 |
Core Project3. Assessment of climate risk based on integrated climate, impact, and land use models |
Research Plan > Core Research Projects > 2008 Research Results |
|
[2008 Research Results] |
|
Development of global agricultural land use scenarios |
The figures below show the proportion of cropland of the total land area in 2000, 2050 and 2100. The larger the red areas, the more extensive the cropland. Generally, there is a global increase in cropland between 2000 and 2050, especially Africa showing a remarkable increase. Apart from this kind of scenarios, we develop land-use scenarios also with regard to forest, grasslands and urban areas, but even in these respects Africa shows the most dramatic change. |
|
Proportion of the total land occupied by cropland (2000 / 2005 / 2100) |
|
(click to enlarge) |
|
|
How to estimate cloud distribution more accurately?
|
A key element in predicting future climate change is to understand the changes in cloud distribution. Therefore, in order to strengthen the credibility of predictions, the estimation methods have been refined. According to the Climate Model, there is a spatial distribution of dense and thin vapor areas within a grid, and it is assumed that clouds exist in the vapor dense areas. Although the earlier assumption was that the vapor distribution within a grid was temporarily stable, in the present research a forecasting method of vapor movement has been developed. When cumulonimbus clouds develop and cloud ice falls, it is possible to show the changing movements of vapor inside the grid. |
|
Rate of skewness change of vapor distribution (red and blue shades) and cloudiness (black line), estimated by Climate Model MIROC4.1 |
|
(click to enlarge) |
|
The skewness indicates the degree of inclination to vapor dense or vapor sparse areas
a) Rate of skewness change
due to cumulus convection
b) Rate of skewness change
due to cloud microphysics
(ice fall) |
|
|
An agricultural impact model to simulate long-term trends in yield |
When we discuss about future food security, it is of utmost importance to consider change in yield due to breeding and fertilization. Based on a crop growth model (EPIC), we developed an agricultural impact model that simulates long-term trends in yield due to breeding and fertilization. A validation study was carried out on paddy rice of Jakarta (Indonesia). The results showed that the present simulation model could successfully confirm the actual growth trend of the paddy rice yield during the past 40 years. |
|
Comparison between simulated and observed paddy rice yield
(1961-2000, Jakarta, Indonesia) |
|
|
The figure above shows a comparison between observed and simulated paddy rice yield in Jakarta between 1961 and 2000. Note that for “observed yield”, the average value over Indonesia reported by the United Nation’s Food and Agriculture Organization (FAO) was used. |
|
|
|
