The lakes on the Tibetan Plateau(TP)account for 57%of the total lake area in China[1],which is a key component of the Asia water tower[2-4].A recent study showed that there are 1424 lakes greater than 1 km^(2) on the ...The lakes on the Tibetan Plateau(TP)account for 57%of the total lake area in China[1],which is a key component of the Asia water tower[2-4].A recent study showed that there are 1424 lakes greater than 1 km^(2) on the TP,while the total lake area reached 5.0-10^(4)±791.4 km^(2) in 2018[5].Most lakes on the TP are closed and are less disturbed by human activities than the other inland water bodies in China.展开更多
Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau.In this study,combined with satelli...Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau.In this study,combined with satellite images and bathymetric data,we comprehensively evaluate the accuracy of a multi-factor combined linear regression model(MLR)and machine learning models,create a depth distribution map and compare it with the spatial interpolation,and estimate the change of water level and water storage based on the inverted depth.The results indicated that the precision of the random forest(RF)was the highest with a coefficient of determination(R2)value(0.9311)and mean absolute error(MAE)values(1.13 m)in the test dataset and had high reliability in the overall depth distribution.The water level increased by 9.36 m at a rate of 0.47 m/y,and the water storage increased by 1.811 km3 from 1998 to 2018 based on inversion depth.The water level change was consistent with that of the Shuttle Radar Topography Mission(SRTM)method.Our work shows that this method may be employed to study the water depth distribution and its changes by combining with bathymetric data and satellite imagery in shallow lakes.展开更多
基金This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences(XDA19020303)the National Natural Science Foundation of China(41831177)+1 种基金the Second Tibetan Plateau Scientific Expedition and Research(2019QZKK0202)the Alliance of Field Observation Stations of Chinese Academy of Sciences(KFJ-SW-YW038).
文摘The lakes on the Tibetan Plateau(TP)account for 57%of the total lake area in China[1],which is a key component of the Asia water tower[2-4].A recent study showed that there are 1424 lakes greater than 1 km^(2) on the TP,while the total lake area reached 5.0-10^(4)±791.4 km^(2) in 2018[5].Most lakes on the TP are closed and are less disturbed by human activities than the other inland water bodies in China.
基金supported by 2020 Science and technology project of innovation ecosystem construction,National Supercomputing Zhengzhou center-Research on Key Technologies of intelligent fine prediction based on big data analysis[grant number:201400210800]Second Tibetan Plateau Scientific Expedition and Research(STEP)[grant number:2019QZKK0202]+3 种基金the CAS Alliance of Field Observation Stations[grant number:KFJ-SW-YW038]CAS Strategic Priority Research Program:[Grant Number XDA19020303,XDA20020100]the Ministry of Science and Technology of China Project[grant number:2018YFB05050000]National Natural Science Foundation of China project[grant number:41831177,41901078].
文摘Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau.In this study,combined with satellite images and bathymetric data,we comprehensively evaluate the accuracy of a multi-factor combined linear regression model(MLR)and machine learning models,create a depth distribution map and compare it with the spatial interpolation,and estimate the change of water level and water storage based on the inverted depth.The results indicated that the precision of the random forest(RF)was the highest with a coefficient of determination(R2)value(0.9311)and mean absolute error(MAE)values(1.13 m)in the test dataset and had high reliability in the overall depth distribution.The water level increased by 9.36 m at a rate of 0.47 m/y,and the water storage increased by 1.811 km3 from 1998 to 2018 based on inversion depth.The water level change was consistent with that of the Shuttle Radar Topography Mission(SRTM)method.Our work shows that this method may be employed to study the water depth distribution and its changes by combining with bathymetric data and satellite imagery in shallow lakes.
