Odhad dopadu zmeny klimatu na výnosy zemedelských plodin s vyuzitím rustového modelu a stochastického generátoru

+ Zdenek Zalud, Milada Stastna (MZLU Brno; simulace rustovým modelem)

+ Ivana Nemesova, Jaroslava Kalvova; (príprava scénáre zmeny klimatu)

obsah * zakladni schema * stochasticky generator (model, validace, pouzitelnost SG pro RM * citlivostní analýza * validace rustového modelu [CERES-Maize, Barley, Wheat] * scénár zmeny klimatu * odhad dopadu zmeny klimatu (stressed yields vs potential yields, direct vs. indirect effects, sensitivity analysis) * adaptation analysis * záver (zdroje chyb, co dál) * Seznam obrazku

Obsah

odhad dopadu zmeny klimatu s vyuzitím rustového modelu (schéma)

bylo udeláno:

validace stochastického generátoru (SG)
pouzitelnost SG pro RM
citlivostní analýza
validace rustového modelu (RM) [CERES-Maize, Barley, Wheat]
scénár zmeny klimatu
odhad dopadu zmeny klimatu

- stressed yields vs potential yields
- direct vs. indirect effects
- sensitivity analysis

záver

zdroje chyb:
co dál:

Seznam obrazku:
Fig. 1: Estimating impact of climate change on crop yields - scheme.
Fig. 2: prímá modifikace pozorovaných rad.
Fig. 3: stochastický generátor.
Fig. 4: Reproduction of the mean annual cycle of TMAX.
Fig. 5: Reproduction of the parameters of the precipitation model.
Fig. 6: Annual cycle of lag-0 correlations among SRAD, TMAX and TMIN.
Fig. 7: Reproduction of the variability of monthly and annual means by Met&Roll.
Fig. 8: Validation of the variability of model maize yields.
Fig. 9: Sensitivity of model yields to statistical structure of input daily weather series.
Fig. 10: Validation of CERES-Maize
Fig. 11: Validation of CERES-Wheat
Fig. 12: Grid points of ECHAM GCM
Fig. 13: Climate Change Scenario
Fig. 14: Potential and stressed yields simulated with stochastically generated weather.
Fig. 15: Effect of changes in individual weather characteristics on model grain yields
Fig. 16: Effect of the planting date on model yields (1´ CO2 weather; 1´ CO2 in the atmosphere)
Fig. 17: Effect of the planting date on model yields (2´ CO2 weather; 1´ CO2 in the atmosphere) ______________________________________________________________

další informace: www.ufa.anet.cz/dub.htm#wg

poster z konference ESA /Lleida, cerven 1999/ (Word 97, *.prdf)
workshop z Potsdami ríjen 1999 (Word 97)
Met&Roll (exe)

scheme

Fig. 1: Estimating impact of climate change on crop yields - scheme

Two approaches to multi-year crop growth simulations:

The crop model simulations with observed pedological, physiological and cultivation data specific for each individual year. Observed weather series is used for present climate simulations; the weather series for changed climate simulations is obtained by direct modification of observed series according to the climate change scenario.

Pedological, physiological and cultivation data are taken from a single "representative" year and arbitrarily long synthetic weather series is created by the stochastic weather generator. Parameters of the generator derived from the observed series are used to generate weather series representing present climate; parameters of the generator are modified in accordance with climate change scenario to generate series representing changed climate.

terminology:

stressed yields - the yields limited by available water and nutrients
potential yields - the yields at optimum supply of water and nutrients
direct effect of CO₂: only c (~ ambient CO₂ concentration varies)
indirect effect of CO₂: only climate (due to CO₂ increase) varies
combined effect of increased CO₂: both climate and c varies

Jak zajistit rady denních meteorologických charakteristik pro zmenené klima ?
prímý výstup z GCM (prízemní charakteristiky)

denní cirkulacní charakteristiky z GCM + downscaling

mezomerítkový model "vnorený" do GCM

metoda analogu (zalozená na podobnosti velkoprostorových klimatických charakteristik v case nebo prostoru)

prímá modifikace pozorovaných rad

stochastický generátor syntetických rad

Fig. 2 prímá modifikace pozorovaných rad:

Fig. 3 stochastický generátor:

stochastický Met&Roll - 4-variate stochastic daily weather generator

model:

variable	model	parameters
precip. occurrence	Markov chain (order=1)	_PI1_{, PI}₀₁
precipitation amount (RAIN)	Gamma distribution	ALPHA, BETA
solar radiation (SRAD) max. temperature (TMAX) min. temperature (TMIN)	AR model (order=1)	two 3x3 matrices - 3 ´ (wet/dry) ´ (avg’s/std's)

all parameters of the generator [including matrices of the AR(1) model [!new!] may vary during a year
interannual variability of monthly means may be controlled [!new!]

available procedures:

analysis (= calculating parameters of the generator’s model and other statistical characteristics from the observed series; range checking is included)

modification of parameters of the generator’s model according to given climate change scenario

stochastic generation of synthetic weather series with use of original or modified WG’s parameters

direct modification of existing series (according to given climate change scenario)

web: http://www.ufa.cas.cz/dub/dub.htm#met&roll

Validation of the stochastic structure of synthetic weather series motivation:
stochastic structure of observed and synthetic weather series should be the same

validation tests were focused on:

parameters of the weather generator [Fig. 4, Fig. 5]

distribution of daily precipitation totals

distribution of length of dry and wet periods

normality of variables of AR model (SRAD, TMAX, TMIN)

annual cycle of lag-0 and lag-1 correlations among variables of the autoregressive model [Fig. 6]

variability of monthly and annual means [Fig. 7]

results (of the comparison of synthetic vs. observed series):

the weather generator underestimates:

- frequency of occurrence of long dry spells
- extreme values of daily precipitation amount
- variability of annual and monthly means [Fig. 7]

daily sums of global solar radiation (SRAD) and daily extreme temperatures (TMAX and TMIN) do not follow normal distribution assumed by AR model
correlations and lag-1 correlations among SRAD, TMAX and TMIN exhibit significant annual cycle not assumed by older version of the generator’s model [Fig. 6]
on the whole, the model of the generator performs better in summer months

Fig. 4 Reproduction of the mean annual cycle of TMAX.

lines: smoothed annual cycles derived from observed data
symbols: smoothed annual cycles derived from synthetic data

Fig. 5 Reproduction of the parameters of the precipitation model

solid lines with filled symbols: parameters derived from observed series dashed lines with empty symbols: parameters derived from synthetic series

Fig. 6: Annual cycle of lag-0 correlations among SRAD, TMAX and TMIN.

The sample correlation coefficients for individual weeks were calculated from the 30-year observed series. The vertical bars at the right part of the graphs indicate the 95% confidence intervals of the all-year correlations. Note: the new version of Met&Roll allows to consider annual cycles of the lag-0 and lag-1 correlations.

Fig. 7: Reproduction of the variability of monthly and annual means by Met&Roll.

The figure displays the ratios of observed to synthetic sample standard deviations of monthly and annual (Y) means of SRAD, TMAX, TMIN and RAIN. These ratios were averaged over 17 stations in the Czech Republic. Note: the new version of Met&Roll allows to increase the interannual variability of monthly means

Applicability of the Weather Generator for crop growth model CERES (validation of variability of model grain yields)

Motivation: How the generator's imperfections (in reproducing stochastic structure of daily weather series) affect model crop yields simulated by CERES-Maize?

Hypothesis: the lower variability of synthetic series may imply lower variability of model grain yields

Validation experiment:

input daily weather series (17 Czech stations):

OBS: 30-year observed series

SYN: 30-year synthetic series generated by Met&Roll

other (pedological, planting, management, ...) input data:

- the same settings for each model year

testing characteristic: distribution function of model grain yields from the 30-year run
comparison of distributions obtained with OBS and SYN weather series:

- visually (quantile characteristics of grain yields; Fig. 8)
- Wilcoxon test

conclusion: No statistically significant difference was found (~Wilcoxon test) and it is thus assumed that the synthetic weather series generated by Met&Roll are applicable to crop growth simulations

Fig. 8 Validation of the variability of model maize yields. The minima, 5^th smallest values, medians and maxima of the grain yields were calculated from the 30-year CERES-Maize simulations with use of observed (lines + rectangles) and synthetic (circles) weather series related to 17 Czech stations.

sensitivity of grain yields to statistical structure of weather series

motivation:

to gain a notion on possible errors resulting from
- inaccuracy of climate change scenarios
- ambiguities in projecting climate change scenario into WG’s parameters
to demonstrate possibilities of the weather generator

experiment (for each climate change scenario):

step 1: modification of weather generator’s parameter(s) [according to given scenario; see the list below]

step 2: generation of 99-year synthetic weather series (Met&Roll)

step 3: simulation of 99-year growth series (CERES-Maize)

step 4: determining quantile characteristics of model grain yields [>>> results are displayed in Fig. 9]

list of scenarios used in the sensitivity analysis:

A: modification of means of daily extreme temperatures (daily temperature amplitude is preserved)

B: modification of daily temperature amplitude (daily temperature means are preserved)

C: modification of standard deviations of SRAD, TMAX and TMIN

D: modification of interdiurnal variability in AR model

E: modification of mean daily precipitation amount (by modifying scale parameter of the Gamma distribution)

F: modification of frequency of wet days

G: shape of distribution of daily precipitation amount is modified

H: simultaneous modification of frequency of wet days & mean daily precipitation amount (monthly precipitation sums are preserved)

I: modification of interdiurnal variability of precipitation occurrence

Fig. 9: Sensitivity of model yields to statistical structure of input daily weather series. Quantiles of the sets of grain yields obtained in 99-year crop growth simulations for various scenarios (see the list). The numbers to the right of each bar are values of the standardised Wilcoxon statistic for testing the hypothesis that the distribution of grain yields under a given scenario does not differ from the present-climate distribution ("no change" scenario).

[note: input data for crop model simulations slightly differ from those used in Figs......]

Validation of crop models

Fig. 10 Validation of CERES-Maize:

Fig. 11 Validation of CERES-Wheat:

Climate Change Scenario

(Nemešová et al., 1998)

Fig. 12 Grid points of ECHAM GCM

Fig. 13 Climate Change Scenario:

changes of daily extreme temperatures (TMAX and TMIN) are based on daily GCM output (ECHAM3/T42)

changes of precipitation (PREC) are based on other GCMs' output and IPCC recommendations

changes of solar radiation (SRAD) are based on statistical model relating monthly characteristics []

Effect of increased CO₂ on stressed and potential yields

Fig. 14 Potential (water and N are non-limiting) and stressed (water and N routines are "switched on") yields simulated with stochastically generated weather.

Bars represent quantiles (5^th, 25^th, median, 75^th, 95^th) from 99-year simulations.

comments:

the increase due to direct effect is greater that the decrease due to the indirect effect (maize yields increase in doubled CO₂climate)
effects of increased CO₂ is less pronounced in water and nitrogen unlimited conditions (potential yields)

Sensitivity analysis

Fig. 15 Effect of changes in individual weather characteristics on model grain yields

[The bars represent quantiles (5^th, 25^th, 50^th, 75^th, 95^th) from 99-year simulations.]

List of scenarios used in the sensitivity analysis

present WG’s parameters derived from observed series (Zabcice, 1961- 90)
2´CO2 parameters of WG modified according to the climate change scenario (Fig.8)
PREC=const as "2´ CO2" but the precipitation is unmodified
only PREC only precipitation is modified
only SRAD only solar radiation is modified
only TEMP only temperature characteristics are modified
var=const as "2´ CO2" but variances of TMAX, TMIN and SRAD unmodified

Comments:

stressed yield: change in any characteristic lowers the yields
potential yield: slight increase of yields under 2´ CO2 conditions appears to be a superposition of decrease due to temperature rise and increase due to solar radiation rise

adaptation analysis

Fig. 16 Effect of the planting date on model yields
(1´ CO2 weather; 1´ CO2 in the atmosphere):

Fig. 17 Effect of the planting date on model yields
(2´ CO2 weather; 1´ CO2 in the atmosphere):

ZAVER

stochastický generátor: je pouzitelný

výsledky jsou srovnatelné se studiemi jiných autori

zdroje chyb:

verohodnost meteorologických rad pro zmenené klima (scénár rustu emisí skleníkových plynu /a aerosolu/, presnost GCM, spolehlivost SG)

presnost rustového modelu

zmeny v technologii zemedelské výroby pro období zmeneného klimatu (jiné odrudy, jiné agrotechnologické postupy)

co dál:

scénáre zmeny klimatu - vyuzití novejších verzí GCM

stochastický generátor - vylepšení modelu (zahrnutí mezirocní variability, Markovuv retezec vyššího rádu, ...)

další plodiny, ...

adaptacní analýza

rozšírení jednobodové analýzy na plošnou analýzu (odhad produkcního potenciálu pro dané území)