soil organic carbon

Spatial and seasonal dynamics of surface soil carbon in the Luquillo Experimental Forest, Puerto Rico

Wang, Hongqing; Cornell, Joseph D.; Hall, Charles A.S.; Marley, David P. 2002. Spatial and seasonal dynamics of surface soil carbon in the Luquillo Experimental Forest, Puerto Rico.. Ecological Modelling 147 105-122.

We developed a spatially-explicit version of the CENTURY soil model to characterize the storage and flux of soil organic carbon (SOC, 0–30 cm depth) in the Luquillo Experimental Forest (LEF), Puerto Rico as a function of climate, vegetation, and soils. The model was driven by monthly estimates of average air temperature, precipitation, and potential evapotranspiration (PET), which in turn were simulated as a function of elevation, slope, and aspect using a spatially-explicit and validated model (TOPOCLIM) of solar insolation/microclimate in mountainous areas. We simulated forest gross primary productivity (GPP) and distribution of above- and below-ground biomass production using a forest productivity model (TOPOPROD). Output from TOPOCLIM and TOPOPROD models was used to run the CENTURY soil model for 1200 months under current climate conditions and in response to potential global warming. We validated our version of CENTURY soil model using 69 soil samples taken throughout the LEF. Simulated SOC storage agrees reasonably well with the observed storage (R2=0.71). The simulated SOC storage in the top 30 cm within the LEF is highly variable, ranging from approximately 20–230 Mg/ha. The rates of decomposition were especially sensitive to changes in elevation. Carbon release rates due to decomposition were close to carbon assimilation rates and ranged from 0.6–0.96 Mg/ha per year at high elevations to 1.2–1.68 Mg/ha per year at lower elevations. Our simulations indicated that differences in elevation affect decomposition and SOC content primarily by changing microclimate. Finally, we found that a projected warming of 2.0 °C is likely to result in losses of SOC in the lower and higher elevation, but increased storage in the middle elevations in the LEF.

Spatial dependence and the relationship of soil organic carbon and soil moisture in the luquillo experimental forest, puerto rico

Wang H, Hall CAS, Cornell JD, Hall MHP.
2002. Spatial dependence and the relationship
of soil organic carbon and soil moisture in Luquillo experimental forest. Landsc.
Ecol. 17:671–84

We used geo-spatial statistical techniques to examine the spatial variation and relationship of soil organic carbon (SOC) and soil moisture (SM) in the Luquillo Experimental Forest (LEF), Puerto Rico, in order to test the hypothesis that mountainous terrain introduces spatial autocorrelation and crosscorrelation in ecosystem and soil properties. Soil samples (n = 100) were collected from the LEF in the summer of 1998 and analyzed for SOC, SM, and bulk density (BD). A global positioning system was used to georeference the location of each sampling site. At each site, elevation, slope and aspect were recorded. We calculated the isotropic and anisotropic semivariograms of soil and topographic properties, as well as the cross-variograms between SOC and SM, and between SOC and elevation. Then we used four models (random, linear, spherical and wave/hole) to test the semivariances of SOC, SM, BD, elevation, slope and aspect for spatial dependence. Our results indicate that all the studied properties except slope angle exhibit spatial dependence within the scale of sampling (200 – 1000 m sampling interval). The spatially structured variance (the variance due to the location of sampling sites) accounted for a large proportion of the sample variance for elevation (99%), BD (90%), SOC (68%), aspect (56%) and SM (44%). The ranges of spatial dependence (the distances within which parameters are spatially dependent) for aspect, SOC, elevation, SM, and BD were 9810 m, 3070 m, 1120 m, 930 m and 430 m, respectively. Cross correlograms indicate that SOC varies closely with elevation and SM depending on the distances between samples. The correlation can shift from positive to negative as the separation distance increases. Larger ranges of spatial dependence of SOC, aspect and elevation indicate that the distribution of SOC in the LEF is determined by a combination of biotic (e.g., litterfall) and abiotic factors (e.g., microclimate and topographic features) related to elevation and aspect. This demonstrates the importance of both elevation and topographic gradients in controlling climate, vegetation distribution and soil properties as well as the associated biogeochemical processes in the LEF.

Predicting Soil Organic Carbon Stock Using Profile Depth Distribution Functions and Ordinary Kriging

Mishra, U., Lal, R., Slater, B., Calhoun, F., Liu, D. S., and
Van Meirvenne, M.: Predicting Soil Organic Carbon Stock Using
Profile Depth Distribution Functions and Ordinary Kriging,
Soil Sci. Soc. Am. J., 73, 614–621, 2009.

The objective of this study was to predict and map SOC stocks at different depth intervals within the upper 1-m depth using profi le depth distribution functions and ordinary kriging. These approaches were tested for the state of Indiana as a case study. A total of 464 pedons representing 204 soil series was obtained from the National Soil Survey Center database. Another 48 soil profi le samples were collected to better represent the heterogeneity of the environmental variables. Two methods were used to model the depth distribution of the SOC stocks using negative exponential profi le depth functions. In Procedure A, the functions to describe the depth distribution of volumetric C content for each soil profi le were fi tted using nonlinear least squares. In Procedure B, the exponential functions were fi tted to describe the depth distribution of the cumulative SOC stocks. The parameters of the functions were interpolated for the entire study area using ordinary kriging on 81% of the data points (n = 414). The integral of the exponential function up to the desired depth was used to predict SOC stocks within the 0- to 1-, 0- to 0.5-, and 0.5- to 1-m depth intervals. These estimates were validated using the remaining 19% (n = 98) of the data. Procedure B showed a higher prediction accuracy for all depths, with higher r and lower RMSE values. The highest prediction accuracy (r = 0.75, RMSE = 2.89 kg m−2) was obtained for SOC stocks in the 0- to 0.5-m depth interval. Using Procedure B, SOC stocks within the top 1 m of Indiana soils were estimated to be 0.90 Pg C.
Syndicate content