Monitoring lake level changes in China using multi-altimeter data (2016–2019)

Chen, Jiaming; Liao, Jingjuan

doi:10.1016/j.jhydrol.2020.125544

Cited by 28 publications

(17 citation statements)

References 39 publications

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“…Many researchers have monitored variations of the lake level and storage successfully based on remote sensing data (Chen & Liao, 2020; S. Li et al., 2019; X. Wang et al., 2013; Yuan et al., 2018; Zheng et al., 2016). Yuan et al.…”

Section: Discussionmentioning

confidence: 99%

Century‐Scale Reconstruction of Water Storage Changes of the Largest Lake in the Inner Mongolia Plateau Using a Machine Learning Approach

Fan

Song

et al. 2021

Water Resources Research

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Lake Hulun is the fifth‐largest lake in China, playing a substantial role in maintaining the balance of the grassland ecosystem of the Mongolia Plateau, which is a crucial ecological barrier in North China. To better understand the changing characteristics of Lake Hulun and the driving mechanisms, it is necessary to investigate the water storage changes of Lake Hulun on extended timescales. The main objective of this study is to reconstruct the water storage time series of Lake Hulun over the past century. We employed a machine learning approach termed the extreme gradient boosting tree (XGBoost) to reconstruct the water storage changes over a one‐century timescale based on the generated bathymetry and satellite altimetry data and investigated the relationships with hydrological and climatic variables in long term. Results show that the water storage changes from 1961 to 2019 were featured by four fluctuation phases, with the highest water storage observed in 1991 (14.02 Gt) and the lowest point in 2012 (5.18 Gt). The century‐scale reconstruction result reveals that the water storage of Lake Hulun reached the highest point in the 1960s within the period of 1910–2019. The lowest stage occurred in the sub‐period of the 1930s–1940s, which was even lower than the alerted shrinkage stage in 2012. The predictive model results indicate the effective performance of the XGBoost model in reconstructing century‐scale water storage variations, with the mean absolute error of 0.68, normalized root mean square error of 0.11, Nash–Sutcliffe efficiency of 0.97, and correlation coefficient of 0.94. The annual fluctuations of water storage were mostly affected by precipitation, followed by vapor pressure, temperature, potential evapotranspiration, and wet day frequency. The dominating characteristics of different variables vary evidently with different sub‐periods. The atmospheric circulations of the Arctic Oscillation, El Nino Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation have tight associations with the water storage variations of Lake Hulun, which change with different study periods.

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Section: Discussionmentioning

confidence: 99%

Century‐Scale Reconstruction of Water Storage Changes of the Largest Lake in the Inner Mongolia Plateau Using a Machine Learning Approach

Fan

Song

et al. 2021

Water Resources Research

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“…Changes in lake levels are considered to be an important indicator of climate balance and variability; Though, there is some relationship of water levels depending on prevailing weather conditions, i.e. there is a relationship in temperature or precipitation change with the intra annual season [29]. It should be noted that this analysis found a strong relationship of the climatic conditions prevailing in the basin with the surface behavior of Lake Chapala, where a decrease in surface water is observed in the lake, over time, in the basin and in the lake.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Climate Indices and NDWI Analysis in Lerma Chapala Basin

Castro-Lazcarro¹,

Belitskaya²,

Tristán³

2021

Preprint

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Lake Chapala is a natural ecosystem of Mexico, declared Ramsar site, currently, is an area with importance in the agricultural sector, tourism, and important source of drinking water in the Metropolitan area of Guadalajara, considered as a thermo regulatory body. This study used tools such as Rhtest, as well as analyzing extreme climate indices using RClimdex. In addition, the relationship with the change in water surface of Lake Chapala was assessed through the NDWI index, using Landsat images, between 1985 and 2018. According to the obtained results, the signs of climatic variability are detected in the study region, which, directly affects the basin runoff and water levels in the lake, as well as, the factor of water overexploitation in the basin, so it is considered that the lake conditions mostly depend on the type of management that is given to the resource. Finally, the results will help the understanding of climate variability and its water resources influence of the Lerma Chapala basin, which will assist in future research.

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“…As the headwater catchment of the Yangtze River, the Qinghai‐Tibet Plateau is called the “Roof of the World” and “Asian Water Tower,” with more than 1,500 lakes (Song et al., 2014). This region is slightly impacted by human activities and is sensitive to climate change; thus, it is usually used to evaluate global climate change (Chen & Liao, 2020). Additionally, the middle and lower reaches of the Yangtze River are the most densely distributed freshwater lake regions in China.…”

Section: Study Area and Data Setmentioning

confidence: 99%

What Drive Regional Changes in the Number and Surface Area of Lakes Across the Yangtze River Basin During 2000–2019: Human or Climatic Factors?

Xing

Gun

2022

Water Resources Research

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The spatiotemporal distribution of lakes in the Yangtze River basin (YRB) has changed tremendously; however, research on how and why lakes changed across this basin is limited. In this study, based on Google Earth Engine, Landsat images were used to track lakes (>1 km2) across the YRB from 2000 to 2019. Then, the anthropogenic and climatic impacts on the evolution of lakes were fully discussed. Results showed that the distribution of lakes across the YRB was extremely uneven. From 2000 to 2019, the total number and the area (referring to the surface area) of lakes increased by 30 and declined by 885 km2, respectively, but the trends of them were unobvious. In contrast, these changes and related causes exhibited high spatial heterogeneity. Lakes in the upper reaches significantly increased (P < 0.01). The expanded and new lakes were mainly distributed in the headwater catchment of the Yangtze River, which was closely related to increased precipitation. The number of lakes in the middle reaches decreased significantly (P < 0.05), which was primarily affected by intense human activities, such as land reclamation and agricultural irrigation. Precipitation played a dominant role in the fluctuation of the lake area here. In the lower reaches, the number of lakes increased (P < 0.01) as a result of the policy of “returning farmland to lakes,” while the expansion of area (P < 0.05) was closely related to the precipitation and runoff. These findings have important policy implications for the conservation of lake resources.

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Monitoring lake level changes in China using multi-altimeter data (2016–2019)

Cited by 28 publications

References 39 publications

Century‐Scale Reconstruction of Water Storage Changes of the Largest Lake in the Inner Mongolia Plateau Using a Machine Learning Approach

Century‐Scale Reconstruction of Water Storage Changes of the Largest Lake in the Inner Mongolia Plateau Using a Machine Learning Approach

Assessment of Climate Indices and NDWI Analysis in Lerma Chapala Basin

What Drive Regional Changes in the Number and Surface Area of Lakes Across the Yangtze River Basin During 2000–2019: Human or Climatic Factors?

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