# Drought and Carbon Cycling of Grassland Ecosystems under Global Change: A Review
## 3 Syntheses and Perspectives
### Combined Effects of Multiple Factors
Moreover, the amplification or suppression of one factor's impact by another factor on soil \(C\) has not been observed in most studies [161; 171]. These effects are the key to identifying the most sensitive factors affecting \(C\) cycling. Therefore, evaluating multi-factor interactions influencing ecosystem structure and functioning is critical to understanding their response to global change. Indeed, once interactive effects can regulate the main effects of single factors, single-factor experiments will become less useful in understanding ecosystem changes [171]. There are a number of single factors that help assess the impacts of global change, such as rainfall manipulation experiments [10], FACE experiments, and N-addition experiments [12]. A multi-factor experimental approach explains ecosystem responses to multiple factors, especially under different drought stress scenarios [172]. Usually, multi-factor experiments are quite expensive and provide imprecise results and undefined interactive mechanisms. They also cannot be properly managed in many ecosystems due to financial constraints. To make multi-factor experiments more effective, models can help imitate the formation of a scientific hypothesis in its initial stages and extrapolate experimental results [171]. Ecosystem models should incorporate the direct and indirect effects of climate change on soil moisture to accurately predict drought feedback and the long-term effects of \(C\) cycling. Models can be informed by single-factor experiments that provide ecosystem-level information for single-factor responses. Multi-factor experiments are important for testing concepts, thus indicating the reality of multi-factor influences. However, large uncertainties remain in most current models that evaluate the feedbacks between \(C\)-cycle and climate change over the past few decades. Fortunately, close cooperation between experimentalists and modelers make it easier to understand water and \(C\) cycling via explicit programs of model-data fusion, such as data sharing, data assimilation, and clarity of model processes. A major improvement in current observations may be gained by the combination of long-term, multi-factor experiments at the ecosystem level, such as whole-ecosystem flux measurements. To some extent, the coupled \(C\)-cycle-climate models can demonstrate the importance of potential carbon-cycle-climate system feedbacks [17]. However, much work towards these experiments and models is needed to increase our understanding of the mechanisms between drought and grassland \(C\) cycling in the multi-factor world at the ecosystem level. In general, the emerging gaps are as follows:1. Under particular scenarios such as droughs, recognizing the key environmental factors impacting grassland \(C\) cycling in the real, multi-factor world is important. It is necessary to carry out multi-factor experiments to determine the contribution of each factor and their interactive effects with other factors on grassland \(C\) cycling at the ecosystem scale. 2. The quantitative impacts between drought and other factors (e.g., elevated CO\({}_{2}\) concentrations, global warming, \(N\) deposition, grazing, and other land-use change) on grassland \(C\) cycling are not clearly proposed at the ecosystem level. 3. The next challenge is to establish the quantitative relationships between different \(C\) fluxes and different global change factors under different drought scenarios as soon as possible. 4. Multi-factor response models should be developed with better coupling mechanisms to examine the interactive effects of global change multi-factors on the carbon and water processes of ecosystems, especially under different levels of drought stress or other extreme scenarios. The data-model fusion has become essential to assess the interactive effects of multiple factors in global change research.