Land cover is one of the most basic input elements of land surface and climate models. Currently, the direct and indirect effects of land cover data on climate and climate change are receiving increasing attentions. I...Land cover is one of the most basic input elements of land surface and climate models. Currently, the direct and indirect effects of land cover data on climate and climate change are receiving increasing attentions. In this study, a high resolution(30 m) global land cover dataset(Globe Land30) produced by Chinese scientists was, for the first time, used in the Beijing Climate Center Climate System Model(BCC_CSM) to assess the influences of land cover dataset on land surface and climate simulations. A two-step strategy was designed to use the Globe Land30 data in the model. First, the Globe Land30 data were merged with other satellite remote sensing and climate datasets to regenerate plant functional type(PFT) data fitted for the BCC_CSM. Second, the up-scaling based on an area-weighted approach was used to aggregate the fine-resolution Globe Land30 land cover type and area percentage with the coarser model grid resolutions globally. The Globe Land30-based and the BCC_CSM-based land cover data had generally consistent spatial distribution features, but there were some differences between them. The simulation results of the different land cover type dataset change experiments showed that effects of the new PFT data were larger than those of the new glaciers and water bodies(lakes and wetlands). The maximum value was attained when dataset of all land cover types were changed. The positive bias of precipitation in the mid-high latitude of the northern hemisphere and the negative bias in the Amazon, as well as the negative bias of air temperature in part of the southern hemisphere, were reduced when the Globe Land30-based data were used in the BCC_CSM atmosphere model. The results suggest that the Globe Land30 data are suitable for use in the BCC_CSM component models and can improve the performance of the land and atmosphere simulations.展开更多
The use of landscape covariates to variability of soil properties in similar estimate soil properties is not suitable topographic and vegetation conditions. for the areas of low relief due to the high A new method wa...The use of landscape covariates to variability of soil properties in similar estimate soil properties is not suitable topographic and vegetation conditions. for the areas of low relief due to the high A new method was implemented to map regional soil texture （in terms of sand, silt and clay contents） by hypothesizing that the change in the land surface diurnal temperature difference （DTD） is related to soil texture in case of a relatively homogeneous rainfall input. To examine this hypothesis, the DTDs from moderate resolution imagine spectroradiometer （MODIS） during a selected time period, i.e., after a heavy rainfall between autumn harvest and autumn sowing, were classified using fuzzy-c-means （FCM） clustering. Six classes were generated, and for each class, the sand （〉 0.05 mm）, silt （0.002-0.05 mm） and clay （〈 0.002 mm） contents at the location of maximum membership value were considered as the typical values of that class. A weighted average model was then used to digitally map soil texture. The results showed that the predicted map quite accurately reflected the regional soil variation. A validation dataset produced estimates of error for the predicted maps of sand, silt and clay contents at root mean of squared error values of 8.4%, 7.8% and 2.3%, respectively, which is satisfactory in a practical context. This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.展开更多
基金supported by the National High Technology Research and Development Program of China (Grant No. 2009AA122005)the Public Welfare Meteorology Research Project of China (Grant Nos. 201506023, 201306048)the National Natural Science Foundation of China (Grant Nos. 41275076, 40905046)
文摘Land cover is one of the most basic input elements of land surface and climate models. Currently, the direct and indirect effects of land cover data on climate and climate change are receiving increasing attentions. In this study, a high resolution(30 m) global land cover dataset(Globe Land30) produced by Chinese scientists was, for the first time, used in the Beijing Climate Center Climate System Model(BCC_CSM) to assess the influences of land cover dataset on land surface and climate simulations. A two-step strategy was designed to use the Globe Land30 data in the model. First, the Globe Land30 data were merged with other satellite remote sensing and climate datasets to regenerate plant functional type(PFT) data fitted for the BCC_CSM. Second, the up-scaling based on an area-weighted approach was used to aggregate the fine-resolution Globe Land30 land cover type and area percentage with the coarser model grid resolutions globally. The Globe Land30-based and the BCC_CSM-based land cover data had generally consistent spatial distribution features, but there were some differences between them. The simulation results of the different land cover type dataset change experiments showed that effects of the new PFT data were larger than those of the new glaciers and water bodies(lakes and wetlands). The maximum value was attained when dataset of all land cover types were changed. The positive bias of precipitation in the mid-high latitude of the northern hemisphere and the negative bias in the Amazon, as well as the negative bias of air temperature in part of the southern hemisphere, were reduced when the Globe Land30-based data were used in the BCC_CSM atmosphere model. The results suggest that the Globe Land30 data are suitable for use in the BCC_CSM component models and can improve the performance of the land and atmosphere simulations.
基金Supported by the Basic Research Program of Jiangsu Province,China (No. BK2008058)the Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX2-YW-409)
文摘The use of landscape covariates to variability of soil properties in similar estimate soil properties is not suitable topographic and vegetation conditions. for the areas of low relief due to the high A new method was implemented to map regional soil texture （in terms of sand, silt and clay contents） by hypothesizing that the change in the land surface diurnal temperature difference （DTD） is related to soil texture in case of a relatively homogeneous rainfall input. To examine this hypothesis, the DTDs from moderate resolution imagine spectroradiometer （MODIS） during a selected time period, i.e., after a heavy rainfall between autumn harvest and autumn sowing, were classified using fuzzy-c-means （FCM） clustering. Six classes were generated, and for each class, the sand （〉 0.05 mm）, silt （0.002-0.05 mm） and clay （〈 0.002 mm） contents at the location of maximum membership value were considered as the typical values of that class. A weighted average model was then used to digitally map soil texture. The results showed that the predicted map quite accurately reflected the regional soil variation. A validation dataset produced estimates of error for the predicted maps of sand, silt and clay contents at root mean of squared error values of 8.4%, 7.8% and 2.3%, respectively, which is satisfactory in a practical context. This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.
