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Soil organic carbon (SOC) storage and redistribution across the landscape (through erosion and deposition) are linked to soil physicochemical properties and can affect soil quality. However, the spatial and temporal variability of soil erosion and SOC remains uncertain. Whether soil redistribution leads to SOC gains or losses continues to be hotly debated. These considerations cannot be modelled using conventional soil carbon models and digital soil mapping. This paper presents a coupled-model combining RothPC-1 which considers soil carbon (C) down to 1 m and a soil redistribution model. The soil redistribution component is based on a cellular automata technique using the multi-direction flow (FD8) algorithm. With the optimized input values based on land use, we simulated SOC changes upon soil profiles to 1 m across the Lower Hunter Valley area (11,300 ha) in New South Wales, Australia from the 1970s to 2016. Results were compared to field observations and showed that erosion was predicted mostly in upslope areas and deposition in low-lying areas. We further simulated SOC trends from 2017 until ~2045 in the area under three climate scenarios and five land use projections. The variation in the magnitude and direction of SOC change with different projections shows that the main factors influencing SOC changes considering soil redistribution are climate change which controlled the trend of SOC stocks, followed by land use change. Neglecting soil erosion in carbon models could lead to an overestimation of SOC stocks. This paper provides a framework for incorporating soil redistribution into the SOC dynamics modelling and also postulates the thinking that soil erosion is not just a removal process by surface runoff. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuum set of compounds with different chemical composition, origin and susceptibilities to decomposition, that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia's soils. The three SOC fractions: mineral-associated organic carbon (MAOC), particulate organic carbon (POC) and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric log-ratio transformation (ilr) to account for the closed compositional nature of SOC fractions. The resulting, back-transformed ilr components were mapped across Australia. SOC fraction stocks for the 0-30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments and soil thickness. Mapping was done by quantile regression forest fitted with the ilr transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59% ±17.5%, whereas 28% ± 17.5% was PyOC and 13% ± 11.1% was POC. The allocation of TOC into the MAOC fractions increased with depth. SOC vulnerability (i.e., POC/[MAOC + PyOC]) was greater in areas with Mediterranean and temperate climate. TOC and the distribution among fractions were the most influential variables on SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates importance metric. We estimated the total SOC stocks (0-30 cm) to be 12.7 Pg MAOC, 2 Pg POC and 5.1 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as response to land use change, management, and climate change. [ABSTRACT FROM AUTHOR]

• Estimation of nutrient transport from both water and wind erosion. • Assessment of spatial and temporal variations of nutrient transport. • Impact analysis of land uses and management on nutrient transport. • Valuation of economic loss of soil nutrient transport from erosion. Nutrient transported from soils to water bodies not only threatens agricultural productivity and food security but also causes the degradation of water quality and the environment in many parts of the world. However, nutrient transport through soil erosion is often ignored in nutrient cycle studies; there is little understanding of how much nutrient is lost through water and wind erosion. In this study, we attempted to assess soil nutrient transport due to both water and wind erosion and its spatial and temporal variability across New South Wales (NSW), Australia. We estimated the mass fraction (%) of total nitrogen (N), total phosphorus (P) and soil organic carbon (SOC) in the eroded soil, and the total nutrient stock of the soil layer down to 200 cm using water and wind erosion models and digital soil mapping. The estimated average N, P and SOC stocks in NSW topsoils (0–5 cm) are 160, 43, and 2970 kg ha−1 respectively. There are great variations in the transport of nutrients by erosion in space and time, ranging from near zero in the Western region to 395 kg ha−1 yr−1 in the North Coast region. The average total nutrient transport rate is about 2.4 % of the surface soil (0–5 cm) total stock in NSW due to both water and wind erosion. The total cost of nutrient transport is estimated at 4.2 billion Australian dollars for the entire state of NSW and 0.2 billion dollars from the cropping lands per year. The areas with the highest nutrient transport rates are the North Coast and Hunter regions due to the relatively high water erosion and nutrient content of the soils. On average, water erosion contributes up to 98 % of the total nutrient transport across the state as a whole, but wind erosion can contribute up to 12 % of the total transport in Spring (i.e., September). This study is the first attempt to investigate nutrient transport from both water and wind erosion in Australia. The methodology and findings contribute to the knowledge of nutrient transport due to erosion in broad nutrient cycle studies. [ABSTRACT FROM AUTHOR]

• MLR and RF were used to estimate current SOC stocks in south-eastern Australia. • Rainfall and minimum temperature were major drivers in determining SOC stocks. • The multi-GCM ensemble means suggested an overall decrease in SOC stocks. • The largest mean decrease of SOC stocks occurs in the high-rainfall alpine regions. Soil organic carbon (SOC) plays a key role in the sequestration of carbon that could otherwise be warming the atmosphere. Climate change including increased temperature and changed rainfall will greatly impact the global SOC cycle. There are still significant gaps in our knowledge of the size of the global SOC pool and how future climate will affect SOC stocks and flows in many parts of the world, including Australia. In this study, we used SOC data in a Digital Soil Mapping framework to predict current and future SOC stocks across the state of New South Wales (NSW) in south-eastern Australia. In the first phase of the study we estimated the current SOC stock using multiple linear regression (MLR) and random forest (RF) modelling, and in the second phase we projected the change of SOC stocks in the near future (2050s) and far future (2090s) under two Shared Socio-economic Pathways (SSPs) scenarios based on 25 global climate models (GCMs) from the Coupled Model Inter-comparison Project Phase 6 (CMIP6). Our spatial modelling showed that estimated current SOC stocks in NSW decreased from east to west. Multi-GCM ensemble means suggested SOC stocks would decrease by 7.6–12.9% under SSP2-4.5 and 9.1–20.9% under SSP5-8.5 across NSW under future climate. The extent of change in SOC stocks varied spatially with the largest mean decrease of SOC stocks occurring in the North Coast and South East (alpine) regions of NSW. Our findings can support decision-making in land management and climate change mitigation strategies in NSW at the regional level. Furthermore, the modelling methods can be applied to other areas where edaphic and landscape properties, land use, and climate data are available. [ABSTRACT FROM AUTHOR]

• 8 field sampled soil organic carbon datasets were compared with topography. • Attributes were derived from a 5 m, 25 m, 30 m and 90 m digital elevation model. • A random forest model was applied across three spatial scales. • Spatial scale affected model performance and the variables selected to predict SOC. • Local scale soil organic carbon prediction needs high resolution data. Baseline estimations of soil organic carbon (SOC) have been made globally using SOC models, earth system models, and digital soil mapping techniques. Digital elevation models (DEMs) underpin these analyses to incorporate the effect of topography on SOC. Here, we test the effect of DEM resolution on the relationships between topography and SOC across catchment to hillslope scales. Samples were collected using a nested field sampling approach in a grazed catchment on Eastern Australia. Topographic attributes were derived from 5 m, 25 m, 30 m, and 90 m resolution DEMs and then used to predict SOC using a Random Forest model. This was trained on a catchment-wide dataset and tested using the repeat samples of this dataset and finer scale field data. SOC was able to be predicted using topography in the catchment-wide datasets (model R2 = 0.37–0.51), however not in the finer-scale data (model R2 = 0–0.21). Variable importance was calculated from the modelling process, with elevation (as a surrogate for climate) being the main driver at the catchment-scale. In the finer scale datasets, topographic variables linked to soil redistribution were more important. As a result, SOC estimation methods using coarse resolution DEM data and large-scale sampling may be limited in capturing the effect of topography, having implications for SOC management and modelling. [ABSTRACT FROM AUTHOR]