Modeling Approaches to Assess Soil Erosion by Water at the Field Scale with Special Emphasis on Heterogeneity of Soils and Crops

Information on soil erosion and related sedimentation processes are very important for natural resource management and sustainable farming. Plenty of models are available for studying soil erosion but only a few are suitable for dynamic soil erosion assessments at the field-scale. To date, there are no field-scale dynamic models available considering complex agricultural systems for the simulation of soil erosion. We conducted a review of 51 different models evaluated based on their representation of the processes of soil erosion by water. Secondly, we consider their suitability for assessing soil erosion for more complex field designs, such as patch cropping, strip cropping and agroforestry (alley-cropping systems) and other land management practices. Several models allow daily soil erosion assessments at the sub-field scale, such as EPIC, PERFECT, GUEST, EPM, TCRP, SLEMSA, APSIM, RillGrow, WaNuLCAS, SCUAF, and CREAMS. However, further model development is needed with respect to the interaction of components, i.e., rainfall intensity, overland flow, crop cover, and their scaling limitations. A particular shortcoming of most of the existing field scale models is their one-dimensional nature. We further suggest that platforms with modular structure, such as SIMPLACE and APSIM, offer the possibility to integrate soil erosion as a separate module/component and link to GIS capabilities, and are more flexible to simulate fluxes of matter in the 2D/3D dimensions. Since models operating at daily scales often do not consider a horizontal transfer of matter, such modeling platforms can link erosion components with other environmental components to provide robust estimations of the three-dimensional fluxes and sedimentation processes occurring during soil erosion events.Peer reviewe

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

Modelling monthly soil losses and sediment yields in Cyprus

The aim of this study was to map soil erosion on the Mediterranean island of Cyprus. The G2 model, an empirical model for month-time step erosion assessments, was used. Soil losses in Cyprus were mapped at a 100 m cell size, while sediment yields at a sub-basin scale of 0.62 km2 mean size. The results indicated a mean annual erosion rate of 11.75 t ha−1 y−1, with October and November being the most erosive months. The 34% of the island’s surface was found to exceed non-sustainable erosion rates (>10 t ha−1 y−1), with sclerophyllous vegetation, coniferous forests, and non-irrigated arable land being the most extensive non-sustainable erosive land covers. The mean sediment delivery ratio (SDR) was found to be 0.26, while the mean annual specific sediment yield (SSY) value for Cyprus was found to be 3.32 t ha−1 y−1. The annual sediment yield of the entire island was found to be 2.746 Mt y−1. This study was the first to provide complete and detailed erosion figures for Cyprus at a country scale. The geodatabase and all information records of the study are available at the European Soil Data Centre (ESDAC) of the Joint Research Centre (JRC).JRC.D.3-Land Resource

Intermittent rivers and ephemeral streams: what water managers need to know

info:eu-repo/semantics/publishedVersio

Intermittent rivers and ephemeral streams: what water managers need to know

Peer reviewe

Soil erosion risk map for Swiss grasslands : a dynamic approach to model the spatio-temporal patterns of soil loss

Soil erosion by water on grassland does not attract the same attention like erosion on arable land as it is usually assumed that the closed vegetation cover prevents soil loss. However, the complex terrain and intensive pasture use of mountain grasslands can potentially induce high soil loss. With a share of 72% of the total agricultural area, grassland is one of the most dominant land use in Switzerland and therefore should not be neglected in topics concerning soil protection. Previous soil erosion studies revealed that soil erosion rates in Switzerland are not constant over time but rather are highly dynamic within a year. Such seasonal variability is mainly caused by rainfall patterns and plant growth cycles. Hence, modeling of soil loss based on a seasonal resolution enables improved insights in the erosion dynamics within a year. The present work aims to model soil erosion with a sub-annual resolution for Swiss grasslands. Thereby we will focus on the most dynamic soil erosion risk factors namely rainfall erosivity and land cover and management. The soil erosion model itself relies on the Revised Universal Soil Loss Equation (RUSLE). Each of the erosion factors of the RUSLE (rainfall erosivity R, soil erodibility K, cover and management C, slope length L, slope steepness S, and support practices P) is modified according to the specific environmental conditions of Swiss grasslands. The factors R and C are the most variable factors within a year as they are directly related to the parameters rainfall intensity and plant growth cycle. Therefore, both factors are modeled on a monthly scale to capture the temporal variations of soil loss within the year. For flexibility and transparency reasons, we derived each factor separately with the most state-of-the-art data and methodology as each of the factor transmit information about its effect on the overall model. Support practices (P-factor) are not considered in the model as the parametrization of grassland management practices and their effect for erosion control is difficult due to a lack of data and studies. Monthly estimates of the rainfall erosivity (R-factor) are based on 10-minutes rainfall data of 87 gauging stations distributed all over Switzerland. Subsequently, the monthly rainfall erosivity is interpolated with spatial covariates representing snow cover, precipitation, and topography. For the C-factor, the fraction of green vegetation cover (FGVC) was derived from the 0.25 m spatial resolution Swissimage orthophotos by a linear spectral unmixing technique. A temporal normalization of the spatial distribution of the FGVC combined with R-factor weighting results in spatial and temporal patterns of the C-factor. Soil erodibility (expressed as the K-factor of the RUSLE equation) was modeled with cubist regression and multilevel B-splines on a national scale based on a total of 199 Swiss and 1639 European Land Use/Cover Area frame statistical Survey (LUCAS) topsoil samples. The LS-factor was adopted to the steep alpine environment by limiting the slope length to 100 m and using a fitted S-factor of empirical slope steepness factors. The mean monthly modeled R-factor for Switzerland is 96.5 MJ mm ha-1 h-1 month-1. On average, rainfall erosivity is 25 times higher in August (263.5 MJ mm ha-1 h-1 month-1) then in January (10.5 MJ mm ha-1 h-1 month-1). In general, the winter has relatively low R-factor values (average of 14.7 MJ mm ha-1 h-1 month-1). The mean monthly C-factor on Swiss grasslands is 0.012 with a maximum from May until September. The national average K-factor of Switzerland is 0.0327 t ha h ha-1 MJ-1 mm-1. The LS-factor for Switzerland is relatively high (14.8) compared to other countries but is mainly driven by the complex topography of the Alps with its steep slopes. The soil erosion modeling reveals distinct seasonal variations. July and August are identified to be the months with the highest soil loss rates (1.25 t ha-1 month-1) by water on Swiss grasslands. Spatially, hotspots of soil erosion are in the Central Swiss Alps (parts of the cantons Fribourg, Bern, Obwalden, Nidwalden, St. Gallen, Appenzell Innerrhoden, and Appenzell Ausserrhoden) in summer. Winter is the season with the lowest risk of soil loss due to low rainfall erosivity on snow-covered ground. The average annual soil loss for Switzerland, expressed as the sum of all monthly erosion rates, is 4.55 t ha-1 yr-1. The spatial rainfall erosivity patterns are heterogeneous in all months, but spatial differences are less pronounced in winter due to the low rainfall erosivity. The small-scale variability of rainfall erosivity is less distinct in all months as homogenous rainfall patterns usually cover larger regions controlled mainly by topography. However, the Swiss Alps are not equally affected by rainfall erosivity with a very low variability within a year in the western and eastern Alps. In contrast, the small-scale variability of the cover and management factor is higher in most of the months due to the impact of grassland land use. The average C-factor for Swiss grassland of 0.012 matches the commonly applied C-factor for grasslands (0.01) proposed in the literature. The Swiss K-factor is low to medium with a clear reduction under consideration of the surface stone cover. We expected a high LS-factor for Switzerland as steep slopes are frequently in the Swiss Alps. The dominance of soil erosion risk on grasslands in summer is surprising as it is commonly assumed that the closed vegetation cover protects soils. Though, the individual consideration of all factors, especially of the R- and C-factor, reveal their strong effect and interaction within the erosion model. The average annual soil loss prediction for Swiss grassland exceeds the maximum tolerable soil loss of Switzerland (2 t ha-1 yr-1; Schaub and Prasuhn, 1998) by a factor of 2. That modeling result highlights that soil erosion on grasslands is of high concern for the Swiss agricultural productivity and environmental protection of a large proportion of the Swiss territory. Based on the increased temporal resolution of soil erosion predictions, spatial and temporal patterns of soil loss by water on Swiss grasslands can be captured. The simultaneous identification of spatial and temporal patterns of soil loss on Swiss grasslands makes a targeted soil erosion control feasible. The knowledge about where and when soil erosion occurs enables the implementation of selective erosion control measures specifically for time periods and regions with high susceptibility. Developing a comprehensive soil erosion assessment on Swiss grassland that is comparable and connectable with available risk assessments such as the erosion risk map 2 for Swiss arable lands (Prasuhn et al., 2013) and the European Union’s assessment RUSLE2015 (Panagos et al., 2015) provides a national and even continental valuation of soil erosion risk. The soil erosion risk map can be seen as a prototype for other erosion modeling on grassland in the Alpine region

Soil Improving Cropping Systems for Sustainable and Profitable Farming in Europe

A major challenge for the future is combining both the profitability and sustainability of agriculture. The European H2020 project SoilCare aims to identify, test, and evaluate soil-improving cropping systems (SICS) which contribute to the implementation of agricultural solutions across Europe (See: https://soilcare-project.eu/en/ for the project website). The project includes 16 study sites distributed across Europe. Each study site implemented short-term experiments during the duration of the project, and most also ran long-term experiments comparing soil quality as a function of different treatments, such as soil amendments, tillage, cover crops, nutrients, and organic matter inputs. In addition, eight work-packages assess different aspects encompassing reviewing the soil-improving cropping systems, the participatory analysis of implementation and selection, methodology and analysis, upscaling at the European level, policy analysis and support, and dissemination and communication. In this way, SoilCare works on a providing a holistic approach to soil quality, spanning from biophysical to human interactions at different scales. In this Special Issue, we aim to compile scientific findings on soil-improving cropping systems (SICS) based on field experiments, including the study of policy, upscaling, and dissemination

3. Water Physicochemistry in IRES

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