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，也是动物和植物群的栖息地，为人类提供了宝贵的生态系统服务。更好地从数量上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，地表水量动态的地面数据很少，特别是在发展中国家。卫星技术可以得出面积-体积关系的时间序列，这是地面数据的有力替代方法。对于非洲、亚洲、欧洲和南美洲的三个大型湖泊和三个大型湿地，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，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数据，补充或替代河流流量观测数据。为此，将开发一种可考虑到观测到的地表水量和面积变化的强化多标准校准方法。通过将表征地表沃茨的卫星数据与全球尺度水文建模相结合，将能够更好地表征地球仪周围的大型湖泊和湿地的动态。

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.