Transcript APPLICATION OF THE WEATHER GENERATOR IN …

PERUN - The System for the Crop
Yield Forecasting and Assessing
Impacts of Climate Change
Martin Dubrovský (1)
Zdeněk Žalud (2), Mirek Trnka (2),
Jan Haberle (3), Petr Pešice (1)
(1) Institute of Atmospheric Physics, Prague, Czech Republic
(2) Mendel University of Agriculture and Forestry, Brno, Czech Republic
(3) Research Institute of Crop Production, Prague, Czech Republic
this presentation
The stress is put on the methodology, not
on the results !
PERUN =
system for crop model simulations
under various meteorological conditions
• 1st version: developed within project “Prediction
of yields of selected crops” (National Agency for
Agricultural Research, Czech Republic; 2001-2002)
• specific tasks solved by PERUN:
 crop yield forecasting
 climate change/sensitivity impact analysis
 + some components of PERUN are used (NATO project)
for assessing drought climatology (PDSI and SPI)
PERUN - components
1) WOFOST crop model (v. 7.1.1.; executable and source code provided
by Alterra Wageningen)
modification: Makkink formula for evapotranspiration implemented
(motivation: Makkink does not need WIND and HUMIDITY data)
2) Met&Roll weather generator
- Met&Roll = WGEN-like stochastic 4/6-variate daily weather generator;
(Dubrovský, 1997)
- 3 modifications were made (see the following slide)
3) user interface
- input for WOFOST (• crop • soil and water • weather & climate • start/end
of simulation • production levels • fertilisers ...)
- launching the process (preparing weather series, crop model simulation)
- statistical and graphical processing of the simulation output
Modifications of previous version of the
4-variate Met&Roll generator
(1) 4-variate  6-variate: To generate all six daily weather
characteristics required by WOFOST (PREC, SRAD, TMAX,
TMIN, VAP, WIND), the separate module adds values of VAP
and WIND to the previously generated four weather
characteristics (SRAD, TMAX, TMIN, PREC) using the nearest
neighbours resampling from the observed data.
(2) The generator may produce series which consistently
follow with the observed data at any day of the year.
(3) The second additional module allows to modify the synthetic
weather series so that it fits the weather forecast
sample:
A) 4-variate  6-variate: learning
@DATE SRAD TMAX TMIN
4-variate series:
@DATE
...
99001
99002
99003
99004
99005
...
...
SRAD
TMAX
TMIN
RAIN
1.9
2.1
1.5
2.4
1.4
-2.7
-3.6
0.1
0.3
-1.4
-6.3
-3.7
-1.3
-2.7
-5.1
0.3
0.7
2.4
0.6
0.1
6-variate series:
@DATE
...
99001
99002
SRAD
TMAX
TMIN
RAIN
VAPO
WIND
1.9
2.1
-2.7
-3.6
-6.3
-3.7
0.3
0.7
0.34
0.28
3.0
3.0
99003
99004
99005
...
1.5
2.4
1.4
0.1
0.3
-1.4
-1.3
-2.7
-5.1
2.4
0.6
0.1
0.61
0.57
0.47
3.0
3.0
3.0
...
xx001
xx002
xx003
xx004
xx005
xx006
xx007
xx008
xx009
xx010
xx011
xx012
xx013
xx014
xx015
...
1.6
1.6
3.9
4.5
1.6
1.6
3.8
1.7
1.7
1.7
1.7
2.9
1.8
4.0
4.0
1.3 -1.5
-0.8 -3.8
-2.3 -9.9
-2.3 -11.4
-6.1 -12.9
-1.8 -12.4
1.2 -2.3
-0.1 -4.3
-1.8 -6.7
-3.8 -8.0
0.0 -3.9
3.7 -0.3
2.6 -0.8
2.9 -3.3
2.4 -5.9
RAIN
VAPO
WIND
3.3
0.3
0.0
0.0
0.0
1.1
0.0
0.0
0.4
1.0
8.3
2.8
1.0
0.0
0.0
0.63
0.53
0.23
0.38
0.33
0.23
0.52
0.39
0.42
0.36
0.46
0.57
0.62
0.45
0.37
1.0
1.7
2.0
1.0
1.3
3.3
4.7
1.3
4.0
2.0
2.0
1.7
2.0
2.7
1.3
 nearest neighbours
resampling
B) series which consistently follow
with the observed data
C) modification of the synthetic weather
series so that it fits the weather forecast:
user interface
PERUN - output - daily series for a single year
PERUN - output - series of annual model characteristics
PERUN - output - summary statistics of the series of annual characteristics
PERUN - sensitivity analysis (day D0) - output (summary statistics)
probabilistic seasonal crop
yield forecasting
seasonal crop yield forecasting
1. construction of weather series
seasonal crop yield forecasting
2. running the crop model
weather forecast is given in terms of:
a) expected values valid for the forthcoming days
(e.g., first day/week: 12±2 °C, second day/week: 7±3 °C, …)
alternative formats of the weather forecast (useful in
climate change/sensitivity analysis):
b) increments with respect to long-term means
(1st day/week/decade:
temperature = + 2 C above normal;
precipitation = 80% of normal;
2nd day/week/decade: ….., …. )
c) increments to existing series
a) weather forecast given in terms of the
expected values
* weather forecast
METHOD = 1
...averages...
@JD-from JD-to TMAX TMIN PREC
99121
99130
17
6 30
99131
99140
14
4 60
99141
99150
21
10 10
@
random component
..std. deviation..
TMAX
TMIN
PREC
2
2
10
3
3
20
4
4
10
b,c) increments with respect to the longterm means or w.r.t. existing series
* weather forecast
METHOD = 3
...averages...
@JD-from JD-to TMAX TMIN PREC
99121
99130
1
1
1.2
99131
99140
0
0
1.0
99141
99150
-1
-1
0.9
@
random component
..std. deviation..
TMAX
TMIN
PREC
2
2
0.1
2
2
0.1
2
2
0.1
crop yield forecasting at various days of the year
probabilistic forecast <avg±std> is based on 30 simulations
input weather data for each simulation =
[obs. weather till D−1] + [synt. weather since D ~ mean climatology)
a) the case of good fit between model and observation
site
crop
year
emergency day
maturity day
observed yield
model yield
=
=
=
=
=
=
=
Domanínek, Czech Rep.
spring barley
1999
122
225
4739 kg/ha
4580 kg/ha
(simulated with
obs. weather series)
enlarge >>>
crop yield forecasting at various days of the year
a) the case of good fit between model and observation
crop yield forecasting at various days
of the year
b) the case of poor fit between model and observation
site
crop
year
emergency day
maturity day
observed yield
model yield
=
=
=
=
=
=
=
Domanínek, Czech Republic
spring barley
1996
124
232
3956 kg/ha
5739 kg/ha
(simulated with
obs. weather series)
enlarge >>>
crop yield forecasting at various days of the year
b) the case of poor fit between model and observation
crop yield forecasting at various days of the year
b) the case of poor fit between model and observation
indicators
task for future research: find indicators of the crop
growth/development (measurable during the growing period)
which could be used to correct the simulated
characteristics, thereby allowing more precise crop yield
forecast
climate change
impact analysis
climate change impact analysis:
input weather series
a) direct modification approach:
present climate:
observed weather series
changed climate: observed weather series modified by
climate change scenario
b) weather generator approach:
present climate:
WG with parameters derived from the
observed series
changed climate: parameters of WG are modified according
to the climate change scenario
climate change impact analysis:
climate change scenario (based on GCMs)
a) changes in the means of climatic characteristics
@SCENARIO: ECHAM4, SRES-A2, high
MONTH
DTR
PRE
--+
*
0
0.08
-0.6
1
-0.11
8.6
2
-0.08
12.6
3
0.06
8.4
4
0.34 -18.0
5
-0.22
9.0
6
-0.19
-2.7
7
0.24
-7.8
8
0.61 -16.1
9
0.00
10.3
10
0.20
-6.2
11
0.06
5.4
12
0.02
-4.9
climate sensitivity; 2050
RAD
TMN
TMP
TMX
*
+
+
+
2.2
2.99
3.01
3.07
0.6
3.84
3.77
3.73
7.9
4.51
4.44
4.44
6.0
3.42
3.38
3.48
6.6
2.74
2.91
3.08
-5.2
2.18
2.01
1.96
-0.6
2.12
2.02
1.93
3.6
2.75
2.89
2.99
7.1
3.27
3.55
3.88
-2.3
2.54
2.58
2.54
4.8
2.52
2.59
2.73
5.9
3.17
3.18
3.23
7.6
2.78
2.77
2.80
VAP
*
18.8
29.8
35.6
23.8
17.9
13.5
14.1
17.5
19.6
15.1
15.4
23.1
21.3
WND
*
0.2
3.2
4.5
3.3
-3.4
1.8
-0.3
-4.8
-9.7
1.2
2.0
1.1
-2.5
b) changes in the WG parameters
- including changes in variability, precipitation frequency, …
- problem: reliability of daily outputs from GCMs
climate change impacts - (multiple scenarios)
summary statistics from 30-year series
tools for batch analysis
• sensitivity analysis
• multi-site analysis
sensitivity analysis
PERUN - sensitivity analysis (soil) - output (summary statistics)
PERUN - sensitivity analysis (temperature) - output (summary statistics)
PERUN - sensitivity analysis (day D0) - output (summary statistics)
sensitivity analysis
3 parameters are varied: soil - station - RDmax
multi-site analysis
multi-station analysis: input table
# multi-station analysis
@idx soil
crop
wea
001 EC1.NEW BAakc.cab DOKS
002 EC2.NEW BAakc.cab LEDN
003 EC3.NEW BAakc.cab ZABC
004 EC5.NEW BAakc.cab ZATC
005 EC2.NEW BAakc.cab KROM
006 EC1.NEW BAakc.cab HOLE
.
.
.
.
***
lat
50.2
50.9
48.1
49.9
51.1
49.1
lon RDMsol
14.3
100
17.2
80
15.2
130
16.1
120
16.2
70
15.6
90
multi-station analysis: summary statistics
plans for future
• implementation of other crop models: … CERES?
• weather generator: other method for WIND & HUMID
• crop yield forecasting: improve the forecast skill by
finding indicators for statistical correction of model yields
• new applications:
– multi-site analysis: agroclimatic potential of Czechia (already
running!)
– climate change impacts: new scenarios
– in other regions
• … and improve programming (user interface, graphs, …)
ask me for the software
demonstration …
(if you are interested)
e n d
[email protected]
www.ufa.cas.cz/dub/crop/crop.htm