基于全球陆面数据同化系统蒸散量的GSAC模型率定

摘要

在识别缺资料流域水文模型参数时,目前常采用的区域化方法存在相似流域间降雨径流关系差别较大、模型参数与流域属性间的相关性不明显、在大范围缺资料地区难于选取参考流域等问题.本文从全球陆面数据同化系统(GLDAS)获取流域蒸散量数据,提出利用GLDAS蒸散量率定GSAC模型的方法.首先,通过合并网格建立GSAC模型模拟的蒸散量与GLDAS蒸散量在时间和空间方面的对应关系;其次,基于纳什效率系数的定义构建了一个模型率定指标,以评价GSAC模型模拟的蒸散量对GLDAS蒸散量的拟合效果;最后,依据GLDAS蒸散量与GSAC模型模拟蒸散量之间的拟合关系率定GSAC模型.呼兰河流域应用结果表明,GLDAS提供的蒸散量能够较好反映流域实际蒸散量的变化情况,为率定GSAC模型提供了一种有效的输入数据;在率定期与验证期,利用GLDAS蒸散量率定的GSAC模型对流量模拟的纳什效率系数分别为0.81和0.77,与利用流量数据率定的GSAC模型模拟结果相近.%Since International Association of Hydrological Science (IAHS) initiated the prediction in ungauged basin (PUB) programe,the regionalization has become a common method for identifying hydrological model parameters in ungauged basins.However,some problems exist in the regionalization method,which was commonly used for parameter identification of hydrological model in ungauged basins,such as different relationships between rainfall and runoff in similar basin,unobvious correlation between hydrological model parameters and basin characteristics,and difficult to select a reference basin in wide range of data-deficient areas,etc.It is significant to study the method of calibrating hydrological model by using the data outside the stream flow.A method was presented to calibrate the parameters of grid-based Sacramento (GSAC) model by using evapotranspiration (ET) data from global land data assimilation system (GLDAS).Firstly,a spatiotemporal-grid corresponding relationship between GLDAS ET and ET simulated by GSAC model was established.Secondly,a evaluation index based on Nash-Sutcliffe efficiency coefficient was constructed to measure the fitting effect between GLDAS ET and ET simulated by GSAC model on 0.25 ° grid.Finally,validation of GSAC model was carried out based on the GLDAS ET.The results of application in the Hulan River Basin indicated that GLDAS ET can better simulate the actual ET of the Hulan River Basin so that provided a useful input data for calibrating parameters of GSAC;the runoff Nash-Sutcliffe efficiency coefficient of GSAC model calibrated by GLDAS ET were 0.81 and 0.77 in the calibration and validation periods,respectively,and the results were similar to the corresponding values of GSAC model calibrated by runoff data.