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A previous modeling study of the lake-floodplain system of Poyang Lake (China) revealed complex hysteretic relationships between stage, storage volume and surface area. However, only hypothetical causal factors were presented, and the reasons for the occurrence of both clockwise and counterclockwise hysteretic functions were unclear. The current study aims to address this by exploring further Poyang Lake’s hysteretic behavior, including consideration of stage-flow relationships. Remotely sensed imagery is used to validate the water surface areas produced by hydrodynamic modeling. Stage-area relationships obtained using the two methods are in strong agreement. The new results reveal a three-phase hydrological regime in stage-flow relationships, which assists in developing improved physical interpretation of hysteretic stage-area relationships for the lake-floodplain system. For stage-area relationships, clockwise hysteresis is the result of classic floodplain hysteretic processes (e.g., restricted drainage of the floodplain during recession), whereas counterclockwise hysteresis derives from the river hysteresis effect (i.e., caused by backwater effects). The river hysteresis effect is enhanced by the time lag between the peaks of catchment inflow and Yangtze discharge (i.e., the so-called Yangtze River blocking effect). The time lag also leads to clockwise hysteresis in the relationship between Yangtze River discharge and lake stage. Thus, factors leading to hysteresis in other rivers, lakes and floodplains act in combination within Poyang Lake to create spatial variability in hydrological hysteresis. These effects dominate at different times, in different parts of the lake, and during different phases of the lake’s water level fluctuations, creating the unique hysteretic hydrological behavior of Poyang Lake. [ABSTRACT FROM AUTHOR]

The extreme cycles of flood and drought in the Poyang Lake catchment (China) place immense pressure on the region's water users and ecosystems. This study examines potential impacts of future climate change in the Poyang Lake catchment using the popular regional climate model, COSMO-CLM, and a distributed hydrological model, WATLAC. Near-future projections (2016–2035) indicate that the mean annual precipitation and temperature are expected to increase over the catchment, with the exception of some northern regions. Relative to the baseline period (1986–2005), the monthly mean precipitation is projected to increase in spring, summer and autumn (March-October), and to decrease in winter (November-February), with the most significant changes in September (62%) and January (−39%). Projected increases in monthly mean temperatures range from 0.3 to 1.4 °C, 0.2 to 0.7 °C, and 0.2 to 1.2 °C for Representative Concentration Pathways (RCP) climate scenarios RCP2.6, 4.5 and 8.5, respectively. Winter temperatures are expected to increase significantly regardless of the climate scenarios. WATLAC simulations indicate that future climate changes will lead to increased high flows in summer and reduced low flows in winter, in terms of both frequency and magnitude, suggesting a high likelihood of an increase in frequency and severity of flooding and droughts in the Poyang Lake catchment. [ABSTRACT FROM AUTHOR]

Lake hydrological simulations using physically based models are cumbersome due to extensive data and computational requirements. Despite an abundance of previous modeling investigations, realtime simulation tools for large lake systems subjected to multiple stressors are lacking. The backpropagation neural network (BPNN) is applied as a first attempt to simulate the water-level variations of a large lake, exemplified by the Poyang Lake (China) case study. The BPNN investigation extends previous modeling efforts by considering the Yangtze River effect and evaluating the influence of the Yangtze River on the lake water levels. Results indicate that the effects of both the lake catchment and the Yangtze River are required to produce reasonable BPNN calibration statistics. Modeling results suggest that the Yangtze River plays a significant role in modifying the lake water-level changes. Comparison of BPNN models to a 2D hydrodynamic model (MIKE 21) shows that comparable accuracies can be obtained from both modeling approaches. This implies that the BPNN approach is well suited to long-term predictions of the water-level responses of Poyang Lake. The findings of this work demonstrate that BPNN can be used as a valuable and computationally efficient tool for future water resource planning and management of the Poyang Lake. [ABSTRACT FROM AUTHOR]