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The objective of this study was to link hydrodynamic disturbance with sediment resuspension, phosphorus release, and algal growth in Lake Tai, a typical shallow lake located in the south of the Yangtze River Delta in China. With this regard, a sediment-water-algae laboratory experiment was conducted and extrapolated to the real situation in terms of field observations. The results show that the algal growth rate synchronically increased with dissolved total phosphorus (DTP) release rate. The DTP decreased with increase of bottom flow velocity, indicating that the phosphorus release rate was lower than its transfer rate into algal biomass. While all levels of hydrodynamic disturbances could increase sediment resuspension and phosphorus release, a low to moderate disturbance was beneficial, but a strong disturbance was harmful for algal growth. Also, a low to moderate disturbance caused the dissolved alkaline phosphatase activity (DAPA) to increase with time, which provided the enzyme for hydrolyzing a variety of organic phosphorus compounds from bed sediment into algae-needed nutritional DTP. The experiment proved to be an efficient means to understanding eutrophication mechanisms of large shallow lakes such as Lake Tai.

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Several modified versions of the Monod model have been proposed to simulate algal dynamics in lakes by keeping the parent model's advantages of simplicity and low data requirement. This study evaluated the performance of a widely-used modified Monod model in predicting algal dynamics at various time scales in Lake Tai, a typical shallow lake in east China, using multiple time series. Chlorophyll-a (Chl-a) concentration was used as a surrogate for algal (CyanoHABs: cyanobacterial harmful algal blooms) growth and the independent variables were total nitrogen (TN), total phosphorus (TP), and either water temperature or air temperature. The evaluation indicated that the model parameters could have distinctly different values, depending on whether or not constraints are imposed, time scales, and types of nutrients. The model performance varied in terms of time scales as well as magnitudes and fluctuations of Chl-a and TN or TP concentrations, achieving a relative OPEN ACCESSWater 2015, 7 3627 better performance for the monthly rather than three-day time scale and for the central part rather than bays of the study lake. The model with TP as the independent variable had a better performance than the model with TN as the independent variable, regardless of the time scale used. The temperature-nutrient interactions were important for algal growth when the temporal fluctuations of these two factors were large but the interactions could become minimal otherwise.

This study examined the discharge and recharge relationships between lake and groundwater in Lake Hulun using a novel tracer method that tracks hydrogen and oxygen isotopes and chloride ions. The hydrogen and oxygen isotopes in precipitation falling in the Lake Hulun Basin were compared with those in water samples from the lake and from the local river, well and spring water during both freezing and non-freezing periods in 2017. The results showed that the local meteoric water line equation in the Lake Hulun area is δD = 6.68 δ18O − 5.89‰ (R2 = 0.96) and the main source of water supply in the study area is precipitation. Long-term groundwater monitoring data revealed that the groundwater is effectively recharged by precipitation through the aeration zone. Exchanges between the various compounds during the strong evaporative fractionation process in groundwater are responsible for the gradual depletion of δ18O. The lake is recharged by groundwater during the non-freezing period, as shown in the map constructed to show the recharge and discharge relationships between the lake and groundwater. The steadily rising lake water levels in the summer mean that the water level before the freeze is high and consequently the water in the lake drains into the surrounding groundwater via faults along both sides of the lake during the frozen period. The groundwater is discharged into the lake in the west and into the Urson River in the east due to the Cuogang uplift.

Although some previous studies examined temporal trends of nutrients in lakes, few of them considered nonstationarity caused by step change in time series and might not capture peaks and troughs. The objectives of this study were to: (1) present an innovative statistical approach that takes into account such nonstationarity; and (2) use this approach to analyze long-term nutrient data from four Chinese lakes, including Lake Dongting, Poyang, Chao and Tai, to investigate the temporal trends of TN and TP in the past decades. The results revealed that both TN and TP in Lake Dongting and Lake Poyang exhibited an upward trend at annual, seasonal, and monthly timescales. Nutrient concentration in Lake Dongting displayed a step change around 2003, a downward trend in April before 2003 but an upward trend in April after 2003 because of point and non-point source pollution, overexploitation, and dam construction. TN concentration for Lake Chao and Lake Tai experienced step changes around 1999 and 2002, respectively. For Lake Chao, spanning the years before the step change, significant (significance level of a = 0.05) upward trends in TN were detected at the annual scale as well as in summer and May, but spanning the years after the step change, significant downward trends were detected. For Lake Tai, significant upward trends before the step change and downward trends after the step change were detected at the annual scale and in October.

An accurate estimation of soil electrical conductivity (EC) using hyperspectral techniques is of great significance for understanding the spatial distribution of solutes and soil salinization. Although spectral transformation has been widely used in data pre-processing, the performance of different pre-processing techniques (or combination methods) on different models of the same data set is still ambiguous. Moreover, extremely randomized trees (ERT) and light gradient boosting machine (LightGBM) models are new learning algorithms with good generalization performance (soil moisture and above-ground biomass), but are less studied in estimating soil salinity in the visible and near-infrared spectra. In this study, 130 soil EC data, soil measured hyperspectral data, topographic factors, conventional salinity indices such as Salinity Index 1, and two-band (2D) salinity indices such as ratio indices, were introduced. The five spectral pre-processing methods of standard normal variate (SNV), standard normal variate and detrend (SNV-DT), inverse (1/OR) (OR is original spectrum), inverse-log (Log(1/OR) and fractional order derivative (FOD) (range 0–2, with intervals of 0.25) were performed. A gradient boosting machine (GBM) was used to select sensitive spectral parameters. Models (extreme gradient boosting (XGBoost), LightGBM, random forest (RF), ERT, classification and regression tree (CART), and ridge regression (RR)) were used for inversion soil EC and model validation. The results reveal that the two-dimensional correlation coefficient highlighted EC more effectively than the one-dimensional. Under SNV and the second order derivative, the two-dimensional correlation coefficient increased by 0.286 and 0.258 compared to the one-dimension, respectively. The 13 characteristic factors of slope, NDI, SI-T, RI, profile curvature, DOA, plane curvature, SI (conventional), elevation, Int2, aspect, S1 and TWI provided 90% of the cumulative importance for EC using GBM. Among the six machine models, the ERT model performed the best for simulation (R2 = 0.98) and validation (R2 = 0.96). The ERT model showed the best performance among the EC estimation models from the reference data. The kriging map based on the ERT simulation showed a close relationship with the measured data. Our study selected the effective pre-processing methods (SNV and the 2 order derivative) using one- and two-dimensional correlation, 13 important factors and the ERT model for EC hyperspectral inversion. This provides a theoretical support for the quantitative monitoring of soil salinization on a larger scale using remote sensing techniques.