Long term simulations
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1 Representation of micrometeorological and physiological parameters with numerical models influencing the vineyard ecosystem: the case of Piemonte (Italy) Valentina Andreoli(1), Claudio Cassardo(1), Silvia Cavalletto(2), Silvia Ferrarese(1), Silvia Guidoni(2), Elena Mania(2) and Federico Spanna(3) Department of Physics and Natrisk Center, University of Torino Alma Universitas Taurinorum, Torino, Italy, 2Department of Agricultural, Forest and Food Sciences, University of Torino Alma Universitas Taurinorum, Torino, Italy, and 3Phytosanitary sector, Regione Piemonte, Torino, Italy 1 1
2 Aims of the project To simulate micro-meteorological conditions within vineyards using a numerical model (land surface SVAT scheme UTOPIA) To simulate physiological vineyard conditions using a numerical model (VINEYARD crop model) To infer micro-meteorological and physiological variables in the past and present climate To analyze which micro-climatic conditions could improve grape quality To analyze the chance of employ numerical model to evaluate agronomic variables trends by means of gridded databases 2
3 Long term simulations - Selected time period: January 1970 December Intercomparison of UTOPIA and NOAH land surface models driven by the global dataset GLDAS2.0 - Preliminary simulations performed by the VINEYARDS crop model driven by UTOPIA output data and by GLDAS2.0 dataset 3
4 Supplementary slides 4
5 The Piemonte region and selected sites Piedmontese sites: A = Cocconato (AT) B = Fubine (AL) C = Castiglione Falletto (CN) Vineyards characterized by the same climate but by different microclimatic conditions Different site characteristics Selected cultivar: Nebbiolo 5
6 UTOPIA model University of TOrino model of land Process Interaction with Atmosphere ( Cassardo, 2016) Diagnostic 1-D model Vegetation 1 layer BIG LEAF optimized for vineyards Physical processes: Partition of heat fluxes among air, vegetation and soil Soil and canopy energy balance and temperatures Hydrological processes Hydrological balance components Soil moisture Vegetation wetness Carbon assimilation rate and photosynthesis 6
7 The UTOPIA model simulations - The long term simulations were carried out using gridded datasets as input from 1970 to Selected gridded database: GLDAS2.0 (Global Land Data Assimilation System) - Selection of the grid point (0.25 ) nearest to the site - Check on input data quality GLDAS database - Possibility of intercomparison UTOPIA vs NOAH 7
8 Output data: UTOPIA vs NOAH Daily sensible heat flux SHF (W/m2) Time period: mean Standard deviation media n min max Range skew kurtosis Standard error UTOPIA NOAH R =
9 Output data: UTOPIA vs NOAH Daily latent heat flux LHF (W/m2) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
10 Output data: UTOPIA vs NOAH Daily net radiation NET (W/m2) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
11 Output data: UTOPIA vs NOAH Daily soil temperature ( C) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
12 Output data: UTOPIA vs NOAH Daily soil volumetric water content (m3/m3) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
13 Output data: UTOPIA vs NOAH Daily sensible heat flux SHF (W/m2) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
14 Output data: UTOPIA vs NOAH Daily latent heat flux LHF (W/m2) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
15 Output data: UTOPIA vs NOAH Daily net radiation NET (W/m2) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
16 Output data: UTOPIA vs NOAH Daily soil temperature ( C) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
17 Output data: UTOPIA vs NOAH Daily soil volumetric water content (m3/m3) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
18 Output data: UTOPIA vs NOAH Daily sensible heat flux SHF (W/m2) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
19 Output data: UTOPIA vs NOAH Daily latent heat flux LHF (W/m2) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R = 0.96
20 Output data: UTOPIA vs NOAH Daily net radiation NET (W/m2) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
21 Output data: UTOPIA vs NOAH Daily soil temperature ( C) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
22 Output data: UTOPIA vs NOAH Daily soil volumetric water content (m3/m3) Time period: mean sd median min max Range skew kurtosis se UTOPIA NOAH R =
23 Output data: UTOPIA Simulation of variables of agronomic interest: the evapotranspiration 23
24 Output data: UTOPIA Castiglione Falletto Simulation of variables of agronomic interest: soil temperature and volumetric water content Soil category: loam Volumetric water content at field capacity: (m3/m3) Volumetric water content at wilting point: (m3/m3) 24
25 Output data: UTOPIA Cocconato Simulation of variables of agronomic interest: the soil temperature and the volumetric water content Soil category: loam Volumetric water content at field capacity: (m3/m3) Volumetric water content at wilting point: (m3/m3) 25
26 Output data: UTOPIA Fubine Simulation of variables of agronomic interest: the soil temperature and the volumetric water content Soil category: loam Volumetric water content at field capacity: (m3/m3) Volumetric water content at wilting point: (m3/m3) 26
27 VINEYARD CROP model (Andreoli V., Cassardo C., 2016) Measured data Air temperature(a), Air relative humidity(b), Solar global radiation(c), Photosynthetically active radiation(d), Soil temperature(e), Soil water content(f), Wind speed and direction(g), Rainfall(h), Leaf wetness(i) Vineyard and soil characteristics Geography (latitude, longitude, slope, height) Soil hydrology, Plant density (plants/ha), Variety characteristics (clusters/plants, berries/cluster,..), Vineyard management (trimming, severity of trimming). VINEYARD CROP MODEL Main model outputs Predawn leaf water potential(f,h), Timing of the main phenological phases (dormancy break, budburst...)(a,b,c,e,f,g), Leaf development(a,f), Yield(a,d,f), Sugar concentration(a,b,c,e,f,g). -I.Cortazar Atauri et al., Asynchromous dynamics of grapevine (VITIS VINIFERA) maturation: experimental study for a modelling approach. J. Int. Sci. Vigne Vin, 2009, 43, n.2, G.Cola et al.,description and testing of a weather-based model for predicting phenology, canopy development and source-sink balance in Vitis Vinifera L. cv. Barbera. Agricultural and Forest Meteorology 184 (2014) A.Singels et al.,refinement and validation of the PUTU wheat crop growth model 2.leaf area expansion. S.Afr.J.Plant Soil 1991, 8(2) 27
28 The CROP model simulations The long term simulations were carried out using as input data: UTOPIA long term output: - soil temperature - soil water content - air relative humidity - leaf wetness and GLDAS2.0 data: - air temperature - solar global radiation - photosyntetically active radiation - wind speed and direction - rainfall - atmospheric pressure Time period: Selected sites: Cocconato, Fubine and Castiglione Falletto 28
29 Output data: VINEYARD CROP model Simulation of Leaf Area Index maximum value The high LAI value of Cocconato site can be mainly associated with the higher soil moisture value 29
30 Output data: VINEYARD CROP model Simulation of berry sugar content (October 1st) Increasing trend of berry sugar content due to the higher air temperature value The low sugar content value of Cocconato site can be mainly associated with the higher soil moisture 30
31 Output data: VINEYARD CROP model Simulation of yield/vine The high value of yield in Cocconato can be mainly associated with the higher LAI and soil moisture value 31
32 Output data: VINEYARD CROP model Simulation of main phenological phases The decreasing phenological timing trends is due to the increase of value of air temperature 32
33 Conclusions... We have simulated 40 years of data ( ) using GLDAS2.0 dataset as input for driving the land surface model UTOPIA, and compared some output with those of NOAH land surface scheme, already stored in GLDAS2.0. The simulations show that the two models give reasonably close output, but with some differences, which change according with the variable considered. We have implemented a phenological model able to infer micro-meteorological and physiological variables, and we have applied it to the same 40-years period in order to have a long time trend relative to the past climate and allow us to make a sort of climatology of such variables The analysis of the output of two models (UTOPIA and VINEYARD CROP models) is ongoing. We present here the first interpretations on the main data....and future perspectives to simulate different cultivars to parameterize other variables as: acids, alcohol content... The author acknowledges a grant jointly supportedby the Lagrange Project CRT Foundation/ISI Foundation (Torino, ITALY) and IWAYs.r.l. (Cernusco sul Naviglio (MI), ITALY) 33
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