Rainfall-induced Soil Erosion Research Articles

Rainstorm erosion difference and topographical changes induced by heavy rainfall between afforestation and grassland restoration catchments on the Chinese Loess Plateau

The frequency and intensity of heavy rainfall on the Chinese Loess Plateau (CLP) are increasing due to global climate change, which has caused severe soil erosion. However, precise evaluation of heavy rainfall-induced soil erosion, deposition, and related topographical changes at the catchment scale is lacking, and there is uncertainty as to how effectively trees can inhibit erosion, especially during heavy rainfall. In this study, we selected a pair of catchments with afforestation and natural grassland restoration on the CLP. Precise evaluation of erosion and topographic changes induced by heavy rainfall was conducted based on airborne light detection and ranging (LiDAR) data. In the grassland catchment, the gully bed area increased by 17.6 %, the gully slope and inter-gully land area decreased by 1.4 %, and the length of the gully shoulder line decreased by 4.4 %. Conversely, the forestland catchment experienced a decrease in gully bed area of 8.8 %, an increase in gully slope area of 5.3 %, a decrease in inter-gully land area of 9.9 %, and a decrease in the length of the gully shoulder line of 11.4 %. Following heavy rainfall, both catchments experienced a substantial increase in terrain relief. Soil erosion in the gully slope accounted for >90 % of the total erosion in both catchments, while deposition in the same area contributed to >75 % of the total deposition. The grassland catchment experienced a unit area erosion of 233,908 m3 km−2, which was 4.7 times greater than that of the forestland catchment (49,768 m3 km−2). In both catchments, erosion predominantly transpired in the steep slope region adjacent to the gully shoulder line, accounting for >15 % of the gully slope erosion area. The grassland catchment exhibited a continuous distribution pattern of erosion and deposition, whereas the forestland catchment exhibited a scattered and intertwined distribution pattern. This study is essential for further research on erosion and topographic changes induced by extreme weather events and to determine appropriate measures.

Does biochar mitigate rainfall-induced soil erosion? A review and meta-analysis

Biochar has emerged as a promising soil amendment for improving soil structure. Yet, its impact on rainfall-induced soil erosion varies across individual studies. To address this gap, we conducted a statistical meta-analysis of 174 paired comparisons from 45 published studies to integratedly evaluate the impacts of biochar on rainfall-induced soil erosion through biochar and soil properties, as well as experimental conditions. Overall, biochar significantly reduced soil erosion by 27.86%. The response ratio (lnRR) of biochar-induced soil erosion exhibited significant variability across different subgroups. Concerning biochar properties, a more favorable influence was observed in other sources biochar (e.g., manure and sewage sludge biochar) compared to wood based and crop waste biochar, and those produced at lower pyrolysis temperatures (< 500 °C). Increasing biochar dosage was not consistently effective. The optimal range was 0.8%–2%, resulting in a 36.07% reduction in soil erosion. Regarding the soil properties, a higher sand/clay ratio of soil significantly enhanced the performance of biochar (p < 0.0001). Specifically, an insignificant effect was observed in fine-grained soils, whereas the highest reduction of 52.97% was noted in coarse-grained soils. Moreover, long-term field experiments induced greater reductions in soil erosion with biochar (35.30%) compared to short-term laboratory studies (29.62% and 12.59%). This meta-analysis demonstrates that biochar, as a potential soil amendment, could effectively mitigate rainfall-induced soil erosion by considering a combination of soil properties along with specific biochar properties.

How Effective Are Palm-Fiber-Based Erosion Control Blankets (ECB) against Natural Rainfall?

Rainfall-induced soil erosion is a significant environmental issue that can lead to soil degradation and loss of vegetation. The estimated global annual loss increased by 2.5% over 11 years, from 35 billion tons in 2001 to 35.9 billion tons in 2012, mainly due to spatial changes. Indonesia is predicted to be among the largest and most intensively eroded regions among countries with higher soil erosion, regarded as hot-spots higher than 20 Mg yr−1 ha−1. Due to climate change, natural rainfall patterns in the tropical regions have been subject to change, with a lower number of rainy days and increased intensity of precipitation. Such changes trigger more soil erosion due to heavier rainfall kicking up dried soil particles that are exposed in the bare embankments. Unfortunately, there is no prevention available in developing countries due to the lack of availability and high prices of mitigation techniques such as terraces and covering areas with geotextiles or blankets. Erosion control blankets (ECBs) have emerged as a potential solution to mitigate soil erosion. This research article aims to evaluate the effectiveness of sugar-palm-fiber-based ECB in reducing soil erosion caused by natural rainfall. The study investigates the effectiveness of sugar-palm-based ECB in protecting against erosion at the designated embankment. During the three months of typical rainy seasons (February to April 2023), total eroded mass (kg) was collected and measured from two adjacent microplots (10 m2 each), one covered with ECB and the other one left as uncovered soil (bare soil). The results indicate that eroded mass is proportional to rainfall, with coefficients of 0.4 and 0.04 for bare soil and ECB-covered embankments, respectively. The total soil loss recorded during the monitoring period was 154.6 kg and 16.7 kg for bare and ECB-covered soil, respectively. The significantly high efficiency of the up to 90% reduction in soil losses was achieved by covering the slope with sugar-palm-fiber-based ECB. The reason for this may be attributed to the intrinsic surface properties of sugar palm fiber ropes and the soil characteristics of the plot area. Sugar palm (Arenga pinnata) fiber has higher lignocellulosic contents that produce a perfect combination of strong mechanical properties (higher tensile strength and young modulus) and a higher resistance to weathering processes. Although the cost of production of handmade sugar-palm-fiber-based ECB is now as high as 4 EUR, further reductions in cost production can be achieved by introducing machinery. Compared to typical ECBs which have smaller openings, sugar-palm-based ECB has larger openings that allow for vegetation to grow and provide it with a lower density. As such, we recommend improvements in the quality of palm-fiber-based ECB via the introduction of further automation in the production process, so that the price can be reduced in line with other commercially available natural fibers such as jute and coir.

A quantitative analysis method for ecological-health risks caused by rainfall-induced soil erosion at the Laronde Mine

Understanding the migration and dispersion of potentially toxic elements (PTEs) from soil erosion is integral to managing mine site risks. Modeling PTEs distribution in this mass movement is a significant challenge. This research quantitatively analyzed the ecological-health risks associated with rainfall-induced soil erosion (debris flow) at the LaRonde Mine's tailing pile (Quebec, Canada). The present study adopted a comprehensive approach by employing rheological and infiltration models to correlate ecological-health indices with slurry distribution and calculated various indices to assess five PTEs (As, Cu, Ni, Pb, and Zn). Exposure values, hazardous quotients, and hazard index assessments were used to examine the potential risks to human health. The outcomes show that Zn presents the highest risk, followed by Cu, while As, Ni, and Pb pose no risk. Moreover, the toxic risk index and pollution load index exhibit lower accuracy than other indices. The computed results were visualized using Arc Geographic Information System (ArcGIS), providing a location-dependent risk level distribution and maps of contamination levels. This study demonstrates the importance of quantitative analysis in mine sites' ecological-health risk assessment and provides a framework for developing soil conservation and management strategies at mine sites.

Monitoring soil erosion in support of achieving SDGs: A special focus on rainfall variation and farming systems vulnerability

The Sustainable Development Goals (SDGs) and soil-related activities are closely intertwined. Healthy soil ecosystems promote the survival of life on Earth and uplift agricultural productivity. One of the issues that is putting healthy soil ecosystems at risk is soil erosion. The Central Highlands of Sri Lanka were chosen because it is a humid, tropical nation that is particularly susceptible to climate threats. Soil erosion was tracked and predicted in connection to rainfall variation in order to reduce soil erosion in farming systems and in supporting the achievement of the SDGs. A revised universal soil loss equation (RUSLE) was executed to determine soil loss. Rainfall variance was calculated using trend analyses of extreme rainfall indices and rainfall erosivity. Soil erosions for the year 2040 under the Representative Concentration Pathways (RCP) 2.6 and 8.5 scenarios were predicted using a variety of machine learning techniques, including artificial neural networks (ANN), support vector machines (SVM), and adaptive network-based fuzzy inference system (ANFIS). Results indicated a positive trend in rainfall erosivity and several other extreme indices. In addition, soil erosion rates increased, with October recording the highest monthly erosion rates. Based on 2020 soil erosion rates, soil erosion model forecasts suggest that erosion may range from 4 % to 22 % in 2040. Under the RCP 2.6 and 8.5 climate scenarios, the rate of soil erosion can be raised from 10.5 t/ha/yr to 12.4 t/ha/yr, respectively. These facts indicate that rainfall-induced soil erosion will increase the vulnerability of the Central Highlands and pose a severe danger to the achievement of the SDGs. In order to ensure the well-beingof the soil ecosystem, this research offered strategies to monitor and anticipate soil erosion in support of achieving the SDGs to protect the healthy soil ecosystem. This methodology can be replicated to minimize soil erosion in farming systems elsewhere.

Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale.

Rainfall kinetic energy (RKE) constitutes one of the most critical factors that drive rainfall erosivity on surface soil. Direct measurements of RKE are limited, relying instead on the empirical relations between kinetic energy and rainfall intensity (KE-I relation), which have not been well regionalized for data-scarce regions. Here, we present the first global rainfall microphysics-based RKE (RKEMPH) flux retrieved from radar reflectivity at different frequencies. The results suggest that RKEMPH flux outperforms the RKE estimates derived from a widely used empirical KE-I relation (RKEKE-I) validated using ground disdrometers. We found a potentially widespread underestimation of RKEKE-I, which is especially prominent in some low-income countries with ~20% underestimation of RKE and the resultant rainfall erosivity. Given the evidence that these countries are subject to greater rainfall-induced soil erosion, these underestimations would mislead conservation practices for sustainable development of terrestrial ecosystems.

The size distribution metrics and kinetic energy of raindrops above and below an isolated tree canopy in urban environment

Raindrop impact on bare soils is the initial phase of rainfall-induced soil erosion which is altered under any type of vegetation due to the interactions of rainfall with the canopies. This study examines the drop size distribution (DSD) and kinetic energy (KE) of raindrops above and below the birch tree (Betula pendula Roth.) canopy in a research plot in the city of Ljubljana, Slovenia using a one-year observation of 63 rainfall events and the effect of meteorological variables under moderate continental climate. Simultaneous measurement of the microstructures of open rainfall and throughfall was carried out using an optical disdrometer. The result of our analysis revealed that throughfall DSD showed two distinct major peaks (bimodal) occurring primarily on smaller drop sizes while open rainfall has only one. The cumulative drop number, median drop-volume diameter (D50), and drop fall velocity of throughfall were 16.4%, 26.6%, and 5.0% lower than those of open rainfall, respectively. Also, the relative volume percentage of raindrops > 1.5 mm is 1.5 times higher than those observed in throughfall drops which indicates that the presence of the canopy caused the fractionation of larger drops into smaller droplets. These reductions significantly differ depending on the phenoseasons of the canopy with the leafed state being higher than the leafless state. Similarly, the Kruskal-Wallis H test result revealed that birch tree elicits a statistically significant change in the kinetic energy of open rainfall, thus weakening the mean rainfall KE by 33.7%. On the other hand, KE is positively affected by the phenological condition of the canopy with higher attenuation being observed during its leafed state. Also, the correlation analysis demonstrated that vapor pressure deficit, air temperature, and relative humidity have stronger associations with throughfall kinetic energy among meteorological variables considered. These findings underscore the necessity of an optimized selection of tree species for afforestation programs.

Rainfall Induced Soil Erosion and Sediment Yield Assessment in Upper Brahmaputra River Basin

Abstract Riverbank erosion, aggradation, and river bed degradation are significant threats to most Indian watersheds. A quantitative valuation of the spatial distribution of soil erosion at a watershed level is vital for sustainable watershed planning and management practice. Very few studies can be found that focus on soil loss with sediment yield patterns in the upper Brahmaputra river basin due to massive erosion-deposition processes. Therefore, it is intended to study the soil loss and sediment yield pattern in the upper Brahmaputra river basin at the selected outlet of Majuli Island, located in South Asia. The principal objective is to evaluate soil erosion and sediment yield transported downstream of Majuli Island using the revised universal soil loss equation (RUSLE) and sediment delivery ratio (SDR) models, respectively, for 1979-2014. The study is conducted based on data availability and efficient models like RUSLE and SDR to measure soil erosion and sediment yield. Also, sediment yield maps for 36 years are prepared for the whole catchment considering rainfall as a variable. The maximum area contributing to soil erosion is observed around the hilly region having steep slopes. However, the highest sediment deposition is found near Majuli Island. This study would help take preventive measures for the erosion process and watershed management to minimize soil erosion in the watershed.

Assessment of rainfall-induced soil erosion rate and severity analysis for prioritization of conservation measures using RUSLE and Multi-Criteria Evaluations Technique at Gidabo watershed, Rift Valley Basin, Ethiopia

Soil erosion due to rainfall is the most dominant form of land resources deterioration in many parts of the world, including Ethiopia. Hence, assessing soil rate and understanding its major drivers are vital to implementing management interventions. This study intended to assess soil erosion rate, severity analysis of hotspot areas and prioritize it using Geographic Information System (GIS) based Revised Universal Soil Loss Equation (RUSLE) and multi-criteria Evaluation analysis Methods for Gidabo watershed, Rift Valley basin, Ethiopia. The Rainfall, Soil, land use/cover, Digital Elevation Model (DEM), and support practice data were used as input parameters of this study. The result revealed that the mean annual soil loss of the watershed was 44.2 t ha−1 yr−1, and it was found to be above the tolerable soil erosion rate. The outcome indicated that 6.8% (21909.6 ha) of the watershed categorized under high to severe (>10 t ha−1 yr−1) and need urgent mitigation measures. The study also demonstrated that the upstream parts of the watershed were more threatened by soil erosion risk due to the area's steep slope and rugged landforms. Therefore, mapping erosion hotspot areas would be helpful to watershed management planner and decision makers for prioritization of watershed management plan. It is too early to say that the finding would help policymakers plan and implement erosion mitigation measures to curb land degradation and support sustainable development in the Gidabo watershed and the Rift Valley basin in general.

SILTspread: Performance-Based Approach for the Design and Installation of Silt Fence Sediment Barriers

AbstractConstruction operations are susceptible to high rates of rainfall-induced soil erosion due to the earth-disturbing nature of land-grading activities. Silt fence sediment barriers are a temp...

Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine

Soil erosion control is a complex, integrated management process, constructed based on unified planning by adjusting the land use structure, reasonably configuring engineering, plant, and farming measures to form a complete erosion control system, while meeting the laws of soil erosion, economic and social development, and ecological and environmental security. The accurate prediction and quantitative forecasting of soil erosion is a critical reference indicator for comprehensive erosion control. This paper applies a new swarm intelligence optimization algorithm to the soil erosion classification and prediction problem, based on an enhanced moth-flame optimizer with sine–cosine mechanisms (SMFO). It is used to improve the exploration and detection capability by using the positive cosine strategy, meanwhile, to optimize the penalty parameter and the kernel parameter of the kernel extreme learning machine (KELM) for the rainfall-induced soil erosion classification prediction problem, to obtain more-accurate soil erosion classifications and the prediction results. In this paper, a dataset of the Vietnam Son La province was used for the model evaluation and testing, and the experimental results show that this SMFO-KELM method can accurately predict the results, with significant advantages in terms of classification accuracy (ACC), Mathews correlation coefficient (MCC), sensitivity (sensitivity), and specificity (specificity). Compared with other optimizer models, the adopted method is more suitable for the accurate classification of soil erosion, and can provide new solutions for natural soil supply capacity analysis, integrated erosion management, and environmental sustainability judgment.

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

Rainfall induced soil erosion assessment, prioritization and conservation treatment using RUSLE and SYI models in highland watershed of Ethiopia

Soil erosion and the subsequent sedimentation are the major problem in Ethiopia. We assessed the impact of rainfall-induced soil erosion and prioritize the sub-watersheds using RUSLE and SYI models. We procured mean annual precipitation, Abay Basin soil map, Digital Elevation Model (DEM) and LANDSAT 8 OLI/TIRS, 2017 datasets. Results show that annual estimated soil loss was ≤270 and ≤120 t ha−1 yr−1 whereas, mean rates were 37.72 and 23.92 t ha−1 yr−1 for RUSLE and SYI models respectively. Both the models have identified ∼25% area of sub-watersheds (SW19 to SW23) has similar average soil loss (>20 t ha−1 yr−1) at higher elevations. Interestingly, similar erosion severity, priority level and ranks were observed through RUSLE and SYI. Findings indicate a strong relationship between erosion rate and topography. The severe erosion prone area requires immediate action for conservation treatment proposed in study.

Assessment of rainfall-induced soil erosion on hillslope: a case study at the Guthrie Corridor Expressway, Malaysia

The effect of northeast monsoon rainfall to soil erosion on hillslope adjacent the Guthrie Corridor Expressway, Malaysia, is investigated by examining a relationship between vegetation covers and slope steepness. Five plot-scale study areas [i.e., NBNM, PDM, PDNM, NBM and natural dense microbe (NDM)] are marked on the hillslope with different percentage and type of vegetation densities. Experimental equipment is set up at those plots to collect rainwater and soil loss starting from November 2014 until March 2015 with twice collection per month. Based on the data collected, the effect of different vegetation densities associated to soil loss is examined. These are the conclusions that can be made based on this investigation: (1) the observed rainfall pattern shows that mostly the recorded rainfall depth is about 5 mm with 20 min duration of rainfall; (2) plot without vegetation cover (i.e., NBNM) yields greater soil loss with the maximum runoff, whereas the least is exhibited by the plot having NDM. Meanwhile, the rate of soil loss is found greater for the planted vegetation compared to the natural plant; (3) a positive relationship is found between runoff and slope steepness regardless of the vegetation cover types.

Applying Biomineralization Technology to Study the Effects of Rainfall Induced Soil Erosion

The rainless days and drought seasons reveal a tendency to lengthen the wet and dry period in recent years in Taiwan. In the bare riverbeds in central Taiwan, such as the Dajia and Zhuoshui rivers, fugitive dust is the common problem during the winter’s dry period with northeast monsoon. The study aims to use the biological method, Microbial-induced carbonate precipitation (MICP), to solidify the soil and implement a series of rainfall simulator experiments to reduce the Aeolian dust emission problems. Accordingly, the relationship between rainfall-induced soil erosion and its soil specimens are also discussed. The soil conducted the MICP for seven days as the curing age, and then be analyzed the degree of soil solidification under different conditions, rainfall intensity of 41 mm/h and 61 mm/h by the soil erosion experiment. The effect of soil solidification with various relative density of soils of 60%, 70%, and 80%, and hillside slopes of 5°, 10°, 20° were tested. The result indicated that, the higher the relative density of soils, the better the effect of soil solidification would be. The relative density of soils from 60% to 80% all kept the effect of soil solidification as applying to MICP. Therefore, it was important to select the curing age with the matching relative density of soils. Moreover, the most appropriate condition for the effects of soil solidification by MICP was the slopes below 10° and the curing age for seven days. The effect of soil solidification was still preserved in that with the high intensity rainfall (66 mm/h) due to the Aeolian dust emission commonly happening in the bare riverbeds with mild slopes.

Mapping rainfall aggressiveness from physiographical data: application to the Grândola Mountain Range (Alentejo, Portugal)

ABSTRACT The South of the Iberian Peninsula is subject to long periods of drought followed by heavy rain events over shallow soils, promoting soil loss. The Modified Fournier Index (MFI) is a good indicator of this process; however, MFI is sometimes difficult to assess due to the scarcity of rainfall data. This study proposes a methodology using MFI and supported by a geographic information system (GIS) and geostatistics to map rainfall aggressiveness with scarce spatial rainfall data, where physiographic variables are used to overcome the lack of rainfall data. The Grândola Mountain Range in the Alentejo region, Portugal, is presented as a case study. This area is a CORINE biotope, currently under application to the Natura 2000 network, and should be considered as a priority for the conservation of the environment. The model allowed us to create a map of rainfall aggressiveness, classified according to CORINE-CEC, found to be Moderate in the mountains and Low in the coastal area of the Grândola Mountain Range. This cartography is an important tool for local and national stakeholders and authorities with responsibilities in planning and protection of the territory. The methodology can be used in regions with scarce spatial rainfall data to assess areas susceptible to rainfall-induced soil erosion.

Runoff initiation, soil detachment and connectivity are enhanced as a consequence of vineyards plantations

Rainfall-induced soil erosion is a major threat, especially in agricultural soils. In the Mediterranean belt, vineyards are affected by high soil loss rates, leading to land degradation. Plantation of new vines is carried out after deep ploughing, use of heavy machinery, wheel traffic, and trampling. Those works result in soil physical properties changes and contribute to enhanced runoff rates and increased soil erosion rates. The objective of this paper is to assess the impact of the plantation of vineyards on soil hydrological and erosional response under low frequency – high magnitude rainfall events, the ones that under the Mediterranean climatic conditions trigger extreme soil erosion rates. We determined time to ponding, Tp; time to runoff, Tr; time to runoff outlet, Tro; runoff rate, and soil loss under simulated rainfall (55 mm h−1, 1 h) at plot scale (0.25 m2) to characterize the runoff initiation and sediment detachment. In recent vine plantations (<1 year since plantation; R) compared to old ones (>50 years; O). Slope gradient, rock fragment cover, soil surface roughness, bulk density, soil organic matter content, soil water content and plant cover were determined. Plantation of new vineyards largely impacted runoff rates and soil erosion risk at plot scale in the short term. Tp, Tr and Tro were much shorter in R plots. Tr-Tp and Tro-Tr periods were used as connectivity indexes of water flow, and decreased to 77.5 and 33.2% in R plots compared to O plots. Runoff coefficients increased significantly from O (42.94%) to R plots (71.92%) and soil losses were approximately one order of magnitude lower (1.8 and 12.6 Mg ha−1 h−1 for O and R plots respectively). Soil surface roughness and bulk density are two key factors that determine the increase in connectivity of flows and sediments in recently planted vineyards. Our results confirm that plantation of new vineyards strongly contributes to runoff initiation and sediment detachment, and those findings confirms that soil erosion control strategies should be applied immediately after or during the plantation of vines.

Rainfall-induced Soil Erosion and Sediment Sizes of a Residual Soil under 1D and 2D Rainfall Experiments

Raindrop impact and surface flow trigger the downstream movement of soil particles by the processes of rainfall-induced soil erosion. A set of laboratory simulated rainfall experiments was carried out to study soil loss and size characteristics of discharged sediments of a soil under a rainfall intensity of 70mm/h, controlled initial soil suctions and moistures. The rainfall simulation was instrumented with tensiometers and moisture sensors. A new device capable of deriving impact force, velocity, and kinetic energy of a falling waterdrop was developed. Sediment sizes in runoff were characterized by a laser particle size analyzer in order to correlate with the properties of rainfall. 1D simulated rainfall experiments were also employed to study soil detachment and soil susceptibility to rainfall under both saturated and unsaturated soil conditions. The processes of soil erosion and outflow size characteristics of sediments relating to effect of suction were discussed. The proposed set of experiments will be a viable tool for measuring soil loss, sediment runoff, and sediment sizes discharged from a farmland pertaining to properties of rain, soil and flow.

A Physically—Based Geometry Model for Transport Distance Estimation of Rainfall-Eroded Soil Sediment

Estimations of rainfall-induced soil erosion are mostly derived from the weight of sediment measured in natural runoff. The transport distance of eroded soil is important for evaluating landscape evolution but is difficult to estimate, mainly because it cannot be linked directly to the eroded sediment weight. The volume of eroded soil is easier to calculate visually using popular imaging tools, which can aid in estimating the transport distance of eroded soil through geometry relationships. In this study, we present a straightforward geometry model to predict the maximum sediment transport distance incurred by rainfall events of various intensity and duration. In order to verify our geometry prediction model, a series of experiments are reported in the form of a sediment volume. The results show that cumulative rainfall has a linear relationship with the total volume of eroded soil. The geometry model can accurately estimate the maximum transport distance of eroded soil by cumulative rainfall, with a low root-mean-square error (4.7–4.8) and a strong linear correlation (0.74–0.86).

Prediction of Experimental Rainfall-Eroded Soil Area Based on S-Shaped Growth Curve Model Framework

Rainfall-induced soil erosion of a mountain area plays a significant role in supplying sediment and shaping the landscape. The related area of soil erosion, as an index of the changed landscape, is easier to calculate visually using some popular imaging tools. By image analysis, our work shows that the changing of the soil erosion area admits the structure of an S-growth curve. Therefore, we propose to establish an S-curve model, based on incremental learning, to predict the soil erosion area. In the process of incremental learning, we dynamically update the accumulative rainfall and rainfall intensity to train the parameters of our S-curve model. In order to verify our prediction model, the index of area is utilized to express the output of eroded soil in a series of experiments. The results show that the proposed S-growth curve model can be used to estimate the growth of the soil erosion area (average relative error 3%–9.7%) according to variable soil material and rainfall intensity. The original S-growth curve model can calculate the erosion areas of just one soil material and one rainfall condition whose average relative error is 7.5%–12.2%; compared to the simple time series analysis-moving average method (average relative error 5.7%–12.1%), our proposed S-growth curve model can reveal the physical mechanism and evolution of the research object.