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基于SWAT模型模拟乌江三岔河生态系统产流服务及其空间变异
引用本文:侯文娟,高江波,戴尔阜,彭韬,吴绍洪,王欢. 基于SWAT模型模拟乌江三岔河生态系统产流服务及其空间变异[J]. 地理学报, 2018, 73(7): 1268-1282. DOI: 10.11821/dlxb201807007
作者姓名:侯文娟  高江波  戴尔阜  彭韬  吴绍洪  王欢
作者单位:1. 中国科学院地理科学与资源研究所 中国科学院陆地表层格局与模拟重点实验室,北京 1001012. 中国科学院大学,北京 1000493. 中国科学院地球化学研究所 环境地球化学国家重点实验室,贵阳 5500024. 普定喀斯特生态系统观测研究站,贵州 普定 562100
基金项目:国家重点基础研究发展计划(2015CB452702);国家自然科学基金项目(41671098, 41530749);北京大学地表过程分析与模拟教育部重点实验室开放基金
摘    要:产流是生态系统水源涵养服务的重要内容,也是保水固土等服务的关键驱动。脆弱的喀斯特生态系统叠加人类活动的影响引起生态退化,在此背景下亟需深刻理解喀斯特地区产流服务空间格局及其影响因子。选择典型的喀斯特峰丛洼地流域,基于率定校准的SWAT水文模型,对喀斯特流域产流服务(包括地表、地下以及总径流量)进行模拟,并结合空间梯度分析和局部回归模型,剖析不同服务变量的空间变异特征。结果表明:研究区产流量空间分异规律明显,总径流系数约为70.0%;地表径流整体处于较低水平,地下径流量丰富(约为地表径流量的2~3倍)。地形因子对喀斯特流域产流服务具有宏观控制作用,随海拔和坡度升高,总径流量、地下径流量显著增加;植被分布特征对地表径流有重要影响,在上游林地和下游园地分布区存在空间差异,在植被因素的影响下随坡度的增加呈现转折性的变化趋势。此外,空间叠加分析表明林地总径流及地下径流最大:一方面是由于森林生态系统更强的土壤水分涵养能力以及水分快速入渗的地质背景特征;另一方面反映了地表覆盖与地形特征的复合效应,即林地大多分布在相对海拔和坡度较大之处。本文有助于推进喀斯特生态系统服务研究领域的发展与创新,并为生态恢复建设提供科技支撑。

关 键 词:产流服务  SWAT模型  空间变异  影响因子  喀斯特  
收稿时间:2017-06-19

The runoff generation simulation and its spatial variation analysis in Sanchahe basin as the south source of Wujiang
HOU Wenjuan,GAO Jiangbo,DAI Erfu,PENG Tao,WU Shaohong,WANG Huan. The runoff generation simulation and its spatial variation analysis in Sanchahe basin as the south source of Wujiang[J]. Acta Geographica Sinica, 2018, 73(7): 1268-1282. DOI: 10.11821/dlxb201807007
Authors:HOU Wenjuan  GAO Jiangbo  DAI Erfu  PENG Tao  WU Shaohong  WANG Huan
Affiliation:1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China2. University of Chinese Academy of Sciences, Beijing 100049, China3. State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, CAS, Guiyang 550002, China4. Puding Karst Ecosystem Research Station, Puding 562100, Guizhou, China
Abstract:Runoff generation is an important part of water conservation service, and also plays a critical role in soil and water retention. Under the background of the ecosystem degradation, which was caused by the vulnerable karst ecosystem combined with human activity, it is necessary to understand the spatial pattern and impact factors of runoff services in the karst region. The typical karst peak-cluster depression was selected as the study area. And the calibrated and verified Soil and Water Assessment Tool (SWAT) was one of the main techniques to simulate the runoff services of typical karst basin. Further, the spatial variability of different services were analyzed with the assistance of the methods of gradient analysis and local regression. Results indicated that the law of spatial difference was obvious. And the surface runoff was at a low level, but the groundwater runoff was rich (about 2-3 times the surface runoff). The runoff coefficients of total and groundwater were 70.0% and 23.9%, respectively. Terrain is a significant factor contributing to macroscopic control effect on the runoff service in the Sanchahe River Basin, where the total and groundwater runoff increased significantly with the rising elevation and slope. Then, the distribution of vegetation has great effects on surface runoff. There were spatial differences between the forest land in the upstream and orchard land in the downstream, in turn the surface runoff presented a turning point due to the influence of vegetation. Moreover, the results of spatial overlay analysis showed that the highest value of total and groundwater runoff was observed in the forest land. It is not only owing to the stronger capacity of soil water conservation of forest ecosystem, and geologic feature of rapid infiltration in this region, but also reflected the combined effects on the land cover types and topographical features, that is, forest land was mostly distributed in the area with relatively great elevation and slope. Overall, this study will promote the development and innovation of ecosystem services fields in the karst region, and further provide a theoretical foundation for ecosystem restoration and reconstruction.
Keywords:runoff services  SWAT  spatial variation  impact factors  karst ecosystem  
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