Streamflow data assimilation for fully coupled atmosphere-surface-subsurface systems

该项目探讨了在数据同化框架中使用流量观测的价值，以提高完全耦合的陆地系统的预测能力。径流观测给出了水文集水区行为的综合反应，但它们也提供了不同水文组成部分的汇总信息，例如，地下水的贡献或总的土壤水分条件。主要的挑战是同化的DA计划的数据（在我们的情况下流量），包含聚合信息的本地参数字段或状态。为了实现这一目标，项目需要确定一种方法，通过放大/缩小操作将不同的尺度联系起来。几个数据同化测试将被用来确定最佳的战略，同化径流的基础上，不同的复杂性与TerrSysMP-PDAF产生的情况。将与其他项目进行比较和联合试验，以评估综合数据同化框架的一致性。

The project explores the use and the value of streamflow observations in a data assimilation framework for improving the predictive capability of fully coupled terrestrial systems. Streamflow observations give an integrated response of the hydrological catchment behaviour but they also provide aggregated information of the different hydrological components e.g., groundwater contribution or aggregated soil moisture conditions. The main challenge is to assimilate in a DA scheme data (in our case streamflow) that contain aggregated information on local parameter fields or states. To achieve this goal, the project needs to identify a way to link the different scales via an upscaling/downscaling operation. Several data assimilation tests will be used to identify the best strategy to assimilate streamflow based on scenarios of different complexity generated with TerrSysMP-PDAF. Comparisons and joint experiments with the other projects will be conducted to assess the consistency of the integrated data assimilation framework.