Evaluating the influence of different environmental water allocation schemes on the water level of a typical shallow lake in semiarid regions: From the perspective of an integrated modeling approach.

Many lakes in semiarid regions around the world rely on environmental water allocation to maintain the health of the lake ecosystem. However, under changing environments, the competition for water resources between human society and natural ecosystems has intensified. How to manage environmental water allocation more reasonably and precisely has become an important issue. The largest lake on the North China Plain, Baiyangdian Lake (BYDL), is a typical lake facing such challenges. To provide feasible strategies for sustainable water allocation to BYDL, with the proper parameterization of hydrological processes, this study developed a 10-day temporal scale lake water level prediction model to quantify how environmental water allocation regulates the BYDL water level under different hydroclimatic conditions. Evaluation of model performance revealed that environmental water allocation rather than natural climatic periodicity dominates the variation in the BYDL water level. The model structure could be further improved with consideration of more detailed observations of both the surface runoff entering BYDL and the water area beneath the canopy of the reeds in BYDL. Analysis of 72 model simulation scenarios indicated that water allocations from multiple sources are indispensable and that the water resources that guarantee maintaining the BYDL water level within the ecologically suitable range vary substantially under different hydroclimatic conditions. More elaborate allocation plans are required both to improve the water quality and health of the aquatic ecosystem of BYDL and to reduce the risk of flooding. The findings from this study are valuable for guiding the implementation of environmental water allocations to lakes in semiarid regions worldwide.

Anthropogenic influences on the water quality of the Baiyangdian Lake in North China over the last decade.

Baiyangdian Lake, the largest shallow lake in the North China Plain, is essential for maintaining ecosystem functioning in this highly populated region. To explore the influences of human activities on the lake's water quality, an improved Water Quality Index (WQI) method and multivariate statistical techniques were adopted to assess the temporal and spatial variations of the lake's water quality and explore the dominant factors of these variations. Datasets for 11 water quality parameters from six monitoring stations were used to evaluate the period spanning from 2006 to 2016. Assessment of the annual WQI showed that the water quality of the lake has generally improved over the past decade. Cluster analysis divided 12 months into the dry and wet periods and the six monitoring stations into those located in the western and eastern parts of the lake. Discriminant analysis demonstrated that with only two parameters (water temperature and fluoride) and six parameters (dissolved oxygen, ammonia nitrogen, total nitrogen, total phosphorus, anionic surfactant, and fecal coliform), 96.0% and 93.8% of the water quality data can be classified into the correct spatial and temporal clusters, respectively. For the principal component analysis and factor analysis, the varifactors detected for the two temporal clusters were similar, and varifactors related to pollution explained more variance in the water quality variation than the ones representing natural factors. For the two spatial clusters, the varifactors were different, indicating they are influenced by different types of anthropogenic activities. Correlation analysis between lake water level and water quality indicated that environmental water allocation to the lake generally improve water quality. These findings provide a more thorough understanding of driving mechanism of water quality and may be helpful for making environmental management decisions in Baiyangdian Lake and other large, shallow lakes in highly populated dryland regions.