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Soil erodibility estimation using LUCAS point survey data of Europe
Abstract
Modelling soil erosion is mostly hampered by low data availability, particularly of soil parameters. One key parameter for soil erosion modelling is the soil erodibility, expressed as the K-factor in the commonly used soil erosion model USLE (Universal Soil Loss Equation). The K-factor is related to crucial soil factors triggering erosion (organic matter content, soil texture, soil structure, permeability). We calculated soil erodibility using measured soil data, collected during the 2009 LUCAS (Land Use and Cover Area frame Survey) soil survey campaign across the member states of the European Union. The soil erodibility dataset overcomes the problems of limited data availability for K-factor assessment and presents a high quality resource for modellers who aim at soil erosion estimation on local/regional, national or European scale.
... As a consequence of soil loss caused by water, land degradation is estimated to be a significant problem in many parts of the world, especially in semi-arid regions such as Iran [1,2]. Soil erodibility (K-factor) can be regarded as a key indicator of soil sensitivity to land degradation, which can predict soil erosion (soil loss) [3,4]. The term soil erodibility refers to soil surface erosion caused by raindrops and runoff. ...
... Due to the difficulty and expense of fieldwork, researchers have developed models indicating a relationship between specific soil parameters and accessible soil properties [1][2][3]. Because soil erodibility is a key parameter for estimating erosion, studying how this varies spatially will help better understand the mechanisms that drive soil erosion, improve the accuracy of the empirical soil erosion model, and characterize the effects of environmental factors, such as topography, on land degradation [3,4]. ...
... Sunny slopes retain less soil water content due to being exposed to higher solar radiation and soil evaporation. Hence, plants on these slopes are more likely to be in drought conditions and radiation-resistance, affecting the carbon fluxes and dynamics [4,42], resulting in a decrease in robust soil aggregates and a potentially increased risk of clay loss (decreased D value). As far as soil erosion is concerned, slope is a crucially important factor to consider. ...
This study examines the effects of land use and slope aspect on soil erodibility (K-factor) and the fractal dimension (D) of soil particle size distribution (PSD) in calcareous soils at the watershed scale in western Iran. The study analyzed 113 soil samples collected from four land uses (slope-farmland, farmland, pasture, and woodland) at a depth of 0–20 cm, measuring common soil properties such as soil texture, soil organic matter (SOM), calcium carbonate (CaCO3), pH, and cation exchange capacity (CEC). The PSD of soil samples was measured using the international system of soil size fractions, and the D for PSD was calculated. The K-factor was calculated using the RUSLE model. The results showed that the K-factor was highest in slope farmlands with SOM at 1.6% and lowest in woodlands at 0.02 Mg h MJ−1 mm−1 with SOM at 3.5%. The study also found that there were significant correlations between D and clay content (r = 0.52), sand content (r = −0.29), and CEC (r = 0.36). Woodland soils had the highest SOM content, with a mean D value of 2.895, significantly higher than the mean D value of slope farmland soils, which had the lowest SOM at 1.6%. The study concludes that woodland soils retain finer particles, particularly clay, resulting in lower soil loss and land degradation compared to other land uses. Finally, the study suggests that shady slope aspects (south aspect) contain more organic matter due to less solar radiation and higher soil water content, resulting in lower soil erodibility (0.02 Mg h MJ−1 mm−1) and higher D values compared to other slope aspects.
... Van der Knijff et al. (2000) delineated soil erodibility for Europe, at 1:1,000,000 scale, using a method based on the five textural classes of the European Soil Database (ESDB) (King et al., 1994). Panagos et al. (2012a) estimated the K-factor for Europe considering measured pedological data, using the nomograph of Wischmeier et al. (1971). The data derived from the Land Use/Cover Area frame Survey (LUCAS) database (Toth et al., 2013;Orgiazzi et al., 2018). ...
... The level of spatial association is assessed through the cross-variogram. IDW was chosen based on literature review (Angulo-Martinez et al., 2009;Panagos et al., 2012a), having been applied at similar research efforts. Ordinary kriging was selected since it is considered the optimal unbiased predictor for the random process at un-sampled locations (Cressie, 1993). ...
... At the Panagos et al. (2012a) pan-European study, the K-factor estimation for Greece was 0.040 t ha h ha -1 MJ -1 mm -1 , with a maximum value of 0.073 t ha h ha -1 MJ -1 mm -1 and standard deviation of 0.010. Panagos et al. (2014a) re-assessed the K-factor for Greece at 0.0298 t ha h ha -1 MJ -1 mm -1 , with standard deviation 0.0057 t ha h ha -1 MJ -1 mm -1 . ...
Erodibility designates soils' vulnerability to the abrading effect of erosion's climatic drivers i.e. precipitation and overland flow. This parameter is represented in the Universal Soil Loss Equation (USLE) and its successor (RUSLE), by the respective component (K-factor). Its accurate quantification is critical for modelling soil erosion. Yet, in many countries – Greece included – the process is hindered by the lack of high-quality field observations, evenly distributed in time and space. In light of the above, the study aims to develop the new soil erodibility map of Greece. The K-factor was calculated using the algebraic expression of the Wischmeier and Smith (1978) nomograph. The latter considers basic soil properties like granulometry, soil organic matter content, structure and permeability. Approximately 2800 soil samples were utilized, deriving from the European Land Use/Cover Area frame Survey (LUCAS) and the Greek National Agricultural Research Foundation (NAGREF) databases. Of them, 577 corresponded to croplands and 2113 to forests/rangeland. Erodibility was spatially interpolated utilizing the ordinary cokriging methodology. The mean nationwide value was estimated at 0.024 t ha h ha⁻¹ MJ⁻¹ mm⁻¹, ranging from 0.013−0.044 t ha h ha⁻¹ MJ⁻¹ mm⁻¹. After incorporating the surface stone cover effect, given the country's extensive coverage, mean K-factor value was reduced by 22 %, at 0.018 t ha h ha–1 MJ–1 mm–1. The new maps (500 m resolution) improve the accuracy of the available K-factor delineation, developed by the EU at pan-European level. The sample network is denser (1 sample per 48 km²) and more evenly distributed throughout the Greek territory. Also, the geostatistical model was fitted focusing entirely on the specific attributes of Greece. The results were proportional to those yielded by national and international studies of local and national interest.
... At the same time, for the convenience of users, they also mapped this relationship into the famous soil erodibility factor Nomograph, which was formally adopted in the 1978 universal soil loss equation. Most researchers calculate K values with this nomographic equation on a watershed or regional scale (Panagos et al. 2012(Panagos et al. , 2014Ferreira et al. 2015). ...
... Most studies that assess regional soil erodibility are based on field soil sampling and analysis of soil physical and chemical properties focused on obtaining the K value for a given sampling point. The spatial distribution of the K value is then obtained by interpolation methods (Zhu and Lin 2010;Bonilla and Johnson 2012;Panagos et al. 2012Panagos et al. , 2014Li and Heap 2014;Addis and Klik 2015;Ferreira et al. 2015;Pereira et al. 2017). Large spatial variability of soil erodibility with complex influencing factors at regional scale was acknowledged by numerous studies (Panagos et al. 2012(Panagos et al. , 2014Wang et al. 2016;Zhu et al. 2019). ...
... The spatial distribution of the K value is then obtained by interpolation methods (Zhu and Lin 2010;Bonilla and Johnson 2012;Panagos et al. 2012Panagos et al. , 2014Li and Heap 2014;Addis and Klik 2015;Ferreira et al. 2015;Pereira et al. 2017). Large spatial variability of soil erodibility with complex influencing factors at regional scale was acknowledged by numerous studies (Panagos et al. 2012(Panagos et al. , 2014Wang et al. 2016;Zhu et al. 2019). Therefore, it remains a challenge to accurately express the soil spatial variability by spatial interpolation. ...
Soil erodibility is a key parameter to measure soil susceptibility to water erosion and thus to soil erosion modelling, introduced as K factor in the Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE). However, subject to soil spatial heterogeneity and data availability, large-scale K value acquisition has not been properly addressed. In this study, authors aimed to construct a new large-scale soil erodibility mapping technique to provide methodological support for regional soil erosion surveys. Taking the mountainous Yunnan province as a case, 743 soil samples from 252 typical soil series were collected. A “GIS-based pedological method” was developed to construct a large-scale soil erodibility database by linking a soil map with soil properties. K value in Yunnan varied between 0.0004 and 0.0169 t ha h ha⁻¹ MJ⁻¹ mm⁻¹, with an average of 0.0065 t ha h ha⁻¹ MJ⁻¹ mm⁻¹. Significantly different K values were found among different types of soils and land use (p < 0.05). Redundancy analysis indicated that annual average temperature and latitude were the dominant control factors. The study showed that regional K value estimated by “GIS-based pedological method” manifested an improvement compared to the traditional spatial interpolation method.
... The K factor is related to crucial soil factors triggering erosion (organic matter content, soil texture, soil structure, permeability) (Panagos et al. 2012). The soil erodibility factor is a lumped parameter that represents an integrated average annual value of the soil profile reaction to the processes of soil detachment and transport by raindrop impact and surface flow (Panagos et al. 2012;Renard et al. 1997). ...
... The K factor is related to crucial soil factors triggering erosion (organic matter content, soil texture, soil structure, permeability) (Panagos et al. 2012). The soil erodibility factor is a lumped parameter that represents an integrated average annual value of the soil profile reaction to the processes of soil detachment and transport by raindrop impact and surface flow (Panagos et al. 2012;Renard et al. 1997). ...
The present study aims to estimate the annual soil loss in the Soummam watershed in the northeast of Algeria, using the Revised Universal Soil Loss Equation (RUSLE), geographic information system (GIS), and remote sensing (RS). RUSLE model has been used for modelling the main factors involved in erosive phenomena. The Soummam watershed covers a surface area of 9108.45 km 2 of irregular shape, northeast-southwest towards southeast. It is characterized by an altitude varying between 2 m in the northeast and 2308 m in the northwest. Results showed that the average erosivity factor (R) is 70.64 (MJ·mm)/(ha·h·year) and the maximum value reaches 140 (MJ·mm)/(ha·h·year), the average soil erodibility factor (K) is 0.016 (t·h·ha)/(MJ·ha·mm) and maximum values reach 0.0204 (t·h·ha)/(MJ·ha·mm) in the southeast regions of the watershed, the average slope length and steepness factor (LS) is 9.79 and the mean C factor is estimated to be 0.62. Thematic maps integration of different factors of RUSLE in GIS with their database, allowed with a rapid and efficient manner to highlight complexity and factors interdependence in the erosion risk analyses. The resulting map for soils losses, with an average erosion rate of 6.81 t/(ha·year) shows a low erosion (<7.41 t/(ha·year)) which covers 73.46% of the total area of the basin, and a medium erosion (7.42 to 19.77 t/(ha·year)), which represents 17.66% of the area. Areas with extreme erosion risk exceeding 32.18 t/(ha·year) cover more than 3.54% of the basin area. The results can certainly aid in implementation of soil management and conservation practices to reduce the soil erosion in the Soummam watershed. Citation: Sahli Y, Mokhtari E, Merzouk B, et al. (2019) Mapping surface water erosion potential in the Soummam watershed in Northeast Algeria with RUSLE model. Journal of Mountain Science 16(7). https://doi.
... Initially, these properties were determined using laboratory-based methods [6]. At the European level, Panagos et al. [20] assessed soil erodibility based on properties (such as texture, organic carbon) that were available from the Land Use-Cover Area Frame Survey (LUCAS) topsoil data. ...
... At the European level, the mean K-factor value was estimated to be 0.032 t ha h ha −1 MJ −1 mm −1 [15]. At the country level, according to the study by Panagos et al. [20], the mean value of K-factor for Greece was 0.0298 t ha h ha −1 MJ −1 mm −1 . At the catchment level, Kfactor values ranged between 0.009 and around 0.04 t ha h ha −1 MJ −1 mm −1 in the northern central and northwestern parts of Chania, Crete [3,50]. ...
Soil erosion is a severe and continuous environmental problem caused mainly by natural factors, which can be enhanced by anthropogenic activities. The morphological relief with relatively steep slopes, the dense drainage network, and the Mediterranean climate are some of the factors that render the Paleochora region (South Chania, Crete, Greece) particularly prone to soil erosion in cases of intense rainfall events. In this study, we aimed to assess the correlation between soil erosion rates estimated from the Revised Universal Soil Loss Equation (RUSLE) and the landscape patterns and to detect the most erosion-prone sub-basins based on an analysis of morphometric parameters, using geographic information system (GIS) and remote sensing technologies. The assessment of soil erosion rates was conducted using the RUSLE model. The landscape metrics analysis was carried out to correlate soil erosion and landscape patterns. The morphometric analysis helped us to prioritize erosion-prone areas at the sub-basin level. The estimated soil erosion rates were mapped, showing the spatial distribution of the soil loss for the study area in 2020. For instance, the landscape patterns seemed to highly impact the soil erosion rates. The morphometric parameter analysis is considered as a useful tool for delineating areas that are highly vulnerable to soil erosion. The integration of three approaches showed that there is are robust relationships between soil erosion modeling, landscape patterns, and morphometry.
... The soil erodibility factor (K-factor) is a lumped parameter that represents an integrated average annual value of the soil profile reaction to the processes of soil detachment and transport by raindrop impact and surface flow [24]. Consequently, K-factor is best obtained from direct measurements on natural plots [39]. However, this is a difficult task on a national or larger scale. ...
... However, this is a difficult task on a national or larger scale. To overcome this problem, measured K-factor values have been related to soil properties [39] estimating soil erodibility at the European level, based on attributes such as texture, organic matter, soil structure, and permeability, which were available from the Land Use/Cover Area frame Survey (LUCAS) [40] topsoil data [25]. Inverse distance weighting (IDW) was used to interpolate erodibility to a map with a grid-cell resolution of 10 km [6]. ...
Soils provide important regulating ecosystem services and have crucial implications for human well-being and environmental conservation. However, soil degradation and particularly soil erosion jeopardize the maintenance and existence of these services. This study explores the spatio–temporal relationships of soil erosion to understand the distribution patterns of sediment retention services in mainland Portugal. Based on Corine Land Cover maps from 1990 to 2018, the InVEST Sediment Delivery Ratio (SDR) model was used to evaluate the influence of sediment dynamics for soil and water conservation. Spatial differences in the sediment retention levels were observed within the NUTS III boundaries, showing which areas are more vulnerable to soil erosion processes. Results indicated that the Region of Leiria, Douro and the coastal regions have decreased importantly in sediment retention capacity over the years. However, in most of the territory (77.52%), changes in sediment retention were little or were not important (i.e., less than 5%). The statistical validation of the model proved the consistency of the results, demonstrating that the InVEST SDR model is an appropriate tool for estimating soil loss potential by water at regional/national levels, although having its limitations. These findings can be relevant to support strategies for more efficient land-use planning regarding soil erosion mitigation practices and to stimulate further investigation at a national level on this important ecosystem service.
... The soil erodibility factor (K-factor) is a lumped parameter that represents an integrated average annual value of the soil profile reaction to the processes of soil detachment and transport by raindrop impact and surface flow [24]. Consequently, K-factor is best obtained from direct measurements on natural plots [39]. However, this is a difficult task on a national or larger scale. ...
... However, this is a difficult task on a national or larger scale. To overcome this problem, measured K-factor values have been related to soil properties [39] estimating soil erodibility at the European level, based on attributes such as texture, organic matter, soil structure, and permeability, which were available from the Land Use/Cover Area frame Survey (LUCAS) [40] topsoil data [25]. Inverse distance weighting (IDW) was used to interpolate erodibility to a map with a grid-cell resolution of 10 km [6]. ...
Soils provide important regulating ecosystem services and have crucial implications for human well-being and environmental conservation. However, soil degradation and particularly soil erosion jeopardize the maintenance and existence of these services. This study explores the spatio-temporal relationships of soil erosion to understand the distribution patterns of sediment retention services in mainland Portugal. Based on Corine Land Cover maps from 1990 to 2018, the InVEST Sediment Delivery Ratio (SDR) model was used to evaluate the influence of sediment dynamics for soil and water conservation. Spatial differences in the sediment retention levels were observed within the NUTS III boundaries, showing which areas are more vulnerable to soil erosion processes. Results indicated that the Region of Leiria, Douro and the coastal regions have decreased importantly sediment retention capacity over the years. However, in most of the territory (77.52%) changes in sediment retention were little or not important (i.e., less than 5%). The statistical validation of the model proved the consistency of the results, highlighting the usefulness of this methodology to analyse the state of soil erosion in the country. These findings can be relevant to support strategies for more efficient land use planning regarding soil erosion mitigation practices.
... However, this behavior can be explained by considering first of all that the above remark concerns the Mediterranean basin as a whole and does not consider the extreme variability of the soils in the area and consequently of their erodibility. Secondly, it should also be kept in mind that in addition to the erodibility factor, soil erosion also depends on other factors, such as rainfall erosivity, slope, crop management and support practices (Panagos et al. 2012). Furthermore, another factor that plays an important role in reducing erosion, is the protection effect of surface stone cover. ...
Flood events, whose number and intensity are predicted to increase in the Mediterranean region, are difficult to monitor. This causes the number of observations of suspended sediment and total phosphorus concentration (|SS| and |TP|, respectively) during their occurrence to be still scarce. Non-perennial or temporary water bodies, which react more promptly to rainfall events, represent ideal natural observatories. In this study, observations of streamflow, |SS| and |TP|, carried out during some flood events, in the Celone river basin, a temporary river located in south-eastern Italy, are presented. The research examined the correlations between flows, concentrations and loads of sediment and phosphorus and investigated factors that influence sediment and phosphorous dynamics in the river basin. The results show no relationship between the time of the year and the precipitation quantity of each event. The high coefficient of determination of the |SS|–|TP| correlations (R² = 0.67 on average) proves the importance of soil erosive processes in the delivery of phosphorus to the river. More than 73% of the total suspended sediment load and 83% of total phosphorus load in the period 2010–2011 were transported during the 11 monitored events. In addition to the discharge, |SS| and |TP| also depend on numerous other factors related to land management, such as soil cover and fertilizations. The study, thanks to the improved understanding of the mechanisms governing sediment and phosphorus losses, represents a useful contribution for river basin authorities who have to draw up management plans aimed at preventing eutrophication phenomena and soil fertility reduction.
... It was recognized from Figure 4, that the distribution of erodibility factor in Erbil basin is highly affected by soil texture (Silt content) and percentage of organic matter. Panagos et al. [17], mentioned that erodibility represents the susceptibility of soil to erosion, taking into account its inherent properties, such as texture, structure, and organic matter content. Soils with higher erodibility are more vulnerable to erosion, while those with lower erodibility are more resistant to erosion processes. ...
This article presents a comprehensive study on the integrated use of the Universal Soil Loss Equation
(USLE), Geographic Information System (GIS), and remote sensing (RS) techniques for soil erosion
mapping in the Erbil Basin. Soil erosion is a critical environmental issue that affects soil productivity,
water quality, and ecosystem health. Understanding the spatial distribution and severity of soil erosion is
essential for effective land management and erosion control measures. The study utilizes the USLE
model, which considers multiple factors contributing to soil erosion, including rainfall erosivity, soil
erodibility, slope length, slope steepness, and land cover. GIS tools were employed to process and analyze
the spatial data, including digital elevation models (DEMs), soil maps, rainfall data, and land cover
information. Remote sensing data from satellite imagery were incorporated to enhance the accuracy and
spatial coverage of the soil erosion mapping. By integrating these approaches, a comprehensive soil
erosion map of the Erbil Basin was generated, providing insights into the extent and severity of erosion
across the study area. The results depicted variations in soil erosion rates, identifying high-risk areas
prone to erosion and highlighting the factors contributing to erosion vulnerability. The estimated yearly
soil loss in the context of USLE was ranged from 1.9 to 6.3 ton per hectare.
... The mean soil erodibility of the field plots was found as Kfactor = 0.0306 Mg·ha·h·ha −1 ·MJ −1 ·mm −1 (±0.0014), which is a moderate soil erodibility based on the USLE (Universal Soil Loss Equation) [26,57,[87][88][89][90]. ...
The objective of our investigation was to study the various effects of correct and incorrect application of fuzziness exponent, initial parameterization and fuzzy classification algorithms modeling on homogeneous management zones (MZs) delineation of a Coriandrum sativum L. field by using precision agriculture, soil chemical, granular and hydraulic analyses, fuzzy k-means zoning algorithms with statistical measures like the introduced Percentage of Management Zones Spatial Agreement (PoMZSA) (%), factor and principal components analysis (PCA) and geostatistical nutrients GIS mapping. Results of the exploratory fuzzy analysis showed how different fuzziness exponents applied to different soil parameter groups can reveal better insights for determining whether a fuzzy classification is a correct or incorrect application for delineating fuzzy MZs. In all cases, the best results were achieved by using the optimal fuzziness exponent with the full number of parameters of each soil chemical, granular and hydraulic parameter group or the maximum extracted PCAs. In each case study where the factor analysis and PCA showed optimal MZs > 2, the results of the fuzzy PoMZSA clustering metric revealed low, medium and medium to high spatial agreement, which presented a statistically significant difference between the soil parameter datasets when an arbitrary or commonly used fuzziness exponent was used (e.g., φ = 1.30 or φ = 1.50). Soil sampling and laboratory analysis are tools of major significance for performing exploratory fuzzy analysis, and in addition, the FkM Xie and Benny’s index and the introduced fuzzy PoMZSA clustering metric are valuable tools for correctly delineating management zones.
... Where A: actual soil loss (t/ha), R: rainfall erosivity, C: vegetation cover, K: soil erodibility, LS: topographic influence, and P: Management factor. The Rainfall (R) and vegetation cover (C) are the main dynamic factors in this assessment, although there are some changes in other parameters in the long run (Panagos et al., 2012). Hence, the monthly rainfall and vegetation input datasets determine the level of temporal detail of the erosion outputs in a particular study area (Supplementary 2). ...
The Sustainable Development Goals (SDGs) and soil-related activities are closely intertwined. Healthy soil
ecosystems promote the survival of life on Earth and uplift agricultural productivity. One of the issues that is
putting healthy soil ecosystems at risk is soil erosion. The Central Highlands of Sri Lanka were chosen because it
is a humid, tropical nation that is particularly susceptible to climate threats. Soil erosion was tracked and predicted
in connection to rainfall variation in order to reduce soil erosion in farming systems and in supporting the
achievement of the SDGs. A revised universal soil loss equation (RUSLE) was executed to determine soil loss.
Rainfall variance was calculated using trend analyses of extreme rainfall indices and rainfall erosivity. Soil
erosions for the year 2040 under the Representative Concentration Pathways (RCP) 2.6 and 8.5 scenarios were
predicted using a variety of machine learning techniques, including artificial neural networks (ANN), support
vector machines (SVM), and adaptive network-based fuzzy inference system (ANFIS). Results indicated a positive
trend in rainfall erosivity and several other extreme indices. In addition, soil erosion rates increased, with
October recording the highest monthly erosion rates. Based on 2020 soil erosion rates, soil erosion model
forecasts suggest that erosion may range from 4 % to 22 % in 2040. Under the RCP 2.6 and 8.5 climate scenarios,
the rate of soil erosion can be raised from 10.5 t/ha/yr to 12.4 t/ha/yr, respectively. These facts indicate that
rainfall-induced soil erosion will increase the vulnerability of the Central Highlands and pose a severe danger to
the achievement of the SDGs. In order to ensure the well-being of the soil ecosystem, this research offered
strategies to monitor and anticipate soil erosion in support of achieving the SDGs to protect the healthy soil
ecosystem. This methodology can be replicated to minimize soil erosion in farming systems elsewhere.
... The saturated hydraulic conductivity results were classified as slow to moderate. The mean soil erodibility of the field was categorized as moderate based on the USLE (Universal Soil Loss Equation) [89][90][91][92]. It is remarked that the results for θfc and θwp obtained from the hydraulic analysis of the soil are within the normal limits given by Allen et al. [17]. ...
Precision agriculture (PA), management zone (MZ) strategies at the field level, soil analyses, deficit irrigation (DI), and fertilizer Variable Rate Application (VRA) are management strategies that help farmers improve crop production, fertilizer use efficiency, and irrigation water use efficiency (IWUE). In order to further investigate these management strategies, the effects of four soil MZ treatments, which were delineated using PA with fuzzy k-means clustering, two irrigation levels [IR1:FI = full drip irrigation (>90% of θfc), IR2:VDI = variable deficit drip irrigation (60–75% of θfc)], and four VRA fertilizations were studied on coriander yield and essential oil content in a two-year research project in Greece. A daily soil-water-crop-atmosphere (SWCA) balance model and a daily depletion model were developed using sensor measurements (climatic parameter sensors as well as soil moisture sensors). Unbalanced one-way ANOVA (p = 0.05) statistical analysis results revealed that correct delineation of MZs by PA with fuzzy k-means clustering, if applied under deficit irrigation and VRA fertilization, leads to increased essential oil content of coriander with statistically significant differences (SSD) and lower fruit yields; however, without SSD differences among management zones, when appropriate VRA fertilization is applied to leverage soil nutrient levels through the different fuzzy clustered MZs for farming sustainability. Moreover, VDI compared to full irrigation in different MZs yields 22.85% to 29.44% in water savings, thus raising IWUE (up to 64.112 kg m−3), nitrogen efficiency (up to 5.623), and N-P-K fertilizer productivity (up to 5.329).
... The literature provides a detailed description of the methodologies used to estimate the aforementioned factors. In particular, the K-factor was estimated using measured soil data collected during the 2009 LUCAS soil survey campaign across European Union member states, along with the nomograph of Wischmeier and Smith [8] as described by Panagos [10]. The high-resolution EU-DEM was utilized as input to the algorithm developed by Desmet and Govers [11] to estimate the LS factor [12], whereas the P-factor takes into account the Good Agricultural and Environmental Condition (GAEC) measures applied in the EU Member States and the ground observations both on land use/cover and landscape features (LUCAS) [13]. ...
Soil loss by water is a major form of land degradation with environmental and economic consequences. In particular, erosion rates are sensitive to both climate and land cover changes. The present study investigates the temporal changes in soil loss rate over South Europe during the 1980-2018 period. To that end, the Revised Universal Soil Loss Equation (RUSLE) was applied by integrating information from freely available geospatial datasets to conduct a multi-decadal assessment. In this frame, the temporal variability of the two dynamic RUSLE factors, namely rainfall erosivity (R) and cover management (C), was explored. Specifically, the rainfall erosivity values per decade were acquired from a newly developed dataset from the European Soil Data Center (ESDAC), coupling the Rainfall Erosivity Database at European Scale (REDES) and UERRA regional reanalysis rainfall data. On the other hand, land cover data were retrieved from the CORINE dataset (CLC) through the Copernicus Land Monitoring Service for different reference years. The appropriate values were assigned to each CLC category per country according to the recent literature to determine the C-factor. In terms of the other three static RUSLE factors, namely soil erodibility (K), slope length and steepness (LS) and support practice (P), these were obtained from the ESDAC database by exploiting the results of previous pan-European assessments. The results indicate that the mean annual soil erosion rates in South Europe were 6.82, 4.90, 4.89 and 5.26 t/ha/year for the decades 1981
... However, this behavior can be explained by considering rst of all that the above remark concerns the Mediterranean basin as a whole and does not consider the extreme variability of the soils in the area and consequently of their erodibility. Secondly, it should also be kept in mind that in addition to the erodibility factor, soil erosion also depends on other factors such as rainfall erosivity, slope, crop management and support practices (Panagos et al. 2012). Furthermore, another factor that plays an important role in reducing erosion, is the protection effect of surface stone cover. ...
Flood events, whose number and intensity are predicted to increase in the Mediterranean region, are difficult to monitor. This causes the number of observations of suspended sediment and total phosphorus concentration (|SS| and |TP| respectively) during their occurrence to be still scarce. Non-perennial or temporary water bodies, which react more promptly to rainfall events, represent ideal natural observatories. In this study, observations of streamflow, |SS| and |TP|, carried out during some flood events, in the Celone river basin, a temporary river located in south-eastern Italy, are presented.
The research examined the correlations between flows, concentrations and loads of sediment and phosphorus and investigated factors that influence sediment and phosphorous dynamics in the river basin. The results show no relationship between the time of the year and the precipitation quantity of each event. The high coefficient of determination of the |SS|-|TP| correlations (R² = 0.67 on average) proves the importance of soil erosive processes in the delivery of phosphorus to the river. More than 73% of the total suspended sediment load and 83% of total phosphorus load in the period 2010–2011 were transported during the 11 monitored events. In addition to the discharge, |SS| and |TP| also depend on numerous other factors related to land management, such as soil cover and fertilizations.
The study, thanks to the improved understanding of the mechanisms governing sediment and phosphorus losses, represents a useful contribution for river basin authorities who have to draw up management plans aimed at preventing eutrophication phenomena and soil fertility reduction.
... where, SAN= Sand percentage, SIL= Silt percentage, CLA = percentage of Clay, C = Soil Organic Carbon, SN1=Sand content subtracted from 1 divided by 100. The data obtained were extrapolated to the whole watershed using the Inverse Distance Weighing technique [37,38,39]. ...
The rising unpredictability in the food supply chain in many parts of the world is related to soil loss and poor agricultural output. The Revised Universal Soil Loss Equation (RUSLE), widely used for estimating soil loss, was applied in the western mid-hills in Nepal, with steep slopes and fragile geology. This region is at high risk for rapid soil erosion and mass wasting. To estimate soil loss, this study utilized the RUSLE model with experimental erosion plots in the Aadhikhola and Tinahukhola watersheds, capturing real-time erosion in the field. The annual soil loss for the Aadhikhola watershed is estimated at ∼41.4 tons ha−1 yr−1. In contrast, in the Tinahukhola watershed, soil loss is low (∼24.1 tons ha−1 yr−1). Although annual rainfall showed an increasing trend in both watersheds, the change in soil loss was statistically insignificant. The high erosion rates from the experimental plots in both watersheds support the model outputs. Results from the experimental plots recorded the rate of soil erosion for different land use as: irrigated agricultural land > rainfed agricultural land > forests. The trends highlight the role of human activities in enhancing soil erosion in these mountainous terrains in terms of medium to long-term perspectives. Therefore, sustainable agriculture practices in these terrains must investigate alternate ways to decrease soil erosion to support people's livelihoods.
... Several annual soil parameters used in the computation of the RUSLE model and SE (Table 4) were extracted from the ESDAC (Panagos et al. 2015b): (1) P-factor is the support conservation practices factor at a regional level (Panagos et al. 2020); (2) LS-factor is the slope Length and Steepness factor (Panagos et al. 2015a); and (3) K-factor is the Soil Erodibility in Europe at a high resolution (Panagos et al. 2012). Also, the computation of the RUSLE model and SE used a single R-factor for the entire study area, which is the rainfall erosivity factor computed for the Campia weather station (altitude = 448 m; latitude = 40.674°N; ...
Background: The models currently used to predict post-fire soil erosion risks are limited by high data demands and long computation times. An alternative is to map the potential hydrological and sediment connectivity using indices to express the general properties of the burnt landscape.
Aims: In this study, we aimed to answer the question: Do these tools identify post-fire sediment mobilisation hotspots?
Methods: To achieve this, we assessed the spatial variability distribution of the location of soil erosion hotspots using the Index of Connectivity, Revised Universal Soil Loss Equation and the Sediment Export, and compared it with the simulation results of a more complex Landscape Evolution Model (LAPSUS model). Additionally, we evaluated statistical measures of association between the four tools.
Key results: The three tools tested in this study are suitable for identifying sediment mobilisation hotspots, where the erosion rates are above the 95th percentile, and differences between their performance are small.
Conclusions: The results indicate that these tools help locate extreme erosion locations in recently burnt areas.
Implications: These results can be considered for post-fire and water contamination risk management, especially for fast prioritisation of areas needing emergency post-fire intervention.
... The K factor is a complicated soil attribute, which is the ease with which the soil is degraded by waterdrop splashing during rain or irrigation (mainly by sprinklers or waterjets), water runoff, or their combination [3]. The capturing of erosion's principal variable (K factor) in forecasting modeling has proved to be a difficult task [7]. To overcome this issue, implicit methods are used to assess the K factor and allow these studies to be carried out [8]. ...
... The K factor is a complicated soil attribute which is the easiness of the soil been degraded by water splashing during a rainfall or irrigation (mostly with sprinklers or waterjets) event or by water run-off or their combination [3]. It is considered hard to capture the principal variables of erosion forecasting models, like soil's erodibility, represented as K [7]. To overcome this issue, implicit methods are used to assess the K factor and allow these studies to be carried out [8]. ...
The aim of our study is the modeling at the field level of the soil erodibility (K factor) by water (rainfall and irrigation) on traditional tillage (CoTl) and no-tillage (NoTl) plots cultivated with Helianthus annuus utilizing plot observations, soil sampling laboratory analyses, GIS, precision agriculture (PA), and Kriging geostatistical modeling. A split-plot layout consisting of four handlings × three replicates of trial blocks (with a southeast facing 7.5% slope) was used. Grid template surface soil core (0.0–5.0 cm) samples were taken to characterize the textures (sandy, silty, clayey, very fine sandy, and gravelly), organic matter concentrations, and the soil’s microstructure and water permeability categories. One GPS satellite tracker system was utilized to define the sampled positions, and 40 soil cores were air-dried and sieved with a 2 mm sieve to identify the soil’s mechanical microtexture using the Bouyoucos methodology. The organic matter was extracted by chemical oxidation with 1 mol L−1 K2Cr2O7 and titration of the remaining reagent with 0.5 mol L−1 FeSO4. The soil microstructure and permeability categories were defined following the USDA classification system. The soil erodibility by water modeling of K (Mg·ha·h·ha−1·MJ−1·mm−1) was derived according to the Wischmeier nomographic method by incorporating it into a developed GIS geospatial model using Kriging geostatistics. The statistical results of the ANOVA test (p = 0.05) among the soil erodibility datasets showed significant differences between the two tillage systems, as well as between the four management treatments. Moreover, it was found that the no-tillage (NoTl) plots and the treatment of no tillage plus vegetative coverage were the best tillage and agricultural practices for hillslope farm fields and can be considered environmentally friendly farming methods to curb soil erodibility by water, reduce runoff hazard, and maintain the soil’s environment and its beneficial nutrients.
... To estimate the edaphic parameters of the native soil of each A. thaliana accession, location coordinates and public maps from the European Soil Data Centre (ESDAC) database (Panagos et al., 2012) were combined using Q-GIS1. Natural accession coordinates were extracted from GWAPP2 in WGS84 system (latitude and longitude). ...
Carbonate-rich soils limit plant performance and crop production. Previously, local adaptation to carbonated soils was detected in wild Arabidopsis thaliana accessions, allowing the selection of two demes with contrasting phenotypes: A1 (carbonate tolerant, c+) and T6 (carbonate sensitive, c-). Here, A1 and T6 parental lines and F3 population derived from their crossing were cultivated on carbonated soil to evaluate growth, fitness, and the segregation pattern of the progeny. To understand the genetic architecture beyond the contrasted phenotypes a bulk segregant analysis sequencing (BSA-Seq) was performed. In parallel, A1 and T6 seedlings were grown hydroponically under control (pH 5.9) and bicarbonate conditions (10 mM NaHCO , pH 8.3) for conducting transcriptomic analysis. Based on BSA-Seq analysis, we identified 69 candidate genes associated with carbonate tolerance; most of them involved in catalytic activities. Root transcriptome analysis revealed a total of 784 differentially expressed genes (DGEs) in the tolerant line A1 . Association analysis of BSA-Seq and transcriptomics of roots and leaves discovered 18 candidate genes involved in bicarbonate stress responses. The screening of the knock-out mutants of these 18 genes suggested that DAO1 (At1G14130), TBL19 (At5G15900), AHH (AT4G20070), JAZ10 (AT5G13220), and INV-E (At5G22510) may have relevant roles in soil carbonate tolerance.
... To estimate the edaphic parameters of the native soil of each A. thaliana accession, location coordinates and public maps from the European Soil Data Centre (ESDAC) database (Panagos et al., 2012) were combined using Q-GIS1. Natural accession coordinates were extracted from GWAPP2 in WGS84 system (latitude and longitude). ...
In nature, multiple stress factors occur simultaneously. The screening of natural diversity panels and subsequent Genome-Wide Association Studies (GWAS) is a powerful approach to identify genetic components of various stress responses. Here, the nutritional status variation of a set of 270 natural accessions of Arabidopsis thaliana grown on a natural saline-carbonated soil is evaluated. We report significant natural variation on leaf Na (LNa) and Fe (LFe) concentrations in the studied accessions. Allelic variation in the NINJA and YUC8 genes is associated with LNa diversity, and variation in the ALA3 is associated with LFe diversity. The allelic variation detected in these three genes leads to changes in their mRNA expression and correlates with plant differential growth performance when plants are exposed to alkaline salinity treatment under hydroponic conditions. We propose that YUC8 and NINJA expression patters regulate auxin and jasmonic signaling pathways affecting plant tolerance to alkaline salinity. Finally, we describe an impairment in growth and leaf Fe acquisition associated with differences in root expression of ALA3, encoding a phospholipid translocase active in plasma membrane and the trans Golgi network which directly interacts with proteins essential for the trafficking of PIN auxin transporters, reinforcing the role of phytohormonal processes in regulating ion homeostasis under alkaline salinity.
... According to the soil-forming factors and scorpan framework, we selected soil properties, topography, vegetation, climate, and human activities as environmental covariables to map the K factor. Soil properties data were obtained by sample measurements in this study, and their spatial distributions were predicted by the widely used inverse distance weighting method (Olaniya et al., 2020;Panagos et al., 2012;Zhang et al., 2018), with a unified resolution of 90 m. The source and resolution of other environmental covariables are provided in Table 1. ...
Quantifying the spatial distribution of soil erodibility (K factor) in the Qinghai-Tibet Plateau is essential for global soil erosion management. However, many K factor maps have a coarse spatial resolution at the regional scale, and high-resolution mapping is still a challenge. Quantitative analysis of the influence of environmental factors such as soil, topography, climate, and vegetation, and human activities on the K factor is also lacking. Therefore, we mapped the high-resolution (90 m) spatial distribution of the K factor values in southeastern Tibet using a random forest model with multiple environmental variables, based on remote sensing, ground observations (114 sampling points), and gridded datasets. The K factor estimates based on soil particle size composition and organic carbon content ranged from 0.09 to 0.35, showing moderate variation. The random forest model yielded a coefficient of determination > 0.9 and provided detailed information on the spatial distribution of K factor values, especially in large unsampled areas. The predicted K factor values tended to be high in the eastern area and low in the western area. Partial least squares path modeling showed that soil physical properties such as fractal dimension and mean weight diameter of aggregates had the largest influence on the K factor (path coefficient 0.695). Climate and topography also had a considerable influence on the K factor (path coefficient −0.489 and −0.469, respectively), while the influence of vegetation and human activities was minimal. Accordingly, the random forest model is an effective tool for high-resolution spatial distribution mapping of the K factor with limited sampling data.
... where, SAN= Sand percentage, SIL= Silt percentage, CLA = percentage of Clay, C = Soil Organic Carbon, SN1=Sand content subtracted from 1 divided by 100. The data obtained were extrapolated to the whole watershed using the Inverse Distance Weighing technique [37,38,39]. ...
... (1) where, K: soil erodibility (t ha hr ha -1 MJ −1 mm −1 ); M: textural factor defined as percentage of silt plus very fine sand fraction content (0.002-0.1 mm) multiplied by the factor: 100 -clay fraction; OM: organic matter content in percent (%); s: soil structure class (s = 1: very fine granular, s = 2: fine granular, s = 3: medium or coarse granular, s= 4: blocky, platy or massive); and p permeability class (p = 1: very rapid, …, p = 6: very slow) (Panagos et al., 2012). In the present study, the soil data of the study area was extracted from the Harmonized World Soil Database (HWSD). ...
Understanding the contribution of different land uses in soil erosion leads to optimal management and conservation practices to reduce the severity of erosion and consequently, the sustainable management. Changeability of the most effective factors on soil erosion especially soil erodibility and topography in different land uses is a first step to have a general view of soil erosion in the watersheds. Therefore, the present research was carried out to study the soil erodibility (S) and terrain influence (T) factors in different land uses in the Iranian part of the Caspian Sea Basin and identification of erosion critical areas based on topography and soil erodibility factors. In order to prepare land use, S and T maps for the study area, were prepared by using satellite data of moderate resolution imaging spectroradiometer (MODIS), shuttle radar topography mission (SRTM 90m) and harmonized world soil database (HWSD) and the use of geographic information system (GIS) and remote sensing (RS), respectively. The results showed that the mean soil erodibility in the Iranian part of the Caspian Sea Basin varied from zero (soilless areas) to 0.044 (t ha hr ha-1 MJ-1 mm-1). While, among eight studied land use, the highest and lowest mean values of soil erodibility were obtained in the rangeland and permanent snow-water body equal to 0.040 and zero (t ha hr ha-1 MJ-1 mm-1), respectively. Also, the mean terrain influence (T) factor varied from 0.01 to 35.83 and shows more changeability in the study basin. As a result, by considering the high soil erodibility and terrain influence, the maximum erosion potential in the study area are located in the middle parts of the basin, where the highest slope gradients have high soil erodibility values. These areas are mainly located in the south slopes of the Alborz mountains. In this regard, defined critical regions based on topography and soil erodibility factors along with natural and anthropogenic factors can be considered in the planning of soil erosion control in watersheds and soil and water conservation programs.
... Soil erodibility is a key parameter in assessing the soil's susceptibility to erosion; it is essential for predicting soil loss and evaluating its environmental effects (Panagos et al., 2012). The most commonly utilised soil erodibility term is the soil erodibility factor (K) of the Universal Soil Loss Equation (USLE) (Wischmeier et al., 1971). ...
... Modelling for erosion in Niger is difficult, due to a lack of data, especially the data related to soils. Key parameters of erosion prediction models, such as soil erodibility, expressed as k-factor, are difficult to obtain [43,44]. While, wind and water erosion are commonly believed to be prevalent in the Sahel, there is no evidence of the rate of soil erosion or the impact on agriculture [43]. ...
A systematic method, incorporating the revised universal soil loss equation model (RUSLE), remote sensing, and the geographic information system (GIS), was used to estimate soil erosion potential and potential area in the Maradi region of south-central Niger. The spatial trend of seasonal soil erosion was obtained by integrating remote sensing environmental variables into a grid-based GIS method. RUSLE is the most commonly used method for estimating soil erosion, and its input variables, such as rainfall erosivity, soil erodibility, slope length and steepness, cover management, and conservation practices, vary greatly over space. These factors were calculated to determine their influence on average soil erosion in the region. An estimated potential mean annual soil loss of 472.4 t/ac/year, based on RUSLE, was determined for the study area. The potential erosion rates varied from 14.8 to 944.9 t/ac/year. The most eroded areas were identified in central and west-southern areas, with erosion rates ranging from 237.1 to 944.9 t/ac/year. The spatial erosion maps can serve as a useful reference for deriving land planning and management strategies and provide the opportunity to develop a decision plan for soil erosion prevention and control in south-central Niger.
... The mean value of soil erodibility USLE-K factor in the water erosion area was calculated as 0.0321 with a standard deviation of 0.0107 [46]. However, Mediterranean countries (Italy, Spain, Greece, and Portugal) have mean K factor values between 0.039 and 0.042 (ton·ha·h/ha·MJ·mm), which is higher than the mean erodibility in Europe [47]. ...
... The mean value of soil erodibility USLE-K factor in the water erosion area was calculated as 0.0321 with a standard deviation of 0.0107 [46]. However, Mediterranean countries (Italy, Spain, Greece, and Portugal) have mean K factor values between 0.039 and 0.042 (ton·ha·h/ha·MJ·mm), which is higher than the mean erodibility in Europe [47]. ...
... The mean value of soil erodibility USLE-K factor in the water erosion area was calculated as 0.0321 with a standard deviation of 0.0107 [46]. However, Mediterranean countries (Italy, Spain, Greece, and Portugal) have mean K factor values between 0.039 and 0.042 (ton·ha·h/ha·MJ·mm), which is higher than the mean erodibility in Europe [47]. ...
Soil erosion is a severe and complex issue in the agriculture area. The main objective of this study was to assess the soil loss in two regions, testing different methodologies and combining different factors of the Revised Universal Soil Loss Equation (RUSLE) based on Geographical Information Systems (GIS). To provide the methodologies to other users, a GIS open-source application was developed. The RUSLE equation was applied with the variation of some factors that compose it, namely the slope length and slope steepness (LS) factor and practices factor (P), but also with the use of different sources of information. Eight different erosion models (M1 to M8) were applied to the two regions with different ecological conditions: Montalegre (rainy-mountainous) and Alentejo (dry-flat), both in Portugal, to compare them and to evaluate the soil loss for 3 potential erosion levels: 0–25, 25–50 and >50 ton/ha·year. Regarding the methodologies, in both regions the behavior is similar, indicating that the M5 and M6 methodologies can be more conservative than the others (M1, M2, M3, M4 and M8), which present very consistent values in all classes of soil loss and for both regions. All methodologies were implemented in a GIS application, which is free and available under QGIS software.
... Soil erodibility factor, commonly known as the K-factor, is related to the integrated effect of rainfall, runoff, and infiltration on soil loss (Auerswald et al., 2014;Panagos et al., 2012). There are several techniques to measure the K-factor including using soil physiochemical properties, rainfall simulations, wind tunnel experiments, and erosion plot experiments (Shabani et al., 2014). ...
Evaluation of soil loss tolerance and of erosion risk is vital to sustain agricultural productivity and to manage natural resources. In the present study, we attempted to evaluate soil erosion risk of the Dorudzan watershed in Southwest Iran by using the RUSLE model coupled with RS-GIS. The soil erosion risk was compared with the measured soil loss tolerance (T-value). To derive the RUSLE factors, we used a digital elevation map with a 10-m resolution, a 10-year rainfall data set, a digital land use map with soil data obtained from 60 profiles plus Landsat-8 images. The thickness method was employed for determining the T-value. The results revealed that the average of the T-value was 10.4 t ha-1yr-1, ranging from 3.5 to 22.5 t ha-1 yr-1. The T-value in Inceptisols with significantly deeper soils and a higher SOM percentage was noticeably higher than that of in Entisols. Regarding the T-value, the SOM and permeability with correlation coefficients of 0.77 and 0.59, respectively, were the best-correlated properties. The annual soil loss (24.6 t ha-1yr-1), varying from zero in the flat areas up to 153.5 t ha-1 yr-1 in the hilly areas, was more than twice the T-value. Erosion classes of very high, severe, and very severe cover 25.07% (6983.2 ha) of the site. The LS-factor has been identified as the most influential linked to soil erosion.
... • Figure 6, the K value and soil erosion typically exhibited inconsistent trends in most soil types, except for a few soil types that showed consistent trends, suggesting that the K factor had a low correlation with soil erosion and hence it is a secondary factor affecting erosion. Theoretically, under the same conditions of rainfall, land use and slope, soils with high erodibility are more susceptible to erosion than those with low erodibility (Panagos et al., 2012;Liu et al., 2020). However, the way of land use indirectly affects the physical and chemical characteristics of soil, which leads to a certain change in soil erodibility (Panagos et al., 2014). ...
Mastering the spatiotemporal evolution tendency of soil erosion and its influencing factors is of great significance for optimizing regional soil and water conservation measures, ensuring sustainable development. This study calculated the soil erosion modulus and investigated the spatiotemporal dynamics of soil erosion intensity in the Dianchi Lake basin of China during the 1999-2014 period based on the revised universal soil loss equation (RUSLE) model and geographic information system (GIS) and remote sensing (RS) techniques. The results showed that the soil erosion in the Dianchi Lake basin has been improved in the past fifteen years, and the erosion area is shrinking continuously, but the unit erosion intensity is increasing, indicating that local area erosion is still serious. The analysis results show that soil erosion can easily occur of cultivated lands with slopes of 8-25° and a vegetation coverage in 0%-45% forestland. The area of intensity of soil erosion exhibited a decreasing trend, indicating a gradual improvement in the situation of soil erosion. However, the soil erosion phenomenon in cultivated land, forest land and bare land with a slope of 8-25° and a vegetation coverage of 45% or less is severely. This study provides a theoretical foundation and methodological reference for research on soil erosion and its influencing factors in similar highlands Lake basin.
... The soil erodibility factor (K) was estimated for the available soil samples based on the nomograph of Wischmeier et al. (1971). The spatial distribution of K utilized the inverse distance weighing (IDW) method (Angulo-Martinez et al. 2009, Panagos et al. 2012. The study incorporated the protective effect of surface stone cover, in order to avoid soil loss overestimation. ...
The accurate representation of the Earth’s surface plays a vital role in soil erosion modelling. Topography is parameterized in the Universal Soil Loss Equation (USLE) and Revised USLE (RUSLE) by the topographic (LS) factor. For slope gradients of <20%, soil loss values are similar for both models, but when the gradient is increased, RUSLE estimates are only half of those of USLE. The study aims to assess the validity of this statement for complex hillslope profiles. To that end, both models were applied at eight diverse mountainous sub-watersheds. The USLE; RUSLE indices were estimated utilizing the SEAGIS model and a European dataset, respectively. LS factors were in a 3:1 ratio (i.e. USLE:RUSLE) considering the entire basin area. For areas with slopes <20%, gross erosion estimates of both models converged. Sites of strong relief (>20%) USLE yielded significantly higher values than RUSLE.
... Soil erodibility is a key parameter in assessing the soil's susceptibility to erosion; it is essential for predicting soil loss and evaluating its environmental effects (Panagos et al., 2012). The most commonly utilised soil erodibility term is the soil erodibility factor (K) of the Universal Soil Loss Equation (USLE) (Wischmeier et al., 1971). ...
... The K-factor spatial distribution utilized the Inverse Distance Weighing (IDW) method (Angulo-Martinez et al., 2009;Panagos et al., 2012). Overall, high K-factor values characterize the most erodible soils (Fig. 9). ...
In recent years, forest fires have increased in terms of frequency, extent and intensity, especially in Mediterranean countries. Climate characteristics and anthropogenic disturbances lead forest environments to display high vulnerability to wildfires, with their sustainability being threatened by the loss of vegetation, changes on soil properties, and increased soil loss rates. Moreover, wildfires are a great threat to property and human life, especially in Wildland-Urban Interface (WUI) areas. In light of the impacts and trends mentioned above, this study aims to assess the impact of the Mati, Attika wildfire on soil erosion. The event caused 102 fatalities, inducing severe consequences to the local infrastructure network; economy; and natural resources. As such, the Revised Universal Soil Loss Equation (RUSLE) was implemented (pre-; post-fire) at the Rafina, Attika watershed encompassing the Mati WUI. Fire severity was evaluated based on the Normalized Burn Ratio (NBR). This index was developed utilizing innovative remotely sensed Earth Observation data (Sentinel-2). The high post-fire values indicate the fire's devastating effects on vegetation loss and soil erosion. A critical “update” was also made to the CORINE Land Cover (CLC) v. 2018, by introducing a new land use class namely “Urban Forest”, in order to distinguish the WUI configuration. Post-fire erosion rates are notably higher throughout the study area (4.53–5.98 t ha−1 y−1), and especially within the WUI zone (3.75–18.58 t ha−1 y−1), while newly developed and highly vulnerable cites now occupy the greater Mati area. Furthermore, archive satellite data (Landsat-5) revealed how the repeated (historical) wildfires have ultimately impacted vegetation recovery and erosional processes. To our knowledge this is the first time that RUSLE is used to simulate soil erosion at a WUI after a fire event, at least at a Mediterranean basin. The realistic results attest that the model can perform well at such diverse conditions, providing a solid basis for soil loss estimation and identification of high-risk erosion areas.
Context
Livestock grazing throughout Europe has resulted in high diversity of semi-natural areas in past centuries. Currently, most low intensity grazing relying on semi-natural vegetation is found primarily in marginal lands. These areas still host a high-level of biodiversity but are subject to abandonment and agricultural intensification.
Objectives
Spatial information on areas where semi-natural vegetation is still grazed, and how contextual geographic conditions encourage or limit grazing is missing, hindering their protection. We present an interdisciplinary approach to map the spatial distribution of grazing in semi-natural areas of the European Union (EU) and the United Kingdom (UK).
Methods
We first interviewed grazing experts from European countries, who provided us with estimates on grazing across selected land cover classes per environmental zone and Member State. Subsequently, we analysed the spatial distribution of grazing through maximum entropy modeling using pan-European in-situ data on grazing observations (using LUCAS, an EU wide land use survey) and a set of geographic characteristics representing the local socio-economic, terrain, soil and climatic context.
Results
The expert-derived estimates on grazing suggest that 20.6% (or 134 thousand km²) of semi-natural areas in the EU + UK are grazed, although with low livestock densities. In addition, we find that there is great variety across the region in the factors that explain the occurrence of grazing: while in some regions, farmers’ age and distance to markets are most important, in others terrain or climate are influencing the location of grazing. Finally, we were able to map both the grazing probability as well as actual spatial distribution of grazing on semi-natural areas for the whole of EU and UK.
Conclusions
These data can assist in prioritizing future conservation efforts in these unique land systems.
The spatiotemporal variability of rainfall and vegetation play a key role in the soil erosion process. However, most research efforts tend to overlook their strong spatial diversification and time-varying character. The parameters are often expressed in a stationary manner focusing on long-term (usually annual) averages, and/or at rather rough spatial analysis. Apparently, a spatially definite non-static parameterization in the same modelling application leads to more realistic soil loss results. In this context, the study attempted to quantify temporal land degradation at the Sperchios river watershed using the RUSLE model. The dynamic R-factor and C-factor coefficients were delineated on a monthly time step accounting for the climatic and biomass seasonality, respectively. Furthermore, C-factor was estimated at farmland level based on a highly detailed land use/land cover (LULC) dataset that provided explicit definition of its cultivated classes (as to holdings demarcation and crop type identification). Intra-annual soil loss variability, hotspots in conjunction to high-risk seasons, and critical land uses were successfully identified. The outputs will assist agronomists and stakeholders to implement targeted (hence cost-/labour-effective) mitigation measures and optimum erosion control strategies, especially amid the riskiest period-area coupling. The methodology’s reproducibility potential merits the upscaling of dynamic erosion simulation at larger spatial units.
The use of the nomograph by Wischmeier et al. (1971) for calculating the K-factor in the USLE was extremely useful when there was low access to calculators. However, the generalised calculation of this factor requires the development of analytic procedures. This paper presents a detailed analysis of the nomograph and its underlying equation, which is applicable only when the silt plus very find sand fraction does not exceed 70%. We also examined the quality of fit on the nomograph of the adaptations to the equation that have been proposed, as a means of dealing with those areas where the original equation is not applicable. All models are shown to have areas where the fit is deficient or even unacceptable. Besides, the family of curves on the nomograph for the various values taken by the organic matter are not coincident with the mathematical function from which they presumably derive. The study also identifies those areas of the textural triangle in which the soils originally used in developing the USLE are located, with a view to according a lower predictive value to the contrasting areas in which calculations of the K-factor will necessarily be extrapolations. Finally, a new equation for calculating the K-factor is presented, which accurately reproduces the different sections of the nomograph, and allows the poorly functioning graph to be dispensed with. The paper ends with a link to a tool in R for simplifying the procedure for calculating the K-factor, taking into account varying situations of data availability.
निबुवा–तांखुवा जलाधार नेपालको पूर्वी विकास क्षेत्रको कोशी नदी बेसिन अन्तर्गत धनकुटा जिल्लामा ८७.३२
डिग्री देखि ८७.४० डिग्री पूर्वी देशान्तर र २६.९५ डिग्री देखि २७.०५ डिग्री उत्तरी अक्षांशबीच अवस्थित छ । यस जलाधार क्षेत्रको कुल जनसंख्या ३३ हजार ३४९ रहेको छ जसमध्ये महिला ५३% र पुरुस ४७% छन् । यस जलाधार क्षेत्रमा ८ हजार ४३२ घरपरिवार छन् । यो जलाधारले धनकुटा नगरपालिकाको १, २, ३, ४, ५, ६, ७ र ८ वडा एवम् छथर जोरपाटी गाउपालिकाको २ र ३ वडालाई समेटेको छ । यसको कुल क्षेत्रफल ७३ वर्ग किमी छ । यस जलाधार प्राकृतिक स्रोतहरूमा धनी छ । यसले ८० प्रकारका जीव तथा वनस्पतिलाई आश्रय प्रदान गरेको छ । यसले अत्यावश्यक पारिस्थितिकीय सेवाहरू प्रदान गर्दछ, जसले यस जलाधार र तल्लो प्रवाह क्षेत्रमा बसोबास गर्ने मानिसहरूको जीविका र हितलाई निर्धारण गर्दछ । यस जलाधारले खानेपानी, सिंचाइ, दाउरा र अन्य पारिस्थितिकीय सेवाहरू प्रदान गर्नुको साथै स्थानीय विकासमा योगदान पुयाएको छ । तथापि, कोशी नदी बेसिनमा पर्ने यस निबुवा–तांखुवा जलाधारले प्रदान गर्ने सेवाहरूमा जलवायु परिवर्तन र सामाजिक आर्थिक कारकहरू दुवैले विभिन्न प्रकारका चुनौती थपिरहेको छ । यसर्थ, यस जलाधारले प्रदान गर्ने पर्याप्त सेवा तथा अवसरहरू ध्यानमा राखी ती अवसरहरूको सदुपयोग गर्न वर्तमान जलाधार व्यवस्थापन योजना सकेसम्म बढी समावेशी हुनु जरुरी छ । जलवायु र अन्य मानव सिर्जित परिवर्तनहरूले पर्वतीय वातावरण र यहा बसोबास गर्ने मानिसहरूमा धेरै प्रतिकूल प्रभावहरू अगाडि ल्याइदिएको छ । विद्यमान परिवर्तित सन्दर्भमा तथा सामाजिक समावेशितालाई केन्द्रमा राख्दै संरक्षण र विकासबीचको दरिलो सन्तुलनमा ध्यान दिने दिगो जलाधार व्यवस्थापन योजना आजको आवश्यकता हो । यो प्रस्तावित जलाधार व्यवस्थापन योजना सोही दिशामा एक कदम हो । यो जलाधार व्यवस्थापन योजनाको उद्देश्य भनेको नगरपालिका तथा गाउपालिका र तिनका वडा, नदी बेसिन र राष्ट्रिय तहमा रहेका निर्णयकर्ता र पेशाकर्मीहरूलाई निबुवा–तांखुवा जलाधारको स्थिति, समस्या, सवाल, जोखिम र अवसरहरूबारे सचेत गराउदै अल्पकालीन तथा दीर्घकालीन जलाधार व्यवस्थापन तथा सुधारका लागि लगानी बढाउने निर्णयमा मद्दत गर्नु हो । यस योजना तयारीको क्रममा प्रयोग गरिएका कार्यविधि नेपालको मध्य पहाडी क्षेत्रका अन्य जलाधारका लागि पनि सान्दर्भिक हुनेछ । जलाधार तहमा यस प्रकारको योजनाको सफल कार्यान्वयनले ठूला नदी बेसिनहरूलाई समेट्ने एकीकृत नदी बेसिन व्यवस्थापनमा पनि सहयोग गर्न सक्नेछ । सन् २०१७ देखि धनकुटा नगरपालिका र इसिमोडले धनकुटा नगरपालिकाको जलाधार व्यवस्थापन अभ्यासहरूमा सहयोग गर्न सहकार्य गर्दै आएका छन् । नेपालको कोशी नदी बेसिनमा रहेको निबुवा–तांखुवा जलाधारको जैविक—भौतिक र सामाजिक—आर्थिक अवस्थाको बृहत् बुझाइको लागि इसिमोड र वन तथा भू–संरक्षण विभागले पुराना दस्तावेजको (पूर्वकार्य) समीक्षा, स्थलगत अध्ययन, सहभागितामूलक विधिहरू, नमूना विकास र भूस्थानिक प्रविधिहरूबाट जलवायु, जलीय पारिस्थिकीय प्रणाली, पारिस्थितिकीय प्रणाली, लैंगिक, जीविकोपार्जन र सुशासनका पक्षहरूमा तथ्याड्ढ र जानकारी सिर्जना गरेका छन् । यस योजना प्रक्रियालाई इसिमोडको नदी बेसिन तथा हिममण्डल कार्यक्रम अन्तर्गतको कोशी बेसिन पहलबाट सहयोग गरियो । यस जलाधार मूल्याड्ढनले माथिल्लो र तल्लो प्रवाह क्षेत्र जोड्ने दृष्टिकोणबाट एकीकृत निबुवा–तांखुवा जलाधार व्यवस्थापनको लागि आवश्यक कार्यहरूको डिजाइन तथा कार्यान्वयन गर्ने लक्ष्य लिदै प्राकृतिक स्रोतहरू (माटो, भूमि, वन, सतहको पानी र भूमिगत पानी) र पूर्वाधार तथा सेवाहरूको साथै लैंगिक, सामाजिक आर्थिक र सुशासन पक्षहरूसमेतलाई केन्द्रमा राखेको छ ।
Visible–Near-Infrared (Vis-NIR) spectroscopy as a rapid, cost-effective, and non-destructive technique has become an alternative approach to evaluate difficult-to-measured soil properties. Hence, this study aimed to evaluate RUSLE soil erodibility (so-called K-factor) in calcareous soils of a semi-humid watershed located in west of Iran using Vis-NIR spectroscopy combined with topography data in GIS. To this end, the K-factor from the RUSLE model and various soil properties such as texture, organic-matter (SOM), bulk density, pH, EC were determined in 120 soil samples taken from different land uses. Partial least-squares-regression (PLSR) and stepwise-multiple-linear-regression (SMLR) were employed to derive models via spectral data coupled with topographical factors for modeling the K-factor. Results showed that slope-farmlands with SOM = 1.6% and woodlands with SOM = 3.5% had the highest and lowest K-factor, respectively. The PLSR model with respect to R² = 0.63, RMSE = 0.018 Mg h MJ⁻¹ mm⁻¹ and RPD =1.6 had an appropriate performance in determining the K-factor. The results also revealed that the R² and RMSE of the Improved-SMLR model developed from spectral data combined with topographical factors (elevation and slope) increased and decreased by 11% and 15%, respectively compared with the SMLR model developed only from spectral information. We concluded that using Vis-NIR spectroscopy integrated with topographical factor could be an applicable and reliable method for the prediction of K-factor.
Agriculture has been identified as one of the main drivers of environmental degradation in the European Union (EU). It can have negative impacts on air, water, soil and biodiversity. The condition of agroecosystems is affected by soil degradation, especially by soil erosion, which reduces agroecosystems’ capacity to provide essential ecosystem services. Therefore, it is necessary to explore synergies and trade-offs between pressures,
ecosystem condition and services to create relevant information for policy and decision-makers to implement sustainable response actions.
As part of the EU environmental policy, the Mapping and Assessment of Ecosystems and their Services (MAES) Working Group developed appropriate concepts to assess and link ecosystem condition and services. This study aims to test the indicators proposed by MAES to assess ecosystem condition and link it with the ecosystem services provision. For this, we applied a suggested operational framework developed in the context of the
Biodiversity Strategies 2020 and 2030 for the integrated assessment of agroecosystems and regulating ecosystem service control of erosion rates supply at European scale. We quantified and mapped indicators for ecosystem condition, environmental and anthropogenic pressures and soil erosion control. We explored the relationships
between the respective indicators and the capacity of agroecosystems to control soil erosion across the different Environmental Zones (EZ).
Our results indicate that, in general, European agroecosystems have a high capacity to control soil erosion with some variations within the EZ. Supply capacity is positively, negatively and not correlated with the various pressure and condition indicators. Management and climate indicators play a significant role in the assessment of
this service. These results highlight that conservational management practices are fundamental to reduce soil loss and improve agroecosystem condition. However, the design and implementation of such management practices must consider regional and local landscape characteristics, agricultural practices and the needs and opportunities
of stakeholders.
Soil erodibility (K factor) mapping has been accomplished mainly by soil map-linked or geo-statistical interpolation. However, the resulting maps usually have coarse spatial resolution at a regional scale. The objectives of this study were a) to map the K factors using a set of environmental variables and random forest (RF) model, and b) to identify the important environmental variables in the predictive mapping on a regional scale. We collected 101 surface soil samples across southeast China in the summer of 2019. For each sample, we measured the particle size distribution and organic matter content, and calculated the K factors using the nomograph equation. The hyperparameters of RF were optimized through 5-fold cross validation (mtry = 2, ntree = 500, p = 63), and a digital map with 250 m resolution was generated for the K factor. The lower and upper limits of a 90% prediction interval were also produced for uncertainty analysis. It was found that the important environmental variables for the K factor prediction were relief, climate, land surface temperature and vegetation indexes. Since the existing K factor map has an average polygonal area of 6.8 km2, our approach dramatically improves the spatial resolution of the K factor to 0.0625 km2. The new method captures more distinct differences in spatial details, and the spatial distribution of the K factor derived from RF prediction followed a similar pattern with kriging interpolation. This suggests the presented approach in this study is effective for mapping the K factor with limited sampling data.
The aim of this study is the assessment of desertification risk for a typical Mediterranean island, in the frame of climate change and the application of good agricultural practices. Based on the MEDALUS Environmentally Sensitive Area Index (ESAI) approach, the sensitivity in desertification is estimated by employing 15 quantitative parameters divided in to four main quality indices: climate, vegetation, soil, and management quality. The methodology applied for a baseline scenario (current conditions), two future climate change scenarios (RCP 4.5 and RCP 8.5) and a soil quality improvement scenario. According to the results, more than 13% of the island's area is characterized as critically sensitive to desertification in the current conditions. This percentage will increase in the future under both the RCP 4.5 and the RCP 8.5 climate scenarios, where the critical areas will rise above to 15%. By applying the soil quality improvement scenario simultaneously with the climate change scenarios, a slight mitigation of desertification risk in the future could be achieved. The methodology developed in this study may be used to assess desertification process under various climate, soil, and land use management scenarios in regions of the Mediterranean Sea. HIGHLIGHTS
This study investigates, using GIS, the effects of all the above approaches on the sensitivity of desertification risk for the Mediterranean island of Crete (Greece) for the period 2031–2060. This study also highlights the results of the comparison of the future desertification scenarios under the application of GAPs and the maintenance of the traditional-conventional agricultural practices.;
The aim of this study was the spatial and temporal monitoring of soil erosion patterns in Iran. The G2 model was used, as an appropriate tool to provide the required month-time step country-wide soil loss maps and statistical outputs. The input parameters were derived with field surveys and remote sensing imagery (MODIS, SRTM, SPOT, and Sentinel-2 scenes). An innovation for G2 was the consideration of the snow cover effect on the rainfall erosivity. It was also the first time G2 was used to map such a big country like Iran on a country scale. The mean annual soil erosion in Iran was found to be 16.5 t ha⁻¹, which is equal to about 2.7 billion tons of soil loss. In spatial terms, the highest soil loss values were found in the north, west and southwest part of Iran, in the steep slopes in Alborz and Zagros mountains. In temporal terms, the highest and lowest monthly erosion values of 3.2 and 0.09 t ha⁻¹ correspond to January and July, respectively.
Soil erosion is unquestionably the trickiest land degradation that adversely affects agricultural productivity. Since resources are scarce, in developing countries like Ethiopia, implementing soil and water conservation practice at a time on the entire watershed is not feasible. Therefore, recognizing erosion-prone areas (the priority watersheds) based on the magnitude of soil loss rate is indispensable. The purpose of this study is to delineate priority sub-watersheds of the TikurWuha watershed in Ethiopia based upon
the soil loss rate. A universal soil loss equation under the geographic information system environment was employed to estimate the soil loss rate. The result revealed that the average soil loss rate from the watershed is 14.13. It is resulting in a gross soil loss of
962,083 from the entire watershed. A small portion of the watershed (9.22%) is suffering from severe and very severe soil loss rate (> 25). A total of 14.41% of the watershed have soil loss rates above the maximum soil loss tolerance of the area (> 12). Among the seven sub-watersheds in the watershed, four sub-watersheds (SW_3, SW_1, SW_2, and SW_4) are falling under the top priority zone. Soil and water conservation measures should be executed rapidly in the TikurWuha watershed, consistent with the rank of the priority watersheds.
Keywords: Ethiopia; Prioritization; Soil and water conservation; Soil loss rate; Sub-watershed
L'objectif de cette thèse est d’intégrer la notion de Services Écosystémiques (SE), i.e., les bénéfices que la société retire du fonctionnement des écosystèmes, dans le cadre des procédures d'évaluation des projets d'infrastructures de transports terrestres. Pour cela, nous commençons par mettre en lumière les différents défis associés à l'intégration des SE dans les décisions d'implantation d'infrastructures de transport. L'intégration ne peut être réalisée que si l'estimation de la perte de SE est faite de manière spatialement explicite (Chapitre 1). Puis, nous illustrons ce point à travers l'étude de la perte d'un service : la régulation du climat global (Chapitre 2). L'analyse est basée sur l'examen d'un projet d'infrastructure contemporain dans l’ouest de la France, et le même cas d’étude est utilisé dans la suite de cette thèse. À la suite, nous approfondissons la question de la combinaison de la perte directe et de la perte indirecte de SE due aux impacts de l'infrastructure sur la connectivité des entités spatiales (Chapitre 3). Pour les deux types d'impacts, nous intégrons des seuils potentiels sur la fourniture de services en proposant une méthode de prise en compte des effets sur des écosystèmes particulièrement sensibles. Nous comparons différentes options de tracé afin de donner un exemple de la manière dont les choix pourraient être améliorés en cartographiant les pertes directe et indirecte de SE. Enfin, nous montrons l'intérêt de la prise en compte des SE dans l'étude d'impact environnemental et le bilan socio-économique de manière à mesurer l'information supplémentaire qu’apporte une telle intégration (Chapitre 4). Nous montrons que ce type d'analyse peut orienter différentes étapes d'un projet d'infrastructure, des études préliminaires jusqu’à l'étude du tracé final. Dans le cas des études d'impact environnemental, l'intégration de ces considérations permet de mesurer la perte de services engendrée par chaque tracé d'infrastructure et d'intégrer ces pertes en tant que nouveau critère de choix de tr cé. Concernant le bilan socio-économique, la perte de services exprimée en termes monétaires permet de donner des informations quant à la perte sociale engendrée par le tracé final. Ceci peut aider les parties prenantes des projets à mieux appréhender les effets engendrés par la réalisation de l’infrastructure.
The contamination of waters with nutrients, especially nitrogen and phosphorus originating from various diffuse and point sources, has become a worldwide issue in recent decades. Due to the complexity of the processes involved, watershed models are gaining an increasing role in their analysis. The goal set by the EU Water Framework Directive (to reach "good status" of all water bodies) requires spatially detailed information on the fate of contaminants. In this study, the watershed nutrient model MONERIS was applied to the Hungarian part of the Danube River Basin. The spatial resolution was 1078 water bodies (mean area of 86 km 2); two subsequent 4 year periods (2009-2012 and 2013-2016) were modeled. Various elements/parameters of the model were adjusted and tested against surface and subsurface water quality measurements conducted all over the country, namely (i) the water balance equations (surface and subsurface runoff), (ii) the nitrogen retention parameters of the subsurface pathways (excluding tile drainage), (iii) the shallow groundwater phosphorus concentrations, and (iv) the surface water retention parameters. The study revealed that (i) digital-filter-based separation of surface and subsurface runoff yielded different values of these components, but this change did not influence nutrient loads significantly; (ii) shallow groundwater phosphorus concentrations in the sandy soils of Hungary differ from those of the MONERIS default values; (iii) a significant change of the phosphorus in-stream retention parameters was needed to approach measured in-stream phosphorus load values. Local emissions and pathways were analyzed and compared with previous model results.
In the context of the European Union's Soil Thematic Strategy, policy makers require easy access to soil data and information of various types and scales to assess the state of soils at European level. To satisfy this need, the European Commission and the European Environment Agency (EEA) decided to establish the European Soil Data Centre (ESDAC), located at the European Commission's Joint Research Centre.
The ESDAC is one of ten environmental data centres that have been established during the last 4 years in support of policy development, implementation and monitoring by the European Commission's Directorate General for Environment. The ESDAC, located at http://esdac.jrc.ec.europa.eu, has become the focal point for soil data and information at European Union level by hosting a series of soil products and web-based tools that allow access to the data. The ESDAC acts as the primary data contact point for the Commission and EEA to fulfill their information needs. The establishment and the evaluation of harmonised databases should facilitate improved soil protection measures.
Cited By (since 1996): 2, Export Date: 26 April 2012, Source: Scopus
The Universal Soil Loss Equation (USLE) is the most widely used and misused prediction equation in the world. Although it was designed to predict long-term average annual soil loss, it has the capacity to predict event soil losses reasonably well at some geographic locations and not well at others. Its lack of capacity to predict event erosion is highly influenced by the fact the event rainfall–runoff factor used in the USLE and its revisions (RUSLE, RUSLE2) does not consider runoff explicitly. While including direct consideration of runoff in the event rainfall–runoff factor improves the capacity to predict event erosion when runoff is measured, that capacity is reduced by inaccurate runoff prediction methods. Even so, the predictions may be better than when the traditional event rainfall–runoff factor is used if the rainfall–runoff model used to predict runoff works reasonably well. Direct consideration of runoff in the rainfall–runoff factor may improve the ability of the model to account for seasonal effects. It also enhances the ability of the model to account for the spatial variations in soil loss on hillslopes which result from spatial variations in soil and vegetation. However, the USLE model will not provide a capacity to account for deposition taking place on concave hillslopes unless it is coupled with an appropriate sediment transport model, as in done in RUSLE2. Changing the basis of the event rainfall–runoff factor has consequences on a number of the other factors used in the model, in particular new values of the soil erodibility factor need to be determined. Using runoff values from cropped areas is necessary to account for differences in infiltration capacities between vegetated and tilled bare fallow areas, but requires re-evaluation of the crop factors.
Rainfall erosivity is a major causal factor of soil erosion, and it is included in many prediction models. Maps of rainfall erosivity indices are required for assessing soil erosion at the regional scale. In this study a comparison is made between several techniques for mapping the rainfall erosivity indices: i) the RUSLE R factor and ii) the average EI 30 index of the erosive events over the Ebro basin (NE Spain). A spatially dense precipitation data base with a high temporal resolution (15 min) was used. Global, local and geostatistical interpolation techniques were employed to produce maps of the rainfall erosivity indices, as well as mixed methods. To determine the reliability of the maps several goodness-of-fit and error statistics were computed, using a cross-validation scheme, as well as the uncertainty of the predictions, modeled by Gaussian geostatistical simulation. All methods were able to capture the general spatial pattern of both erosivity indices. The semivariogram analysis revealed that spatial autocorrelation only affected at distances of ∼15 km around the observatories. Therefore, local interpolation techniques tended to be better overall considering the validation statistics. All models showed high uncertainty , caused by the high variability of rainfall erosivity indices both in time and space, what stresses the importance of having long data series with a dense spatial coverage.
Rainfall erosivity is a major causal factor of soil erosion, and it is included in many prediction models. Maps of rainfall erosivity indices are required for assessing soil erosion at the regional scale. In this study a comparison is made between several techniques for mapping the rainfall erosivity indices: i) the RUSLE R factor and ii) the average EI30 index of the erosive events over the Ebro basin (NE Spain). A spatially dense precipitation data base with a high temporal resolution (15 min) has been used. Global, local and geostatistical interpolation techniques were employed to produce maps of the rainfall erosivity indices, as well as mixed methods (regression plus local interpolation). To determine the reliability of the maps several goodness-of-fit and error statistics were computed, using a cross-validation scheme. All methods represented correctly the spatial patterns of both erosivity indices, but the mixed approaches tended to be better overall considering the validation statistics. Additionally, they allowed identifying statistically significant relationships between rainfall erosivity and other geographical variables, as elevation and distance to the water bodies. All models had a relatively high uncertainty, caused by the high variability of rainfall erosivity indices both in time and space, what stresses the importance of using the longest data series available with a good spatial coverage.
Scientific planning for soil and water conservation requires knowledge of the relations between those factors that cause loss of soil and water and those that help to reduce such losses. The soil loss prediction procedure presented in this handbook provides specific guidelines which are needed for selecting the control practices best suited to the particular needs of each site. The procedure is founded on an empirical soil loss equation that is believed to be applicable wherever numerical values of it factors are available. KEYWORDS: TROPAG textbar Miscellaneous subjects textbar Climatology textbar Land Conservation and Management textbar USA (Mainland).
The paper focuses on the concept, mapping and discussion of loess distribution in Western, Central and Eastern Europe at a scale of 1:2,500,000. The research work is based on studies and data compilation primarily carried out in the 1970s and 1980s [Fink, J., Haase, G., Ruske, R., 1977. Bemerkungen zur Lößkarte von Europe 1:2,5 Mio. Petermanns Geographische Mitteilungen 2(77), 81–94; Fink, J., 1969 Les progres de l’ etude de loess en Europe. Bulletin de l’ Association française pour l’ etude du Quarternaire 3–12. Haase, G., Ruske, R., Fink, J., 1983. Conception, preparation and some results of the Loess Map of Europe on a scale 1:2,5 Million. INQUA Newsletter 1983(1), 7–10] and completed recently by additional material and literature references. Reference is also made to recent GIS-based data processing and visualisation techniques that were utilised for the final version of the European Loess Map.The paper provides an overview of the history of the conceptualisation of the map as well as on the loess study in Europe, and than considers the cartographic data on loess sediment formation and distribution in Europe. The classification of loess and loess-like sediments and their distribution throughout Europe as reproduced in the map are discussed [Haase, G., Lieberoth, I., Ruske, R., 1970. Sedimente und Paläoböden im Lössgürtel. In: Richter, H., Haase, G., Lieberoth, I., Ruske, R. (Eds.), Periglazial-Löß-Paläolithikum om JUngpleistozän der Deutschen Demokratischen Republik; Petermanns Geographische Mitteilungen 274, 99–212]. The paper illustrates the final state of the loess distribution map of Europe at a scale of 1:2,500,000 and the digital data references on which it is based. Some applications of the map are suggested.
This project takes a look at the variation of the parameters related to soil erodibility (fractions of clay, silt, fine and coarse sand; organic matter, permeability, and structure) coming from soil pits from the Community of Madrid's soil map (Spain), according to Soil Taxonomy at subgroup level. It draws the conclusion that map erodibility shouldn't be estimated from a soil map because the K factor obtained does not present significant differences among the different types of soil. One or more key factors related with soil erodibility must be taken into account if erodibility maps are to be drawn. This research has shown that silt and structure could be considered key factors for erodibility maps of the area, but not significant differences have been found in important factors such as clay or organic matter due to the wide range of data variance. In order to elaborate erosion risk maps the use of the K factor from the physiographical map is a good alternative. When data are grouped according to these criteria significant differences in K factor are shown. Erodibility was greater in soils developed over gypsic material, with a value of 0.63+/-0.28, than in high plateaus (locally know as alcarrias), with a value of 0.40+/-0.18. In order to adequately represent soil erodibility, a kriging geostatistic technique is used, which reduces the variation of the factors considered when they are found to correlate, as is the case with the parameters considered to calculate K factor.
European soil data centre: response to European policy support and public data requirements Spatial variability of the soil erodibility parameters and their relation with the soil map at subgroup level
- Eurostat
- Luxembourg
- P Panagos
- Van Liedekerke
- M Jones
- A Montanarella
- L Perez-Rodriguez
- R Marques
- M J Bienes
Eurostat, Luxembourg. Panagos, P., Van Liedekerke, M., Jones, A., Montanarella, L., 2012. European soil data centre: response to European policy support and public data requirements. Land Use Policy 29 (2), 329e338. Panagos, 2006. The European soil database. GEO: Connexion 5 (7), 32e33. Perez-Rodriguez, R., Marques, M.J., Bienes, R., 2007. Spatial variability of the soil erodibility parameters and their relation with the soil map at subgroup level. Sci. Total Environ. 378 (1e2), 166e173.
Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE) Agricultural Handbook Soil erosion risk assessment in Italy
- K G Renard
- G R Foster
- G A Weesies
- D K Mccool
- D C Yoder
- Van
- J M Knijff
- R J A Jones
- L Montanarella
Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K., Yoder, D.C., 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). Agricultural Handbook, vol. 703. U.S. Department of Agriculture, Washington, DC, 404 pp. Van der Knijff, J.M., Jones, R.J.A., Montanarella, L., 2000. Soil erosion risk assessment in Italy. European Soil Bureau. European Commission, JRC Scientific and Tech-nical Report, EUR 19044 EN, 52 pp.
The European soil database. GEO: Connexion. v5 i7
- Panagos
Land Use and Cover Area frame Survey
LUCAS, 2009. Land Use and Cover Area frame Survey. Web address: http://epp.eurostat.
ec.europa.eu/portal/page/portal/lucas/methodology (accessed September 2011).