李晓玲3,
王琦4,,,
张永梅1, 2
1.中国科学院新疆生态与地理研究所/阜康荒漠生态国家野外科学观测研究站 乌鲁木齐 830011
2.中国科学院大学 北京 100049
3.甘肃农业大学水利水电工程学院 兰州 730070
4.甘肃农业大学草业学院 兰州 730070
基金项目: 国家自然科学基金项目41461062
国家自然科学基金项目41661059
详细信息
作者简介:尹鑫卫, 主要研究方向为干旱区水文生态学。E-mail: xinweiyin@foxmail.com
通讯作者:王琦, 主要从事牧草、草坪及作物节水灌溉方面研究。E-mail: wangqigsau@gmail.com
中图分类号:S152.7计量
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出版历程
收稿日期:2017-08-14
录用日期:2017-11-28
刊出日期:2018-05-01
Sensitivity analysis and optimization of parameters for Laio soil moisture dynamic stochastic model for ridge-furrow rainwater harvesting system
YIN Xinwei1, 2,,LI Xiaoling3,
WANG Qi4,,,
ZHANG Yongmei1, 2
1. Fukang Station for Desert Ecosystem Observation and Experiment/Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
4. College of Grassland Science, Gansu Agricultural University, Lanzhou 730070, China
Funds: This study was supported by the National Natural Science Foundation of China41461062
This study was supported by the National Natural Science Foundation of China41661059
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Corresponding author:WANG Qi E-mail: wangqigsau@gmail.com
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摘要
摘要:水文模型参数的敏感性分析、优化和验证对提高模型计算精度和效率具有重要意义。为探讨Laio土壤水分动态随机模型（Laio模型）各参数在垄沟集雨系统的敏感性，同时，确定参数优化和模型验证的最佳方案，本文结合多因素敏感性分析法以及改进单纯形法（ISM）、粒子群优化算法（PSO）和混合粒子群优化算法（HPSO），利用中国气象局定西干旱气象与生态环境试验基地2012-2013年垄沟集雨燕麦生长季降雨、径流和土壤水分等实测数据，对垄沟集雨系统Laio模型的13个参数进行敏感性分析、优化和验证。结果表明，平均降水量α和凋萎系数sw对土壤水分概率密度函数p（s）最敏感，p（s）对参数α的敏感性在低土壤含水率下更明显，对参数sw的敏感性在高土壤含水率下更明显；3种算法（ISM、PSO和HPSO）的优化参数值均能对垄沟集雨系统土壤水分概率密度函数进行较好模拟，峰值（CPV）、峰值位置（PP）和95%置信区间（CI95%）实测值与模拟值的相对误差均小于10%，CM指数均大于0.5；同时，HPSO算法优化参数的模拟效果和收敛速度均显著优于PSO算法和ISM算法，能较显著克服ISM算法和PSO算法存在的缺陷。HPSO算法可作为垄沟集雨系统土壤水分动态随机模型参数优化的待选方案。
关键词:垄沟集雨系统/
土壤水分动态/
Laio土壤水分动态随机模型/
敏感性分析/
模型参数优化
Abstract:Sensitivity analysis of parameters, calibration and validation of eco-hydrological models are essential for model evaluation and application. It is important in model application to accurately estimate the values of model parameters and to further improve model prediction capacity. Based on eco-hydrological process, the Laio soil moisture dynamics stochastic model (Laio model) was used to describe daily water balance in active soil depth of ridge-furrow rainwater harvest system during growing season to analyze the effects of the interactions among plants, soil and environment under different climatic conditions on soil water balance and plant water conditions. The performance of the Laio model varied with climatic zone due to the heterogeneity of climate, vegetation and soil characteristics. In this study, in order to establish an effective system for parameter sensitivity analysis, calibration and validation of the Laio model in a ridge-furrow rainwater harvesting system in a semi-arid area, a field experiment with a randomized complete block design was conducted during the 2012 and 2013 oat growing seasons at Dingxi Arid Meteorology and Ecological Environment Experimental Station. The experiment was designed to investigate the parameter sensitivity and to determine the optimal mode of parameter optimization of the Laio model under various mulching materials (common plastic film, biodegradable film mulch and manually compacted soil) and various ridge-furrow ratios (60 cm:30 cm, 60 cm:45 cm and 60 cm:60 cm). The methods included multi-factor sensitivity analysis, simplex method (ISM), particle swarm optimization algorithm (PSO) and hybrid particle swarm optimization algorithm (HPSO). Also continuously monitored soil moisture, precipitation runoff and daily precipitation data for 2012-2013 were used to run the model. The results indicated that:(1) mean precipitation per rainfall event (α) and soil saturation degree at wilting point (sw) were the most sensitive parameters for probabilistic density function of soil moisture[p(s)] in different experimental treatments. While the sensitivity of p(s) to α was more obvious under low soil moisture content, that to sw was more obvious under high soil moisture content. (2) There were good agreements among the results of modelling using optimized parameters of the Laio model for the three optimization algorithms (ISM, PSO and HPSO) and the observation values, which were determined from the p(s) curve. This included curve peak value (CPV), curve peak position (PP), 95% confidence interval (CI95%) and consistency measure (CM). All of these indicated that the optimized parameters of the Laio model using the ISM, PSO and HPSO methods correctly estimated p(s) of ridge-furrow rainwater harvesting. (3) The HPSO method not only improved global optimization performance, but also quickened convergence and gave robust results with good quality. It was an effective optimization method for the Laio model calibration and validation. The study improved the efficiency of model parameter calibration, upgraded the accuracy of model simulation results and provided guidance for application of the Laio model in ridge-furrow rainwater harvesting research.
Key words:Ridge-furrow rainwater harvesting system/
Soil moisture dynamic/
Laio soil moisture dynamic stochastic model/
Sensitivity analysis/
Model parameter optimization
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图1垄沟集雨种植燕麦示意图
Figure1.Schematic diagram of ridge-furrow rainwater harvesting system for oats cultivation
下载: 全尺寸图片幻灯片
图2一致性测度(CM)指数定义示意图
Figure2.Schematic diagrams showing definition of consistency measure (CM)
下载: 全尺寸图片幻灯片
图3不同土壤含水率下不同覆盖处理的垄沟集雨系统Laio模型各参数敏感性分析
模型参数的意义见表 2。
Figure3.Sensitivity analysis of parameters in Laio soil moisture dynamic stochastic model in ridge-furrow rainwater harvesting system under different mulching treatments and soil moistures
Meanings of parameters are shown in the table 2.
下载: 全尺寸图片幻灯片
图4基于SOM神经网络聚类法的垄沟集雨系统Laio模型参数敏感性分类
模型参数的意义见表 2。
Figure4.Sensitivity classification of parameters in Laio soil moisture dynamic stochastic model in ridge-furrow rainwater harvesting system based on SOM neural network clustering method
Meanings of parameters are shown in the table 2.
下载: 全尺寸图片幻灯片
图5参数α(生长季平均降水量)和sw(凋萎系数)对垄沟集雨系统土壤水分概率密度函数[p(s)]的影响
Figure5.Influences of parameter α (mean rainfall depth of growing season) and parameter sw (wilting coefficient) on probability density function of soil [p(s)] in ridge-furrow rainwater harvesting system under common plastic film covering
下载: 全尺寸图片幻灯片
图6基于ISM、PSO和HPSO算法的垄沟集雨系统Laio模型参数优化效率和有效性验证
SMC:土壤含水量; NCF:正态曲线拟合; SPSO: PSO优化参数模拟曲线; SISM: ISM优化参数模拟曲线; SHPSO: HPSO优化参数模拟曲线。
Figure6.Optimization efficiency and validation of parameters in Laio soil moisture dynamic stochastic model based on ISM, PSO and HPSO algorithms in ridge-furrow rainwater harvesting system under different mulching treatments
SMC: soil moisture content; NCF: normal curve fitting; SPSO: simulating curves with PSO-optimized parameters; SISM: simulating curves with ISM-optimized; SHPSO: simulating curves with HPSO-optimized parameters.
下载: 全尺寸图片幻灯片表1垄沟集雨种植燕麦试验设计
Table1.Experimental design for oats cultivation in ridge-furrow rainwater harvesting system
| 处理 Treatment | 沟垄比 Furrow:ridge (cm) | 垄面积 Ridge area (m2) | 沟面积 Furrow area (m2) | 小区面积 Plot area (m2) | 垄覆盖方式 Ridge mulch style |
| SR | 60:30 | 13.50 | 18 | 31.50 | 土壤结皮 Soil crust |
| 60:45 | 20.25 | 18 | 38.25 | ||
| 60:60 | 27.00 | 18 | 45.00 | ||
| BMR | 60:30 | 13.50 | 18 | 31.50 | 可降解地膜覆盖 Biodegradable mulch film |
| 60:45 | 20.25 | 18 | 38.25 | ||
| 60:60 | 27.00 | 18 | 45.00 | ||
| CMR | 60:30 | 13.50 | 18 | 31.50 | 普通地膜覆盖 Common plastic film |
| 60:45 | 20.25 | 18 | 38.25 | ||
| 60:60 | 27.00 | 18 | 45.00 |
下载: 导出CSV表2垄沟集雨系统Laio模型参数取值范围及其不同覆盖处理的初始值
Table2.Values ranges and initial values of parameters in Laio soil moisture dynamic stochastic model (Laio model) for ridge-furrow rainwater harvesting system
| 属性 Property | 随机模型参数 Parameter of Laio model | 单位 Unit | 参数取值范围 Value range of parameter | 参数初始值 Initial value of parameter | |||||
| 符号 Symbol | 意义 Meaning | UBV | LBV | SR | CMR | BMR | |||
| 土壤 Soil | n | 土壤孔隙度 Soil porosity | m3·m-3 | 0 | 1 | 0.58 | 0.58 | 0.58 | |
| β | 土壤孔隙大小分布参数 Soil porosity distribution parameter | / | 10.00 | 20.00 | 14.8 | 14.8 | 14.8 | ||
| Zr | 土壤活动层深度 Depth of rooting zone | cm | 140 | 140 | 140 | 140 | 140 | ||
| Ks | 土壤饱和导水率 Saturated hydraulic conductivity of soil | cm·d-1 | 10.00 | 30.00 | 19.5 | 19.5 | 19.5 | ||
| sh | 吸湿系数 Hygroscopic coefficient | m3·m-3 | 0 | 0.10 | 0.026 | 0.026 | 0.026 | ||
| sw | 凋萎系数 Wilting coefficient | m3·m-3 | 0.10 | 0.40 | 0.25 | 0.25 | 0.25 | ||
| s* | 水分胁迫开始点 Critical point of plant undergoes water stress | m3·m-3 | 0.40 | 0.70 | 0.56 | 0.56 | 0.56 | ||
| sfc | 田间持水率 Field capacity | m3·m-3 | 0.70 | 1 | 0.72 | 0.72 | 0.72 | ||
| Ew | 凋萎系数对应土壤蒸发 Soil evaporation at wilting point | cm·d-1 | 0 | 0.02 | 0.012 | 0.012 | 0.012 | ||
| 作物 Crop | Δ | 植物截留阈值 Interception capacity of vegetation | cm | 0 | 0.50 | 0.128 | 0.130 | 0.131 | |
| Emax | 日最大蒸散量 Maximum evapotranspiration | cm·d-1 | 0 | 1.00 | 0.610 | 0.570 | 0.560 | ||
| 降雨 Rainfall | α | 生长季平均降水量 Mean rainfall depth of growing season | d-1 | 同初始值 Same as the initial value | 0.594 9 | 0.816 6 | 0.767 3 | ||
| λ | 生长季平均降水频率 Mean rainfall frequency of growing season | d-1 | 0.33 | 0.33 | 0.33 | 0.33 | 0.33 | ||
| 表中模型参数取值范围由相关实测数据和文献资料[11, 16]获得。UBV:参数取值下限; LBV:参数取值上限。Values ranges of model parameters in the table were obtained from the relevant measured data and literatures[11, 16]. UBV: upper bound value of model parameter; LBV: lower bound value of model parameter. | |||||||||
下载: 导出CSV表3基于ISM、PSO和HPSO算法的垄沟集雨系统Laio模型参数优化结果
Table3.Optimization results of parameters in Laio soil moisture dynamic stochastic model based on ISM, PSO and HPSO algorithms in ridge-furrow rainwater harvesting system under different mulching treatments
| 模型参数 Parameter | 单位 Unit | ISM算法参数优化值 Value after optimization using ISM | PSO算法参数优化值 Value after optimization using PSO | HPSO算法参数优化值 Value after optimization using HPSO | ||||||||
| SR | CMR | BMR | SR | CMR | BMR | SR | CMR | BMR | ||||
| n | m3·m-3 | 0.569 6 | 0.581 4 | 0.581 1 | 0.563 7 | 0.586 3 | 0.580 9 | 0.563 4 | 0.586 6 | 0.580 4 | ||
| β | / | 14.800 3 | 14.801 1 | 14.800 8 | 14.208 4 | 13.502 5 | 14.028 3 | 14.201 2 | 13.548 1 | 14.031 5 | ||
| Zr | cm | 140 | 140 | 140 | 140 | 140 | 140 | 140 | 140 | 140 | ||
| Ks | cm·d-1 | 19.500 4 | 19.501 2 | 19.500 7 | 15.542 6 | 17.394 4 | 17.572 0 | 15.540 7 | 17.345 2 | 17.569 9 | ||
| sh | m3·m-3 | 0.025 8 | 0.026 3 | 0.026 3 | 0.022 1 | 0.028 8 | 0.024 3 | 0.026 1 | 0.027 9 | 0.023 8 | ||
| sw | m3·m-3 | 0.249 5 | 0.249 0 | 0.249 4 | 0.180 1 | 0.206 6 | 0.201 7 | 0.130 2 | 0.186 6 | 0.181 7 | ||
| s* | m3·m-3 | 0.580 3 | 0.570 3 | 0.569 8 | 0.671 3 | 0.561 1 | 0.532 5 | 0.691 0 | 0.571 2 | 0.534 1 | ||
| sfc | m3·m-3 | 0.721 0 | 0.721 0 | 0.720 0 | 0.820 6 | 0.743 9 | 0.745 4 | 0.880 3 | 0.746 8 | 0.743 5 | ||
| Ew | cm·d-1 | 0.009 0 | 0.006 3 | 0.010 0 | 0.015 2 | 0.017 8 | 0.018 2 | 0.013 2 | 0.016 7 | 0.018 1 | ||
| Δ | cm | 0.128 0 | 0.129 3 | 0.131 0 | 0.143 8 | 0.111 9 | 0.117 1 | 0.138 6 | 0.112 3 | 0.116 4 | ||
| Emax | cm·d-1 | 0.611 1 | 0.671 2 | 0.559 8 | 0.611 5 | 0.600 9 | 0.531 6 | 0.661 9 | 0.600 2 | 0.531 1 | ||
| α | d-1 | 0.594 9 | 0.816 6 | 0.767 3 | 0.594 9 | 0.816 6 | 0.767 3 | 0.594 9 | 0.816 6 | 0.767 3 | ||
| λ | d-1 | 0.330 0 | 0.330 0 | 0.330 0 | 0.330 0 | 0.330 0 | 0.330 0 | 0.330 0 | 0.330 0 | 0.330 0 | ||
| 模型参数的意义见表 2。Meanings of parameters are shown in the table 2. | ||||||||||||
下载: 导出CSV表4基于ISM、PSO和HPSO算法的垄沟集雨系统Laio模型参数优化性能比较
Table4.Performance comparison of parameter optimization in Laio soil moisture dynamic stochastic model based on ISM, PSO and HPSO algorithms in ridge-furrow rainwater harvesting system under different mulching treatments
| 算法 Algorithm | SR的p(s)曲线检验指标 Consistency test indicators of SR | CMR的p(s)曲线检验指标 Consistency test indicators of CMR | BMR的p(s)曲线检验指标 Consistency test indicators of BMR | |||||||||||
| CPV | PP | CI95% | CM | CPV | PP | CI95% | CM | CPV | PP | CI95% | CM | |||
| ISM | 0.019 4 | 0.033 3 | -0.149 3 | 0.519 | 0.006 9 | -0.028 6 | -0.043 4 | 0.830 | 0.011 1 | 0.058 8 | -0.043 7 | 0.685 | ||
| PSO | 0.012 6 | 0.033 3 | -0.087 4 | 0.723 | 0.006 8 | -0.028 6 | -0.049 0 | 0.957 | 0.009 3 | 0.000 1 | -0.021 0 | 0.864 | ||
| HPSO | 0.000 1 | 0.000 1 | -0.062 9 | 0.805 | 0.006 1 | -0.028 6 | -0.024 1 | 0.994 | -0.002 4 | 0.000 1 | -0.019 3 | 0.903 | ||
| CPV:峰值; PP:峰值位置; CI95%: 95%置信区间; CM:一致性指数。CPV: the curve peak value; PP: the position of the peak; CI95%: the confidence interval of 95%; CM: the consistency measure. | ||||||||||||||
下载: 导出CSV参考文献
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