How to simulate land-cover change,driven by climate change and human activity,is not only a hot issue in the field of land-cover research but also in the field of sustainable urbanization.A surface-modeling method of ...How to simulate land-cover change,driven by climate change and human activity,is not only a hot issue in the field of land-cover research but also in the field of sustainable urbanization.A surface-modeling method of land cover scenario(SSMLC)driven by the coupling of natural and human factors was developed to overcome limitations in existing land-cover models.Based on the climatic scenario data of CMIP6 SSP1-2.6,SSP2-4.5,and SSP5-8.5 released by IPCC in 2020,which combines shared socioeconomic paths(SSPs)with typical concentration paths(RCPs),observation climatic data concerning meteorological stations,the population,GDP,transportation data,land-cover data from 2020,and related policy refences,are used to simulate scenarios of land-cover change in the Jing-Jin-Ji region using SSP1-2.6,SSP2-4.5,and SSP5-8.5 for the years 2040,2070 and 2100,respectively.The simulation results show that the total accuracy of SSMLC in the Jing-Jin-Ji region attains 93.52%.The change intensity of land cover in the Jing-Jin-Ji region is the highest(plus 3.12%per decade)between 2020 and 2040,gradually decreasing after 2040.Built-up land has the fastest increasing rate(plus 5.07%per decade),and wetland has the fastest decreasing rate(minus 3.10%per decade)between 2020 and 2100.The change intensity of land cover under scenario SSP5-8.5 is the highest among the abovementioned three scenarios in the Jing-Jin-Ji region between 2020 and 2100.The impacts of GDP,population,transportation,and policies on land-cover change are generally greater than those on other land-cover types.The results indicate that the SSMLC method can be used to project the change trend and intensity of land cover under the different scenarios.This will help to optimize the spatial allocation and planning of land cover,and could be used to obtain key data for carrying out eco-environmental conservation measures in the Jing-Jin-Ji region in the future.展开更多
Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this ...Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this study,we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles(MMEs)from the Coupled Model Intercomparison Project Phase 6(CMIP6)under four Shared Socioeconomic Pathway and Representative Concentration Pathway(SSP-RCP)scenarios(SSP126(SSP1-RCP2.6),SSP245(SSP2-RCP4.5),SSP460(SSP4-RCP6.0),and SSP585(SSP5-RCP8.5))during 2015-2100.The bias correction and spatial disaggregation,water-thermal product index,and sensitivity analysis were used in this study.The results showed that the hydrothermal condition is mismatched in the central and southern deserts,whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition.Compared with the historical period,the matched degree of hydrothermal condition improves during 2046-2075,but degenerates during 2015-2044 and 2076-2100.The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions.The result suggests that the optimal scenario in Central Asia is SSP126 scenario,while SSP585 scenario brings further hydrothermal contradictions.This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.展开更多
The Selenge River Basin(SRB)in Mongolia has faced ecosystem degradation because of climate change and overloading.The dynamics of the pastoral system and the extent of overload under future scenarios have not been doc...The Selenge River Basin(SRB)in Mongolia has faced ecosystem degradation because of climate change and overloading.The dynamics of the pastoral system and the extent of overload under future scenarios have not been documented.This study aims to answer the following questions:Will the typical soums in the SRB become more overgrazed in the future?What optimal strategy should be implemented?Multisource data were integrated and utilized to model the pastoral system of typical soums using a system dynamics approach.Future scenarios under three SSP-RCPs were projected using the model.The conclusions are as follows:(1)From upstream to downstream,rational scenarios for pastoral system transferred from SSP1-RCP2.6 to SSP2-RCP4.5,which reflect improved productivity at the expense of ecosystem stability.(2)Compared with that during the historical period of 2000-2020,the projected carrying capacity of the soums decreases by 15.2%-37.3%,whereas the number of livestock continues to increase.Consequently,the stocking rate is expected to increase from 0.32-1.16 during 2000-2020 to 1.26-2.02 during 2021-2050,indicating that rangeland will become more overloaded.(3)A livestock reduction strategy based on future livestock stock and grassland carrying capacity scenarios was proposed to maintain a dynamic forage-livestock equilibrium.It is suggested that reducing livestock is a practical option for harmonizing grassland conservation with livestock husbandry development.展开更多
Land use projections are crucial for climate models to forecast the impacts of land use changes on the Earth’s system.However,the spatial resolution of existing global land use projections(e.g.,0.25°×0.25&#...Land use projections are crucial for climate models to forecast the impacts of land use changes on the Earth’s system.However,the spatial resolution of existing global land use projections(e.g.,0.25°×0.25°in the Land-Use Harmonization(LUH2)datasets)is still too coarse to drive regional climate models and assess mitigation effectiveness at regional and local scales.To generate a high-resolution land use product with the newest integrated scenarios of the shared socioeconomic pathways and the representative concentration pathways(SSPs-RCPs)for various regional climate studies in China,here we first conduct land use simulations with a newly developed Future Land Uses Simulation(FLUS)model based on the trajectories of land use demands extracted from the LUH2 datasets.On this basis,a new set of land use projections under the plant functional type(PFT)classification,with a temporal resolution of 5 years and a spatial resolution of 5 km,in eight SSP-RCP scenarios from 2015 to 2100 in China is produced.The results show that differences in land use dynamics under different SSP-RCP scenarios are jointly affected by global assumptions and national policies.Furthermore,with improved spatial resolution,the data produced in this study can sufficiently describe the details of land use distribution and better capture the spatial heterogeneity of different land use types at the regional scale.We highlight that these new land use projections at the PFT level have a strong potential for reducing uncertainty in the simulation of regional climate models with finer spatial resolutions.展开更多
基金National Key R&D Program of China(2017YFA0603702)National Key R&D Program of China(2018YFC0507202)+3 种基金National Natural Science Foundation of China(41971358)National Natural Science Foundation of China(41930647)Strategic Priority Research Program(A)of the Chinese Academy of Sciences(XDA20030203)Innovation Research Project of State Key Laboratory of Resources and Environment Information System,CAS。
文摘How to simulate land-cover change,driven by climate change and human activity,is not only a hot issue in the field of land-cover research but also in the field of sustainable urbanization.A surface-modeling method of land cover scenario(SSMLC)driven by the coupling of natural and human factors was developed to overcome limitations in existing land-cover models.Based on the climatic scenario data of CMIP6 SSP1-2.6,SSP2-4.5,and SSP5-8.5 released by IPCC in 2020,which combines shared socioeconomic paths(SSPs)with typical concentration paths(RCPs),observation climatic data concerning meteorological stations,the population,GDP,transportation data,land-cover data from 2020,and related policy refences,are used to simulate scenarios of land-cover change in the Jing-Jin-Ji region using SSP1-2.6,SSP2-4.5,and SSP5-8.5 for the years 2040,2070 and 2100,respectively.The simulation results show that the total accuracy of SSMLC in the Jing-Jin-Ji region attains 93.52%.The change intensity of land cover in the Jing-Jin-Ji region is the highest(plus 3.12%per decade)between 2020 and 2040,gradually decreasing after 2040.Built-up land has the fastest increasing rate(plus 5.07%per decade),and wetland has the fastest decreasing rate(minus 3.10%per decade)between 2020 and 2100.The change intensity of land cover under scenario SSP5-8.5 is the highest among the abovementioned three scenarios in the Jing-Jin-Ji region between 2020 and 2100.The impacts of GDP,population,transportation,and policies on land-cover change are generally greater than those on other land-cover types.The results indicate that the SSMLC method can be used to project the change trend and intensity of land cover under the different scenarios.This will help to optimize the spatial allocation and planning of land cover,and could be used to obtain key data for carrying out eco-environmental conservation measures in the Jing-Jin-Ji region in the future.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences,Pan-Third Pole Environment Study for a Green Silk Road(Pan-TPE)of China(XDA2004030202)Shanghai Cooperation and the Organization Science and Technology Partnership of China(2021E01019)。
文摘Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this study,we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles(MMEs)from the Coupled Model Intercomparison Project Phase 6(CMIP6)under four Shared Socioeconomic Pathway and Representative Concentration Pathway(SSP-RCP)scenarios(SSP126(SSP1-RCP2.6),SSP245(SSP2-RCP4.5),SSP460(SSP4-RCP6.0),and SSP585(SSP5-RCP8.5))during 2015-2100.The bias correction and spatial disaggregation,water-thermal product index,and sensitivity analysis were used in this study.The results showed that the hydrothermal condition is mismatched in the central and southern deserts,whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition.Compared with the historical period,the matched degree of hydrothermal condition improves during 2046-2075,but degenerates during 2015-2044 and 2076-2100.The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions.The result suggests that the optimal scenario in Central Asia is SSP126 scenario,while SSP585 scenario brings further hydrothermal contradictions.This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.
基金National Natural Science Foundation of China,No.32161143025,No.42371283,No.W2412155National Key R&D Program of China,No.2022YFE0119200。
文摘The Selenge River Basin(SRB)in Mongolia has faced ecosystem degradation because of climate change and overloading.The dynamics of the pastoral system and the extent of overload under future scenarios have not been documented.This study aims to answer the following questions:Will the typical soums in the SRB become more overgrazed in the future?What optimal strategy should be implemented?Multisource data were integrated and utilized to model the pastoral system of typical soums using a system dynamics approach.Future scenarios under three SSP-RCPs were projected using the model.The conclusions are as follows:(1)From upstream to downstream,rational scenarios for pastoral system transferred from SSP1-RCP2.6 to SSP2-RCP4.5,which reflect improved productivity at the expense of ecosystem stability.(2)Compared with that during the historical period of 2000-2020,the projected carrying capacity of the soums decreases by 15.2%-37.3%,whereas the number of livestock continues to increase.Consequently,the stocking rate is expected to increase from 0.32-1.16 during 2000-2020 to 1.26-2.02 during 2021-2050,indicating that rangeland will become more overloaded.(3)A livestock reduction strategy based on future livestock stock and grassland carrying capacity scenarios was proposed to maintain a dynamic forage-livestock equilibrium.It is suggested that reducing livestock is a practical option for harmonizing grassland conservation with livestock husbandry development.
基金the National Key Research&Development Program of China(2019YFA0607203,2017YFA0604404)the National Natural Science Foundation of China(41901327,41671398,41871318)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(2019A1515010823)the Fundamental Research Funds for the Central Universities(19lgpy41)Natural Resources of the People’s Republic of China(GS(2020)2879)。
文摘Land use projections are crucial for climate models to forecast the impacts of land use changes on the Earth’s system.However,the spatial resolution of existing global land use projections(e.g.,0.25°×0.25°in the Land-Use Harmonization(LUH2)datasets)is still too coarse to drive regional climate models and assess mitigation effectiveness at regional and local scales.To generate a high-resolution land use product with the newest integrated scenarios of the shared socioeconomic pathways and the representative concentration pathways(SSPs-RCPs)for various regional climate studies in China,here we first conduct land use simulations with a newly developed Future Land Uses Simulation(FLUS)model based on the trajectories of land use demands extracted from the LUH2 datasets.On this basis,a new set of land use projections under the plant functional type(PFT)classification,with a temporal resolution of 5 years and a spatial resolution of 5 km,in eight SSP-RCP scenarios from 2015 to 2100 in China is produced.The results show that differences in land use dynamics under different SSP-RCP scenarios are jointly affected by global assumptions and national policies.Furthermore,with improved spatial resolution,the data produced in this study can sufficiently describe the details of land use distribution and better capture the spatial heterogeneity of different land use types at the regional scale.We highlight that these new land use projections at the PFT level have a strong potential for reducing uncertainty in the simulation of regional climate models with finer spatial resolutions.
