Spatiotemporal characteristics of Qinghai Lake ice phenology between 2000 and 2016

Qi, Miaomiao; Yao, Xiaojun; Li, Xiaofeng; Duan, Hongyu; Gao, Yongpeng; Liu, Juan

doi:10.1007/s11442-019-1587-0

Cited by 24 publications

(17 citation statements)

References 27 publications

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“…The freezing temperature of Qinghai Lake is slightly lower than 0 • C. It usually freezes in mid-December each year and completely freezes in early January of the following year. The frozen Qinghai Lake begins to melt in mid-to late March and is completely melted by early April [31]. Ice thickness is on average 50 cm, and the maximum ice thickness is 70 cm [32].…”

Section: Study Areamentioning

confidence: 99%

Monitoring the Ice Phenology of Qinghai Lake from 1980 to 2018 Using Multisource Remote Sensing Data and Google Earth Engine

Liu

Yao

et al. 2020

Remote Sensing

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Lake ice, one of the most direct lake physical characteristics affected by climate change, can reflect small-scale environmental changes caused by the atmosphere and hydrology, as well as large-scale climate changes such as global warming. This study uses National Oceanic and Atmospheric Administration, Advanced Very High Resolution Radiometer (NOAA AVHRR), MOD09GQ surface reflectance products, and Landsat surface reflectance Tier 1 products, which comprehensively used RS and GIS technology to study lake ice phenology (LIP) and changes in Qinghai Lake. Over the past 38 years, freeze-up start and freeze-up end dates were gradually delayed by a rate of 0.16 d/a and 0.19 d/a, respectively, with a total delay by 6.08 d and 7.22 d. The dates of break-up start and break-up end showed advancing trends by −0.36 d/a and −0.42 d/a, respectively, which shifted them earlier by 13.68 d and 15.96 d. Overall, ice coverage duration, freeze duration, and complete freeze duration showed decreasing trends of −0.58 d/a, −0.60 d/a, and −0.52 d/a, respectively, and overall decreased by 22.04 d, 22.81 d, and 9.76 d between 1980 and 2018. The spatial pattern in the freeze–thaw of Qinghai Lake can be divided into two areas; the west of the lake area has similar spatial patterns of freezing and ablation, while, in the east of the lake area, freezing and ablation patterns are opposite. Climate factors were closely related to LIP, especially the accumulated freezing degree-day (AFDD) from October to April of the following year. Furthermore, freeze-up start time was more sensitive to changes in wind speed and precipitation.

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Section: Study Areamentioning

confidence: 99%

Monitoring the Ice Phenology of Qinghai Lake from 1980 to 2018 Using Multisource Remote Sensing Data and Google Earth Engine

Liu

Yao

et al. 2020

Remote Sensing

Self Cite

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“…Previous studies reported that latitude, surface area, volume, depth, salinity, air temperature, and precipitation are factors influencing lake ice phenology [12,19,26,30,35,36,84]. The quantitative relationships between lake ice phenology parameters and these factors were analyzed by linear regression, and the significance of these relationships was also calculated using the F-test with p < 0.05, p < 0.01, and p < 0.001.…”

Section: Driving Factorsmentioning

confidence: 99%

Variation in Ice Phenology of Large Lakes over the Northern Hemisphere Based on Passive Microwave Remote Sensing Data

Che

Dai

2021

Remote Sensing

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Ice phenology data of 22 large lakes of the Northern Hemisphere for 40 years (1979−2018) have been retrieved from passive microwave remote sensing brightness temperature (Tb). The results were compared with site-observation data and visual interpretation from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectivity products images (MOD09GA). The mean absolute errors of four lake ice phenology parameters, including freeze-up start date (FUS), freeze-up end date (FUE), break-up start date (BUS), and break-up end date (BUE) against MODIS-derived ice phenology were 2.50, 2.33, 1.98, and 3.27 days, respectively. The long-term variation in lake ice phenology indicates that FUS and FUE are delayed; BUS and BUE are earlier; ice duration (ID) and complete ice duration (CID) have a general decreasing trend. The average change rates of FUS, FUE, BUS, BUE, ID, and CID of lakes in this study from 1979 to 2018 were 0.23, 0.23, −0.17, −0.33, −0.67, and −0.48 days/year, respectively. Air temperature and latitude are two dominant driving factors of lake ice phenology. Lake ice phenology for the period 2021−2100 was predicted by the relationship between ice phenology and air temperature for each lake. Compared with lake ice phenology changes from 1990 to 2010, FUS is projected to be delayed by 3.1 days and 11.8 days under Representative Concentration Pathways (RCPs) 2.6 and 8.5 scenarios, respectively; BUS is projected to be earlier by 3.3 days and 10.7 days, respectively; and ice duration from 2080 to 2100 will decrease by 6.5 days and 21.9 days, respectively.

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“…1 and Table B.8 (Appendix B). While most of the earlier works (Gou et al, 2015;Qi et al, 2019Qi et al, , 2020Yao et al, 2016) on long time-series monitoring of lake ice with MODIS concentrated on larger lakes, many lakes that freeze are actually small-or medium-sized mountain lakes, especially outside the (sub-)Arctic regions. The lakes we analyse are relatively small in area (0.78 -11.3 km 2 ), representative for this category.…”

Section: Study Areamentioning

confidence: 99%

Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery

Tom¹,

Wu²,

Baltsavias³

et al. 2021

Preprint

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Depleting lake ice can serve as an indicator for climate change, just like sea level rise or glacial retreat. Several Lake Ice Phenological (LIP) events serve as sentinels to understand the regional and global climate change. Hence, monitoring the long-term lake freezing and thawing patterns can prove very useful. In this paper, we focus on observing the LIP events such as freeze-up, break-up and temporal freeze extent in the Oberengadin region of Switzerland, where there are several small-and medium-sized mountain lakes, across two decades (2000-2020) from optical satellite images. We analyse time-series of MODIS imagery (and additionally cross-check with VIIRS data when available), by estimating spatially resolved maps of lake ice for these Alpine lakes with supervised machine learning. To train the classifier we rely on reference data annotated manually based on publicly available webcam images. From the ice maps we derive long-term LIP trends. Since the webcam data is only available for two winters, we also validate our results against the operational MODIS and VIIRS snow products. We find a change in Complete Freeze Duration (CFD) of -0.76 and -0.89 days per annum (d/a) for lakes Sils and Silvaplana respectively. Furthermore, we correlate the lake freezing and thawing trends with climate data such as temperature, sunshine, precipitation and wind measured at nearby meteorological stations.

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Spatiotemporal characteristics of Qinghai Lake ice phenology between 2000 and 2016

Cited by 24 publications

References 27 publications

Monitoring the Ice Phenology of Qinghai Lake from 1980 to 2018 Using Multisource Remote Sensing Data and Google Earth Engine

Monitoring the Ice Phenology of Qinghai Lake from 1980 to 2018 Using Multisource Remote Sensing Data and Google Earth Engine

Variation in Ice Phenology of Large Lakes over the Northern Hemisphere Based on Passive Microwave Remote Sensing Data

Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery

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