APEC 기후센터 기후변화연구팀의 Sanai Li 박사가 오는 2월 26부터 3월 1일까지 태국 방콕에서 열리는 Climate Data Analysis and Crop Modeling Training 워크숍에 참석한다. Sanai Li 박사는 영국 기상청의 요청을 받아 워크숍을 이끌게 될 5명의 교육 전문가 중 1명으로 참석하게 된다.

Li 박사는 워크숍이 진행되는 동안 2개의 발표를 진행할 예정이다. 발표 제목과 abstract은 다음과 같다.
 

(1st Presentation)
 

▶ Title: Assessment of climate change impacts on agriculture, including a case study on crop impacts in China

▶ ABSTRACT

In addition to the impacts of mean climate change, increased frequency of heat stress, droughts and floods has imposed significant negative impacts on crop yields in some parts of the world. Continued crop yield increases will be required to meet the rising population. Food security remains a challenge, particular in developing countries. Rising maximum temperature has imposed a negative impact on rice yield in most of the Asian countries. The high maximum temperature in July and August is the main factor affecting rice yield. The impact of minimum temperature is small. Variability in rice yield is significantly affected by internal variability in rainfall, especially in India and Japan, where about 60% of rice yield variability is determined by rainfall variability. Thus, it is important to adjust the planting date and crop rotation to avoid heat and water drought in July and August to improve yield production. In order to assess the impacts of climate change on agriculture, China was selected as a case study. Regional climate model outputs are linked with crop models and a hydrological model to assess the impacts of climate change and socio-economic factors on crop production and water availability, respectively. Results showed that climate change alone will result in a small decrease in crop production in China. Water scarcity and land use change will further reduce crop production. In China, the reduction in crop production during the 2080s may be offset by elevated CO₂ at and other adaptation measures, but perhaps at very high costs.

(2nd Presentation)

▶ Title: The General Large Area Model for Annual Crops (GLAM)

▶ ABSTRACT

A regional crop model (GLAM) was designed to simulate yields directly at the regional scale and to model the impacts of climatic influences on crop growth and development at the regional level. The selection of the working temporal and spatial scale of GLAM depends on the yield-determining process at the spatial scale of interest. A case study in China showed that, due to the memory effect of the soil moisture, little difference was found when driving the crop model with 5d (pentad) temporally averaged rainfall data. This result is relevant for crop studies, as reliable pentad satellite-based rainfall retrievals are available for a far longer period than daily data. The highly skewed distribution of rainfall implies 10d or longer averaging of rainfall strongly impacts crop yield, although temperature, a spatially smoother field, could be averaged for decades, or even monthly. Spatially averaging the driving climate data shows that the skill of the crop model in yield forecasting improves as the spatial scale of weather data increases from 2° × 2° to 0.5° × 0.5°. GLAM has been successfully used to simulate groundnut in India, to assess the impacts of climate change and flooding on wheat in China, and for exploring climate adaptation in China. The validation and application of GLAM for a wide range of environmental conditions needs to be examined further in diverse agricultural areas. The impacts of climate change on crop growth at the regional level need to be explored further with the improved crop model and input datasets.