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 SM D-F SM D-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 0-Apr 27-Apr 4-May TAM RAM SMD-N SMD-F
-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