High-resolution datasets for lake level changes in the Qinghai-Tibetan Plateau from 2002 to 2021 using multi-altimeter data

Chen, Jiaming; Liao, Jingjuan; Lou, Yanhan; Ma, Shanmu; Guo, Huadong; Zhang, Lianchong

doi:10.5194/essd-2022-313

Cited by 3 publications

(1 citation statement)

References 22 publications

(28 reference statements)

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“…We chose the high-resolution dataset of lake level changes on the Tibetan Plateau from 2002 to 2021 as the comparison data for the ICESat-2 crossover points 42 . The dataset uses eight satellites to fit the elevation changes of lakes larger than 1 km² on the Tibetan Plateau.…”

Section: Technical Validationmentioning

confidence: 99%

High spatial resolution elevation change dataset derived from ICESat-2 crossover points on the Tibetan Plateau

Chen,

Wang,

Che

et al. 2024

Sci Data

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Understanding elevation changes on the Tibetan Plateau is crucial to comprehend the changes in topography, landscape, climate, environmental conditions, and water resources. However, some of the current products that track elevation changes only cover specific surface types or limited areas, and others have low spatial resolution. We propose an algorithm to extract ICESat-2 crossover points dataset for the Tibetan Plateau, and form a dataset. The crossover points dataset has a density of 2.015 groups/km², and each group of crossover points indicates the amount of change in elevation before and after a period of time over an area of approximately 17 meters in diameter. Comparing ICESat-2 crossover points data with existing studies on glaciers and lakes, we demonstrated the reliability of the derived elevation changes. The ICESat-2 crossover points provide a refined data source for understanding high-spatial-resolution elevation changes on the Tibetan Plateau. This dataset can provide validation data for various studies that require high-precision or high-resolution elevation change data on the Tibetan Plateau.

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Section: Technical Validationmentioning

confidence: 99%

High spatial resolution elevation change dataset derived from ICESat-2 crossover points on the Tibetan Plateau

Chen,

Wang,

Che

et al. 2024

Sci Data

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Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region

et al. 2023

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The long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learning approach, and to examine the impact of these hydro-climatological factors on lake dynamics. In situ and remote sensing observations were employed to identify the predominant explanatory pathways for assessing the fluctuations in lake area. The Great Salt Lake (GSL) and Lake Chad (LC) were chosen as study sites due to their semi-arid regional settings, enabling the testing of the proposed approach. The random forest (RF) supervised classification algorithm was applied to estimate the lake area extent using Landsat imagery that was acquired between 1999 and 2021. The long-term lake dynamics were evaluated using remotely sensed evapotranspiration data that were derived from MODIS, precipitation data that were sourced from CHIRPS, and in situ water level measurements. The findings revealed a marked decline in the GSL area extent, exceeding 50% between 1999 and 2021, whereas LC exhibited greater fluctuations with a comparatively lower decrease in its area extent, which was approximately 30% during the same period. The framework that is presented in this study demonstrates the reliability of remote sensing data and machine learning methodologies for monitoring lacustrine dynamics. Furthermore, it provides valuable insights for decision makers and water resource managers in assessing the temporal variability of lake dynamics.

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Estimating Rainfall Anomalies with IMERG Satellite Data: Access via the IPE Web Application

Ekpetere,

Mehta,

Coll

et al. 2024

Remote Sensing

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This study assesses the possibilities of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) to estimate extreme rainfall anomalies. A web application, the IMERG Precipitation Extractor (IPE), was developed which allows for the querying, visualization, and downloading of time-series satellite precipitation data for points, watersheds, country extents, and digitized areas. The tool supports different temporal resolutions ranging from 30 min to 1 week and facilitates advanced analyses such as anomaly detection and storm tracking, an important component for climate change study. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region, the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of −22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash–Sutcliffe Efficiency (NSE) of 0.80, and a Kling–Gupta Efficiency (KGE) of 0.52. In the wet region, the average CC of 0.93, PRB of 24.82%, RMSE of 0.96, MBR of 0.79, NSE of 0.80, and KGE of 0.18 were computed. Median RAI indices from both the IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS and advocating for further global climate change studies.

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High-resolution datasets for lake level changes in the Qinghai-Tibetan Plateau from 2002 to 2021 using multi-altimeter data

Cited by 3 publications

References 22 publications

High spatial resolution elevation change dataset derived from ICESat-2 crossover points on the Tibetan Plateau

High spatial resolution elevation change dataset derived from ICESat-2 crossover points on the Tibetan Plateau

Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region

Estimating Rainfall Anomalies with IMERG Satellite Data: Access via the IPE Web Application

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