Changes In Soil Moisture In China And Their Impact On Gross Primary Productivity Under Different Future Scenario

Water cycle and carbon cycle are important components of terrestrial ecosystem.As an important comprehensive variable in the water cycle,soil moisture（SM）has a vital impact on plant growth and hydrological processes.Gross primary productivity（GPP）of vegetation is an indispensable ecological index to understand the process of surface carbon cycle.Under the background of climate change,the accuracy of SM and GPP estimation is important for the coupling study of the two.Earth System Model（ESM）is a key tool for predicting the response of SM and GPP to future climate change.However,the accuracy of SM and GPP estimated from different ESMs varies geographically as each ESM has its advantages and limitations.In this study,a deep learning model that can simultaneously perform data fusion and spatial downscaling is used to develop a set of SM fusion data and GPP fusion data with high accuracy and spatial resolution in China from 1850 to 2100（including four scenarios:SSP1-2.6,SSP2-4.5,SSP3-7.0 and SSP5-8.5）.The evaluation of the fusion data showed that our merged SM product is significantly better than each of the ESMs and the ensemble mean of all ESMs in terms of accuracy and spatial distribution.These provide data support for subsequent studies.The Dual Extended Kalman Filter-based generalized Partial Directed Coherent（g OPDC）is applied to the study of soil moisture-gross primary productivity interaction.The two-way influence is expressed in time-frequency domain under historical and future climate scenarios,and the interaction period and intensity are identified.The main conclusions of this thesis are as follows:（1）The SM fusion model is constructed by deep learning method,and the SM dataset in history and four future scenarios of future period are obtained.From the perspective of evaluation indicators,the performance of fusion data is better than other data in all aspects.From the spatiotemporal patterns of the results,the trend of future SM in China is roughly presented:the northeastern part of China become wet,while the Yangtze River basins become dry.With the low to a high emission scenario,in the east of 100°E,the drying and wetting trends become apparent.In the west of 100°E,the change in the northwest arid region is not just a simple trend enhancement but a change from a wetting trend to a drying trend.From the temporal scale of the results,under different scenarios,the SM change rate increased with time in the three selected periods.In northern China,the SM wetting rate will increase significantly in summer and autumn,and the SM drying rate will increase in spring.Whereas in southern parts of China,the drying trend in winter will increase,and the wetting trend will increase in summer.（2）The fusion data of gross primary productivity are obtained by deep learning method.In the historical period,Chinese mainland is affected by monsoon climate,vegetation type and other factors.In a year,GPP is the highest in summer,followed by spring and autumn,and the lowest in winter.The spatial distribution of GPP is affected by hydrothermal conditions,showing a trend of increasing gradually from the northwest inland to the southeast coast.There are seasonal differences of GPP changes in the future scenarios,but there is little difference in GPP changes in the early 21^st century under different scenarios of the same season.（3）The causal statistical method g OPDC was used to analyze the soil moisture-GPP coupling relationship.Under the background of global warming,the effect of soil moisture on GPP in arid areas increases with the increase of radiative forcing.The effect of GPP on soil moisture will gradually disappear in the future.The effect of soil moisture on GPP in semi-arid and semi-humid areas increased at first and then decreased.In the humid area,under all SSPs scenarios,the influence intensity of soil moisture on GPP will increase slightly in the early 21^stcentury,but it will continue to decrease in the middle and later 21^st century.