APPLICATION OF THE WEATHER GENERATOR IN PROBABILISTIC CROP YIELD FORECASTING

1
APPLICATION OF THE WEATHER GENERATOR IN
PROBABILISTIC CROP YIELD FORECASTING

• Martin Dubrovský (1), Zdenek 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
project QC1316 of NAZV (National Agency for
Agricultural Research, Czech Republic)
2
PERUN system for crop model simulations under
various meteorological conditions

• development of the software started last year
• tasks to be solved by PERUN
• crop yield forecasting (main task)
• climate change/sensitivity impact analysis (task
  for future)

3
PERUN - components

• 1) WOFOST crop model (v. 7.1.1. provided by
  Alterra Wageningen)
• new Makkink formula for evapotranspiration was
  implemented
• (motivation Makkink does not need WIND and
  HUMIDITY data)
• 2) MetRoll weather generator (Dubrovský, 1997)
• some modifications had to be made (will be
  discussed later)
• 3) user interface
• - input for WOFOST (crop, soil and water,
  start/end of simulation, production levels,
  fertilisers, ...)
• - launching the process (weather generation, crop
  model simulation)
• - statistical and graphical processing of the
  simulation output

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seasonal crop yield forecasting 1. construction
of weather series
5
seasonal crop yield forecasting 2. running the
crop model
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Modifications of previous version of
the4-variate MetRoll 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

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A) 4-variate ? 6-variate
learning sample _at_DATE SRAD TMAX TMIN RAIN
VAPO WIND ... xx001 1.6 1.3 -1.5 3.3
0.63 1.0 xx002 1.6 -0.8 -3.8 0.3 0.53
1.7 xx003 3.9 -2.3 -9.9 0.0 0.23
2.0 xx004 4.5 -2.3 -11.4 0.0 0.38
1.0 xx005 1.6 -6.1 -12.9 0.0 0.33
1.3 xx006 1.6 -1.8 -12.4 1.1 0.23
3.3 xx007 3.8 1.2 -2.3 0.0 0.52
4.7 xx008 1.7 -0.1 -4.3 0.0 0.39
1.3 xx009 1.7 -1.8 -6.7 0.4 0.42
4.0 xx010 1.7 -3.8 -8.0 1.0 0.36
2.0 xx011 1.7 0.0 -3.9 8.3 0.46
2.0 xx012 2.9 3.7 -0.3 2.8 0.57
1.7 xx013 1.8 2.6 -0.8 1.0 0.62
2.0 xx014 4.0 2.9 -3.3 0.0 0.45
2.7 xx015 4.0 2.4 -5.9 0.0 0.37 1.3 ...

• 4-variate series
• _at_DATE SRAD TMAX TMIN RAIN
• ...
• 99001 1.9 -2.7 -6.3 0.3
• 99002 2.1 -3.6 -3.7 0.7
• 99003 1.5 0.1 -1.3 2.4
• 99004 2.4 0.3 -2.7 0.6
• 99005 1.4 -1.4 -5.1 0.1
• ...
• ...

6-variate series _at_DATE SRAD TMAX TMIN RAIN
VAPO WIND ... ...
99001 1.9 -2.7 -6.3 0.3 0.34 3.0
99002 2.1 -3.6 -3.7 0.7 0.28 3.0
99003 1.5 0.1 -1.3 2.4 0.61 3.0
99004 2.4 0.3 -2.7 0.6 0.57 3.0
99005 1.4 -1.4 -5.1 0.1 0.47 3.0
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B) series which consistently follow with the
observed data

• (1) generator working in normal-mode (1st-order
  generator assumed)
• X(t) f X(t-1), e e vector of random
  numbers
• X(0) PDF(X)
• (2) series which follows with the observed data
  at day D0
• (observed weather data are available till D0-1)
• X(t) f X(t-1), e for tgtD0
• X(D0) f XOBS(D0 -1), e

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B) series which consistently follow with the
observed data
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C) modification of the synthetic weather series
so that it fits the weather forecast
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weather forecast is given in terms of - expected
values valid for the forthcoming days (e.g.,
first week 122 C, second week 73 C, )

• alternative formats of the weather forecasts
  (useful in climate change/sensitivity analysis)
• - increments with respect to long-term means
• (first week temperature 2 C above normal
  precipitation 80 of normal
• second week .., .
• - increments to existing series

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crop yield forecasting at various days of the year

• probabilistic forecast ltavgstdgt is based on 30
  simulations
• input weather data for each simulation
• obs. weather till D-1 synt. weather since D
  without forecast!
• (better to say forecast mean climatology)
• a) the case of good fit between model and
  observation
• site Domanínek, Czech Rep.
• crop spring barley
• year 1999
• emergency day 122
• maturity day 225
• observed yield 4739 kg/ha
• model yield 4580 kg/ha
• (simulated with
• obs. weather series)

enlarge gtgtgt
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crop yield forecasting at various days of the year

• a) the case of good fit between model and
  observation

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Crop yield forecasting at various days of the year

• b) the case of poor fit between model and
  observation
• site Domanínek, Czech Republic
• crop spring barley
• year 1996
• emergency day 124
• maturity day 232
• observed yield 3956 kg/ha
• model yield 5739 kg/ha
• (simulated with
• obs. weather series)

enlarge gtgtgt
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crop yield forecasting at various days of the year

• b) the case of poor fit between model and
  observation

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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
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PERUN - users interface
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E N D

• Thank you for your attention!

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PERUN system for crop model simulations under
various meteorological conditions (development of
the software started last year)

• tasks to be solved by PERUN
• crop yield forecasting
• climate change/sensitivity impact analysis
• comprises all parts of the process
• preparing input parameters for the crop model
  (with a stress on the weather data)
• launching the crop model simulation
• statistical and graphical analysis of the crop
  model output

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input data to crop model

• a) non-weather data info about crop, soil and
  hydrology start/end of simulation, nutrients,
  ... (as in WCC)
• b) weather data
• - station
• - day D0 (observed data till D0 -1, synthetic
  since D0
• - weather forecast table (weather series are
  postprocessed to fit the weather forecast)
• - weather series observed till D0-1, synthetic
  since D0
• - formula for evapotranspiration Makkink or
  Penman
• c) N number of re-runs (new weather data are
  generated for each simulation other input data
  are always the same)

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a) weather forecast is given in terms of the
absolute values

• weather forecast random component
• METHOD 1
• ...averages... ..std.
  deviation..
• _at_JD-from JD-to TMAX TMIN PREC TMAX TMIN
  PREC
• 99121 99130 17 6 30 2 2
  10
• 99131 99140 14 4 60 3 3
  20
• 99141 99150 21 10 10 4 4
  10
• _at_

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b) weather forecast is given in terms of
increments to existing series

• weather forecast random component
• METHOD 2
• _at_

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c) increments with respect to the long-term means

• weather forecast random component
• METHOD 3
• ...averages... ..std.
  deviation..
• _at_JD-from JD-to TMAX TMIN PREC TMAX TMIN
  PREC
• 99121 99130 1 1 1.2 2 2
  0.1
• 99131 99140 0 0 1.0 2 2
  0.1
• 99141 99150 -1 -1 0.9 2 2
  0.1
• _at_