Spatiotemporal Variation Of Vegetation Phenology On The Tibetan Plateau Under Climate Change Scenarios

Vegetation is an important part of terrestrial ecosystem.The monitoring of its phenological characteristics plays an important role in meteorological forecasting,agricultural forecasting,and the study of many characteristics of terrestrial ecosystem.Vegetation phenology has been significantly affected by global warming recently.On the background of this trend,the Tibetan Plateau is more sensitive and has become an essential region for early warning global climate change,since it has unique geographical environment.Therefore,the simulation and prediction of vegetation phenology on the Tibetan Plateau is of great significance for the study of climate,phenology and the interactions between them.In this thesis,the Tibetan Plateau was selected as the study area.And its long-term(from 2000 to 2020)vegetation phenological characteristics,including Start of Season(SOS),End of Season(EOS)and Length of Season(LOS),were extracted from MODIS vegetation index product(MOD13Q1).Based on the generated dataset,the temporal and spatial characteristics of the historical vegetation phenology on the Tibetan Plateau were analyzed.In addition,with the physical process-based models,this thesis predicted the future vegetation phenology of the Tibetan Plateau for the first time,using the climate data under four Shared Socioeconomic Pathways(SSPs)in the CMIP6(the sixth phase of the Coupled Model Intercomparison Project)climate model.In fact,vegetation phenology is affected by various of climatic factors and their interactions.The machine-learning model of SOS on the Tibetan Plateau was established,to better simulate the temporal and spatial changes of SOS.Finally,the temporal and spatial characteristics of vegetation phenology in the Tibetan Plateau were deeply discussed under future climate change scenarios.The main accomplished work and results in the thesis can be summarized as in the following three parts:(1)From 2000 to 2020,the SOS of vegetation on the Tibetan Plateau advanced by3.9 days every 10 years,whereas the EOS delayed by 4.3 days every 10 years,and the LOS extended by 8.0 days every 10 years.In term of spatial distribution,from northwest to southeast of the Tibetan Plateau,the average of historical SOS generally shows an overall advance trend,whereas the EOS shows a delayed trend,and the LOS shows an extended trend.The SOS is positively correlated to the altitude,while the EOS and LOS are negatively related to that.When the altitude increases by 1 km,the SOS delays by about 15.1 days,EOS advances by about 6.2 days,and LOS shortens by about 21.5 days.(2)The prediction of vegetation phenology was calculated based on the physical process models.The results indicate that the SOS will advance by about 1.6-2.0 days in2040,compared with that from 2000 to 2014.And the EOS will delay by about 1.1-1.7days,and the LOS will extend by 2.7-3.7 days by 2040 relative to the 2000-2014 mean,irrespective of scenario.The future SOS,EOS and SOS from 2040 to 2100 were also computed under four climate change scenarios(i.e.,SSP1-2.6,SSP2-4.5,SSP3-7.0 and SSP5-8.5).From 2040 to 2100,the predicted SOS will advance by about 2.4,5.6 and 6.8days,the predicted EOS will be delayed by about 3.1,9.0 and 13.6 days,and the predicted LOS will extend by about 5.4,14.4 and 20.2 days under the last three scenarios(i.e.,SSP2-4.5,SSP3-7.0 and SSP5-8.5),respectively.But they will not change significantly under the first scenario(i.e.,SSP1-2.6).In addition,it is obvious that the the predicted SOS will advance in the northeast and southeast,the EOS will delay in the west and southwest,and the LOS will extend in the southwest of the Tibetan Plateau between 2000-2014 and 2086-2100.(3)Compared with the evaluation results of the physical process-based model,the machine learning model proposed in this thesis performed more reliable in the spatiotemporal simulation of vegetation phenology.Based on the machine learning model,SOS is predicted to advance by about 1.8-2.8 days by 2040 relative to the 2000-2014 mean,irrespective of scenarios.And the predicted SOS from 2040 to 2100 will advance about 2.6,6.0 and 7.1 days,under the climate change scenarios of SSP2-4.5,SSP3-7.0and SSP5-8.5,respectively.But no significant advance trend of SOS was observed under the scenario of SSP1-2.6.Furthermore,there is more obvious advance trend of SOS in the southwest and northwest than that in the southeast and east of the Tibetan Plateau between 2000-2014 and 2086-2100.The thesis provides novel insights for the study of temporal and spatial changes of vegetation phenology in the Tibetan Plateau under the different climate change scenarios.