APPLICATION OF THE WEATHER GENERATOR IN PROBABILISTIC

CROP YIELD FORECASTING

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

project QC1316 of NAZV(National Agency for Agricultural Research, Czech Republic)

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)

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) Met&Roll 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

seasonal crop yield forecasting1. construction of weather series

seasonal crop yield forecasting2. running the crop model

Modifications of previous version of the4-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

A) 4-variate 6-variate:

4-variate series:@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

...

...

learning sample:@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

...

6-variate series:@DATE SRAD TMAX TMIN RAIN VAPO WIND

...

...

99001 1.9 -2.7 -6.3 0.3 0.34 3.099002 2.1 -3.6 -3.7 0.7 0.28 3.0

99003 1.5 0.1 -1.3 2.4 0.61 3.099004 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

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 t>D0

X(D0) = f [XOBS(D0 -1), e]

B) series which consistently follow with the observed data

C) modification of the synthetic weather series so that it fits the weather forecast:

weather forecast is given in terms of- expected values valid for the forthcoming days (e.g., first week: 12±2 °C, second week: 7±3 °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

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; without forecast!]

(better to say: forecast = mean climatology)

a) the case of good fit between model and observationsite = 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 >>>

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 = 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 >>>

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

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

indicators

PERUN - user’s interface

E N D

Thank you for your attention!

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

input data to crop modela) 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)

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

* weather forecast random component

METHOD = 1

...averages... ..std. deviation..

@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 @

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

* weather forecast random component

METHOD = 2

@

c) increments with respect to the long-term means

* weather forecast random component

METHOD = 3

...averages... ..std. deviation..

@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

@