# Impacts of Heat and Drought on Gross Primary Productivity in China
## 5 Discussion
### Limitations and Research Prospects  
This study divided the research area into eight vegetation regions for analysis; however, the responses of vegetation to heat and drought are very different in each region due to factors such as climate conditions, topographic differences, and vegetation types. Furthermore, this study only focused on the impact of two types of extreme climate events, heat and drought, which have universal coupling effects on GPP anomalies. However, vegetation growth can be influenced by many other extreme climatic conditions, such as flooding [81], low temperatures, and cold damage [82].  
Additionally, at present, there is no unified definition of heat. Academia generally believes that heat means that the temperature exceeds a certain threshold (including absolute thresholds and relative thresholds), and the absolute threshold defines heat events in terms of specific temperatures. Heat defined by absolute thresholds has more direct and obvious influence on vegetation and increases the comparability between the responses of different vegetation types to heat. However, it is difficult to obtain accurate heat threshold data for different vegetation types due to the rich vegetation types in different vegetation regions. Therefore, this study applied the 99 th, 95 th, and 90 th percentile threshold values of temperature anomaly sequences to define the heat intensities. This method conveniently provides an adaptive heat definition that requires a long temperature time series without frequent small climatic changes. Otherwise, the use of this method may cause uncertainty in experimental results. Therefore, the influence of different definitions of heat on research results and comparisons between different definitions should be considered in future studies.  
Finally, this study used copula functions to estimate ENAG probabilities under different heat intensities and drought levels; this method is more objective than traditional nonprobabilistic evaluation methods. However, copula functions are difficult to use in higher-dimensional research, such as in studies on the synergistic effects of heat, freezing, heavy rain, and drought on GPP. In addition, different vegetation types have different responses to the time of occurrence of extreme climate events, and the responses of vegetation to extreme climate events have certain hysteresis effects. This study did not identify the time of occurrence of extreme climate events, nor did it distinguish between the growing seasons and nongrowing seasons of vegetation. Considering the following three points, our study was conducted on an annual scale. First, if analyzing the GPP of the growing season, we must first determine the start and end time of the growing season of vegetation in different regions (or even each pixel). The growing season of different vegetation in the same area is different. For a given vegetation type and in the same region, its growing season also varies in different years. There are also errors in determining the growing season. Second, the responses of vegetation to extreme climate events have certain hysteresis effects. The drought before the growing season will affect the surface water storage, and then it will affect the growth and development of vegetation [83; 84]. Third, a drought index with a 12-month time scale is often used in studies on the relationship between drought and vegetation response [85; 86; 87; 88]. However, future studies that comprehensively consider these factors and conduct more detailed analyses, such as identifying various extreme climate events within a vegetation region and analyzing their impacts on vegetation productivity within a certain time window, are necessary.