Runoff variability in glacierized transboundary river basins over High Mountain Asia (HMA) directly affects the stability of water supply for more than one billion people in Asia. However, limited by insufficient in-situ gauges and imprecise hydrological model output, it is still a challenge to accurately monitor and comprehensively analyze the HMA runoff change. In this paper, we construct a water budget closure test of water balance equation based on satellite gravimetry constraints to assess the accuracy of hydrological dataset outputs, and propose a multi-dataset merging method to evaluate runoff variability in ten HMA transboundary basins over the past two decades. Results show that the runoff quantified by the hydrological dataset has relatively maximum uncertainty compared to precipitation and evapotranspiration. The performance of the reconstructed terrestrial water storage change (TWSC) from hydrological dataset varies with basins, and the maximum Nash-Sutcliffe Efficiency (NSE) value ranges from 0.31 to 0.94. Nevertheless, the current hydrological dataset struggles to accurately reconstruct the interannual and annual variability of TWSC, with the maximum cyclostationary NSE (NSEc) value ranging from −1.07 to 0.24. Runoff change in HMA exhibits both overall stability and regional climatic condition-related spatial heterogeneity. A significant downstream change-driven increase trend of runoff occurs in Indus Basin (0.2 ± 0.1 mm/mon/yr), while Brahmaputra Basin (−0.5 ± 0.4 mm/mon/yr) and Salween Basin (−0.4 ± 0.2 mm/mon/yr) show significant runoff decrease trends driven by upstream and downstream changes, respectively. Climate change has exacerbated the instability of runoff in the arid basins over northern HMA, leading to evident increase in annual amplitude. Furthermore, negative correlation is found between temperature and runoff at the interannual scale, especially in Ganges Basin (−19.73 ± 12.53 Gt/month per °C) and Mekong Basin (−17.46 ± 9.43 Gt/month per °C). Our multi-dataset merging methodology can improve the reliability of using global hydrological datasets to quantify runoff variability in poorly in-situ gauged regions, and may also be applicable to the evaluation of precipitation and evapotranspiration.

中文翻译：

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亚洲高山跨界流域径流变异性评价——基于卫星重力约束的多数据集合并


亚洲高山 （HMA） 冰川化跨界河流流域的径流变化直接影响亚洲 10 多亿人口的供水稳定性。然而，受限于原位测距仪不足和水文模型输出不精确，准确监测和综合分析HMA径流变化仍然是一个挑战。在本文中，我们构建了基于卫星重力约束的水平衡方程水收支闭合检验，以评估水文数据集输出的准确性，并提出了一种多数据集合并方法来评估过去二十年十个 HMA 跨界流域的径流变化。结果表明，与降水和蒸散相比，水文数据集量化的径流具有相对最大的不确定性。水文数据集重建的陆地储水量变化 （TWSC） 性能随流域而异，最大 Nash-Sutcliffe 效率 （NSE） 值范围为 0.31 至 0.94。然而，目前的水文数据集难以准确重建 TWSC 的年际和年际变化，最大周期平稳 NSE （NSEc） 值范围为 -1.07 至 0.24。HMA 的径流变化表现出整体稳定性和区域气候条件相关的空间异质性。印度河流域（0.2 ± 0.1 mm/mon/yr）呈现显著的下游变化驱动径流增加趋势，而布拉马普特拉河流域（-0.5 ± 0.4 mm/mon/yr）和萨尔温江流域（-0.4 ± 0.2 mm/mon/yr）分别表现出由上游和下游变化驱动的显著径流减少趋势。气候变化加剧了 HMA 北部干旱流域径流的不稳定性，导致年振幅明显增加。 此外，在年际尺度上，温度和径流之间存在负相关，特别是在恒河流域（-19.73 ± 12.53 Gt/月/°C）和湄公河流域（-17.46 ± 9.43 Gt/月/°C）。我们的多数据集合并方法可以提高使用全球水文数据集量化原位测量不佳区域的径流变化的可靠性，也可能适用于降水和蒸散的评估。