Hao Zhou, Zhicai Luo, Natthachet Tangdamrongsub, Zebing Zhou, Lijie He,Chuang Xu, Qiong Li and Yunlong Wu

MOE Key Laboratory of Fundamental Physical Quantities Measurement,Hubei Key Laboratory of Gravitation and Quantum Physics, Institute of Geophysics, School of Physics,Huazhong University of Science and Technology, Wuhan 430074, China

(Received 15 Mar 2018; accepted 4 May 2018; published online 5 May 2018)

The Poyang Lake, the largest freshwater lake in China, is famous for its ecological and economic importance as well as frequent flood characteristics. In this study, multiple satellite remote sensing observations (e.g., GRACE, MODIS, Altimetry, and TRMM), hydrological models, and in situ data are used to characterize the flood phenomena over the Poyang Lake basin between 2003 and 2016. To improve the accuracy of the terrestrial water storage (TWS) estimates over the Poyang Lake basin, a modified forward-modeling method is introduced in the GRACE processing. The method is evaluated using the contaminated noise onboard observations for the first time. The results in both spectral and spatial domains infer a good performance of the method on the suppression of high-frequency noise while reducing the signal loss. After applying forward-modeling method, the TWS derived from the GRACE spherical harmonic coefficients presents a comparable performance with the solution derived from the newly released CSR Release05 mascon product over the Poyang Lake basin. The flood events in 2010 and 2016 are identified from the positive anomalies in non-seasonal TWSs derived by GRACE and hydrological models. The flood signatures also coincide with the largest inundated areas estimated from MODIS data, and the observed areas in 2010 and 2016 are 3370.3 km2 (30% higher than the long-term mean) and 3445.0 km2 (33% higher), respectively. The water levels in the Hukou station exceed the warning water level for 25 days in 2010 and 28 days in 2016. These continuous warning-exceeded water levels also imply the severe flood events, which are primarily driven by the local plenteous precipitation in the rainy season (1528 mm in 2010, 1522 mm in 2016).

http://dx.doi.org/10.3390/rs10050713