This paper compartmentalizes regional land use of rural settlements in China by employing a hierarchical clustering method.The statistic data are sourced from the National Bureau of Statistics of China(NBSC) and the d...This paper compartmentalizes regional land use of rural settlements in China by employing a hierarchical clustering method.The statistic data are sourced from the National Bureau of Statistics of China(NBSC) and the data of land use change from the Ministry of Land and Resources of China(MLRC).The population of rural settlement decreases from the southeast to the northwest of China and the density of rural settlement decreases from the east to the west of China.Land-use scale of rural settlement,the proportion of one-storey houses and the average household area decrease from the north to the south of China.The ratio of area of cultivated land to rural settlement is high in the northeast and southwest of China but low in the southeast of China.The land use regionalization of rural settlement can be divided into four regions,namely:the northern region of China,Qinghai-Tibet,Yunnan-Guizhou,and the middle and eastern region of China.The northern region of China and the middle and eastern region of China can be further divided into nine sub-regions:Xinjiang,Northeast China,Ningxia and Inner Mongolia,North China,the south of the Changjiang(Yantze) River and Sichuan Basin,Jiangsu-Shanghai,South China,the Loess Plateau,and Guangxi.In consideration of the significant regional differences,it is proposed that different policies should be implemented regarding the utilization and management of rural settlements.展开更多
Seasonal and interannual changes in the Earth's gravity field are mainly due to mass exchange among the atmosphere,ocean,and continental water sources.The terrestrial water storage changes,detected as gravity chan...Seasonal and interannual changes in the Earth's gravity field are mainly due to mass exchange among the atmosphere,ocean,and continental water sources.The terrestrial water storage changes,detected as gravity changes by the Gravity Recovery and Climate Experiment(GRACE) satellites,are mainly caused by precipitation,evapotranspiration,river transportation and downward infiltration processes.In this study,a land data assimilation system LDAS-G was developed to assimilate the GRACE terrestrial water storage(TWS) data into the Community Land Model(CLM3.5) using the POD-based ensemble four-dimensional variational assimilation method PODEn4 DVar,disaggregating the GRACE large-scale terrestrial water storage changes vertically and in time,and placing constraints on the simulation of vertical hydrological variables to improve land surface hydrological simulations.The ideal experiments conducted at a single point and assimilation experiments carried out over China by the LDAS-G data assimilation system showed that the system developed in this study improved the simulation of land surface hydrological variables,indicating the potential of GRACE data assimilation in large-scale land surface hydrological research and applications.展开更多
In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 of the dual-pass data assimilation scheme optimizes the model vegetation parameters at the weekly temporal...In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 of the dual-pass data assimilation scheme optimizes the model vegetation parameters at the weekly temporal scale, and Pass 2 optimizes the soil moisture at the daily temporal scale. Based on ensemble Kalman filter(EnKF), the land surface temperature(LST) data derived from the new generation of Chinese meteorology satellite(FY3A-VIRR) are assimilated into common land model(CoLM) for the first time. Six sites, Daman, Guantao, Arou, BJ, Miyun and Jiyuan, are selected for the data assimilation experiments and include different climatological conditions. The results are compared with those from a dataset generated by a multi-scale surface flux observation system that includes an automatic weather station(AWS), eddy covariance(EC) and large aperture scintillometer(LAS). The results indicate that the dual-pass data assimilation scheme is able to reduce model uncertainties and improve predictions of surface flux with the assimilation of FY3A-VIRR LST data.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41001108)
文摘This paper compartmentalizes regional land use of rural settlements in China by employing a hierarchical clustering method.The statistic data are sourced from the National Bureau of Statistics of China(NBSC) and the data of land use change from the Ministry of Land and Resources of China(MLRC).The population of rural settlement decreases from the southeast to the northwest of China and the density of rural settlement decreases from the east to the west of China.Land-use scale of rural settlement,the proportion of one-storey houses and the average household area decrease from the north to the south of China.The ratio of area of cultivated land to rural settlement is high in the northeast and southwest of China but low in the southeast of China.The land use regionalization of rural settlement can be divided into four regions,namely:the northern region of China,Qinghai-Tibet,Yunnan-Guizhou,and the middle and eastern region of China.The northern region of China and the middle and eastern region of China can be further divided into nine sub-regions:Xinjiang,Northeast China,Ningxia and Inner Mongolia,North China,the south of the Changjiang(Yantze) River and Sichuan Basin,Jiangsu-Shanghai,South China,the Loess Plateau,and Guangxi.In consideration of the significant regional differences,it is proposed that different policies should be implemented regarding the utilization and management of rural settlements.
基金supported by the National Natural Science Foundation of China(Grant Nos.41075062,91125016)the National Basic Research Program of China(Grants Nos.2010CB951001,2010CB428403)
文摘Seasonal and interannual changes in the Earth's gravity field are mainly due to mass exchange among the atmosphere,ocean,and continental water sources.The terrestrial water storage changes,detected as gravity changes by the Gravity Recovery and Climate Experiment(GRACE) satellites,are mainly caused by precipitation,evapotranspiration,river transportation and downward infiltration processes.In this study,a land data assimilation system LDAS-G was developed to assimilate the GRACE terrestrial water storage(TWS) data into the Community Land Model(CLM3.5) using the POD-based ensemble four-dimensional variational assimilation method PODEn4 DVar,disaggregating the GRACE large-scale terrestrial water storage changes vertically and in time,and placing constraints on the simulation of vertical hydrological variables to improve land surface hydrological simulations.The ideal experiments conducted at a single point and assimilation experiments carried out over China by the LDAS-G data assimilation system showed that the system developed in this study improved the simulation of land surface hydrological variables,indicating the potential of GRACE data assimilation in large-scale land surface hydrological research and applications.
基金funded by the National Natural Science Foundation of China(Grant Nos.91125002,41201330)the Fundamental Research Funds for the Central Universitiesthe Special Foundation for Free Exploration of State Laboratory of Remote Sensing Science(Grant No.13ZY-06)
文摘In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 of the dual-pass data assimilation scheme optimizes the model vegetation parameters at the weekly temporal scale, and Pass 2 optimizes the soil moisture at the daily temporal scale. Based on ensemble Kalman filter(EnKF), the land surface temperature(LST) data derived from the new generation of Chinese meteorology satellite(FY3A-VIRR) are assimilated into common land model(CoLM) for the first time. Six sites, Daman, Guantao, Arou, BJ, Miyun and Jiyuan, are selected for the data assimilation experiments and include different climatological conditions. The results are compared with those from a dataset generated by a multi-scale surface flux observation system that includes an automatic weather station(AWS), eddy covariance(EC) and large aperture scintillometer(LAS). The results indicate that the dual-pass data assimilation scheme is able to reduce model uncertainties and improve predictions of surface flux with the assimilation of FY3A-VIRR LST data.
