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APPLICATION OF THE WEATHER GENERATOR IN PROBABILISTIC CROP YIELD FORECASTING Martin Dubrovsk (1), Zdenk alud (2), Mirek Trnka (2), Jan Haberle (3) Petr Peice (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) Slide 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) Slide 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) Met&Roll weather generator (Dubrovsk, 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 Slide 4 seasonal crop yield forecasting 1. construction of weather series Slide 5 seasonal crop yield forecasting 2. running the crop model Slide 6 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 Slide 7 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 3.0 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 Slide 8 B) series which consistently follow with the observed data (1) generator working in normal-mode (1 st -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 D 0 : (observed weather data are available till D 0 -1) X(t) = f [X(t-1), e]for t>D 0 X(D 0 ) = f [X OBS (D 0 -1), e] Slide 9 B) series which consistently follow with the observed data Slide 10 C) modification of the synthetic weather series so that it fits the weather forecast: Slide 11 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 Slide 12 crop yield forecasting at various days of the year probabilistic forecast is based on 30 simulations input weather data for each simulation = [obs. weather till D1] + [synt. weather since D; without forecast!] (better to say: forecast = mean climatology) a) the case of good fit between model and observation site =Domannek, Czech Rep. crop=spring barley year=1999 emergency day=122 maturity day=225 observed yield=4739kg/ha model yield=4580kg/ha (simulated with obs. weather series) enlarge >>> Slide 13 crop yield forecasting at various days of the year a) the case of good fit between model and observation Slide 14 Crop yield forecasting at various days of the year b) the case of poor fit between model and observation site = Domannek, Czech Republic crop= spring barley year=1996 emergency day=124 maturity day=232 observed yield=3956kg/ha model yield=5739kg/ha (simulated with obs. weather series) enlarge >>> Slide 15 crop yield forecasting at various days of the year b) the case of poor fit between model and observation Slide 16 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 Slide 17 PERUN - users interface Slide 18 E N D Thank you for your attention! Slide 19 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 Slide 20 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 D 0 (observed data till D 0 1, synthetic since D 0 ; - weather forecast table (weather series are postprocessed to fit the weather forecast) - weather series = observed till D 0 1, synthetic since D 0 - 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) Slide 21 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 @ Slide 22 b) weather forecast is given in terms of increments to existing series * weather forecast random component METHOD = 2 @ Slide 23 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 @