Aquatic vegetation plays an important role in maintaining the balance of lake ecosystems. Thus, classifying and mapping aquatic vegetation is a priority for lake management. Classification tree (CT) approaches have been used successfully to map aquatic vegetation from spectral indices obtained from remotely sensed images. However, due to the effects of extrinsic and intrinsic factors, applying a CT model developed for imagery from one date to imagery from another date or a different dataset likely would reduce the classification accuracy. In this study, three spectral features (SFs) were selected to develop a CT model for identifying aquatic vegetation in Taihu Lake. Three traditional CT models with three SFs were developed using CT analysis based on satellite images acquired on 11 July, 16 August and 26 September 2013, and corresponding ground-truth samples, from the Huangjing-1A/B Charge-Coupled Device (HJ-CCD) images, environment and disaster reduction small satellites that were launched by China Center for Resources Satellite Data and Application (CRESDA). The overall accuracies of traditional CT models were 82%, 80% and 84%. We then tested two methods to modify CT model thresholds to adjust the traditional CT models based on image date to determine if the results would enable us to map and classify aquatic vegetation for periods when no ground-based data were available. We assessed the results with ground-truth samples and area agreement with traditional CT models. Results showed that CT models modified from a linear adjustment based on the relationship between ranked values of SFs between two image dates produced map accuracies comparable with those obtained from the traditional CT models and suggest that the method we proposed is feasible for mapping aquatic vegetation types in lakes when ground data are not available.

In lakes and reservoirs, physical processes control temperature dynamics and stratification, which are important determinants of water quality. In large lakes, even extensive monitoring programs leave some of the patterns undiscovered and unresolved. Lake models can complement measurements in higher spatial and temporal resolution. These models require a set of driving data, particularly meteorological input data, which are compulsory to the models but at many locations not available at the desired scale or quality. It remains an open question whether these meteorological input data can be acquired in a sufficient quality by employing atmospheric models. In this study, we used the European Centre for Medium-Range Weather Forecasts’ (ECMWF) ERA-Interim atmospheric reanalysis data as meteorological forcing for the three-dimensional hydrodynamic General Estuarine Transport Model (GETM). With this combination, we modelled the spatio-temporal variation in water temperature in the large, shallow Lake Chaohu, China. The model succeeded in reproducing the seasonal patterns of cooling and warming. While the model did predict diurnal patterns, these patterns were not precise enough to correctly estimate the extent of short stratification events. Nevertheless, applying reanalysis data proved useful for simulating general patterns of stratification dynamics and seasonal thermodynamics in a large shallow lake over the year. Utilising reanalysis data together with hydrodynamic models can, therefore, inform about water temperature dynamics in the respective water bodies and, by that, complement local measurements.

Chlorophyll a (Chl-a) is an important indicator of algal biomass in aquatic ecosystems. In this study, monthly monitoring data for Chl-a concentration were collected between 2005 and 2015 at four stations in Meiliang Bay, a eutrophic bay in Lake Taihu, China. The spatiotemporal distribution of Chl-a in the bay was investigated, and a statistical model to relate the Chl-a concentration to key driving variables was also developed. The monthly Chl-a concentration in Meiliang Bay changed from 2.6 to 330.0 μg/L, and the monthly mean Chl-a concentration over 11 years was found to be higher at sampling site 1, the northernmost site near Liangxihe River, than at the three other sampling sites. The annual mean Chl-a concentration fluctuated greatly over time and exhibited an upward trend at all sites except sampling site 3 in the middle of Meiliang Bay. The Chl-a concentration was positively correlated with total phosphorus (TP; r = 0.57, p < 0.01), dissolved organic matter (DOM; r = 0.73, p < 0.01), pH (r = 0.44, p < 0.01), and water temperature (WT; r = 0.37, p < 0.01), and negatively correlated with nitrate (NO₃⁻-N; r = −0.28, p < 0.01), dissolved oxygen (DO; r = −0.12, p < 0.01), and Secchi depth (ln(SD); r = −0.11, p < 0.05). A multiple linear regression model integrating the interactive effects of TP, DOM, WT, and pH on Chl-a concentrations was established (R = 0.80, F = 230.7, p < 0.01) and was found to adequately simulate the spatiotemporal dynamics of the Chl-a concentrations in other regions of Lake Taihu. This model provides lake managers with an alternative for the control of eutrophication and the suppression of aggregations of phytoplankton biomass at the water surface.

To improve the water quality and alleviate the eutrophication of Lake Yangchenghu, the third largest freshwater body within the Lake Taihu basin in China and an important source of drinking water, nutrient reduction strategies should be urgently addressed by decision makers, since virtually no improvement of water quality has taken place since the mid-1990s. Due to the lack of sufficient observation data and simulation results, a vertically compressed three-dimensional numerical model, the EcoTaihu model, was used to study the impact of three restoration measures on the water quality—namely, total nitrogen (TN), total phosphorus (TP) and biomass of phytoplankton (BP)—of Lake Yangchenghu: (i) total nutrient reduction, (ii) intensification of flushing by water transfer, and (iii) spatial adjustment of inflow channels. In particular, the spatial effects of the three restoration measures on the water quality were investigated. The results showed that the EcoTaihu model is applicable to other shallow lakes in China. The water quality responses to the different restoration scenarios showed significant spatio-temporal differences. The reduction of nutrient loads from inflows appeared to be the most effective measure for controlling the eutrophication and algal blooms in Lake Yangchenghu. The effectiveness of water transfer on the improvement of water quality for TN and TP was more influenced by the differences of nutrient concentrations between the transferred water and lake water, rather than flow rate, since no proportionate increase of improvement was observable in the case of larger transferred rates (60 m³ s⁻¹). The spatial narrowing of inflowing rivers in the southwestern lake could preferentially improve the water quality in the southern bay of the western lake, but would also result in a deterioration trend of water quality in the total lake and drinking water abstraction areas.