An official website of the United States government
Soil hydraulic properties (SHP) are among the indicators of the diversity and health of an ecosystem and are commonly measured by two criteria: infiltration and water retention capacity. This may be seen as an “Ecological Alteration,” resulting from the sum biological and nonbiological processes which modify the structure of the soil, including bioturbation and the accumulation of organic matter. These changes in soil structure drive the changes in SHP. Central Chile has seen an abrupt and extensive land use/land cover transition from several hundred years of wheat cultivation (annually tilled) to short rotation (~25-30 yr) silviculture. This allows for neighboring assessment of soil impacts of transitioning from cultivated to uncultivated production as a function of time. Further, the region’s climate geography (a NorthSouth primary axis) allows us to view the soil health impacts of this change in planting along a precipitation gradient (850 – 1700 mm/yr) to help tease-out the impact of climate on temporal dynamics of soil properties. We measured infiltration in five recently transitioned first rotation locations along this precipitation gradient. Sampling plots were established for continuous wheat, early-, mid-, and late-stage pine plantations, and Chilean Native Forest. We sampled in both the dry summer months and again in the wet winter months. In the dry sampling period, we found transitions from wheat to silviculture saw an initial decrease in infiltration; however, over time (~30 years) infiltration in the plantations approached that of the Native Forest (increasing approximately by an order of magnitude in 30 years). In the wet sampling period, the results were more inconclusive. Some plots did not show an increase in infiltration capacity while others showed a gradual increase over the same 30-year period.
more »
« less
Soil Carbon Mapping of the Contiguous US Using VNIR Spectra Within A Heterogeneous Spatial Model
Abstract The Rapid Carbon Assessment, conducted by the US Department of Agriculture, was implemented in order to obtain a representative sample of soil organic carbon across the contiguous US. In conjunction with a statistical model, the dataset allows for mapping of soil carbon prediction across the US; however, there are two primary challenges to such an effort. First, there exists a large degree of heterogeneity in the data, whereby both the first and second moments of the data generating process seem to vary both spatially and for different land-use categories. Second, the majority of the sampled locations do not actually have laboratory-measured values for soil organic carbon. Rather, visible and near-infrared (VNIR) spectra were measured at most locations, which act as a proxy to help predict carbon content. Thus, we develop a heterogeneous model to analyze this data that allows both the mean and the variance to vary as a function of space as well as land-use category, while incorporating VNIR spectra as covariates. After a cross-validation study that establishes the effectiveness of the model, we construct a complete map of soil organic carbon for the contiguous US along with uncertainty quantification.
more »
« less
- PAR ID:
- 10573220
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of Agricultural, Biological and Environmental Statistics
- Volume:
- 30
- Issue:
- 2
- ISSN:
- 1085-7117
- Format(s):
- Medium: X Size: p. 517-539
- Size(s):
- p. 517-539
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract. Peatlands have often been neglected in Earth system models (ESMs).Where they are included, they are usually represented via a separate, prescribed grid cell fraction that is given the physical characteristics of a peat (highly organic) soil. However, in reality soils vary on a spectrum between purely mineral soil (no organic material) and purely organicsoil, typically with an organic layer of variable thickness overlying mineral soil below. They are also dynamic, with organic layer thickness and its properties changing over time. Neither the spectrumof soil types nor their dynamic nature can be captured by current ESMs. Here we present a new version of an ESM land surface scheme (Joint UK Land Environment Simulator, JULES) where soil organic matter accumulation – and thus peatland formation, degradation and stability – is integratedin the vertically resolved soil carbon scheme. We also introduce the capacity to track soil carbon age as a function of depth in JULES and compare this to measured peat age–depth profiles. The new scheme is tested and evaluated at northern and temperate sites. This scheme simulates dynamic feedbacks between the soil organic material and its thermal and hydraulic characteristics. We show that draining the peatlands can lead to significant carbon loss, soil compaction and changes in peat properties. However, negative feedbacks can lead to the potential for peatlands to rewet themselves following drainage.These ecohydrological feedbacks can also lead to peatlands maintaining themselves in climates where peat formation would not otherwise initiate in the model, i.e. displaying some degree of resilience. The new model produces similar results to the original model for mineral soils and realistic profiles of soil organic carbon for peatlands.We evaluate the model against typical peat profiles based on 216 northern and temperate sites from a global dataset of peat cores.The root-mean-squared error (RMSE) in the soil carbon profile is reduced by 35 %–80 % in the best-performing JULES-Peat simulationscompared with the standard JULES configuration. The RMSE in these JULES-Peat simulations is 7.7–16.7 kg C m−3 depending on climate zone, which is considerably smaller than the soil carbon itself (around 30–60 kg C m−3). The RMSE at mineral soil sites is also reducedin JULES-Peat compared with the original JULES configuration (reduced by ∼ 30 %–50 %). Thus, JULES-Peat can be used as a complete scheme that simulates both organic and mineral soils. It does not requireany additional input data and introduces minimal additional variables to the model. This provides a new approach for improving the simulation of organic and peatland soils andassociated carbon-cycle feedbacks in ESMs.more » « less
-
Abstract Soil organic matter decomposition and its interactions with climate depend on whether the organic matter is associated with soil minerals. However, data limitations have hindered global-scale analyses of mineral-associated and particulate soil organic carbon pools and their benchmarking in Earth system models used to estimate carbon cycle–climate feedbacks. Here we analyse observationally derived global estimates of soil carbon pools to quantify their relative proportions and compute their climatological temperature sensitivities as the decline in carbon with increasing temperature. We find that the climatological temperature sensitivity of particulate carbon is on average 28% higher than that of mineral-associated carbon, and up to 53% higher in cool climates. Moreover, the distribution of carbon between these underlying soil carbon pools drives the emergent climatological temperature sensitivity of bulk soil carbon stocks. However, global models vary widely in their predictions of soil carbon pool distributions. We show that the global proportion of model pools that are conceptually similar to mineral-protected carbon ranges from 16 to 85% across Earth system models from the Coupled Model Intercomparison Project Phase 6 and offline land models, with implications for bulk soil carbon ages and ecosystem responsiveness. To improve projections of carbon cycle–climate feedbacks, it is imperative to assess underlying soil carbon pools to accurately predict the distribution and vulnerability of soil carbon.more » « less
-
Soil organic carbon (SOC) represents the largest terrestrial carbon pool. Effectively monitoring SOC at high spatial resolution is crucial for estimating carbon budgets at the ecosystem scale and informing climate change mitigation efforts at the regional scale. Traditional soil sampling methods, however, are laborious and expensive. Remote sensing platforms can be used to survey large landscapes to meet the need for rapid and cost-effective approaches for quantifying SOC at landscape to regional scales, if relationships between remotely sensed variables and SOC can be established. We developed a workflow to analyze and predict SOC content based on National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) remote sensing data. First, we benchmarked related tools and developed reproducible workflows using NEON remote sensing datasets. Hyperspectral data were extracted from the locations where NEON soil data exist. Additional variables from the LiDAR data and key metadata (climate and land cover) were extracted for those locations. Random Forest and Partial Least Squares Regression techniques were then used to create models for fine-scale SOC prediction. Cross-validation was embedded in the model creation step. The most important covariates were selected through recursive feature elimination, stepwise selection, and expert judgment. Preliminary results indicate that machine learning models can re-produce SOC measurements in testing datasets. Key predictors include topographic variables, vegetation indices, and specific wavelength bands in hyperspectral images. We are further validating our algorithms using SOC data from ISCN (International Soil Carbon Network) and SoDaH (SOils DAta Harmonization database) that are co-located with NEON sites. We are creating high-resolution SOC maps for 0-30 cm depth at NEON sites and testing our algorithms for different land use types. Our work paves the way for a broader assessment of SOC stocks using remote sensing observations, and our high-resolution SOC maps will potentially help quantify carbon budgets across heterogeneous landscapes.more » « less
-
Abstract Deep soils represent a dynamic interface between surface soils and saprolite or bedrock, influencing water flow, solute and gas exchange, and mineral and organic matter transformations from local to global scales. Root architecture reflects land cover and soil heterogeneity, enabling vegetation access to resources that vary temporally and spatially while shaping soil structure and formation. However, how land use can influence roots and soil structure relatively deep in the subsurface (>30 cm) remains poorly understood. We investigate how cropland‐related land use and subsequent vegetation recovery alter rooting dynamics and soil structure in deeper horizons. Using a large‐scale data set representing multiple land uses as a means of varying root abundance across four soil orders, we demonstrate that B horizon root loss and regeneration are linked to changes in multiple soil structural attributes deep within soil profiles. Our findings further suggest that the degree of soil development modulates the extent of structural transformations, with less‐developed soils showing greater susceptibility to root‐associated structural shifts. The greatest change in structural development and distinctness was observed in Inceptisols, while Ultisols exhibited the least change. Such soil structural changes affect water flowpaths, carbon retention, and nutrient transport throughout the subsurface. This work thus underscores the need for Earth system models to capture dynamic soil structural attributes that respond to land‐use change. We suggest that changes in deep‐rooting abundance, such as those accelerating in the Anthropocene, may be an important agent of subsurface structural change with meaningful implications for contemporary and future ecosystem feedbacks to climate.more » « less
