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Phenological models are built upon an understanding of the influence of environmental factors on plant phenology, and serve as effective tools for predicting plant phenological changes. However, the differences in phenological model predictive performance under different climate change scenarios have been rarely studied. In this study, we parameterized thirteen spring phenology models, including six one-phase models and seven two-phase models, by combining phenological observations and meteorological data. Using climatic data from two Shared Socioeconomic Pathways (SSP) scenarios, namely SSP126 (high mitigation and low emission) and SSP585 (no mitigation and high emission), we predicted spring phenology in Germany from 2021 to 2100, and compared the impacts of dormancy phases and driving factors on model predictive performance. The results showed that the average correlation coefficient between the predicted start of growing season (SOS) by the 13 models and the observed values exceeded 0.72, with the highest reaching 0.80. All models outperformed the NULL model (Mean of SOS), and the M1 model (driven by photoperiod and forcing temperature) performed the best for all the tree species. In the SSP126 scenario, the average SOS advanced initially and then gradually shifted towards a delay starting around 2070. In the SSP585 scenario, the average SOS advanced gradually at a rate of approximately 0.14 days per year. Moreover, the standard deviation of the simulated SOS by the 13 spring phenology models exhibited a significant increase at a rate of 0.04 days per year. On average, two-phase models exhibited larger standard deviations than one-phase models after approximately 2050. Models driven solely by temperature showed larger standard deviations after 2060 compared to models driven by both temperature and photoperiod. Our findings suggest investigating the release mechanisms of endodormancy phase and incorporating new insights into future phenological models to better simulate the changes in plant phenology. [ABSTRACT FROM AUTHOR]

In the background of global climate change and rapid urbanization, extreme climate events are frequent, and highly urbanized areas flooding problems are becoming increasingly prominent. It becomes important to develop qualitative scenario storylines to assess future flooding risk in a changing environment over the highly urbanized areas. In this study, the future (2030–2050) flood risk in the Pearl River Delta (PRD) region of China was assessed based on the MaxEnt model. We have developed four future scenarios under different emission conditions based on the representative concentration pathways (RCPs) and the shared socio-economic pathways (SSPs). The MaxEnt model was trained by using flood hazard sample point data and flood risk indicators, meanwhile, food risk prediction with high accuracy was obtained. We analyzed the influencing factors of flood risk and predicted the flood risk of the PRD under four future scenarios. According to the results, there is a significant increase in the size and proportion of high flood risk areas in most scenarios. Flood risk under scenario SSP5-RCP8.5 are expected to be the most serious in the future. Population density (POPD) and Gross domestic product density (GDPD) have the highest explanatory power for flood risk. This study predict the changes of flood risk under the combined influence of climate change and human activities, and hoped to provide a reference for future planning and disaster mitigation construction in PRD cities.