融雪径流模型参数渐进式优化率定方法

Approach of progressive optimization calibration for Snowmelt-Runoff Model

融雪径流模型(SRM)是一种概念性和半分布式模型,其参数在一定程度上反应了流域对融雪的径流响应内在机制.提出了一种融雪径流模型离散化参数的渐进式优化率定方法,以新疆玛纳斯河流域为实验区,开展SRM模型参数离散化分析及其优化研究.根据模型结构剖析,确定融雪径流系数和降雨径流系数为两个待率定的敏感参数,分析了月、半月、10天和5天等不同离散尺度模型参数表征融雪径流响应时变机制的特性,对旬离散的模型参数,采用不同目标函数及优化方法分别对2001—2012共12年融雪期逐日的融雪径流过程进行模拟率参,精度评价表明渐进式优化率定方法优于分时段优化率定方法.最后以2001—2008年的融雪期作为率参期,以2009—2012年作为检验期,分别面向三个目标对模型参数进行全局优化率定,验证模拟的平均效率系数达到0.87,表明本文提出的融雪径流模型参数率定方法是有效的,经优化的模型可应用于融雪径流预测.

Snowmelt Runoff Model（SRM）is a concept and semi-distributed hydrological Model and its primary parameters can express the internal response mechanism of catchment runoff to snowmelt.In this paper,we proposed a progressive calibration approach for discretized parameters optimization of SRM which was applied to simulate the snowmelt-runoff process of the Manas River basin in Xinjiang,China.The snowmelt-runoff coefficient and rainfallrunoff coefficient were determined as the two sensitive parameters to be calibrated based on the model structure parsing,and it was analyzed that the time-varying characteristic of catchment snowmelt-runoff response expressed bythe model parameters which were discretized to the multiple time scale of month,half month,ten days and five days.The different object functions and optimization methods were used to calibrate the year model parameters discretized ten days scale by simulating the snowmelt-runoff process during snowmelt period from 2001 to 2012.The simulation accuracy evaluation shows that the progressive calibration method is better than the discrete time segment calibration method.Finally,oriented three objectives,the model parameters are optimized with 2001-2008 as calibration period and 2009-2012 as validation period,the results of verification simulations show that the average of efficiency coefficient is 0.87,the proposed calibration method for SRM parameters optimization are proved to be effective and the optimized model can be used to predict snowmelt-runoff process.