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基于地理探测器的干旱区内陆河流域产水量驱动力分析——以疏勒河流域为例
引用本文:郑续,魏乐民,郭建军,周妍妍,陈冠光,岳东霞. 基于地理探测器的干旱区内陆河流域产水量驱动力分析——以疏勒河流域为例[J]. 干旱区地理, 2020, 43(6): 1477-1485. DOI: 10.12118/j.issn.1000-6060.2020.06.08
作者姓名:郑续  魏乐民  郭建军  周妍妍  陈冠光  岳东霞
作者单位:1.兰州大学资源环境学院,甘肃 兰州 730000;2 中国科学院西北生态环境资源研究院,沙漠与沙漠化重点实验室,甘肃 兰州 730000
基金项目:国家自然科学基金项目(41701623,41671516);;国家重点研发计划项目(2017YFC1501005);;中央高校基本科研业务费项目(lzujbky-2020-sp03);
摘    要:明确干旱区产水量的驱动因素,能为区域水资源优化和可持续发展提供科学依据。基于MODIS植被指数、HWSD的土壤数据集以及气象要素数据,采用InVEST模型和地理探测器探究疏勒河流域多年平均产水量的空间分布,揭示不同空间尺度上产水量的单因子及双因子交互驱动机制。结果表明:疏勒河流域多年平均产水量呈现南部>北部>中部。流域尺度上,产水量空间格局的主导驱动力为降水,坡度与降水交互驱动作用最为显著。区域尺度上,南部山区、北部马鬃山地区和中部平原区的主导驱动力各不相同,分别为日照时数、人为干扰强度、降水,双因子交互作用显示人为干扰强度与其它因子的交互最为显著。不同土地利用类型中,耕地产水量的主导驱动力为坡度,而其它地类产水量的主要影响因子为降水。各地类中降水与其他因子的交互均大大增强了单因子驱动力。因此,干旱区产水量多尺度驱动机制研究对区域水资源可持续管理至关重要。

关 键 词:产水量  InVEST模型  地理探测器  疏勒河流域
收稿时间:2019-12-24

Driving force analysis of water yield in inland river basins of arid areas based on geo-detectors: A case of the Shule River
ZHENG Xu,WEI Le-min,GUO Jian-jun,ZHOU Yan-yan,CHEN Guan-guang,YUE Dong-xia. Driving force analysis of water yield in inland river basins of arid areas based on geo-detectors: A case of the Shule River[J]. Arid Land Geography, 2020, 43(6): 1477-1485. DOI: 10.12118/j.issn.1000-6060.2020.06.08
Authors:ZHENG Xu  WEI Le-min  GUO Jian-jun  ZHOU Yan-yan  CHEN Guan-guang  YUE Dong-xia
Affiliation:1.College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, Gansu, China;2.Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environmental and Resources, Chinese Academy of Sciences, Lanzhou 730000, Gansu, China
Abstract:Defining the driving factors of water yield in arid areas can provide a scientific basis for regional water resources optimization and sustainable development. In this paper, the InVEST model and geo-detectors were used to explore the spatial distribution of the average annual water yield in the Shule River Basin, Gansu Province, China and the driving forces at different spatial scales in 2015, based on the MODIS vegetation index, HWSD soil dataset, and meteorological data. Results show that the average annual water yield of the Shule River Basin was in an order of south > north > middle. At the basin scale, the dominant driving force of the spatial pattern of water yield is precipitation. The interaction of slope and precipitation is the most significant for water yield. At the regional scale, the dominant interpretation factors in the southern Qilian Mountains, northern Ma Zong mountains area, and the central plains are precipitation, human disturbance intensity, and precipitation, respectively. Most significant factor interaction is between human disturbance intensity and other factors. In different land uses, the slope in cultivated land is the dominant driving force, while precipitation of other land types is the main influencing factor of water yield. The interaction between precipitation and other factors in various categories has greatly enhanced the single factor driving force. Therefore, the research at the multi- scale driving mechanism of water yield in arid regions is crucial to the sustainable management of regional water resources.
Keywords:water yield  InVEST model   geo-detector  Shule River Basin  
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