Application of Climate Application of Climate Predictions and Simulation Predictions and Simulation Models for the Benefit of Models for the Benefit of Agriculture in RomaniaAgriculture in Romania

Adriana MARICA, Aristita BUSUIOC, RoxanaBOJARIU, Constanta BORONEANT

WMO/CAgM Expert Team Meeting on Impact of Climate Change/Variability and Medium-to Long-Range Predictions for

Agriculture, Brisbane, Australia, 15-18 February 2005

Introduction: Impacts of climate variability/change on agriculture in Romania;

Using medium and long-range climate forecasting to reduce impacts of climate variability;

State of the art of climate predictions achieved within the National Meteorological Administration in Romania;

Some examples of application seasonal forecasts to soil moisture deficit and maize yield, using the CROPWAT model;

Conclusions and recommendations.

Summary of the presentation

Introduction: Impacts of climate variability/change on agriculture in Romania

√ Like in many others countries in the south-eastern Europe, in Romania climate variability, including extreme events result in high variability in crop yield levels with negative consequenceson food supply and economy;

√ Some research studies have shown that during history drought events have caused yield losses up to 40-60%, especially in the southern part of the Romanian Plain (Tuinea et al., 2000);

√ Also, in the extremely dry years, such as 2000, the largest water shortage and rainfall variability associated with high maximum temperature during the critical phases of maize crop (silking-grain filling) resulted in significant yield reduction up to 90% (Marica 2003);

Introduction: Impacts of climate variability/change on agriculture in Romania

√ Both climate variability and climate extremes may increase as a result of global warming;

√ It is becoming more and more evident that food supply in our country will be affected by future climate change, particularly in regions with high present-day vulnerability and little potential for adaptation, such as the southern part of Romania (Simota & Marica, 1997; Cuculeanu et. al., 1999);

√ Recent studies show that changes in climate predicted by global climate model HadCM3, SRES scenario A2, may have significant negative effects on water balance elements and maize yield (Marica & Busuioc, 2004);

Using medium and long-range climate forecasting to reduce impacts of climate

variability (1)

Better knowledge of climatic variability together with the availability of climate forecasts and agrometeorological models are key components for improving agricultural decision making at the farm or policy level;

Medium range forecasts are of great usefulness for farmers in short-term decisions:

• whether to carry out or not specific agricultural practices

• to schedule farm work (to decide if to sow or not, to

spray or not, to irrigate or not)

• if the decision is made to irrigate what should be the

amount of irrigation.

Prediction of seasonal climate fluctuations play an important role in long-term agricultural planning and can have many benefits for agriculture:

• can be used to reduce some of the losses associated with climate variability;

• can help agricultural managers maintain their agricultural productivity in spite of extreme climatic events;

• can help water resources managers ensure reliable water deliveries;

• can offer the potential for agricultural producers to plan ahead and modify decisions to decrease unwanted impacts or take advantage of expected favorable conditions.

Using medium and long-range climate forecasting to reduce impacts of climate

variability (2)

National Forecasting Centre - Bucharest - ROU

Regional Forecasting Centres

- Bucharest - MUN - Constanta - DOB - Bacau - MOL - Cluj - TRN - Sibiu - TRS - Arad - BAC - Craiova - OLT

MUN

TRN

DOBOLT

TRS

BACMOL

Bucharest

Forecasting network in RomaniaForecasting network in RomaniaForecasting network in Romania

11 TM

S and 41

CM

S

11 TM

S and 41

CM

S

connect

ions

connect

ions

Territorial Meteorological Station (TMS) • County Meteorological Station (CMS)

State of the art of climate predictions State of the art of climate predictions achieved within the NMA achieved within the NMA

State of the art of climate predictions State of the art of climate predictions achieved within the NMA achieved within the NMA

Medium-range forecasts (up to 7 days in advanceup to 7 days in advance)-based on numerical weather prediction models and statistical methods

Long-range forecasts:

• Monthly forecastsMonthly forecasts - using statistical methods: analogies, self-regressive models

• Seasonal forecastsSeasonal forecasts- based on the integration of statistical methods

(conditional probabilities, autoregressive model and multi-field analog prediction)

- lead time: 3 months and 1-3 seasons

TemperatureDECEMBER 2004

Rainfall

Monthly / Seasonal ForecastsMonthly / Seasonal Forecasts

JANUARY 2005Temperature

Rainfall

Long-range climate forecasting

FEBRUARY 2005Temperature

Rainfall

TEMPERATURE TEMPERATURE

Winter 2004/2005Winter 2004/2005––Autumn 2005Autumn 2005

RAINFALL RAINFALL

Winter 2004/2005Winter 2004/2005––Autumn 2005Autumn 2005

Seasonal ForecastsSeasonal Forecasts

Long-range climate forecasting

Medium range weather forecast

of weekly precipitation and temperature used

in combination with a simple soil

water balance model (SWB) for estimating soil

moisture content

Soil moisture forecasting for 31 July 2003 / maize crop / 0-100cm soil depth

Exemple of application medium-range weather forecasts

Available soil moisture at 31July 2003 / maize crop / 0-100cm soil depth

Examples of application seasonal forecasts to soil moisture deficit and

maize yield

seek to demonstrate how seasonal climate forecast seek to demonstrate how seasonal climate forecast combined with the CROPWAT model can estimate the combined with the CROPWAT model can estimate the soil water deficits and maize yield reduction due to soil water deficits and maize yield reduction due to crop stress undercrop stress under rainfedrainfed conditions or deficit conditions or deficit irrigation.irrigation.

describe the 2003 results and 2005 preliminary describe the 2003 results and 2005 preliminary investigations as an example of application of seasonal investigations as an example of application of seasonal climate forecasting in the agriculture sector;climate forecasting in the agriculture sector;

CROPWAT modelCROPWAT model

⇒⇒ reference reference evapotranspirationevapotranspiration

⇒⇒ crop water requirementcrop water requirement

⇒⇒ irrigation requirementirrigation requirement

⇒⇒ actual crop actual crop evapotranspirationevapotranspiration

⇒⇒ soil moisture deficitsoil moisture deficit

⇒⇒ estimated yield estimated yield reduction due to crop reduction due to crop stressstress

⇒⇒ irrigation schedulingirrigation scheduling

•• Monthly means of Monthly means of min. and max. min. and max. temperature, relative temperature, relative humidity, sunshine humidity, sunshine duration, wind speedduration, wind speed••rainfall data Monthlyrainfall data Monthly

•• KcKc, crop , crop description, max. description, max. rooting depth, % area rooting depth, % area covered by plantcovered by plant

•• initial soil moisture initial soil moisture condition and condition and available soil moistureavailable soil moisture

•• irrigation scheduling irrigation scheduling criteriacriteria

ClimaticClimatic

CropCrop

SoilSoil

IrrigationIrrigation

OUTPUTOUTPUTINPUTINPUTDATADATA

Input data usedInput data used

Monthly means climatic data:Monthly means climatic data:••measured during Aprilmeasured during April--May 2003 May 2003 (min.& max. temp. humidity, sunshine duration, wind speed and rainfall)••estimated for 2003 summer season & 2005 spring and estimated for 2003 summer season & 2005 spring and summer season summer season (temperature and rainfall)

Crop data:Crop data:•• sowing date: sowing date: 20 April / 5 May 2003 /20 April 2005•• standard crop coefficient (standard crop coefficient (KcKc), crop yield data (), crop yield data (KyKy))

and depletion fraction (P)and depletion fraction (P)

Soil data:Soil data:•• total available moisture: total available moisture: 227/191 /227 mm•• initial available soil moisture: initial available soil moisture: 170/163/185 mm•• maximum root infiltration rate: maximum root infiltration rate: 40 mm/day•• maximum rooting depth: maximum rooting depth: 1m

Model application:Model application:

•• For the case studies in 2003, at Calarasi and Tg.Jiu sites, the CROPWAT model was run with rainfed and irrigated maize in the forecasted and real weather conditions;

•• For the case study in 2005, only in Calarasi site, the model was run only with rainfed maize in the forecasted and “normal” weather conditions.

Summer 2003 forecast

Temperature Rainfall

CALARASI 20030

50

100

150

200

250

20-Apr

27-Apr

4-May

11-May

18-May

25-May

1-Jun

8-Jun

15-Jun

22-Jun

29-Jun

6-Jul

13-Jul

20-Jul

27-Jul

3-Aug

10-Aug

17-Aug

24-Aug

31-Aug

mm

TAM RAM SMD -F SMD - R

TARGU-JIU 20030

50

100

150

200

250

5-May

12-May

19-May

26-May

2-Jun

9-Jun

16-Jun

23-Jun

30-Jun

7-Jul

14-Jul

21-Jul

28-Jul

4-Aug

11-Aug

18-Aug

25-Aug

1-Sep

8-Sep

15-Sep

mm

TAM RAM SMD-F SMD-R

The 2003 ResultsThe 2003 Results

Daily soil moisture Daily soil moisture deficit simulated with deficit simulated with

CROPWAT model CROPWAT model during during rainfedrainfed maize maize

growing season, in the growing season, in the weather forecast weather forecast

conditions for summer conditions for summer 2003, as compared 2003, as compared with the real onewith the real one

TAM:TAM: total available total available moisture,moisture,

RAM:RAM: easily available easily available moisturemoisture

SMD:SMD: soil moisture soil moisture deficitdeficit

FORECAST

REAL

FORECAST

REAL

TOTAL RAINFALL

0

100

200

300

400

500

CALARASI TARGU JIU

Rain

(mm

)

ForecastReal

SOIL MOISTURE DEFICIT

0

100

200

300

400

500

600

CALARASI TARGU JIU

SM

D (m

m)

ForecastReal

The 2003 ResultsThe 2003 Results

Changes in growing season rainfall and soil Changes in growing season rainfall and soil moisture deficit in the seasonal weather forecast moisture deficit in the seasonal weather forecast

as compared with the real weather conditions as compared with the real weather conditions

-35%

-60%

11%

91%

ESTIMATED MAIZE YIELD

-70

-60

-50

-40

-30

-20

-10

0CALARASI TG.JIU

%

ForecastReal

Effects of Effects of estimated and real estimated and real weather conditions weather conditions on on rainfedrainfed maize maize yield reduction due yield reduction due to crop stressto crop stress

The 2003 ResultsThe 2003 Results

Effects of different irrigation schedules on maize Effects of different irrigation schedules on maize yield simulated with CROPWAT at yield simulated with CROPWAT at Calarasi Calarasi sitesite

53%53%24%24%10%10%

----

--240240366366405405449449

RainfedRainfedIrrIrr.fixed.fixed intint&depth&depthIrrIrr. 70% of TAM. 70% of TAMIrrIrr. 70% of RAM. 70% of RAMIrrIrr. 100% of RAM. 100% of RAM

Yield Yield reductionreduction

(%)(%)

Net Net irrigationirrigation

(mm)(mm)

OptionsOptions

The 2003 ResultsThe 2003 Results

Spring & summer 2005 forecast

Temperature

Rainfall

Temperature

Rainfall

The 2005 Preliminary ResultsThe 2005 Preliminary Results

Calarasi 2005

0

1

2

3

4

5

6

7

20-Ap

r27-Ap

r4-M

ay11-May18-May25-May1-J

un8-J

un15-Ju

n22-Ju

n29-Ju

n6-J

ul13-Ju

l20-Ju

l27-Ju

l3-A

ug10-Au

g17-Au

g24-Au

g31-Au

g

mm

/per

iod

EToCWRIrr.Req.

Daily reference evapotranspiration (ETo), maize water requirements (CWR), irrigation requirements (Irr.Req)

Daily soil moisture deficit during maize growing season, in the 2005 weather forecast conditions, as compared with the “normal”conditions

Calarasi 2005020406080100120140160180200220240

20-Ap

r27-Ap

r4-M

ay11-May18-May25-May1-J

un8-J

un15-Ju

n22-Ju

n29-Ju

n6-J

ul13-Ju

l20-Ju

l27-Ju

l3-A

ug10-Au

g17-Au

g24-Au

g31-Au

g

mm

TAM RAM SMD-N SMD-F

FORECAST

NORMAL

The 2005 Preliminary ResultsThe 2005 Preliminary Results

ESTIMATED MAIZE YIELD REDUCTION

-40

-35

-30

-25

-20

-15

-10

-5

0

Normal Forecast

Yiel

d re

duct

ion

%

SOIL MOISTURE DEFICIT

270280290300310320330340350360370

Normal Forecast

mm

Changes in growing season soil moisture deficit under

forecast weather conditions of the 2005 spring and summer, as

compared with the normal

Effects of the forecasted weather conditions on

rainfed maize yield reduction due to crop

stress, as compared with the normal

-15.3%

CONCLUSIONSCONCLUSIONS

The application of seasonal weather forecasts together with CROPWAT model allows the estimation of soil water supply conditions with 3-6 months ahead and in case a skillful forecast can help farmers and decision makers to minimize negative consequences of unfavorable weather conditions or take advantages of favorable conditions;

Examples given in this paper have shown that the combination of seasonal forecast information and agrometeorological models give promising results for estimating maize yield reduction due to crop stress;

The use this technology of simulation models, as an essential component of agricultural applications of seasonal climate prediction, provides useful information to the benefit of agriculture.

• Improve the skill level of seasonal weather forecasts and develop methods for adapting such forecasts in order to enhance the planning activities in agriculture as well as to avoid crop yield looses;

• Using the results of new climate research projects such as ENSEMBLES (Ensemble-based Predictions of ClimateChange and their impacts) and enhancing collaboration with ECMWF, UK-MetOffice and EUMETSAT in order to increase theprecision and accuracy of long-term climate predictions in Romania;

• Efforts in the next future will be needed to focus on operationalapplication of seasonal forecasts together with simulation capabilities of agrometeorological models to choose the best agricultural management options and assess the likelihood ofimproving the crop yield level.

RecommendationsRecommendations

Thank You Thank You