随机森林模型在径流变化归因分析中的适用性研究

王艺璇; 刘夏; 沈彦军

doi:10.12357/cjea.20210652

随机森林模型在径流变化归因分析中的适用性研究

doi: 10.12357/cjea.20210652

王艺璇^{1, 2,},
刘夏¹,
沈彦军^{1, 2, ,}

1.
中国科学院遗传与发育生物学研究所农业资源研究中心/中国科学院农业水资源重点实验室　石家庄　050022
2.
中国科学院大学　北京　100049

基金项目: 国家自然科学基金项目(41807177)和河北省“三三三人才工程”项目(A202001062)资助

详细信息

作者简介:
王艺璇, 主要研究方向为流域生态水文模拟。E-mail: wangyixuanjy@foxmail.com

通讯作者:
沈彦军, 主要从事流域生态水文模拟与水环境管理方向研究。E-mail: shenyanjun@sjziam.ac.cn

中图分类号: TV11
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出版历程
- 收稿日期: 2021-09-27
- 录用日期: 2021-12-30
- 网络出版日期: 2021-12-31
- 刊出日期: 2022-05-18

Applicability of the random forest model in quantifying the attribution of runoff changes

WANG Yixuan^{1, 2
,},
LIU Xia¹,
SHEN Yanjun^{1, 2
, ,}

1.
Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences / Key Laboratory of Agricultural Water Resources, Chinese Academy of Sciences, Shijiazhuang 050022, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: This research was supported by the National Natural Science Foundation of China (41807177) and “ 333 Talent Project” of Hebei Province (A202001062).

More Information

Corresponding author: E-mail: shenyanjun@sjziam.ac.cn

摘要

摘要: 气候变化和人类活动是影响流域径流变化的两大因素, 定量识别二者对流域径流的影响对水资源合理开发和流域综合治理具有重要意义。随机森林模型作为一种易于使用的机器学习方法, 被越来越多地应用于水文学领域, 然而随机森林模型在径流变化归因分析中是否具有适用性值得探讨。本文以永定河上游的洋河流域为例, 基于随机森林模型对流域径流进行了模拟, 采用径流影响评价模型定量分析了气候变化和人类活动对径流变化的贡献率; 并利用基于物理机制的SWAT模型对研究结果进行了对比验证。研究结果表明: 1)在对不同子流域径流模拟效果方面, SWAT分布式水文模型对东洋河(柴沟堡东站)、南洋河(柴沟堡南站)、洋河(响水堡站)流域突变前的径流量模拟结果较为可信, 3个流域率定期和验证期的R²均在0.65以上, 纳什系数(NSE)也大部分在0.65以上; 随机森林模型对3个流域模拟径流的NSE和R²多在0.80以上, 均高于SWAT模型的NSE和R², 随机森林模型模拟表现优于SWAT模型; 2)对比验证发现, 基于随机森林模型和SWAT模型的流域径流变化归因分析结果较为相近, 人类活动是导致永定河上游流域径流变化的主要原因, 对径流减少的贡献率为84.3%~97.6%。总体上, 随机森林模型在永定河上游流域径流变化归因研究中具有一定的适用性, 为流域径流变化贡献率的定量识别提供了一种新的方法和思路。
- 永定河流域 /
- 随机森林模型 /
- SWAT模型 /
- 径流 /
- 归因分析
Abstract: Climate change and human activity have a significant impact on runoff in basins. As an important ecological barrier,the upper Yongding River Basin of has undergone significant changes in the ecological environment over the past 50 years, and the problem of water shortage has become increasingly prominent. It is necessary to restore the water ecology and analyze the influence of climate and human activities on runoff dynamics. Therefore, this study established a comparative approach between the random forest and Soil and Water Assessment Tool (SWAT) models in the Yanghe River Basin, which is greatly affected by human activities in the upper Yongding River Basin. The main conclusions were as follows: 1) in terms of the runoff simulation effect, the SWAT model was reliable for revealing the runoff dynamics in the Dongyanghe River Basin, Nanyanghe River Basin, and Yanghe River Basin. The R² values of the simulated and observed runoff in the three basins were above 0.65 in both the calibration and verification periods, and the Nash coefficients (NSE) were also above 0.65 in the three basins. However, the random forest model outperformed the SWAT model in terms of NSE and R² in the three basins, and its NSE and R² values were mostly above 0.80. 2) In quantifying the attribution of runoff changes, the results based on the SWAT model showed that the contribution rates of climate change to runoff decline in the three basins were generally between 5.0% and 15.7%, and those of human activities were 84.3%–95.0% in the three basins. The results based on the random forest model were similar to the attribution results of runoff decline based on the SWAT model; the contribution rates of climate change and human activities to runoff decline in the three basins were generally 2.4%–11.5% and 88.5%–97.6% in the three basins. This is consistent with the research results of other experts and scholars that human activities are the main cause of runoff decline in the Yanghe River Basin. Random forest can be applied in runoff simulation in the Yanghe River Basin, and the simulated model results can be used in water resource management. In this study, the SWAT model and random forest were combined to reveal the impacts of climate change and human activities on the changes in runoff in the Yanghe River Basin. Additionally, the applicability of the random forest model in the Yanghe River Basin was evaluated, which demonstrated the possibility of integrating the random forest model in hydrological modeling in further research. However, random forest is a black-box model in theory and lacks consideration of hydrological processes. Although this study preliminarily explored the method and it has been proven to be applicable in runoff simulation, the uncertainty of this method in runoff simulation or runoff evolution needs to be further explored.
- Yongding River Basin /
- Random forest model /
- SWAT /
- Runoff /
- Attribution analysis

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图 1 洋河流域示意图

Figure 1. Schematic diagram of the Yanghe River Basin

下载: 全尺寸图片幻灯片

图 2 基于SWAT模型的东洋河、南洋河、洋河流域突变前月径流实测值和模拟值对比

图中虚线左侧为率定期, 右侧为验证期。

Figure 2. Observed and simulated monthly runoff before abrupt change in the Dongyanghe River Basin, Nanyanghe River Basin, Yanghe River Basin based on SWAT

In the figure, the left side of the dotted line is the calibration period, and the righ side of the dotted line is the verification period.

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图 3 基于随机森林模型的东洋河、南洋河、洋河流域突变前月径流实测值和模拟值对比

Figure 3. Observed and simulated monthly runoff before abrupt change in the Dongyanghe River Basin, Nanyanghe River Basin, Yanghe River Basin based on random forest model

下载: 全尺寸图片幻灯片

图 4 基于SWAT模型(a,b,c)和随机森林模型(d, e, f)的东洋河、南洋河、洋河流域径流影响评价模型结果

Figure 4. Observed and simulated monthly runoff after calibration and validation periods based on the SWAT and random forest model in the Dongyanghe River Basin, Nanyanghe River Basin, Yanghe River Basin

下载: 全尺寸图片幻灯片

表 1 洋河流域水文站、气象站基本信息

Table 1. Basic information of hydrological stations and weather stations in the Yanghe River Basin

分类 Category	站名 Name	经度 Longitude (°)	纬度 Latitude (°)	海拔/控制面积 Altitude (m) /area (km²)	时间序列 Period
气象站 Meteorological station	天镇 Tianzhen	114.1	40.4	1015.6	1984—2015
	尚义 Shangyi	114.0	41.1	1358.6	1984—2015
	怀安 Huai’an	114.4	40.7	1142.4	1984—2015
	宣化 Xuanhua	115.0	41.6	629.3	1984—2015
	崇礼 Chongli	115.3	41.0	1246.7	1984—2015
	涿鹿 Zhuolu	115.2	40.3	529.6	1984—2015
	兴和 Xinghe	113.9	40.9	1253.1	1984—2015
水文站 Hydrometric station	柴沟堡东 Chaigoubu East	114.1	40.7	3674	1984—2015
	柴沟堡南 Chaigoubu South	114.4	40.7	2427	1984—2015
	响水堡 Xiangshuibu	115.2	40.5	14 507	1984—2015

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表 2 基于SWAT模型的气候变化及人类活动对东洋河、南洋河、洋河流域径流变化影响的贡献率(1986—2015年)

Table 2. Contribution rates of climate change and human activities to runoff changes in the Dongyanghe River Basin, Nanyanghe River Basin, Yanghe River Basin based on SWAT model (1986 to 2015)

水文站 Hydrometric station	时段 Time interval	实测径流 Observed runoff (×10⁸ m³)	模拟径流 Simulated runoff (×10⁸m³)	径流变化量 Runoff variation (×10⁸ m³)	贡献值 Variable quantity (×10⁸ m³)		贡献率 Contribution rate (%)
水文站 Hydrometric station	时段 Time interval	实测径流 Observed runoff (×10⁸ m³)	模拟径流 Simulated runoff (×10⁸m³)	径流变化量 Runoff variation (×10⁸ m³)	气候变化 Climate change	人类活动 Human activities	气候变化 Climate change	人类活动 Human activities
柴沟堡东 Chaigoubu East Station	突变前 Before abrupt change	0.93
柴沟堡东 Chaigoubu East Station	突变后 After abrupt change	0.42	0.85	−0.51	−0.08	−0.43	15.7	84.3
柴沟堡南 Chaigoubu South Station	突变前 Before abrupt change	0.47
柴沟堡南 Chaigoubu South Station	突变后 After abrupt change	0.19	0.45	−0.28	−0.01	−0.27	5.0	95.0
响水堡 Xiangshuibu Station	突变前 Before abrupt change	2.14
响水堡 Xiangshuibu Station	突变后 After abrupt change	0.95	2.04	−1.20	−0.11	−1.09	8.9	91.1

下载: 导出CSV

表 3 基于随机森林模型的气候变化和人类活动对东洋河、南洋河、洋河流域径流变化的贡献率(1986—2015年)

Table 3. Contribution rate of climate change and human activities to the runoff changes in the Dongyanghe River Basin, Nanyanghe River Basin, Yanghe River Basin based on the random forest model (1986−2015)

水文站 Hydrometric station	时段 Time interval	实测径流 Observed runoff (×10⁸ m³)	模拟径流 Simulated runoff (×10⁸ m³)	径流变化量 Runoff variation (×10⁸ m³)	贡献值 Variable quantity (×10⁸ m³)		贡献率 Contribution rate (%)
水文站 Hydrometric station	时段 Time interval	实测径流 Observed runoff (×10⁸ m³)	模拟径流 Simulated runoff (×10⁸ m³)	径流变化量 Runoff variation (×10⁸ m³)	气候变化 Climate change	人类活动 Human activities	气候变化 Climate change	人类活动 Human activities
柴沟堡东 Chaigoubu East Station	突变前 Before abrupt change	0.93
柴沟堡东 Chaigoubu East Station	突变后 After abrupt change	0.42	0.87	−0.51	−0.06	−0.45	11.5	88.5
柴沟堡南 Chaigoubu South Station	突变前 Before abrupt change	0.47
柴沟堡南 Chaigoubu South Station	突变后 After abrupt change	0.19	0.46	−0.28	−0.01	−0.27	2.4	97.6
响水堡 Xiangshuibu Station	突变前 Before abrupt change	2.14
响水堡 Xiangshuibu Station	突变后 After abrupt change	0.95	2.10	−1.20	−0.04	−1.15	3.4	96.6

下载: 导出CSV

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