Rainfall Erosivity Research Articles

Climate change impairs the effects of vegetation improvement on soil erosion control in the Qinghai-Tibetan Plateau

Water erosion is a wide ecosystem degradation threat for the Qinghai-Tibetan Plateau (QTP) region and surrounding areas. Climate wetting and vegetation greening in the QTP could have complex impacts on water erosion. However, there is limited information about the erosion dynamics under climate change and the effects of vegetation improvement on erosion control in this alpine region. By calibrating the Revised Universal Soil Loss Equation (RUSLE) model, we estimated water erosion in the QTP and simulated future changes using different Shared Socioeconomic Pathway-Representative Concentration Pathway (SSP) scenarios. We showed intensified erosion and that the benefits of vegetation improvement to erosion control was impaired under climate change. Under the SSP585 scenario, annual water erosion in the QTP was predicted to increase by 0.35 t∙ha−1∙yr−1 and be dominated by an accelerative rise of rainfall erosivity (R factor of RUSLE). Compared with historical observations, the C factor of RUSLE under the SSP585 scenario was believed to reduce by 4.35 % on average during the 2081–2100 period, but predicted soil loss will increase by more than 90 %. Although more considerable vegetation improvement could occur under high climate forcing, there may also be more intensified rainfall erosivity and water erosion across the QTP than those under SSP126 and SSP245 scenarios. Our results suggest that mitigating climate change rather than vegetation improvement is essential to address climate-dominated ecosystem degradation threats such as water erosion in the QTP.

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.

Suitability of satellite-based rainfall products for estimating rainfall erosivity in areas with contrasted climate and terrain properties: Example of west-central Morocco

Suitability of satellite-based rainfall products for estimating rainfall erosivity in areas with contrasted climate and terrain properties: Example of west-central Morocco

An integrated modeling approach for estimating monthly global rainfall erosivity

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001–2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha−1 h−1 month−1 in June–August over the Northern Hemisphere and ~ 700 MJ mm ha−1 h−1 month−1 in December–February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December–February and June–August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10–30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

An assessment of the shade and ground cover influence on the mitigation of water-driven soil erosion in a coffee agroforestry system

Coffee cultivation under agroforestry systems is one of the main agricultural activities in Central America, but climate change is putting its sustainability at risk. Rainfall erosivity has worsened, thus driving soil losses. Although the vegetal covers in coffee agroforestry systems play a crucial role in controlling erosion, the specific influence of each cover layer remains unclear. In this study, we assessed the influence of the canopy (tree and banana cover and coffee cover) and ground cover (weed and litter) on water driven soil erosion, to determine which type of cover has the most influence on soil erosion control. The study site is situated in the core and buffer zones of the Macizo de Peñas Blancas National Park (Nicaragua), where seventeen coffee sampling plots with an agroforestry system composed of an Inga spp and Musa spp canopy cover were analysed. The results showed that the 19.2 ± 3.4% of the soil surface was affected by erosion and it was mainly related to the litter ground cover (r = –0.95, P < 0.001). Also, this cover presented the best partial correlation (ryl.tcw = –0.93, P < 0.001) when the effects of the other vegetal covers were eliminated. Specifically, the litter cover accounted for 90% of the erosion variability, while the impact of the other types of cover was negligible, accounting for just 1% of the erosion. We conclude that litter layer is more important than canopy cover for effective erosion control, and the main function of shade trees is as a source of litter biomass.

Research on Early Warning Method of Plough Layer Thickness of Cultivated Land in Black Soil Driven by Rainfall Events

The thinning of the arable layer causes soil quality degradation, seriously affecting crop growth and even threatening national food security. This article focuses on the problems and needs of poor timeliness, high cost, and inability to dynamically warn about the thinning of the tillage layer in black soil farmland, and conducts research on corresponding warning methods. Based on daily rainfall data, the percentile method is used to calculate the threshold for erosive rainfall event driven warning. Based on erosive rainfall event driven coupling of external soil erosion stress meteorological elements and influencing processes, as well as the connotation of geographic events, the concept definition of geographic events for thinning warning of arable layer thickness is given. The semantic description and discovery method of geographic events are provided, and geographic event driven rules and the technical process of thinning warning of arable layer thickness are formulated. There is a significant correlation between rainfall and topsoil thickness, with a correlation coefficient of 0.83. A soil thickness thinning warning simulation was conducted based on the daily rainfall data of Hailun City, Heilongjiang Province in 2020. On June 14, 2020, when the daily rainfall exceeded the event threshold, an automatic warning for thinning of the plow layer was triggered, achieving a dynamic warning process for thinning of the plow layer. The research results indicate that this method is helpful in solving the problem of thinning warning of the cultivated layer thickness in black soil farmland, and can provide technical support for the quality construction, protection, and management of black soil farmland.

Rainfall erosivity across Austria's main agricultural areas: Identification of rainfall characteristics and spatiotemporal patterns

Study regionTarget area of this study are the main agricultural production zones of Austria. Most important croplands cover the flat to pre-alpine areas concentrated in the north, east, and southeast of Austria. Study focusThe novelty of our study is the spatiotemporal assessment of rainfall characteristics that drive erosivity at the event level as well as the identification of erosive rainfall distribution patterns within the events. Our assessment approach allows the definition of both typical and extreme erosivity. Long-term and high temporal resolution rainfall datasets were used to apply a clustering approach, seasonal and spatial analyses, and rainfall distributions assessment (isopleths) of the identified rainfall types (clusters). New hydrological insights for the regionThree dominant erosive event-types (clusters) were identified that strongly relate with Austria’s seasonality and complex topography. The most erosive rainfall events (cluster C1) are characterized by a high intensity and short duration. C1 events have the largest occurrence frequency in pre-alpine southern Austria and occur from May to September. Unlike the less erosive and more evenly distributed event types (C2 and C3) the highly erosive C1 events have a pronounced maximum rainfall intensity at the onset of the event.

Serra da Canastra National Park: Influence of forest fires on the RUSLE C factor and its impact on water erosion

The adverse impacts of soil degradation and nutrient loss resulting from water erosion are significant environmental concerns that have profound implications for both water quality and biodiversity. This study aims to evaluate the impact of forest fires on soil loss through water erosion in the Serra da Canastra National Park, in Minas Gerais state, Brazil. Using the Revised Universal Soil Loss Equation (RUSLE), which considers rainfall erosivity, soil erodibility, slope length and slope and vegetation cover, the C factor (vegetation cover) values were obtained from data from literature and methods based on the Normalized Differential Vegetation Index (NDVI). Validation was carried out using data on total sediment, water flow and daily runoff from the hydrosedimentological station and the InVEST software. The results highlight the significant impact of wildfires on soil loss through water erosion and indicate that areas recently affected by wildfires, especially on steep slopes and with more erodible soils, are subject to the highest rates of soil loss. Soil loss rates varied from 0.75 to 12.55 Mg ha-1 yr-1, in part due to the different ways of obtaining factor C. The research emphasizes the need to conserve vegetation cover to prevent soil erosion, particularly in regions impacted by forest fires. This study offers valuable insights that can contribute to enhancing the sustainable environmental management of the Serra da Canastra National Park.

Estimation of erosion rate in a watershed of a deforestation area in the Brazilian Amazon, eastern of Mato Grosso

The central region of Brazil, geographically located in Mato Grosso, presents changes in land cover with replacement of the forest for soybean and cotton cultivation, intensifying soil losses in a hydrographic basin located in the tropical climate. The objective of the research is to estimate the loss of soil in the Curisevo River Basin, from the Universal Soil Loss Equation. The methodologies used were the elaboration of a cartographic base in the QGIS geographic information system, with the spatial information of slopes, soil types and land cover, of the Curisevo River Basin. The rainfall erosivity was estimated with rainfall data from the National Institute of Meteorology and the erodibility of the soils was estimated according to the indications of the State of Mato Grosso. The land cover of the Curisevo River Basin was analyzed based on remote sensing images with the vectorization of the areas of the agriculture, pasture and forest classes. The spatial data of slopes of the Curisevo River Basin were obtained from the Shuttle Radar Topography Mission base, with spatial analysis treatment in QGIS. In the basin with an area of 1.18 million hectares, an erosion rate of 134,526 t/ha/year was estimated, corresponding to 16 million tons of sediments. This study contributes to the geographical studies, and the indices used in the equation to estimate soil losses can be analyzed by climatic, pedological, geomorphological and landscape analysis studies in land cover changes in a watershed.

Evaluation of GPM IMERG-FR Product for Computing Rainfall Erosivity for Mainland China

Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend to underestimate rainfall erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) and GPM_3IMERGDF (daily and 0.1°), in estimating two erosivity indices in mainland China: the average annual rainfall erosivity (R-factor) and the 10-year event rainfall erosivity (10-yr storm EI), by comparing with that derived from gauge-observed hourly precipitation (Gauge-H). Results indicate that GPM_3IMERGDF yields higher accuracy than GPM_3IMERGHH, though both products generally underestimate these indices. The Percent Bias (PBIAS) is −55.48% for the R-factor and −56.38% for the 10-yr storm EI using GPM_3IMERGHH, which reduces to −10.86% and −32.99% with GPM_3IMERGDF. A bias-correction method was developed based on the systematic difference between SSPs and Gauge-H. A five-fold cross validation shows that with bias-correction, the accuracy of the R-factor and 10-yr storm EI for both SPPs improve considerably, and the difference between two SSPs is reduced. The PBIAS using GPM_3IMERGHH decreases to −0.06% and 0.01%, and that using GPM_3IMERGDF decreases to −0.33% and 0.14%, respectively, for the R-factor and 10-yr storm EI. The rainfall erosivity estimated with SPPs with bias-correction shows comparable accuracy to that obtained through Kriging interpolation using Gauge-H and is better than that interpolated from gauge-observed daily precipitation. Given their high temporal and spatial resolution, and timely updates, GPM_3IMERGHH and GPM_3IMERGDF are viable data products for rainfall erosivity estimation with bias correction.

Geospatial modeling and mapping of soil erosion in India

Soil erosion generally removes the topmost fertile layer of soil, affecting agricultural productivity on a larger scale. As a significant portion of the Indian economy depends on agricultural productivity, granular assessment of the impact of soil erosion becomes critical. However, a national-scale assessment of soil erosion and an impact classification system currently doesn’t exist over India. Given the resource-intensive and time-consuming nature of field experiments required for the measurement of soil loss across a vast country, the Revised Universal Soil Loss Equation (RUSLE) was extensively utilized for soil erosion calculations due to its simplicity, streamlined data requirements, and accuracy. This study estimates the yearly potential soil loss throughout India at a spatial resolution of 250 m and quantifies its variability considering districts, soil texture, soil type, land use and land cover, and basins. The relative importance of individual and combined impact of multiple parameters on quantified soil loss has been assessed using a random forest model. Rainfall erosivity (R-factor) emerges as the most crucial feature in estimating soil erosion in Indian conditions while rainfall intensity, combined with the topographic factor, demonstrated the highest influence on soil erosion in Indian conditions when the combined impact was assessed. Further, we mapped the Sediment Delivery Ratio (SDR) and Specific Sediment Yield (SSY) to assess the actual soil loss reaching downstream of basins across the national boundary. The national mean values for Sediment Delivery Ratio (SDR) and Sediment Yield (SSY) stand at 0.11 and 2.61 tons per hectare per year, respectively. The yearly potential soil loss for India is calculated at 21 tons per hectare per year, and nine out of the twenty districts with the highest susceptibility to soil erosion are in the state of Assam. Finally, a novel impact-based erosion-severity classification system has been introduced which finds that 29.46% of the landmass is prone to minor erosion while 3.17% experiences catastrophic erosion. This is the first comprehensive national-scale assessment of both soil erosion and sediment yield mapping over India, and the consequent classification system will enable the planning and implementation of soil conservation strategies locally as well as nationally.

Assessment of rainfall erosivity for Bundelkhand region of central India using long-term rainfall data

Prevention of soil erosion requires long term assessment of rainfall erosivity and other related variables of the region. In the present study, four variables related to rainfall erosivity i.e. modified Fournier index (MFI), rainfall erosivity (R) factor, erosivity density (ED) and precipitation concentration index (PCI) were calculated. Long-term (1901-2021) daily rainfall data of Bundelkhand region (Central India) were used in the analysis. The above variables were assessed for spatial and temporal variability on annual and seasonal scale. The R-factor values range from 3010.61 MJ.mm ha-1 h-1 to 5346.53 MJ.mm ha-1 h-1, showing the region belongs to moderate to severe erosivity class. The mean annual R-factor, MFI, ED and PCI values for the Bundelkhand region were calculated as 4072.86 MJ.mm ha-1 h-1, 270.55 mm, 19.13 MJ ha-1 h-1 and 28.88, respectively. This study provides the insights of soil erosion problems of Bundelkhand region and would help in adopting the preventive measures and watershed development activities.

Effect of rainfall parameters on soil erosion in Chwalimski Brook catchment, NW Poland

Soil erosion by water is one of the most important factors affecting contemporary landscape changes within the lowland geoecosystems in Central Europe. Soil erosion mainly depends on rainfall (especially its intensity and erosivity), length of slope and its inclination, type of cultivation and usage of the land, antierosion treatments, and susceptibility of soils to erosion. The main goal of this research was to evaluate the impact of rainfall on soil erosion processes. This was realized by stationery observations and quantitative research conducted within the Chwalimski Brook catchment (NW Poland) since the early 90s. Additionally, the rainfall characteristics such as intensity, kinetic energy, and erosivity (represented by the rainfall erosivity indices EI5, EI10, EI15, EI20, EI30 and EI60) were computed. The presented results contain data on runoff, soil loss, and rainfall collected at a testing plot over 16 years (2001–2016 hydrological years). Annual rainfall erosivity (EI30) ranged from 144.7 to 782.1 MJ mm ha−1 h−1, precipitation totals varied from 524.9 to 919.7 mm, and soil loss ranged from 0.074 to 4.471 kg m−2. The greatest rates of soil loss were established in the most rainy years, although significant soil loss also occurred in below-average rainfall years, such as in 2013 and 2014. Consequently, the achieved results show that annual soil erosion primarily depends on individual rainfall and erosive events. Hence, the magnitude of soil erosion cannot be solely estimated based on the rainfall totals, but it is also necessary to consider the intensity and erosivity of each rainfall event.

Responses of runoff and sediment yield to slope length and gravel content of Lou soil engineering accumulation slope in Guanzhong region, Northwest China.

With the economic development, a large number of engineering accumulation bodies with Lou soil as the main soil type were produced in Guanzhong area, Northwest China. We examined the characteristics of runoff and sediment yield of Lou soil accumulation bodies with earth (gravel content 0%) and earth-rock (gravel content 30%) under different rainfall intensities (1.0, 1.5, 2.0, 2.5 mm·min-1) and different slope lengths (3, 5, 6.5, 12 m) by the simulating rainfall method. The results showed that runoff rate was relatively stable when rainfall intensity was 1.0-1.5 mm·min-1, while runoff rate fluctuated obviously when rainfall intensity was 2.0-2.5 mm·min-1. The average runoff rate varied significantly across different rainfall intensities on the same slopes, and the difference of average runoff rate of the two slopes was significantly increased with rainfall intensity. Under the same rainfall intensity, the difference in runoff rate between the slope lengths of the earth-rock slope was more obvious than that of the earth slope. When the slope length was 3-6.5 m, flow velocity increased rapidly at first and then increased slowly or tended to be stable. When the slope length was 12 m, flow velocity increased significantly. In general, with the increases of rainfall intensity, inhibition effect of gravel on the average flow velocity was enhanced. When rainfall intensity was 2.5 mm·min-1, the maximum reduction in the average flow velocity of earth-rock slope was 61.5% lower than that of earth slope. When rainfall intensity was less than 2.0 mm·min-1, sediment yield rate showed a trend of gradual decline or stable change, while that under the other rainfall intensities showed a trend of rapid decline and then fluctuated sharply. The greater the rainfall intensity, the more obvious the fluctuation. There was a significant positive correlation between the average sediment yield rate and runoff parameters, with the runoff rate showing the best fitting effect. Among the factors, slope length had the highest contribution to runoff velocity and rainfall erosion, which was 51.8% and 35.5%, respectively. This study can provide scientific basis for soil and water erosion control of engineering accumulation in Lou soil areas.

Annual and Seasonal Characteristics of Rainfall Erosivity in the Eastern Rhodopes (Bulgaria)

Rainfall, with its intensity, duration, and seasonal distribution, is among the main factors causing soil erosion, which is a widespread environmental problem in Bulgaria. Rainfall erosivity shows the potential of precipitation to generate erosion processes and is an essential indicator of the climate vulnerability of a region. This paper aims to evaluate rainfall erosivity in a part of the Eastern Rhodopes Mountains, an area that is characterised by high-intensity erosion processes and high erosion risk. Local peculiarities of rainfall erosivity were revealed by the calculation of some precipitation indices based on the monthly precipitation for the period 2000–2021, such as the precipitation concentration index (PCI), Angot precipitation index, Fournier index (FI), and modified Fournier index (MFI). The analysis of the extremely wet and extremely dry months at the annual and seasonal (October–March and April–September) levels was performed to evaluate the susceptibility to erosion. The results from the study show that rainfall erosivity in the studied area varies from low to moderate in the northern part of the study area and from high to very high in the south. According to the MFI, high and very high erosivities have been observed mainly since 2012. The erosivity increases from north to south, to the area with a complex relief, where the combination of orography and atmospheric circulation make favourable conditions for the occurrence of extreme precipitation. The analyses of the calculated indices show that the precipitations in most of the studied area generally have from a low to a moderate erosivity, but this does not exclude the occurrence of cases with high and very high erosivities, which are characteristic of recent years and are related to the increase in annual precipitations and extreme precipitation months. The results of this study can contribute to the development and implementation of measures and preventive activities for the reduction and possible elimination of the negative impacts of extreme precipitation.

Soil Erosion Mapping and Severity Analysis Based on RUSLE Model on Welmel Catchments of Ganale-Dewa River Basin Bale Lowland South Eastern Ethiopia

Soil erosion is a kind of environmental deterioration through decreased agricultural output, increased floods, and habitat loss, soil degradation has an impact on many people's livelihoods, either directly or indirectly. In many developing tropical nations throughout the world where soil and water conservation (SWC) planning and management initiatives are either ineffective or nonexistent, soil erosion is on the rise. Because there isn't precise scientific research that gives information, identifying locations that are susceptible to soil erosion is also insufficient. The Oromia area of Ethiopia, where the majority of livelihoods mainly rely upon agriculture, is home to the little-recognized watershed of Welmel. A thorough understanding of the spatial variability across various climatic zones, land uses, and soil erosion is crucial for the planning of successful SWC management approaches. Through the use of a geographic information system (GIS), remote sensing (RS), and numerous characteristics such as land uses, and climate, this study has attempted to identify prospective places in need of SWC techniques by undertaking a spatial modeling of soil erosion within the Welmel watershed's Genale Dawa basin. The revised universal soil loss equation (RUSLE), which was previously modified for Ethiopian circumstances, was utilized to predict potential soil loss after laboratory study and processing multiple input datasets in ArcGIS to get the requisite six RUSLE model input parameters. It made use of data from a digital elevation model (DEM) on interpolated rainfall erosivity (R), soil erodibility (K), plant cover (C), topography (LS), and conservation practices (P) from satellite pictures. The study demonstrates that the RUSLE using GIS taking into account various climates and land management practices provides a great advantage in that it enables one to spatially analyze multilayer data to identify areas that are susceptible to soil erosion and subsequently develop the be

Soil erosion-based sub-watershed prioritization through coupling various crop management and erosivity scenarios using game theory

The soil erosion of the watershed is different in temporal and spatial scales due to complex climatic and environmental conditions and their dynamic state over time. Accordingly, it is necessary to prioritize sub-watersheds based on their dynamic conditions to reduce the destructive effects of soil erosion. However, this issue has to be sufficiently reported on the scale of the watershed. Towards that, a game theory-based prioritization approach was planned concerning changes in vegetation cover and rainfall in the Shazand Watershed of the Markazi Province, Iran. To achieve the purpose of the study, the amount of soil erosion was first estimated in different scenarios resulting from the changes in rainfall erosivity and vegetation cover using the RUSLE model. Next, pairwise comparisons between 24 sub-watersheds in all scenarios were made based on the sub-watersheds' ranks in soil erosion in each scenario. Considering all scenarios, the priority of the sub-watersheds was assessed using the Condorcet approach. The results showed that the mean soil erosion in the watershed ranged from 5.4 and 36 t ha−1 y−1 while applying various scenarios. It is worth mentioning that the prioritization results obtained from the Condorcet algorithm and the current soil erosion rate are highly similar. On the other hand, sub-watersheds 2 and 9 were selected as the first and last priority, providing an appropriate roadmap for managers and relevant departments to adopt appropriate soil erosion inhibition measures.

Applicability Comparison of GIS-Based RUSLE and SEMMA for Risk Assessment of Soil Erosion in Wildfire Watersheds

The second-largest wildfire in the history of South Korea occurred in 2022 due to strong winds and dry climates. Quantitative evaluation of soil erosion is necessary to prevent subsequent sediment disasters in the wildfire areas. The erosion rates in two watersheds affected by the wildfires were assessed using the revised universal soil loss equation (RUSLE), a globally popular model, and the soil erosion model for mountain areas (SEMMA) developed in South Korea. The GIS-based models required the integration of maps of the erosivity factor, erodibility factor, length and slope factors, and cover and practice factors. The rainfall erosivity factor considering the 50-year and 80-year probability of rainfall increased from coastal to mountainous areas. For the LS factors, the traditional version (TV) was initially used, and the flow accumulation version (FAV) was additionally considered. The cover factor of the RUSLE and the vegetation index of the SEMMA were calculated using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images acquired before and after the wildfire. After one year following the wildfire, the NDVI increased compared to during the year of the wildfire. Although the RUSLE considered a low value of the P factor (0.28) for post-fire watersheds, it overestimated the erosion rate by from 3 to 15 times compared to the SEMMA. The erosion risk with the SEMMA simulation decreased with the elapsed time via the vegetation recovery and stabilization of topsoil. While the FAV of RUSLE oversimulated by 1.65~2.31 times compared to the TV, the FAV of SEMMA only increased by 1.03~1.19 times compared to the TV. The heavy rainfall of the 50-year probability due to Typhoon Khanun in 2023 generated rill and gully erosions, landslides, and sediment damage in the post-fire watershed on forest roads for transmission tower construction or logging. Both the RUSLE and SEMMA for the TV and FAV predicted high erosion risks for disturbed hillslopes; however, their accuracy varied in terms of the intensity and extent. According to a comparative analysis of the simulation results of the two models and the actual erosion situations caused by heavy rain, the FAV of SEMMA was found to simulate spatial heterogeneity and a reasonable erosion rate.

Assessing rainfall erosivity changes over China through a Bayesian averaged ensemble of high-resolution climate models

Spatiotemporal variation in rainfall erosivity resulting from changes in rainfall characteristics due to climate change has implications for soil erosion in developing countries. To promote soil and water conservation planning, it is essential to understand past and future changes in rainfall erosivity and their implications on a national scale. In this study, we present an approach that uses a Bayesian model averaging (BMA) method to merge multiple regional climate models (RCMs), thereby improving the reliability of climate-induced rainfall erosivity projections. Our multi-climate model and multi-emission scenario approach utilize five RCMs and two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios for the baseline period (1986–2005) and future periods (2071–2090) to characterize the spatiotemporal projection of rainfall erosivity and assess variations in China. Our results indicate that the two models outperform other models in reproducing the spatial distribution and annual cycle of rainfall erosivity in China. Moreover, we found an increasing trend in the annual rainfall erosivity from the baseline climate up to the RCMs for all models, with an average change in erosivity of approximately 10.9% and 14.6% under RCP4.5 and RCP8.5, respectively. Our BMA results showed an increase in the absolute value of rainfall erosivity by 463.3 and 677.0 MJ·mm·hm−2·h−1, respectively, in the South China red soil region and the Southwest China karst region under the RCP8.5 scenario. This increase indicates that climate warming will significantly enhance the potential erosion capacity of rainfall in these regions. Additionally, our study revealed that the Southwest China karst region and the Northwest China Loess Plateau region are more sensitive to radiation forcing. To mitigate the risk of soil erosion caused by climate change, it is necessary to consider changes in rainfall erosivity, local soil conditions, vegetation coverage, and other factors in different regions and take appropriate soil and water conservation measures.

Integrated Universal Soil Loss Equation (USLE) and Geospatial Approach to Assess Soil Erosion in Machhu Sub-watershed, Morbi, India

Abstract Soil erosion is a severe and rapidly rising issue in many parts of the world due to human activities such as farming practices, land excavation for development and deforestation. Moreover, it can negatively impact water availability, agricultural growth, and ultimately, countries’ long-term economy. A quantitative and consistent land degradation assessment is vital for proper planning of soil conservation activities in a catchment or watershed. The Universal Soil Loss Equation (USLE) model is applied in this study to address the issue of soil erosion in the Machhu-sub watershed (24769.63 ha), located in Saurashtra, India. The landscape feature of the study area includes basalt type rock and water body. In this USLE model study we have used input parameters such as rainfall erosivity (R), soil erosivity (K), cover management (C), slope length and steepness (LS), and conservation practice (P) integrated with Geographical Information System (GIS) to analyze and obtain the estimated annual soil loss. Results indicated that the overall soil loss in the study area can be classified into five categories: Very Low (0-1), Low (1-3), Low moderate (3-5), Moderate (5-10), and High ( &amp;gt;10 tons/ha/year). The finding includes the overall soil potential loss of the Machhu sub-watershed is 14.90 tons/ha/year. Furthermore, 60.86 % of the agricultural area is affected by soil erosion. Therefore, the necessary soil conservation methods can be planned in the Machu-sub watershed area based on the USLE analysis. These findings may assist researchers, scientists, and policymakers in building a concrete strategy for sustainable development of not only study area but other catchments also.