RCPs气候情景下珠江三角洲地区城市用地扩展的预测与模拟

随着社会经济的快速发展,中国城市规模和数量不断扩大,城市土地利用系统内部变化错综复杂.本研究以珠江三角洲地区城市群为例,研究了在自然环境条件和社会经济条件共同作用下城市化进程中城市用地动态变化的驱动机制,并设计了规划情景和RCPs气候情景,运用决策树元胞自动机模型对这几种情景下珠江三角洲地区城市用地的动态变化进行预测模拟.结果表明: 非农业人口和社会经济的增长对城市化过程起着决定性的推动作用,交通干线在整个城市化进程中始终起着重要的基础性作用,高程较高和坡度较大的区域制约了该地区的城市化进程.随着时间的推移,无论哪种情景,城市用地扩张的态势不变,但扩张速度到一定时间节点将会减缓,不同情景下减缓的时间点不同;规划情景、MESSAGE模式和AIM模式下的城市用地发展速度依次增加,但MESSAGE气候模式下的城镇发展较符合当前的城镇发展态势;城市用地扩张的区域主要集中在广州、东莞、佛山、珠海、深圳、湛江和潮汕等城市化相对较高的区域.

Prediction and simulation of urban area expansion in Pearl River Delta Region under the RCPs climate scenarios.

The sizes and number of cities in China are increasing rapidly and complicated changes of urban land use system have occurred as the social economy develops rapidly. This study took the urban agglomeration of Pearl River Delta Region as the study area to explore the driving mechanism of dynamic changes of urban area in the urbanization process under the joint influence of natural environment and social economic conditions. Then the CA (cellular automata) model was used to predict and simulate the urban area changes until 2030 under the designed scenarios of planning and RCPs (representative concentration pathways). The results indicated that urbanization was mainly driven by the non agricultural population growth and social economic development, and the transportation had played a fundamental role in the whole process, while the areas with high elevation or steep slope restricted the urbanization. Besides, the urban area would keep an expanding trend regardless of the scenarios, however, the expanding speed would slow down with different inflection points under different scenarios. The urban expansion speed increased in the sequence of the planning scenario, MESSAGE scenario and AIM scenario, and that under the MESSAGE climate scenario was more consistent with the current urban development trend. In addition, the urban expansion would mainly concentrate in regions with the relatively high urbanization level, e.g., Guangzhou, Dongguan, Foshan, Shenzhen, Zhanjiang and Chaoshan.