Progress in plant phenology modeling under global climate change

Plant phenology is the study of the timing of recurrent biological events and the causes of their timing with regard to biotic and abiotic forces.Plant phenology affects the structure and function of terrestrial ecosystems and determines vegetation feedback to the climate system by altering the carbon,water and energy fluxes between the vegetation and near-surface atmosphere.Therefore,an accurate simulation of plant phenology is essential to improve our understanding of the response of ecosystems to climate change and the carbon,water and energy balance of terrestrial ecosystems.Phenological studies have developed rapidly under global change conditions,while the research of phenology modeling is largely lagged.Inaccurate phenology modeling has become the primary limiting factor for the accurate simulation of terrestrial carbon and water cycles.Understanding the mechanism of phenological response to climate change and building process-based plant phenology models are thus important frontier issues.In this review,we first summarized the drivers of plant phenology and overviewed the development of plant phenology models.Finally,we addressed the challenges in the development of plant phenology models and highlighted that coupling machine learning and Bayesian calibration into process-based models could be a potential approach to improve the accuracy of phenology simulation and prediction under future global change conditions.

Coupling the Xinanjiang model with geomorphologic instantaneous unit hydrograph for flood forecasting in northeast China

The Xinanjiang(XAJ)model has been successfully applied in humid and semi-humid regions.Considering the geomorphologic factors to accurately estimate floods,this study adopted the geomorphologic instantaneous unit hydrograph(GIUH)method to calculate the surface runoff instead of the experience unit hydrograph(EUH)in the original model.The geomorphologic factors of the case study basin were obtained by using a digital elevation model(DEM)and the Terrain analysis using Digital Elevation Models(TauDEM).Furthermore,the dynamic Muskingum model was used for the channel flood routing.This study focused on the simulation of heavy precipitation and floods over the Chong River,which is a tributary river to the Songhua River on the right bank in northeast China.The detailed steps of the method were shown,up to the estimated value of flood runoff discharges and flood peaks and their comparison with observed values.The average deterministic coefficients(DCs)of model calibration and validation were 0.89 and 0.83,respectively.The results show that the model precision is high and the model is feasible for flood forecasting.Lastly,some methodological perspectives to enhance the method are presented.

Contrasting phenology responses to climate warming across the northern extra-tropics

Climate warming has substantially advanced the timing of spring leaf-out of woody species at middle and high latitudes,albeit with large differences.Insights in the spatial variation of this climate warming response may therefore help to constrain future trends in leaf-out and its impact on energy,water and carbon balances at global scales.In this study,we used in situ phenology observations of 38 species from 2067 study sites,distributed across the northern hemisphere in China,Europe and the United States,to investigate the latitudinal patterns of spring leaf-out and its sensitivity(S T,advance of leaf-out dates per degree of warming)and correlation(R_(T),partial correlation coefficient)to temperature during the period 1980-2016.Across all species and sites,we found that S_(T) decreased significantly by 0.15±0.02 d℃^(-1)°N^(-1),and R_(T) increased by 0.02±0.001°N^(-1)(both at P<0.001).The latitudinal patterns in R_(T) and S_(T) were explained by the differences in requirements of chilling and thermal forcing that evolved to maximize tree fitness under local climate,particularly climate predictability and summed precipitation during the pre-leaf-out season.Our results thus showed complicated spatial differences in leaf-out responses to ongoing climate warming and indicated that spatial differences in the interactions among environmental cues need to be embedded into large-scale phenology models to improve the simulation accuracy.