A Geographic Information System (GIS)-based approach to adaptation to regional climate change: a case study of Okutama-machi, Tokyo, Japan

作者：Bai Yingjiu^*; Kaneko Ikuyo; Kobayashi Hikaru; Kurihara Kazuo; Takayabu Izuru; Sasaki Hidetaka; Murata Akihiko

来源：Mitigation and Adaptation Strategies for Global Change, 2014, 19(5): 589-614.

DOI：10.1007/s11027-013-9450-6

• Climate change projection
• Database management system (DMS)
• Geographic information system (GIS)
• Local government decision-making
• Okutama-machi
• Regional climate change

Recently, local governments have an increasing need to take extensive and effective local measures to adapt to regional climate change, but have difficulty knowing how and when to adapt to such change. This study aims: 1) to characterize an efficient and cost-effective database management tool (DMT) for developing a Geographic Information System (GIS) based approach to using observed and projected data, for decision-making by non-expert government authorities, and 2) to document how DMT can be used to provide specialized yet understandable climate change information to assist local decision-makers in clarifying regional priorities within a wide array of adaptation options. The DMT combines climate change mapping, statistical GIS, and a vulnerability assessment. Okutama-machi, a 225.63 km(2) sparsely populated mountainous region (2012 population 5,856) northwest of Tokyo, Japan, was chosen for this pilot study. In this paper, the most recent regional climate projections (5 km resolution) are transcribed into an understandable form for use by non-expert citizens who use the GIS-based DMT. Results illustrate qualitative agreement in projection of summer daily mean temperatures; the mean temperature increase at Okutama-machi is the greatest of any area in Tokyo. In comparing near future and future conditions, August monthly mean temperature will increase more than 0.7-0.9 A degrees C and 2.8-2.9 A degrees C, and monthly precipitation by 50 % and 25-41 %, respectively. However, the root mean square (RMS) errors and bias of percentage change for monthly precipitation in summertime are 26.8 % and 4.3 %, respectively. These data provide an early warning and have implications for local climate policy response.

• 出版日期2014-6
• 单位中国气象科学研究院