RT6 -Assessments of Impacts and Climate Change
Seasonal predictions of wheat yield and irrigation needs
in Northern Italy
Fausto Tomei, Valentina Pavan, Giulia Villani, Vittorio Marletto
Arpa Emilia-Romagna, Servizio Idro-Meteo-Clima, Bologna, Italy
Results
This work involves the application of
downscaled
multi-model
seasonal
predictions to forecasting useful agricultural
information for the Italian region of
Emilia-Romagna up to three months in
advance.
The wheat yield prediction method has been
run for the years 1987-1999 using observed
data until the last day of April and, after that
day, the daily predictions of both meteorological variables and watertable depth
obtained calibrating the seasonal multimodel forecasts for the May-June-July (MJJ)
period. Figure 5 shows the comparison
between observed and predicted yield
distributions using data collected in the
experimental farm of Cadriano, Bologna.
Comparison between the mean of the
observational data and the median of
ensemble forecasts gives a determination
coefficient of 0.48.
In particular we concentrated on the
prediction of wheat yield and irrigation
needs for kiwifruit orchards.
Brisighella
Fig. 1 - locations of the study
For both we were able to obtain from local
researchers important field data for
validating the models and checking
forecasting results.
Simulations were carried out by with the CRITERIA/WOFOST modelling software, produced and/or adapted
by us in the past and driven using ENSEMBLES Stream 2 downscaled multi-model predictions for the AMJ
(wheat) and JJA (kiwifruit) periods.
To carry out this work the modelling suite was integrated by a new routine to assess water table level (essential
for better wheat yield prediction) from rainfall data, and by a weather generator enabling to transform seasonal
predictions from monthly statistical anomalies to daily data series.
The results were very interesting for both applications, and encouraged us to perform a first operational
seasonal forecast of wheat yield for the current 2009 season, using output from the operational Ecmwf
ensemble predictions system.
10,00
8,00
Wheat yield (t/ha)
Introduction
6,00
4,00
2,00
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Fig 5
- Comparison between observed wheat yield at Cadriano, Bologna
(blue band, indicating the whole range of yields from all the experimental
plots) and the predicted distribution (box and whiskers) using multi-model
downscaled seasonal forecasts, for the MJJ period.
Figure 6 shows the comparison between actual irrigation data for kiwifruit measured at Brisighella, in the hills
of Emilia-Romagna, and the hindcasts computed using downscaled STREAM2 dataset for the months JJA, in
the years 1996-2005. The multi-model runs use five models, nine members, and five weather generator
replicates (225 member replicates).
Materials and method
Figure 2 shows the computational scheme developed at ARPA-SIMC for agronomical predictions: core of the
scheme is the coupled soil water balance and crop growth model, CRITERIA/WOFOST (C/W), which
describes the dynamics of water in agricultural soils with or without crops. The C/W software environment
(Marletto et al., 2007) requires the input of soil and crop parameters and daily meteorological data, namely
extreme temperatures, total precipitation and, if present, hypodermic water table depth.
In order to obtain a statistical prediction, observational meteorological data are used up to the day of
prediction. Starting from the first day of prediction, meteorological daily data are synthetically produced with a
weather generator (wg), based on Richardson scheme, using as input predicted and downscaled seasonal
anomalies added to the local climatology.
Observed
data
Fig. 6 - Comparison between actual irrigation data (red line) for kiwifruit measured in Brisighella, Italy, and the assessment of
irrigation
water needs computed using downscaled STREAM2 dataset for the JJA period (box and whiskers). Blue stars represent the irrigation
water need computed with CRITERIA model using actual weather data.
Output
(wheat yield,
irrigation needs,
…)
Fig. 2 - Agronomical predictions computational scheme.
The climate anomalies used for predictions are obtained by calibrating and downscaling the multi-model
seasonal ensemble forecasts extracted from the ENSEMBLES Stream 2 data-set and from the EUROSIP
ECMWF special project framework. The calibration and downscaling method is based on the MOS version of
a statistical multi-linear regression scheme developed in recent years at ARPA-SIMC (Pavan et al, 2005). The
high resolution anmalies forecasts needed as input for the wg consist of monthly cumulated precipitation, wet
day frequency, averaged minimum and maximum temperature and the mean difference of temperature between
dry and wet days. The observational data used to define the local climate predictands are obtained starting from
the daily analysis produced operationally by UCEA covering the whole Italian territory from 1987 to present,
with an approximate resolution of 35 km.
The synthetic precipitation data produced
by wg are used as input in an empirical
equation developed at ARPA-SIMC
(Tomei et al., 2009) to predict hypodermic
groundwater level, that is essential for a
correct analysis of water dynamics that
influence crop growth.
0.00
water level (m)
-0.50
-1.00
-1.50
-2.00
-2.50
January-03
May-04
September-05
observed watertable data
February-07
June-08
estimated values
Fig. 3
(kg / ha)
(mm)
- Comparison between observed groundwater depth in Ferrarese plain
(blue points) and forecast values (red line, R2 = 0.71).
Wheat
yield
Fig. 4 - Example of
wheat yield prediction, year 2007. Left: observed cumulated
rainfall for the first 6 months of 2007 at Cadriano (black solid line) and 10 runs of
WG using as input the ensemble mean of seasonal predictions (grey thin lines).
Right: wheat yield simulation from observed daily data (black solid line) and wheat
yield simulations obtained using WG data (grey thin lines).
The starting hypothesis of the formula is
that trend of groundwater depth during a
year can be approximated with a sinusoidal
curve and that observed variations related
to this curve are well correlated to
precipitation anomalies previous to the
data to estimate. Figure 3 shows the
validation of formula using the data of a
well located in the Ferrara plain (R2 =
0.71).
Finally the WOFOST routines, when
activated in C/W model, compute dry
biomass accumulation, so it is possible to
predict a statistical distribution of wheat
yield. Figure 4 shows an example of wheat
yield prediction for the year 2007 at
Cadriano, Bologna.
Figure 7 shows the comparison between
hindcasts of irrigation water needs for
kiwifruit at Brisighella computed using
downscaled multi-model STREAM2 dataset
for the JJA period and the values computed
with CRITERIA model using actual
weather data (tier-2 verification), for the
years 1987-2005.
Table 1 shows two performance indices
(the determination coefficient R2 and the
modelling efficiency index EF) for the 20
years period of tier-2 verification. EF is
computed as follows:
n
EF  1 
 ( Predicted
i 1
i 1
400
 Observed i ) 2
1987
n
 (Observed
i
Kiwifruit irrigation water need
300
CRITERIA/
WOFOST
model
200
Hypodermic
groundwater
depth model
Weather
Generator
100
Statistical
downscaling
mm
Multi-model
ensemble
seasonal
forecasts
i
 AvgObserved ) 2
where n represents the number of data
pairs, i is the pair index and AvgObserved is
the average of the observed data. EF
provides a simple index of model
performance on a relative scale, where
EF=1 indicates a perfect fit, EF=0 suggests
that the model predictions are no better
than a simple average, and a negative value
would indicate an eventually poor model
performance.
All STREAM2 model predictions perform
better than a simple average of observed
data (EF>0). The multi-model R2 is the
highest, while its efficiency is comparable to
the one of the best performing models.
1989
1991
1993
1995
1997
1999
2001
2003
2005
Fig. 7
- Comparison between irrigation water needs for kiwifruit
computed with CRITERIA model (grey diamonds) using actual weather
data of Brisighella, and the assessments of irrigation water needs (box and
whiskers), computed using downscaled multi-model STREAM2 dataset for
the JJA period.
Model
R2
EF
MULTI-MODEL
0.51
0.32
LFPW
0.27
0.21
INGV
0.42
0.36
IFMK
0.41
0.32
EGRR
0.22
0.17
ECMF
0.22
0.15
Table 1
- Tier-2 verification of the 1987-2005 hindcasts of irrigation
water needs for kiwifruit at Brisighella, Italy, using STREAM2 dataset, JJA
period.
References
Marletto V., Ventura F., Fontana G., Tomei F. (2007). Wheat growth simulation and yield prediction with seasonal forecasts and a
numerical model. Agricultural and forest meteorology 147, pp.71-79.
Pavan V. et al. (2005). Downscaling of DEMETER winter seasonal hindcasts over Northern Italy. Tellus, 57A:424-434.
Tomei F. et al. (2008). Seasonal weather predictions and crop modelling for wheat yield forecasting in Northern Italy, European Society for
Agronomy Congress Proceedings, Bologna, 15-19 September 2008.
Tomei F. et al. (2009). Sviluppo di un’equazione empirica per la stima e la previsione del livello piezometrico utilizzando dati pregressi e
anomalie nelle precipitazioni. AIAM Congress Proceedings, Sassari, 15-17 June 2009 (in Italian).
Tomei F., Villani G., Pavan V., Pratizzoli W. And Marletto V. (2009). Report on the quality of seasonal predictions of wheat yield and
irrigation needs in Northern Italy.. Ensembles Project, 6th EU R&D Framework Programme, Research Theme 6, Assessments
of Impacts and Climate Change, available as Deliverable 6.22 from www.ensembles-eu.org.
ENSEMBLES Final Symposium - A Changing Climate in Europe
17-19 November 2009, Exeter, UK