In China,numerous cities are expanding into sloping land,yet the quantitative distribution patterns of urban built-up land density along the slope gradient remain unclear,limiting the understanding of sloping land urb...In China,numerous cities are expanding into sloping land,yet the quantitative distribution patterns of urban built-up land density along the slope gradient remain unclear,limiting the understanding of sloping land urbanization.In this paper,a simple negative exponential function was presented to verify its applicability in 19 typical sloping urban areas in China.The function fits well for all case urban areas(R^(2)≥0.951,p<0.001).The parameters of this function clearly describe two fundamental attributes:initial value a and decline rate b.Between 2000 and 2020,a tends to increase,while b tends to decrease in all urban areas,confirming the hypothesis of mutual promotion between flatland densification and sloping land expansion.Multiple regression analysis indicates that the built-up land density and the ruggedness of background land can explain 70.7%of a,while the average slope ratio of built-up land to background land,the built-up land density and the built-up land area can explain 82.1%of b.This work provides a quantitative investigative tool for distribution of urban built-up land density along slope gradient,aiding in the study of the globally increasing phenomenon of sloping land urbanization from a new perspective.展开更多
Deep soil organic carbon(SOC)plays an important role in carbon cycling.Precisely predicting deep SOC at the regional scale is crucial for the accurate assessment of carbon sequestration potential in soils but has been...Deep soil organic carbon(SOC)plays an important role in carbon cycling.Precisely predicting deep SOC at the regional scale is crucial for the accurate assessment of carbon sequestration potential in soils but has been challenging for a century.Herein,we developed a depth distribution function-based empirical approach to predict SOC in deep soils at the regional scale.We validated this approach with a dataset from four regions of the world and examined the application of this approach in China’s Loess Plateau.We found that among the reported depth distribution functions describing vertical patterns of SOC,the negative exponential function performed best in fitting SOC along the soil profile in various regions.Moreover,the parameters(i.e.,Ceand k)of the negative exponential function were linearly correlated to surface SOC(0–20 cm)and the changing rates of SOC within the topsoil(0–40 cm).Based on the above relationships,the empirical equations for predicting the negative exponential parameters are established.The validation results from site-specific and regional dataset showed that combining the negative exponential function and such empirical equations can precisely predict SOC concentration in soils down to 500 cm depth.Our study provides a simple,rapid and accurate method for predicting deep soil SOC at the regional scale,which could simplify the assessment of deep soil SOC in various regions.展开更多
基金supported by the project of the National Natural Science Foundation of China entitled“Distribution and change characteristics of construction land on slope gradient in mountainous cities of southern China”(No.41961039).
文摘In China,numerous cities are expanding into sloping land,yet the quantitative distribution patterns of urban built-up land density along the slope gradient remain unclear,limiting the understanding of sloping land urbanization.In this paper,a simple negative exponential function was presented to verify its applicability in 19 typical sloping urban areas in China.The function fits well for all case urban areas(R^(2)≥0.951,p<0.001).The parameters of this function clearly describe two fundamental attributes:initial value a and decline rate b.Between 2000 and 2020,a tends to increase,while b tends to decrease in all urban areas,confirming the hypothesis of mutual promotion between flatland densification and sloping land expansion.Multiple regression analysis indicates that the built-up land density and the ruggedness of background land can explain 70.7%of a,while the average slope ratio of built-up land to background land,the built-up land density and the built-up land area can explain 82.1%of b.This work provides a quantitative investigative tool for distribution of urban built-up land density along slope gradient,aiding in the study of the globally increasing phenomenon of sloping land urbanization from a new perspective.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDA23070202 and XDB40020000)the National Key Research and Development Program(Grant No.2022YFF1302804)+1 种基金the National Natural Science Foundation of China(Grant Nos.41977068 and 41622105)the Program from Chinese Academy of Sciences(Grant No.QYZDB-SSWDQC039)。
文摘Deep soil organic carbon(SOC)plays an important role in carbon cycling.Precisely predicting deep SOC at the regional scale is crucial for the accurate assessment of carbon sequestration potential in soils but has been challenging for a century.Herein,we developed a depth distribution function-based empirical approach to predict SOC in deep soils at the regional scale.We validated this approach with a dataset from four regions of the world and examined the application of this approach in China’s Loess Plateau.We found that among the reported depth distribution functions describing vertical patterns of SOC,the negative exponential function performed best in fitting SOC along the soil profile in various regions.Moreover,the parameters(i.e.,Ceand k)of the negative exponential function were linearly correlated to surface SOC(0–20 cm)and the changing rates of SOC within the topsoil(0–40 cm).Based on the above relationships,the empirical equations for predicting the negative exponential parameters are established.The validation results from site-specific and regional dataset showed that combining the negative exponential function and such empirical equations can precisely predict SOC concentration in soils down to 500 cm depth.Our study provides a simple,rapid and accurate method for predicting deep soil SOC at the regional scale,which could simplify the assessment of deep soil SOC in various regions.
