Using GIS Spatial Distribution to Predict Soil Organic Carbon in Subtropical China

Spatial distribution of organic carbon in soils is difficult to estimatebecause of inherent spatial variability and insufficient data. A soil-landscape model for a region,based on 151 samples for parent material and topographic factors, was established using a GISspatial analysis technique and a digital elevation model (DEM) to reveal spatial distributioncharacteristics of soil organic carbon (SOC). Correlations between organic carbon and topographicfactors were analyzed and a regression model was established to predict SOC content. Results forsurface soils (0-20 cm) showed that the average SOC content was 12.8 g kg^(-1), with the SOC contentbetween 6 and 12 g kg^(-1) occupying the largest area and SOC over 24 g kg^(-1) the smallest. Also,soils derived from phyllite were the highest in the SOC content and area, while soils developed onpurple shale the lowest. Although parent material, elevation, and slope exposure were allsignificant topographic variables (P < 0.01), slope exposure had the highest correlation to SOCcontent (r = 0.66). Using a multiple regression model (R^2 = 0.611) and DEM (with a 30 m X 30 mgrid), spatial distribution of SOC could be forecasted.

Soil Organic Carbon Density in Hebei Province, China: Estimates and Uncertainty

In order to improve the precision of soil organic carbon (SOC) estimates, the sources of uncertainty in soil organic carbon density (SOCD) estimates and SOC stocks were examined using 363 soil profiles in Hebei Province, China, with three methods: the soil profile statistics (SPS), GIS-based soil type (GST), and kriging interpolation (KI). The GST method, utilizing both pedological professional knowledge and GIS technology, was considered the most accurate method of the three estimations, with SOCD estimates for SPS 10% lower and KI 10% higher. The SOCD range for GST was 84% wider than KI as KI smoothing effect narrowed the SOCD range. Nevertheless, the coefficient of variation for SOCD with KI (41.7%) was less than GST and SPS. Comparing SOCD’s lower estimates for SPS versus GST, the major sources of uncertainty were the conflicting area of proportional relations. Meanwhile, the fewer number of soil profiles and the necessity of using the smoothing effect with KI were its sources of uncertainty. Moreover, for local detailed variations of SOCD, GST was more advantageous in reflecting the distribution pattern than KI.

Functional Rehabilitation of the "Soil Reservoir"in Degraded Soils to Control Floods in the Yangtze River Watershed

The reasons for the Yangtze River flood calamity in 1998 are briefly introduced. The authors believe that using a 'soil reservoir' concept is an important means to help control flooding of the Yangtze River.A 'soil reservoir' has a large potential storage capacity and its water can be rapidly 'discharged' into the underground water in a timely fashion. The eroded, infertile soils of the Yangtze River Watershed are currently an obstacle to efficient operation of the 'soil reservoir'. The storage capacity of this 'soil reservoir'has been severely hampered due to intensive soil erosion and the formation of soil crusts. Therefore, possible measures to control floods in the Yangtze River Watershed include: rehabilitating the vegetation to preserve soil and water on the eroded infertile soils, enhancing infiltration of the different soil types, and utilizing the large 'soil reservoir' of the upper reaches of the Yangtze River.