Application of Climate Predictions and Simulation Models for the ...

Application of Climate
Predictions and Simulation
Models for the Benefit of
Agriculture in Romania
Adriana MARICA, Aristita BUSUIOC, Roxana
BOJARIU, 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

Summary of the presentation
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.

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 consequences
on 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.

Using medium and long-range climate
forecasting to reduce impacts of climate
variability (2)
‣ 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.

11 11 TMS TMS and and 41 41 CMS CMS
connections connections
BAC
State of the art of climate predictions
achieved within the NMA
Forecasting network in Romania
TRN
MOL
National
Forecasting Centr
- Bucharest - ROU
TRS
Regional
Forecasting Centre
OLT
MUN
Bucharest
DOB
- Bucharest - MUN
-Constanta-DOB
- Bacau - MOL
- Cluj - TRN
- Sibiu - TRS
- Arad - BAC
- Craiova - OLT
Territorial Meteorological Station (TMS) • County Meteorological Station (CMS)

State of the art of climate predictions
achieved within the NMA
Medium-range forecasts (up to 7 days in advance)
-based on numerical weather prediction models and
statistical methods
Long-range forecasts:
• Monthly forecasts - using statistical methods: analogies,
self-regressive models
• Seasonal forecasts
Seasonal 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

Long-range climate forecasting
Monthly / Seasonal Forecasts
DECEMBER 2004
Temperature
JANUARY 2005
Temperature
FEBRUARY 2005
Temperature
Rainfall
Rainfall
Rainfall

Long-range climate forecasting
Seasonal Forecasts
TEMPERATURE
inter 2004/2005–Autumn 2005
RAINFALL
Winter 2004/2005–Autumn 2005

Exemple of application medium-range
weather forecasts
Soil moisture forecasting for 31 July 2003 / maize crop / 0-100cm soil depth
Available soil moisture at 31July 2003 / maize crop / 0-100cm soil depth
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

Examples of application seasonal
forecasts to soil moisture deficit and
maize yield
describe the 2003 results and 2005 preliminary
investigations as an example of application of seasonal
climate forecasting in the agriculture sector;
seek to demonstrate how seasonal climate forecast
combined with the CROPWAT model can estimate the
soil water deficits and maize yield reduction due to
crop stress under rainfed conditions or deficit
irrigation.

CROPWAT model
DATA
Climatic
Crop
Soil
Irrigation
INPUT
• Monthly means of
min. and max.
temperature, relative
humidity, sunshine
duration, wind speed
•rainfall data Monthly
• Kc, , crop
description, max.
rooting depth, % area
covered by plant
• initial soil moisture
condition and
available soil moisture
• irrigation scheduling
criteria
OUTPUT
⇒ reference
evapotranspiration
⇒ crop water requirement
⇒ irrigation requirement
⇒ actual crop
evapotranspiration
⇒ soil moisture deficit
⇒ estimated yield
reduction due to crop
stress
⇒ irrigation scheduling

Input data used
Monthly means climatic data:
•measured during April-May 2003 (min.& max. temp.
humidity, sunshine duration, wind speed and rainfall)
•estimated for 2003 summer season & 2005 spring and
summer season (temperature and rainfall)
Crop data:
• sowing date: 20 April / 5 May 2003 /20 April 2005
• standard crop coefficient (Kc(
Kc), crop yield data (Ky(
Ky)
and depletion fraction (P)
Soil data:
• total available moisture: 227/191 /227 mm
• initial available soil moisture: 170/163/185 mm
• maximum root infiltration rate: 40 mm/day
• maximum rooting depth: 1m

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

0
CALARASI 2003
The 2003 Results
mm
50
100
150
200
20-Apr
27-Apr
4-May
11-May
18-May
250
0
50
REAL
25-May
1-Jun
8-Jun
15-Jun
22-Jun
29-Jun
6-Jul
13-Jul
20-Jul
FORECAST
27-Jul
3-Aug
10-Aug
17-Aug
TAM RAM SMD -F SMD - R
TARGU-JIU 2003
FORECAST
24-Aug
31-Aug
Daily soil moisture
deficit simulated with
CROPWAT model
during rainfed maize
growing season, in the
weather forecast
conditions for summer
2003, as compared
with the real one
mm
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
REAL
TAM RAM SMD-F SMD-R
TAM: total available
moisture,
RAM: easily available
moisture
SMD: soil moisture
deficit

The 2003 Results
Changes in growing season rainfall and soil
moisture deficit in the seasonal weather forecast
as compared with the real weather conditions
TOTAL RAINFALL
SOIL MOISTURE DEFICIT
Rain (mm)
500
400
300
200
100
Forecast
Real
-35%
-60%
SMD (mm)
600
500
400
300
200
100
11%
Forecast
Real
91%
0
CALARASI
TARGU JIU
0
CALARASI
TARGU JIU

The 2003 Results
ESTIMATED MAIZE YIELD
%
0
-10
-20
-30
-40
-50
-60
CALARASI
Forecast
Real
TG.JIU
Effects of
estimated and real
weather conditions
on rainfed maize
yield reduction due
to crop stress
-70

The 2003 Results
Effects of different irrigation schedules on maize
yield simulated with CROPWAT at Calarasi site
Options
Rainfed
Irr.fixed
int&depth
Irr. . 70% of TAM
Irr. . 70% of RAM
Irr. . 100% of RAM
Net
irrigation
(mm)
-
240
366
405
449
Yield
reduction
(%)
53%
24%
10%
-
-

Spring & summer 2005 forecast
Temperature
Temperature
Rainfall
Rainfall

7
6
5
4
3
2
1
0
The 2005 Preliminary Results
The 2005 Preliminary Results
ETo
CWR
Irr.Req.
Calarasi 2005
Calarasi 2005
Daily soil moisture deficit
during maize growing
season, in the 2005
weather forecast
conditions, as compared
with the “normal”
conditions
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
Daily reference
evapotranspiration (ETo),
maize water
requirements (CWR),
irrigation requirements
(Irr.Req)
1-May
8-May
5-May
1-Jun
8-Jun
5-Jun
2-Jun
9-Jun
6-Jul
13-Jul
20-Jul
27-Jul
3-Aug
0-Aug
7-Aug
4-Aug
1-Aug
0-Apr
7-Apr
4-May
0
20
40
60
80
00
20
40
60
80
00
20
40
NORMAL
FORECAST
TAM RAM SMD-N SMD-F
0-Apr
27-Apr
4-May

-40
The 2005 Preliminary Results
mm
370
360
350
340
330
320
310
300
290
280
270
SOIL MOISTURE DEFICIT
-15.3%
Changes in growing season
soil moisture deficit under
forecast weather
conditions of the 2005
spring and summer, as
compared with the normal
Normal
Forecast
Yield reduction %
0
-5
-10
-15
-20
-25
-30
-35
ESTIMATED MAIZE YIELD REDUCTION
Normal Forecast
Effects of the forecasted
weather conditions on
rainfed maize yield
reduction due to crop
stress, as compared with
the normal

CONCLUSIONS
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.

Recommendations
• 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 Climate
Change and their impacts) and enhancing collaboration with
ECMWF, UK-MetOffice and EUMETSAT in order to increase the
precision and accuracy of long-term climate predictions in
Romania;
• Efforts in the next future will be needed to focus on operational
application of seasonal forecasts together with simulation
capabilities of agrometeorological models to choose the best
agricultural management options and assess the likelihood of
improving the crop yield level.

Thank You