Redirigiendo al acceso original de articulo en 22 segundos...
ARTÍCULO
TITULO

Stratified Data Reconstruction and Spatial Pattern Analyses of Soil Bulk Density in the Northern Grasslands of China

Yuxin Qiao    
Huazhong Zhu    
Huaping Zhong and Yuzhe Li    

Resumen

The spatial pattern of soil bulk density in the grasslands of northern China largely remains undefined, which raised uncertainty in understanding and modeling various soil processes in large spatial scale. Based on the measured data of soil bulk density available from soil survey reports from the grasslands of northern China, we constructed a soil Stratified Pedotransfer function (SPTF) from the surface soil bulk density. Accordingly, the stratified bulk density data of soil vertical profile was reconstructed, and the estimation of soil bulk density data in horizontal space was performed. The results demonstrated that the soil bulk density of the grasslands of northern China was typically high in the central and northwestern regions and low in the eastern and mountainous regions. Mean soil bulk density of the grasslands was 1.52 g·cm-3. According to geographical divisions, the highest soil bulk density was observed in the Tarim basin, with mean soil bulk density of 1.91 g·cm-3. Conversely, the lowest soil bulk density was observed in the Tianshan Mountain area, with mean soil bulk density of 1.01 g·cm-3. Based on data obtained on various types of grasslands, the soil bulk density of alpine meadow was the lowest, with a mean soil bulk density of 0.75 g·cm-3, whereas that of temperate desert was the highest, with mean soil bulk density of 1.80 g·cm-3. Mean prediction error, root mean square deviation, relative error, and multiple correlation coefficient of soil bulk density data pertaining to surface layer (0?10 cm) in the grasslands of northern China were 0.018, 0.223, 16.2%, and 0.5386, respectively. The approach of employing multiple data sources via soil transfer function improved the estimation accuracy of soil bulk density from stratified soils data at the large scale. Our study would promote the accurate assessment of grassland carbon storage and fine land characteristics mapping.

 Artículos similares

       
 
Stefano Guarino, Enrico Mastrostefano, Massimo Bernaschi, Alessandro Celestini, Marco Cianfriglia, Davide Torre and Lena Rebecca Zastrow    
The definition of suitable generative models for synthetic yet realistic social networks is a widely studied problem in the literature. By not being tied to any real data, random graph models cannot capture all the subtleties of real networks and are ina... ver más
Revista: Future Internet

 
Fuan Tsai, Jhe-Syuan Lai, Kieu Anh Nguyen and Walter Chen    
The universal soil loss equation (USLE) is a widely used empirical model for estimating soil loss. Among the USLE model factors, the cover management factor (C-factor) is a critical factor that substantially impacts the estimation result. Assigning C-fac... ver más

 
Kieu Anh Nguyen, Walter Chen, Bor-Shiun Lin and Uma Seeboonruang    
Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the... ver más

 
Yeboah Gyasi-Agyei    
Rain gauges continue to be sources of rainfall data despite progress made in precipitation measurements using radar and satellite technology. There has been some work done on assessing the optimum rain gauge network density required for hydrological mode... ver más
Revista: Water

 
Roxanne Ahmed, Terry Prowse, Yonas Dibike, Barrie Bonsal and Hayley O?Neil    
Runoff from Arctic rivers constitutes a major freshwater influx to the Arctic Ocean. In these nival-dominated river systems, the majority of annual discharge is released during the spring snowmelt period. The circulation regime of the salinity-stratified... ver más
Revista: Water