Modeling Soil Organic Matter Distribution in a Northern Hardwood Forest and Tropical Watershed. Kris Johnson* Fred Scatena* and Yude Pan** *University of Pennsylvania **USDA Forest Service The Green Mountains Bisley Estimating Soil Organic Matter Content - The Post and Curtis and University of Pennsylvania Studies During the years B. W. Post and R. O. Curtis set out to establish forest plots for the purpose of modeling a site index useful for timber production. Plots were selected to minimize variations in vegetation type and parent material. All the plots are northern hardwood forest over well-drained acid-till soils. A total of 78 plots were measured for biomass and soil nutrients – including Soil Organic Matter (SOM) (Figure 2). To estimate SOM for the entire site, three pits were excavated and described. Each horizon was sampled and combined with the other horizons to make a composite sample. Next, soil depth was measured in the pits and also by using a soil auger at 17 points along a transect in each plot. SOC content was then calculated for the whole plot by estimating bulk density by regression on percent loss on ignition (%LOI). These plots were revisited during the years by the University of Pennsylvania team led by Arthur Johnson. The soils were sampled in a similar way by digging three pits, however this time one of the pits was also measured quantitatively. The most direct measure of SOM is the quantitative pit method that divides the soil profile into forest floor, 0-10 cm, cm, and 20+ layers. This method accurately measures bulk density by weighing the rocks from the pit as they are excavated (Huntington et al. 1988). A site average for SOM content was also calculated in the same way as the Post and Curtis study (by regression on %LOI). Therefore, three separate measurements of SOC are possible for each site, although the quantitative pit estimate is only representative of the soils in the precise location where it was excavated (Figure 1). SOM was multiplied by 0.45 to convert to SOC. Objectives Use the Green Mountain dataset to: 1)Estimate SOC content for a Northern Hardwood forest (Table 1). 2)Parameterize the Century model for a Northern Hardwood forest. Results - Century Model Performance The SOC content for the top 20 cm of soil was simulated by the Century model (Parton et al. 1988) and compared to the three measurements of SOC for 20 sites in the Green Mountains. The model was allowed to spin up for 2000 years. Precipitation, temperature, soil texture, bulk density, soil depth and drainage were varied from site to site. With a few exceptions, the Century model simulated SOC in the top 20 cm of mineral soil within the range of measurements made for each site (Figure 3). However, the measured SOC and simulated SOC were poorly correlated (adjusted R2 0.20) no matter which measurement was used as a benchmark. This reflects the challenge of simulating forest soils, especially in this region which is dominated by glacial till soils, tree-throws (i.e. “pit and mound topography”) and the formation of spodic horizons. Many sites that were over-estimated were more developed in their soil formation, having well-defined spodic horizons. In addition, a few very rocky sites (>50% rocks) were also overestimated by the model. Therefore, it seems that the model could not account for leaching of organic materials from the surface horizons as well as the fraction of rocks which makes the carbon content smaller. It is not as clear why some sites were underestimated by the model. One source of error is that soils with true A horizons and thick forest floors (10-20cm) are sometimes difficult to separate into mineral and organic horizons, which make the measurements uncertain. Also, the climate data, which is also modeled, may be inaccurate for some locations. Model errors for 20 sites ranged from about 20% to 30% (Table 2). Figure 1 (above). Variability chart for 20 selected sites used for Century Model parameterization. Figure 2 (right). The Green Mountain Physiographic Region. Green areas are Northern Hardwood (i.e. maple-beech-birch) stands. White dots are plot locations and bigger white dots correspond to higher SOC. The Bisley Experimental Watershed is a small tropical watershed covering 13 hectares (about 3 city blocks) located in the Caribbean National Forest, Puerto Rico (Figures 4). Three main vegetation types occur throughout the National Forest at different elevations. Bisley is located in the lower elevation “tabonuco forest” (Dacryodes excelsa). The tabonuco is a hurricane-resilient tree that has smaller leaves than the surrounding vegetation. During high winds, the leaves are quickly dropped in order to reduce wind resistance and minimize stress on the tree. Additionally, individual trees are often linked to each other in a network of root grafts so as to avoid being uprooted. The tree and its roots provide physical stability for the soil so that much of the SOM is saved from being transported during heavy rainfall. Further, the landscape is dissected with many small ephemeral stream channels sometimes separated by only a few meters. Therefore, more SOM preferentially accumulates in stable ridge areas where tabonuco vegetation dominates (Scatena and Lugo 1995). SOM was measured at gridpoints spaced every 40 m (85 total observations) and at three depths (0-10cm, 10-35cm, 35-60cm) with a 1-inch coring device (Figure 6). Objective To model ridge, slope and valley landforms for SOM estimation. Topographic variability, even within individual plots, is very high in this dissected landscape. To improve results, only those plots which were believed to have reliable SOM measurements were used. To assess this, two separate measurements of the same plots in the years 1988 and 1990 were compared. Only those plots with relatively low variability between measurements were used for analysis (about 60 of 85 total plots) (Figure 5). Topographic position index (TPI) was used to identify ridge areas from valley areas (Figure 6). This was the best way to account for spatial variation of SOC in the watershed, even after considering spatial autocorrelation models. TPI classes adequately describe the differences in SOC variability in the watershed (Figure 7). Figure 4 (above). The location of the Bisley Experimental Watershed. Figure 5 (below). Variability chart of SOC measurements at Bisley. Figure 7. SOM distribution among landform types as observed in the field (above left) and SOM distribution among landforms modeled by TPI (above right). Figure 6 (left). Ridge, slope and valleys identified by TPI. Darkest colors are ridges, medium are slopes and lightest colors are valleys. White dots represent SOM in the top 60 cm where bigger dots are higher in SOM. Acknowledgements Many thanks to Arthur Johnson and David Vann for providing the data and helping with interpretations. William Parton and Cindy Keogh at the Natural Resource Ecology Lab, Fort Collins, CO for training and helping with calibration of the model. Also, Richard Birdseye and the U.S. Forest Service for support and collaboration. Figure 3