Harmonizing models and measurements: Assessing soil erosion through RUSLE model.

Soil erosion poses significant ecological and socioeconomic challenges, driven by factors such as inappropriate land use, extreme rainfall events, deforestation, farming methods, and climate change. This study focuses on the Kozhikode district in Kerala, South India, which has seen increased vulnerability to soil erosion due to its unique geographical characteristics, increase in extreme events, and recent land use trends. The research employs RUSLE (Revised Universal Soil Loss Equation), considering multiple contributing factors such as rainfall erosivity (R), slope length and steepness (LS), cover management (C), conservation practices (P), and soil erodibility (K). The study is unique and novel, since it integrates extensive field data collected from agricultural plots across Kozhikode with the RUSLE model predictions, providing a more accurate and context-specific understanding of soil erosion processes and also suggesting management strategies based on risk priority. The study found that Kozhikode experiences an average annual soil loss of 28.7 tons per hectare. A spatial analysis revealed varying erosion risk levels across the district. 52.0% of the area experiences very slight erosion, 10.31% has slight erosion, 6.18% undergoes moderate erosion, 3.88% is moderately severe, 7.34% is at severe erosion risk, 5.6% has very severe erosion, and 14.65% faces extremely severe erosion. Field data collected from agricultural plots across Kozhikode were compared with RUSLE-predicted values, revealing a low root mean square error, indicating a strong correlation between observed and simulated data. Based on these findings, the district was categorized into low, medium, and high-priority regions, with tailored recommendations proposed for each. Implementing these measures could mitigate erosion, preserve soil fertility, and support the long-term sustainability of natural and agricultural ecosystems in Kozhikode. Given the practical challenges in estimating RUSLE factors in Southern India, where data scarcity is a common issue, this preliminary study underscores the need for expanded, long-term field observations to enhance understanding of soil erosion processes at the watershed level.

Analysis of the coupling and coordination between soil erosion and land use in the Northeastern black soil region of China: a case study of Lishu County.

Lishu County, which is located in the black soil region of Northeast China, represents a key site for the analysis of soil erosion intensity. This study offers a scientific foundation for the development of targeted soil and water conservation strategies within the region. The Revised Universal Soil Loss Equation (RUSLE) was employed to compute the soil erosion modulus in Lishu County, with the objective of conducting a quantitative analysis of the temporal and spatial distribution patterns of soil erosion. Additionally, the changing characteristics of soil erosion were examined from the perspectives of land use types and slope variations. The Generalized Connectivity Causality Model (GCCM) was utilized to identify the causal relationship between soil erosion and land use types through the reconstruction of state space and cross-mapping predictions. (1) Soil erosion in Lishu County between 2000 and 2020 predominantly exhibited mild to moderate levels, characterized by patchy and sporadic erosion, with relatively severe occurrences in the northern and central regions. (2) Soil erosion was correlated with land use and slope variations, with more than 90% of erosion incidents transpiring in cultivated land areas. The 3°-5° slope range in Lishu County emerged as a focal point for erosion, necessitating targeted prevention and control measures. (3) The GCCM model illustrated a discernible causal relationship between soil erosion and land use, revealing mutual influences between the two factors. Between 2000 and 2020, both the area and intensity of soil erosion in Lishu County exhibited an initial increase, followed by a subsequent decrease. This suggests an overall trend of amelioration in soil erosion conditions. However, notable spatial disparities persist in the erosion distribution across the region.

[Analysis of Spatial Distribution of Ecosystem Services and Driving Factors in Northeast China].

Northeast China is an important ecological barrier in China, and an in-depth understanding of the spatial distribution in ecosystem services （ESs）, and the driving factors is crucial for realizing the subsequent management and protection of ESs. In the study, we quantitatively assessed the characteristics of spatial distribution in ESs in Northeastern China using the InVEST, RWEQ, and RUSLE models and identified the driving factors of ESs spatial distribution in conjunction with the geodetector based on meteorological data, remote sensing data, and socio-economic data. The results showed that the spatial distribution of ESs in Northeast China had obvious spatial heterogeneity. The high values of habitat quality （HQ）, carbon sequestration （CS） services, and soil conservation （SC） services were mainly distributed in the northern part of the four eastern leagues of the Inner Mongolia Autonomous Region, the northern part of Heilongjiang Province, and the eastern part of Northeast China, which were high in fraction vegetation cover, and low values were mainly found in southwestern and eastern Heilongjiang Province, western Jilin Province, and western Liaoning Province. The high values of the water yield （WY） service and wind prevention and sand fixation （WPSF） service were distributed in the east of the Inner Mongolia Autonomous Region and the east of Liaoning Province. The high values of WY services and WPSF services were distributed in the eastern part of Northeast China and the four eastern provinces of the Inner Mongolia Autonomous Region. According to the geodetector results, slope had the strongest explanatory power for the spatial distribution of SC services with a q-value of 0.31, land use/cover change had the strongest explanatory power for the spatial distribution of HQ and CS services with q-values of 0.64 and 0.52, respectively, and fraction vegetation coverage and annual precipitation had the strongest explanatory power for the spatial distribution of WPSF and WY services with q-values of 0.24 and 0.64, respectively, and there were interactions among all the driving factors. The spatial distribution of ESs in Northeast China was mainly influenced by natural factors. The results will provide a scientific basis for subsequent management and enhancement of ESs in Northeast China.

Comparative analysis of RUSLE and SWPT for sub-watershed conservation prioritization in the Ayu watershed, Abay basin, Ethiopia.

Ethiopia is currently facing a major environmental problem caused by soil erosion. In order to tackle this problem, it is essential to implement a comprehensive watershed management approach and give priority to conservation efforts depending on the level of severity. Therefore, the objective of this research is to evaluate the mean annual soil erosion and rank the sub-watersheds for conservations in the Ayu watershed, utilizing the Revised Universal Soil Loss Equation (RUSLE) model and the Sub-Watershed Prioritization Tool (SWPT). RUSLE was utilized to predict the annual average soil erosion rate, while SWPT was applied to conduct Weighted Sum Analysis (WSA) for ranking sub-watersheds. Support Vector Machine (SVM) was employed for classifying land use and land cover. The Relative importance of morphometric and topo-hydrologic features in the SWPT was analyzed using a Random Forest model. The Bland-Altman plot and Wilcoxon Signed Rank Test were employed to assess the agreement in prioritizing watersheds between RUSLE results and the SWPT. Furthermore, field observations were conducted to validate the land use classification by collecting ground data. In addition, the study was enhanced with local viewpoints by conducting focus group discussions with agricultural experts and farmers to obtain qualitative insights and validation of resuts. The findings showed that soil loss varied from 0 to 110 t/ha/yr, with an average of 8.95 t/ha/yr, resulting in a total loss of 384365.3 tons annually. The comparison of RUSLE and SWPT showed a moderate positive relationship (r = 0.59). The results of the Bland-Altman plot indicate a consistent agreement between the two methods. However, there is inconsistency among the five sub watersheds. This study enhances the knowledge of soil erosion patterns and offers useful guidance for watershed conservation techniques. It can be also used as a beneficial framework for managing watersheds, with possible uses outside of the Ayu watershed.

Variations in soil erodibility (K-factor) for the Chernozems depending on the method of texture determination.

Soil erodibility (K-factor) is an important parameter in erosion modeling, is one of five factors of the Revised Universal Soil Loss Equation (RUSLE), and generally represents the soil's response to rainfall and run-off erosivity. The erodibility could be determined based on direct measurements of soil properties and mathematical calculations. In this study, the K-factor was calculated based on a formula from RUSLE, proposed by Renard et al. (1997). All input parameters: soil organic carbon (SOC), soil structure, and permeability classes were measured by one method, but particle size distribution - in two ways by sedimentation and laser diffraction methods to assess the impact the K-factor variability and the values of soil erosion rates. The 107 soil samples of Chernozems from Kursk Oblast (Russia) were studied. The texture for the most of samples was classified as silty loam in both analyses. However, the laser diffraction underestimates the clay content by an average of 13.2 % compared to the pipette method. The average K-factor estimated based on laser diffraction data was 0.050, and 0.034 t ha h ha-1 MJ-1 mm-1 - sedimentation method. Thus, depending on the method of soil texture analysis, the RUSLE calculated soil loss could underestimated/overstated by 32 % (or 4 t ha-1 yr-1 on average in the study site). Therefore, we propose a regression equation-based conversion method of laser diffraction data to sedimentation method data for Chernozems.•The Laska-TM laser analyzer measured on ∼ 13 % less clay fraction (more on ∼ 8 % silt and ∼ 5 % fine sand) compared with sedimentation method data.•For erosional researchers/modelers it is suggested to state the method of soil texture analysis (based on sedimentation law or laser diffraction) was used for RUSLE K-factor calculations.•To convert K-factor values (for Chernozems) calculated and based on data of the sedimentation method to laser sedimentation - it suggested utilize the coefficient 1.47 (0.68 - vice versa).

Spatial analysis and assessment of soil erosion in the southern Western Ghats region in India.

Soil erosion is expected to worsen in the future as a result of climate change, growing population demands, improper land use, and excessive exploitation of natural resources in India. Due to the growing population and changes in land use, it has become increasingly crucial to map and quantitatively assess soil for the purpose of sustainable agricultural usage and planning conservation efforts. The problem of soil erosion is mainly on steeper slopes with intense rainfall in parts of Western Ghats. The 20.17% of geographical area have been converted into wasteland due to soil erosion. The Revised Universal Soil Loss Equation (RUSLE) is a highly prevalent and effective technique utilized for estimating soil loss in order to facilitate the planning of erosion control measures. Despite the fact that RUSLE is accurately estimate sediment yields from gully erosion, it is an effective tool in estimating sheet and rill erosions losses from diverse land uses like agricultural to construction sites. The current study is mainly about combining the RUSLE model with GIS (Geographic Information System) to find out how much soil is being lost, particularly in Noyyal and Sanganur watersheds which is located in Coimbatore district of Tamil Nadu, India. This analysis is based on the soil order, with a significant proportion of alfisols and inceptisols being considered. The obtained outcome is contrasted with the established soil loss tolerance threshold, leading to the identification of the areas with the highest susceptibility to erosion. Within the narrower and more inclined section of the watershed, yearly soil loss scales from 0 to 5455 tonnes/ha/year, with an average annual loss of soil of 2.44 tonnes/ha. The severe soil erosion of 100 to 5455 tonnes/ha/year is found along the steep and greater slope length. The generated soil map was classified into six categories: very slight, slight, moderate, high, severe, and very severe. These classifications, respectively, occupied 6.23%, 14.88%, 10.56%, 15.70%, 7.73%, and 6.63% of the basin area. Based on the results of cross-validation, the estimated result of the present study was found to be very high compared to past studies conducted 0 to 368.12 tonnes/ha/year especially in very severe erosion zones. But very slight to severe erosion zones nearly matched with same level of soil loss. To protect the soil in the study area from erosion, more specific actions should be taken. These include micro-catchment, broad bed furrows, up-and-down farming, soil amendment with coconut coir pith composition, streambank stabilization with vegetation, and micro-water harvesting with abandoned well recharge. These actions should be carried out over time to make sure to work.

Impact of forest landscape restoration in combating soil erosion in the Lake Abaya catchment, Southern Ethiopia.

As an effect of forest degradation, soil erosion is among Ethiopia's most pressing environmental challenges and a major threat to food security where it could potentially compromise the ecosystem functions and services. As the effects of soil erosion intensify, the landscape's capacity to support ecosystem functions and services is compromised. Exploring the ecological implications of soil erosion is crucial. This study investigated the soil loss and land degradation in the Lake Abaya catchment to explore forest landscape restoration (FLR) implementation as a possible countermeasure to the effects. The study used a geographic information system (GIS)-based approach of the Revised Universal Soil Loss Equation (RUSLE) to determine the potential annual soil loss and develop an erosion risk map. Results show that 13% of the catchment, which accounts for approximately 110,000 ha, is under high erosion risk of exceeding the average annual tolerable soil loss of 10 t/ha/year. Allocation of land on steep slopes to crop production is the major reason for the calculated high erosion risk in the catchment. A scenario-based analysis was implemented following the slope-based land-use allocation proposal indicated in the Rural Land Use Proclamation 456/2005 of Ethiopia. The scenario analysis resulted in a reversal erosion effect whereby an estimated 3000 t/ha/year of soil loss in the catchment. Thus, FLR activities hold great potential for minimizing soil loss and contributing to supporting functioning and providing ecosystem services. Tree-based agroforestry systems are among the key FLR measures championed in highly degraded landscapes in Ethiopia. This study helps policymakers and FLR implementors identify erosion risk areas for future FLR activities. Thereby, it contributes to achieving the country's restoration commitment.

Does spatial resolution matter in the estimation of average annual soil loss by using RUSLE?-a study of the Urmodi River Watershed (Maharashtra), India.

The study investigates the influence of multispectral satellite data's spatial resolution on land degradation in the Urmodi River Watershed in which Kaas Plateau, a UNESCO World Heritage site, is located. Specifically, the research focuses on soil erosion and its risk zonation. The study employs Landsat 8 (30-m resolution) and Sentinel-2 (10-m resolution) data to assess soil erosion risk. The Revised Universal Soil Loss Equation (RUSLE) is used to quantify the average annual soil erosion output denoted by (A), by using its factors such as rainfall (R), soil erodibility (K), slope-length (LS), cover management (C), and support practices (P). R-factor was computed from MERRA-2 rainfall data, K-factor was derived from field soil sample-based analysis, LS factor was from Cartosat Digital Elevation Model-based data. The C factor was derived from NDVI of Landsat 8 and Sentinel-2, and the P factor was prepared from LULC derived from Landsat 8, and Sentinel-2 was incorporated in the final integration. The soil erosion hazard map ranged from slight to extremely severe. Remote sensing (RS)-based parameters like Land Use Land Cover (LULC) are derived from the Landsat 8 and Sentine-2 satellite data and used to compute the difference in the final outcome of the integration. The study found similarities in average annual soil loss (A) in plain areas, but differences in final soil erosion risk zone (A) were influenced by LULC map variations due to different cell sizes, P factor, and slope gradient. Notable differences were observed in soil erosion risk categories, particularly in high to very severe zones, with a cumulative difference of 73.85 km2. In addition to this, a scatterplot between the final outputs was computed and found the moderate (R2 = 42.08%) correlation between Landsat 8 and Sentinel-2 imagery-based final average annual soil erosion (A) of RUSLE. The study area encompasses various landforms ranging from the plateau to pediplain, and in such situation, the water-led soil erosion categories vary depending on terrain condition along with its biophysical factors and, hence, need to analyze the need of such factors on the average annual soil erosion quantification. Different spatial resolution has an effect on the final output, and hence, there is a need to track this change at various spatial resolutions. This analysis highlights the significant impact of spatial resolution on land degradation assessment, providing precise identification of surface features and enhancing soil erosion risk zoning accuracy.

Dynamic analysis of soil erosion in the affected area of the lower Yellow River based on RUSLE model.

With the accelerated development of urbanization, the exploration and usage of land resources is becoming more and more frequent, which leads to the decline of soil quality, resulting in a series of soil ecological issues, such as soil nutrient loss, soil quality degradation and destruction. At present, the contradiction between soil erosion and sustainable development of human society has become one of the hot issues studied by scholars. The Yellow River Basin is an important experimental area for high-quality development in China, constructing the Yellow River Ecological Economic Belt play an important role in China's regional coordinated development. Although most of the affected area of the Lower Yellow River (AALYR) is in the plain, they have a large population density and are in the historical farming area. In latest years, because of the development and transformation of modern society, their ecological environment has become more fragile and soil erosion problems has become increasingly serious. Studying and analyzing soil erosion is of vital meaning for ecological protection and can provide scientific support for soil conservation work. Depending on the data of precipitation, soil properties, land use, population, etc., this paper studies and analyzes the soil erosion in AALYR from 2000 to 2020 through the RUSLE. We found that during the 20 years the proportion of very slight and slight grade area increased, and the distribution of moderate and above erosion grade was less, mainly in Zibo, Jinan, Anyang, Zhengzhou, and Tai 'an. Nearly three quarters of the regional soil erosion grade didn't change, apart from the increase of slight grade area, the other erosion grades area showed a downward trend. We take the city, county and town zoning analysis find that as the scale decreases, the area of serious erosion grades increases, and the distribution is gradually detailed. Land use is the main influencing factor of erosion except DEM. Forestland and grassland are larger of the soil erosion in various types of land use. Through these conclusions in this paper, it is promising to provide theoretical references for the ecological environment governance and high-quality and sustainable development of great river basins of the world and similar regions.

Assessment of sediment transport in Luxiapuqu watershed using RUSLE-TLSD and InSAR techniques: Yarlung Tsangpo River, China.

The Yarlung Tsangpo River Basin is characterized by its intricate topography and a significant presence of erosive materials. These often coincide with heavy localized precipitation, resulting in pronounced hydraulic erosion and geological hazards in mountainous regions. To tackle this challenge, we integrated the RUSLE-TLSD (Revised Universal Soil Loss Equation-Transportation-limited sediment delivery) model with InSAR (Interferometric Synthetic Aperture Radar) data, aiming to explore the sediment transport process and pinpoint hazard-prone sites within mountainous small watershed. The RUSLE-TLSD model aids in evaluating multi-year sediment transport dynamics in mountainous zones. And, the InSAR data precisely delineates changes in sediment scouring and siltation at sites vulnerable to hazards. Our research estimates that the potential average soil erosion within the watershed stands at 52.33 t/(hm2 a), with a net soil erosion of 0.69 t/(hm2 a), the sediment transport pathways manifest within the watershed's gullies and channels. Around 4.32% of the watershed area undergoes sedimentation, predominantly at the base of slopes and within channels. Notably, areas (d) and (e) emerge as the most susceptible to disasters within the watershed. Further analysis of the InSAR data highlighted four regions in the typical area (e) from 2017 that are either sedimentation- or erosion-prone, referred to as "hotspots." Among them, R1 exhibits a strong interplay between water and sediment, rendering it highly sensitive to environmental factors. In contrast, R4, characterized by a sharp bend in siltation, remains relatively impervious to external elements. The NDVI (normalized difference vegetation index) stands out as the pivotal determinant influencing sediment transport within the watershed, exerting a pronounced impact on the outlet section, especially in spring. By employing this approach, we gained a deeper understanding of sediment transport mechanisms and potential hazards in small watershed in uninformative mountainous areas. This study furnishes a robust scientific framework beneficial for erosion mitigation and disaster surveillance in mountainous watersheds.

Spatial assessment of soil erosion by water using RUSLE model, remote sensing and GIS: a case study of Mellegue Watershed, Algeria-Tunisia.

Soil erosion is an important global phenomenon that can cause many impacts, like morphometry and hydrology alteration, land degradation and landslides. Moreover, soil loss has a significant effect on agricultural production by removing the most valuable and productive top soil's profile, leading to a reduction in yields, which requires a high production budget. The detrimental impact of soil erosion has reached alarming levels due to the exacerbation of global warming and drought, particularly in the arid climates prevalent in Tunisia and Algeria and other regions of North Africa. The influence of these environmental factors has been especially evident in the catchment of Mellegue, where profound vegetation loss and drastic changes in land use and cover, including the expansion of urban areas and altered agricultural practices, have played a significant role in accelerating water-induced soil loss between 2002 and 2018. The ramifications of these developments on the fragile ecosystems of the region cannot be overlooked. Accordingly, this study aimed to compare soil losses between 2002 and 2018 in the catchment of Mellegue, which is a large cross-border basin commonly shared by Tunisian-Algerian countries. The assessment and mapping of soil erosion risk were carried out by employing the Revised Universal Soil Loss Equation (RUSLE). This widely recognised equation provided valuable insights into the potential for erosion. Additionally, changes in land use and land cover during the same period were thoroughly analysed to identify any factors that may have contributed to the observed risk. By integrating these various elements, a comprehensive understanding of soil erosion dynamics was achieved, facilitating informed decision-making for effective land management and conservation efforts. It requires diverse factors that are integrated into the erosion process, such as topography, soil erodibility, rainfall erosivity, anti-erosion cultivation practice and vegetation cover. The computation of the various equation factors was applied in a GIS environment, using ArcGIS desktop 10.4. The results show that the catchment has undergone significant soil water erosion where it exhibits the appearance of approximately 14,000 new areas vulnerable to erosion by water in 2018 compared to 2002. Average erosion risk has also increased from 1.58 t/ha/year in 2002 to 1.78 in 2018, leading to an increase in total estimated soil loss of 54,000 t/ha in 2018 compared to around 25,500 t/ha in 2002. Maps of erosion risk show that highly eroded areas are more frequent downstream of the basin. These maps can be helpful for decision-makers to make better sustainable management plans and for land use preservation.

Spatial soil loss prediction impacted by long-term land use/land cover change: a case study of Swat District.

Soil erosion is a destructive consequence of land degradation caused by deforestation, improper farming practices, overgrazing, and urbanization. This irreversible effect negatively impacts the limited renewable soil resource, causing soil truncation, reduced fertility, and unstable slopes. To address the anticipation of erosion modulus resulting from long-term land use and land cover (LULC) changes, a study was conducted in the Swat District of Khyber Pakhtunkhwa (Kpk), Pakistan. The study aimed to predict and evaluate soil erosion concerning these changes using remote sensing (RS), geographic information systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE) model. We also evaluated the impact of the Billion Tree Tsunami Project (BTTP) on soil erosion in the region. Model inputs, such as rainfall erosivity factor, topography factor, land cover and management factor, and erodibility factor, were used to calculate soil erosion. The results revealed that significant soil loss occurred under 2001, 2011, and 2021 LULC conditions, accounting for 67.26%, 61.78%, and 65.32%, falling within the category of low erosion potential. The vulnerable topographical features of the area indicated higher erosion modulus. The maximum soil loss rates observed in 2001, 2011, and 2021 were 80 t/ha-1/year-1, 120 t/ha-1/year-1, and 96 t/ha-1/year-1, respectively. However, the observed reduction in soil loss in 2021 as compared to 2001 and 2011 suggests a positive influence of the BTTP on soil conservation efforts. This study underscores the potential of afforestation initiatives like the BTTP in mitigating soil erosion and highlights the significance of environmental conservation programs in regions with vulnerable topography.

Soil erosion vulnerability and soil loss estimation for Siran River watershed, Pakistan: an integrated GIS and remote sensing approach.

Soil erosion is a problematic issue with detrimental effects on agriculture and water resources, particularly in countries like Pakistan that heavily rely on farming. The condition of major reservoirs, such as Tarbela, Mangla, and Warsak, is crucial for ensuring an adequate water supply for agriculture in Pakistan. The Kunhar and Siran rivers flow practically parallel, and the environment surrounding both rivers' basins is nearly identical. The Kunhar River is one of KP's dirtiest rivers that carries 0.1 million tons of suspended sediment to the Mangla reservoir. In contrast, the Siran River basin is largely unexplored. Therefore, this study focuses on the Siran River basin in the district of Manshera, Pakistan, aiming to assess annual soil loss and identify erosion-prone regions. Siran River average annual total soil loss million tons/year is 0.154. To achieve this, the researchers integrate Geographical Information System (GIS) and remote sensing (RS) data with the Revised Universal Soil Loss Equation (RUSLE) model. Five key variables, rainfall, land use land cover (LULC), slope, soil types, and crop management, were examined to estimate the soil loss. The findings indicate diverse soil loss causes, and the basin's northern parts experience significant soil erosion. The study estimated that annual soil loss from the Siran River basin is 0.154 million tons with an average rate of 0.871 tons per hectare per year. RUSLE model combined with GIS/RS is an efficient technique for calculating soil loss and identifying erosion-prone areas. Stakeholders such as policymakers, farmers, and conservationists can utilize this information to target efforts and reduce soil loss in specific areas. Overall, the study's results have the potential to advance initiatives aimed at safeguarding the Siran River watershed and its vital resources. Protecting soil resources and ensuring adequate water supplies are crucial for sustainable agriculture and economic development in Pakistan.

A comparative study of morphometric, hydrologic, and semi-empirical methods for the prioritization of sub-watersheds against flash flood-induced landslides in a part of the Indian Himalayan Region.

The flash flood-induced erosion is the primary contributor to soil loss within the Indian Himalayan Region (IHR). This phenomenon is exacerbated by a confluence of factors, including extreme precipitation events, undulating topographical features, and suboptimal soil and water conservation practices. Over the past few decades, several flash flood events have led to the significant degradation of pedosphere strata, which in turn has caused landslides along with fluvial sedimentation in the IHR. Researchers have advocated morphometric, hydrologic, and semi-empirical methods for assessing flash flood-induced soil erosion in hilly watersheds. This study critically examines these methods and their applicability in the Alaknanda River basin of the Indian Himalayan Region. The entire basin is delineated into 12 sub-watersheds, and 13 morphometric parameters are analyzed for each sub-watershed. Thereafter, the ranking of sub-watersheds vulnerability is assigned using the Principal Component Analysis (PCA), compounding method (CM), Geomorphological Instantaneous Unit Hydrograph (GIUH), and Revised Universal Soil Loss Equations (RUSLE) approaches. While the CM method uses all 13 parameters, the PCA approach suggests that the first four principal components are the most important ones, accounting for approximately 89.7% of the total variance observed within the dataset. The GIUH approach highlights the hydrological response of the catchment, incorporating dynamic velocity and instantaneous peak magnifying the flash flood susceptibility, lag time, and the time to peak for each sub-watershed. The RUSLE approach incorporates mathematical equations for estimating annual soil loss utilizing rainfall-runoff erosivity, soil erodibility, topographic, cover management, and supporting practice factors. The variations in vulnerability rankings across various methods indicate that each method captures distinct aspects of the sub-watersheds. The decision-maker can use the weighted average to assign the overall vulnerability to each sub-watershed, aggregating the values from various methods. This study considers an equal weight to the morphometric, hydrological GIUH, and semi-empirical RUSLE techniques to assess the integrated ranking of various sub-watersheds. Vulnerability to flash flood-induced landslides in various sub-watersheds is categorized into three classes. Category I (high-priority) necessitates immediate erosion control measures and slope stabilization. Category II (moderate attention), where rainwater harvesting and sustainable agricultural practices are beneficial. Category III (regular monitoring) suggests periodic community-led soil assessments and afforestation. Sub-watersheds WS11, WS8, WS5, and WS12 are identified under category I, WS7, WS4, WS9, and WS6 under category II, and WS1, WS3, WS2, and WS10 under category III. The occurrence of landslides and flash-flood events and field observations validates the prioritization of sub-watersheds, indicating the need for targeted interventions and regular monitoring activities to mitigate environmental risks and safeguard surrounding ecosystems and communities.

"Estimating soil surface roughness by proximal sensing for soil erosion modeling implementation at field scale".

Soil Surface Roughness (SSR) is a physical feature of soil microtopography, which is strongly influenced by tillage practices and plays a key role in hydrological and soil erosion processes. Therefore, surface roughness indices are required when using models to estimate soil erosion rates, where tabular values or direct measurements can be used. Field measurements often imply out-of-date and time-consuming methods, such as the pin meter and the roller chain, providing inaccurate indices. A novel technique for SSR measurement has been adopted, employing an RGB-Depth camera to produce a small-scale Digital Elevation Model of the soil surface, in order to extrapolate roughness indices. Canopy cover coverage (CC) of the cover crop was also detected from the camera's images. The values obtained for SSR and CC indices were implemented in the MMF (Morgan-Morgan-Finney) model, to validate the reliability of the proposed methodology by comparing the models' results for sediment yields with long-term soil erosion measurements in sloping vineyards in NW Italy. The performance of the model in predicting soil losses was satisfactory to good for a vineyard plot with inter-rows managed with recurrent tillage, and it was improved using spatialized soil roughness input data with respect to a uniform value. Performance for plot with permanent ground cover was not so good, however it was also improved using spatialized data. The measured values were also useful to obtain C-factor for RUSLE application, to be used instead of tabular values.

Analysis of spatiotemporal variations and influencing factors of soil erosion in the Jiangnan Hills red soil zone, China.

Soil erosion is an important environmental problem in China. The hilly region of Jiangnan is characterized by severe soil erosion due to its unique climate and intensive human activities. Therefore, assessing soil erosion in this area is of great significance for achieving regional sustainable development. Based on the spatial zoning of natural resources and the spatial differences in precipitation, land cover, topographic features, and soil texture, we estimated soil erosion from 2000 to 2020 using the Revised Universal Soil Loss Equation (RUSLE) model. The study showed that micro-erosion dominates spatially in the subtropical forest subzone of the eastern hills, accounting for more than 60% of the total erosion area. Intense erosion was found in woodlands and grasslands and the erosion intensity tended to be lower in the plains. Erosion occurred mainly in areas with slopes >8°. The areas with significantly lower erosion were mainly distributed at the boundaries between forests, arable land, and artificial land surfaces. The areas where soil erosion significantly increased over the study period were mainly found in farmland areas (31.70%). Soil erosion occurred because of a combination of factors, among which vegetation cover played a prominent role. Elevation and slope were correlated with soil erosion intensity. Severe erosion in different parts of the study area showed two trends of spatial aggregation and discrete distribution. This analysis of soil erosion in the study area by the RUSLE model provides reference data for the eastern subtropical forest subregion including the Jiangnan Hills.

Upscaling plot-based measurements of RUSLE C-factor of different leaf-angled crops in semi-arid agroecosystems.

Several models have been used to assess temporal cover change trends by using remote and proximal sensing tools. Particularly, from the point of hydrologic and erosional processes and sustainable land and soil management, it is crucial to determine and understand the variation of protective canopy cover change within a development period. Concordantly, leaf angle distribution (LAD) is a crucial parameter when using the vegetation indices (VIs) to define the radiation reflected by the canopy when estimating the cover-management factor (C-factor). This research aims to assess the C-factor of cultivated lands with sunflower and wheat that have different leaf orientations (planophile and erectophile, respectively) with the help of reduced models of NDVI and LAI for estimating crop-stage SLR values with the help of a stepwise linear regression. Those equations with R-squared values of 0.85 and 0.93 were obtained for sunflower and wheat-planted areas, respectively. The Normalized Difference Vegetation Index (NDVI), one of the two plant indices used in this study, was measured by remote and proximal sensing tools. At the same time, the Leaf Area Index (LAI) was obtained by a proximal hand-held crop sensor alone. Soil loss ratio (SLR) was upscaled for the establishment period (1P) of sunflower and the maturing period (3P) of wheat to present different growth stages simultaneously with plant-specific equations that can be easily adapted to those aforementioned crops instead of doing field measurements with conventional techniques in semi-arid cropping systems.

Soil erosion and degradation assessment integrating multi-parametric methods of RUSLE model, RS, and GIS in the Shaqlawa agricultural area, Kurdistan Region, Iraq.

This study evaluated soil erosion rates in the Shaqlawa district using the Geographical Information System (GIS)-based Revised Universal Soil Loss Equation (RUSLE) model. The primary objective was to identify areas within the district that are prone to significant erosion and develop appropriate soil conservation schemes accordingly. A combination of primary and secondary data from diverse sources was utilized to achieve this objective. The GIS-based RUSLE model used variables like soil erodibility (K), soil coverage (C), topographic effect (LS), rainfall runoff (R), and erosion control practices (P) to estimate the amount of soil that had been washed away in the study area. The study provided valuable information that can be used to plan and administer soil protection in the Shaqlawa district. The average yearly soil loss in the study region is estimated to be 65.66 t ha-1 year-1. The district is experiencing significant soil erosion rates, which may have detrimental effects on agricultural productivity, water quality, and environmental health. The analysis revealed that Balisan, Hiran, Shaqlawa center, and part of the Salahaddin subdistrict are the most affected areas, with high values of LS and R factors contributing to significant soil erosion rates. These results underscore the importance of soil protection and management efforts in the Shaqlawa district. The combination of the RUSLE with GIS and remote sensing techniques has been recognized as an essential, cost-effective, and highly accurate approach for estimating soil erosion.

Comparison of ecosystem health in different geomorphic regions of Chishui River Basin, Southwest China.

Ecosystem health of the Chishui River Basin (CRB, a crucial ecological barrier in the upper reaches of the Yangtze River) is vital for the ecological security and sustainability of the Yangtze River Basin. We used RUSLE model, SWAT model, Fragstats and geographic detectors to construct a theoretical framework of ecosystem health assessment for CRB, and examined the spatiotemporal variations and driving factors of ecosystem health in CRB under ecological restoration from 2010 to 2020. The results showed that ecosystem service in the CRB decreased and then increased during 2010-2020 and the overall trend was downward. The overall ecosystem service function was higher in the Danxia (non-karst) area than that in the karst area. The ecosystem health was generally subhealthy, with the Danxia area being mostly extremely healthy and healthy, whereas the karst area mostly subhealthy and unhealthy. There were differences in the dominant drivers of ecosystem health between karst and Danxia areas. Vegetation, precipitation, and bedrock bareness rate were the dominant drivers in the karst area, while vegetation, land use, and precipitation were the dominant factors in Danxia area. After interaction detection, the explanatory power of impact factors increased, and the dominant interaction factor combinations in different geomorphological type regions had shown great differences. Among them, precipitation∩normalized difference vegetation index (NDVI), precipitation∩digital elevation model (DEM) and precipitation ∩ bedrock bareness rate were the dominant interaction factor combinations in the karst area, and NDVI∩precipitation, NDVI∩land use and NDVI∩DEM were the dominant interaction factor combinations in Danxia area. These results would provide scientific support for health maintenance and conservation of CRB ecosystem.

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-1 year-1 from northeastern and 101.72, 107.83, and 114.04 ton ha-1 year-1 from southeastern region, respectively. Although total annual rainfall was high in 2017, soil loss was found higher in 2021 which indicates the impact of land use change on erosion. This investigation will help the policymakers to identify the erosion-vulnerable areas in the hill tracts that require immediate soil conservation practices. Additionally, there is no latest field-based data available for the country for 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.