On the capabilities of the SWOT satellite to monitor the lake level change over the Third Pole

Abstract

The lake level dynamics of the Qinghai–Tibetan Plateau (QTP, also called the ‘Third Pole’) are a crucial indicator of climate change and human activities; however, they remain poorly measured due to extremely high elevation and cold climate. The existing satellite altimeters also suffer from relatively coarse temporal resolution or low spatial coverage, preventing effective monitoring of lake level change at such a large spatial scale. The recently launched surface water and ocean topography (SWOT) mission is expected to greatly enhance the current lake level monitoring capabilities. However, a systematic evaluation is still lacking in the region. To elucidate this potential, here, we generated SWOT-like lake products for 38 major lakes (>150 km2) over the QTP during 2000–2018 using a large-scale SWOT hydrology simulator with the input of satellite altimetry and water mask databases. The comparative assessments between the satellite altimetry data and SWOT simulations using various statistical metrics and decomposed time series components demonstrate that SWOT can successfully monitor both short-term dynamics and long-term trends. Extended experiments to derive SWOT-like data of 783 lakes (>1 km2) based on the synthetic lake level series present the spatial pattern of SWOT performance that tends to improve with the increasing lake area. Our findings provide comprehensive inferences and confidence for lake level monitoring in the Third Pole in the early period of the SWOT satellite.

Lake‐Area Expansion Alters Downwind Precipitation Patterns on the Tibetan Plateau: Insights From the Most Dramatically Expanded Lake

“…(2015) would be a good solution to calculate the LSWT. However, due to a lack of observed lake depth, extinction coefficient, and LSWT data on the TP, it is difficult to identify and calibrate the key model parameters and to parameterize the schemes for the lake processes, which may cause significant biases in the simulation of lake–atmosphere interactions (X. Ma et al., 2022; Y. Wu et al., 2020; Xiong et al., 2023). Consequently, it is still challenging in simulating LSWT on TP and requires detailed modification of lake process schemes according to observed data (X. Ma et al., 2022).…”

Characterizing the Water Storage Variation of Kusai Lake by Constructing Time Series from Multisource Remote Sensing Data

“…However, the availability of consistent and continuous temporal sampling can be limited, making it difficult to capture detailed features of lake area variations, such as seasonal and annual fluctuations. Therefore, most current studies focus on detecting abrupt changes corresponding to the outburst and long-term trends in the ZL-SL basin [32].…”