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.

基于卫星测高的全球原位河流流量扩展数据集（Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements, SAEM）依托卫星测高技术，为填补全球原位河流流量实测数据的空白提供了一套完整解决方案。本数据集通过整合多颗卫星任务的观测数据，并与现有河流水文测站网络相结合，显著提升了河流流量估算的空间覆盖范围，可为可持续发展提供支撑，并助力应对气候变化加剧的各类复杂水相关挑战。

SAEM的首个版本包含以下三类核心内容：
1. 8730个河道测站的基于水位的流量估算结果，覆盖了全球实测总流量的约88%。该批估算结果的克林-古普塔效率系数（Kling-Gupta Efficiency, KGE）中位数为0.48，性能优于当前已有的全球同类数据集。
2. 虚拟站（Virtual Stations, VSs）目录：该目录基于特定标准构建，包含各虚拟站的坐标、关联的卫星测高任务、与流量测站的距离以及质量标记等信息。
3. 测高水位时间序列与非参数分位数映射函数：其中测高水位时间序列指由可提供高质量流量估算结果的虚拟站生成的水位时间序列数据，该数据来源于已公开的Level-3数据集以及本研究中新生成的观测数据，数据源包括Hydroweb.Next、DAHITI、GRRATS和HydroSat等；非参数分位数映射函数则针对虚拟站，采用非参数随机分位数映射函数（Nonparametric Stochastic Quantile Mapping Function）方法，实现水位时间序列到流量数据的转换建模。

搜集汇总

背景与挑战

背景概述

SAEM数据集通过卫星测高技术提供全球河流流量估算，覆盖了全球88%的测量站流量，并包含虚拟站目录和水位时间序列，支持气候变化下的水资源管理。