地理学报 ›› 2018, Vol. 73 ›› Issue (9): 1809-1822.doi: 10.11821/dlxb201809015

• 气候变化与生态环境 • 上一篇    

基于贝叶斯网络的水源涵养服务空间格局优化

曾莉1(),李晶1(),李婷1,杨晓楠2,王彦泽1   

  1. 1. 陕西师范大学地理科学与旅游学院,西安 710119
    2. 西北农林科技大学水土保持研究所,杨凌 712100
  • 收稿日期:2017-06-16 出版日期:2018-09-25 发布日期:2018-09-19
  • 基金资助:
    国家自然科学基金项目(41771198, 41771576, 41571512);中央高校基本科研业务费专项资金(2017CSY011);National Natural Science Foundation of China, No.41771198, No.41771576, No.41571512;The Fundamental Research Funds For the Central Universities, Shaanxi Normal University, No.2017CSY011

Optimizing spatial patterns of water conservation ecosystem service based on Bayesian belief networks

ZENG Li1(),LI Jing1(),LI Ting1,YANG Xiaonan2,WANG Yanze1   

  1. 1. School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
    2. Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, Shaanxi, China
  • Received:2017-06-16 Online:2018-09-25 Published:2018-09-19

摘要:

以渭河流域关中—天水经济区段(简称“渭河流域关天段”)为研究区,基于贝叶斯网络和水量平衡原理建立了水源涵养服务网络模型;将CA-Marcov模型与贝叶斯网络模型相结合,预测了2050年不同土地利用情景及其水源涵养服务分布概率;提出了关键变量关键状态子集方法,对研究区水源涵养服务空间格局进行优化。结果表明:① 保护情景下,林地面积增长了18.12%,其主要来源为耕地;草地和城市面积增长缓慢,分别增加了0.73%和0.38%;水体和未利用地分别减少了5.08%和0.92%,该情景下的水源涵养量值偏高的概率在3种情景中最大,保护情景的设计对未来的土地利用政策制定具有一定参考价值。② 水源涵养服务的关键影响因子是降水、蒸散发和土地利用,水源涵养量最高状态对应的关键变量关键状态子集是:﹛降水= 1,蒸散发= 2,土地利用= 2﹜,该子集主要分布在年平均降雨量和蒸散发量较大,植被覆盖率高的地区。③ 研究区适宜优化水源涵养的区域主要分布在天水市麦积区南部、宝鸡市陇县西南部和渭滨区南部、咸阳市旬邑县东北部和永寿县西北部,以及铜川市耀州区西部。结合贝叶斯网络模型研究水源涵养服务的优化区域,不仅有助于提升对生态系统水源涵养服务过程的直观认识,而且增加了情景设计和格局优化的合理性。在此基础上提出的关键状态关键因子方法,对研究区水源涵养生态环境建设和政策制定都具有重要的意义。

关键词: 水源涵养, 生态系统服务, 贝叶斯网络, 情景分析, 空间格局优化

Abstract:

This study, taking the Weihe River Basin in the Guanzhong-Tianshui Economic Region of China as a case, establishes a water conservation ecosystem service network model. Based on Bayesian belief networks, the model forecasts the distribution probability of water conservation ecosystem services projected under different land-use scenarios for the year 2050 with a CA-Marcov model. A key variable subset method is proposed to optimize the spatial pattern of the water conservation ecosystem service. There were three key study findings. First, under the protection scenario, the area of woodland increased by 18.12%, mainly from the conversion of cultivated land. The grassland and cities increased by 0.73% and 0.38%, respectively. The water and unused land were reduced by 5.08% and 0.92%, respectively. The probability of high water conservation value under this scenario is the largest in the three scenarios, and the design of protection scenario is conducive to the formulation of future land use policies. Second, the key factors influencing water conservation ecosystem service include precipitation, evapotranspiration and land use. The state set corresponding to the highest state of water conservation ecosystem service is {precipitation = Highest, evapotranspiration = High, land use = High}, mainly distributed in areas with high annual average rainfall and evapotranspiration and high vegetation coverage. Third, the regions suitable for optimizing water conservation ecosystem service are mainly distributed in the southern part of Maiji District in Tianshui, southwest of Longxian and south of Weibin District in Baoji, northeast of Xunyi County and northwest of Yongshou County in Xianyang, and west of Yaozhou District in Tongchuan. Identifying the optimization regions of water conservation ecosystem service based on Bayesian belief networks, not only helps to develop a better understanding of the water conservation ecosystem services processes, but also increases the rationality of the scenario design and pattern optimization. On this basis, the key variable subset method is crucial to sound eco-environment construction and policy formulation in the study area.

Key words: water conservation, ecosystem services, Bayesian belief networks, scenario analysis, spatial pattern optimization