1e:T41d,China's Loess Plateau is both the largest and deepest loess deposit in the world, and it has long been one of the most severely eroded areas on Earth. Since the 1970s, numerous soil- and water-conservation practices have been implemented: terracing, planting of vegetation, natural vegetation rehabilitation, and check-dam construction. With the implementation of the Grain-for-Green Project in 1999, the Loess Plateau has become the most successful ecological restoration zone in China. However, these large-scale restoration measures and drought have significantly reduced both runoff and sediment from the Loess Plateau. This situation has both advantages and disadvantages for the lower Yellow River. Some local soil erosion has been successfully controlled, but the whole regional ecosystem remains very fragile. Therefore, it is necessary to balance each ecosystem service, for example, by determining the region's vegetation capacity and its spatial distribution for the sustainable development of the socioecological system of the Loess Plateau.1f:T4d4,The "Global Aridity Index and Potential Evapotranspiration Database - Version 3" (Global-AI_PET_v3) provides high-resolution (30 arc-seconds) global hydro-climatic data averaged (1970-2000) monthly and yearly, based upon the FAO Penman-Monteith Reference Evapotranspiration (ET₀) equation. An overview of the methods used to implement the Penman-Monteith equation geospatially and a technical evaluation of the results is provided. Results were compared for technical validation with weather station data from the FAO "CLIMWAT 2.0 for CROPWAT" (ET₀: r² = 0.85; AI: r² = 0.90) and the U.K. "Climate Research Unit: Time Series v 4.04" (ET₀: r² = 0.89; AI: r² = 0.83), while showing significant differences to an earlier version of the database. The current version of the Global-AI_PET_v3 supersedes previous versions, showing a higher correlation to real world weather station data. Developed using the generally agreed upon standard methodology for estimation of reference ET₀, this database and notably, the accompanying source code, provide a robust tool for a variety of scientific applications in an era of rapidly changing climatic conditions.20:T50f,Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called 'early warning signals', and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.

	Year	Citations

References