中国农业科学 ›› 2017, Vol. 50 ›› Issue (20): 3953-3969.doi: 10.3864/j.issn.0578-1752.2017.20.011

• 土壤肥料·节水灌溉·农业生态环境 • 上一篇    下一篇

关中-天水经济区生态系统固碳服务空间流动及格局优化

李婷1,2,李晶1,2,3,王彦泽1,2,曾莉1,2

 
  

  1. 1陕西师范大学地理科学与旅游学院,西安710119;2地理学国家级实验教学示范中心(陕西师范大学),西安710119;3宝鸡文理学院/陕西省灾害监测与机理模拟重点实验室,陕西宝鸡721013
  • 收稿日期:2017-04-27 出版日期:2017-10-16 发布日期:2017-10-16
  • 通讯作者: 李晶,E-mail:lijing@snnu.edu.cn
  • 作者简介:李婷,E-mail:lite@snnu.edu.cn。
  • 基金资助:
    国家自然科学基金(41371020)、中央高校特别支持项目(GK2015020210)、陕西省教育厅重点实验室科研计划(12JS014)

The Spatial Flow and Pattern Optimization of Carbon Sequestration Ecosystem Service in Guanzhong-Tianshui Economical Region

LI Ting1,2, LI Jing1,2,3, WANG YanZe1,2, ZENG Li1,2   

  1. 1School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119; 2National Demonstration Center for Experimental Geography Education (Shaanxi Normal University), Xi’an 710119; 3Baoji University of Arts and Sciences/Key Laboratory of Disaster Monitoring and Mechanism Simulation of Shaanxi Province, Baoji 721013, Shaanxi
  • Received:2017-04-27 Online:2017-10-16 Published:2017-10-16

摘要: 【目的】量化区域生态系统固碳服务(以下简称“固碳服务”)的供需平衡状况,模拟固碳服务的空间流动,揭示区域固碳服务功能转变的空间规律,并据此给出区域固碳空间布局优化策略,为引导区域的低碳发展提供直观的科学参考。【方法】通过多源数据融合模型模拟人口空间密度,进而估算关中-天水经济区生态系统固碳服务需求量;使用CASA模型计算研究区生物碳库的固碳服务供给量,在此基础上结合遥感碳循环过程模型估算土壤碳库的固碳服务供给量;使用流动比率量化区域固碳服务的供需平衡状况,以空间可视化的方式梳理固碳服务从供给区到受益区的流动过程;基于贝叶斯原理计算环境变量对固碳服务供给的状态条件概率,将其可视化表达为条件概率像素图,通过熵减模型筛选关键变量,利用关键因子最优状态子集分布探讨固碳格局的不确定性,并给出固碳空间布局优化策略。【结果】(1)研究区固碳服务总体上供大于求,供需平衡空间差异明显,需求的高值区主要分布在关中平原的人口高度聚集区,供给的高值区则主要沿秦岭山脉和北山山系分布。(2)根据流动比率分布,研究区大致可分为三大碳源集聚中心:以天水市区为中心的碳源集聚中心(Ri >0.04),以彬县为中心的碳源集聚中心(Ri >0.04),以及以最高值西安市为中心的多级碳源集聚中心(Ri>0.20)。其空间流动则可分为四组供需对应区域:秦岭中段、东段及北山的永寿县等流向以西安市为中心的关中城市群,秦岭西段流向天水市,麟游县、旬邑县等流向彬县,铜川市、澄城县、华县等流向蒲城县。(3)根据条件概率和熵减度计算,研究区可取{DEM=3, PET=1}作为生物碳库的关键变量最优状态子集,该子集主要分布在宝鸡市南部秦岭山脉和天水市西南角,该区域中生物固碳状态为最优的概率可以达到54.36%;取{NPP=3, DEM=3}作为土壤碳库的关键变量最优状态子集,该子集主要分布在秦岭山脉沿线、天水市西南角和咸阳市东北角,这些区域中土壤固碳状态为最优的概率高达92.84%与现有固碳格局对比得出,生物碳库的适宜优化区主要分布在天水市武山县和秦州区,土壤碳库的适宜优化区主要分布在秦岭中段各区县。【结论】研究区固碳服务总体上需求小于供给,区域内存在较明显的固碳服务空间流动,未来的固碳格局优化中以天水和秦岭中段作为固碳功能优化的主要区域,能够得到良好固碳格局优化成效的概率较大。

关键词: 固碳, 生态系统服务, 空间流动, 贝叶斯, 关中-天水经济区

Abstract: 【Objective】The aims of this paper were to quantify the balance of supply and demand situation of carbon sequestration service in regional ecosystem(hereinafter referred to as "carbon sequestration service"), simulate the spatial flow of carbon sequestration service, reveal the function change spatial rule of carbon sequestration service in regional ecosystem, and give the regional spatial layout optimization strategy which will provide an intuitive scientific reference for guiding the low carbon development of regions.【Method】Population spatial density was simulated with a multi-source data fusion model, and then the demand quantity of carbon sequestration service was estimated in Guanzhong-Tianshui Economic Region. The supply quantity of biologic carbon sequestration service in study area was calculated using CASA model, the soil carbon sequestration service supply was estimated using remote sensing model of carbon cycle. Based on these, the regional carbon balance was quantified with current ratio and the flow process of carbon sequestration service from generation to the use of land was elaborated with spatial visualization method. The conditional probability of environmental variables to the supply of carbon sequestration was calculated rely on Bayes principle, and the key variables were screened by entropy reduction model, the uncertainty of carbon sequestration pattern was discussed by using the distribution of key factor subsets with optimal state, and at last, the carbon sequestration space layout optimization strategy was given.【Result】(1) Carbon sequestration service in the study area overall oversupply, the spatial difference of balance between supply and demand is obvious, the high value areas of demand are mainly distributed in the Guanzhong plain high population areas, the high value areas of supply are mainly distributed in the areas along the Qinling Mountains and the Beishan mountains. (2) The spatial flow of carbon sequestration service in ecosystems is obviously different. According to the current ratio distribution, the study area can be divided into three carbon source concentration centers: Tianshui carbon source concentration center (Ri>0.04), Binxian carbon source concentration center (Ri>0.04), and Xi’an multi-level carbon source concentration center, with the highest value (Ri>0.20). The carbon sequestration service space flow in the study area can be divided into four groups: the flow from the Middle and East of Qinling Mountains, Yongshou County in Beishan Muontain to Guanzhong City Group, the flow from the west of Qinling Mountains to Tianshui city, the flow from Linyou county and Xunyi County to Binxian County, the flow from Tongchuan City, Chengcheng County, Hua County to Pucheng County. (3) According to the conditional probability and entropy calculation, taking set{DEM=3, PET=1} as a key variable optimal state subset of biology carbon pool, which is mainly distributed in Baoji City in southern Qinling Mountains and the southwest corner of Tianshui, where the optimal biological carbon fixation probability can reach 54.36%; taking set {NPP=3, DEM=3} as a key variable in the optimal state subset of soil carbon pool, which is mainly distributed in the Qinling Mountains, along the southwest corner of Tianshui and the northeast corner of the city of Xianyang, where the optimal probability is as high as 92.84%. The suitable areas of biology carbon pool mainly distributed in Tianshui and Wushan County, Qinzhou District and the suitable areas of soil carbon pool are mainly distributed in the middle part of Qinling Mountains district.【Conclusion】In general, carbon sequestration service demand is less than the supply in study area with obvious spatial flow. Taking Tianshui City and the middle areas of Qinling Mountains as the main region to optimize the carbon sequestration function can get a higher probability of good carbon sequestration pattern.

Key words: carbon sequestration, ecosystem services, spatial flow, Bayes, Guanzhong-Tianshui Economic Region