Abstract Single-mission altimetric water level observations of rivers are spatially and temporally limited, and thus they are often unable to quantify the full extent of extreme flood events. Moreover, only missions with a short-repeat orbit, such as Envisat, Jason-2, or SARAL, could provide meaningful time series of water level variations directly. However, long or non-repeat orbit missions such as CryoSat-2 have a very dense spatial resolution under the trade-off of a repeat time insufficient for time series extraction. Combining data from multiple altimeter missions into a multi-mission product allows for increasing the spatial and temporal resolution of the data. In this study, we combined water level data from CryoSat-2 with various observations from other altimeter missions in the Mekong River Basin between 2008 and 2016 into one multi-mission water level time series using the approach of universal kriging. In contrast to former multi-mission altimetry methods, this approach allows for the incorporation of CryoSat-2 data as well as data from other long or non-repeat orbit missions, such as Envisat-EM or SARAL-DP. Additionally, for the first time, data from tributaries are incorporated. The multi-mission time series including CryoSat-2 data adequately reflects the general inter-annual flood behaviour and the extreme floodings in 2008 and 2011. It performs better than single-mission time series or multi-mission time series based only on short-repeat orbit data. The Probability of Detection of the floodings with the multi-mission altimetry was around 80% while Envisat and Jason-2 single-mission altimetry could only detect around 40% of the floodings correctly. However, small flash floods still remain undetectable.

中文翻译：

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观察湄公河流域的极端水位：多任务卫星测高方法中长重复轨道任务的好处

摘要 河流的单任务高度水位观测在空间和时间上都是有限的，因此往往无法量化极端洪水事件的全部范围。此外，只有具有短重复轨道的任务，例如 Envisat、Jason-2 或 SARAL，才能直接提供有意义的水位变化时间序列。然而，在重复时间不足以进行时间序列提取的权衡下，诸如 CryoSat-2 之类的长或非重复轨道任务具有非常密集的空间分辨率。将来自多个高度计任务的数据组合到一个多任务产品中，可以提高数据的空间和时间分辨率。在这项研究中，我们使用通用克里金法将 CryoSat-2 的水位数据与 2008 年至 2016 年湄公河流域其他高度计任务的各种观测结果结合成一个多任务水位时间序列。与以前的多任务测高方法相比，这种方法允许合并 CryoSat-2 数据以及来自其他长或非重复轨道任务的数据，例如 Envisat-EM 或 SARAL-DP。此外，第一次纳入了来自支流的数据。包括 CryoSat-2 数据在内的多任务时间序列充分反映了 2008 年和 2011 年的一般年际洪水行为和极端洪水。它比单任务时间序列或仅基于短重复的多任务时间序列表现更好轨道数据。使用多任务高度计检测洪水的概率约为 80%，而 Envisat 和 Jason-2 单任务高度计只能正确检测约 40% 的洪水。然而，小规模的山洪暴发仍然无法检测到。