Soil Erosion Potential Research Articles

Tools for USLE-CP-factor calculation and actual erosion risk on field block level for Switzerland

The calculation of the cover management factor (C-factor) and support practices factor (P-factor) is an important element in the Universal Soil Loss Equation (USLE). In Switzerland, a potential soil erosion risk map of arable land and a field block map that represents the basis of the agriculturally used areas in the country are available. A CP-factor tool was developed adapted to Swiss agronomic and environmental conditions, which allows to calculate CP-factors easily for various crop rotations and management practices. The calculated CP-factor values can be linked to any field block in the potential soil erosion risk map to determine the actual soil erosion risk for the field block. A plausibility check with other C-factor tools showed a sound match. This user-friendly calculation makes the CP-Tool and the actual erosion risk more accessible for authorities and GIS users. With Python and QGIS as open source resources, it is also possible to easily improve the tools. Linking the two tools provides substantial added value for education and training, advising farmers and policy, as well as scientific research, and can serve as a reference for other countries.•USLE-CP-factor and actual erosion risk calculation on small scale field block level.•Developed and programmed based on open source resources for further improvements.•Both tools increase the knowledge of management practices for GIS- and non GIS users.

Vulnerability of Potential Soil Erosion and Risk Assessment at Hilly Farms using InSAR Technology

Vulnerability of Potential Soil Erosion and Risk Assessment at Hilly Farms using InSAR Technology

Influence of Biochar on Soil Physicochemical Properties, Erosion Potential, and Maize (Zea mays L.) Grain Yield under Sandy Soil Condition

ABSTRACT Degraded soils in humid tropics are characterized by low fertility and high soil erosion potential. Hence, field experiments were conducted during 2018 and 2019 cropping seasons on an Alfisol at Owo in the forest–savanna transition zone of southwest Nigeria to evaluate the effects of the application of biochar (B) produced from waste wood of trees on physicochemical characteristics, erosion potential, and maize yield. Four treatments considered were control (no biochar application, 10 t ha−1 B, 20 t ha−1 B and 30 t ha−1 B). The treatments were laid out in a randomized complete block design with three replications. Results in both years indicated that B application significantly improved grain yield of maize and soil chemical properties, and reduced bulk density (BD) and penetration resistance (PR), and increased porosity (PO), moisture content (MC), and the mean weight diameter (MWD) of soil aggregates. Pooled over 2 years, B applied at 10, 20 and 30 t ha−1 increased grain yield of maize by 44.4%, 66.7%, and 88.9%, respectively, compared with the control. Averaged over 2 years, incorporating B into the soil significantly reduced soil loss (SL) by 32.3%, 56.9%, and 69.7% at 10, 20, and 30 t ha−1 application rates, respectively, compared with the control. The formation of macroaggregates in the B-amended soils is the critical factor to improve soil erosion potential. Therefore, the application rate of 30 t ha−1 B is considered as suitable for severely degraded soil because this application rate efficiently improves maize yield and soil properties and reduces SL.

Impact of soil erosion potential uncertainties on numerical simulations of the environmental fate of radiocesium in the Abukuma River basin

The Fukushima Dai-ichi Nuclear Power Plant accident in March 2011 resulted in the deposition of significant quantities of radionuclides, including radiocesium (137Cs), over a wide area. Most of the deposited 137Cs is strongly adsorbed on fine soil particles such as clay and silt near the ground surface. Therefore, to estimate the environmental fate of 137Cs, it is necessary to predict its transport with eroded sediment in rainfall-runoff processes. In this study, a distributed radiocesium prediction model was applied to simulations of 137Cs transport associated with hydrological processes in the Abukuma River Basin, the largest river system in Fukushima, over the period of 2011–2012. The soil erosion potential, which is a key input to the distributed radiocesium prediction model, was estimated using the Universal Soil Loss Equation (USLE). This study focused on the uncertainty in estimating the environmental fate of 137Cs associated with the USLE factors. The USLE has five physically meaningful factors: the rainfall and runoff factor (R), soil erodibility factor (K), topographic factor (LS), cover and management factor (C), and support practice factor (P). Because the USLE factors were determined using various methods, R, LS, and the product of C and P (C×P) were divided into two, three, and five cases, respectively, based on previous studies. Therefore, we conducted 30 different simulations. The average total 137Cs outflow during the computational period in the simulation cases using the same USLE factors was 13.3 and 11.7 TBq for R (two cases), 12.6, 13.9 and 10.9 TBq for LS (three cases), and 26.5, 8.64, 0.47, 22.8 and 4.03 TBq for C×P (five cases). For the total outflow, C and P had the highest uncertainty of all the USLE factors. The outflow rates of the average total 137Cs in the simulation cases using the same C and P from the croplands and forest areas and from the undisturbed croplands and paddy fields were 62–91% and 18–34%, respectively. These results were due to the high erodibility of the croplands, the large forest areas in grids with high 137Cs deposition density, and the high concentration of 137Cs in the soil of the undisturbed croplands and paddy fields. This study indicates that land use, especially forest areas, croplands, and undisturbed paddy fields, has a significant impact on the environmental fate of 137Cs.

Spatial Assessment of the Effects of Land Cover Change on Soil Erosion in Hungary from 1990 to 2018

As soil erosion is still a global threat to soil resources, the estimation of soil loss, particularly at a spatiotemporal setting, is still an existing challenge. The primary aim of our study is the assessment of changes in soil erosion potential in Hungary from 1990 to 2018, induced by the changes in land use and land cover based on CORINE Land Cover data. The modeling scheme included the application and cross-valuation of two internationally applied methods, the Universal Soil Loss Equation (USLE) and the Pan-European Soil Erosion Risk Assessment (PESERA) models. Results indicate that the changes in land cover resulted in a general reduction in predicted erosion rates, by up to 0.28 t/ha/year on average. Analysis has also revealed that the combined application of the two models has reduced the occurrence of extreme predictions, thus, increasing the robustness of the method. Random Forest regression analysis has revealed that the differences between the two models are mainly driven by their sensitivity to slope and land cover, followed by soil parameters. The resulting spatial predictions can be readily applied for qualitative spatial analysis. However, the question of extreme predictions still indicates that quantitative use of the output results should only be carried out with sufficient care.

Integrated GIS-based RUSLE approach for quantification of potential soil erosion under future climate change scenarios.

Human-induced agricultural and developmental activities cause substantial alteration to the natural geography of a landscape; thereby accelerates the geologic soil erosion process. This necessitates quantification of catchment-scale soil erosion under both retrospective and future scenarios for efficient conservation of soil resources. Here, we present a revised universal soil loss equation (RUSLE) based soil erosion estimation framework at an unprecedentedly high spatial resolution (30 × 30m) to quantify the average annual soil loss and sediment yield from an agriculture-dominated river basin. The input parameters were derived by using the observed rainfall data, soil characteristics (soil texture, hydraulic conductivity, organic matter content), and topographic characteristics (slope length and percent slope) derived from digital elevation model (DEM) and satellite imageries. The developed approach was evaluated in the Brahmani River basin (BRB) of eastern India, wherein the different RUSLE inputs, viz., rainfall erosivity (R factor), soil erodibility (K factor), topographic (LS factor), crop cover (C factor), and management practice (P factor) factors have the magnitude of 1937 to 4867MJmmha-1h-1year-1, 0.023 to 0.039thhaMJ-1ha-1mm-1, 0.03 to 74, 0.16 to 1, and 0 to 1, respectively. The estimated average annual soil loss over the BRB ranged from 0 to 319.55tha-1year-1, and subsequent erosion categorization revealed that 54.2% of basin area comes under extreme soil erosion zones in the baseline period. Similarly, the sediment yield estimates varied in the range of 0.96 to 133.31tha-1year-1, and 35.81% area were identified as high soil erosion potential zones. The extent of erosion under climate change scenario was assessed using the outputs of HadGEM2-ES climate model for the future time scales of 2030, 2050, 2070, and 2080 under the four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The severity of soil erosion under climate change is expected to have a mixed impact in the range of -25 to 25% than the baseline scenario. The outcomes of this study will serve as a valuable tool for decision-makers while implementing management policies over the BRB, and can be well extended to any global catchment-scale applications.

Multi-time scale variability of rainfall erosivity and erosivity density in the karst region of southern China, 1960–2017

Rainfall erosivity, a measure of the potential for soil erosion by water, is an important factor for estimating soil loss. Understanding the variation tendency of rainfall erosivity is especially critical for soil and water conservation in the fragile ecological environment of the karst region in southern China. This study analysed the rainfall erosivity at multiple spatial and temporal scales based on daily rainfall data observed at 223 meteorological stations during the period of 1960–2017. A daily rainfall erosivity model, co-kring interpolation, regression analysis, gravity model and the Mann-Kendall test were applied in the analysis process. The results indicate the following: (i) The mean annual rainfall erosivity is 5130.00 MJ·mm·ha−1·h−1 in southern China, with a range of 3964.24 to 6425.87 MJ·mm·ha−1·h−1, and varies greatly among different provinces. (ii) The magnitude of rainfall erosivity varies unevenly among seasons, with the mean rainfall erosivity in summer being almost 15 times higher than that in winter. (iii) The annual and seasonal rainfall erosivity has increased in the karst region of southern China over the past 58 years, whereas at the province scale, the seasonal trend in rainfall erosivity is more complex, and the trends are not necessarily linear and positive. Furthermore, at the interdecadal scale, there is no regular trend, and the data exhibit considerable variation. (iv) The temporal variation characteristics of erosivity density are basically consistent with those of rainfall erosivity, and the two show a significant high correlation. (v) The gravity centre of annual rainfall erosivity is located in Tongdao County, while the monthly gravity centre has shifted across Guangxi, Hunan and Guizhou. In summary, knowledge of rainfall erosivity patterns is valuable for assessing the risk of soil erosion and formulating countermeasures.

Assessment of Soil Erosion Using the RUSLE Model for the Epworth District of the Harare Metropolitan Province, Zimbabwe

Urban development without adequate soil erosion control measures is becoming a major environmental concern in developing urban areas across Africa. These environmental disturbances encompass rampart Land Use and Land Cover changes (LULC) due to a high population growth rate and increased economic activities. To understand the influence of accelerated LULC changes and urban expansion as major drivers in landscape degradation in the Epworth district of the Harare Metropolitan Province, the RUSLE model was employed. This considers land use, soil, climate and topography as input parameters in the assessment of the extent and impact of these drivers on soil erosion. The Revised Universal Soil Loss Equation (RUSLE) was used to predict the potential erosion between 1984 and 2018 and soil erosion risk for the years 2000 and 2018. The mean rate of the predicted potential soil erosion was 13.2 t ha−1 yr−1 (1984–2018); areas especially vulnerable to erosion were predicted for foot slope areas with direct tributaries to the major streams and steep sloping zones. The average soil erosion risk was estimated at 1.31 t ha−1 yr−1 for the year 2000 and 1.12 t ha−1 yr−1 for 2018. While the overall potential soil loss decreased between 2000 and 2018, the potential soil loss was observed to increase tremendously in residential areas, which doubled in extent between 2000 and 2018. The findings reveal that about 40% of the Epworth district was threatened by unsustainable soil loss resulting from increased soil erosion risk within the built-up areas.

Assessment of Soil Erosion at Multiple Spatial Scales Following Land Use Changes in 1980-2017 in the Black Soil Region, (NE) China.

Impact of land use and land cover change on soil erosion is still imperfectly understood, especially in northeastern China where severe soil erosion has occurred since the 1950s. It is important to identify temporal changes of soil erosion for the black soil region at different spatial scales. In the present study, potential soil erosion in northeastern China was estimated based on the Revised Universal Loss Equation by integrating satellite images, and the variability of soil erosion at different spatial scales following land use changes in 1980, 1990, 2000, 2010, and 2017 was analyzed. The regionally spatial patterns of soil loss coincided with the topography, rainfall erosivity, soil erodibility, and use patterns, and around 45% of soil loss came from arable land. Regionally, soil erosion rates increased from 1980 to 2010 and decreased from 2010 to 2017, ranging from 3.91 to 4.45 Mg ha−1 yr−1 with an average of 4.22 Mg ha−1 yr−1 in 1980–2017. Areas with a rate of soil erosion less than 1.41 Mg ha−1 yr−1 decreased from 1980 to 2010 and increased from 2010 to 2017, and the opposite changing patterns occurred in higher erosion classes. Arable land continuously increased at the expense of forest in the high-elevation and steep-slope areas from 1980 to 2010, and decreased from 2010 to 2017, resulting in increased areas with erosion rates higher than 7.05 Mg ha−1 yr−1. At a provincial scale, Liaoning Province experienced the highest soil erosion rate of 9.43 Mg ha−1 yr−1, followed by Jilin Province, the eastern Inner Mongolia Autonomous Region, and Heilongjiang Province. At a county scale, around 75% of the counties had a soil erosion rate higher than the tolerance level. The county numbers with higher erosion rate increased in 1980–2010 and decreased in 2010–2017, resulting from the sprawl and withdrawal of arable land.

The use of RUSLE and GCMs to predict potential soil erosion associated with climate change in a monsoon-dominated region of eastern India

Soil is one of the most important natural resources; therefore, there is an urgent need to estimate soil erosion. The subtropical monsoon-dominated region also faces a comparatively greater problem due to heavy rainfall with high intensity in a very short time and the presence of longer dry seasons and shorter wet seasons. The Arkosa watershed faces the problem of extreme land degradation in the form of soil erosion; therefore, the rate of soil erosion needs to be estimated according to appropriate models. GCM (general circulation model) data such as MIROC5 (Model for Interdisciplinary Climate Research) of CMIP5 (Coupled Model Intercomparison Project Phase 5) have been used to project future storm rainfall and soil erosion rates following the revised universal soil loss equation (RUSLE) in various influential time frames. Apart from that, different satellite data and relevant primary field-based data for future prediction were considered. The average annual soil erosion of Arkosa watershed ranges from 6 t/ha/year. The very high (> 6 t/ha/year) and high (5–6 t/ha/year) soil loss areas are found in the southern, south-eastern, and eastern part of the watershed. Apart from this, low (1–2 t/ha/year) and very low (< 1 t/ha/year) soil loss areas are associated with the western, northern, southern, and major portion of the watershed. Extreme precipitation rates with high kinetic energy due to climate change are favorable to soil erosion susceptibility. The results of this research will help to implement management strategies to minimize soil erosion by keeping authorities and researchers at risk for future erosion and vulnerability.

Modelling the Effect of Conservation Measures on Potential Soil Erosion: A USLE and GIS Approach

Modelling the Effect of Conservation Measures on Potential Soil Erosion: A USLE and GIS Approach

Quantifying the soil erosion legacy of the Soviet Union

We examine the historical legacy of the Soviet Union on the current rate of human-induced soil erosion in its successor countries. We use a spatial regression discontinuity design and high-resolution soil erosion data. Our results suggest strong discontinuities in current soil erosion rates along the former border of the Soviet Union. We find that soil erosion in countries that were part of the former Soviet Union is 26% lower than in neighboring countries (0.77 tons per hectare and year). In contrast, we do not find such discontinuity in potential soil erosion under natural vegetation, underlining that this effect is man-made. We show that the main mechanism is a sharp discontinuity in forest dynamics, whereas general economic and demographic differences are less important.

Estimation of soil erosion risk in southern part of Syria by using RUSLE integrating geo informatics approach

Soil erosion is one of the major problems that threatens agricultural production and sustainability of natural resources in Syria. More than 85% of Syrian agricultural land is exposed to soil erosion at different rates. The present study estimated soil erosion in the eastern part of Yarmouk Basin in Al-Swida governorate (Southern Syria), by integrating the Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) approach. The parameters used for the RUSLE model were prepared from climatic data, field data, and satellite imageries. Results showed that average erosivity was 374.19 MJ mm ha−1 h−1 yr−1, while the K-factor ranged from 0.22 to 0.36 ton.ha.MJ−1.mm−1, and LS-factor reached 45% in some places. The estimated potential soil erosion ranged from 1.26 to 350.5 t ha− 1 yr− 1, with an average of 137.4 t ha− 1 yr− 1. Meanwhile, ninety-five percent of the study area experienced acceptable rate of erosion with soil loss, which ranged between 0 to 5 t ha− 1 yr− 1. While, rest of the area experienced unacceptable erosion rate, which ranged from 5 to 350 t ha− 1 yr− 1. Therefore, the areas which are experienced unacceptable erosion rate need immediate conservation plan from soil and water conservation point of view.

Land use and climate change impacts on global soil erosion by water (2015-2070)

Soil erosion is a major global soil degradation threat to land, freshwater, and oceans. Wind and water are the major drivers, with water erosion over land being the focus of this work; excluding gullying and river bank erosion. Improving knowledge of the probable future rates of soil erosion, accelerated by human activity, is important both for policy makers engaged in land use decision-making and for earth-system modelers seeking to reduce uncertainty on global predictions. Here we predict future rates of erosion by modeling change in potential global soil erosion by water using three alternative (2.6, 4.5, and 8.5) Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. Global predictions rely on a high spatial resolution Revised Universal Soil Loss Equation (RUSLE)-based semiempirical modeling approach (GloSEM). The baseline model (2015) predicts global potential soil erosion rates of [Formula: see text] Pg yr-1, with current conservation agriculture (CA) practices estimated to reduce this by ∼5%. Our future scenarios suggest that socioeconomic developments impacting land use will either decrease (SSP1-RCP2.6-10%) or increase (SSP2-RCP4.5 +2%, SSP5-RCP8.5 +10%) water erosion by 2070. Climate projections, for all global dynamics scenarios, indicate a trend, moving toward a more vigorous hydrological cycle, which could increase global water erosion (+30 to +66%). Accepting some degrees of uncertainty, our findings provide insights into how possible future socioeconomic development will affect soil erosion by water using a globally consistent approach. This preliminary evidence seeks to inform efforts such as those of the United Nations to assess global soil erosion and inform decision makers developing national strategies for soil conservation.

Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India

Land degradation is very severe in the subtropical monsoon-dominated region due to the uncertainty of rainfall in the long term, and most of the rainfall occurs with high intensity and kinetic energy over short time periods. So, keeping this scenario in view, the main objective of this work is to identify areas vulnerable to soil erosion and propose the most suitable model for soil erosion susceptibility in subtropical environment. The implementation of machine learning and artificial intelligence techniques with a GIS environment for determining erosion susceptibility is highly acceptable in terms of optimal accuracy. The point-specific values of different elements from random sampling were considered for this study. Sensitivity analysis of the predicted models (i.e., analytical neural network, geographically weighted regression and GWR–ANN ensemble) was performed using the maximum causative factors and related primary field observations. The area under curve of receiver operating system reveals precision with 87.13, 89.57 and 91.64 for GWR, ANN and ensemble GWR–ANN, respectively. The ensemble GWR–ANN is more optimal than the GWR, ANN for determining water-induced soil erosion susceptibility. The process of soil erosion is not a unidirectional process, so the multidimensional impacts from the conditioning factors have to be determined precisely by considering the maximum possible factors as well as selecting optimal models for specific regions.

Space-Time Drought Dynamics and Soil Erosion in Puruliya District of West Bengal, India: A Conceptual Design

This paper integrates drought and soil erosion problems of Puruliya district of West Bengal. The space-time dynamics of vegetative drought is assessed based on the mean monthly time-series MODIS data for the period of 2000–2013. The drought recurrence ranges from 0 to 29 months, and the mean frequency was assessed to be 7.09 months for the entire period under deliberation. The potential soil disintegration of the district was estimated using the Revised Universal Soil Loss Equation in the geo-spatial environment. The locale has been arranged into six potential soil erosion classes ranging from low-to-severe risk depending upon the computed soil erosion amount. The mean soil erosion rate of the district was anticipated as 76.29 t ha−1 year−1. The erosion amount is high in gently sloping grounds covering mainly harvested agricultural fields and open fields. These areas contribute mostly the soil erosion in this district. It is found that in this area the slope length-steepness factor is the major erosion-controlling factor. About one-fourth of the total area comes under the threat of a very high-to-severe erosion zone. Also, it was observed that the drought condition of the district is strongly correlated with the soil moisture condition in the drought-affected months and the precipitation amount. The study likewise distinguished the conceivable interrelationship between the soil erosion problem and drought frequency for better monitoring and policy decision making. A considerable increasing trend is observed in mean soil erosion with increasing frequency of drought-affected months for the study period.

Effects of land use and land cover change on soil erosion potential in Berhe district: a case study of Legedadi watershed, Ethiopia

ABSTRACT This research was done in Legedadi watershed since the area was highly susceptible to soil erosion problem that could be aggravated by heavy rainfall, and steep slope. The aim of the present study was to examine the impact of LULC change on soil erosion potential in Legedadi watershed, and its implication on sedimentation problems. Multi-temporal Landsat imagery of TM (1985), TM (1997) and ETM+ (2013) were deployed to categorize a LULC map of the study watershed. Soil erosion risk was calculated by using GIS and RUSLE. Digital image processing and classification technique have been done using ERDAS Imagine 9. 2. Results indicate that there was a continuous and active LULC change observed in the watershed for the last 28 years. The settlement area and cultivated land were increased by 14.34% and 18.3%, whereas grazing land and bare land classes were reduced continuously by 25.74% and 18.47% between (1985–2103) respectively. The estimated annual soil losses were 0.0-330 t ha−1year−1in 2013 whereas in 1997 its range was 0-257.1 t ha−1year−1. A mean annual soil loss in the watershed was 54.19 t ha−1year−1 in 1997and 66.21 t ha−1year−1 in 2013, respectively. The average annual soil loss from each sub-watershed ranges from 34.57–89.19 t ha−1year−1. This calls for the coordinated action of the local communities and government in ensuring sustainable natural resource conservation systems considering the Climate Resilient Green Economy strategy of Ethiopia.

Study on a soil erosion sampling survey in the Pan-Third Pole region based on higher-resolution images

Soil erosion is one of the most severe global environmental problems, and soil erosion surveys are the scientific basis for planning soil conservation and ecological development. To improve soil erosion sampling survey methods and accurately and rapidly estimate the actual rates of soil erosion, a Pan-Third Pole region was taken as an example to study a methodology of soil erosion sampling survey based on high-spatial-resolution remote sensing images. The sampling units were designed using a stratified variable probability systematic sampling method. The spatiotemporal characteristics of soil erosion and conservation were taken into account, and finer-resolution freely available and accessible images in Google Earth were used. Through the visual interpretation of the free high-resolution remote sensing images, detailed information on land use and soil conservation measures was obtained. Then, combined with the regional soil erosion factor data products, such as rainfall-runoff erosivity factor (R), soil erodibility factor (K), and slope length and steepness factor (LS), the soil loss rates of some sampling units were calculated. The results show that, based on these high-resolution remote sensing images, the land use and soil conservation measures of the sampling units can be quickly and accurately extracted. The interpretation accuracy in 4 typical cross sections was more than 80%, and sampling accuracy, described by histogram similarity in 11 large sampling sites, show that the landuse of sampling uints can represent the structural characteristics of regional land use. Based on the interpretation of data from the sample survey and the regional soil erosion factor data products, the calculation of the soil erosion rate can be completed quickly. The calculation results can reflect the actual conditions of soil erosion better than the potential soil erosion rates calculated by using the coarse-resolution remote sensing method.

Effects of Landscape Changes on Soil Erosion in the Built Environment: Application of Geospatial-Based RUSLE Technique

The world’s ecosystem is severely affected by the increase in the rate of soil erosion and sediment transport in the built environment and agricultural lands. Land use land cover changes (LULCC) are considered as the most significant cause of sediment transport. This study aims to estimate the effect of LULCC on soil erosion potential in the past 20 years (2000–2020) by using Revised Universal Soil Loss Equation (RUSLE) model based on Geographic Information System (GIS). Different factors were analyzed to study the effect of each factor including R factor, K factor, LS factor, and land cover factor on the erosion process. Maps generated in the study show the changes in the severity of soil loss in the Chitral district of Pakistan. It was found out that 4% of the area was under very high erosion risk in the year 2000 which increased to 8% in the year 2020. An increase in agricultural land (4%) was observed in the last 20 years which shows that human activities largely affected the study area. The outcomes of this study will help the stakeholders and regulatory decision makers to control deforestation and take other necessary actions to minimize the rate of soil erosion. Such an efficient planning will also be helpful to reduce the sedimentation in the reservoir of hydraulic dam(s) constructed on Chitral river, which drains through this watershed.

Global rainfall erosivity changes between 1980 and 2017 based on an erosivity model using daily precipitation data

Rainfall erosivity (R) is the most important factor affecting soil erosion. It is crucial to estimate it on a global scale to identify the potential soil erosion risk area and to develop mitigation strategies. On a large scale, R is often estimated based on available precipitation data, such as annual, monthly and daily rainfall. Compared with the rainfall erosivity model driven by monthly and annual rainfall data, the daily erosivity model can provide more information about intense erosive events, producing more accurate results. Given the climate variability in different regions, regional parameters are needed when applying the daily erosivity model on a global scale. However, regional parameter estimation is a challenge, and few studies have addressed this.Here, we present a method for parameterising the daily erosivity model for different regions of the world by generating equations for estimating the required parameters of the model in each climate zone. This was based on a literature review to collect the data sites with estimated parameter values globally. We then applied the model to compare the global average annual and monthly rainfall erosivity changes between 1980–1999 (P1) and 2000–2017 (P2). The results showed that globally, average annual R decreased in all climate zones in P2, except for a substantial increase in Df (Cold without dry season), located in high latitudes of the northern hemisphere. At the monthly scale, except for the region 40-70°N, all regions experienced a considerable decrease. Even though the global total annual and monthly rainfall erosivity was reduced between 1980 and 2017, the risk of extreme daily erosivity is high. The acceptable agreements between the values estimated in our model, estimated from high-resolution rainfall data and retrieved from the literature, indicate that the present approach could assess the effects of climate change on soil erosion on a global scale, particularly regarding extreme rainfall events.