Water quality safety is the key factor to maintain the ecosystem service functions of lakes. Field investigations and statistical analyses were carried out to study the water quality of a large, agriculture-stressed lakes (e.g., Chagan Lake) in Northeast China. The hydro-chemical properties of the Chagan Lake are HCO₃·CO₃-Na. Nutrient (N and P) and non-nutrient (pH and F⁻) were found to be the major factors that threaten water quality safety of the lake. The concentration of total nitrogen (TN) and total phosphorus (TP) was found to vary seasonally and at different locations. The overall lake water had mean TN and TP values of 2.19 mg/L and 0.49 mg/L, respectively, in summer. TN was the major factor for water quality deterioration in the western region of the lake, while TP was the principal factor in the other regions, as determined by a principal component analysis (PCA). Fluoride (F⁻) concentration in the lake water were related to the values of total dissolved solid (TDS), pH, and electrical conductivity (EC). In addition, eutrophication is a fundamental index that has been affecting the ecological evaluation of water quality. The results showed that trophic level index (TLI), trophic state index (TSI), and eutrophication index (EI) were evaluated to quantify the risk of eutrophication. However, TLI and TSI can better describe the purification effect of the wetland. These indices showed that the lake water was hyper-eutrophic in summer, with TLI, TSI, and EI values of 60.1, 63.0, and 66.6, respectively. Disparities in water quality were observed among whole areas of the lake. Overall, this study revealed that controlling agriculture drainage is crucial for lake water quality management. The study generated critical data for making water quality management plans to control the risk.

Derived from Landsat imagery and capable of enhancing the contrast between surface water bodies and the background, water indices are widely used in surface water body extraction. Whether one index and its optimal threshold can maintain the best all year round is a question. At present, however, little research has considered the effect of time or conducted experiments with data from different months. To identify the best index for surface water body extraction, two regions in Jilin Province were selected for the case study and a comprehensive comparative analysis considering the imagery acquisition time was conducted. Ten water indices were selected and calculated based on the 30 m spatial resolution Landsat TM/OLI imagery acquired in 1999 and 2001 and 2019 and 2021 from May to October. The indices included the Modified Normalized Difference Water Index (NDWI3 and MNDWI), Automated Water Extraction Index (AWEI) for images with and without shadow, Multi-Band Water Index (MBWI), New Water Index (NWI), Water Ratio Index (WRI), Sentinel-2 Water Index (SWI) originally calculated based on the Sentinel-2 imagery, New Comprehensive Water Index (NCIWI), Index of Water Surfaces (IWS), and Enhanced Water Index (EWI). The OTSU algorism was adopted to adaptively determine the optimal segmentation threshold for each index and the indices were compared in terms of inter-class separability, threshold sensitivity, optimal threshold robustness, and water extraction accuracy. The result showed that NWI and EWI performed the best in different months and years, with the best water enhancement effect that could suppress background information, especially for the water-related land use types and cloud pollution. Their optimal segmentation thresholds throughout the study period were more stable than others, with the ranges of −0.342 to −0.038 and −0.539 to −0.223, respectively. Based on the optimal thresholds, they achieved overall accuracies of 0.952 to 0.981 and 0.964 to 0.981, commission errors of 0 to 28.2% and 0 to 7.7%, and omission errors of 0 to 15% and 0 to 8%, with a kappa coefficient above 0.8 indicating good extraction results. The results demonstrated the effectiveness of NWI and EWI combined with the OTSU algorithm in better monitoring surface water during different water periods and offering reliable results. Even though this study only focuses on the lakes within two regions, the indices have the potential for accurately monitoring the surface water over other regions.

Many studies have found that damming a river can change downstream hydrology, sediment transport, channel morphology, and fish habitat. However, little is known about river dam effects on downstream riparian wetland dynamics and their quantitative relationship with hydrological alterations. In this study, hydrological time series and wetland distribution data spanning nearly 40 years (1978–2016) before and after the construction of a large dam in 2005 across the Nenjiang River in Northeast China were used to reveal the impact of dam on the downstream discharge regime and wetland degradation. Hydro-statistical and stepwise multiple regression analyses were performed to quantify the relationship of riparian wetland area with a metrics of 33 hydrological indicators. Dam construction caused decline in peak discharge, flood frequency, and magnitude. Moreover, 150 km riparian wetlands along the downstream of the dam was largely reduced. The count and duration of high flow pulses, 1-day maximum, and date of maximum discharge changed significantly after the dam construction. The hydrological changes have made a significant contribution to the 44% reduction in riparian wetlands following the dam construction. Our results indicated that hydrological alterations caused by dam regulation led to the area reduction of downstream riparian wetlands. The findings provide relevant information for developing best dam operation practices to protect and restore downstream wetland ecosystems.