Understanding the effect of land use/land cover (LULC) on water quality is essential for environmental improvement, especially in urban areas. This study examined the relationship between LULC at buffer-zone scales and water quality in a lakeside city near Poyang Lake, which is the largest freshwater lake in China. Representative indicators were selected by factor analysis to characterize the water quality in the study area, and then the association between LULC and water quality over space and time was quantified by redundancy analysis. The results indicated that the influence of LULC on water quality is scale-dependent. In general, the LULC could explain from 56.9% to 31.6% of the variation in water quality at six buffer zones (from 500 m to 1800 m). Forest land had a positive effect on water quality among most buffer zones, while construction land and bare land affected the representative water quality indicators negatively within the 1200 m and 1500 m buffer zones, respectively. There was also a seasonal variation in the relationship between LULC and water quality. The closest connection between them appeared at the 1000 m buffer zone in the dry season, whereas there was no significant difference among the buffer zones in the wet season. The results suggest the importance of considering buffer-zone scales in assessing the impacts of LULC on water quality in urban lakeshore areas.

Hydrological processes are known as major driving forces in structuring wetland plant communities, but the specific relationships are not always well understood. The recent dry conditions of Poyang Lake (i.e., the largest freshwater lake in China) are having a profound impact on its wetland vegetation, leading to the degradation of the entire wetland ecosystem. We developed an integrated framework to quantitatively investigate the relationship between the spatial distribution of major wetland herbaceous communities and the hydrological regimes of Poyang Lake. First, the wetland herbaceous community classification was built using a support-vector machine and simultaneous parameter optimization, achieving an overall accuracy of over 98%. Secondly, based on the inundation conditions since 2000, four hydrological drivers of the spatial distribution of these communities were evaluated by canonical correspondence analysis. Finally, the hydrological niches of the communities were quantified by Gaussian regression and quantile methods. The results show that there were significant interspecific differences in terms of the hydrological niche. For example, Carex cinerascens Ass was the most adaptable to inundation, while Triarrhena lutarioriparia + Phragmites australis Ass was the least. Our integrated analytical framework can contribute to hydrological management to better maintain the wetland plant community structure in the Poyang Lake area.

Data-driven models are widely used in the field of water level prediction due to their generalizability and predictive abilities. In long-series prediction, however, data-driven models degrade rapidly due to the uncertainty and constraints of model data and parameters. To address the problem of inaccurate continuous water level prediction, this study introduced a data assimilation technique, the unscented Kalman filter (UKF), and embedded support vector regression (SVR) into the framework and applied it to Dongting Lake, the second largest freshwater lake in China. The results demonstrated that the assimilation model is significantly better than the non-assimilation model in predicting water levels and is not affected by the characteristics of lake level changes, with the R² increasing from 0.975–0.982 to 0.998–0.999 and the RMSE decreasing from 0.436–0.159 m to 0.105–0.042 m. The prediction lead time also increased with the increase of continuous assimilation data. Further analysis of the assimilation model showed that when there was an assimilation cycle, the prediction remained stable for successive sets of two or more assimilated data, and the prediction lead time increased with successive assimilated data, from 4–8 days (one successive assimilation data) to 9–12 days (five successive assimilation data). Overall, this study found that the data assimilation framework can improve the prediction ability of data-driven models, with assimilated models having a smaller fluctuation range and higher degree of concentration than non-assimilated models. The increase in assimilated data will improve model accuracy as well as the number of days of prediction lead time when an assimilation cycle exists.

Hydrological processes are known as driving forces in structuring wetland plant communities, but still specific relationships are not always well understood. The dynamic, seasonally inundated wetland at Poyang Lake (less than 1000  km2 in the dry season and more than 3000 km2 in the wet season), the largest freshwater lake in China, underpinnes critical regional ecosystem services (e.g. flood water retention, water supply and biodiversity conservation). However, recent drier conditions of Poyang Lake are having a profound impact on its wetland vegetation leading to degradation of the entire wetland ecosystem, thereby also threatening the winter habitat of migratory birds. We aim to develop an integrated framework to quantitatively investigate the spatial distribution of major herbaceous communities that provide habitat for the migratory birds in response to Poyang Lake flood inundation. First, ground references are obtained from a combination of drone imagery and field surveys as an input for the wetland herbaceous community classification model. Our classification model is based on a machine learning technique applied to Sentinel-2 satellite data. This new search strategy provides an accurate classification based on the more optimal input variables and model parameters gained simultaneously. Secondly, based on the dynamic changes in water levels since 2000, we statistically evaluate the key environmental drivers of the hydrological regime on the spatial distribution of the wetland vegetation communities. This showed that: 1) different plant communities exhibited varying tolerance to flood inundation and 2) two key factors, i.e., average water depth and average duration of the inundation events, were found to be able to characterize the communities’ tolerance independently. For example, Carex cinerascens Ass. which had the widest inundation stress tolerance, being adapted to an inundation duration of 120~230 d and depth of 1.5~1.7 m, accounted for the largest herbaceous community (>27% cover) within the entire study area. Different survival strategies to inundation stress, such as dormancy and morphological restructuring, can explain the varying tolerance of plant species/communities. Our work elucidated the linkages between hydrological processes and herbaceous plants’ distribution in wetlands, and the approach can be readily applied at small to large catchment scales and provides a straightforward practical tool to predict the possible responses of the lake wetland vegetations to potential hydrological changes.