在全球气候变化及中国社会经济迅速发展的背景下,为了解中国湿地分布的时空动态特征及演化规律,以4期(1978年、1990年、2000年、2008年)中国湿地遥感制图数据和3期(1990年、2000年、2005年)土地利用数据为基础,同时考虑到对湿地变化的影响程度和数据的可获取性,选取12个影响因子(平均温度、平均湿度、累计降水量、人口数量、地区生产总值、农林牧渔产值、耕地面积、粮食产量、有效灌溉面积、水库库容量、除涝面积、治碱面积)研究1978年-2008年这30年间中国湿地变化的驱动机制。考虑到地理现象的空间非平稳性,本文采用地理加权回归的方法分析驱动因子对湿地变化的影响作用。地理加权回归作为一种局部线性回归方法,能够直观地反映湿地驱动因子对湿地作用的地域差异。结果表明:不同类型的湿地变化的主要影响因素不同,内陆湿地与温度、降水以及农业耕作灌溉等密切相关;人工湿地与经济发展水平和水利设施兴建密切相关;滨海湿地与农林牧渔产业和人口等密切相关。同一类型湿地变化的主要影响因素随着时间推移也有所变化,并且影响程度在地域上也存在较为明显的南北和东西差异。本次研究结果基本反映了1978年-2008年中国湿地变化的特征规律。

The factors that influence China's wetlands are greatly complicated because of global climate changes and rapid economic development. This study investigates the dynamic characteristics and evolution laws of the temporal and spatial distribution of wetlands in China, as well as the driving forces behind these changes. Considering the relevance with wetland change and data availability, we chose 12 impact factors as independent variables(average temperature, average humidity, accumulative precipitation, population, gross regional domestic product, agricultural production value, agricultural acreage, grain production, effective irrigation area, reservoir capacity, drainage area, and saline-alkali management area), in which three were natural factors and nine were social economic factors. The wetland change driving mechanism from 1978 to 2008 was studied using Geographically Weighted Regression(GWR) based on the wetland remote sense mapping in four years(1978, 1990, 2000, and 2008) and land use data in three years(1990, 2000, and 2005). GWR is a local linear regression method that can effectively reflect the regional disparity of driving factors influencing wetlands and can present intuitive results. The main influencing factors of different types of wetlands vary. Inland wetlands were closely associated with average temperature, accumulative precipitation, and activities related to farming irrigation, whereas economic development and water infrastructure significantly influenced artificial wetlands. Coastal wetlands were closely associated with population and fishery industry. The main factors influencing a wetland changed with time, and obvious differences in the degree of influence over the space were observed. For inland wetlands, accumulative precipitation affected the northwest arid region from 1978 to 1990. The average temperature significantly positively correlated with inland wetlands in the north areas, where snow and permafrost were distributed from 1990 to 2000. Both of them can increase the wetland water supply to expand the wetlands area. The drainage areas on inland wetlands significantly influenced the southeast coastal area. Agricultural acreage, effective irrigation, and grain production significantly influenced the north, especially in three northeast provinces and the Inner Mongolia autonomous region. Due to these factors, inland wetlands sharply reduced because of drainage, reclamation, and increasing agricultural demand for water. Artificial wetlands are consistent with changes in economic development in China from 1978 to 2008. During this period, economic development moved from south to north and from east to west, and artificial wetlands increased accordingly in those areas. In the past 30 years, the reduction of coastal wetlands was mainly caused by fisheries development, tideland reclamation, oilfield development, infrastructure, and water conservancy facility construction. Among these factors, fishery production mainly affected Jiangsu and Zhejiang provinces, tidal land reclamation affected Fujian and Guangdong provinces, and oil field development significantly affected the areas around the Bohai Sea. At the same time, the population growth rate was faster in coastal areas than in other regions, resulting in the conversion of wetlands into a large number of artificial facilities.The results of this study basically reflect the characteristic changes in China's wetlands from 1978 to 2008, which could provide helpful policy support for the management and rational utilization of wetlands on a national scale.