基于多层土壤湿度数据同化的小流域径流模拟

Streamflow Simulation Based on Multi-layer Soil Moisture Data Assimilation in a Small Catchment

土壤水分状态在水文循环中起关键作用,对径流预报的准确性影响很大。以美国宾西法尼亚州中部的Shale Hills流域为例,研究多层实测土壤湿度数据同化对径流模拟的作用。首先利用新安江模型对研究区内2008~2012年的日径流进行模拟,同时通过集合卡尔曼滤波算法构建基于新安江模型的水文数据同化模型,并设置未同化的开环试验作为对比,研究模型同化土壤湿度对改善径流模拟和土壤水分的能力。模拟结果显示,新安江模型在该流域有较好的适用性;相比于同化前,模型同化多层土壤湿度后径流量相对误差降低了5.2%,纳什效率系数从0.62提高到0.66,土壤含水率的均方根误差从16.4 mm减小到3.0 mm。其次对单层土壤含水率同化结果进行比较,结果表明,下层土壤含水率同化比上层同化对径流模拟作用更明显,但仅加入上层实测土壤含水率同化能够降低下层土壤水模拟的不确定性。研究结果表明同化多层土壤湿度可提高径流模拟精度,同时也能够有效优化土壤水的模拟结果。

Soil moisture status plays a critical role in the hydrologic cycle and has a significant impact on the accuracy of streamflow forecasting.In this paper,the role of assimilating soil moisture data is investigated and the Shale Hills catchment in central Pennsylvania,USA,is used as the study area.Firstly,the daily streamflow process in the study area from 2008 to 2012 is simulated by using the Xin'anjiang model.Meanwhile,an assimilation model based on the ensemble Kalman filter algorithm,and an unassimilated open-loop experiment was set up to study the effect of multi-layer soil moisture data assimilation on streamflow simulation.The simulation results show that the Xin'anjiang model has good applicability in this catchment.Compared with the open-loop,the relative error of streamflow is reduced by 5.2%,the Nash coefficient is increased from 0.62 to 0.66,and the root mean square error of soil moisture content decreased from 16.4 mm to 3.0 mm after assimilating multi-layer soil moisture in the model.The assimilation of soil moisture content of the lower layer has a more obvious effect on streamflow simulation than that of the upper layer,but adding the upper layer of measured soil moisture content can reduce the uncertainty of the lower layer.Assimilating multi-layer soil moisture can improve the accuracy of streamflow simulation to a certain extent,and also can effectively improve the simulation results of soil moisture.