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地理学报    2018, Vol. 73 Issue (9): 1809-1822     DOI: 10.11821/dlxb201809015
  气候变化与生态环境 本期目录 | 过刊浏览 | 高级检索 |
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. 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
Optimizing spatial patterns of water conservation ecosystem service based on Bayesian belief networks
ZENG Li1(),LI Jing1(),LI Ting1,YANG Xiaonan2,WANG Yanze1
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
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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。

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曾莉
李晶
李婷
杨晓楠
王彦泽
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 wordswater conservation    ecosystem services    Bayesian belief networks    scenario analysis    spatial pattern optimization
收稿日期: 2017-06-16      出版日期: 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
引用本文:   
曾莉, 李晶, 李婷等 . 基于贝叶斯网络的水源涵养服务空间格局优化[J]. 地理学报, 2018, 73(9): 1809-1822.
ZENG Li, LI Jing, LI Ting et al . Optimizing spatial patterns of water conservation ecosystem service based on Bayesian belief networks[J]. Acta Geographica Sinica, 2018, 73(9): 1809-1822.
链接本文:  
http://www.geog.com.cn/CN/10.11821/dlxb201809015      或      http://www.geog.com.cn/CN/Y2018/V73/I9/1809
Fig. 1  渭河流域关天段行政区划图
植被类型 降水 蒸散发
Highest High Medium Low
Highest Highest 0.0647 52.15 38.991 8.794
Highest High 0.104 55.123 37.712 7.061
Highest Medium 0.16 58.139 35.251 6.451
Highest Low 0.314 80.038 19.586 0.0628
High Highest 1.036 48.705 26.425 23.834
High High 1.104 25.153 49.202 24.54
High Medium 1.139 22.322 47.41 29.129
High Low 0.704 48.415 50.792 0.088
Medium Highest 0.813 0.675 12.462 86.049
Medium High 3.265 2.664 41.71 52.36
Medium Medium 2.248 5.154 34.65 57.948
Medium Low 1.249 8.005 90.68 0.0662
Low Highest 4.66 1.553 9.709 84.078
Low High 32.642 2.554 26.257 38.547
Low Medium 15.281 2.488 23.099 59.133
Low Low 17.687 4.082 77.751 0.68
Tab. 1  蒸散发节点的条件概率表
Fig. 2  水源涵养服务贝叶斯网络构建原理
序号 因子 状态名称 状态编号 实际值范围
1 降水(mm/a) Highest 1 817~996
High 2 681~817
Medium 3 529~681
Low 4 0~529
2 植被类型 Highest 1 常绿阔叶林,常绿针叶林,落叶阔叶林,落叶针叶林,乔木园地,稀疏林,针阔混交林
High 2 灌木绿地,灌木园地,落叶阔叶灌,乔木绿地,稀疏灌木林
Medium 3 草本绿地,草本沼泽,草丛,旱地,水田,稀疏草地,草原
Low 4 采矿场,工业用地,河流,湖泊,交通用地,居住地,空,裸土,裸岩,水库,运河,沙漠/沙地
3 土地利用 Cropland 1 耕地
Forest 2 林地
Grassland 3 草地
Water 4 水体
Urban 5 城市
Heath 6 未利用地
4 土壤类型 Highest 1 沼泽土,水稻土,淤淀黑土,潮土,灰褐色土型淤淀土,褐土型淤淀土,轻度盐化淤淀土
High 2 亚高山草甸土,山地棕壤,山地淋溶褐土,暗棕壤,棕壤,山地草原土,粗骨土,褐土,黄墡土,黄棕壤,山地草甸草原土
Medium 3 耕种山地淋溶褐土,耕种山地灰褐土,耕种山地碳酸盐褐土,耕种山地草甸草原土,耕种山地褐土
Low 4 塿土,新积土,紫色土,红土,风沙土,黄褐土,石质土,黄绵土,石灰岩土
5 蒸散发(mm/a) Highest 1 10913~65535
High 2 6003~10913
Medium 3 4668~6003
Low 4 1765~4668
6 地表径流(mm/a) Highest 1 59~89
High 2 34~59
Medium 3 15~34
Low 4 0~15
7 水源涵养(t/hm2·a) Highest 1 1103~1468
High 2 917~1103
Medium 3 163~917
Low 4 0~163
Tab. 2  水源涵养因子状态分级
地区 生产总值增长率(%) 常住人口增长率(%)
2015年 计划 保护 开发 2015年 计划 保护 开发
西安 11.53 12 10 15 4.64 6 4.5 8
渭南 7.71 7.5 6 9 3.46 4 3.5 6
宝鸡 6.27 6.5 5.5 8 3.55 4 3.5 6
铜川 0.65 0.6 0.5 1 3.79 4 3.8 4.5
咸阳 12.08 13 11 15 3.98 5 4.0 6
杨凌示范区 13.57 14 10 15 4.93 6 5.0 6.5
天水 8.9 9 8 10 0.35 0.4 0.3 0.6
Tab. 3  研究区社会经济统计值及情景设计
Fig. 3  水源涵养服务的预测和优化原理
Fig. 4  2010年水源涵养服务贝叶斯网络
实际水源
涵养等级
预测水源涵养等级
最高 行总和
最高 92 9 0 0 101
38 59 0 0 97
1 1 0 0 2
0 0 0 0 0
列总和 131 69 0 0 200
总体精度(%) 75.5
Tab. 4  水源涵养服务等级预测误差矩阵
土地利用类型 保护情景 计划情景 开发情景
耕地 -13.22 -8.42 -11.52
林地 18.12 13.07 13.27
草地 0.73 -1.00 -0.94
水体 -5.08 -5.05 -5.00
城市 0.38 2.25 5.04
未利用地 -0.92 -0.85 -0.85
Tab. 5  研究区土地利用变化(%)
Fig. 5  研究区土地利用类型图
节点名称 方差缩减 相对百分比
水源涵养 1.49285 100
降水 0.53235 35.7
蒸散发 0.11014 7.38
土地利用(2010) 0.10628 7.12
地表径流 0.05903 3.95
植被 0.02189 1.47
土壤类型 0.00015 0.00975
Tab. 6  水源涵养服务对各节点的敏感性
Fig. 6  3种情景下水源涵养服务关键变量关键状态子集分布
Fig. 7  水源涵养服务优化区
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