Presentation

Vittorio Marletto
ARPA Emilia-Romagna
[email protected]
Meteorological Technology World Expo 2015 Brussels
Contents
 Arpa Emilia-Romagna & the regional weather service
 Background:
 soil water modelling
 crop remote sensing
 seasonal predictions
 The iColt system in Emilia-Romagna, description and
results
 The next step, an overview of the EU Horizon 2020
MOSES innovation action (2015-18)
Meteorological Technology World Expo 2015 Brussels
Emilia-Romagna, a region of Italy
Meteorological Technology World Expo 2015 Brussels
What is Arpa?
 More than 1000 staff, mainly technical
 Monitoring the environment and collecting a large
amount of physical, chemical and biological data on air,
water, soil and biota
 Controlling pollution from industries and other sources
 Providing technical support to the regional govt. of
Emilia-Romagna and local authorities
 Including a robust and experienced regional hydrometeo-climate service (started in 1984)
Arpa, Hydro-Meteo-Climate Service
Meteorological Technology World Expo 2015 Brussels
Hydromet and radar monitoring
network
ERG5 Emilia-Romagna 5 km hourly weather
grid for plant disease and irrigation
applications
Plant disease
models and advice
(run by Regional
offices)
Agrometeorological Bulletin
Weekly and monthly information for farmers
Maps of
Temperature
Precipitation
Evapotranspiration
SOIL WATER
CONTENT:
Phenology:
Local measurements
and regional
simulations
Local measurements
and regional
simulations
Heat sums
IRRINET
Irrigation advice
to farmers (run
by CER)
Allergenic Pollen Bulletin
Drought and desertification
Emilia-Romagna observatory (www.arpa.emr.it/siccita)
Bullettins
Drought
NDVI anomalies
Temperature anomaliies
Indicators and Data
Precipitazton amount and anomalies
River flow
SPI Index
Decils
Data and forecasts
Available water
Traspiration Deficit
iColt - Background
 Soil water and crop modelling – CRITERIA
(www.tinyurl.com/criteriamodel )
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Mathematical modelling:
Criteria at a glance
 Soil water balance: numerical model (based on Richard’s
equation) and empirical model
 Simple crop leaves and roots development model (phenology)
 Evaluation functions (potential and actual ET, capillary rise…)
 Water stress and irrigation
 Crop growth model (for wheat and maize)
 Nitrogen model
Bittelli, M., Tomei, F., Pistocchi, A., Flury, M., Boll, J., Brooks, E.S., Antolini, G. (2010)
Development and testing of a physically based, three-dimensional model of surface and subsurface
hydrology, Advances in Water Resources, 33 (1), 106-122.
Regional soil map and database
Soil
Texture
Soil profile
properties
Criteria soil moisture and irrigation
irrigation
[mm]
soil
depth
[cm]
Criteria geographical system
Forecasting irrigation
demand needs a
geographical approach for
input data and to produce
statistical analysis of
outputs.
Here an example of
average seasonal
irrigation water needs
(mm).
iColt - Background
 Soil water and crop modelling – CRITERIA
(www.tinyurl.com/criteriamodel )
 Remote sensing – crop mapping
Meteorological Technology World Expo 2015 Brussels
Sat images acquisition windows
1.
02/11/2014 UK-DMC2 & 01/11/2015 Landsat 8 (clouds)
2.
19/02/2015 UK-DMC2
3.
31/03/2015 UK-DMC2
Results
During the acquisition windows a team of two technicians go around the study area to collect field
information such as: crop, BBCH, etc. E. g. in 2015 there were 826 plot surveyed
We are in the sixth year of application
50
40
30
Eeee summer crops
EAgc winter crops
EPpm meadows and alfalfa
LPfv fruit orchards and vineyards
EEri rice
20
10
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iColt - Background
 Soil water and crop modelling – CRITERIA
(www.tinyurl.com/criteriamodel )
 Remote sensing – crop mapping
 Seasonal downscaled predictions
Meteorological Technology World Expo 2015 Brussels
Seasonal forecasts are among us…
Seasonal predictions
 Global probabilistic seasonal forecasts “multi-model
ensemble” are produced at ECMWF by means of 2
models: ECMWF (SFEC) and Météo France (LFPW)
and are available from 1991 up to now.
 At Arpa global forecasts are calibrated and
downscaled to local climate, from large scale fields
Z500 (geopotential at 500 hPa) and T850
(temperature at 850 hPa).
 High resolution final prediction for each model
consists in an ensembles of seasonal anomalies for
several variables needed as input of the weather
generator scheme
Format of the seasonal predictions
(anomalies over northern Italy)
Agronomical impact simulations
Modelling scheme of seasonal predictions
local
weather
observed
data
Multi-model
seasonal
forecasts
Statistical
downscaling
prec., wet days,
tmin, tmax (mean and
std. deviation)
Weather
Generator
(daily meteo
series)
Watertable
equation
local
watertable
observation
AgriModel
(Criteria)
Output
(Agronomical
impacts)
Weather Generator input variables
Name
Input data of WG
Unit
Tmax
Tmin
Txsd
Tnsd
mean of maximum temperature
mean of minimum temperature
standard deviation of maximum temperature
standard deviation of minimum temperature
°C
°C
°C
°C
Prcp
mean of total precipitation
mm
Fwet
fraction of wet days
-
Tdw
difference between maximum temperatures on dry and wet
days
°C
Richardson, C. W., and Wright, D. A. (1984). WGEN: A model for generating daily weather variables. U.S. Department of
Agriculture, Agricultural Research Service, ARS-8, 83 pp.
Stöckle, C.O., Campbell, G.S., and Nelson, R. (1999). ClimGen manual. Biological Systems Engineering Department,
Washington State University, Pullman, WA. 28 pp.
Synthetic weather series
Previous 9 months
obs. data
Climate: local observed
data
(min. 20 years)
54
34
b
c
d
e
f
g
65
Precipitation
32
b
c
d
e
f
g
Tmin
30
30
60
c
d
e
f
g
28
Tmean
b
c
d
e
f
g
26
Tmax
c
d
e
f
g
Climate
55
25
50
24
15
40
20
35
18
30
16
Temperature [°C]
45
10
5
0
-5
-10
01/07/2001
31/12/2002
01/07/2004
31/12/2005
02/07/2007
31/12/2008
02/07/2010
01/01/2012
02/07/2013
14
12
20
8
10
6
5
4
0
31/12/2014
b
c
d
e
f
g
Precipitation
Tmin
Tmean
Tmax
Climate
20
18
16
2
14
12
10
0
8
6
4
-4
-6
-8
-10
01/09/2009
b
c
d
e
f
g
26
24
22
10
15
b
c
d
e
f
g
b
c
d
e
f
g
34
32
30
28
-2
2
0
04/10/2009
07/11/2009
11/12/2009
14/01/2010
17/02/2010
23/03/2010
26/04/2010
30/05/2010
03/07/2010
06/08/2010
54
34
32
30
28
26
24
20
b
c
d
e
f
g
Precipitation
b
c
d
e
f
g
Tmin
46
44
42
b
c
d
e
f
g
Tmean
b
c
d
e
f
g
Tmax
b
c
d
e
f
g
Climate
16
14
12
26
24
22
10
8
Precipitazioni [mm]
34
32
30
28
18
20
18
16
6
4
2
14
12
10
0
-2
8
6
4
-4
-6
-8
-10
01/09/2009
52
50
48
40
38
36
22
Temperature [°C]
-15
01/01/2000
25
22
b
c
d
e
f
g
46
44
42
Precipitazioni [mm]
20
52
50
48
40
38
36
Precipitation [mm]
70
35
Seasonal
forecasts
anomalies
2
0
04/10/2009
07/11/2009
11/12/2009
14/01/2010
17/02/2010
23/03/2010
26/04/2010
30/05/2010
03/07/2010
06/08/2010
Number of years of synthetic series: models x members x replicates
Watertable depth assessment
using daily temperature and precipitation
0
H  H 0    wi (( Pi  PETi )  ( Pavg  PETavg ))
i  n
Where:
n is the number of days in the recharge
period,
P (mm) is the daily precipitation,
PET (mm) is the daily potential
evapotranspiration,
H0 (m) is the mean watertable depth,
α (m mm-1) is a correlation parameter
and wi is the daily weight:
Tomei, F., Antolini, G., Tomozeiu, R., Pavan, V., Villani,
G., & Marletto, V. (2010, May). Analysis of precipitation
in Emilia-Romagna (Italy) and impacts of climate
change scenarios. In Proceedings of Statistics in
hydrology Working Group (STAHY-WG) International
workshop, Taormina (pp. 23-25).
wi  1 
i
n
Model vs obs. watertable depth in Cadriano (Bologna)
The iCOLT system workflow
iColt forecasts – (11/6/2015) Mm3
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iColt results
•The eight panels refer to EmiliaRomagna reclamation consortia
•They show the box plot for the
probabilistic seasonal predictions of
irrigation need anomaly (in m3/ha)
obtained using the iCOLT system for the
years 2011, 2012 and 2013.
•Climatological values and validation
values (red dots) are estimated using the
CRITERIA water balance model forced
with observed meteorological data.
•Boxes cover from the 25th to the 75th
percentile, whiskers extend to the 5th and
95th percentile while extreme values are
indicated by black dots.
•More details on the Ecmwf newsletter,
March 2014, available online
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iCOLT service evolution
Year
Remote sensing coverage
Seasonal irrigation
predictions
2007-2008
Plain between Bologna and
Reggio-Emilia
NO
2008-2009
Emilia-Romagna
NO
2009-2010
Emilia-Romagna
YES
2010-2011
Emilia-Romagna
YES
2011-2012
Emilia-Romagna
YES
2012-2013
Emilia-Romagna with watertable
YES
2013-2014
Emilia-Romagna with watertable
YES
2014-2015
Emilia-Romagna with watertable
YES
What next?
Meteorological Technology World Expo 2015 Brussels
EU H2020 MOSES innovation action (2015-2018)
Managing crOp water Saving with Enterprise Services
 MOSES aims at putting in place and demonstrate at
the real scale of application an information platform
devoted to planning of irrigation water resources, to
support water procurement & management
agencies (e.g. reclamation consortia, irrigation
districts, etc.).
Its main goals are:
 saving water
 improving services to farmers
 reducing monetary and energy costs
MOSES Context Diagram
MOSES Timeline activity
MOSES Functionalities
 The platform results from the trans-disciplinary
integration of many different innovative approaches like
satellite remote sensing, seasonal and medium-term
weather forecasting, agronomic modelling, economy, and
online GIS Decision Support System (DSS)
Its main functionalities are:




Seasonal probabilistic forecasting /downscaling
Early in-season crop mapping
In-season water demand monitoring
Long and medium term irrigation water demand forecasting
MOSES Service areas
MOSES Product Portfolio
Temporal
Validity
Temporal Update
MOSES PRIMARY PRODUCTS
1
2
3
Long term water irrigation demand forecast maps –
before season
Evapotranspiration and water availability
monitoring maps – in season
Short term water irrigation demand forecast maps –
in season
Once for Season
Season
Weekly Update
Day/week
Weekly Update
Day/Week
MOSES DERIVED PRODUCTS
4
Topographic/cadastral maps, soil maps
Once for project
-
5
Land use/land cover
Once for season
Season
6
Climatic reference maps (precipitation,
temperature, PET, simplified water budgets etc.)
20-30 yrs
20-30 yrs
7
Climatic anomaly maps for current and former
year(s)
quarterly
3 months
8
Seasonal forecasted anomalies (prec. T, other
variables)
yearly
3 months
9
Crop classification maps
yearly
grow season
10
Numerical weather forecasts
daily
10 days
11
Rivers discharge
Weekly/monthly
Monthly/seasonal
12
Soil humidity
Weekly/monthly
Monthly/seasonal
MOSES Framework and Organization
 16 partners: environmental agencies, universities, research
institutes, space associations, water consortia, irrigator
associations, SME & industries (5 European countries, 3
continents)
 3 stake-holders
 4 Demonstration Area located in: Italy, Spain, Romania and
Morocco
 Core Activities: Project management, Scientific and Demo
Areas coordination
 Partners points of strength and roles identified
 Researchers, Stake-holders and end-user involvement
Web site www.moses-project.eu, coming soon
Stay tuned… and thanks from all of us!
ArpaER agromet, remote sensing and climate group
 G. Antolini, L. Botarelli, V. Marletto, A. Pasquali, V. Pavan, W. Pratizzoli, A.
Spisni, F. Tomei, R. Tomozeiu, G. Villani, A. Volta, L. Sapia
 [email protected]
This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 642258