Land-use - DNE21+

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Model Documentation - DNE21+

Corresponding documentation
Previous versions
Model information
Model link https://www.rite.or.jp/system/en/global-warming-ouyou/modeltodata/overviewdne21/
Institution Research Institute of Innovative Technology for the Earth (RITE), Japan, http://www.rite.or.jp/en/.
Solution concept
Solution method
Anticipation

Potentially available land area for energy crop production and afforestation, which is one of the input data of DNE21+ model, is evaluated by using a land-use model to maintain consistency with the land area required for food crop production and forest conservation and water-stressed basins. The land-use mode is basically a 15-minute-grid model, for every decade from 2000 to 2050, and at time points for 2070 and 2100. It is integrated with the water-use model; therefore, the water-stressed basins are estimated under the same scenario regarding socioeconomic development, climate change, and land use for food crop production.

a) Land area required for food crop production

Food crop types include wheat, rice, maize, sugar cane, soybeans, oil palm fruit, rapeseed and others, in consideration of their importance in terms of principal food, favorite food, vegetable oil and feed considerations. The available land area for food crop production are allocated in order to meet the regional food demands. If there is a shortage of food crop production due to a lack of available land, production is reallocated to regions where land is still available and any shortage is fulfilled by means of trade.

Impacts due to climate change and adaptations for planting can be taken into account through changes in the potential production, which can be calculated using the information on crop characteristics and soil types provided in the in the Global Agro-Ecological Zones (GAEZ) model (Fischer et al. 2002) for both rain-fed and irrigated conditions. Impacts on crop productivity due to technological progress associated with economic growth and production adjustments to avoid falling crop prices and so on are taken into account through a scenario of ?yield (or management) factor? for each of crops and for each of the 32 regions. For all crops, a greater growth of the ?yield factor? is expected in developing regions than in developed regions. The grids available for irrigation are based on an irrigation map for the year 2000 (Siebert et al. 2007). The regional areas for irrigation maintain the consistency with those of the original map, although the share of irrigation area for each of grids is simplified to be either 0 or 100%. No expansion of the irrigation available grids is allowed in the future according to the assumptions by Alcamo et al. (2007). For details of the land-use model, please refer to Hayashi et al. (2013).

(Reference) Hayashi, Ayami, et al. "Global evaluation of the effects of agriculture and water management adaptations on the water-stressed population." Mitigation and Adaptation Strategies for Global Change (2013): 1-28.

b) Available land area for energy crop production and afforestation

The agro-land use model is equipped with a land cover map for eight major land-cover categories (i.e., rainforest, other forests, arable land, grassland, pasture, barren, built-up, and water), which was constructed by reference to data from around 2000 (Fischer et al. 2008, PBL 2009, USGS 2000). Fallow land is estimated by subtracting the land required for food crop production from arable land. Then, fallow land and grassland are treated as candidate land for energy crop production and afforestation. They don?t include either cropland or pasture; therefore, there is no worrying about competition with food production technically. Furthermore, they don?t include rainforests and other forests; therefore, concerns regarding CO2 emissions and biodiversity loss through the use of land for energy crop production are minimal. In the next step, water-stressed basins are excluded from the candidate land. Water-stressed basins are estimated based on a criterion (0.4 ? the annual water withdrawal-to-availability ratio) while maintaining consistency in terms of agricultural water use for irrigation, domestic and industrial water use, and water availability. Data for the river basins are derived from the Total Runoff Integrating Pathways (TRIP) database (Oki 2001).

We focus on land satisfying conditions for energy crop yield and land accessibility of the candidate land, and we calculate the available energy from the energy crop production on the land by level of conditions for yield and land accessibility. Energy crop types include cereals (wheat, rice, or maize), sugar cane, oil palm fruit, other oil crops (soybeans, or rapeseed), and lignocellulosic crops.

c) CO2 emissions from LULUCF

Four types of CO2 emission/fixation are taken into account; (1) CO2 emissions due to expansions of crop land for food crop production, (2) CO2 fixation by forests planted and naturally-regenerated before 2010, (3) CO2 fixation by afforestation after 2010, and (4) CO2 emissions by other sources (i.e., emission abandoned land after deforestation).

CO2 emission due to expansions of crop land for food crop production is estimated by multiplying the land area converted to crop land from other types of land, by CO2 emission coefficients. The former is evaluated by using the land-use model, and the latter was constructed by reference to Houghton?s studies (1999, 2001) for each of the 32 regions.

Land area for forests planted and naturally-regenerated during the last 60 years were estimated based on the FRA 2010 report (FAO, 2010) for each of the 54 regions. The amount of CO2 fixation by the forests were calculated based on the estimated forest area and the NPP (Net Primary Production) for each of the regions. CO2 fixation by afforestation after 2010 is one of the mitigation options in the DNE21+ model.

CO2 emissions by the other sources in 2005 were estimated so that the total CO2 emission to consists to the amount for the year by RCP database (IIASA database). After that, it was assumed to decrease associated with increase in per capita GDP.