Assessing the spatiotemporal dynamics of water volumes in large wetlands and lakes by combining remote sensing with macro-scale hydrological modelling (WLDYN)

大型湖泊和湿地是全球水循环的重要组成部分，也是动植物的栖息地，为人类提供宝贵的生态系统服务。更好地定量了解大型湖泊和湿地的时空水量变化也有助于改进对下游水资源的评估。然而，地表水流量动态的地面数据很少，特别是在发展中国家。能够得出面积-体积关系时间序列的卫星技术为地面数据提供了一种强有力的替代方法。对于非洲、亚洲、欧洲和南美洲的三个大湖泊和三个大湿地，我们将以一致的方式获得这种基于遥感的数据。然后，这些数据将支持开发更好的算法，利用全球尺度水文模型WGHM模拟湖泊和湿地，并将作为模型校准的观测，作为河流流量观测的补充或替代。为此，将开发一种改进的多标准校准方法，该方法可以考虑到观测到的地表水体积和面积变化。通过将表征地表水的卫星数据与全球范围的水文模型相结合，将改进对全球大型湖泊和湿地动态的表征。

Large lakes and wetlands are important elements of the global water cycle and habitat for fauna and flora, providing valuable ecosystem services to humans. A better quantitative understanding of the spatiotemporal water volume variations of large lakes and wetlands also contributes to an improved assessment of downstream water resources. However, ground data of surface water volume dynamics are scarce, in particular in developing countries. Satellite techniques that allow deriving time series of area-volume relations provide a powerful alternative to ground data. For three large lakes and three large wetlands in Africa, Asia, Europe and South America, we will derive such remote-sensing based data in a consistent manner. These data will then support the development of better algorithms for modelling lakes and wetlands with the global-scale hydrological model WGHM, and will serve as observations for model calibration, in addition to or as an alternative to river discharge observations. To this end, an enhanced multi-criteria calibration method that can take into account observed surface water volume and area variations will be developed. By combining satellite data that characterize surface waters with global-scale hydrological modelling, an improved characterization of the dynamics of large lakes and wetlands around the globe will be achieved.