Regime shifts in shallow lakes can lead to great changes in ecosystem structures and functions, making ecosystem management more complicated. Lake Yilong, located in Yunnan Province, is one of the most eutrophic lakes in China. Although there is a high possibility that this lake has undergone regime shift one or more times, the presence of regime shifts and their drivers remain unknown. Here, we employed the sequential t-test analysis of regime shifts to detect the regime shifts based on the long-term (1989–2018) dataset of the lake. We further determined their potential drivers, and explored the nutrient thresholds of regime shifts and hysteresis. The results showed that during the testing period, three regime shifts occurred in 1996 (restorative type), 2009 (catastrophic type) and 2014 (restorative type). The potential key drivers for the first two regime shifts (1996 and 2009) were both related to aquaculture. The abolition of cage fish culture may have led to the restorative regime shift in 1996, and the stocking of crabs and excessive premature releasing of fry possibly caused the catastrophic regime shift in 2009. However, the third regime shift, which occurred in 2014, was possibly related to the drought and succedent hydration. These results indicate that adjustments of aquaculture strategy and hydrological conditions are critical for the lake ecosystem’s recovery. Moreover, the total phosphorus thresholds were identified to be lower than 0.046 mg/L (restorative type) and higher than 0.105 mg/L (catastrophic type), respectively. In addition, an obvious hysteresis was observed after 2014, suggesting that nutrient reduction is important for this lake’s management in the future.

Lake eutrophication is governed by persistent anthropogenic nutrient inputs, primarily nitrogen (N), phosphorus (P) and cryptic internal nutrient cycling processes that sustain bioavailable nutrient pools. While the impact of external nutrient loads on lake eutrophication has been extensively studied, the role of internal nutrient cycling in lake ecosystems remains underexplored. In this study, the hierarchical bootstrap generalized linear model (HBGLM) to long-term summer water quality data (1999–2020) from Lake Dianchi, China, to explore the relative importance of nitrogen (N), phosphorus (P), as well as the limitations of N and P on the growth of phytoplankton. The results revealed that from 1999 to 2020, the Chla and TP concentrations decreased by 49% and 78%, respectively, and that internal nutrient cycling significantly influenced changes in nutrient concentrations, reflecting the relationships among N, P, and chlorophyll a (Chla). Particularly in 2007, 2013, and 2017, the long-term trends of the TN:TP ratio, an indicator of potential nutrient limitation in the lake, were consistent with changes in the distributions of the average slopes of TN and TP across different periods, indicating that these years primarily exhibited patterns of colimitation by N and P or P limitation, indirectly confirming that Lake Dianchi will transition from N and P colimitation to being limited primarily by P. This study reveals that N is typically the primary limiting element, while P is a key element promoting water eutrophication. To further validate improvements to existing eutrophication mitigation models, conducting carefully de-signed experiments at different scales is recommended.

Secchi depth (SD) is a valuable and feasible water quality indicator of lake eutrophication. The establishment of an automated system with efficient image processing and an algorithm suitable for the inversion of transparency in lake-rich regions could provide sufficient temporal and spatial information for lake management. These are especially critical for lake-rich regions where in situ monitoring data are scarce. This study demonstrated the implementation of an atmospheric correction algorithm (ACOLITE algorithm) in conjunction with the Google Earth Engine platform to generate remote-sensing reflectance products of specific points efficiently. The study also evaluated the performance of an algorithm for inverting lake SDs in Yunnan Plateau lakes, which is one of the five lake districts in China, since there is a lack of in situ data for most of the lakes in the region. The in situ data from four lakes with large SD ranges and imagery from Landsat Operational Land Imager were used to train and evaluate the performance of two algorithms: an empirical algorithm (stepwise regression) and machine learning (support vector machines and multi-layer perception). The results revealed that the retrieval accuracy of models with bands and band ratio combinations could be substantially improved compared with models with a single band or band combinations. A negative correlation was also observed between the temporal match between observations and the model accuracy. This study found that the MLP model with sufficient training data was more suitable for transparency estimation of lakes belonging to the dataset; the SVM model was more suitable for transparency prediction outside the training set, regardless of the adequacy of the training data. This study provides a reference for monitoring lakes within the Yunnan region using remote sensing.

In this study, we analyzed the main climatic factors influencing nine plateau lakes in Yunnan Province over the past 40 years (from 1980 to 2019) based on trend and mutation analyses. The results revealed that the air temperatures of these plateau lakes have increased, with an annual average air temperature increase of 0.18 °C per decade, during this 40-year period. From 2000 to 2005, there was an abrupt change in the air temperature increase, the rate of which was 0.20 °C per decade, and there were reductions in long-wave radiation, relative humidity, wind speed, precipitation, and snowfall. In addition, the rising trend in the air temperature of these lakes located in northwest Yunnan (temperate climate, higher elevation) was found to be significantly higher than that of the lakes in central Yunnan (subtropical climate, lower elevation), whereas in contrast, the declining trends in long-wave radiation, relative humidity, wind speed, and precipitation were more pronounced in central than in northwestern Yunnan.

Lake eutrophication is a major environmental concern in southwest China, where external inputs of nutrients and organic pollutants adversely impact plateau lakes like Yangzonghai (YZH) Lake. This study applied the two-dimensional CE-QUAL-W2 model to simulate the hydrodynamics and water quality of YZH Lake from 2016 to 2018, aiming to (i) assess the model’s performance for water level and surface temperature, (ii) identify spatial and temporal patterns of water quality, and (iii) determine pollutant reduction targets to meet Class II water standards. The model accurately simulated observed water levels (RMSE = 0.078 m, AME = 0.066 m, R = 0.91, p < 0.001) while moderately predicted surface temperatures (RMSE = 1.82 °C, AME = 1.53 °C, R = 0.80, p < 0.001). Total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (CODMn) exceeded Class III standards during strong thermal stratification periods, especially near major river inflows like the Baiyi and Yangzonghai Rivers. High nutrient and chlorophyll-α levels with the lowest transparency were observed during summer stratification. External inflows contributed over 70% of the total nutrient and organic matter input. Reducing external loads by 43% (TN), 26% (TP), and 10% (CODMn) annually is crucial to meet Class II standards. At the 75th percentile, 50%, 35%, and 40% reductions, respectively, are required. The internal cycling and hydrodynamics result in a non-linear relationship between load reductions and in-lake concentrations. The study demonstrates both the strengths and limitations of CE-QUAL-W2 model, offering a scientific foundation for establishing realistic pollutant reduction targets for sustainable lake management.