Dongting Lake wetland is a typical lake wetland in the Middle and Lower Yangtze River Plain in China. Due to the influence of natural and human activities, the landscape pattern has changed significantly. This study used 12 Landsat images from 1991 to 2022 and applied three common classification methods (support vector machine, maximum likelihood, and CART decision tree) to extract and classify the landscape information, with the latter having a superior annual accuracy of over 90%. Based on the CART decision tree classification results, the dynamic characteristics of wetland spatial patterns were analyzed through the landscape pattern index, dynamic degree model, and transition matrix model. Redundancy and grey correlation analysis were employed to investigate the driving factors. The results showed increased landscape fragmentation, reduced heterogeneity, and increased complexity from 1991 to 2022. The water and mudflat areas exhibited three distinct stages: gradual decline until 2001 (−3.06 km²/a); sharp decrease until 2014 (−19.44 km²/a); and steady increase (22.93 km²/a). Vegetation conversion, particularly between sedge and reed, dominated the change in landscape pattern. Reed area initially increased (18.88 km²/a), then decreased (−35.89 km²/a), while sedge showed the opposite trend. Woodland area fluctuated, peaking in 2016 and declined by 2022. The construction of the Three Gorges Dam significantly altered landscape dynamics through water level changes, reflected by a 4.03% comprehensive dynamic degree during 2001–2004. Potential evaporation also emerged as a significant natural factor, exhibiting a negative correlation with the landscape index. During 1991–2001 and 2004–2022, the comprehensive explanatory rates of temperature, precipitation, potential evaporation, and water level on landscape pattern dynamics were 88.56% and 52.44%, respectively. Other factors like policies and socio-economic factors played a crucial role in wetland change. These findings offer valuable insights into the dynamic evolution and driving mechanisms of Dongting Lake wetland.

Wetlands are critical components of the global carbon cycle, yet their carbon sink dynamics under hydrological fluctuations remain insufficiently understood. This study employed the Carnegie-Ames-Stanford Approach (CASA) model to estimate the net ecosystem productivity (NEP) of the Dongting Lake wetland and explored the spatiotemporal dynamics and driving mechanisms of carbon sinks from 2000 to 2022, utilizing the Theil-Sen median trend, Mann-Kendall test, and attribution based on the differentiating equation (ADE). Results showed that (1) the annual mean spatial NEP was 50.24 g C/m²/a, which first increased and then decreased, with an overall trend of −1.5 g C/m²/a. The carbon sink was strongest in spring, declined in summer, and shifted to a carbon source in autumn and winter. (2) Climate variability and human activities contributed +2.17 and −3.73 g C/m²/a to NEP, respectively. Human activities were the primary driver of carbon sink degradation (74.30%), whereas climate change mainly promoted carbon sequestration (25.70%). However, from 2000–2011 to 2011–2022, climate change shifted from enhancing to limiting carbon sequestration, mainly due to the transition from water storage and lake reclamation to ecological restoration policies and intensified climate anomalies. (3) NEP was negatively correlated with precipitation and water level. Land use adjustments, such as forest expansion and conversion of cropland and reed to sedge, alongside maintaining growing season water levels between 24.06~26.44 m, are recommended to sustain and enhance wetland carbon sinks. Despite inherent uncertainties in model parameterization and the lack of sufficient in situ flux validation, these findings could provide valuable scientific insights for wetland carbon management and policy-making.

China’s Dongting Lake area is intertwined with rivers and lakes and possesses many water systems. As such, it is one of the most complicated areas in the Yangtze River Basin, in terms of the complexity of its flood control. Over time, siltation and reclamation in the lake area have greatly weakened the river discharge capacity of the lake area, and whether it can endure extreme floods remains an open question. As there is no effective scenario simulation model for the lake area, this study constructs a hydrological model for the Jingjiang–Dongting Lake system and verifies the model using data from 11 typical floods occurring from 1954 to 2020. The parameters derived from 2020 data reflect the latest hydrological relationship between the lake and the river, while meteorological data from 1954 and 1998 are used as inputs for various scenarios with the aim of evaluating the flood pressure of the lake area, using the water levels at the Chengglingji and Luoshan stations as indicators. The preliminary results demonstrate that the operation of the upstream Three Gorges Dam and flood storage areas cannot completely offset the flood pressure faced by the lake area. Therefore, the reinforcement and raising of embankments should be carried out, in order to cope with potential extreme flood events. The methodology and results of this study have reference value for policy formation, flood control, and assessment and dispatching in similar areas.