Slope Length Research Articles

Estimation of Soil Loss by Revised USLE Model using Geospatial Techniques: A Case Study of Sainj Valley, Northwestern Himalaya, India

Soil erosion is a severe environmental problem. The current study is being conducted in Sainj valley of Himachal Pradesh. An increase in soil erosion rates has significant effects on land degradation, biodiversity loss, productivity and other factors. The Revised Universal Soil Loss Equation (RUSLE) model along with, Geographic Information System (GIS) and Remote Sensing (RS), was used in current study to quantify soil loss in the Sainj valley. Five important parameters like, Rainfall erosivity factor (R), Soil erodibility factor (K), Slope length and steepness (LS), Cropping management factor (C), and support practice factor (P) have been used to estimate the amount of soil loss in the study area. All these maps were created in GIS software using a variety of data preparation methods. The rainfall erosivity factor ranged between 501.19 to 1097.23 mt ha-1 cm-1. Spatial distribution of conservation support practice on soil loss indicated the variability (0–1) where lower value represents the higher conservation practice. The results show that average annual predicted soil loss is between 0 to 541.52tons/ha/yr and the predicted Average soil erosion rate was 11.15 tons/ha/Yr. Predicted soil loss was classified into six Erosion intensity classes Negligible (0-5), Low Erosion (5-10), Moderate Erosion (10-25), Moderately high (25-75), High Erosion (75-100), Extremely High Erosion (>100) From soil loss estimation it was observed that 62 % of the area is having very low erosion intensity and 0.37% of the study area having Extremely High erosion intensity. These findings can help the decision makers further in developing a suitable conservation program to prevent soil erosion.

The sensitivity level of landslide risk using Geographic Information System on the slopes of Mount Argopura, East Java, Indonesia

Jember is surrounded and limited by highlands such as Mount Argopura, Mount Ijen, Mount Argopura, and the southern karst mountains. In 2015-2022, the Jember area flooded during the rainy season and dried during the dry season. Changes in land cover that do not follow the science of soil preservation will cause disasters, including landslides and erosion. The purpose of this study is to assess the risk of landslides on the slopes of Mount Argopura through the Geographic Information System. The study used a field survey method that was divided into several stages, including making a working map, conducting a field survey, and analyzing the data in the laboratory. The sensitivity analysis of the landslide level used as the basis for the assessment used the relationship between the parameters of soil erodibility, soil erosion, slope and soil conservation, and slope length. The sensitivity of the level of landslide risk on Mount Argopura is divided into five classes, from very light to very heavy. The very light category covers 4.92% of the total area with erosion of 0.47 t/ha/year. The very heavy class covers 39.70% of the total area, with 1,360.79 t/ha/year erosion.

On the nature of Tone 4 alternation in Taiwan Mandarin: Insights from acoustic analysis of trisyllabic right-branching word productions

Tone 4 (T4) alternations refer to the phenomenon where the tone values of a T4 syllable reduces from ‘51' to ‘53' when it is followed by another T4 syllable in Mandarin Chinese. It is inconclusive if the nature of T4 alternations is tone sandhi (an abstract phonological rule) (Chao, 1948; Jang, 2021) or tonal coarticulation (an articulatory phenomenon in speech production) (Lin, 20007; Shen, 1990). The current study invited 30 Taiwan Mandarin speakers to produce 10 pairs of right-branching trisyllabic words, including sequences of [T4[T4-T4]] (e.g., dà diàn shì 'big television') and [T4[T4-Tone1]] syllables (e.g., dà diàn jiā ‘big store'). The T4 sandhi view predicted that the surface tone values of the first syllables in these two sequences would differ (“51i” vs. “53”), while the T4 coarticulation view predicted that the tone values would be the same (“53”). Results from the acoustic analysis on fundamental frequency (f0) contour, f0 slope and vowel length of the first T4 syllables indicated that the differences were not statistically significant. These results supported the tonal coarticulation view. Discussions pertaining to the mental grammar for the tone value distances of two adjacent T4 syllables and the typology of tonal coarticulation in Mandarin Chinese are formulated.

Modeling Dynamics of Soil Erosion by Water Due to Soil Organic Matter Change (1980–2020) in the Steppe Zone of Russia

This research aims to evaluate the dynamics of soil loss through soil erosion by water in agricultural lands in steppe areas using a modification of the RUSLE2 model from the 1980s to the 2010s. The calculation was performed using a raster model of data that included a model of the slope angle, slope length, soil erodibility, rainfall and snowmelt erosivity factors, types of land use, and cover management factor. All data were taken from open sources. The average soil erosion in the territory studied amounted to 1.48 t ha−1 year−1 in the 1980s and 1.72 t ha−1 year−1 in the 2010s. The discrepancy with other studies was 12% for the level of the 1980s and 2–7% for the level of the 2010s. The main factor leading to an increase in soil loss was soil erodibility due to the loss of soil organic matter, which affected about 52% of the studied lands. The increase in the amount of soil loss occurred against a background of compensating processes: reduction in precipitation and climate change (getting drier), as well as the overgrowth of agricultural lands with natural steppe vegetation. Modified model RUSLE2 has shown good results correlated with other studies for the research area.

Spatiotemporal dynamics and driving factors of soil erosion in the Beiluo River Basin, Loess Plateau, China

Soil erosion reduces soil fertility and land productivity while enhancing desertification, which seriously threatens ecological security, carbon sequestration, and sustainable economic and social development. Therefore, it is fundamental to precisely assess the long-term dynamics of soil erosion and explore its drivers to control the risk of soil erosion. Currently, machine learning-based analyses of the factors driving soil erosion are lacking in the Beiluo River Basin (BRB), China. This paper aimed to investigate the spatiotemporal dynamics of soil erosion by the Chinese soil loss equation coupled with the gully erosion factor, and then reveal its driving factors using the Boosting Regression Tree method (BRT) and estimate the influence of drivers' interactions on soil erosion in the BRB between 1990 and 2017. The findings demonstrated that the expanded Chinese soil loss equation accurately explained the variations in soil erosion in the BRB. The range of average soil erosion rates was 2,852.83–800.35 t km−2 a−1. Steep gully farmland, particularly areas with relatively rough terrain, was more vulnerable to intense erosion. Soil erosion rates for different geomorphologies decreased as follows: hilly–gully areas > gully–plateau areas > alluvial river plains > Rocky Mountains. The major drivers affecting the soil erosion rate were the biomass control factor (B) and slope, followed by slope length (L) and slope steepness (S). B–soil erodibility (1990), B–LS (2000), LS–rainfall erosivity (2010), and slope–rainfall erosivity (2017) interactions had the greatest influence on soil erosion, with interaction intensities of 32.3%, 17.75%, 46.05%, and 35.69%, respectively. The average soil erosion rates according to land classification types decreased as follows: farmland > grassland > forest. The results indicate that water and soil conservation in the study area benefited greatly from implementation of the Grain for Green Program, with forests reducing erosion more effectively than grassland.

Effect of Flume Width on the Hydraulic Properties of Overland Flow from Laboratory Observation

The effect of flume width (b) on overland flow dynamics was investigated in this study. Experiments were conducted with five different flow discharges and five flume widths (0.05–0.30 m, with an interval of 0.05 m). The findings revealed that a narrow flume had a noticeable impact on flow acceleration as the slope length increased. Relative average deviation (RAD) was calculated to evaluate the influence of sidewall effects on flow velocity. The coefficient of variation in the RAD ranged from 1.90% to 3.65%. The RAD has extremely significant differences between different widths when the flow is 0.02–0.08 m2/min. The significant differences in the RAD at different widths decrease as the flow rate increases. The flow regime was evaluated using the ratio of the thickness of the viscous sublayer to the water depth (δ/h), which proved to be a better indicator than the Reynolds number for studying flow regimes in different flume widths. Furthermore, it was observed that the energy variation was smaller in narrow flumes (b = 0.5–0.10 m) compared to wider flumes (b = 0.25–0.30 m). When the flume width ranged from 0.15 to 0.30 m, the specific energy change increased. These results contribute to further understanding of the hydraulic characteristics of overland flow and provide theoretical references for optimizing experimental design.

Adaptability analysis and model development of various LS-factor formulas in RUSLE model: A case study of Fengyu River Watershed, China

The slope length and slope steepness factor (LS-factor) formula in the Revised Universal Soil Loss Equation (RUSLE) has a considerable level of uncertainty due to the existence of multiple methods. In this study, four commonly used formulas for the slope length factor and two formulas for the slope gradient factor were chosen and combined based on their applicability to the specific research context. Based on the ModelBuilder in ArcGIS Pro 3.0, a RUSLE calculation model (RUSLE-Cal model) was constructed in the study, which can automatically calculate the soil erosion modulus using four commonly used RUSLE formulas and one combination formula. Taking the Fengyu River watershed in China as a case study, this research analyzes the uncertainty of different LS-factor formulas and validates the accuracy of RUSLE simulation results using measured sediment data. The optimal combination of LS-factor formulas is selected, and an in-depth analysis is conducted on the origins and suitability of each formula. The accuracy validation results indicate that, for the Fengyu River watershed, the optimal combination of L-factor and S-factor formulas were determined based on the slope gradient. Specifically, L1 formula was used when slope ≤ 10°, and L3 formula was used when slope greater than 10°. Similarly, S1 formula was used when slope ≤ 18°, and S2 formula was used when slope greater than 18°. The RUSLE model achieved the best simulation results with a relative error of 5.55%. The results of the uncertainty analysis indicate that the four formulas have a significant impact on the simulated soil erosion, with a RE ranging from −99.18% to 31.49%. Therefore, based on literature review and formula analysis, a suitability selection table for L-factor and S-factor formulas is provided, which can provide formula basis for the improvement of soil erosion in watershed models.

Three-dimensional reliability stability analysis of earth-rock dam slopes reinforced with permeable polymer

Polymer is a new repair and reinforcement material for earth-rock dams developed in recent years. Models are developed where spatial variability is incorporated into the analytical framework to evaluate the stability and reliability of reinforced three-dimensional dam slopes. The Karhunen-Loeve (K-L) expansion method is employed to generate three-dimensional random fields. The simplified Bishop method and Monte Carlo method are used to calculate the safety factors and failure probabilities of the dam slopes. With the implementation of polymer grouting reinforcement, the safety factors are improved and the failure probabilities are reduced. The reinforcement effect will be affected by the amount of polymer dosing, dam slope characteristics, and polymer grouting parameters. The impact of various longitudinal autocorrelation distances on reliability is influenced by the length of the dam slope itself. An increase of the safety factor by approximately 37% can be achieved through a polymer dosing of around 3%. Nevertheless, with the elevation of dam slope height and slope ratio, the effectiveness of polymer reinforcement is diminished.

Soil Erosion Estimation of Kunthippuzha Watershed Using GIS and RUSLE Model

Soil erosion is an environmental crisis in the world today that threatens the natural environment and also agriculture. Erosion removes the top fertile soil, degrades soil fertility, water quality and soil productivity. The soil erosion risk assessment is helpful for land evaluation in the regions where soil erosion is a major threat for sustainable agriculture. Modelling can provide a quantitative and consistent approach to estimate soil erosion and sediment yield under a wide range of conditions. The soil erosion and erosion prone areas in the Kunthippuzha sub-watershed of Bharathapuzha river basin, Kerala, India was estimated using RUSLE. The study area is having a drainage area of 950 km2 up to the gauging station. The estimation was done for 1990 to 2021. The estimated rainfall erosivity, soil erodibility, Slope length and slope steepness factor and crop management factors range from 929.36 and 980.38 MJ mm ha-1 h-1 yr-1, 0.01387 to 0.0385 t ha h ha−1 MJ−1 mm−1, 0 to 9.77 and 0.057796 to 1.0999. The results indicate that the estimated total annual potential soil loss of about 842175 t/y is comparable with the measured sediment of 845500 t/yr during the years 1990-2021. The soil erosion rate categorized into six classes based on the erosion severity, the major portion (60%) of the study area comes under very slight erosion zone and only a small portion (14%) comes under severe and very severe erosion zone. Result suggests the area of the north-eastern part suffers from a high soil erosion risk due to steep slope. The results can certainly aid in implementation of soil management and conservation practices to reduce the soil erosion in the Kunthippuzha sub-watershed.

The response of organic carbon fractions in sediment to rainfall characteristics and slope lengths: Based on four-years on-site monitoring data

The effect of soil erosion processes on the properties of sediment and organic carbon fractions is not well understood under complex erosion environments, such as various rainfall characteristics and transport distances, which leads to a gap in knowledge regarding the role of soil erosion in the global carbon cycle. This study investigated the impact of different rainfall characteristics (patterns and parameters) and slope lengths (5 m ∼ 20 m) on the sediment yield, particle size distribution (PSD) and properties of organic carbon fractions on China’s Loess Plateau. The results show that runoff was highest under rainfall pattern A (extreme rainfall), while the sediment concentration peaked under rainfall pattern B (heavy rainfall), with significant correlation with slope length, rainfall intensity, kinetic energy, rainfall erosivity and precipitation (p less than 0.05). Silt- and clay-sized fractions were transported as primary particles and enriched under rainfall patterns B and C with no differences between slope lengths. The enrichment ratio (ER) of sediment, effective and ultimate particles were primarily influenced by rainfall intensity-related parameters. In contrast, the aggregation ratio (AR) tended to be influenced by rainfall kinetic energy. The content and loss of organic carbon fractions correlated to rainfall parameters and slope length, although no significant difference was observed between different rainfall patterns and slope lengths. Lost organic carbon fractions were predominantly Mineral-associated organic carbon (MOC), with great enrichment under rainfall pattern C and rapid depletion as the slope length increased. Structural equation models (SEM) indicated the effect of rainfall characteristics and slope length on organic carbon loss was mainly due to the regulation of soil loss and particle size distribution of sediment in selective transport erosion processes. We highlighted the need to consider the effect of soil erosion and particle size distribution comprehensively on the carbon cycle from the perspective of organic carbon fractions to describe the potential effects of soil erosion on the carbon budget quantitatively, especially in complex environmental conditions with consideration of scale effect.

Assessment of Soil Erosion by RUSLE Model using remote sensing and QGIS: A Case Study on Sina River Basin

Soil erosion is a significant environmental problem that affects river basins globally, leading to the loss of fertile topsoil, degradation of agricultural lands, and water pollution. The Revised Universal Soil Loss Equation (RUSLE) model is commonly used to assess soil erosion by considering various factors such as rainfall erosivity, soil erodibility, slope length and steepness, vegetation cover, and conservation practices. To obtain the necessary data for accurate predictions, remote sensing techniques and Quantum Geographic Information Systems (QGIS), specifically QGIS software, are utilized. The objectives of this research are to determine to estimate soil loss using the Revised Universal Soil Loss Equation (RUSLE) model for the Sina River watershed using QGIS. This research paper focuses on assessing soil erosion in the Sina River Basin located in Ahmednagar, Maharashtra, India, using the RUSLE model and QGIS, having a drainage area of 12,372 km2 . The parameters of the RUSLE model were estimated using remote sensing data and the erosion was determined using QGIS. The estimated rainfall erosivity, soil erodibility, topographic and crop management factors range from 83 to 84 MJ/mm·ha−1hr−1/year, 0 to 0.25t ha−1·MJ−1·mm−and 0 to 957.083, 0 to 0.19 respectively. The results indicate that the estimated total annual potential soil loss of about 14.37 t/yr. The predicted soil erosion rate due to the increase in a barren area is about 9.76 t/yr. The results can certainly aid in the implementation of soil management and conservation practices to reduce soil erosion in the Sina River Basin.

Soil erosion estimation by RUSLE model using GIS and remote sensing techniques: A case study of the tertiary hilly regions in Bangladesh from 2017 to 2021.

Soil erosion is one of the major environmental threats in Bangladesh, especially in the tertiary hilly regions located in the northeastern and southeastern parts of the country. The revised universal soil loss equation(RUSLE), combined with Geographic Information System, is a reliable methodology to estimate the potential soil loss in an area. This research aimed to use the RUSLE model to estimate the soil erosion in the tertiary hill tracts of Bangladesh from 2017 to 2021. The erosivity factor was determined from the annual average precipitation, and erodibility factor was estimated from FAO soil database. The elevation model was used to analyze slope length steepness factors, while land use land cover was used to compute cover management factor. Lastly, land use and elevation were integrated to estimate the support practice factor. Results revealed that the potential mean annual soil loss in 2017, 2019, and 2021 was 68.77, 69.84, and 83.7 ton ha-1year-1 from northeastern and 101.72, 107.83, and 114.04 ton ha-1year-1 from southeastern region, respectively. Although total annual rainfall was high in 2017, soil loss was found higher in 2021which indicates the impact of land use change on erosion. This investigation will help the policymakers to identify the erosion-vulnerable areasin the hill tracts that require immediate soil conservation practices. Additionally, there is no latest field-based data available for the countryfor the validation, and hence, it is recommended to conduct field-based studies for validating the model-derived results and creating a reliable soil erosion database for the country.

Predicting potential reforestation areas by Quercus ilex (L.) species using machine learning algorithms: case of upper Ziz, southeastern Morocco.

The selection of appropriate areas for reforestation remains a complex task because of influence by several factors, which requires the use of new techniques. Based on the accurate outcomes obtained through machine learning in prior investigations, the current study evaluates the capacities of six machine learning techniques (MLT) for delineating optimal areas for reforestation purposes specifically targeting Quercus ilex, an important local species to protect soil and water in upper Ziz, southeast Morocco. In the initial phase, the remaining stands of Q. ilex were identified, and at each site, measurements were taken for a set of 12 geo-environmental parameters including slope, aspect, elevation, geology, distance to stream, rainfall, slope length, plan curvature, profile curvature, erodibility, soil erosion, and land use/land cover. Subsequently, six machine learning algorithms were applied to model optimal areas for reforestation. In terms of models' performance, the results were compared, and the best were obtained by Bagging (area under the curve (AUC) = 0.98) and Naive Bayes (AUC = 0.97). Extremely favorable areas represent 8% and 17% of the study area according to Bagging and NB respectively, located to the west where geological unit of Bathonian-Bajocian with low erodibility index (K) and where rainfall varies between 250 and 300 mm/year. This work provides a roadmap for decision-makers to increase the chances of successful reforestation at lower cost and in less time.

Relationships among apparent electrical conductivity and plant and terrain data in an agroforestry system in the Ozark Highlands

AbstractMinimal research has been conducted relating apparent electrical conductivity (ECa) surveys to plant and terrain properties in agroforestry systems. Objectives were to identify: (i) ECa–forage yield, –tree growth, and –terrain attribute relationships within ECa‐derived soil management zones (SMZs) and (ii) terrain attributes that drive ECa variability within a 20‐year‐old, 4.25‐ha, agroforestry system in the Ozark Highlands of northwest Arkansas. The average of 12 monthly perpendicular (PRP) and horizontal coplanar (HCP) ECa surveys (August 2020 to July 2021) and 14 terrain attributes were obtained. Tree diameter at breast height (DBH) and height (TH) measurements were made in December 2020 and March 2021, respectively, and forage yield samples were collected during Summer 2018 and 2019. Apparent EC‐tree property relationships were generally stronger within the whole site (averaged across tree property and ECa configuration, |r| = 0.38) than within the SMZs (averaged across tree property, ECa configuration, and SMZ, |r| = 0.27). The strength of the SMZs’ terrain‐attribute‐PRP‐ECa relationships were 9% to 205% greater than that for the whole site. In whole‐site, multi‐linear regressions, slope length and steepness factor (10.5%), mid‐slope (9.4%), and valley depth (7.2%) had the greatest influence (i.e., percent of total sum of squares) on PRP ECa variability, whereas valley depth (15.3%), wetness index (11.9%), and mid‐slope (11.2%) had the greatest influence on HCP ECa variability. Results show how ECa relates to plant (i.e., DBH, TH, and forage yield) and terrain data within SMZs in agroforestry systems with varying topography and could be used to precisely manage agroforestry systems.

Modification of the RUSLE slope length factor based on a multiple flow algorithm considering vertical leakage at karst landscapes

Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing area. The existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensity is constant at any slope length. The objective of this study was to modify the L-equation by establishing the functional relationship between surface-runoff intensity and karst slope length, and to evaluate its predictive capability at different resolution DEMs. Transfer grid layers were generated based on the area rate of surface karstification and considered the runoff transmission percentage at the exposed karst fractures or conduits to be zero. Using the multiple flow direction algorithm united with the transfer grid (MFDTG), the flow accumulation of each grid cell was simulated to estimate the average surface-runoff intensity over different slope lengths. The effectiveness of MFDTG algorithm was validated by runoff plot data in Southwestern China. The simulated results in a typical peak-cluster depression basin with an area rate of surface karstification of 6.5% showed that the relationship between surface-runoff intensity and slope length was a negative power function. Estimated by the proposed modified L-equation ((alx(b+1)/22.13)m), the L-factor averages of the study basin ranged from 0.35 to 0.41 at 1, 5, 25 and 90 m resolutions respectively. This study indicated that the modified L-equation enables an improved prediction of the much smaller L-factor and the use of any resolution DEMs on karst landscapes. Particular attention should be given to the variation of surface-runoff intensity with slope length when predicting L-factor on hillslopes with runoff scale effect.

Decentralization of criticality for watershed protection and sustainable agricultural production orientation in Lai Giang River Basin, South Central Coast, Vietnam

Watershed vulnerability assessment is a crucial task reflecting the risk of land degradation due to erosion of a river basin. This study assessed criticality by applying GIS to assess soil erosion potential and for solutions to reduce soil erosion in the most vulnerable. The potential erosion level is applied, RUSLE’s loss equation with four parameters of rainfall (R), slope (S), slope length (L), and soil properties (K) according to the formula: Y = K*R*S*L assuming no land cover. The research results revealed that the Lai Giang River basin has a very high potential for soil erosion with no land cover. After combining with land cover in the basin through index land cover (C) and index farming activities (P), soil loss is gradually lower. This study shows the positive effects of land cover in reducing soil erosion. We tried to come up with possible solutions on the area required for protection forest development to limit the possibility of soil erosion, that is, the area of protective forest land that needs to achieve 58.000 ha. The improved use of RUSLE integration with GIS and remote sensing is more efficient and less expensive in estimating vulnerability to land protection priorities in river basins.

Geomorphic factors influencing the spatial distribution of eroded Chernozems in automated digital soil erosion mapping

Among the factors influencing the intensity of soil erosion by water, the relief sets the basic conditions for the occurrence of erosion-sedimentation processes: the geometry of the contact surface with water runoff and its primary spatial characteristics. We developed a model that describes the categories of the intensity of soil erosion by water only by geomorphic parameters using a DEM. The study was conducted in arable soils in the southeastern part of the Central Russian Upland, namely, typical Chernozems (TCh) in the forest-steppe zone and ordinary Chernozems (OCh) of the northern steppe of the temperate climate zone. Based on 1146 ground soil-erosion survey points, the relationship between the category of soil erosion intensity and terrain parameters (slope steepness, slope length and exposure, topographic position index, and slope profile curvature, etc.) was analyzed. The results of the revealed dependencies for the studied soils made it possible to develop prognostic models using ordinal logistic regression with an assessment of their accuracy. It was found that the model for OCh soils shows in all parameters a stronger relationship between geomorphic factors and the category of soil erosion intensity than the model for TCh soils. The regression model for OCh exceeds the model for TCh by 12 % in overall accuracy. For both soils, eroded areas are determined with much less accuracy (50–60 %) than non-eroded areas (80–95 %). Based on the modeling results, maps of soil erosion by water were constructed, where belonging to the category of soil erosion intensity was determined by the maximum probability. It is also shown that soil erosion intensity modeling based only on a set of geomorphic predictors is not inferior in accuracy to the conventional visual-expert cartographic method and can be a more objective and efficient alternative in automated digital soil erosion mapping.

Optimization of land management measures for soil erosion risk using GIS in agricultural landscape of western Hararghe highlands, Ethiopia

Soil loss through water is one of the major environmental problems leading to land degradation worldwide. Though soil erosion is an inevitable natural phenomenon, it is possible to reverse and minimize the rate at a local level. This requires spatially unambiguous evidence on rate of soil loss so as to implement site specific land management decisions. The aim of this study was to estimate the mean annual soil loss and optimize land management measures in the Midhagdu watershed of western Hararghe highlands, Ethiopia. The study used five soil loss factors as input variables to estimate the soil loss. A thematic layer map was generated for each factor with spatial resolution of 30 m using Geographical Information System (GIS), and combined them with revised universal soil loss equation (RUSLE) model to estimate soil loss and identify erosion risky area in the watershed. According to the results, the distribution of annual soil loss varied between 0 and 179 t ha−1 yr−1, with a mean annual loss rate of 44.24 t ha−1 yr−1. The total annual soil loss in the entire watershed was 240,250 tons. Of this, 93.48% (224,593 tons) of the loss occurred in 49% (2,674 ha) of the catchment area,which was above the maximum tolerable soil loss limits. Most of the soil erosion affected areas are spatially situated in the upper and hillsides of the watershed. This is due to factors attributed to the steepness of the slope, dominance of Leptosols with shallow soil depth, coupled with absence of supporting practice in these parts of the watershed. Thus, slope length and slope steepness (LS) factor was the primary RUSLE parameters that controlled the soil loss in the studied watershed followed by soil erodibility (K) and support practice (P) factor. Hence, this study optimized land management options for future intervention based on the inherent limitation of the biophysical environment, socioeconomic and cultural conditions of the watershed community.

Fast and robust vision-based cable force monitoring method free from environmental disturbances

For long-term vision-based cable force monitoring, it is essential to fully consider different types of challenges, such as complex backgrounds in the field of view, illumination changes, occlusion, camera motion, and real-time realization. To address these challenges, this study proposes a vision-based, robust real-time cable force measurement method. In terms of innovation, three unique aspects were incorporated. First, to solve the difficulty of cable edge recognition caused by complex backgrounds, a linear feature detection operator called edge drawing lines was optimized by setting screening strategy based on the slope and line segments length. Second, the average of bilateral edge displacements was used as the final value to offset the displacement deviation caused by the illumination change. Third, to overcome obstacles to identifying the cable frequency caused by the camera motion, an interference frequency elimination method without reference targets or additional sensors was proposed. Furthermore, the integrated cable force calculation method was developed into a real-time measurement software. Finally, the proposed method was applied to a long-span cable-stayed bridge, where the camera was vibrated by the bridge deck or the wind significantly. The reliability of the proposed method was proved by comparing the results obtained with those achieved by accelerometers.

Modeling feedback processes between soil detachment and sediment transport along hillslopes on the Loess Plateau of China

A dynamic model of soil erosion along hillslopes considering soil detachment and sediment transport is still a major challenge in terms of its applicability to field conditions. Data availability for model calibration and validation is very limited for physically process models. An improved hillslope erosion dynamic model (HED) with a simple structure and strong application on field plots was established based on the sediment feedback mechanism in this study. Observed runoff and sediment data from field plots with slope gradients of 14.1 %–62.5 % and slope lengths of 7.9–64.7 m within runoff events for the Chagagou catchment on the Loess Plateau of China were used to evaluate the HED. We confirmed that the power function can reproduce the soil detachment capacity (Φ) and sediment transport capacity (Tc) under varying field slope conditions (gradients and lengths). The two parameters associated with the power function of Φ or Tc are consistent across the variable conditions. When the HED model simulates the process and event sediment discharge, the unified model parameters could be obtained. The simulation precision of above results ranged from 0.44 to 0.95 for Nash–Sutcliffe simulation efficiency (NSE), from 0.65 to 0.96 for R2 and from −32.37 % to 31.61 % for relative error (RE). The feedback term of HED was close to one as the slope length approached zero. Decreasing of sediment yield as slope length increased was quite consistent with the measured data due to the reasonable sediment feedback term. The critical slope lengths were more easily reached, and the feedback term value along the slope decreased more rapidly at steeper slopes and higher runoff events. HED can be further integrated into distributed watershed models for predicting sediment discharge within runoff events.