Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM)

The Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM) dataset provides a comprehensive solution for addressing gaps in river discharge measurements by leveraging satellite altimetry. This dataset offers enhanced coverage for river discharge estimations by utilizing data from multiple satellite missions and integrating it with existing river gauge networks. It supports sustainable development and helps address complex water-related challenges exacerbated by climate change. The first version of SAEM includes (1) height-based discharge estimates for 8,730 river gauges, covering approximately 88% of the total gauged discharge volume globally. These estimates demonstrate a median Kling-Gupta Efficiency (KGE) of 0.48, surpassing the performance of current global datasets. (2) Catalog of Virtual Stations (VSs): a catalog of VSs defined by specific criteria, including each station’s coordinates, associated satellite altimetry missions, distance to discharge gauges, and quality flags. (3) Altimetric Water Level Time Series: time series data of water levels from VSs that provide high-quality discharge estimates. The water level data are sourced from both existing Level-3 datasets and newly generated data within this study, including contributions from Hydroweb.Next, DAHITI, GRRATS, and HydroSat. Non-parametric quantile mapping functions: for VSs, which model the transformation of water level time series into discharge data using a Nonparametric Stochastic Quantile Mapping Function approach.

基于卫星测高技术的全球尺度实测河流流量扩展数据集（SAEM）提供了一种全面解决方案，以弥补河流流量测量中的空白。该数据集通过利用卫星测高技术，提供了河流流量估计的增强覆盖范围，它通过整合多个卫星任务的数据与现有的河流测流网络相得益彰。SAEM数据集支持可持续发展，并有助于应对气候变化加剧的复杂水资源挑战。SAEM的第一个版本包括以下内容：(1) 基于8,730个河流测站的流量估算，覆盖全球约88%的测流总量。这些估算的平均Kling-Gupta效率（KGE）为0.48，超越了现有全球数据集的性能。(2) 虚拟测站（VSs）目录：根据特定标准定义的VSs目录，包括每个站点的坐标、相关卫星测高任务、至流量测站的距离和质量标志。(3) 测高水位时间序列：来自VSs的高质量流量估计的水位时间序列数据。水位数据来源于现有的Level-3数据集以及本研究中新生成数据，包括Hydroweb、DAHITI、GRRATS和HydroSat的贡献。对于VSs，采用非参数分位数映射函数：利用非参数随机分位数映射函数方法，将水位时间序列转换成流量数据。