats511 Water courses512 Water bodies521 Coastal lagoons522 Estuaries523 Sea and ocean

1

0

not used

2

7

8

10

11

13

Simulation


Firstly, 24 driving factors were chosen as considered influencing the land use. 1. Elevations (digital elevation model IGMI at 20 m)2. Slope (calculated from 1)3. Aspect (calculated from 1)4. Soil carbon content (European Soil Database at 1 km)5. Soil clay content (European Soil Database at 1 km)6. Soil silt content(European Soil Database at 1 km)7. Soil sand content(European Soil Database at 1 km)8. Soil pH (European Soil Database at 1 km)9. Soil density (European Soil Database at 1 km)10.Soil depth (European Soil Database at 1 km)11.Field capacity (European Soil Database at 1 km)12.Population density (ISTAT, municipality scale)13.Labor force in agriculture (ISTAT, provincial scale)14.Labor force in commerce (ISTAT, local system work scale)15.Labor force in industry and services (ISTAT, local system work scale)16.Labor force in institutions (ISTAT, local system work scale)17.Annual mean precipitation (MARS-STAT, 50 km)18.Annual mean temperature (MARS-STAT, 50 km)19.Distance from road (road database)20.Distance from rivers (river network database)21.Distance from sea (coastal boundary database)22.Distance from cities (from Corine Land Cover database)23.Topographic index (calculated from 1)24.Water deficit index (calculated from 17, 18, 12 and EUROPEAN SOIL DATABASE)

For each of continuous variables, the Kolmogorov-Smirnov test was performed to verify the normality of distribution

Then the Spearman test was carried out to determine the non-correlation among parameters

Driving factors


Influencing factor β computation

FINAL DRIVING FACTORS1. Elevations2. Slope3. Aspect4. Soil carbon content5. Soil clay content6. Soil silt content7. Soil sand content8. Soil pH9. Soil density10. Soil depth11. Filed capacity12. Population density13. Labor force in agriculture14. Labor force in commerce15. Labor force in industry and services16. Labor force in institutions17. Annual mean precipitation18. Annual mean temperature19. Distances from the cities20. Topographic index

Calcolo dei fattori di influenza relativa perché i fattori predisponenti hanno diverse scale ed unità di misura

Logistic Regression


Driving factorsBinary maps: existence (1) or not (0) for each land use

nni

i xxxxP

P

...1

log 3322110

For each land use inversion of the logistic regression

Driving factors

β factors

Pil (0-1) for each map unit (pixel)

Comparison

)β...ββββexp(1

)β...ββββexp(

3322110

3322110

nn

nnil xxxx

xxxxP

Initial dicothomic map about the presence/absence of land use


It is possible to test the regression model performances considering different driving factors

For each Pil[0,1]

classes ROClu00 0.986lu01 0.959lu02 0.938lu03 0.885lu04 0.876lu05 0.889lu06 0.977lu07 0.863lu08 0.817lu09 0.915lu10 0.814lu11 0.821lu12 0.835lu13 0.711

media 0.878

Regression model accuracy


ROC (Receiving Operating Characteristics)

AREA BELOW THE CURVE

=0.5 random

=1 perfect

true positive rate (TPR) or sensitivityTPR = TP / P = TP / (TP + FN) false alarm rate (FPR)FPR = FP / N = FP / (FP + TN)

We included in the model a distance/enrichment factor (Santini et al., in prep.) avoiding to chose subjectively the distance into which calculating such factor as in (Verburg et al., 2004)

distance/enrichment effects among land uses in neighboring


ELASTICITY FACTORFUTURE LAND USE DEMAND

Demands are in ha, “c” indicates the central demographic hypothesis (slight population increase) while “h” the high demographic hypothesis (strong population increase)

Elasticity factors computed for mean and high impacts demands


c: central demographic hypothesis h: high demographic hypothesisa2: climate scenario a2 b2: climate scenario b2v: vicinity/enrichment nv: no vicinity/enrichmentp: protected areas np: no protected areas


16 simulations

Es. Results c_a2_nv


Both for A2 and B2 climate scenarios the largest changes interest the centre of Italy (about 20%) whereas the smallest ones the north (about 18%). Anyway in the A2 scenarios changes are stronger.

Simulation results have been analyzed in terms of landscape fragmentation due to land use changes, using appropriate patch and landscape indices

PR: PATCH RICHNESS (number of classes)SHDI: SHANNON’S DIVERSITY INDEXSIDI: SIMPSON’S DIVERSITY INDEXMSIDI: MODIFIED SIMPSON’S DIVERSITY INDEXSHEI: SHANNON’S EVENNESS INDEXSIEI: SIMPSON’S EVENNESS INDEXMSIEI: MODIFIED SIMPSON’S EVENNESS INDEXAI: AGGREGATION INDEX…

e.g. Radius of Gyration (m)

2000c_a2_nv

c_b2_nv


Analyzing patch metric we obtained that classes of artificial land use have a behaviour differing from the one of natural or semi-natural areas

period zone 2071-2080 2081-2090 2091-2100 2071-2080 2081-2090 2091-2100 2071-2080 2081-2090 2091-2100italy 8133 12889 4967 8566 13322 5400 9021 13776 5854north 26480 34725 19524 27134 35378 20178 27795 36040 20839center 1646 7819 -468 2162 8335 47 2692 8866 578south -2076 -1025 -3258 -1867 -817 -3050 -1615 -565 -2797island -2949 -2928 -3136 -2820 -2799 -3007 -2661 -2640 -2848italy 5920 14874 1770 6353 15307 2203 6807 15761 2657north 27725 38351 19897 29040 39666 21212 39713 39004 20550center -3063 11286 -7017 -2016 12332 -5971 13552 11801 -6501south -7169 -1672 -7429 -6708 -1211 -6969 12793 -1463 -7221island -3722 -3532 -5961 -3433 -3244 -5673 6746 -3403 -5832

Demographic Impact hypothesis

scenario A2

scenario B2

high medium low

period zone 1996-2005italy 22242north 38390center 23097south 14490island 3062

1996-2005

WRI = (EI-HC)

Scenarios-Climate scenario for the decades 2071-2080, 2081- 2090, 2091-2100- Lithological maps of Italy- Demographic prediction ISTAT up to 2051

Actual- Climate data MARS-STAT decade 1996-2005- Lithological maps of Italy- Population density ISTAT

There are many drought indices (e.g., Standardize Precipitation Index) used as indicators of water resources in terms of natural recharge, but not able to represent the water reserve as they do not take into account the groundwater balance, the land use etc. Recently a Groundwater Resource Index (Mendicino et al., 2008) has been developed that considers the soil component. But in order to perform a most complete evaluation of water resources it is necessary to account for human, agricultural and industrial consumption strictly dependent from climate and land use changes.

Water Resource Index

Land use change model validation for the period from 1950 to 2000(in progress)

High resolution land use change model validation (hydrographic basin scale) using land use maps for 1800 (pre-industrial age), 1878, 1939, 1980, 2000(in progress)

High resolution Climate scenarios CMCC (ANS division) + land use changes scenarios

Desertification risk evaluation and scenariosFocusing on water resources (human, agriculture, industry, energy use)

Forest type habitat migration according to forest spreading or reduction due to land use changes.

Future directions …

Socio-economic (CIP division) and land use change model coupling (Mediterranean Europe)Input to carbon model (ANS division)

- Magnani F. et al., co-author R. Valentini. 2008. Ecologically implausible carbon response? Reply. Nature, 451(7180): E3-E4- Vuichard N. et al., co-author R. Valentini. 2008. Carbon sequestration due to the abandonment of agriculture in former USSR since 1990 Global Biogeochemical Cycles, 22( GB4018): doi:10.1029/2008GB003212. - Wohlfahrt GA. et al., co-author R. Valentini. 2008. Biotic, Abiotic, and Management Controls on the Net Ecosystem CO2 Exchange of European Mountain Grassland Ecosystems. Ecosystems ,11: 1338–1351- Ciais P. et al., co-author R. Valentini. 2008. Carbon accumulation in European forests Nature Geosciences, 1(7): 425-4

- Dolman A.J., A. Freibauer and R. Valentini. 2008. The continental scale greenhouse gas balance of Europe Springer, New York, 2008- Papale D. et al., 2008. ASPIS, A Flexible Multispectral System for Airborne Remote Sensing Environmental Applications Sensors (8), 3240-3256.- Richardson A.D. et al., co-author D.Papale. 2008. Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals. Agricultural and Forest Meteorology, (148), 38-50 2008- Göckede M. et al., co-authors D.Papale, R. Valentini. 2008. Quality control of CarboEurope flux data - Part 1: Coupling footprint analyses with flux data quality assessment to evaluate sites in forest ecosystem. Biogeosciences (5), 433-450.- Carvalhais N. et al., co-authors D. Papale, R. Valentini. 2008. Implications of the carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval. Global Biogeochemical Cycles, (22), GB2007- Vetter M. et al., co-author D.Papale. 2008. Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models. Biogeosciences, (5), 561-583.- Desai A.R. et al., co-author D.Papale, 2008. Cross-site evaluation of eddy covariance GPP and RE decomposition techniques. Agricultural and Forest Meteorology, (148), 821-838.- Lasslop G. et al., co-author D.Papale. 2008. Influences of observation errors in eddy flux data on inverse model parameter estimation. Biogeosciences (5), 1311-1324.-Jung M. et al., co-author D. Papale. 2008. Diagnostic assessment of European gross prige mary production. Global Change Biology (14), 2349-2364.-Garbulsky M.F. et al., co-author D. Papale. 2008. Remote estimation of carbon dioxide uptake by a Mediterranean forest. Global Change Biology (14) 2860-2867.-Claus Beier et al., co-authors Duce P. and Spano D. Carbon and nitrogen balances for 6 shrublands across Europe. Global Biogeochemical Cycles (accepted)-Prieto P. Et al., co-authors Cesaraccio C., Pellizzaro G., and Sirca C. Changes in the onset of shrubland species spring growth in response to an experimental warming along a north-south gradient in Europe. Global Ecology and Biogeography (accepted).-M.J. Calejo, N. Lamaddalena, J.L. Teixeira, L.S. Pereira. 2008. Performance analysis of pressurized irrigation systems operating on demand using flow-driven simulation modelling. Journal of Agricultural Water Management. n. (95) 2008, pp 154-162.

Pubblications

Documents

Division on Impacts on Agriculture, Forest and Natural Ecosystems (IAFENT) Responsible Prof. Riccardo Valentini Lecce, 25 Marzo 2009