Observing water level extremes in the Mekong River Basin: The benefit of long-repeat orbit missions in a multi-mission satellite altimetry approach

TL;DR

This study demonstrates that combining multi-mission satellite altimetry data, including long-repeat orbit missions like CryoSat-2, improves the detection and analysis of water level extremes and floods in the Mekong River Basin.

Contribution

The paper introduces a novel multi-mission altimetry approach using universal kriging that incorporates data from long and non-repeat orbit missions, including tributaries, to better monitor river floods.

Findings

Multi-mission approach improves flood detection accuracy.

CryoSat-2 data enhances spatial and temporal resolution.

Flood detection probability around 80%, outperforming single-mission data.

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.