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

利用遥感和数据同化方法预测海河流域水文通量

• 批准号: 162231035
• 负责人: Professor Dr. Harry Vereecken
• 金额: --
• 依托单位: School of Geography
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2010
• 资助国家: 德国
• 起止时间: 2009-12-31 至 2011-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/162231035?language=en
• 关键词: Predicting; hydrological; fluxes; Haihe; river

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).

为了改进对实际蒸散量、地下水补给量和土壤蓄水量的预测，将使用遥感数据和数据同化方法。在一个创新的方法，我们将专注于土壤水分和叶面积指数（LAI）数据的同化和全面的验证活动在当地，足迹和区域尺度。数据同化系统的开发将包括HYDRUS与SUCROS和CLM与SUCROS的耦合以及辐射传输模型CMEM的集成。分析了集合卡尔曼滤波（EnKF）、粒子滤波（PF）、逆模型方法（NSGA-II）和偏差校正方法的优点。除了更新叶面积指数和土壤水分的状态外，还更新了土壤水力特性和根系吸水量的参数。通过正演模拟虚拟土壤和区域水文系统，分析了由于过程描述和参数化不准确而引起的模型误差对地下水补给量和蒸散量预测的影响。通过海河流域的试验研究，验证了模型的正确性。在局部尺度上，可称量的蒸渗仪被用来确定地下水补给量、土壤储水量和蒸散量。在足迹尺度上，我们确定实际蒸散使用涡度相关系统（EC）和大口径蒸散仪（LAS），这将是比较遥感蒸散值。将该方法应用于海河流域两个区域的多源遥感数据同化。该提案旨在确定额外水文信息的必要性和所需的准确性，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，尤其是在未测量的河流流域或数据可用性减少的情况下。此外，还对水文通量，特别是地下水补给和实际蒸散量进行监测，这将能够对迅速变化的环境条件作出反应，并利用未来的使命数据（如SMOS、SMAP）。