SWAT模型多目标率定与评价——以梅川江流域为例

doi:10.7523/j.issn.2095-6134.2021.05.003

中国科学院大学学报 ›› 2021, Vol. 38 ›› Issue (5): 590-600.DOI: 10.7523/j.issn.2095-6134.2021.05.003

SWAT模型多目标率定与评价——以梅川江流域为例

李景¹, 马天啸¹, 陆妍如¹, 宋现锋^1,2,3, 李润奎^1,2, 刘军志⁴, 段峥⁵

1. 中国科学院大学资源与环境学院, 北京 100049;
2. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室, 北京 100101;
3. 中国科学院空天信息创新研究院中国科学院定量遥感信息技术重点实验室, 北京 100094;
4. 南京师范大学虚拟地理环境教育部重点实验室, 南京 210023;
5. 隆德大学自然地理与生态系统科学学院, 隆德 22362, 瑞典

收稿日期:2019-12-16 修回日期:2020-03-19 发布日期:2021-09-13
通讯作者: 宋现锋
基金资助:
国家重点研发计划项目（2020YFC1807103）、广西重大科技专项（GK-AA17202033）和国家自然科学基金（40771167，41601486）资助

Multi-objective calibration and evaluation of SWAT modela case study in Meichuan River basin

LI Jing¹, MA Tianxiao¹, LU Yanru¹, SONG Xianfeng^1,2,3, LI Runkui^1,2, LIU Junzhi⁴, DUAN Zheng⁵

1. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
3. Key Laboratory of Quantitative Remote Sensing Information Technology of CAS, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
4. Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, China;
5. Department of Physical Geography and Ecosystem Science, Lund University, Lund 22362, Sweden

Received:2019-12-16 Revised:2020-03-19 Published:2021-09-13

摘要/Abstract

摘要： 基于水文站径流数据的SWAT模型参数率定，尽管在站点处模拟精度较高，但率定参数在空间分布上存在不确定性，尤其当站点稀疏时，参数间补偿作用使模型深受异参同效现象的干扰。利用植被模块多目标率定SWAT模型，可提高总体建模质量。一方面，植被在水文过程中至关重要，对植被模块率定加强了对SWAT中间过程的精度控制；另一方面，植被数据属于面源数据，不同于水文站的点源数据，提高了模型在全空间模拟上的精度控制。以梅川江流域为研究区，考虑径流、作物产量、MODIS LAI等数据对SWAT开展多目标率定，并同径流单目标模拟结果对比分析。结果表明，本方法可明显减弱异参同效对模型结果的影响，降低参数的不确定性，提高模型的模拟精度和稳健性。

关键词: SWAT模型, 异参同效, 多目标优化, 叶面积指数, 作物产量

Abstract: SWAT model calibrated by streamflow data from gauging stations often provides a relatively high accuracy of hydrologic simulation, nevertheless those calibrated model parameters are still with uncertainty, particularly in the area with sparse gauging stations where the compensation between parameters would make SWAT model deeply disturbed by the phenomenon of equifinality. In this paper, a multi-objective calibration method is proposed to calibrate SWAT model and improve modeling quality by using leaf area index (LAI) from remote sensing data. Generally, vegetation has a crucial impact on eco-hydrological process that is also the core component of hydrological process. Therefore, the calibration of vegetation module facilitates to increase the precision of hydrologic model output. In-situ measured streamflow data is usually collected at gauging points, while remote sensed data is a snapshot over a large space. So LAI data may presents much more spatial details in the model other than streamflow data. The test was carried out with streamflow, crop yield yearbook, and MODIS LAI datasets in the Meichuan River basin, and the results showed that the multi-objective method significantly reduced the impact of equifinality on model parameters and improved the simulation accuracy as well as the robustness of the SWAT model because it took full advantages of vegetation remote sensing and concerned the great role of vegetation in hydrological process.

Key words: SWAT model, equifinality, multi-objective optimization, leaf area index, crop yield

中图分类号:

P333

李景, 马天啸, 陆妍如, 宋现锋, 李润奎, 刘军志, 段峥. SWAT模型多目标率定与评价——以梅川江流域为例[J]. 中国科学院大学学报, 2021, 38(5): 590-600.

LI Jing, MA Tianxiao, LU Yanru, SONG Xianfeng, LI Runkui, LIU Junzhi, DUAN Zheng. Multi-objective calibration and evaluation of SWAT modela case study in Meichuan River basin[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(5): 590-600.

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SWAT模型多目标率定与评价——以梅川江流域为例

Multi-objective calibration and evaluation of SWAT modela case study in Meichuan River basin

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