High resolution characterization of the soil organic carbon depth profile in a soil landscape affected by erosion

Abstract

A B S T R A C TThe identification of soil management strategies as well as the evaluation of their effectiveness requires detailed information on the spatial and temporal patterns of soil organic carbon storage. High-resolution SOC profile data are generally not available and traditional methods for collecting these are time consuming and costly. Recent studies use geo-statistical approaches to assess the three-dimensional patterns of SOC storage. However, there is still a large discrepancy between the continuous and high resolution mapping of the horizontal SOC variability on the one hand, and the coarse and discontinuous mapping of the vertical SOC profile on the other. In this study, we combine spectroscopic techniques with spatial modeling in a small, cultivated catchment in Germany and we evaluate the contribution of soil redistribution processes and topographical parameters to the observed spatial and vertical patterns. Using high-resolution data from soil cores, we evaluated the robustness of a third order polynomial function to model the vertical SOC profile. Using a crossvalidation, our results show that this approach results in a robust model (RSME = 0.24%) and performs better than the widely used exponential depth model (RMSE = 0.39%). In a next step, we evaluated the relationship between the parameters of the SOC depth model and co-variables including soil redistribution (inferred from 137 Cs data) and topographical indices using a multiple linear regression model. The performance was calculated by cross-validation and we found a low robustness of the models because of the low number of profiles (i.e. n = 19). A statistical evaluation of the co-variables highlighted two key factors influencing the SOC vertical distribution. Soil redistribution processes mainly influenced the surface SOC content (first centimeters) whereas the shape of the depth distribution was controlled by slope curvature alone. The mapping of polynomial parameters was validated using an external SOC profile dataset and showed a poor prediction of the surface content but a good prediction of the depth distribution once the surface SOC content is known (RMSE = 0.15-0.25%C). This suggests that estimating the vertical SOC profile from topsoil data by applying remote sensing data, in combination with our SOC profile model, is promising and can will result in an accurate mapping of 3D SOC patterns at a very high resolution.

Spatial variability analysis of soil quality parameters in a watershed of Sub-Himalayan Landscape - A case study

“…Outlier values at both depths were also identified using the inter quartile range (IQR) relationship. These results are in agreement with the findings of various researchers who reported higher SOC contents at the surface soil in hilly watershed (Wang et al, 2010;Wen et al, 2015), mountainous landscape , terraced rice fields (Li et al, 2015), erosion affected landscape (Jague et al, 2016), as well as an altitudinal gradient in the mountainous region (Parras-Alcántara et al, 2015). Similar variation of soil organic carbon with depth, has also been reported by Bera et al (2016), under corn production systems with addition of various organic amendments.…”

“…This can be attributed to the fact that presence of SOM/SOC has been found to be beneficial for nutrient retention/recycling, soil productivity, water holding capacity, carbon sequestration (Prescott et al, 2000;Munson and Carey, 2004;Seely et al, 2010;Six and Paustian, 2014). Studying soil organic carbon on a regional or watershed scale invites special attention these days as it is considered a key parameter, playing central roles in various environmental issues such as climate regulation, food and water security (Jague et al, 2016). Quantifying and estimating spatial distribution of SOC is vital for evaluating various soil functions and aids in understanding different soil carbon sequestration processes (Venteris et al, 2004).…”

Tracing the temporal evolution of soil redistribution rates in an agricultural landscape using 239+240Pu and 10Be

“…All models react in a similar range to an increased tracer content (i.e. In general, Pu-based, local erosion and deposition rates are in the range rates inferred from 137 Cs inventories at the same investigation area (Aldana Jague et al, 2016). The PDM approach and the depth function seem to show a slightly higher reactivity and are, thus, more sensitive to changes.…”

Reconstructing rates and patterns of colluvial soil redistribution in agrarian (hummocky) landscapes

“…Average annual temperature in the same period was 9.1°C, with 0.6 and 17.7°C as average winter and summer temperature (DWD Climate Data Center, ). The study site is part of the experimental CarboZALF‐D field (Sommer et al, ), which represents a landscape laboratory with a research focus on the feedbacks between erosion processes and carbon dynamics (Rieckh et al, ; Aldana Jague et al, ; Gerke et al, ; Hoffmann et al, ). Before the establishment of the CarboZALF‐D experimental site in 2009, an extensive coring – based on a GIS analysis of soil forming factors – took place in the surrounding Quillow catchment (165 km 2 ) to assure representativeness of the soil pattern at the regional scale.…”