Abstract:In recent years, the increased intensity and duration of droughts have dramatically altered the structure and function of grassland ecosystems, which have been forced to adapt to this change in climate. Combinations of global change drivers such as elevated atmospheric CO 2 concentration, warming, nitrogen (N) deposition, grazing, and land-use change have influenced the impact that droughts have on grassland C cycling. This influence, to some extent, can modify the relationship between droughts and grassland carbon (C) cycling in the multi-factor world. Unfortunately, prior reviews have been primarily anecdotal from the 1930s to the 2010s. We investigated the current state of the study on the interactive impacts of multiple factors under drought scenarios in grassland C cycling and provided scientific advice for dealing with droughts and managing grassland C cycling in a multi-factor world. Currently, adequate information is not available on the interaction between droughts and global change drivers, which would advance our understanding of grassland C cycling responses. It was determined that future experiments and models should specifically test how droughts regulate grassland C cycling under global changes. Previous multi-factor experiments of current and future global change conditions have studied various drought scenarios poorly, including changes in precipitation frequency and amplitude, timing, and interactions with other global change drivers. Multi-factor experiments have contributed to quantifying these potential changes and have provided important information on how water affects ecosystem processes under global change. There is an urgent need to establish a systematic framework that can assess ecosystem dynamic responses to droughts under current and future global change and human activity, with a focus on the combined effects of droughts, global change drivers, and the corresponding hierarchical responses of an ecosystem.

No abstract

Flood simulation and forecasting in various types of watersheds is a hot issue in hydrology. Conceptual hydrological models have been widely applied to flood forecasting for decades. With the development of economy, modern China faces with severe flood disasters in all types of watersheds include humid, semi-humid semi-arid and arid watersheds. However, conceptual model-based flood forecasting in semi-humid semi-arid and arid regions is still challenging. To investigate the applicability of conceptual hydrological models for flood forecasting in the above mentioned regions, three typical conceptual models, include Xinanjiang (XAJ), mix runoff generation (MIX) and northern Shannxi (NS), are applied to 3 humid, 3 semi-humid semi-arid, and 3 arid watersheds. The rainfall-runoff data of the 9 watersheds are analyzed based on statistical analysis and information theory, and the model performances are compared and analyzed based on boxplots and scatter plots. It is observed the complexity of drier watershed data is higher than that of the wetter watersheds. This indicates the flood forecasting is harder in drier watersheds. Simulation results indicate all models perform satisfactorily in humid watersheds and only NS model is applicable in arid watersheds. Model with consideration of saturation excess runoff generation (XAJ and MIX) perform better than the infiltration excess-based NS model in semi-humid semi-arid watersheds. It is concluded more accurate mix runoff generation theory, more stable and efficient numerical solution of infiltration equation and rainfall data with higher spatial-temporal resolution are main obstacles for conceptual model-based flood simulation and forecasting.