Soil erosion modelling as a tool for future land management and conservation planning

Maintaining future agricultural productivity and ensuring soil security is of global concern and requires evidence-based management practices. Moreover, understanding where and when land is at risk of erosion is a fundamental step to combatting future soil loss and reach Land Degradation Neutrality (LDN). However, this is a difficult task because of the high spatial and temporal variability of the controlling factors involved. Therefore, tools investigating the impact and frequency of extreme erosive events are crucial for land managers and policymakers to apply corrective measures for better erosion management in the future. While the utility of using wind and water erosion models for management is well established, there is a paucity of work on the impact of climate change and extreme environmental conditions (e.g. wildfires) on soil erosion by wind and water simultaneously. Both erosion types are controlled by different environmental variable that vary highly in space and time. Therefore, the overarching aim of this study was to develop a joint wind-water erosion modelling method and demonstrate the utility of this approach to identify (1) the spatio-temporal variability of extreme erosion events in the South Australian agricultural zone (Australia) and (2) assess the likely increase of this variability in the face of climate change and the recurrence of wildfires. To fulfil the aim of the research project, we adapted two state-of-the-art wind and water (hillslope) erosion models to integrate modern high-resolution datasets for spatial and temporal analysis of erosion. The adaptation of these models to local conditions and the use of high-resolution datasets was essential to ensure reliable erosion assessment. First, we applied these models separately in the Eyre Peninsula and Mid-North agricultural regions. We evaluated the spatio-temporal variability of extreme erosion events between 2001 and 2017 and described the complex interactions between each erosional process and their influencing factors (e.g. soil types, climate conditions, and vegetation cover). Hillslope erosion was very low for most of the Eyre Peninsula; however, a large proportion of the central Mid-North region frequently recorded severe erosion (> 0.022 t ha-1) two to three months per year, for most of the years in the time-series. The most severe erosion events were primarily driven by topography, low ground cover ( 500 MJ mm ha-1 h-1). Average annual wind erosion was very low and comparable in the two regions. Nonetheless, most of the west coast of the Eyre Peninsula frequently registered severe erosion (> 0.000945 t ha-1 or 0.945 kg ha-1) two to three months per year, for most of the years. The most severe erosion events were largely driven by the soil type (sandy soils), recurring low ground cover ( 68 km h-1). We identified that erosion severity was low for the vast majority of the study area, while 4% and 9% of the total area suffered severe erosion by water and wind respectively, demonstrating an extreme spatial and temporal skewness of soil erosion processes. Then we combined the modelling outputs from the wind and water erosion models and tested the models’ response to major wildfire events. This research demonstrated how erosion modelling could be used to predict the impact of severe wildfire events on soil erosion. The two models satisfactorily captured the spatial and temporal variability of post-fire erosion. However, a very small fraction of the region (0.7%) was severely impacted by both wind and water erosion. We observed that soil erosion increased immediately after the wildfires or within the first six months for the ten fire-affected regions. For three of the wildfire events, the models showed an increase in wind and water erosion in consecutive months or at the same time. These results highlighted the importance to consider wind and water erosion simultaneously for post-fire erosion assessment in dryland agricultural regions. Finally, we had the rare opportunity to assess the impact of a catastrophic wildfire event on wind erosion in an agricultural landscape by examining the influence of unburnt stubble patches on adjacent burnt or bare plots using a spatio-temporal sampling design. The field study allowed a quantitative assessment of spatial and temporal patterns of wind erosion and sediment transport after a catastrophic wildfire event. It showed very high levels of spatial variability of erosion processes between burnt and bare patches and demonstrated how measuring field-scale sediment transport could complement fine-scale experimental studies to assess environmental processes at the field scale. This research highlights the utility of erosion models to inform corrective measures for future land management. We have implemented tools that allow a realistic assessment of the influence of climate change and extreme environmental conditions scenarios on soil erosion for a wide range of land cover over large regions. Here, the models enabled the identification of the relative post-fire wind or water erosion risk in dryland agricultural landscapes, making them particularly useful for land management under future uncertainty. Spatial patterns compared well with previous modelling approaches and underpinned the benefit of erosion models to assess spatial differences in erosion risk and evaluate corrective measures at the regional scale. However, modelled soil erosion magnitudes strongly depend on how the influence of soils is implemented in the models, making it difficult to set absolute quantitative soil loss targets for land management. The thesis has provided a proof of concept of the approach for South Australia. However, all input data can be freely sourced Australia-wide and similar dataset are available globally.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 202

Remote sensing applications for soil erosion assessment

The main focus of my research lies in the modelling of soil erosion by wind and water in low rainfall cropping regions using remotely sensed data and Geographic Information Systems (GIS). The application of remote sensing techniques and modern satellite imagery can facilitate the evaluation of natural processes such as soil erosion over very large regions (e.g. 160,000 km2). Indeed, higher resolution datasets are now available, with high temporal imaging capabilities, ranging from daily to every 30 minutes. This research therefore aims at combining and comparing some of these products and select the best combination to assess soil losses linked to land management and climate conditions.<br><br>The main outcome of this project is to propose a modelling tool, which can be used for more efficient decision making, to assess the influence of changes in land uses and climate conditions on soil erosion and wind-to-water erosion balance.<br

Soil erosion modelling: a global review and statistical analysis

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named ‘Global Applications of Soil Erosion Modelling Tracker (GASEMT)’, includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.</p

Soil erosion modelling: a bibliometric analysis

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper