, Volume 58, Issue 31, pp 3815-3829,
Open Access This content is freely available online to anyone, anywhere at any time.
Date: 18 May 2013

Water balance estimates of ten greatest lakes in China using ICESat and Landsat data

Abstract

Lake level and area variations are sensitive to regional climate changes and can be used to indirectly estimate water balances of lakes. In this study, 10 of the largest lakes in China, ∼1000 km2 or larger, are examined to determine changes in lake level and area derived respectively from ICESat and Landsat data recorded between 2003 and 2009. The time series of lake level and area of Selin Co, Nam Co, and Qinghai Lake in the Tibetan Plateau (TP) and Xingkai Lake in northeastern China exhibit an increasing trend, with Selin Co showing the fastest rise in lake level (0.69 m/a), area (32.59 km2/a), and volume (1.25 km3/a) among the 10 examined lakes. Bosten and Hulun lakes in the arid and semiarid region of northern China show a decline in both lake level and area, with Bosten Lake showing the largest decrease in lake level (−0.43 m/a) and Hulun Lake showing the largest area shrinkage (−35.56 km2/a). However, Dongting, Poyang, Taihu, and Hongze lakes in the mid-lower reaches of the Yangtze River basin present seasonal variability without any apparent tendencies. The lake level and area show strong correlations for Selin Co, Nam Co, Qinghai, Poyang, Hulun, and Bosten lakes (R 2 >0.80) and for Taihu, Hongze, and Xingkai lakes (∼0.70) and weak correlation for East Dongting Lake (0.37). The lake level changes and water volume changes are in very good agreement for all lakes (R 2 > 0.98). Water balances of the 10 lakes are derived on the basis of both lake level and area changes, with Selin Co, Nam Co, Qinghai, and Xingkai lakes showing positive water budgets of 9.08, 4.07, 2.88, and 1.09 km3, respectively. Bosten and Hulun lakes show negative budgets of −3.01 and −4.73 km3, respectively, and the four lakes along the Yangtze River show no obvious variations. Possible explanations for the lake level and area changes in these four lakes are also discussed. This study suggests that satellite remote sensing could serve as a fast and effective tool for estimating lake water balance.

This article is published with open access at Springerlink.com