Lake Hulun, the fifth largest lake in China, is a typical eutrophic steppe lake located in the Hulun Buir Prairie. The dissolved organic matter (DOM) in the water of Lake Hulun has a high concentration. However, little is known about the occurrence characteristics and source of the DOM in Lake Hulun. The spatial and temporal distribution characteristics of DOM concentration in Lake Hulun were thoroughly surveyed, and the optical characteristics, fluorescence components and sources of DOM were analyzed by excitation emission matrix (EMM) and parallel factor analysis (PARAFAC) technology. The DOM concentration was 6.46–42.87 mg C/L, and was highest in summer and lowest in winter. The difference in the spatial distribution of DOM in winter was significant due to the ice over, and showed a trend where the concentration near the shore was higher than that in the center of the lake. Three humic-like components and one component consisting of a mixture of humic-like and protein-like substances of DOM were identified, with the former being prevalent. The humification index of DOM was 2.22–9.92, indicating that the DOM has a high degree of humification. The DOM is mainly derived from terrestrial sources, with the highest proportion (91.0% ± 8.1%) found in winter and the lowest (66.2% ± 7.7%) in summer. Given that the DOM in Lake Hulun is mainly dominated by humic-like components with a high degree humification, the DOM may have low bioactivity. However, this is just a preliminary analysis and judgment, and it is necessary to conduct other experiments such as biodegradation experiments to further study the bioavailability of DOM in Lake Hulun.

The related dynamic change in meteorological and hydrological parameters is critical for available water resources, development management options, and making informed decisions. In this study, to enhance the resolution of the predicted meteorological and hydrological parameters under climate change, the statistical downscaling method (SDSM), the generalized regression neural network (GRNN) model, the Soil and Water Assessment Tool (SWAT) model, and the improved Tennant method were integrated into a framework. The available water resources were assessed in the Huangshui watershed of the Qinghai–Tibet Plateau, which has the highest average elevation in the world. The meteorological parameters were obtained by the SDSM model and the GRNN model. The SWAT model used the meteorological parameters to simulate the hydrological data under climate change scenarios. Considering the meteorological conditions and the high sediment content in the basin, the available water resources are evaluated by the improved Tennant method. The meteorological data of the Xining station from 1958 to 2011 were used to analyze the dynamic changes and mutation trends in the data. The results indicated that the precipitation would have a great increase during the wet season from May to September, and the flows and available water resources would decrease with increasing carbon emissions under different representative concentration pathways (RCPs).