1. 1 Vegetation dynamics and soil water balance in a water-limited Mediterranean ecosystem on Sardinia, Italy Nicola Montaldo 1, John D. Albertson 2 and Marco Mancini 3 3- Dipartimento di Ingegneria Idraulica, Ambientale, e del Rilevamento, Politecnico di Milano, Italy 2- Department of Civil and Environmental Engineering, Pratt School of Engineering, Duke University, USA 10–14 December 2007, AGU FALL MEETING 1- Dipartimento di Ingegneria del Territorio, Università di Cagliari (nicola.montaldo@unica.it)

2. 2  Field monitoring of land surface fluxes, soil moisture and vegetation dynamics for years with different hydro-meteorological conditions of a water-limited Mediterranean heterogeneous ecosystem;  Development of a 3-component (bare soil, grass and woody vegetation) coupled VDM-LSM for modeling land surface dynamics;  Assess the influence of key environmental factors on the vegetation dynamics for the different annual hydrologic conditions Goals Methodology  Experimental field campaign at Orroli (Sardinia) for monitoring land surface fluxes and vegetation growth… started in May 2003  Development of a coupled VDM-LSM for competing vegetation species (grass, woody vegetation)  test the coupled model for the Orroli site and data analysis

3. 3 The experiment: Orroli site (From April 2003) Flumendosa dam Mulargia dam

4. 4 The experiment: Orroli site in the Flumendosa basin

5. 5 The experiment: Eddy correlation tower for monitoring land surface fluxes a b c d a- CNR1 Integral radiometer b- H 2 O/CO 2 gas analyzer c- Soil heat d- CSAT3 Sonic anemometer Energy balance H+LE=R n -G 3 infrared transducers, IRTS-P (Apogee)

6. 6 The experiment: LAI estimate with the CEPTOMETER LP-80 PAR (photosynthetically active radiation) sensor (LI-190SB) Soil moisture probes (CS616 Campbell sci.) Silt loam

7. 7 Quickbird image Spring: 4 May 2004 (spatial resolution 2.8 m) 1 km The tower Orroli The experiment: Remote sensing observations Quickbird image Summer: 3 August 2003 (spatial resolution 2.8 m) The tower

8. 8 The field heterogeneity (Detto et al., WRR 2006) The interpretation of eddy- covariance measurements through the foot print model (a revised 2-D version 2-D version of the foot print model of Hsieh et al. [2000] through the foot print model (a revised 2-D version 2-D version of the foot print model of Hsieh et al. [2000] ) f v,wv (fraction of woody vegetation) Estimate the source area of the flux at each time step Normalized difference vegetation index (NDVI) of woody vegetation NDVI/NDVI MAX

9. 9 Bare soil Grass Woody veg. Woody vegetation transpiration Bare soil evaporation competition for root zone soil water content Grass transpiration f bs f v,wv f v,g Patch mosaic Patchs Decomposition of the Landscape The Land Surface model (LSM).. 3-components Infiltration,I Drainage, Q dr Soil moisture,  Runoff (Albertson and Kiely, J. Hydrology, 2001; Montaldo and Albertson, J. Hydrometeorology,2(6), 2001)   Root zone budget: (f v,g +f v,wv + f bs =1)

10. 10 with k= w (woody vegetation) or g (grass) Penmann-Monteith  Evapotranspiration ET= E v,g + E v,w + E bs Canopy resistance E bs =f bs  (  ) E p f1()f1()  wilt  lim 1 0 f 2 (T) T min T opt 1 0 T max Grass Woody veg. Bare soil [  (  )] From observations, using the foot print model Woody veg. Detto et al. WRR, 2007

11. 11 Vegetation dynamic model of the generic vegetation type  Green (leaves) biomass  Root biomass  Dead biomass     a g, a s, a r allocation coefficients, dinamically estimated  P g : Gross photosynthesis    Maintenance and growth respirations Senescence Litter fall ProductionDestruction Derived from Montaldo et al., [WRR, 2005]; Nouvellon et al., 2001  Stem biomass

12. 12 Gross photosynthesis fraction of PAR absorbed by the canopy  P is the leaf photochemical efficiency [g dry mass/ PAR] PAR (0.38-0.71  m) Substomatal cavity Montaldo et al., [WRR, 2005]

13. 13 Allocation coefficients Derived from Arora and Boer [GCB, 11, 39-59, 2005] Woody vegetation Grass

14. 14 VDM+LSM (3-components) at the Orroli site: Soil Moisture…

15. 15 VDM+LSM at the Orroli site: Surface temperature

16. 16 VDM+LSM at the Orroli site: Energy balance components

17. 17 VDM+LSM (3-components) at the Orroli site:ET

18. 18 VDM+LSM (3-components) at the Orroli site:LAI

19. 19 Allocation coefficients in VDM

20. 20 Comparison of observed and hystorical mean monthly Precipitation and temperature

21. 21 Influence of soil moisture and temperature on grass dynamics during the observed years

22. 22 Correlation between grass LAI and precipitation (April and May) mean 15-day values of grass LAI versus the aggregated 15-day precipitation values time lagged by 15 days

23. 23Conclusions  The yearly variability of hydro-meteorological conditions offered a wide range of conditions for testing the developed 3-component (bare soil, grass and woody vegetation) coupled VDM-LSM model. The model performed well for the whole period of observation and was able to accurately predict vegetation dynamics, soil water balance and land surface fluxes.  Interannual variability of hydromet-conditions can significantly affect vegetation growth in these water limited ecosystems: importance to include VDMs in LSM  The correlation was found to be high when the values of precipitation and LAI are aggregated at 15-day time intervals, and there is a sufficient time lag (15-days) between the forcing (precipitation) and the answer (LAI) Nicola Montaldo (nicola.montaldo@unica.it)

24. 24 Throughfall Soil water balance Drainage Evapotranspiration Balance of intercepted water by vegetation LAI grass Rainfall Atmospheric forcings (R i, RH, u, T, PAR) Biomass budget Photosynthesis Respiration Translocation Senescence Land Surface Model Grass VDM Energy balance Soil heat dynamic LSM+VDM coupled model LAI woody veg. Biomass budget Senescence Respiration Translocation Photosynthesis Woody veg. VDM Competition for water

25. 25 Allocation coefficient model of Arora and Boer (GCB, 11, 39-59, 2005) Woody vegetation Grass