Water level changes of Hulun Lake in Inner Mongolia derived from Jason satellite data

Li, Shi; Chen, Jianping; Xiang, Jie; Pan, Yun; Huang, Zhiyong; Wu, Yongliang

doi:10.1016/j.jvcir.2018.12.031

Cited by 38 publications

(33 citation statements)

References 23 publications

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“…The problem of dammed water level prediction in reservoirs can be tackled by considering very different predictive variables (input data). Many authors [14,15] have considered hydro-meteorological data, but alternative input data for prediction are also available, such as images from video cameras [16] or satellite-based information [17,18], among other possibilities. Regarding the computational methods applied in dammed water level prediction problems, there have been different attempts using time series processing algorithms [19], empirical orthogonal functions [20], error correction-based forecasting [21], multivariate approaches [22], or ensemble-based algorithms [23].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

Castillo-Botón

Casillas-Pérez

Casanova-Mateo

et al. 2020

Water

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This paper presents long- and short-term analyses and predictions of dammed water level in a hydropower reservoir. The long-term analysis was carried out by using techniques such as detrended fluctuation analysis, auto-regressive models, and persistence-based algorithms. On the other hand, the short-term analysis of the dammed water level in the hydropower reservoir was modeled as a prediction problem, where machine learning regression techniques were studied. A set of models, including different types of neural networks, Support Vector regression, or Gaussian processes was tested. Real data from a hydropower reservoir located in Galicia, Spain, qwew considered, together with predictive variables from upstream measuring stations. We show that the techniques presented in this paper offer an excellent tool for the long- and short-term analysis and prediction of dammed water level in reservoirs for hydropower purposes, especially important for the management of water resources in areas with hydrology stress, such as Spain.

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Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

Castillo-Botón

Casillas-Pérez

Casanova-Mateo

et al. 2020

Water

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“…Wang et al, 2013;Yuan et al, 2018;Zheng et al, 2016). Yuan et al (2018) and S Li et al (2019) reconstructed the time series water storage variations of Lake Hulun by integrating altimetry data and Landsat images after 2002. However, it makes the application to long-term series impractical for Lake Hulun with limited remote sensing data (mainly since the 1990s).…”

Section: Comparison Of Time-series Water Storage Reconstruction Results With Previous Studiesmentioning

confidence: 99%

“…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%

“…The Lake Hulun basin is transboundary and supports a unique wetland ecosystem, thus, the changes in water storage and associated hydrological processes of the lake basin attracted increased attention from the scientific community and the public. The lake is featured by an irregular oblique rectangle from southwest to northeast with a maximum width of 41 km and a length of 93 km (S. Li et al., 2019). At the water level of 543.60 m, the lake water area is 2,134.05 km 2 (as measured in 2019).…”

Section: Study Areamentioning

confidence: 99%

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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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“…When the RMSE is lower, the deviation between altimetry data and in situ data is smaller; therefore, the accuracy of ICESat is higher. The Pearson correlation coefficient was also used to determine the consistency between the trend of in situ data and that of ICESat water level calculated based on the no-buffer water mask [16], [51], [62].…”

Section: Accuracy Of Water Level Retrieved From Icesatmentioning

confidence: 99%

Obtaining Accurate Water Level Measurements in Lakes: Analysis of Changes in ICESat Altimetry Accuracy With Buffer Changes

Yin

et al. 2020

IEEE Access

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Determining the accuracy of lake water levels calculated based on Ice, Cloud, and land Elevation Satellite (ICESat) data mainly relies on identifying lake water footprints (LWFs), which are obtained using an overlay analysis of lake water masks (LWMs) and ICESat tracks. However, most previous studies that have conducted a buffer analysis based on LWMs have set the buffer size subjectively without providing a detailed explanation for this or conducting a system analysis. In this study, the effects of using inside and outside buffers to obtain LWMs for seven lakes are analyzed. The Modified Normalized Difference Water Index (MNDWI) was applied to extract LWMs from Thematic Mapper (TM) images. The boxplot was used to remove footprints with abnormal elevations, and then the average of the remaining footprints was calculated as the ICESat water level. To compare with the in situ measured data, the root mean square error (RMSE) was used for accuracy evaluation. Results show the following: (1) for Yamzhog Yumco, which is a narrow lake, the altimetry accuracy is higher when using the outside buffer than for the inside buffer or with no buffer, and the highest accuracy is obtained with an outside buffer of approximately 100 m.(2) For other relatively wide lakes, such as Lake Michigan, Lake Erie, Lake Huron, Lake Ontario and Lake Superior, the inside buffer method does not always improve altimetry accuracy, and this result differs from those presented previously. (3) For different lakes, the range of change in altimetry accuracy is affected by the number of LWFs. This study is of value for use in studies that apply ICESat altimetry data to obtain changes in lake water levels, especially for relatively narrow lakes, and the results imply that the altimetry accuracy can be improved by using the outside buffer.

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Water level changes of Hulun Lake in Inner Mongolia derived from Jason satellite data

Cited by 38 publications

References 23 publications

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

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

Obtaining Accurate Water Level Measurements in Lakes: Analysis of Changes in ICESat Altimetry Accuracy With Buffer Changes

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