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林炳青,陈兴伟,陈莹,刘梅冰.流域景观格局变化对洪枯径流影响的SWAT模型模拟分析.生态学报,2014,34(7):1772~1780 本文二维码信息
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流域景观格局变化对洪枯径流影响的SWAT模型模拟分析
Simulations and analysis on the effects of landscape pattern change on flood and low flow based on SWAT model
投稿时间:2013-04-22  修订日期:2013-11-13
DOI: 10.5846/stxb201304220769
关键词景观格局  洪枯  径流  SWAT模型  晋江流域
Key Wordslandscape pattern  flood  low flow  SWAT model  Jinjiang watershed
基金项目国家自然科学基金资助项目(50979015);福建省公益科研院所专项重点项目(2013R04);2012年福建省公益科研院所专项(N00298)
作者单位E-mail
林炳青 福建师范大学地理科学学院, 福州 350007  
陈兴伟 福建师范大学地理科学学院, 福州 350007
湿润亚热带山地生态国家重点实验室培育基地, 福州 350007
福建省陆地灾害监测评估工程技术研究中心, 福州 350007 
cxwchen215@163.com 
陈莹 福建师范大学地理科学学院, 福州 350007
湿润亚热带山地生态国家重点实验室培育基地, 福州 350007 
 
刘梅冰 福建师范大学地理科学学院, 福州 350007
湿润亚热带山地生态国家重点实验室培育基地, 福州 350007 
 
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摘要:
为了进一步揭示流域景观格局变化的水文效应,以晋江流域为研究区,基于SWAT模型分别模拟1985年和2006年两种景观格局下,2002-2010年气象条件时的日径流过程;应用景观格局分析法和Pearson相关分析法,定量分析了景观格局变化对洪枯径流的影响,探讨了影响机制。研究表明:(1)SWAT模型能够较精确模拟晋江流域的日径流过程;(2)2002-2010年气象条件时,与1985年景观格局相比,在2006年景观格局下晋江流域年平均最大1 d和连续最大5 d径流深分别增加5.46%和4.97%,年平均最小1 d和连续最小7 d径流深分别减少3.79%和2.55%;(3)洪水径流与景观格局相关性最显著,最大1 d和连续最大5 d与林地景观面积呈显著负相关,相关系数分别为-0.764和-0.721;最大1 d与Shannon多样性指数和Shannon均匀度指数呈显著正相关,相关系数分别为0.721和0.736;与最大斑块指数和蔓延度呈显著负相关,相关系数分别为-0.61和-0.596,说明林地景观面积的减少降低了流域对强降水的截留能力;景观类型均衡化分布,连通性降低,降低了水分在流域的内部循环能力,导致洪峰流量的增加;(4)景观格局指数与最小1 d和连续最小7 d的相关性不显著,说明景观格局变化对枯水径流的直接影响较小。
Abstract:
The effects of landscape pattern change on hydrological processes have become one of the major ecological concerns in the world. Some researches have been working on the yearly and monthly runoff response to the landscape pattern change based on distributed hydrological model. How the daily runoff, such as flood and low flow, responds to landscape pattern change has not been well studied. The study area of Jinjiang watershed, which is situated at southeastern China, is one of the regions where the economy grow fast and the landscape pattern change dramatically. It is worth to reveal how the change in landscape pattern influences the flood and low flow for the regional ecological security and economic development. In this paper, an integrated approach of distributed hydrological modeling and Pearson correlation analysis was applied to quantify the contributions of the changes of landscape indices on the variation in flood and low flow. Daily stream flow under the meteorological condition from 2002 to 2010 was simulated with the landscape pattern maps in two periods (1985 and 2006) using the Soil and Water Assessment Tool (SWAT), respectively. The changes of annual maximum 1-day, 5-day, and minimum 1-day, 7-day stream-flow between two simulations were calculated. Finally, the changes of stream-flow were related to the changes of landscape indices in Pearson correlation to quantify the impacts of changes in landscape on that of flood and low flow.
The results show as following: (1) All Ens and R2 are above 0.75, and Ers are in the range of ±10% in the calibration and validation period for three gauge stations, suggesting SWAT model is well performance. (2) Compared with the landscape in 1985, the annual mean maximum 1-day and 5-day stream flow from 2002 to 2010, which was simulated by SWAT model with the landscape in 2006, increases by 5.46% and 4.97% in the Jinjiang watershed, while the annual mean minimum 1-day and 7-day stream flow decreases by 3.79% and 2.55%, respectively. (3) There are significant correlations between the variation of flood flow and landscape indices. The forestland area is negatively related with annual maximum 1-day and 5-day stream flow with the correlation coefficients of -0.764 and -0.721. The glass area is positively related with annual minimum 1-day and 7-day stream flow with the correlation coefficients of 0.461 and 0.478, respectively. It demonstrates that the increase of forest area can reduce the discharge of flood, while the increase of glass area can increase the discharge of low flow. Furthermore, the relationships between landscape metrics and flood are also revealed. SHDI and SHEI are significantly positively related with maximum 1-day flow with the correlation coefficients of 0.721 and 0.736, respectively. On the other hand, LPI and CONTAG show negative relationships with maximum 1-day flow with the coefficients of -0.61 and -0.596, respectively. The relationships between landscape metrics and flood reveal that equilibrium distribution of landscape classes and low connectivity reduces hydrologic cycle of the basin and eventually leads to the increase of flood flow. (4) Landscape metrics are not significantly related with low flow indicate that the impact of the change in landscape on that of the low flow is not so important.
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