基于变动饱和带的产汇流模型及其参数确定方法

A runoff generation and concentration model based on the variable saturation zone concept and its parameter determination

当前分布式水文模型的参数确定仍主要依赖率定方式,在缺资料地区应用受到限制。建立一种基于变动饱和带产流模式和网格水滴汇流方法的分布式产汇流模型,提出利用下垫面特征来确定模型参数的方法。结合野外入渗试验和参数敏感性分析,建立地表饱和水力传导度(K_(0z))和饱和水力传导度随深度衰减系数(f)2个敏感性产流参数与地形参数、土壤类型数据的定量统计关系,利用野外坡面流观测试验确定坡面汇流参数,并在多个实际流域进行应用验证。结果表明:①利用地形参数确定K_(0z)与使用遥感资料确定K_(0z)的模型精度进行对比,在姜湾实验流域场次洪水模拟的平均确定性系数从0.82提高至0.86,洪峰与洪量误差的平均绝对值分别降低了2.2%和0.95%,但峰现时间误差平均绝对值增大了4%(仍控制在2 h内)。②建立姜湾等14个流域参数f率定值与不同深度土壤类型数据的定量关系,移用至七邻等6个流域进行验证,表明参数f关系式与模型率定的精度非常接近,相对误差的平均绝对值为2.8%,场次洪水模拟的平均确定性系数为0.83,洪峰与洪量误差的平均绝对值为10.07%和6.86%,峰现时间误差的平均绝对值为2.61 h。提出的敏感性产流参数确定方法与野外实测、模型率定、遥感资料推求等方式进行对比,均具有较高的参数估计精度和场次洪水模拟精度,在缺资料地区具有适用性。

The calibration processes of current distributed hydrological model has been a critical issue in data-scared or ungauged regions.In this study,we setup a hydrological model,in which,the variable saturated zone concept originated from the real-time interactive basin simulator is applied for runoff generation and the grid water droplet method for flow concentration.We also proposed a method for parameter estimation based on the characteristics of underlying surface.Based on field infiltration experiments and parameter sensitivity analysis,the quantitative statistical relationships were built between two sensitive parameters(surface saturated hydraulic conductivity K_(0z),coefficient of attenuation of saturation hydraulic conductivity with depth f)and the topographic parameters and soil types.The overland confluence parameters were determined by field overland flow observation experiments.The proposed parameter estimation method was verified in selected basins.Our results showed that:①The proposed method for K_(0z) estimation contributes to a better modeling performance for flood simulation in Jiangwan experimental watershed,the average Nash-Sutcliffe efficiency coefficient increased from 0.82 to 0.86,and the average absolute values of peak and flood volume errors decreased by 2.2%and 0.95%,respectively,but the average absolute value of the peak present time error increased by 4%(still controlled within 2 h).②Using the measured flood data of 14 basins such as Jiangwan to calibrate the parameter f,we established the quantitative relationship between the calibrated parameter f and the soil type data of different depths was built in 14 basins including Jiangwan,and further tested in other six basins.The parameter f estimated by the soil type data of different depths was very close to that determined by traditional model calibration processes,the average absolute relative error is 2.8%,the average Nash-Sutcliffe efficiency coefficient of the flood simulation is 0.83,and the average absolute values of flood peak error and flood volume error were 10.07%and 6.86%,respectively,and the average absolute peak present time error was 2.61 h.Our results indicated that the proposed methods for determining sensitive runoff generation parameters are applicable in data-sparse areas and could provide a better or comparable parameter estimation and flood simulation than that determine by field measurements,model calibration,remote sensing data estimation,and other methods.