Annual Erosion Research Articles

Wind Speed and Vegetation Coverage in Turn Dominated Wind Erosion Change With Increasing Aridity in Africa

AbstractWind erosion is one of the main causes of land degradation and desertification. Clarifying the spatiotemporal variations of wind erosion and the dominant factors of its spatial characteristics and the temporal trend will contribute to the establishment of appropriate wind erosion control and management practices, which is essential for combating global land degradation and strengthening ecological protection in drylands. Here, we assessed wind erosion in Africa during 2001–2020 based on the Revised Wind Erosion Equation (RWEQ). We also analyzed the influential factor of spatial characteristics and temporal variation based on machine learning and other methods under different aridity. Results revealed that the average annual wind erosion modulus was 16,672 t/km2/a in Africa during 2001–2020, with hyper‐arid areas and arid areas accounting for more than 90% of the total wind erosion modulus. The spatial characteristics of wind erosion were dominated by natural factors but not anthropogenic activities. Except in hyper‐arid areas, wind speed and vegetation coverage together dominated the spatial characteristics. Wind speed was the dominant factor in wind erosion change, while in arid and semi‐arid areas, the capability of vegetation coverage to affect wind erosion change was comparable to wind speed. It can be concluded that, although revegetation does contribute to the reduction of wind erosion in arid and semi‐arid areas, taking into account water resource constraints and land use conflicts, large plantations can be replaced with windbreaks to increase vegetation coverage while reducing near‐surface wind speed, which improves the sustainability of ecological projects aimed at combating land degradation and desertification.

Future changes in global rainfall erosivity: Insights from the precipitation changes

Quantifying future changes in rainfall erosivity is essential to address potential water erosion risks. This study reveals a significant power function relationship between rainfall erosivity and precipitation both globally and across various climatic zones. Building on this foundation, new empirical models were developed to project relative changes in rainfall erosivity. These models, predicted on the understanding that General Circulation Models (GCMs) provide more accurate predictions of climate change trends than exact meteorological values, utilizes the ratio of modeled historical precipitation to future precipitation under various scenarios as the variable, formulated as a power function. Data from three GCMs runs from both the Coupled Model Intercomparison Project phase 5 (CMIP5) and CMIP6–including historical, mid-term, and long-term periods–were employed. The analysis suggests that by the end of the 21st century, global annual rainfall erosivity could increase by 2.6% under the Shared Socio-Economic Pathway (SSP)1–2.6 scenario, by 3.5% under SSP2-4.5, and by a significant 6.4% under SSP5-8.5, relative to the 2000–2010 baseline. Furthermore, over 76% of the global land area is projected to experience an increase in rainfall erosivity over the century. Regions with projected changes in rainfall erosivity, whether increase or decrease, are likely to face more pronounced changes from mid-century onwards. The CMIP6 exhibits improved model consistency over its predecessor, CMIP5, indicating a greater water erosion risk as global warming progresses. These projections offer insights for strategies to combat soil degradation due to climate changes.

No increase of soil wind erosion with the establishment of center pivot irrigation system in Mu-Us sandy land

Center Pivot Irrigation system (CPIs) is widely used in newly exploited arable land in sandy lands. These sandy lands are currently stable because of climate change and ecological restoration efforts since the beginning of the 21st century in northern China. The exploitation of these fixed sandy lands to arable land with CPIs may affect the soil wind erosion, yet it remains unknown. The temporal changes of CPIs and its effect on wind erosion module were analyzed and modeled from 2000 to 2020 in Mu-Us sandy land using satellite images and Revised Wind Erosion Equation (RWEQ). The establishment of CPIs started from 2010, boomed in 2015 and peaked in 2020. They were mainly transformed from woodland, grassland, and barren land near rivers in east and southeast, and from cropland in inter-dunes in west and southwest of Mu-Us sandy land. The temporal and spatial pattern of CPIs well aligns with the land consolidation and requisition-compensation balance policies. In most of the Mu-Us sandy land, the annual erosion module is <25 t ha−1 a−1. Despite great variation, the annual, Winter and Spring erosion module of the Mu-Us sandy land or in Otog Qian and Yuyang, the CPIs concentrated counties, all decreased during 2000–2019. Although, wind erosion module in CPIs was lower than the surrounding area, it increased in 2019 given the same climate conditions as in 2010. Our results suggest 1) the establishment of CPIs in Mu-Us sandy land greatly depends on the local policy and natural endowment, and 2) although the set-up of CPIs showed no impact on the wind erosion with CPIs accounting for <1 % of Mu-Us sandy land, post-harvest of CPIs should be carefully concerned to prevent soil wind erosion.

Scaling of soil organic carbon in space and time in the Southern Coastal Plain, USA

Soil organic carbon (SOC) is a dynamic soil property (DSP) that represents the largest portion of terrestrial carbon. Its relevance to carbon sequestration and the potential effects of land use on SOC storage, make it imperative to map across both space and time. Most regional-scale studies mapping SOC give static estimates and train different models for different periods with varying accuracies. We developed a flexible modeling approach called DSP-Scale to map SOC in both space and time. DSP-Scale uses ecological concepts and empirical data to predict DSP dynamics using inherent soil properties (static factors) and land cover changes (dynamic factors). We compiled SOC data for the 0–20 cm depth (SOC20) from 1441 points spanning a 25 million ha study area in the southeastern U.S. Coastal Plain, incorporating data from the Rapid Carbon Assessment, National Cooperative Soil Survey Soil Characterization database, and other regional studies. We developed a random forest model using climate, topography, soil survey, and land cover changes to predict SOC20 dynamics for five-year periods between 2001 and 2019. Our model explained 66 % and 59 % of the variation for the training and test data, respectively. Top predictors included mean annual precipitation, slope, and soil erosion class. Land cover 10 years before measurements of SOC20 was more important than current land cover for estimating SOC20. We estimated total SOC stocks of 207.1 and 208.3 Tg for 2001 and 2019, respectively. Highest gains of total SOC stock (0.9 Tg from 2001 to 2019) were associated with land cover change from mixed to evergreen forest. The greatest loss of total SOC stock (0.2 Tg) in the same period was associated with land cover change from pasture/hay to evergreen forest. We concluded that the DSP-Scale approach provides a flexible way to use dynamic and static factors affecting SOC stocks to predict changes in space and time at regional scales.

Assessment of Soil Erosion within the Gayathripuzha River Basin Using RUSLE Model and GIS Tools

Soil erosion, a natural phenomenon accelerated by human activities, became a major problem for agricultural land use and for water quality concerns. Soil erosion by water is influenced by various factors like climate, nature of land surface, slope steepness, soil properties, land cover, and management practices. Estimation of soil erosion is essential to know the nature and quantity of soil loss and create specific strategies for soil conservation. This research paper thoroughly examines the susceptibility of soil erosion in the Gayathripuzha river basin in Kerala, India, which is encompassing a wide area of around 961 km². Employing the Revised Universal Soil Loss Equation (RUSLE), this investigation assesses the influential factors affecting soil erosion, which comprise rainfall (R Factor), soil erodibility (K Factor), topography (LS Factor), land usage (C Factor), and the impact of conservation measures (P Factor). The study was conducted using Geographic Information System (GIS) tools like ArcGIS 10.8 Desktop and integrating data on rainfall, soil properties, land topographic features, and land use. Rainfall erosivity is a major factor causing erosion in Gayathripuzha river basin and it ranges from 746.03 to 1222.35 MJ mm ha−1 h−1 y−1 in the river basin. Soil erodibility factor also varies widely within the region. Slope length and steepness factor showed that highland ecosystems are present only in a small area of the river basin. Variation in land use and land cover also influenced the soil erosion within the area. The estimated average annual soil erosion ranges from 0 to 133.18 t ha-1 yr-1 within the research site. The research found that soil erosion varied greatly across the study area. Regions with slopes over 50% had much higher erosion rates, while most of the study area had low erosion levels. Notably, the intensity of soil erosion demonstrated a positive correlation with slope percentage, with areas classified as bare ground demonstrating the highest erosion rates. These findings emphasize the necessity for targeted and comprehensive conservation strategies tailored to the diverse landscape of the region to effectively manage soil erosion and mitigate its adverse impacts.

Water soil erosion modeling with RUSLE, GIS & remote sensing: A case study of the AL-Sanaoubar River basin (Syria)

Soil erosion forms a serious ecological challenge to future production and sustainability, with implications of the environmental crisis and the food supply. It is one of the more critical geomorphological problems that threaten environmental sustainability and cause soil degradation along the Syrian coast. The object of this research is to evaluate the rate of probable annual water-induced soil erosion in the AL-Sanaoubar River Basin. By Integrating geographic information system (GIS), and remote sensing techniques with the Universal Soil Loss Equation (RUSLE). The results showed that the soil loss values in the AL-Sanaoubar River Basin ranged between 0–138.15 tons/ha/year, an average of 52.4 tons/ha/year. The total soil loss reached 12,606.11 tons/year for the entire studied area.The classification of possible spatial risk included five grades: very low (91.91%), low (7.62%), medium (0.35%), high (0.10%), and very high (0.01%).It has been noticed high soil erosion values in few portions of the study area, indicating that they had reached a risk stage; these sites were concentrated in the center and east of the studied basin, especially in the plateau and mountainous parts, Slope was a major factor that caused a direct impact on soil erosion. Soil erosion gradually increased with increasing slope in all types of land uses.To test the effectiveness of some soil conservation practices against water erosion, an experiment was conducted at the Mazar Qatrya site in Lattakia governorate during two consecutive seasons. Contour cultivation of the pea crop was applied at different distances, the agricultural method showed a high efficiency in reducing water erosion. Land use managers and Planners can benefit from this research to make suitable provisions and activities to conserve the soil.

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.

P120 Characteristics of difficult-to-treat rheumatoid arthritis: results from a systematic literature review

Abstract Background/Aims Estimates predict between 10-20% of RA patients meet difficult-to-treat Rheumatoid Arthritis (D2T-RA) classification, representing a clinically distinct cohort. There is a significant unmet need in the identification and management of these patients. Discerning significant characteristics across cohorts may identify predictors for D2T-RA, which may prompt earlier clinical recognition and patient-tailored, holistic management to improve clinical outcomes. This review aims to describe the clinical characteristics of patients with EULAR-defined D2T-RA to help identify and manage predictors, contributory factors, or consequences of difficult-to-treat disease. Methods A systematic literature search of PubMed, Embase and Web of Science databases for studies pertaining to characteristics of D2T-RA was undertaken from database inception to May 2023. Results were screened against strict inclusion/exclusion criteria. Risk-of-bias (RoB) assessments were performed using the Newcastle-Ottawa Scale (NOS) and a modified version for cross-sectional studies (mNOS). Significant (p &amp;lt; 0.05) baseline characteristics between D2T-RA and non-D2T patients were extracted for comparison following thematic analysis. Where there was conflict in the direction of the extracted characteristics; the number and quality of studies were used to resolve evidential disagreements. Results 1687 studies were identified, of which seven fulfilled the inclusion criteria. Included studies were observational, published between 2021 and 2023, with mean D2T and non-D2T sample sizes of 83 and 417 patients, respectively. Five of the seven cohorts were European, with two from Japan. Following RoB and dispute resolution, all seven identified studies were included for analysis. Key themes elucidated are shown in table 1. Of note, significant characteristics included female sex, autoantibody positivity, greater annual bone erosion progression, interstitial lung disease, mental health conditions, and non-adherence. Conclusion Greater awareness of clinical characteristics of D2T-RA patients will assist clinicians by promoting the assessment of traits associated with poor treatment response. This is the first review to systematically describe characteristics of D2T-RA patients across multiple cohorts. Several commonly recorded, modifiable characteristics were associated with D2T-RA. Evidential disagreements and heterogeneous study designs were analytical barriers, and clinical intervention studies are needed to determine if addressing these factors in the routine clinical setting improves clinical outcomes and prevention in D2T-RA. Disclosure S. Chambers: None. A.P. Croft: None. R. Singh: None.

Assessing basin-wide soil erosion in the Citarum watershed using USLE method

Citarum is the largest watershed in West Java, which is home to 25 million people. In such a large and populated watershed, land conversion is an inevitability. This condition directly encouraged an increase in erosion rates, which impacted increasing sedimentation rates in rivers, lakes, and dams. This research aimed to assess the spatial characteristics of soil erosion in the Citarum Watershed using the USLE method. The results showed that 45 % of the land in the Citarum watershed was in class I erosion hazard with an erosion rate of ≤15 tons/ha/year, 18 % in class II (15–60 tons/ha/year), 26 % in class III (60–180 tons/ha/year), 5 % in class IV (180–480 tons/ha/year), and the remaining 6 % in class V (>480 tons/ha/year). Total annual erosion in the Citarum watershed reaches 80.1 million tons, of which 52 % is contributed by the Middle Citarum Watershed (42.0 million tons/year), 26 % is the Upper Citarum Watershed (21.2 million tons/year), and the remaining 22 % contributed by the Lower Citarum Watershed (17.0 million tons/year). The proposed control measures for erosion hotspot locations throughout the watershed, which include structural and nonstructural approaches, are also proposed.

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.

Long‐term effects of soil erosion on dryland crop yields in the Songnen Plain, Northeast China

AbstractThe elucidation of the influence of soil erosion on crop yields is of paramount importance to ensure food security. Remote sensing data has facilitated the acquisition of long‐term spatially‐distributed information on soil erosion modulus and crop yields. The Revised Universal Soil Loss Equation (RUSLE) model was employed to estimate the soil erosion modulus, while the Carnegie‐Ames‐Stanford Approach (CASA) model has been utilized to calculate the net primary productivity (NPP). This study integrated models to assess the impact of soil erosion on dryland crop yields in the Songnen Plain from 2000 to 2020. The findings reveal that the annual average soil erosion modulus for dry cropland in black soil region (BSR) of the Songnen Plain is 4.2 (t ha−1 yr−1), which is 1.5 times greater than that of the other region (OR). The greater cropland erosion within the BSR limited the trend for increasing crop yields, whereas the impact of erosion on crop yield dynamics in the OR was not significant. The areas where dryland crop yields diminished in the Songnen Plain because of soil erosion were predominantly concentrated within the BSR. Advancements in agricultural technology have partially mitigated the adverse consequences of erosion on yields.

Effects of sediment-storage dams on suspended sediment transport in the Sera Lake Watershed, northeast Turkey

In this study, the effects of cascade sediment-storage dams (SSDs) on the suspended sediment (SS) transport in the Sera Lake Watershed (northeast Turkey) were investigated. Ten SS monitoring stations from upstream (S01) to downstream (S10) were selected for the study considering the locations of the SSDs and Sera Lake. In a gauging station, where S05 was selected, the water stage monitoring studies were also conducted, and daily discharges and SS loads were computed. The Spearman correlation coefficient between SS and discharges data was computed as 0.592. The deterioration of the naturalness of the watershed with external interventions was effective on this value. While the Sera Lake trapped 33% of the SS transport, this value varied between –25.8 and 44% for SSDs. Although the SSDs have been proportionately succeed in trapping the SS, they have been insufficient in preventing sedimentation and problems caused by sediment transport in the watershed. In addition, the annual average soil erosion amount was determined by the revised universal soil loss equation (RUSLE) method in the watershed. According to the RUSLE method, the annual average soil loss of the watershed is 1.65 tons/ha. The SDR value of the watershed was calculated as 0.104. Integrating different environmental management tools to achieve a balanced and sustainable decision, a strategic environmental management plan should be constituted.

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.

SPATIAL ESTIMATION OF SOIL EROSION USING GEOSPATIAL TECHNIQUE – A CASE STUDY OF JANJEVA RIVER CATCHMENT (KOSOVO)

The aim of the study is to analyse the spatial estimation of soil erosion in a small size river catchment of Janjeva (Kosovo). Soil erosion is becoming a serious threat and its spatial estimation is crucial for soil resource protection and management. The Revised Universal Soil Loss Equation (RUSLE) methodology integrated with GIS techniques was applied to estimate the annual erosion rate. Different datasets including open-source data were used to find main components of soil erosion. The results show different soil erosion classes depending on lithological settings, landforms, climate conditions, soil properties, land cover and soil conservation practices. By calculating RUSLE parameters for Janjeva River catchment (84 km²), we found out that mean annual erosion rate is 1 t·ha-¹·yr-¹. Erosion rate class &lt;5 t·ha-¹·yr-¹ dominates the catchment’s area with 92.5%, while other classes have 7.5% of catchments total area. Highest rates of soil erosion are found in volcanic rocks, steep slopes and soils without conservation practices. Soils located in western part of the catchment with adequate conservation practice have minimum erosion rate.

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.

Effects of Erosion Control Works: Case Study–Reservoir Celije, Rasina River Basin, the Zapadna Morava River (Serbia)

The aim of this research was to analyze the impact of implemented erosion control works (ECW) on soil erosion intensity in the watershed of the Ćelije reservoir (Rasina River) in the period between 1968 and 2022. The Erosion Potential Method was used to calculate the annual gross erosion (W), sediment transport (G), and erosion coefficient (Z) in the study area. As a result of the performed ECW there was a general decreasing trend in the intensity of soil erosion processes in the last 54 years. The specific annual gross erosion was 1189.12 m3/km−2/year−1 in 1968, while in 2022 it was 554.20 m3/km−2/year−1. The specific sediment transport was 540.18 m3/km−2/year−1 in 1968 and 253.55 m3/km−2/year−1 in 2022. Due to the changes in the intensity of erosion processes, the specific annual gross erosion decreased by 634.92 m3/km−2/year−1 and the specific sediment transport decreased by 286.63 m3/km−2/year−1. The erosion coefficient was reduced from Z = 0.62 to Z = 0.35. A dependence between the slope of siltation and the natural bed slope was defined. The results show a significant correlation between erosion intensity and performed ECW, providing a basis for future watershed management and defining a strategy for soil erosion control in the Ćelije reservoir watershed.

Erosion Prediction and Soil and Water Conservation Scenario Using The SWAT Model

Increasing population and development has resulted in inappropriate land use within watersheds that increase soil erosion. One of the hydrologic models that can be used to predict erosion and soil and water conservation in a watershed is SWAT. This research was conducted to predict erosion and the guidance of soil and water conservation in the Yeh Empas Watershed. This research was carried out in several stages, including data collection, data analysis and map validation using the ROC (receiver operating characteristic) and AUC (area under curve) methods. Results revealed that Yeh Empas Watershed had a very light EHL (erosion hazard level) category was found in the downstream area with an annual erosion value of 0 – 14.68 t/ha. In the middle area, the EHL was categorized as light with an erosion value of 15.44 – 57.22 t.ha-1.y-1. The upstream area has moderate EHL with a value of 65.20 – 178.64 t.ha-1.y-1. The validation of the EHL map using the ROC method showed a result of 0.83. This value was classified as very good. It can be concluded that SWAT predictions can be used in estimating erosion and soil and water conservation scenario to reduce 85% erosion. Keywords: Erosion prediction, Soil and water conservation scenario, SWAT models, Yeh Empas Watershed.

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.

The Impact of Group- and Single-Tree-Selection Cuttings on Runoff and Sediment Yield in Mixed Broadleaved Forests, Northern Iran

Silvicultural treatment and the forest harvesting operations using different methods can lead to an increase in the production of runoff and sediment by changing the canopy and soil surface where they are conducted. In order to investigate this issue, sampling plots were established in the Namkhaneh district of the Kheyrud forest with three replications for every treatment: control stand and tree harvesting systems using single-selection cuttings and group-selection cuttings. The amount of runoff and sediment was collected and estimated from precipitation over a period of one year. Also, some soil physical properties such as bulk density, penetration resistance, sand, silt, and clay content, soil moisture, and soil organic matter were measured. The results showed that tree harvesting systems has a significant effect on runoff, the runoff coefficient, and sediment but the season (growing season and fall) and the combined effect of tree harvesting systems and the season have no significant effect on the runoff coefficient and sediment. The mean runoffs of each rainfall event for the control, single-tree, and group-selection treatments were 5.67, 8.42, and 10.28 mm, respectively, and the sediment amounts were 3.42, 6.70, and 11.82 gr/m2, respectively. Furthermore, the total annual erosion amounts of the control, selection, and grouping treatments were 0.427, 0.838, and 2.178 t/ha, respectively. The bulk density, penetration resistance, and percentage of sand and silt were positively related and the percentages of clay and organic matter were negatively related with the amount of runoff and sediment. In the method of individual selection cuttings, the damage to the forest in terms of the amount of runoff and soil erosion was less than for the group-selection cuttings. Forest harvesting by the selection method (single-selection and group-selection) has caused different changes in the vegetation canopy. The final summary of our results could be the advice to predominantly use the single-selection method in high-slope stands.

Modelling Soil Erosion Using RUSLE and GIS for Kadalundi River Basin in Kerala, India

The main objective of the study was to estimate the potential average annual soil loss from Kadalundi river basin using RUSLE model and to prepare the spatial distribution map of soil erosion hazard using GIS to suggest suitable soil conservation and management measures for the basin. The individual factor maps for each factor in the RUSLE equation were prepared and multiplied in raster calculator to obtain the spatial distribution map of soil erosion with the help of ArcGIS. The daily rainfall data, basic soil data, Digital Elevation Model (DEM) and satellite imageries were used as the input data for the calculation of rainfall erosivity factor, soil erodibility factor, topographic factor, cover management factor and conservation practice factor. The results indicated an average annual erosion of 5.48 t·ha-1·yr-1 in the basin during the period of 2001 to 2021. The total quantity of soil washed away from the basin was 694836.60 t·yr-1. The estimated values of R-factor ranged from 938.97 MJ·mm·ha-1·h-1·yr-1 to 1102.65 MJ·mm·ha-1·h-1·yr-1, and the computed erodibility factor values varied between 0.0075 to 0.025 t·h·MJ⁻1·mm⁻1. The topographic factor value of the basin was found between 0 to 9.07. The basin had C-factor values of 0.08 to 1.10 and the P-factor values varied from 0.1 to 1. The spatial distribution map of erosion suggest that major part of the basin is under slight erosion class and only a minor part is under very severe erosion class. Areas in the very severe erosion class is characterised by moderate to steep slopes, or in other words areas with less slope is having slight erosion. As most of the area of the basin is having slight erosion, simple agronomical measures can be adopted for the mitigation purposes.