Based on 11004 satellite images from CBERS CCD and Landsat TM/ETM, changes in the spatial characteristics of all lakes in China were determined following pre-established interpretation rules. This dataset was supported by 6843 digital raster images (1:100000 and 1:50000), a countrywide digital vector dataset (1:250000), and historical literature. Comparative data were corrected for seasonal variations using precipitation data. There are presently 2693 natural lakes in China with an area greater than 1.0 km 2 , excluding reservoirs. These lakes are distributed in 28 provinces, autonomous regions and municipalities and have a total area of 81414.6 km 2 , accounting for ~0.9% of China's total land area. In the past 30 years, the number of newly formed and newly discovered lakes with an area greater than 1.0 km 2 is 60 and 131, respectively. Conversely, 243 lakes have disappeared in this time period.

Lake size is sensitive to both climate change and human activities, and therefore serves as an excellent indicator to assess environmental changes. Using a large volume of various datasets, we provide a first complete picture of changes in China's lakes between 1960s–1980s and 2005–2006. Dramatic changes are found in both lake number and lake size; of these, 243 lakes vanished mainly in the northern provinces (and autonomous regions) and also in some southern provinces while 60 new lakes appeared mainly on the Tibetan Plateau and neighboring provinces. Limited evidence suggested that these geographically unbalanced changes might be associated primarily with climate change in North China and human activities in South China, yet targeted regional studies are required to confirm this preliminary observation.

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Extremely low water level events have frequently occurred in the middle–lower Yangtze River (MLYR) in recent years (2006–2011). Most of these drought events coincided with the initial operation of the Three Gorges Dam (TGD). The TGD was therefore the focus of controversy about the causes of the hydrological droughts of the rivers and lakes of the region. We quantified the effects of the TGD's operation on water levels from 2006 to 2011 using a newly developed hydrodynamic model. The operation of the TGD significantly exacerbated the severe hydrological droughts that occurred in late September to November because of water impoundment, but it increased water levels from April to early June in the MLYR because of the drawdown of TGD water levels. Evidence suggests that the recent extremely low water levels were mainly because of the remarkable decline in inflows to the MLYR resulting from precipitation changes and possible human activities. Nevertheless, it should be noted that the effects of the TGD on downstream rivers and lakes will be intensified in the foreseeable future. Copyright © 2013 John Wiley & Sons, Ltd.

Runoff modeling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data driven models. In this paper, we propose a data driven approach using the state-of-the-art Long-Short-Term-Memory (LSTM) network. The proposed model was applied in the Poyang Lake Basin (PYLB) and its performance was compared with an Artificial Neural Network (ANN) and the Soil & Water Assessment Tool (SWAT). We first tested the impacts of the number of previous time step (window size) in simulation accuracy. Results showed that a window in improper large size will dramatically deteriorate the model performance. In terms of PYLB, a window size of 15 days might be appropriate for both accuracy and computational efficiency. We then trained the model with 2 different input datasets, namely, dataset with precipitation only and dataset with all available meteorological variables. Results demonstrate that although LSTM with precipitation data as the only input can achieve desirable results (where the NSE ranged from 0.60 to 0.92 for the test period), the performance can be improved simply by feeding the model with more meteorological variables (where NSE ranged from 0.74 to 0.94 for the test period). Moreover, the comparison results with the ANN and the SWAT showed that the ANN can get comparable performance with the SWAT in most cases whereas the performance of LSTM is much better. The results of this study underline the potential of the LSTM for runoff modeling especially for areas where detailed topographical data are not available.

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Based on long-term NDVI (Normalized Difference Vegetation Index) derived from Global Inventory Modeling and Mapping Study (GIMMS) and daily meteorological observations from 14 stations in the Poyang Lake Basin, this study investigated the relationship between vegetation variation and climatic extremes during 1982-2006. Ten typical indices were adopted to describe climatic extreme, including two precipitation-related and eight temperature-related indices. Correlation analysis shows that monthly averaged NDVI variations are generally determined by temperature but not precipitation extremes. Positive correlations appear between NDVI and temperature indices, and the correlations are more significant in spring and autumn. Significant negative correlations are found in summer and winter between NDVI and precipitation-related indices. In addition, spatial heterogeneity analysis shows that NDVI is more vulnerable to climate change for the middle basin than other regions. Finally, we demonstrate that NDVI can currently responds to temperature extremes or with a lag of 1 month. With respect to precipitation extremes, the strongest response may occur 2 months later. Our study highlights the role of climate extremes to the NDVI, and is helpful to improve the understanding of vegetation vulnerability to climate fluctuations.