Spatial Variability Of Soil Erosion Research Articles

Exploring driving factors of soil erosion using a Multiscale GWR model: a case study at Central Citarum Watershed, West Java, Indonesia

To address challenges in soil erosion management and ecological rehabilitation, understanding the determinants of soil erosion is crucial. This research aims to achieve two primary objectives: (1) delineating the spatial patterns of soil erosion within the designated region and (2) identifying the influential factors using the Multiscale Geographical Weighted Regression (MGWR) methodology. The methodological framework involved the creation of grid datasets, with soil erosion as the response variable and a combination of physical and socioeconomic attributes as predictors. We extracted 550 data points from raster datasets, specifically centered on village locations, using the ‘extract multi-value to point’ tool in ArcGIS. The R Studio environment was utilized to select the relevant factors influencing soil erosion. The geographical detector technique was applied to determine the explanatory power of each determinant concerning the spatial patterns of soil erosion. Subsequently, data from the Ordinary Least Squares (OLS) model underwent MGWR analysis. The findings reveal that the Central Citarum Watershed experiences an estimated annual soil erosion of 23.16 million tons, averaging 102.01 tons per hectare. The analysis identified LS (slope length and gradient) and CP (vegetative cover and supportive practices) as the primary variables influencing the spatial variability of soil erosion. Notably, the MGWR model demonstrated enhanced explanatory capacity and effectiveness compared to both the OLS and Geographically Weighted Regression (GWR) methodologies.

GIS-Based RUSLE Reservoir Sedimentation Estimates: Temporally Variable C-Factors, Sediment Delivery Ratio, and Adjustment for Stream Channel and Bank Sediment Sources

The empirical Revised Universal Soil Loss Equation (RUSLE) has been adapted to geographical information system (GIS) frameworks to study the spatial variability of soil erosion across landscapes and has also been used to estimate reservoir sedimentation. The literature presents contradictory results about the efficacy of using RUSLE in a GIS context for quantifying reservoir sedimentation, requiring further evaluation and validation of its estimates relative to measured reservoir sedimentation. Our primary objective was to determine if these contradictory results may be a function of the RUSLE’s inability to account for sediments derived from gullies, stream channels, or stream banks; the temporal variability of some of RUSLE’s empirically based factors such as the land cover/land management (C-) factor; and in some model renditions, the choice of value for the sediment delivery ratio (SDR). The usefulness of adjusting these estimates using a regional representative value of gully/stream bank sediment contributions was also assessed. High-spatial horizontal resolution (2 m) digital elevation models (DEMs) for 12 watersheds were used together with C-factor data for five representative years in a GIS-based RUSLE model that incorporates SDR within a sediment routing routine to study the impacts of choice of C-factor and SDR on reservoir sedimentation estimates. Choice of image date for developing C-factors was found to impact reservoir estimates. We also found that the value of SDR for some of the study watersheds would have to be unrealistically small to produce sedimentation estimates comparable to measured values. Estimates of reservoir sedimentation were comparable to measured data for 5 of the 12 watersheds, when the regionally based adjustment for gully/stream bank contributions was applied. However, differences remained large for the remaining seven watersheds. Statistical analysis revealed that certain combinations of geomorphic, pedologic, or topographic variables could be used to predict the degree of sediment underestimation with a significant and high level of correlation (0.72 < R2 ≤ 0.99; p-value < 0.05). Our findings indicate that the level of agreement between GIS-based RUSLE estimates of reservoir sedimentation and measured values is a function of watershed characteristics; for example, the area-weighted soil erodibility (K-) factor of the soils within the watershed and stream channels, the stream entrenchment ratio and bank full depth, the percentage of the stream corridor having slopes ≥ 21°, and the width of the stream flood way as a percentage of the watershed area. Within the context of GIS, these metrics are easily obtained from digital elevation models and publicly available soils data and may be useful in prioritizing reservoirs’ assessments for function and safety.

Spatial modeling of soil erosion hazards and crop diversity change with rainfall variation in the Central Highlands of Sri Lanka

The spatial variation of soil erosion is essential for farming system management and resilience development, specifically in the high climate hazard vulnerable tropical countries like Sri Lanka. This study aimed to investigate climate and human-induced soil erosion through spatial modeling. Remote sensing was used for spatial modeling to detect soil erosion, crop diversity, and rainfall variation. The study employed a time-series analysis of several variables such as rainfall, land-use land-cover (LULC) and crop diversity to detect the spatial variability of soil erosion in farming systems. Rain-use efficiency (RUE) and residual trend analysis (RESTREND) combined with a regression approach were applied to partition the soil erosion due to human and climate-induced land degradation. Results showed that soil erosion has increased from 9.08 Mg/ha/yr to 11.08 Mg/ha/yr from 2000 to 2019 in the Central Highlands of Sri Lanka. The average annual rainfall has increased in the western part of the Central Highlands, and soil erosion hazards such as landslides incidence also increased during this period. However, crop diversity has been decreasing in farming systems, namely wet zone low country (WL1a) and wet zone mid-country (WM1a), in the western part of the Central Highlands. The RUE and RESTREND analyses reveal climate-induced soil erosion is responsible for land degradation in these farming systems and is a threat to sustainable food production in the farming systems of the Central Highlands.

Assessment of Intra-Annual and Inter-Annual Variabilities of Soil Erosion in Crete Island (Greece) by Incorporating the Dynamic “Nature” of R and C-Factors in RUSLE Modeling

Under the continuously changing conditions of the environment, the exploration of spatial variability of soil erosion at a sub-annual temporal resolution, as well as the identification of high-soil loss time periods and areas, are crucial for implementing mitigation and land management interventions. The main objective of this study was to estimate the monthly and seasonal soil loss rates by water-induced soil erosion in Greek island of Crete for two recent hydrologically contrasting years, 2016 (dry) and 2019 (wet), as a result of Revised Universal Soil Loss Equation (RUSLE) modeling. The impact of temporal variability of the two dynamic RUSLE factors, namely rainfall erosivity (R) and cover management (C), was explored by using rainfall and remotely sensed vegetation data time-series of high temporal resolution. Soil, topographical, and land use/cover data were exploited to represent the other three static RUSLE factors, namely soil erodibility (K), slope length and steepness (LS) and support practice (P). The estimated rates were mapped presenting the spatio-temporal distribution of soil loss for the study area on a both intra-annual and inter-annual basis. The identification of high-loss months/seasons and areas in the island was achieved by these maps. Autumn (about 35 t ha−1) with October (about 61 t ha−1) in 2016, and winter (about 96 t ha−1) with February (146 t ha−1) in 2019 presented the highest mean soil loss rates on a seasonal and monthly, respectively, basis. Summer (0.22–0.25 t ha−1), with its including months, showed the lowest rates in both examined years. The intense monthly fluctuations of R-factor were found to be more influential on water-induced soil erosion than the more stabilized tendency of C-factor. In both years, olive groves in terms of agricultural land use and Chania prefecture in terms of administrative division, were detected as the most prone spatial units to erosion.

Landslide-controlled soil erosion rate in the largest tableland on the Loess Plateau, China

Hillslopes in the steep mountainous terrains are eroded by landslides, however the quantitative characterization of the interaction between the soil erosion and landslide is rare. Here, taking the largest tableland in the Loess Plateau of China as an example, we calculated the area and volume of 5,420 shallow loess landslides and compared these against the Chinese Soil Loss Equation (CSLE) derived soil erosion rate of 15 sub-catchments. The results on the spatial variability of soil erosion suggest that the average soil erosion modulus in the study area is 50.54 tons·ha−1·year−1, but the value varies widely across the whole research area. Overall, soil erosion is more severe in the north, which is similar to the distribution of landslide scars extracted from Google imagery. The volume of the landslides in the northern watershed was approximately 4-fold that of the landslides in the southern watershed. Our analysis reveals a satisfactory linear fitting result (R2 = 0.68) between the area of landslides and soil erosion. Based on the results, we believe that restoring vegetation cover in landslide scars should become the most urgent action to mitigate soil erosion.

Estimating Non-Sustainable Soil Erosion Rates in the Tierra de Barros Vineyards (Extremadura, Spain) Using an ISUM Update

Monitoring soil erosion processes and measuring soil and water yields allow supplying key information to achieve land degradation neutrality challenges. Vineyards are one of the most affected agricultural territories by soil erosion due to human and natural factors. However, the spatial variability of soil erosion, the number of sampling points, and plot size necessary to estimate accurate soil erosion rates remains unclear. In this research, we determine how many inter-rows should be surveyed to estimate the soil mobilization rates in the viticulture area of Tierra de Barros (Extremadura, SW Spain) using the Improved Stock Unearthing Method (ISUM). This method uses the graft union of the vines as a passive biomarker of the soil surface level changes since the time of plantation and inter-row measures. ISUM was applied to three inter-row and four rows of vines (5904 sampling points) in order to determine how many surfaces and transects must be surveyed as all the previous surveys were done with only one inter-row. The results showed average values of soil depletion reaching −11.4, −11.8, and −11.5 cm for the inter-rows 1, 2, and 3, respectively. The current soil surface level descended 11.6 cm in 20 years. The inter-rows 1, 2, and 3 with a total area of 302.4 m2 each one (2016 points) recorded 71.4, 70.8, and 74.0 Mg ha−1 yr−1, respectively. With the maximum number of sampling points (5904), 71.2 Mg ha−1 yr−1 were obtained. The spatial variability of the soil erosion was shown to be very small, with no statistically significant differences among inter-rows. This could be due to the effect of the soil profile homogenization as a consequence of the intense tillage. This research shows the potential predictability of ISUM in order to give an overall overview of the soil erosion process for vineyards that follow the same soil management system. We conclude that measuring one inter-row is enough to get an overview of soil erosion processes in vineyards when the vines are under the same intense tillage management and topographical conditions. Moreover, we demonstrated the high erosion rates in a vineyard within the viticultural region of the Tierra de Barros, which could be representative for similar vineyards with similar topographical conditions, soil properties, and a possible non-sustainable soil management system.

Potential soil erosion estimation and area prioritization for better conservation planning in Gumara watershed using RUSLE and GIS techniques\u2019

BackgroundWater induced soil erosion has been continued to threaten the land resources in sub humid northwestern highlands of Ethiopia. Soil and water conservation measures have been implemented without site-specific scientifically quantified soil erosion data and priority bases. In this regard, quantitative analysis of soil erosion and its spatial variation plays a decisive role for better evidence and priority based implementation. Thus, this study aimed to estimate potential soil loss, identify hotspot areas, and prioritize for conservation measures in Gumara watershed using RUSLE, GIS and remote sensing techniques’.ResultThe study result showed that soil loss due to water erosion was found to be a critical problem in the watershed. It ranges from nearly zero in gentle slope of forest lands to 442.92 t ha−1 year−1 on very steep slope cultivated lands. A total of 9.683456 million t of gross surface soil has been lost annually, with an average soil erosion rate of 42.67 t ha−1 year−1. Of which 62.1% was generated from cultivated land. The model result indicated a high spatial variability of soil erosion within the watershed. High intensity of soil erosion has been principally attributed to slope and land use/covers. The study further estimated that about 63.1% of the total soil loss was generated from only 29.3% of the area delineated as very severe soil erosion severity class. Soil erosion rate for 71.7% of the watershed area was beyond the maximum tolerable soil erosion limit estimated for Ethiopian highlands (> 18 t ha−1 year−1). The sub-watershed severity class map revealed that 3814 ha of the sub-watershed area was evaluated as very severe level of soil erosion severity class.ConclusionSoil erosion in the watershed has been a threatening problem for agricultural production to day, its sustainability and to be worsening in the future unless remedial measures were taken, mainly due to human intervention. Therefore, Gumara watershed needs immediate intervention for better conservation planning by considering identified priority classes and hotspot areas.

Current and future hot-spots and hot-moments of nitrous oxide emission in a cold climate river basin

An ecosystem in a cold climate river basin is vulnerable to the effects of climate change affecting permafrost thaw and glacier retreat. We currently lack sufficient data and information if and how hydrological processes such as glacier retreat, snowmelt and freezing-thawing affect sediment and nutrient runoff and transport, as well as N2O emissions in cold climate river basins. As such, we have implemented well-established, semi-empirical equations of nitrification and denitrification within the Soil and Water Assessment Tool (SWAT), which correlate the emissions with water, sediment and nutrients. We have tested this implementation to simulate emission dynamics at three sites on the Canadian prairies. We then regionalized the optimized parameters to a SWAT model of the Athabasca River Basin (ARB), Canada, calibrated and validated for streamflow, sediment and water quality. In the base period (1990–2005), agricultural areas (2662 gN/ha/yr) constituted emission hot-spots. The spring season in agricultural areas and summer season in forest areas, constituted emission hot-moments. We found that warmer conditions (+13% to +106%) would have a greater influence on emissions than wetter conditions (−19% to +13%), and that the combined effect of wetter and warmer conditions would be more offsetting than synergetic. Our results imply that the spatiotemporal variability of N2O emissions will depend strongly on soil water changes caused by permafrost thaw. Early snow freshet leads to spatial variability of soil erosion and nutrient runoff, as well as increases of emissions in winter and decreases in spring. Our simulations suggest crop residue management may reduce emissions by 34%, but with the mixed results reported in the literature and the soil and hydrology problems associated with stover removal more research is necessary. This modelling tool can be used to refine bottom-up emission estimations at river basin scale, test plausible management scenarios, and assess climate change impacts including climate feedback.

Estimating landscape susceptibility to soil erosion using a GIS-based approach in Northern Ethiopia

Soil erosion is a very critical form of land degradation resulting in the loss of soil nutrients and downstream sedimentation of water storages in the highlands of Ethiopia. As it is technically and financially impossible to conserve all landscapes affected by erosion, identification of priority areas of intervention is necessary. Spatially distributed erosion models can help map landscape susceptibility to erosion and identify high erosion risk areas. Integration of erosion models with geographic information systems (GIS) enables assessing evaluate the spatial variability of soil erosion and plan implementing conservation measures at landscape levels. In this study, the Revised Universal Soil Loss Equation adjusted for sediment delivery ratio was used in a GIS system to assess landscape sensitivity to erosion and identify hotspots. The approach was applied in three catchments with size being 10–20km2 and results were compared against quantitative and semi-quantitative data. The model estimated mean soil loss rates of about 45tha−1y−1 with an average variability of 30% between catchments. The estimated soil loss rate is above the tolerable limit of 10tha−1y−1. The model predicted high soil loss rates at steep slopes and shoulder positions as well as along gullies. The results of the study demonstrate that knowledge of spatial patterns of high soil loss risk areas can help deploy site-specific conservation measures.

Erosion rate predictions from PESERA and RUSLE at a Mediterranean site before and after a wildfire: Comparison & implications

Mediterranean ecosystems are commonly vulnerable to wildfires. Accelerated erosion processes due to wildfires in those environments constitute a major restrictive factor in their sustainability. This study aims at evaluating the use of the Revised Universal Soil Loss Equation (RUSLE) and the Pan-European Soil Erosion Risk Assessment (PESERA) models in predicting the changes in spatial variability of soil erosion following a wildfire event. A site in Greece on which a wildfire occurred in the summer of 2007 is used as a case study. Soil erosion rates for the site before and after the fire outbreak were estimated by the two models. Inputs for both models included climatic, land-use, soil type, topography, Earth Observation (EO) as well as management and other ancillary data.Both models showed a substantial increase of soil erosion rates in the affected area as a result of the fire, particularly towards the steeper slopes and on areas of high burning severity. Yet, there were noticeable differences in the predictions between the 2 models in terms of absolute estimates of soil erosion rates before and after the fire event. Mean pre-fire erosion rates from RUSLE were ~2.5 times higher than those from PESERA. RUSLE predicted considerably higher mean erosion in comparison to PESERA for the post-fire conditions, yet of much less spatial variability. Average post-fire soil loss value, compared to pre-fire, was about nine and six times greater when using the RUSLE and the PESERA model respectively. RUSLE predictions were most sensitive to topographic and rainfall erosivity factors. PESERA showed high sensitivity to the vegetation coverage as well as to the soil characteristics inserted as crusting and erodibility variables.To our knowledge, this is the first study performing an intercomparison of soil erosion rate predictions between these two models, particularly so in the context of the influence of wildfire events. This study provides a key contribution towards our ability to better understand the effect of fire on soil erosion in the Mediterranean and elsewhere, as well as the ability of those widely used models as efficient tools to be used for this purpose. The latter is of key significance and practical value for research and policy decision making purposes alike, where information on spatiotemporal estimates of soil erosion rates may be required.

Temporal and spatial variations of the water erosion rate

Understanding the erosion dynamics and recognizing sensitive times and places to erosion within the catchment are necessary for erosion control. The seasonal and spatial variability of soil erosion under different contrasting lithologic conditions was studied in Vartavan catchment of Qazvin province of Iran. For this purpose, sediment trappers and erosion pins were placed in each parent material unit and were monitored at the end of each season. The results showed that the erosion rate of Vartavan catchment has large variations spatially and temporally. Based on the average weight of sediments in trappers, soil erosion rate is reduced correspondingly in units of light tuff (178.2 g), black shale (34.4 g), red mudstone (29.9 g), andesite (21.7 g), dark tuff (16.7 g), sandstone (14.9 g), red sandstone (9.8 g), shale limestone (9.5 g), and eventually orbitolina limestone (6.5 g). The seasonal variation of erosion revealed that autumn has the maximum rate of erosion (71 %) which then decreases during the spring (19 %) and winter (10 %) until it reaches the minimum rate in summer. Understanding seasonal variations and identifying the critical months when the most amount of erosion occurs are essential for outlining soil conservation plans in specific lithological units.

Effects of Canopy and Roots of Patchy Distributed Artemisia capillaris on Runoff, Sediment, and the Spatial Variability of Soil Erosion at the Plot Scale

Laboratory-simulated rainfall experiments were conducted to quantify the effects of patterns of Artemisia capillaris on runoff and soil loss. The spatial variability of soil erosion and deposition were also preliminarily analyzed using classic statistical and geostatistical methods. Simulated storms (90 mm/h) were applied on a bare plot (CK) and three different plant patterns: a checkerboard pattern (CP), a banded pattern perpendicular to the slope direction (BP), and a single long strip parallel to slope direction (LP). Each patterned plot underwent two sets of test—intact plants and roots only, respectively. All treatments had three replicates. The results showed that A. capillaris of different patterns can effectively reduce runoff and sediment. Compared with CK, the intact plant plots had a 12% to 25% less runoff and a 58% to 92% less sediment. The contributions of roots and canopies of A. capillaris to the reductions in runoff and sediment were different. Roots contributed more (46%–70%) to the sediment reduction, whereas canopies contributed more (57%–81%) to the runoff reduction. BP and CP exhibited a preferable controlling effect on soil erosion than LP. Coefficient of variation indicated that soil erosion, expressed as probe height difference (Δh), was strongly variable. Geostatistical analysis revealed that semivariograms of soil erosion can be fitted with spherical or exponential models and showed a moderate or strong spatial dependence. Interpolation using kriging demonstrated a spatial similarity among the treatments. Erosion (Δh > 0), primarily 0- to 2.67-mm level, spread over the bare plot. For intact plant experiments, the erosion area was relatively smaller than that for CK, and no significant differences were observed between erosion and deposition (Δh < 0) area. The erosion area increased after removing vegetation canopies.

ASSESSING THE PERFORMANCE OF A SPATIALLY DISTRIBUTED SOIL EROSION AND SEDIMENT DELIVERY MODEL (WATEM/SEDEM) IN NORTHERN ETHIOPIA

ABSTRACTMost regional‐scale soil erosion models are spatially lumped and hence have limited application to practical problems such as the evaluation of the spatial variability of soil erosion and sediment delivery within a catchment. Therefore, the objectives of this study were as follows: (i) to calibrate and assess the performance of a spatially distributed WATEM/SEDEM model in predicting absolute sediment yield and specific sediment yield from 12 catchments in Tigray (Ethiopia) by using two different sediment transport capacity equations (original and modified) and (ii) to assess the performance of WATEM/SEDEM for the identification of critical sediment source areas needed for targeting catchment management.The performance of the two model versions for sediment yield was found promising for the 12 catchments. For both versions, model performance for the nine catchments with limited gully erosion was clearly better than the performance obtained when including the three catchments with significant gully erosion. Moreover, there is no significant difference (alpha 5 per cent) between the performances of the two model versions.Cultivated lands were found to be on average five times more prone to erosion than bush–shrub lands. The predicted soil loss values in most parts of Gindae catchment are generally high as compared with the soil formation rates. This emphasises the importance of implementing appropriate soil and water conservation measures in critical sediment source areas prioritising the steepest part of the catchment (i.e. areas with slope &gt;50 per cent).The applicability of the WATEM/SEDEM model to environments where gully erosion is important requires the incorporation of permanent gully and bank gully erosion in the model structure. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Soil erosion in the Swiss midlands: Results of a 10-year field survey

Long-term field monitoring of soil erosion by water was conducted on arable land in the Swiss midlands. All visible erosion features in 203 fields were continuously mapped and quantified over 10 years. The eroded soil volume associated with linear erosion features was calculated by measuring the length and cross-sectional area in rills at representative positions and the extent of interrill erosion was estimated. Averaged across the 10 study years, just under one-third (32.2%) of the fields exhibited erosion. With 0.75 t ha −1 yr −1 (mean) and 0.56 t ha −1 yr −1 (median), the average annual soil loss of the region was relatively small. The year-to-year variation in soil loss of the region was great and ranged from 0.16 to 1.83 t ha −1 yr −1. The maximum annual soil erosion in a single field was 96 t yr −1 or 58 t ha −1 yr −1, thus demonstrating that only a few erosion events on a few fields may decisively contribute to the total extent of soil erosion in a region. Linear and interrill erosion accounted for 75% and 25% of total soil loss, respectively. Wheel tracks, furrows, headlands, and slope depressions were important on-site accelerators of erosion. Run-on from adjacent upslope areas was an important trigger of erosion. Of the soil moved by erosion, 52% was deposited within the field of origin. A high proportion (72%) of the linear erosion features caused off-site damage. Part of the total eroded soil (20%) was transported into water, thereby contributing to their contamination. The long-term field assessment of soil erosion helps to fill existing knowledge gaps concerning temporal and spatial variability of soil erosion on arable land, the extent and severity of soil erosion and its sources and causes, as well as subsequent off-site damage.

Spatial variability of erosion and soil organic matter content estimated from 137Cs measurements and geostatistics

The spatial variability of soil erosion was studied through the use of fallout radionuclides (FRNs) and geostatistics. The spatial correlation structures of radiocesium ( 137Cs), soil redistribution and organic matter (OM) content have been established in a 2.16 ha agricultural field located 30 km east of Quebec City, Canada. A significant relationship was found between 137Cs (Bq m − 2 ) and OM (%) in the 0–20 cm soil depth ( n = 42; r 2 = 0.63, p < 0.001), highlighting the relation between these two parameters. The conversion of the areal activities of 137Cs into soil redistribution (t ha − 1 yr − 1 ) was done using the Mass Balance Model 2 (MBM 2). The magnitude of soil redistribution, at the sampled points, ranged from an erosion rate of 62 t ha − 1 yr − 1 to a deposition of 17 t ha − 1 yr − 1 . Geostatistics coupled with a geographic information system (GIS) were used to create a map of soil redistribution, based on the spatial variability of FRNs, and to establish a sediment budget. Prior to mapping, semivariograms were produced, taking into account the autocorrelation present in the data. A significant autocorrelation and reliable variograms were obtained for the three tested parameters ( 137Cs, OM content and soil redistribution) (0.87 ≤ r 2 ≤ 0.95; 0.7 ≤ Scale/Sill ≤ 0.96 and 4% ≤ ‘nugget-to-sill’ < 20%). Using the Kriging interpolation and ‘area weighted mean’ of the soil redistribution map, a sediment budget was estimated for the whole field. A net sediment output was estimated as 16.6 t ha − 1 yr − 1 , for a sediment delivery ratio (SDR) of 99%. This high SDR is believed to reflect the joint impact of tillage, water and snowmelt erosion on the net sediment production. Approximately 85% of the agricultural field surface was estimated to be affected by erosion rates approaching or exceeding the suggested tolerance level of 6 t ha − 1 yr − 1 for most Canadian soils. The geostatistics concept is a powerful tool in soil science and especially for FRNs use in order to characterize the spatial variability of erosion and sedimentation processes.

Patterns of water and tillage erosion on topographically complex landscapes in the North American Great Plains

Two field sites located in the northern region of the North American Great Plains were examined to investigate the contributions of water and tillage erosion towards total soil erosion in topographically complex landscapes (hummocky and undulating landscapes). Results indicated that both water and tillage erosion contributed substantially to total erosion in the undulating landscape while tillage erosion dominated in the hummocky landscape. The patterns of water, tillage and total soil erosion can be predicted using landscape segmentation in such landscapes. Soil properties and crop yield are also related to soil erosion. Landscape segmentation can be used as a simple tool to more easily represent the spatial variability of soil erosion and affected biophysical processes such as crop production, nutrient cycling, greenhouse gas emission and pesticide fate, and to target soil conservation practices toward the most intensive erosion processes on given landform elements.

The influence of aspect and vegetation on seasonal changes in erosion under rainfall simulation on a clay soil in Spain

The seasonal and spatial variability of soil erosion under contrasting slope aspects in southeastern Spain was studied by performing and interpreting 84 rainfall simulation experiments conducted at an intensity of 55 mm h−1 during 1 h. The vegetated soils on the north-facing slope and the upper afforested parts had negligible sediment yield, runoff and erosion, while the bare soils on the south-facing slope had very high runoff rates. Runoff sediment concentration decreased over time during simulated rainfall events on the vegetated areas while it increased on the bare ones. Solute release decreased over time on both surface types. Seasonally, runoff sediment concentration was highest in autumn, decreasing in winter and spring, due to the exhaustion of erodible soil and the vegetation growth. Sediment concentrations increased slightly in summer when runoff and erosion rates where very low. Increasing seasonal variability corresponded with increasing runoff and soil loss rates. Key words: Runoff, soil loss, Mediterranean, erodibility