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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