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Lakes and vegetation are important factors of the Earth's hydrological cycle and can be called an “indicator” of climate change. In this study, long-term changes of lakes' area and vegetation coverage in the Qinghai-Tibetan Plateau (QTP) and their relations to the climate change were analyzed by using Mann-Kendall method during the past 30 years. Results showed that: 1) the lakes' area of the QTP increased significantly during the past 30 years as a whole, and the increasing rates have been dramatically sped up since the year of 2000. Among them, the area of Ayakekumu Lake has the fastest growing rate of 51.35%, which increased from 618 km 2 in the 1980s to 983 km 2 in the 2010s; 2) overall, the Normalized Difference Vegetation Index (NDVI) increased in the QTP during the past 30 years. Above 79% of the area in the QTP showed increasing trend of NDVI before the year of 2000; 3) the air temperature increased significantly, the precipitation increased slightly, and the pan evaporation decreased significantly during the past 30 years. The lake area and vegetation coverage changes might be related to the climate change. The shifts in the temporal climate trend occurred around the year 2000 had led the lake area and vegetation coverage increasing. This study is of importance in further understanding the environmental changes under global warming over the QTP. [ABSTRACT FROM AUTHOR]

Uncertainty is recognized as a critical consideration for accurately predicting stream water nitrogen (N) loading, but identifying the relative contribution of individual uncertainty sources within the total uncertainty remains unclear. In this study, a powerful method, referred to as the Bayesian inference combined with analysis of variance (BayeANOVA) was adopted to detect the timing and magnitude of multiple uncertainty sources and their relative contributions to total uncertainty in simulating daily loadings of three stream water N species (ammonium-N: NH 4 + -N, nitrate-N: NO 3 − -N and total N: TN) in a rice agricultural watershed (the Tuojia watershed) as influenced by non-point source N pollution. Five sources of uncertainty have been analyzed in this study, which arise from model structure, parameters, inputs, interaction effects between parameters and inputs, and internal variability (induced by random errors of model or environment). The results show that uncertainty in parameters relating to the processes of both N and hydrologic cycles contributed the largest fractions of total uncertainty in N loading simulations (58.83%, 63.48% and 61.64% for NH 4 + -N, NO 3 − -N and TN loading, respectively). Additionally, three of the largest uncertainties (i.e. parameters, inputs and interaction effects) in all three simulated N loadings were on average significantly greater in the rice-growing season relative to the fallow season, primarily due to the excess fertilization application during the rice-growing season. The predicted TN uncertainty was mainly attributed to the inaccuracy of NO 3 − -N simulation, which contributed to 75.48% of predicted TN uncertainty. It is concluded that reducing the parameter uncertainty of NO 3 − -N loading simulation during the rice-growing season is the key factor to improving stream water N modeling precision in rice agricultural watersheds. [ABSTRACT FROM AUTHOR]