基于贝叶斯网络的生态系统服务空间格局优化——以泾河流域为例

doi:10.12118/j.issn.1000-6060.2021.465

Abstract

Abstract:

Changes in regional land use patterns due to rapid urbanization affect the quality of the ecological environment and the spatial pattern of ecosystem services. Therefore, optimizing ecosystem services is crucial. Using the spatial assessment of net primary production, crop production, soil conservation, and water yield services in the Jing River Basin, northwest China from 2000 to 2020 as the basis, this study combined Bayesian networks and ecosystem services and employed a subset of key variables and a visualized subset of optimal states to determine the areas necessitating the optimization of the aforementioned ecosystem services for regional economic and ecological harmonious development. Results showed that (1) the SWAT model can accurately simulate the regional runoff and generate a high coefficient of determination (R²>0.6) and a Nash-Sutcliffe efficiency coefficient (NSE>0.5) when comparing simulated and observed values, thus providing a guarantee for the further assessment of water yield services. (2) The spatial and temporal variations of the four ecosystem services in the Jing River Basin from 2000 to 2020 were substantial. All four ecosystem services showed an upward fluctuation trend on the temporal scale and a stable trend on the spatial scale. (3) Overlay analysis of the optimized areas for the four ecosystem services revealed that the integrated optimized areas were concentrated in the central and southwestern parts of Pengyang County, Ningxia Province and the scattered areas of Huan County, Gansu Province. This study is important for guiding region optimization for the sustainable management and reducing the degradation of ecosystems.

Key words: ecosystem services, Bayesian networks, spatial and temporal pattern optimization, Jing River Basin

YU Yuyang,LI Jing,ZHOU Zixiang,TANG Chengyan. Spatial pattern optimization of ecosystem services based on Bayesian networks: A case of the Jing River Basin[J].Arid Land Geography, 2022, 45(4): 1268-1280.

Figures/Tables 12

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数据类型	数据描述	数据来源
基础地理信息数据	行政区划、河流、道路等	国家基础地理信息中心（http://www.ngcc.cn/ngcc/）
高程数据	来源于地理空间数据云,空间分辨率为30 m	地理空间数据云（http://www.gscloud.cn/）
土壤数据	第二次全国土地调查南京土壤研究所提供的1:100000土壤数据,空间分辨率为1 km×1 km	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/zh-hans/）
土地利用数据	来源于GlobalLand 30,全球地理信息公共产品,空间分辨率为30 m	全球地理信息公共产品（http://www.globallandcover.com/）
气象数据	2000—2020年逐日数据,指标包括降水量、最高气温、最低气温、风速、相对湿度数据	国家气象科学数据中心（http://data.cma.cn/）
水文数据	2000—2020年张家山水文站逐日径流数据	张家山水文站（http://61.163.88.227:8006/hwsq.aspx）
统计数据	2000—2020年人口密度以及GDP等统计数据	统计年鉴

因子	状态名称	实际值范围
蒸散发量/mm	低	2256~3903
	中	3903~5362
	高	5362~9827
	最高	9827~65534
土地利用水平分级	低	1
	中	2
	高	3
	最高	4
植被覆盖度	低	-0.17~0.14
	中	0.14~0.30
	高	0.30~0.46
	最高	0.46~0.70
水土保持措施因素	低	0.00
	中	0.00~0.10
	高	0.10~0.35
	最高	0.35~1.00
土壤类型	低	1~4
	中	4~9
	高	9~14
	最高	14~20
太阳辐射/MJ·m^-2	低	4989~5331
	中	5331~5576
	高	5576~5732
	最高	5732~5911
降水量/mm	低	323~422
	中	422~506
	高	506~584
	最高	584~693
坡度/(°)	低	0.00~3.50
	中	3.50~7.00
	高	7.00~10.50
气温/℃	低	<8
	中	8~9
	高	9~10
	最高	10~12
土地利用类型	耕地	-
	林地	-
	草地	-
	水域	-
	建设用地	-
	未利用地	-
土壤保持/t·hm^-2	低	0.00~41.30
	中	41.30~93.60
	高	93.60~174.14
	最高	174.14~555.09
农业生产力/t·hm^-2	低	28.64~57.75
	中	57.75~60.00
	高	60.00~90.00
	最高	90.00~120.00
NPP/g C·m^-2	低	0.23~11.97
	中	11.97~135.89
	高	135.89~204.47
	最高	204.47~388.90
产水量/m³	低	0.00~408.61
	中	408.61~1001.75
	高	1001.75~2095.70
	最高	2095.70~3361.15

参数名称	相对显著性（T）	显著性检验（P）	参数范围	最优校准值
土壤饱和导水率	2.5450	0.0112	-0.9~0.9	-0.8425
降雪温度	-2.2385	0.0256	-20~20	7.9700
主河道水力传导度	1.8577	0.0638	0~150	58.8517
地下水滞留系数	-1.7717	0.0771	0~500	390.1691
土壤蒸发补偿系数	-1.2422	0.2148	0.01~1.00	0.0294
土壤有效含水率	-1.1148	0.2655	-0.5~0.5	0.3959
土壤湿密度	1.0494	0.2945	-0.5~0.5	-0.4496
基流系数	0.5965	0.5512	0~1	0.9276
地下水再蒸发系数	-0.5050	0.6138	0.02~0.20	0.1400
浅层地下水径流系数	0.0329	0.9737	0~5000	2409.2715

土地利用水平	蒸散发量	农业生产力
土地利用水平	蒸散发量	最高	高	中	低
最高	最高	0.353	0.002	0.284	0.361
高	高	0.229	0.032	0.620	0.119
中	高	0.099	0.111	0.299	0.491
低	中	0.108	0.422	0.282	0.188

土地利用水平	降水量	气温	土地利用类型	产水量
土地利用水平	降水量	气温	土地利用类型	最高	高	中	低
最高	最高	高	未利用地	0.159	0.020	0.205	0.616
中	最高	低	未利用地	0.065	0.138	0.579	0.217
中	最高	低	草地	0.031	0.805	0.084	0.080
最高	最高	最高	草地	0.488	0.124	0.191	0.196

Spatial pattern optimization of ecosystem services based on Bayesian networks: A case of the Jing River Basin

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