Managing lake water levels from an ecological perspective has become an urgent issue in recent years in efforts to protect, conserve, and restore lake eco-environments. In this study, we considered the actual situation of Ebinur Lake basin to develop a lake water balance model using a System Dynamics (SD) method. The objective of this study is based on the lake water balance model to sufficiently understand the variation and relationship between the lake depth–area–volume. We combined field investigations and hydrological data analysis to expose the major factors affecting lake water level fluctuations (WLFs), as well as the impact of WLFs on lake eco-environments. All with the aim of providing a theoretical basis to manage Ebinur Lake ecosystems for conservation and restoration. The main findings of this study include: (I) The model’s calculation results agree with the observation value, as the monthly lake surface area was used to validate the model. (II) The factors influencing the dynamic changes in the water level of the lake are ranked in ascending order (from the lowest to the highest) as follows: Precipitation, groundwater recharge, evaporation, river inflow. (III) Fluctuations in water level play a significant role in lake shoreline displacement variation, and when the lake’s water level drops below 1 m, the surface area of the water body decreases to approximately 106 km². (IV) The magnitude and frequency of WLFs drive major differences in the ecology of lake littoral zones, influencing not only the structure and functioning of benthic assemblages but also littoral habitat structure. These results established a quantitative linkage between hydrological variables and ecosystem health for the Ebinur Lake wetlands. These findings could be widely used in managing the Ebinur Lake basin as well as other similar water bodies, and could provide a useful tool for managing lake ecosystems for conservation and restoration.

The simulation and prediction of glacially derived runoff are significant for water resource management and sustainable development in water-stressed arid regions. However, the application of a hydrological model in such regions is typically limited by the intricate runoff production mechanism, which is associated with snow and ice melting, and sparse monitoring data over glacierized headwaters. To address these limitations, this study develops a set of mathematical models with a certain physical significance and an efficient particle swarm optimization algorithm by applying long- and short-term memory networks on the glacierized Muzati River basin. First, the trends in the runoff, precipitation, and air temperature are analyzed from 1990 to 2015, and differences in their correlations in this period are exposed. Then, Particle Swarm Optimization–Long Short-Term Memory (PSO-LSTM) and Bi-directional Long Short-Term Memory (BiLSTM) models are combined and applied to the precipitation and air temperature data to predict the glacially derived runoff. The prediction accuracy is validated by the observed runoff at the river outlet at the Pochengzi hydrological station. Finally, two other types of models, the RF (Random Forest) and LSTM (Long Short-Term Memory) models, are constructed to verify the prediction results. The results indicate that the glacially derived runoff is strongly correlated with air temperature and precipitation. However, in the study region over the past 26 years, the air temperature was not obviously increasing, and the precipitation and glacially derived runoff were significantly decreasing. The test results show that the PSO-LSTM and BiLSTM runoff prediction models perform better than the RF and LSTM models in the glacierized Muzati River basin. In the validation period, among all models, the PSO-LSTM model has the smallest mean absolute error and root-mean-square error and the largest coefficient of determination of 6.082, 8.034, and 0.973, respectively. It is followed by the BiLSTM model having a mean absolute error, root-mean-square error, and coefficient of determination of 6.751, 9.083, and 0.972, respectively. These results imply that both the particle swarm optimization algorithm and the bi-directional structure can effectively enhance the prediction accuracy of the baseline LSTM model. The results presented in this study can provide a deeper understanding and a more appropriate method of predicting the glacially derived runoff in glacier-fed river basins.

With the increasing contribution of household carbon footprint to the global carbon footprint, household carbon footprint as a critical component of future carbon reduction has become a research hotspot to mitigate global warming and maintain sustainable economic development. The Web of Science (WOS) database is the literature data source. The literature on household carbon footprint is analyzed with the help of the visualization software CiteSpace. The collected data are analyzed using bibliometric analysis tools for knowledge base analysis, cooperative network analysis, and knowledge evolution analysis to grasp the developmental pulse of household carbon footprint. The findings show that the co-citation analysis reveals that household carbon footprint research has a clear knowledge base and shows a clear trend over time. The classic literature lays the foundation for subsequent diversified and interdisciplinary articles. The subsequent research hotspots show apparent inheritance and development characteristics, with many diversified and interdisciplinary studies appearing after 2008. Based on the three-level cooperation network analysis, household carbon footprint research has a clear structure of research cooperation network. Cross-institution and cross-country articles collaborate frequently; most authors tend to co-author articles, and there are still a small number of authors who write independently, among which China, Chinese institutions, and Chinese authors make significant contributions. Developed countries represented by the U.S. have chosen cross-region cooperation at the macro level through multifaceted research. The co-word and journal overlay analyses reveal that household carbon footprint research encompasses various research topics. The apparent shift of keywords within each research phase directly proves that household carbon footprint research is gradually developing into an interdisciplinary and diversified homogeneous field. This paper analyzes the evolution of household carbon footprint in detail and composes the basic knowledge which can provide a reliable reference for subsequent scholars.

Urbanization is a comprehensive process of mutual influence among the population, economy, society and living environment, and it depends on the synergy of a series of factors. This paper uses the statistical data of 76 counties in Xinjiang from 1996 to 2018 to construct a comprehensive urbanization evaluation system. Based on the entropy method, comprehensive evaluation model and coupling coordination model, from the scales of time and space, this paper discusses the current situation of the coordinated development of population, economy, society and living environment factors in counties in Xinjiang in the process of urbanization. Local spatial autocorrelation analysis is used to further study the spatial agglomeration effect of the coupling and coordination of urbanization development in the counties. The results show the following: (1) The comprehensive urbanization level of 76 counties in Xinjiang has the characteristics of "center-periphery" development, and high-level counties are clustered on the northern slopes of the Tian Mountains. (2) Most counties are in a serious state of imbalance; notably, the degree of population-economy-society-living environment coupling and coordination in the border counties and towns is in an unsatisfactory state. (3) The county-level cities in Northern Xinjiang belong to the diffusion and spillover areas, the county-level cities in southern Xinjiang belong to the polarization benefit areas, and most other counties are in the state of no spillover effect.