FOR 2630: Understanding the global freshwater system by combining geodetic and remote sensing information with modelling using a calibration/data assimilation approach (GlobalCDA)

当代全球水文（或陆地表面）模型提供了相互矛盾的估计，例如，河流流域的平均年蒸散量或低流量、平均流量和高流量，导致对当前水资源可用性或气候变化对淡水资源影响的估计存在很大差异。拟议的研究单位（RU）的中心目标是提高我们对全球淡水资源的理解，并获得更好的估计大陆水通量（径流，地下水补给，实际蒸散量和可再生水资源）和存储（在雪，土壤，地下水和地表水体以及冰川）。这是我们的假设，一个重大的改进只能通过结合国家的最先进的水文建模和多个新的和最佳处理的大地测量和遥感数据在一个基于集合的校准和数据同化（C/DA）的方法，允许一个灵活的参数（校准）和状态（数据同化）调整量身定制的建模目的。这种方法还考虑到了由于模型结构和输入而产生的不确定性，但尚未得到实施。因此，在本RU中，我们制定了第一阶段的两个主要目标：（1）发展一种基于多观测集合的C/DA方法，以便在第二阶段在全球范围内将模式输出变量的观测数据联合收割机结合起来（基于水位计的流量和GRACE和GRACE-FO总蓄水量异常的时间序列以及积雪的遥感数据，（2）利用全球水文模型WaterGAP改进对淡水通量和储存量的定量评估，包括其对气候和人为强迫的不确定性的响应，关键盆地和估计海洋质量变化由于大陆水储存的变化。C/DA方法包括用于数据同化和参数校准的Enclusive Kalman滤波方法（EnCDA）和帕累托最优校准方法（POC），该方法能够优化时间常数参数，但需要在第二阶段扩展，以考虑校准数据的不确定性和模型输出不确定性的估计。敏感性分析、C/DA提供的不确定性信息以及针对独立数据的模型验证将允许评估应用C/DA方法的附加值。

Contemporary global hydrological (or land surface) models provide conflicting estimates of e.g., mean annual evapotranspiration or low, mean, and high flows in river basins, resulting in strongly differing estimates of current water availability or of climate change impacts on freshwater resources. The central objective of the proposed Research Unit (RU) is to improve our understanding of global freshwater resources and to obtain better estimates of continental water fluxes (streamflow, groundwater recharge, actual evapotranspiration, and renewable water resources) and storages (in snow, soil, groundwater and surface water bodies as well as in glaciers). It is our hypothesis that a major improvement can only be achieved through combining state-of-the-art hydrological modelling and multiple new and optimally processed geodetic and remote sensing data in an ensemble-based calibration and data assimilation (C/DA) approach that allows a flexible parameter (calibration) and state (data assimilation) adjustment tailored to the modelling purpose. Such an approach, which also takes into account uncertainty due to model structure and input, has not been implemented yet. Therefore, in this RU we formulated two primary goals for the first phase: (1) develop a multi-observation ensemble-based C/DA methodology to combine, in the second phase at the global scale, observational data of model output variables (time series of gauge-based streamflow and GRACE and GRACE-FO total water storage anomalies as well as remotely-sensed data on snow cover, extent and water level of surface water bodies and streamflow) with hydrological models in an optimal manner, and (2) exploit this methodology with the global hydrological model WaterGAP to provide an improved quantitative assessment of freshwater fluxes and storages including their uncertainties in response to climate and anthropogenic forcing, for critical basins and for estimating ocean mass change due to continental water storage changes. The C/DA approach encompasses an Ensemble Kalman Filter approach (EnCDA) for both data assimilation and parameter calibration and a Pareto-optimal calibration approach (POC) that enables optimization of temporally constant parameters but needs to be expanded, in the second phase, to take into account the uncertainty of calibration data and estimation of model output uncertainty. Sensitivity analyses, uncertainty information provided by C/DA as well as model validation against independent data will allow evaluating the added value of applying the C/DA approach.