Influences Of Climate Change On The Carbon And Water Fluxes Of Forest And Cropland Ecosystems In The Qinling Mountains

Climate change is one of the biggest challenges facing the world today.The carbon and water cycles of ecosystems and their coupling relationships are important factors in assessing the ecosystem responses to climate change.The Qinling Mountains is a hotspot for global climate change and regional adaptation research.Therefore,it is of great significance to explore the temporal and spatial changes of ecosystem carbon and water fluxes in the Qinling Mountains under future climate change.First,we constructed combined GPP and ET datasets suitable for the Qinling Mountains in China based on the multi-source remote sensing products of gross primary productivity（GPP）and evapotranspiration（ET）of ecosystems.Next,through coupling the Biome-BGC model and the independent parameter optimization software of PEST and based on the constructed datasets,we optimized the relevant parameters of the Biome-BGC model and then verified the calibrated model to simulate the growth of typical vegetations in the Qinling Mountains.Driven by historical and future climatic data obtained from the GCMs（Global climate models）of the CMIP6（the Coupled Model Intercomparison Project Phase 6）,the optimized Biome-BGC model was used to simulate the GPP and ET in historical and future periods in the Qinling Moutains.Then,the ecosystem water use efficiency was calculated as well.As a result,we explored and analyzed the temporal and spatial variations of regional carbon and water fluxes under future climate change.In this study,the biome-BGC model parameters were automatically optimized and the regional simulations were conducted.The results improved the simulation performace of carbon and water fluxes in the Qinling Mountains,and predicted the future change of carbon and water fluxes in the regional ecosystem.This study provided a theoretical basis and technical reference for the optimization of single-point process-based ecology models and the simulations of regional ecosystem carbon and water fluxes.The main conclusions were drawn as follows.（1）The GPP product with the best overall performance was the VPM GPP across the eight different ecosystem research sites in China,while the ET product with the best performance was the China ET.The GPP products,which were used in the combined dataset for the Qinling Mountain,included:RF GPP（for evergreen needle-leaved forest）,PML-V2GPP（for deciduous broad-leaved forest）,VPM GPP（for crop）,and AVHRR GPP（for shrub meadow）.Similarly,the ET products included:PML-V2 ET（for evergreen needle-leaved forest,and deciduous broad-leaved forest）,China ET（for crop）,and CR ET（for shrub-meadow）.（2）Under typical vegetation coverage in the Qinling Mountains,the influential parameters of the Biome-BGC model for GPP and ET simulations mainly included:the atmospheric deposition of N,symbiotic+asymbiotic fixation of N,cuticular conductance,maximum stomatal conductance,C:N of leaves,annual fire mortality fraction,canopy water interception coefficient,fraction of leaf N in Rubisco,annual live wood turnover fraction,litter fall as fraction of growing season,canopy light extinction coefficient,current growth proportion,annual whole-plant mortality fraction,and transfer growth period as fraction of growing season.After optimization of the influential parameters of the Biome-BGC model,the R² of simulated and observed GPP and ET values increased by 34.49%and 52.92%,respectively;the RMSE values decreased by 37.02%and 55.00%,respectively.（3）The average value of GPP_year in the historical period was 1420.77 g C m^-2 in the Qinling Mountains,presenting a spatial distribution of high in the south and low in the north,as well as high in the west and low in the east.The change rate of GPP_year was 3.544 g C m^-2a^-1.The GPP_month change of crops showed a"double peak"pattern in each single year,while other vegetations showed a"single peak"pattern.Under the SSP245 scenario,the change of GPP_year was:far future>near future>historical period.The change of GPP_year change rate was:near future>far future≈historical period;the change of GPP_month in the future period was similar to the historical period.Under the SSP585 scenario,the change of GPP_year was:far future>historical period>future near-term;the change of GPP_year change rate was:far future>near future>historical period.The change of GPP_month in the future period was similar to the historical period.（4）The average ET_year in the historical period was 572.52 mm in the Qinling Mountains,presenting a spatial distribution of high in the middle and low in the surrounding.The ET_yearchange rate was 3.016 mm a^-1.The ET_month of each vegetation showed a"single peak"pattern in each each.Under the SSP245 scenario,the change of ET_year was:far future>near future>historical period;the change of ET_year change rate was:historical period>near future>far future.The change of ET_month in the future period was similar to the historical period.Under the SSP585 scenario,the change of ET_year was:far future>near future>historical period;the change of ET_year rate of change was:far future>near future>historical period.The change of ET_month in the future period was similar to the historical period.（5）In the Qinling Mountains,the average value of WUE_year was 2.48 g C kg^-1 H₂O in the historical period,which was higher in the north part and lower in the northeast,northwest corners and south-central parts.The change rate of WUE_year was-0.062 g C kg^-1 H₂O 10a^-1.The WUE_month of evergreen needle-leaved forest showed a"valley-like"pattern in a single year,while other vegetations showed a"double-peak"pattern.Under the SSP245 scenario,the change of WUE_year was:historical period>far future>near future;the change of WUE_yearchange rate was:near future>far future>historical period.The change of WUE_month in the future period was similar to the historical period.Under the SSP585 scenario,the change of WUE_year was:historical period>far future>near future;the change of WUE_year change rate was:far future>near future>historical period.The change of WUE_month in the future period was similar to the historical period.