Lake ice phenology of the Nam Co at Tibetan Plateau: Remote sensing and modelling

被引：0
|
作者：
Wu Y. [1 ,2 ]
Guo L. [1 ,2 ]
Fan L. [1 ,2 ]
Wen M. [1 ]
Chi H. [1 ,2 ]
Zhang B. [1 ,2 ]
机构：
[1] Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing
[2] College of Resources and Environment, University of Chinese Academy of Sciences, Beijing
来源：
National Remote Sensing Bulletin | 2022年 / 26卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Lake ice phenology; Lake model; Multi-source remote sensing; Nam Co; Tibetan Plateau;
D O I：
10.11834/jrs.20221288
中图分类号：
学科分类号：
摘要：
Lake ice phenology refers to the dates of lake freeze-up and break-up and period of ice cover; it is considered a valuable indicator of regional climate change. The shifts of lake ice phenology in association with a warming climate is widely interesting because it not only serves as evidence of the changes in climate but could show substantial impacts on regional hydrological processes and the aquatic ecosystem. Ground-based records of lake ice phenology over the Tibetan Plateau are limited because of the harsh geographical conditions and the high observation costs. Satellite-based observation and modeling are expected to be effective in investigating the long-term changes in lake ice phenology for regions with poor ground observations. We aim to reconstruct the lake ice phenology time series and to identify the long-term changes of lake ice phenology in responding to the climate of Nam Co Lake at the Tibetan Plateau and for the past 60 years based on a process-based model, where remotely sensed lake surface water temperature is used to calibrated the process-based model.The research framework includes retrieving lake surface water temperature and lake ice phenology information from remotely sensed data, calibrating the process-based model against the remotely sensed lake surface water temperature, determining lake ice phenology according to the simulated water temperature, validating the simulated lake ice phenology by comparing against that derived from the remotely sensed data, detecting the long-term trends in the reconstructed lake ice phenology, and modeling the response of lake ice phenology to changes in air temperature. Four different remotely sensed datasets and the corresponding approaches are used to retrieve lake ice phenology of the Nam Co for the period 2000-2015. The process-based model (LAKE 2.3) is a 1D lake surface energy balance model. It is used to reconstruct lake ice phenology of Nam Co for the period 1963 to 2018 and investigate the sensitivity of lake ice phenology to climate change. The Mann-Kendall nonparametric statistical test approach is used in detecting the trend of lake ice phenology.Lake ice phenology derived using different remotely sensed data and approaches with consistency in the trend but with considerable uncertainties due to the temporal and spatial resolution of the sensors. The reconstructed lake ice breaking-up date based on the model is more comparable to that remotely sensed data than the other lake ice phenology indicators. The reconstructed time series of lake ice phenology shows that, during the previous 57 years, the freezing-up date was significantly delayed whereas the breaking-up date was earlier, thereby resulting in a shortened ice cover duration. The ice cover duration is shortened at a rate of 6.4 days/10a during the period 1963 to 2018. Sensitivity analysis shows that the breaking-up date would be significantly earlier in a warm climate. Under the 2 ℃ warmer scenario, the breaking-up date would be 12.4 days earlier on the average, and the ice cover duration would be shortened by 19.7 days, on the average.This study combines the strengths of remote sensing and numerical modeling in forming a novel research framework to reconstruct lake ice phenology of regions with poor ground-observation, such as the Tibetan Plateau. The results show that the framework is reliable and valuable to explore the long-term changes in lake ice phenology and its response to climate change. However, uncertainties exist in the remotely sensed lake ice phenology and the numerical modeling, which needs to be improved and further validated where or when ground-based observations are available. © 2022, Science Press. All right reserved.
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页码：193 / 200
页数：7
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