Predicting hydrological fluxes in the Haihe river basin using remote sensing and data assimilation methods

为了提高对实际蒸散量、地下水补给和土壤蓄水量的预测，将使用遥感数据和数据同化方法。在一种创新的方法中，我们将专注于土壤水分和叶面积指数(LAI)数据的同化，以及在地方、足迹和区域尺度上的综合验证活动。数据同化系统的开发将包括Hydrus与SUCROS的耦合、CLM与SUCROS的耦合以及辐射传输模式CMEM的集成。我们将分析使用集成卡尔曼滤波(EnKF)、粒子滤波(PF)、逆建模法(NSGA-II)和偏差校正方法的优势。除了LAI和土壤水分的状态更新外，还进行了土壤水力性质和根系吸水率的参数更新。通过正演模型模拟了虚拟土壤和区域水文系统，分析了模型误差对地下水补给和蒸散量预测的影响。通过对海河流域的试验研究，验证了模拟结果的正确性。在当地尺度上，可称重的蒸渗仪被用来确定地下水补给、土壤蓄水量和蒸散。在足迹尺度上，我们使用涡旋协方差系统(EC)和大口径闪烁仪(LAS)来确定实际的蒸散量，并将其与遥感蒸散值进行比较。在海河流域的两个区域，利用多个遥感数据，应用所提出的数据同化方法。该提案旨在确定额外水文信息的需要和所需精度，这些信息可能有助于限制模型参数空间的不确定性和模型预测的不确定性，尤其是在未经测量的河流流域或在数据可获得性减少的情况下。此外，还对水文通量进行监测，特别是地下水补给和实际蒸散，这将能够对迅速变化的环境条件作出反应，并利用未来特派团的数据(如SMOS、SMAP)。

In order to improve the prediction of actual evapotranspiration, groundwater recharge and soil water storage, remotely sensed data and data assimilation methods will be used. In an innovative approach we will focus on the assimilation of soil moisture and leaf area index (LAI) data and comprehensive validation activities on local, footprint and regional scale. The development of the data assimilation system will include a coupling of HYDRUS with SUCROS and CLM with SUCROS and the integration of the radiative transfer model CMEM. We will analyze the advantage of using ensemble Kalman filter (EnKF), particle filter (PF), inverse modeling methods (NSGA-II) and bias correction methods. In addition to the state update of LAI and soil moisture, a parameter update of soil hydraulic properties and root water uptake is performed. By forward model runs virtual soils and regional hydrological systems are simulated to analyze the role of model errors from inaccurate process description and parameterization on the prediction of groundwater recharge and evapotranspiration. The modeling results are validated by experimental studies within the Haihe river basin. On the local scale weighable lysimeters are used to determine groundwater recharge, soil water storage and evapotranspiration. On the footprint scale we determine actual evapotranspiration using eddy covariance systems (EC) and large aperture scintillometer (LAS) which will be compared to remotely sensed evapotranspiration values. To two regions in the Haihe river basin the proposed data assimilation approach will be applied by using multiple remote sensing data. The proposal aims to identify the need and the required accuracy of additional hydrological information that may contribute in constraining the uncertainties of the model parameter space and the model prediction uncertainty, above all over ungauged river basins or in case of reduced data availability. In addition, a monitoring of hydrological fluxes, in particular groundwater recharge and actual evapotranspiration, is performed which will be able to react to rapidly changing environmental conditions and to utilize future mission data (e.g. SMOS, SMAP).