High Rainfall Erosivity Research Articles

Modelling and Estimation of Average Annual Soil Loss in Ika South Local Government Area of Delta State, Nigeria

Soil-related phenomenon and the Sustainable Development Goals (SDGs) particularly SDG-15 are strictly tied. A healthy soil ecosystem encourages the existence of life on Earth and for agricultural purposes. One of the key factors that is contributing to unhealthy soil ecosystems is soil erosion. The research involves the assessment and modelling of soil erosion using the Revised Universal Soil Loss Equation (RUSLE II) with the sole aim of ascertaining the annual Soil loss within the study area, this will be a veritable tool in estimating soil loss in farming systems and in supporting the achievement of the SDGs. It provides a concrete estimation of the distribution of soil erosion at Ika South LGA, through the combination of the influencing factors such as rainfall aggressivity, soil erodibility, topography, vegetation cover, and soil conservation practices. The soil loss estimated at Ika South ranges from 0 to 49119.93 t/ha/year. Results have shown that 5% of the surface area displays a high rate of soil loss, and it represents zones with moderate to severe soil erosion. These areas are mainly situated in moderate to very high slope where the runoff is high. This loss is favoured by the other factors of erosion, which are also combined to accelerate erosion losses significantly (58% of the total area has a very high rainfall erosivity), moderately erodible soils (49% of the soils show a K factor between 0.128 and 0.27t ha H/ha MJ mm), 78% of the soil have nearly level to gently undulating plains, and 14% of the areas representing steep slopes. The results revealed that, there is a probability that the rate of erosion will increase in the future in this area which pose a great threat to the achievement of the SDG-15. Therefore, proper land-use planning should be adopted such as cropping pattern for agricultural land in local area. Accurate planning should be done as a means for restoring degraded vegetation.

Rainfall Erosivity Projection in South‐East Australia Using the Improved Regional Climate Simulations

ABSTRACTRainfall erosivity is one of the most dynamic factors in the soil erosion process. The increase in soil erosion caused by high rainfall erosivity, and the subsequent loss of soil nutrients, can lead to reduced food production and ecosystem services. This research program under the New South Wales (NSW) Climate Change Adaptation Strategy, assesses rainfall pattern change, rainfall erosivity and erosion risk across NSW under future climate conditions. Daily rainfall erosivity and erosion risk were modelled by Revised Soil Loss Universal Equation (RUSLE) approach and compared with that driven by observed rainfall data. Future rainfall erosivity and soil erosion risk change were investigated from daily precipitation projection of the updated NSW and Australian Regional Climate Modelling (NARCliM1.5) for two future scenarios, RCP4.5 and RCP8.5, from the historical (1986–2005) to far future (2060–2079) periods. The annual average rainfall erosivity is projected to increase about 8% under RCP 4.5 and further decrease 5% under RCP 8.5 in NSW due to the predicted temperature rises. More frequent heavy rainfall events are projected to occur during summer (December–January–February), and the rainfall from these extreme rainfall events is expected to account for 51% of the total annual rainfall in the far future. NARCliM‐derived results underestimate annual rainfall erosivity compared with observation‐derived erosivity. There are greater instability (root mean squared error [RMSE]: 803.2) and erosivity uncertainty (Bias: 16%~48%) in high rainfall zones. At a monthly scale, dry months (June–July–August) are becoming drier, while wet months (December–January–February) are becoming wetter and more erosive. 67% of NSW is predicted to experience increased rainfall erosivity under RCP4.5, whereas most of NSW will shift to drought and its consequent effects under the high‐end emission scenario (RCP 8.5). To address the dual challenges of excessive wetness in coastal and north‐east NSW and increasing aridity in Western NSW, it is necessary to develop climate change adaptation management strategies based on high‐risk areas and monthly or seasonal conditions. With the emerging launch of NARCliM2.0, we anticipate further improvements of these predictions will be achieved by more accurate models and data at higher spatial and temporal resolutions.

Soil erosion risk assessment of the Lakhmess watershed (northwestern Tunisia) via the SEAGIS model: Inferred prioritization of risky sub-watersheds

Water erosion in Tunisian semi-arid regions causes harmful effects by silting reservoirs and reducing agricultural lands and soil fertility. Several factors are involved in the erosion process: rain erosivity, soil fragility, and degraded land cover on steep slopes associated with the intensification of inappropriate human practices. Thus, identifying erosion vulnerable sub-watersheds based on the assessed soil loss rate is very important to apply suitable conservation measures. The current research aimed to prioritize risky areas in the Lakhmess watershed, north-west Tunisia via the Soil Erosion Assessment using Geographical Information System (SEAGIS) model. To prioritize sub-watersheds vulnerable to soil erosion and sediment yield, the Lakhmess watershed, covering an area of 162 km2, was divided into 16 sub-watersheds (L1–L16), according to the hydrographic network. Then, the mean annual soil erosion rate and the mean annual sediment yield in the watershed were estimated by integrating the Revised Universal Soil Loss Equation (RUSLE) and the Sediment Delivery Ratio (SDR) in the SEAGIS model and their spatial distribution was determined. The obtained results indicate that the estimated average annual soil erosion rate is 4.2 t/ha/y and the annual sediment yield is 2.6 t/ha/y. Maner's SDR model was selected as the best model for estimating SY, with standard error, standard deviation, and coefficient of variation values of 0.75%, 0.01, and 0.45%, respectively. The prioritization of the Lakhmess sub-watersheds based on the estimated soil loss rate reveals that among the 16 sub-watersheds, three sub-watersheds (L10, L12, and L15) were identified as being in a very high priority soil erosion class. The high soil erosion rate and sediment yield in these sub-watersheds is explained by the steep slope and a high rainfall erosivity factor. Six sub-watersheds (L2, L4, L5, L6, L7, and L16) were found to belong to a very low priority soil erosion class, as they are characterized by a very gentle slope, which appears to be an extremely determining factor. These findings constitute a basis for decision makers to plan effective conservation measures to conserve agricultural lands, soil, and water resources in northwestern Tunisia.

Evaluating rainfall erosivity on the Tibetan Plateau by integrating high spatiotemporal resolution gridded precipitation and gauge data

High-precision rainfall erosivity mapping is crucial for accurately evaluating regional soil erosion on the Tibetan Plateau (TP) under the backdrop of climate warming and humidification. Although high spatiotemporal resolution gridded precipitation data provides the foundation for rainfall erosivity mapping, the increasing spatial heterogeneity of rainfall with decreasing temporal granularity can lead to greater errors when directly computing rainfall erosivity from gridded precipitation data. In this study, a site-scale conversion coefficient was established so that rainfall erosivity calculated using hourly data can be converted to rainfall erosivity calculated using per-minute data. A revised model was established for calculating the rainfall erosivity based on high-resolution hourly precipitation data from the Third Pole gridded precipitation dataset (TPHiPr). The results revealed a notable underestimation in the original calculation results obtained using the TPHiPr, but strong correlation was observed between the two sets of results. There was a significant improvement in the Nash–Sutcliffe coefficient of efficiency (from −0.39 to 0.80) and the Percent Bias (from −63.95 % to 0.37 %) after model revision. The TPHiPr effectively depict the spatial characteristics of rainfall erosivity on the TP. It accurately reflected the rain shadow area on the northern flank of the Himalayas and the dry-hot valley in the Hengduan Mountains. It also showed high rainfall erosivity values in the tropical rainforest area on the southern flank of the eastern Himalayas. The overall trend of rainfall erosivity has increased on the TP during the period 1981 to 2020, with 65.91 % of the regions exhibiting an increasing trend and 22.25 % showing significant increases, indicating an intensified risk of water erosion. These findings suggest that the 40-year-high spatial resolution rainfall erosivity dataset can provide accurate data support for a quantitative understanding of soil erosion on the TP.

Correlation between flood couplet-based sediment yield and rainfall patterns in a small watershed on the Chinese Loess Plateau

Understanding how rainfall patterns impact sediment yields during rainfall events is important to explore soil erosion changes and to prevent watershed flood disasters. However, few studies identified sediment yields variation affected by rainfall patterns at continuous deposited flood couplet scale. This study analyzed sediment yields under different rainfall patterns and correlated particle depositional features on the condition that the erosive environment was obviously changed by anthropogenic activities in a check dam-controlled small watershed on the Chinese Loess Plateau. Results showed that erosive rainfall events, having formed 40 flood couplets behind the check dam between 1978 and 2010, can be grouped into three rainfall patterns (i.e., rainfall patterns I, II, and III) according to changes in rainfall amount, erosivity, and duration. Rainfall pattern III, characterized by high rainfall amount and erosivity within a short duration (i.e., rainstorm) accounted for only 15.0 % of erosive rainfall events while yielding 44.7 % of the total sediment. Conversely, rainfall pattern I (the most common, 55.0 %), characterized by its long duration and relatively low rainfall amount and erosivity, only contributed 25.3 % of the total sediment. Both sediment yields and thickness proportions of coarse particle layer in flood couplets exhibited a decreasing trend under all three rainfall patterns. Meanwhile, the main factor that impacted sediment yields changed from rainfall in the 1980s and 1990s to the “Grain for Green” program dominated anthropogenic activities since 1999. For a small watershed with a few terraces and only one check dam at the outlet, artificial vegetation restoration significantly reduced coarse soil particle loss and sediment yield. Findings showed that sediment yield and deposition feature variations in flood couplets under different rainfall patterns are helpful in improving watershed soil and water conservation measures against flood disasters, particularly under rainstorms.

Differences in sediment provenance from rainfall and snowmelt erosion in the Mollisol region of Northeast China

AbstractDifferences in erosion patterns inevitably lead to different soil loss processes. However, the differences in sediment sources between rainfall and snowmelt erosion are not well studied. To address this gap, a study was conducted in a small agricultural catchment (3.5 km2) using the fingerprinting technique to quantify the various sources contributing to sediment, as well as determine the magnitude of sediment total nitrogen (TN), total phosphorus (TP), total potassium (TK) and soil organic matter (SOM) loads from three sources (cultivated land, unpaved roads and gullies) during the rainy season and spring thawing period. Sediment sources from 17 rainfall erosion and seven snowmelt erosion events of varying magnitudes were compared. The Collins and Bayesian mixing models, coupled with geochemical tracers (Cs, V, Cd, Pb and Ag), were applied to assess the contribution proportion of individual sediment sources to suspended sediment. According to the results obtained from the Bayesian mixing model with higher accuracy, cultivated land, unpaved roads and gullies contributed 60.3% (load‐weighted mean contribution [LMC] = 82.8%), 11.0% (LMC = 6.5%) and 28.7% (LMC = 10.7%) of the sediment in the rainy season, respectively. Cultivated land was the dominant sediment source for rainfall erosion, with high rainfall erosivity and sediment concentration. However, for snowmelt erosion, gullies were the primary sediment source (LMC = 66.2%), followed by cultivated land (LMC = 19.8%) and unpaved roads (load‐weighted = 14.0%). Meanwhile, in the spring thawing period, the contributions of unpaved roads to sediment were higher than in the rainy season. During the rainy season, the greatest contributions of TN, TP, TK and SOM were observed from cultivated land, with respective LMC values of 94.2%, 89.4%, 81.1% and 94.8%. With the increase of sediment contribution from gullies in the spring thawing period, its LMC of TN, TP, TK and SOM increased to 42.6%, 58.3%, 65.0% and 38.0%, respectively. The results of the study could be useful for understanding the processes of soil erosion, clarifying the driving mechanism of compound erosion, developing the compound erosion prediction model and optimizing the spatial configuration of erosion control measures.

Spatial and temporal pattern of soil erosion in Crimea

In the paper spatial and temporal variability of soil erosion for the periods of 1958–1988 and 1989–2018 is estimated on the basis of RUSLE in Crimea. Erosion rates vary from very slight to very high in the region. Strongly eroded areas are found in the mountainous regions in the Southern Crimea, mainly attributable to a combination of a high rainfall erosivity (R-factor) and relatively steep slopes (LS-factor). Soil loss in 1958–1988 exceeded the values in 1989–2018 on approximately 13%, while the share of moderate, high and very high-eroded areas varied within the range of less than 1%. € ese changes are associated with both the reduction of agricultural cultivated lands and the variability of soil erosion factors. € e soil erosion formation is mainly connected with the factors of rainfall erosivity, slope steepness as well as the cover-management factor in Crimea. Comparison of actual suspended sediment yield in the gauges with the values of soil erosion demonstrated that most rivers area characterized by the accumulation of eroded material when 10–50% of the washed products reach the gauges.

Unveiling the Wrath of Erosion: Assessing the Impacts of Soil Erosion on the Coastal Region of Bangladesh

Study on soil erosion in the coastal region of Bangladesh is crucial due to its severe environmental and socio-economic impacts. Understanding erosion patterns, identifying vulnerable areas, and developing effective conservation strategies are essential to safeguard agricultural productivity, preserve coastal ecosystems, and protect the livelihoods of coastal communities. This study focuses on soil erosion in the low-lying, flood-prone coastal regions of Bangladesh. The Revised Universal Soil Loss Equation (RUSLE) technique was used to calculate soil erosion rates. The southern section of the study area, characterized by high rainfall erosivity (R) and soil erodibility (K), exhibited the highest erosion rates at 1446 t/ha/year. The region experiences significantly higher rainfall erosivity than other parts of the world. Cox’s Bazar, Chittagong, Feni, and Patuakhali receive over 3000 mm/yr of precipitation, while Teknaf and Hatiya surpass 4000 mm/yr. The vulnerability of the soft, sand-rich topsoil to water-driven gully and rill erosion contributes to the high erodibility factor. Conversely, the northwest and low-lying marshy areas exhibit low erosion rates due to the flat terrain and deposition of eroded soil mass from the hinterland. The hilly areas of Chittagong, Teknaf, and Cox’s Bazar experience above-average erosion rates. Effective soil conservation and management strategies are essential for reducing soil erosion in Bangladesh’s coastal regions, considering local environmental conditions such as precipitation and soil type. The findings of this research provide valuable insights for future studies and policies on soil erosion management in the coastal districts of Bangladesh. By guiding efficient conservation efforts, this information can help mitigate the adverse impacts of soil erosion on the environment and economy. IUBAT Review, 6(2): 1-24

Exploring biochar addition impacts on soil erosion under natural rainfall: A study based on four years of field observations on the Loess Plateau

Biochar’s potential impact on soil erosion has gained attention, but remained inconsistent, particularly lacks the results from continuous field monitoring under diverse rainfall conditions. This study aimed to explore the long-term variation in soil erosion induced by biochar addition under distinct natural rainfalls. Six runoff plots, treated with different amounts of biochar derived from apple tree branches (0, 1%, 2.5%, 4%, 5.5% and 7%), were established. Over a four-year period from 2017 to 2020, runoff and soil loss from these plots were continuously monitored and analyzed. Among 215 recorded rainfall events, 27 events resulted in runoff and sediment production were classified into three rainfall patterns (RI, RII and RIII) using K-means clustering. RIII characterized by high mean rainfall depth (138.80 mm), duration (3443 min), and erosivity (879 MJ.mm (ha. h)−1), but infrequent occurrence, followed by RII and RI with moderate and low mean depth, duration and erosivity, but higher frequency. Biochar addition, relative to control, resulted in an average annual runoff reduction, ranging from 9% to 36%, with a mean reduction of 28%, and reduced runoff for each rainfall, ranging from 4% to 28%, with a mean reduction of 12%. Similarly, biochar addition also reduced average annual soil loss, ranging from 43% to 79%, with a mean reduction of 61%, and reduced soil loss for each rainfall, ranging from 29% to 74%, with a mean reduction of 52%. The reduction of runoff and soil loss was positively correlated with biochar addition rates. Notably, the soil loss reduction was exceeded that of runoff. Soil erosion was negatively linked to surface area of soil particles (p < 0.05), total carbon, and total organic carbon (p < 0.01), but positively correlated with sand content (p < 0.01) and bulk density (BD) (p < 0.05) under biochar addition. Comparison of the mean reduction of runoff and soil loss among three rainfall patterns, the order was as follows: RII>RI>RIII. The optimal controlling of soil erosion was observed in 7% biochar addition under RII. Runoff-soil loss relationships well-fitted by power equation, showed soil incorporated with biochar treatments exhibited heightened susceptibility to erosion under RIII with high intensity compared to RII and RI with moderate or low rainfall intensity. Thus, the findings indicate biochar is a potential soil amendment to improve a degraded or degrading sloping farmland, however, the rainfall characteristics need to be considered when it is applied to the sloping farmland prone to erosion.

Rainfall erosivity in the Republic of North Macedonia: Fournier index approach

Rainfall erosivity is a crucial factor influencing soil erosion, which results from the detachment, transportation, and deposition of soil particles caused by rainfall and runoff. Rainfall erosivity assessment is a fundamental process aimed at quantifying the erosive power of rainfall events in a given region. The paper provides an overview of rainfall erosivity assessment in several heterogeneous regions of the Republic of North Macedonia based on rainfall data from meteorological stations characterized by different climatic and geographical conditions. Having into consideration that rainfall erosivity can be quantified by using various indices, this study uses two common ones: Fournier Index (FI) and the Modified Fournier Index (MFI) based on monthly and annual precipitation totals for the period 1951-2020. The results imply that in general, the studied stations belong to a low class of erosivity. It is found that FI has greater sensitivity and provides more details, also showing years with high and even very high rainfall erosivity while MFI gives more information for moderate rainfall erosivity.

Assessment, regionalization, and modeling rainfall erosivity over Brazil: Findings from a large national database

In this study, we used a large national database to assess the rainfall erosivity (RE) patterns in time and space over the Brazilian territory. Thereby, RE and erosivity density (ED) values were obtained for 5166 rainfall gauges. Also, the concentration of the RE throughout the year and the RE's gravity center locations were analyzed. Finally, homogeneous regions regarding RE values were delimited and estimative regression models were established. The results show that Brazil's mean annual RE value is 5620 MJ mm ha−1 h−1 year−1, with considerable spatial variation over the country. The highest RE magnitudes were found for the north region, while the northeast region shows the lowest values. Regarding the RE's distribution throughout the year, in the southern region of Brazil, it is more equitable, while in some spots of the northeastern region, it is irregularly concentrated in specific months. Further analyses revealed that for most of the months, the RE's gravity centers for Brazil are in the Goiás State and that they present a north-south migration pattern throughout the year. Complementarily, the ED magnitudes allowed the identification of high-intensity rainfall spots. Additionally, the Brazilian territory was divided into eleven homogeneous regions regarding the RE patterns and for each defined region, a regression model was established and validated. These models' statistical metrics were considered satisfactory and, thus, can be used to estimate RE values for the whole country using monthly rainfall depths. Finally, all database produced are available for download. Therefore, the values and maps shown in this study are relevant for improving the accuracy of soil loss estimates in Brazil and for the establishment of soil and water conservation planning on a national scale.

Improving satellite-based global rainfall erosivity estimates through merging with gauge data

Rainfall erosivity is a key factor that influences soil erosion by water. Rain-gauge measurements are commonly used to estimate rainfall erosivity. However, long-term gauge records with sub-hourly resolutions are lacking in large parts of the world. Satellite observations provide spatially continuous estimates of rainfall, but they are subject to biases that affect estimates of rainfall erosivity. We employed a novel approach to map global rainfall erosivity based on a high-temporal-resolution (30-min), long-term (2001–2020) satellite-based precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG)—and mean annual rainfall erosivity from the Global Rainfall Erosivity Database (GloREDa) stations (n = 3286). We used a residual-based merging scheme to integrate GPM-IMERG-based rainfall erosivity with GloREDa using Geographically Weighted Regression (GWR). The accuracy of the GWR-based merging scheme was evaluated with a 10-fold cross-validation against GloREDa stations. Based on GPM-IMERG-only, the global mean annual rainfall erosivity was estimated to be 1173 MJ mm ha−1 h−1 yr−1 with a standard deviation of 1736 MJ mm ha−1 h−1 yr−1. The mean value estimated via GPM-IMERG merged with GloREDa was 2020 MJ mm ha−1 h−1 yr−1 with a standard deviation of 3415 MJ mm ha−1 h−1 yr−1. Overall, GPM-IMERG-only estimates underestimated rainfall erosivity. The underestimations were greatest in areas of high rainfall erosivity. The accuracy of rainfall erosivity estimates from GPM-IMERG merged with GloREDa substantially improved (Nash-Sutcliffe efficiency = 0.83, percent bias = −2.4%, and root mean square error = 1122 MJ mm ha−1 h−1 yr−1) compared to estimates by GPM-IMERG-only (Nash-Sutcliffe efficiency = 0.51, percent bias = 27.8%, and root mean square error = 1730 MJ mm ha−1 h−1 yr−1). Improving satellite-based global rainfall erosivity estimates through integrating with gauge data is relevant as it can contribute to enhancing soil erosion modeling and, in turn, support land degradation neutrality programs.

Mercury pollution in the soil and river water of the Ratai watershed by artisanal and small-scale gold mining activities in Pesawaran District, Lampung, Indonesia

&lt;p&gt;The high risk of mercury pollution in the Ratai watershed due to artisanal and small-scale gold mine activities in Pesawaran District, Lampung, Indonesia, was evaluated. Studies are needed to improve the understanding of the effect of heavy metal pollution, especially mercury (Hg), in soil and river water along the watershed because of erosion. The Universal Soil Loss Equation (USLE) integrated with Geographic Information Systems (GIS) model was used to analyze the transport of mercury (Hg) from nonpoint source pollution loads to the Way Ratai River using rainfall-based erosion. Soils and river water samplings were conducted in 2020. Biophysical conditions, the land cover, and the rainfall data of the Ratai watershed were also taken into account. The results indicated that Hg concentrations in the soil and the river water were high, ranging from 0.26-28.9 mg L&lt;sup&gt;-1&lt;/sup&gt; and from 0.08-14.1 mg L&lt;sup&gt;-1&lt;/sup&gt;, respectively. The reason for the high Hg contents in the soils and the river waters was the high erosion rate in the watershed. As the study area was characterized by high rainfall erosivity and low to high soil erodibility, the erosion-caused Hg contamination in soil and water can be significant if no conservation strategies are developed.&lt;/p&gt;

Assessment of rainfall erosivity in Southwest Bulgaria using the Fournier index

Soil erosion is one of the most widespread environmental problems in Bulgaria, which often occurs together with other dangerous phenomena such as landslides, soil degradation, wind erosion, etc. The main factor for erosion is rainfall. Rainfall erosivity is an indicator of the potential of rainfall to generate erosion processes and is of fundamental importance in the analysis of the climate vulnerability of a given region. The purpose of the present study is to assess rainfall erosivity in one of the most populated regions of Bulgaria – the Southwest by comparing the results of two indices - the Fournier index (FI) and the modified Fournier index (MFI). The results show that, in general, the studied stations belong to a low class of erosivity. It is found that MFI has greater sensitivity and provides more detail, showing also years with high and even very high rainfall erosivity.

USLE modelling of soil loss in a Brazilian cerrado catchment

Soil is a fundamental resource that supports much of the Earth's vegetation cover and is indispensable for societies. However, the types of use, changes in cover and inadequate management can cause erosion, with negative results for the economy, for the quality of life of the populations and for an ecologically balanced development. This study aimed to evaluate soil erosion in the Verdinho River Basin, located in Southwestern Goiás, in the Brazilian Cerrado, using geotechnology products and tools, in order to provide qualitative and quantitative results to support the planning and management of the river basin effectively and efficiently. The study uses Universal Soil Loss Equation (USLE) as a modelling tool. The study area is characterized by high rainfall erosivity, medium soil erodability. Potential erosion ranged from weak to very strong. The results on the integration of natural physical aspects with land use (anthropic action) resulted in soil loss corresponding to the category from slight (0–2.5 t ha−1.year−1) to extremely high (>100 t ha−1.year−1). The highest soil losses are observed in the areas of cultivated land, pasture and exposed soil associated with environments with greater natural potential for erosive processes. Inter-rill, rill erosion, as well as inter rill, rill and gully are the recognized soil erosion types gully erosion were observed, indicating which areas in the river basin are exposed to risk of soil erosion at severe level and the need to implement actions of recovery of degraded areas, as well as appropriate soil management and conservation practices.

Rates, factors, and tolerances of water erosion in the Cerrado biome (Brazil): A meta‐analysis of runoff plot data

AbstractDue to the high rainfall erosivity and highly erodible soils, water erosion is severe in Brazil. Soil and ecosystem degradation occurs when erosion exceeds on‐ and off‐site soil loss tolerances, with significant socioeconomic and environmental impacts. In the last 50 years, the Brazilian Cerrado had 53% of its original vegetation converted to agriculture and pastureland. Although erosion plot studies exist in the region, the data are fragmented and unexplored, hindering the development of soil conservation policies. The objective of the present research was to compile, systematize, and statistically analyze the existing erosion plot data in the Brazilian Cerrado, correlating the observed results with different environmental and management factors, and with the corresponding soil loss tolerances. Twenty runoff plot datasets of the Brazilian Cerrado, encompassing 5 states, 10 sites, 108 plots, and 360 plot·years were compiled and thoroughly analyzed. Mean annual rainfall, runoff, and soil loss were 1443.5 mm year−1, 83.1 mm year−1, and 8.9 Mg ha−1 year−1, respectively. After the data were normalized with respect to plot length, steepness, and climate, runoff and soil loss were found to be significantly higher in soils with impermeable horizons and in land uses without permanent soil cover (p &lt; 0.05). Erosion under permanently covered plots was below the on‐ and off‐site soil loss tolerances. A power equation provided the best fit between plot runoff and soil loss (R2 = 0.71; p &lt; 0.05), indicating that runoff volume, easier to estimate, could be used as a proxy for upslope erosion. Although erosion plot data cannot be extrapolated to the whole landscape, the research results provide useful elements for the development of sound conservation policies in the Cerrado and in other similar savannas of the world.

Spatial and temporal variability in rainfall erosivity under Alpine climate - a Slovenian case study using optical disdrometer data

Rainfall erosivity is one of the most important parameters that influence soil erosion rates. It is characterized by a large spatial and temporal variability. For example, in Slovenia, which covers around 20,000 km2, the annual rainfall erosivity ranges from less than 1,000 MJ mm ha1 h1 to more than 10,000 MJ mm ha1 h1. Drop size distribution (DSD) data are needed to investigate rainfall erosivity characteristics. More than 2 years of DSD measurements using optical disdrometers located at six stations in Slovenia were used to investigate the spatial and temporal variability in rainfall erosivity in Slovenia. Experimental results have indicated that elevation is a poor predictor of rainfall erosivity and that erosivity is more strongly correlated to the mean annual precipitation. Approximately 90% of the total kinetic energy (KE) was accounted for in about 35% of 1 min disdrometer data. The highest 1 min intensities (I) and consequently also KE values were measured in summer followed by autumn and spring. The local KE-I equation yielded an acceptable fit to the measured data in case of all six stations. The relatively large percentage of 1 min rainfall intensities above 5 mm/h can at least partially explain some very high annual rainfall erosivity values (i.e., near or above 10,000 MJ mm ha1 h 1). Convective and large-scale precipitation events also result in various rainfall erosivity characteristics. The station microlocation and wind impacts in case of some stations yielded relatively large differences between the data measured using the optical disdrometer and the pluviograph. Preliminary conclusions have been gathered, but further measurements are needed to get even better insight into spatial and temporal variability in rainfall erosivity under Alpine climate in Slovenia.

Spatial and Temporal Variability in Rainfall Erosivity Under Alpine Climate: A Slovenian Case Study Using Optical Disdrometer Data

Rainfall erosivity is one of the most important parameters that influence soil erosion rates. It is characterized by a large spatial and temporal variability. For example, in Slovenia, which covers around 20,000 km2, the annual rainfall erosivity ranges from less than 1,000 MJ mm ha−1 h−1 to more than 10,000 MJ mm ha−1 h−1. Drop size distribution (DSD) data are needed to investigate rainfall erosivity characteristics. More than 2 years of DSD measurements using optical disdrometers located at six stations in Slovenia were used to investigate the spatial and temporal variability in rainfall erosivity in Slovenia. Experimental results have indicated that elevation is a poor predictor of rainfall erosivity and that erosivity is more strongly correlated to the mean annual precipitation. Approximately 90% of the total kinetic energy (KE) was accounted for in about 35% of 1 min disdrometer data. The highest 1 min intensities (I) and consequently also KE values were measured in summer followed by autumn and spring. The local KE-I equation yielded an acceptable fit to the measured data in case of all six stations. The relatively large percentage of 1 min rainfall intensities above 5 mm/h can at least partially explain some very high annual rainfall erosivity values (i.e., near or above 10,000 MJ mm ha−1 h−1). Convective and large-scale precipitation events also result in various rainfall erosivity characteristics. The station microlocation and wind impacts in case of some stations yielded relatively large differences between the data measured using the optical disdrometer and the pluviograph. Preliminary conclusions have been gathered, but further measurements are needed to get even better insight into spatial and temporal variability in rainfall erosivity under Alpine climate in Slovenia.

Estimation of rainfall erosivity based on WRF-derived raindrop size distributions

Abstract. Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driven force for soil erosion, and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy. As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops' kinetic energy. With the development of global atmospheric re-analysis data, numerical weather prediction techniques become a promising way to estimate rainfall kinetic energy directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall kinetic energy, and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, Thompson aerosol-aware had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

Application of RUSLE integrated with GIS and remote sensing techniques to assess soil erosion in Anambra State, South-Eastern Nigeria

Abstract Soil erosion and mass movement processes spread across Anambra State in Nigeria, therefore making management and conservation techniques expensive and difficult in execution across the entire state. This study employed the Revised Universal Soil Loss Equation (RUSLE) model with the integration of geographic information system (GIS) and remote sensing techniques to assess the risk of soil erosion and hotspots in the area. Remotely sensed data such as Landsat 8 imagery, Shuttle Radar Topography Mission (SRTM) imagery, Era-Interim coupled with world soil database were used as digital data sources for land use map, digital elevation model, rainfall and soil data, respectively, to generate the Universal Soil Loss Equation (USLE) parameters. The results indicated vulnerability levels in low, medium and high cover areas of 4,143.62 (91%), 332.29 (7%) and 84.06 (2%) km2, respectively, with a total soil loss between 0 and 181.237 ton/ha/yr (metric ton per hectare per year). This study revealed that high rainfall erosivity, steep and long slopes, and low vegetation cover were the main factors promoting soil loss in the area. Thus, the amount of soil loss in Anambra State is expected to increase with climate change and anthropogenic activities.