Robust, Long-term Lake Level Change from Multiple Satellite Altimeters in Tibet: Observing the Rapid Rise of Ngangzi Co over a New Wetland

Abstract

Satellite altimetry has been successfully applied to monitoring water level variation of global lakes. However, it is still difficult to retrieve accurate and continuous observations for most Tibetan lakes, due to their high altitude and rough terrain. Aiming to generate long-term and accurate lake level time series for the Tibetan lakes using multi-altimeters, we present a robust strategy including atmosphere delay corrections, waveform retracking, outlier removal and inter-satellite bias adjustment. Apparent biases in dry troposphere corrections from different altimeter products are found, and such correctios must be recalculated using the same surface pressure model. A parameter is defined to evaluate the performance of the retracking algorithm. The ICE retracker outperforms the 20% and 50% threshold retrackers in the case of Ngangzi Co, where a new wetland has been established. A two-step algorithm is proposed for outlier removal. Two methods are adopted to estimate inter-satellite bias for different cases of with and without overlap. Finally, a 25-year-long lake level time series of Ngangzi Co are constructed using the TOPEX/Poseidon-family altimeter data from October 1992 to December 2017, resulting in an accuracy of ~17 cm for TOPEX/Poseidon and ~10 cm for Jason-1/2/3. The accuracy of retrieved lake levels is on the order of decimeter. Because of no gauge data available, ICESat and SARAL data with the accuracy better than 7 cm are used for validation. A correlation more than 0.9 can be observed between the mean lake levels from TOPEX/Poseidon-family satellites, ICESat and SARAL. Compared to the previous studies and other available altimeter-derived lake level databases, our result is the most robust and has resulted in the maximum number of continuous samples. The time series indicates that the lake level of Ngangzi Co increased by ~8 m over 1998–2017 and changed with different rates in the past 25 years (-0.39 m/yr in 1992–1997, 1.03 m/yr in 1998–2002 and 0.32 m/yr in 2003–2014). These findings will enhance the understanding of water budget and the effect of climate change.

Improving the Estimation of Water Level over Freshwater Ice Cover using Altimetry Satellite Active and Passive Observations

“…As shown in Figures 3-6, the radar altimetry data that were acquired during these periods are contaminated by the presence of ice in the altimeter footprint. This situation causes changes in the waveform shape over large lakes, from an ocean-like waveform close to the Brown model [40] in the summer, to a peaky waveform during the winter [41]. The changes in shape and amplitude strongly affect backscatter, peakiness and the altimetry-based water level estimate.…”

Detecting Lake Level Change From 1992 to 2019 of Zhari Namco in Tibet Using Altimetry Data of TOPEX/Poseidon and Jason-1/2/3 Missions

“…Surface pressure and vertical integral of water vapor from ERA-Interim are published by the European Centre for Medium-Range Weather Forecasts (ECMWF) 2 . The two sets of data, with a time resolution of 24 h and 6 h, are used to calculate dry troposphere correction (DTC) and wet troposphere correction (WTC), respectively (Wang et al, 2019). Since T/P GDR data do not include WTC in inland areas, the ERA-Interim data should be used to calculate this correction.…”

“…The results with a 3-month and a 6-month window length are rather similar, both keep in energy very well. Relatively, the former has more small spikes than the latter (Wang et al, 2019). Therefore, the 6-month-wide filter window is used.…”

Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt

“…Based on this formula, both propagation and geophysical biases can be compensated. Other geophysical terms, including lake tides, hydrostatic variations, thermal expansion, and wind piling-up effects are neglected, as suggested by a previous study [25]. For detailed information of each correction, refer to [59,62].…”

“…These characteristics hinder the viability of inland waterbody monitoring, especially for rivers, which usually have a more inhomogeneous neighboring topography [23]. Thus, to overcome the waveform difference to the standard Brown model [24], a computation-intensive waveform reprocessing (retracking) analysis is usually required for inland waterbodies [4,[25][26][27]. However, the selection of retrackers (ground processing techniques that estimate the range to the point of closest approach on the surface) varies in different studies, and most of them use in-house algorithms or refinements [4,22,28,29], which largely limits the transferability of the method to other regions.…”