Rainfall Erosivity Research Articles

Measurement and modelling of kinetic energy and erosivity of rainfall and throughfall in a tropical semiarid region

Empirical relationships have been widely used to estimate kinetic energy of rainfall (KE) and its erosivity potential (EI30) due to scarcity of direct and continuous measurements. Two disdrometers were installed − one in an open field and another under the canopy − in a fragment of Tropical Dry Forest (TDF) in a semiarid region, Northeast of Brazil, to quantify the role of the canopy on attenuating rain kinetic energy and erosivity potential, for all events and at the seasonal scale. The data was also used to test available KE and EI30 models, as well as to recalibrate the models for the study area. Data were collected from December/2019 to July/2021, totaling 95 events. Rainfall (R) was higher than throughfall (TF) due to canopy interception, but rainfall duration and the total number of drops were higher under the canopy. The TDF canopy reduces rainfall kinetic energy by 30 % and its erosivity potential by 39 %, with higher KE and EI30 attenuation being observed during the high leaf density stage, which highlights the major seasonal role of this vegetation in protecting the soil surface from the impact of raindrops and, consequently, against erosion. The relationship between KE and mean intensity of R and TF is best expressed as a function of time (J m-2h−1), by a linear model (r2 > 0.98 – p-value = 0.000). The Wischmeier and Smith-WS model for KE and EI30 was recalibrated for the semiarid region, underestimating KE of open field rainfall by 2 % and overestimated TF KE by 1 %, against underestimations of 24 % and 20 % of the original model for R and TF, respectively. The results presented here contribute to understanding the role of dry forests in attenuating the erosivity potential of the rainfall in semiarid environments, as well as to improving the capacity on predicting rainfall and throughfall erosivity under such conditions.

Sediment source tracing in a Brazilian subtropical catchment using diffuse reflectance: Effect of spectral ranges, pre-processing techniques, and multivariate model

ABSTRACT Agriculture intensification in Southern Brazil’s subtropical regions combined with the frequent occurrence of erosive rainfall has rendered the area a global water erosion hotspot. In this scenario, understanding and regulating erosion processes at the river catchment scale is critical for mitigating soil and water resource degradation. Traditional methods for tracing sediment sources are expensive and time-consuming and justify the development of alternative approaches. Therefore, in this study, we employed diffuse reflectance spectroscopy analyses in the ultraviolet-visible (UV-VIS), near-infrared (NIR), and mid-infrared (MIR) ranges, combined with multivariate models and spectral pre-processing techniques to estimate sediment source contributions in a homogeneous subtropical catchment (Conceição River, 804 km²). Soil samples (n = 181) were collected to characterize the four potential sediment sources, including: cropland (n = 78), stream bank (n = 36), unpaved road (n = 40) and pasture (n = 27). Moreover, 44 sediment samples were collected, including suspended sediment (n = 8), fine sediment deposited on the riverbed (n = 15), and suspended sediment samples collected in the water column during storm events (n = 21). Vector machine (SVM) model outperformed the others, with better accuracy and reliability. While UV-VIS spectra proved less effective due to soil homogeneity across the catchment, NIR and MIR spectra provided valuable information for discriminating sediment sources. Furthermore, reducing the number of potential sources (from four to three or two) improved model predictions, especially when distinguishing between surface sources (cropland and pasture) and subsurface sources (unpaved roads and stream banks). The study’s findings shed light on the power of efficient and cost-effective alternative methods for assessing sediment sources, which are vital for promoting effective erosion control and sustainable land management in similar regions.

Effect of soil hydrological conditions on rainfall erosion on coastal saline soil slopes

Effect of soil hydrological conditions on rainfall erosion on coastal saline soil slopes

Assessing monthly dynamics of agricultural soil erosion risk in Poland

Soil erosion in agricultural landscapes poses a significant threat to soil health and productivity. This study investigates the dynamics of soil erosion across agricultural lands in Poland at monthly scale using the Revised Universal Soil Loss Equation (RUSLE) model. The study utilizes the Global Rainfall Erosivity Dataset (GLOREDA), which provides the most up-to-date and highest resolution rainfall erosivity (R) values, derived from 10-min resolution rainfall records. Additionally, satellite imagery spanning 2003 to 2023 was leveraged to estimate the cover-management (C) factor, capturing the spatial and temporal dynamics of vegetation cover and agricultural practices. Results reveal significant spatio-temporal variations in soil erosion rates, with peak erosion occurring during the summer months following crop harvesting, despite not aligning precisely with peak rainfall erosivity. The lowest erosion rates are observed during the winter seasons, attributed to minimal rainfall erosivity. The findings highlight the critical role of agricultural practices, particularly the timing of crop harvesting and temporary exposure of bare soil, in driving soil erosion dynamics in Poland's agricultural landscapes. Specifically, the average annual soil loss for agricultural areas in Poland was determined to be 0.27 t ha−1 yr−1, with peak monthly soil erosion rates reaching up to 0.08 t ha−1 yr−1 in August. Total monthly soil loss from agricultural lands in Poland was estimated to be approximately 4.87 Mt. annually, with 68 % of the total annual soil loss occurring during the summer months. This study contributes valuable insights into understanding and managing soil erosion risks in agricultural systems, aiding in the development of targeted soil conservation strategies and sustainable land management practices.

Assessment of nutrient differences in detached soil particles between cropland and revegetated abandoned land

AbstractCropland (CRL) abandonment is a worldwide phenomenon of land use change with significant impacts on agro‐ecosystems. This research attempts to deepen the analysis of the positive and negative effects arising by focusing on the changes in soil properties and the amount and composition of soil particles detached after several periods of rainfall and the resulting exported sediment that occurs in abandoned areas with natural revegetation compared to CRL. The study was carried out in an agroforestry catchment with a temperate climate, on which CRL and abandoned land with natural vegetation were compared. The soil was sampled along two representative hillslopes integrating elements of the landscape and land use/land covers. Sediments were collected after seven periods in which flood events were recorded during the period July 2016 to December 2017, using artificial‐lawn mats. Soil and sediment composition (texture, soil organic carbon [SOC] and nutrients [total nitrogen and total phosphorous]) under both land uses were assessed and compared and related to rainfall characteristics using principal component analysis. Nutrient enrichment factors in the sediments compared to soils were also evaluated. The results highlight that after abandonment, SOC increased significantly, reaching contents almost three times higher than in CRL. Consequently, soil erodibility decreased, resulting in substantially lower sediment generation after erosive rainfall events. On average, sediment generation was three times lower in abandoned areas than in CRL, despite their steeper slopes. Soil total nitrogen also increased on abandoned lands, reaching values about twice as high as those in CRL. However, total phosphorous content was almost twice as high in CRL than in abandoned land posing a potential risk for water due to higher erosion rates recorded in CRL. The results confirmed the association of phosphorous with smaller particles and also demonstrated the total phosphorous‐SOC link in abandoned land. Furthermore, the effect of rainfall intensity on phosphorous mobilisation was confirmed, whilst nitrogen losses were mainly related to the total amount of rainfall recorded. In a scenario of increasing extreme precipitation, both total amount and increased precipitation intensity may exacerbate water pollution problems following nutrient loss in cultivated areas. This research contributes to identifying the impacts on agroforestry systems within the current context of converting natural areas into cultivated land, whilst in other regions revegetated natural areas are developed from abandoned agricultural land.

Quantifying the contributions of precipitation, topography and human activity and their coupling to the development of permanent gully

Gully erosion poses greater threat to ecological and food security globally. The gully development rate and the contributions of primary influencing factors have not been thoroughly understood, primarily due to the complicated environmental conditions characterized by climate, soil, topography, and human activities. Therefore, this study was conducted to explore the gully development and contributions of its driving factors from 2013 to 2021 based on historical Google Earth images and field investigation in a typical rolling hilly watershed of northeast China. The climatic factors reflected by precipitation, topographical factors related to gully, and anthropological factors reflected by tillage ridge orientation and linear anthropological factors (LAF) including roads and shelterbelts were obtained. The tillage effect index (TEI), LAF effect index (LEI) and the distance from gully to LAF (DL) were calculated to analyze the influence of anthropological factors on gully development. The results showed that the linear, areal and volumetric gully development rates (RL, RA and RV) showed great variabilities in different periods with the average of 5.69 m year−1, 137.37 m2 year−1 and 428.54 m3 year−1, respectively. The accumulated RL, RA and RV increased exponentially or linearly with accumulated precipitation amount (P), erosive rainfall duration (RD), erosive rainfall (ER), maximum 30-min rainfall intensity (I30) and rainfall erosivity (RE) increased (P < 0.001). The slope of the gully head (HS) was identified as the primary topographic factor influencing RL. Both RA and RV showed positive power relationships with the drainage area of the gully (CA) and the upstream gully head (HA). The human activity reflected by tillage ridge orientation, unpaved road and shelterbelts had different effects on RL and RA. The increased orientation angle between gully and tillage ridge (ROA) mitigated headcut retreat for normal precipitation year but it would aggravate headcut retreat in the periods of continuous heavy rainfall. Gully bank with the ROA of 30−90° was more prone to areal development. The TEI, LEI and DL had significantly positive linear, power or exponential relations with gully development rates. The PLSR and VPA analysis indicated that topography, the interaction between topography and human activity, and the coupled effects of precipitation, topography and human activity were the main contributor of RL, RA and RV, respectively, and accounted for 34 %, 50 %, and 40 % of total variability of RL, RA and RA, respectively. This study can provide guidance for the control of gully erosion and the optimization of land use structure in the gully catchment.

Spatiotemporal Trends and Variations in Rainfall Erosivity in the East Qinling Mountains and the Environmental Impacts

Spatiotemporal Trends and Variations in Rainfall Erosivity in the East Qinling Mountains and the Environmental Impacts

Evaluation of soil erosion rate using geospatial techniques for enhancing soil conservation efforts

According to reports, Ethiopia is one of the countries in sub-Saharan Africa with the worst affected by soil erosion. It has both on-site and off-site consequences on biophysical and socioeconomic settings in an area. The study area is heavily affected by soil erosion forming diverse erosion structures, particularly in the upper course of the watershed. Hence, this work seeks to estimate the geographically distributed annual soil loss rate and mapping of soil erosion hazard hotspot areas in the watershed using the Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions. The RUSLE parameters, such as rainfall erosion factor (R-factor), soil erodibility factor (K-factor), slope steepness and slope length factor (LS-factor), land cover factor (C-factor), and conservation practice factor (P-factor) were considered as data input for the analysis to quantify the soil loss rate in the study area. A digital elevation model (DEM) with a 12.5 × 12.5-meter resolution was employed for catchment delineation and determination of the LS factor.The mean yearly rainfall data from the surrounding rain gauge stations was used to analyze the R-factor. The results of the current conditions showed that the average typical soil loss rate from the entire watershed is 23.8 t ha-1 yr-1, and the quantity of soil loss from the study area ranged from 0 to 776.71tan /ha-1 yr-1. Nonetheless, Tiro Afeta experiences mean soil erosion at a rate of roughly 50.2 t ha-1 yr-1, exceeding the acceptable threshold of 11 t ha-1 yr-1. Determining the sustainability of soil production requires assessment, particularly in cases where significant yearly soil erosion occurs. Due to intensive agricultural activities in the Xiro Afeta watershed, significant soil erosion is predominantly occurring in thissteep upper region. Consequently, this area urgently requires appropriate soil protection measures.

Spatial variation of gully erosion determinants across subtropical regions of China

Gully erosion, with an enormous threat to global economic development and ecological security, results from the comprehensive effect of soil inherent properties and external geo-environmental factors. Previous studies on gully causative factors were mainly conducted on a specific site, case, or region, and limited systematic investigation has been performed on their spatial variation. Based on gully inventory in the seven provinces of southern China, this study investigated the spatial variation of thresholds for fifteen potential causative factors by the frequency ratio model, and analyzed the contribution of these factors to gully erosion by the Boruta algorithm. The results showed that gully density generally increased from north to south, characterized by a clustered distribution within a specific range, but the threshold of each potential factor exhibited a slight difference. Particularly, gully formation was predominantly influenced by multi-year average rainfall erosivity, multi-year average rainfall, and population density. However, the relative importance of these factors to gully erosion showed significant spatial heterogeneity. The relative importance increased from north to south for the factors of rainfall erosivity (8.25 %-13.56 %), rainfall (8.62 %-12.01 %), and slope aspect (0.35 %-1.22 %), but decreased for the factors of slope gradient (2.93 %-6.24 %), temperature (5.23 %-8.89 %), normalized difference vegetation index (NDVI) (5.25 %-7.80 %), and fraction vegetation coverage (FVC) (4.11 %-7.23 %). Overall, climatic factors exhibited an increasing contribution to gully erosion from north to south, whereas the influence of topography and vegetation coverage decreased along the same gradient. These findings will facilitate a better gully erosion control in southern China.

GloRESatE: A dataset for global rainfall erosivity derived from multi-source data

Numerous hydrological applications, such as soil erosion estimation, water resource management, and rain driven damage assessment, demand accurate and reliable rainfall erosivity data. However, the scarcity of gauge rainfall records and the inherent uncertainty in satellite and reanalysis-based rainfall datasets limit rainfall erosivity assessment globally. Here, we present a new global rainfall erosivity dataset (0.1° × 0.1° spatial resolution) integrating satellite (CMORPH and IMERG) and reanalysis (ERA5-Land) derived rainfall erosivity estimates with gauge rainfall erosivity observations collected from approximately 6,200 locations across the globe. We used a machine learning-based Gaussian Process Regression (GPR) model to assimilate multi-source rainfall erosivity estimates alongside geoclimatic covariates to prepare a unified high-resolution mean annual rainfall erosivity product. It has been shown that the proposed rainfall erosivity product performs well during cross-validation with gauge records and inter-comparison with the existing global rainfall erosivity datasets. Furthermore, this dataset offers a new global rainfall erosivity perspective, addressing the limitations of existing datasets and facilitating large-scale hydrological modelling and soil erosion assessments.

Spatial and Temporal Variability of Rainfall Erosivity in the Niyang River Basin

Rainfall erosivity is a crucial factor in the evaluation of soil erosion, significantly influencing the complex relationships among water, soil, and the environment. Understanding its attributes and variations in space and time is essential for effective water resource management, erosion mitigation, and land-use planning. This paper utilizes daily precipitation data from 123 grid points in the Niyang River Basin, spanning from 2008 to 2016, to calculate rainfall erosivity using a straightforward algorithmic model. Ordinary Kriging was used to examine the spatial and temporal variations in rainfall erosivity, while Spearman’s correlation analysis was employed to examine the relationships between annual rainfall erosivity and various factors, including multi-year average precipitation, erosive rainfall, dry-season rainfall, wet-season rainfall, temperature, and elevation. The results indicate a year-by-year increase in rainfall erosivity in the basin, with a trend towards stabilization. The average annual rainfall erosivity over the years is 711 MJ·mm·hm−2·h−1, peaking at 1098 MJ·mm·hm−2·h−1 in 2014. A significant 93.9% of rainfall erosivity is concentrated in the wet season, with a maximum of 191 MJ·mm·hm−2·h−1 in July. The left bank of the mainstream, especially the central and lower sections of the main river and its tributaries, experiences the highest rainfall erosivity. Rainfall factors predominantly influence erosivity, with erosive rainfall showing the strongest correlation (rho = 0.93), while temperature and elevation have relatively minor effects. This study enhances the understanding of rainfall erosive forces in the plateau region and provides a scientific basis for predicting soil loss, developing effective erosion control measures, and ensuring sustainable land use.

Mapping Benggang Erosion Susceptibility: An Analysis of Environmental Influencing Factors Based on the Maxent Model

Benggang erosion is one of the most severe geomorphological hazards occurring on deeply weathered crusts in the hilly regions of southern China. Unraveling the susceptibility and pinpointing the risk areas of Benggang erosion are essential for developing effective prevention and management strategies. This study introduced the Maxent model to investigate Benggang erosion susceptibility (BES) and compared the evaluation results with the widely used Random Forest (RF) model. The findings are as follows: (1) the incidence of Benggang erosion is rising initially with an increase in elevation, slope, topographic wetness index, rainfall erosivity, and fractional vegetation cover, followed by a subsequent decline, highlighting its distinct characteristics compared to typical types of gully erosion; (2) the AUC values from the ROC curves for the Maxent and RF models are 0.885 and 0.927, respectively. Both models converge on elevation, fractional vegetation cover, rainfall erosivity, Lithology, and topographic wetness index as the most impactful variables; (3) both models adeptly identified regions prone to potential Benggang erosion. However, the Maxent model demonstrated superior spatial correlation in its susceptibility assessment, contrasting with the RF model, which tended to overestimate the BES in certain regions; (4) the Maxent model’s advantages include no need for absence samples, direct handling of categorical data, and more convincing results, suggesting its potential for widespread application in the BES assessment. This research contributes empirical evidence to study the Benggang erosion developing conditions in the hilly regions of southern China and provides an important consideration for the sustainability of the regional ecological environment and human society.

Soil Erosion Risk Analysis in the Ría de Arosa (Pontevedra, Spain) Using the RUSLE and GIS Techniques

Soil erosion in coastal areas, driven by global change and human activity, poses a significant threat to ecological and economic stability. This research investigates water erosion in the southeast of the Ría de Arosa (Pontevedra, Spain), utilizing the Revised Universal Soil Loss Equation model and Geographic Information System technologies. Key factors analyzed include rainfall erosivity, soil erodibility, topography, land cover, and conservation practices. High-resolution maps (1 × 1 m pixels) identified areas at high risk of erosion. Vulnerable zones, such as coastal cliffs and vineyards, show severe erosion rates exceeding 50 t/ha/year (&gt;5 mm/year), with the most extreme zones reaching up to 200 t/ha/year (&gt;200 mm/year). These results emphasize that intervention could be required or recommended. Suggested measures include reforestation, effective agricultural land management, or the implementation of vegetative barriers to reduce erosion. These areas, characterized by steep slopes and sparse vegetation, are particularly susceptible to soil loss, necessitating specific conservation efforts. The results underscore the need for sustainable coastal management practices and preventive strategies to protect this vulnerable coastal zone. Implementing these measures is crucial to mitigating the impacts of soil erosion, preserving natural resources, and ensuring long-term ecological and economic resilience in the region.

Erosive Rainfall Thresholds Identification Using Statistical Approaches in a Karst Yellow Soil Mountain Erosion-Prone Region in Southwest China

Karst yellow soil is one of the most important cultivated soils in southwest China. At present, only a few studies have dealt with rainfall erosivity and erosive rainfall thresholds in the karst yellow soil region. This paper utilizes statistical methods to identify erosive rainfall thresholds and slope erosion-prone areas in the Qianzhong region. This analysis is based on long-term experimental data from 10 experimental stations and 69 experimental plots within the region in 2006 to 2022. The findings show the following: The rainfall amount threshold was 12.66 mm for woodland plots, 10.57 mm for grassland plots, 9.94 mm for farmland plots, and 8.93 mm for fallow plots. Soil and water conservation measures in forestry and grassland effectively increase the rainfall amount thresholds. Compared to farmland, the rainfall threshold increased by 27.32% for woodland and 6.32% for grassland. Bare land and farmland are erosion-prone areas in the karst yellow soil region. The erosive rainfall thresholds for farmland plots with slopes of 13°, 15°, 20°, 23°, and 25° were 10.41 mm, 10.28 mm, 9.66 mm, 9.52 mm, and 9.15 mm, respectively. With the increase in the 13–25° slope gradient of farmland, the initial rainfall required for runoff generation leads to a reduction. The wrong selection indices (WSI) of all landcover plots were less than 10%, and the efficiency indices (EFF) were between 80.43% and 90.25%. The relative error index (REI) of the erosive rainfall thresholds for all landcover runoff plots was less than 0.50%, very close to 0, indicating that these thresholds have small errors and high accuracy. This study gained a better understanding of natural rainfall-induced erosion characteristics in the study area, determined rainfall thresholds for distinguishing erosive rainfall events from non-erosive across different landcover types, and reduced the workload of calculating rainfall erosivity while enhancing the accuracy of soil erosion forecasting and simulation in the karst mountain yellow soil area.

Spatio-temporal forecasting of landslide hazard in Chongqing National Transmission Protection Regions, China

ABSTRACT Previous studies on landslide susceptibility (LS) and hazard (LH) assessments have overlooked a crucial measure for evaluating validation effectiveness, which involves the use of post-temporal landslide inventories instead of solely relying on historical data. Therefore, this study constructed a regional LH prediction and validation framework at different timescales using the landslide data from 2002 to 2021. Firstly, based on updated landslide data and conditioning factors, the datasets for susceptibility modelling were built for various periods. Then, the performances of traditional ma chine learning, ensemble and deep learning models were compared. Finally, the multi-period rainfall data and susceptibility results were used in innovative LH forecasting. Our study results show that the ensemble learning model (random forest) has stronger generalisation ability in different time periods, with all Area Under Curve (AUC) values exceeding 0.9. The hazard modelling framework based on rainfall erosion intensity and susceptibility can effectively forecast LH, and the shortened timescale for rainfall erosion intensity can improve the accuracy of LH forecasting. At the very high hazard level, the percentage of landslides forecast increased from 25.23% to 42.86%. This study comprehensively explores spatio-temporal dynamic changes in LH, accurately identifying areas posing threats to the National Transmission Line Protection Regions (NTLPR).

Construction and Application of the Phaeozem Health Evaluation System in Liaoning Province, China

Soil degradation has led to a continuous decline in the quality of phaeozem, which is seriously threatening the foundation of national food security. Therefore, precisely evaluating the health status of phaeozem resources and their spatial and temporal variations are crucial for ensuring the effective implementation of soil degradation prevention and control strategies. In this study, soil data from 140 sites were collected, and eight physical and chemical indices (sand content, silt content, pedon thickness, organic matter, total potassium, total nitrogen, total phosphorus, and pH) were utilized to assess the soil health status of phaeozem in Liaoning Province. The results showed the following: (1) The minimum data set is aligned with previous research findings and effectively mirrors the soil’s health condition. (2) The substantial coefficients of variation observed for pedon thickness, sand content, and silt content across regions suggest notable differences, likely influenced by climatic variations, parent material differences, and anthropogenic activities. (3) The study area’s average soil pH of 6.39 indicates an overall acidic nature, potentially attributed to intense soil weathering and suboptimal fertilization practices. (4) The soil health index (SHI) ranged from 0.33 to 0.91, with an average of 0.62, which indicates that the majority of the phaeozem in Liaoning Province are in a sub-healthy state (SHI &lt; 0.6 = unhealthy, SHI 0.6–0.8 = sub-healthy, SHI ≥ 0.8 = healthy). These sub-healthy areas are primarily located in hills, mountains, and the Liaohe Plain, and are significantly impacted by rainfall and wind erosion. Meanwhile, unhealthy areas are concentrated in the south and center of the province, characterized by fragile ecological environments and challenging agricultural conditions.

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.

Changes in erosion susceptibility of a Chromic Haplustert pasture soil by some polymers under simulated rainfall

Due to the climate change that has become evident especially in the last ten years, ecosystems such as forests and pastures in the Mediterranean climate zone have become even more vulnerable to degradation. High erosive precipitation increases the amount of erosion in these soils with increasing their erodibility. The aim of this study was to determine to what extent some polymers, such as polyacrylamide and polyvinylalcohol, can prevent runoff and soil loss from erosion pans where a pasture soil was placed. For this purpose, aggregates of soil samples &lt;8 mm from the A horizon of a Chromic Haplustert were used. Polyacrylamide and polyvinylalcohol solutions at different doses (0, 3.5, 7.0 and 14.0 kg ha-1) were applied to the aggregates by spraying. Erosion pans with slopes of 9% and 15% were exposed to simulated rainfall produced by a drop former rainfall simulator for one hour. The simulation trial was planned as 2 replications according to the randomized plots trial design. During simulated rainfall, runoff water and sediment from pans were collected at 10-minute intervals. Transported soils were expressed as dry weight, and runoff waters were expressed as volumetric. According to the results obtained, the highest runoff occurred from a 15% inclined soil pan (11.06 mm) without polymer applied (control). After one hour of simulated rainfall, runoff did not occur from some pans with high-doses of applied polymer. The amount of soil transported from the control pan by runoff and by splashing to the sides is 362 and 4241 kg ha-1, respectively. The pan, in which the least soil is carried by splashing, was the one with a 9% slope and the highest dose of polyacrylamide (97 kg ha-1). Statistical results show that polyvinylalcohol was&amp;nbsp;&amp;nbsp;more successful than polyacrylamide in reducing soil and water losses especially on 15% slope. These results indicate that the application of polymer to pasture soils can be a valid way to combat erosion, but the treatments should be carried out by choosing the right dose, taking into account factors such as the slope of the land, the erodibility of the soil and the erosivity of rainfall.&amp;nbsp;

Temporal and Spatial Variations in Rainfall Erosivity on Hainan Island and the Influence of the El Niño/Southern Oscillation

Rainfall erosivity (RE), a pivotal external force driving soil erosion, is impacted by El Niño/Southern Oscillation (ENSO). Studying the spatiotemporal variations in RE and their response to ENSO is essential for regional ecological security. In this study, a daily RE model was identified as a calculation model through an evaluation of model suitability. Daily precipitation data from 1971 to 2020 from 38 meteorological stations on Hainan Island, China, were utilized to calculate the RE. The multivariate ENSO index (MEI), Southern Oscillation Index (SOI), and Oceanic Niño Index (ONI) were used as the ENSO characterization indices, and the effects of ENSO on RE were investigated via cross-wavelet analysis and binary and multivariate wavelet coherence analysis. During the whole study period, the average RE of Hainan Island was 15,671.28 MJ·mm·ha−1·h−1, with a fluctuating overall upward trend. There were spatial and temporal distribution differences in RE, with temporal concentrations in summer (June–August) and a spatial pattern of decreasing from east to west. During ENSO events, the RE was greater during the El Niño period than during the La Niña period. For the ENSO characterization indices, the MEI, SOI, and ONI showed significant correlations and resonance effects with RE, but there were differences in the time of occurrence, direction of action, and intensity. In addition, the MEI and MEI–ONI affected RE individually or jointly at different time scales. This study contributes to a deeper understanding of the influence of ENSO on RE and can provide important insights for the prediction of soil erosion and the development of related coping strategies.

Finite Element Method-Peridynamics Coupled Analysis of Slope Stability Affected by Rainfall Erosion

Rainfall is a pivotal factor resulting in the cause of slope instability. The traditional finite element method often fails to converge when dealing with the strongly nonlinear fluid–solid coupling problems, making it impossible to fully analyze the sliding process under the state of slope instability. Therefore, this paper uses the coupling of peridynamics (PD) and the finite element method (FEM) to propose a data exchange mode between the seepage field and the deformation field. The influencing factors of fine particle erosion during rainfall are further considered. According to the damage mechanism of the slope sliding process to the original structure of the soil, a modified erosion constitutive relationship is proposed, which takes into account the destructive effect of plastic deformation on coarse particles. Then, the influence of rainfall duration, rainfall intensity, erosion, and initial saturated permeability coefficient on slope stability was simulated and analyzed. This paper provides a novel concept for slope stability analysis and safety evaluation under rainfall conditions.