面向复杂情景的SWMM水质参数敏感性分析

为了研究多种降雨情景下不同下垫面类型的水质参数敏感性,采用修正Morris筛选法解析了SWMM的水质参数敏感性总体特征,阐明了不同降雨强度和下垫面类型对水质参数敏感性的影响程度,揭示了各水质指标的高敏感参数分异特征。结果表明:高敏感及敏感参数数量及敏感性随降雨强度增大而增加,暴雨条件下道路和绿地水质参数敏感性水平显著升高,而降雨峰现时间滞后会导致部分高敏感水质参数的敏感性降低,敏感性从高到低依次为径流指数、最大积累量、冲刷系数、速率常数、半饱和常数；下垫面类型对高敏感及敏感水质参数数量的影响程度高于降雨条件；屋顶的敏感性参数数量及其敏感性总体较大,主要受下垫面面积占比和透水性影响,农田氨氮的径流指数敏感性显著高于化学需氧量、总磷、总氮,与肥料水解及氨氮流失过程有关。

Sensitivity analysis of water quality parameters in SWMM for complex scenarios

In order to study the sensitivity of water quality parameters of different underlying surface types under various rainfall scenarios, the modified Morris screening method was used to analyze the overall characteristics of water quality parameters sensitivity of the SWMM, clarify the influence degree of different rainfall intensity and underlying surface types on water quality parameter sensitivity, and reveal the differentiation characteristics of highly sensitive parameters of each water quality index. The results show that the number of highly sensitive and sensitive parameters and sensitivity increase with increasing rainfall intensity, and the sensitivity level of water quality parameters for road and grassy area significantly elevated under rainstorm condition, however, the time lag of rainfall peak will reduce the sensitivity of some highly sensitive water quality parameters. In terms of sensitivity, the parameters are runoff index, maximum accumulation, washoff coefficient, rate constant and half-saturation constant in descending order. The significance tests show that the influence degree of underlying surface types on the number of highly sensitive and sensitive parameters is higher than that of rainfall conditions. The number of sensitivity parameters and their sensitivity for roof are generally large, mainly affected by the area proportion and permeability of underlying surfaces. The sensitivities of runoff exponents of ammonia nitrogen for farmland are significantly higher than COD, TP and TN, which is related to the process of fertilizer hydrolysis and ammonia nitrogen loss.