As global warming continues to intensify, the relationship between diurnal temperature range (DTR) and vegetation productivity continues to change over time. However, the impact of DTR changes on vegetation activities remains uncertain. Thus, further study about how DTR changes affect the physiological activities of plants is also urgently needed. In this study, we employed copula function theory to analyze the impact of DTR on Normalized Difference Vegetation Index (NDVI) values during the spring, summer, and autumn seasons from 1982 to 2014 for various land types in the Inner Mongolia Plain (IMP), China. The results showed that the relationship between DTR and NDVI in the IMP was characterized by correlation at the upper tail and asymptotical independence at the lower tail. This demonstrated that the DTR had little effect on NDVI when they reached their minimum value. However, it has a significant impact on NDVI at its maximum values. This study provides valuable insight into the dynamic impact of monthly DTR on different land use types under climate change.

Extreme surges and rainfall represent major driving factors for compound flooding in estuary regions along the Chinese coast. The combined effect of extreme surges and rainfall (that is, compound floods) might lead to greater impacts than if the drivers occurred in isolation. Hence, understanding the frequency and severity of compound flooding is important for improving flood hazard assessment and compound flood resilience in coastal cities. In this study, we examined the dependence between extreme surges and corresponding rainfall events in 26 catchments along the Chinese coastline during typhoon and non-typhoon seasons using copula functions, to identify where the two drivers more often occur together and the implication for flood management in these locations. We found that the interaction between flood drivers is statistically significant in 10 catchments located around Hainan Island (south) and Shanghai, where surge peaks occur mainly during the typhoon season and around the Bohai Sea (north), where surge peaks occur mainly during the non-typhoon season. We further applied the copula-based framework to model the dependence between surge peaks and associated rainfall and estimate their joint and conditional probability in two specific locations—Hainan Island and the Bohai Sea, where the correlation between flood drivers is statistically significant. We observed that in Hainan Island where most of the surge peaks occur during the typhoon season, extreme rainfall events during the typhoon season are generally more intense compared to annual maxima rainfall. In contrast, around the Bohai Sea where surge peaks occur mainly outside the typhoon season, rainfall is less intense than annual maxima rainfall. These results show that the interaction between extreme surges and rainfall can provide valuable insight when designing coastal and urban infrastructure, especially in highly populated urban areas prone to both coastal and pluvial flooding, such as many Chinese coastal cities.

Compound flooding is generated when two or more flood drivers occur simultaneously or in close succession. Multiple drivers can amplify each other and lead to greater impacts than when they occur in isolation. A better understanding of the interdependence between flood drivers would facilitate a more accurate assessment of compound flood risk in coastal regions. This study employed the D-Flow Flexible Mesh model to simulate the historical peak coastal water level, consisting of the storm surge, astronomical tide, and relative sea level rise (RSLR), in Shanghai over the period 1961-2018. It then applies a copula-based methodology to calculate the joint probability of peak water level and rainfall during historical tropical cyclones (TCs) and to calculate the marginal contribution of each driver. The results indicate that the astronomical tide is the leading driver of peak water level, followed by the contribution of the storm surge. In the longer term, the RSLR has significantly amplified the peak water level. This study investigates the dependency of compound flood events in Shanghai on multiple drivers, which helps us to better understand compound floods and provides scientific references for flood risk management and for further studies. The framework developed in this study could be applied to other coastal cities that face the same constraint of unavailable water level records.