Erosion Risk Assessment Research Articles

Spatial Assessment of Soil Erosion Risk Using RUSLE and GIS in Dhumuga Watershed, Ambo, Ethiopia

Soil erosion has become a critical problem leading to land degradation and environmental risks globally. To grasp the rates of soil loss and identify the main factors driving these issues, it is vital to examine the specific impacts of soil erosion across different locations. Therefore, between 2021 and 2023, a research initiative was undertaken to assess, rank, identify, and map sections of the watershed that are particularly susceptible to soil erosion. The RUSLE components for factors R, K, L, S, C, and P were integrated using the ArcGIS 10.4.1 spatial analyst's raster calculator tool to calculate and create maps that illustrate the risk and intensity of soil erosion in the Dhumuga watershed. The Dhumuga watershed was categorized into five groups based on average annual soil loss: 0–5 ton/ha<sup>-1</sup> year<sup>-1</sup> (very slight), 5–10 ton/ha<sup>-1</sup> year<sup>-1</sup> (slight), 10–20 ton/ha<sup>-1</sup> year<sup>-1</sup> (moderate), 20–50 ton/ha<sup>-1</sup> year<sup>-1</sup> (high), and > 50 ton/ha-1 year-1 (very high). The assessment of soil erosion severity was influenced by factors such as rainfall, soil type, DEM, land use, and land cover, employing the GIS-based RUSLE equation. The spatial risk of soil erosion was sorted into five categories based on severity, with 11.58% of the area categorized as very high risk (>50 ton ha<sup>-1</sup> year<sup>-1</sup>), and 54.2% in the very low to low-risk category. On average, the watershed yielded an annual sediment production of up to 13.94 tons/ha/year, which is within an acceptable range. Considering these research findings, GIS-based analyses can be utilized to pinpoint areas at risk of soil erosion and identify vulnerable zones, offering crucial insights for future soil conservation and model enhancement.

Active Gully Head Erosion Rates Characteristics on the Loess Plateau: InSAR‐Based Calculation and Response to Extreme Rainfall

ABSTRACTQuantitative analysis and prediction of gully head erosion hold paramount importance for terrain evolution studies and risk mitigation effort. However, previous findings could not reconcile high temporal resolution with long time series coverage, particularly at the watershed scale. In this study, a calculation method for the active gully head erosion rate (AGHER) was proposed on the basis of interferometric synthetic aperture radar (InSAR) technology, thereby combining the historical climate data to estimate the annual AGHER. Additionally, we investigated the relative importance of extreme rainfall events on erosion rates. The results indicated that the long‐term annual AGHER in the study area ranged from 51.83 to 89.50 mm year−1 from 1980 to 2023 on the Dongzhi Plateau (DZP). Extreme rainfall events (rainfall amount ≥ 150 mm) emerged as the foremost erosion‐causing factor, accounting for a contribution rate ranging from 69.8% to 75.0%. Furthermore, through field surveys, we identified 21 gully heads that represent potential hazards to high‐speed railways (HSRs). Although the proportion of gullies affecting rail infrastructure may be relatively modest in century‐long projections, it notably increases against the background of worldwide escalation due to the occurrence of extreme precipitation events. This study establishes a robust foundation for gully erosion management and risk assessment on the Loess Plateau.

Empirical and Machine Learning Models for Soil Erosion Risk Assessment: A Case Study of Tsageri Municipality, Georgia

Soil erosion caused by water is one of the most common causes of land degradation worldwide. Within framework of this research soil erosion risk in Tsageri municipality, Georgia was evaluated using Revised Universal Soil Loss Equation (RUSLE) and a machine learning-based Random Forest (RF) model. Open access digital datasets and field observations collected in 2023-2024, which included visually identified erosion areas and GPS-recorded data on the presence or absence of erosion, were utilized in modeling process. Data processing and modeling conducted using ArcGIS Pro 3.0 and RStudio software. According to RUSLE results, 39.7% of the study area falls under the very low erosion risk zone, and 20.7% is in the very high risk zone. The RF model results indicated that 16.5% of the territory is under very low risk of erosion and 13.9% - very high risk. It was observed that RUSLE model tends to overestimate erosion rates on steep, forested slopes, while the RF model, by incorporating additional variables, provided more accurate prediction. These findings suggest that combining RUSLE with machine learning improves soil erosion risk assessment, particularly in complex landscapes such as in Tsageri municipality. Future researche should focus on testing additional variables to refine the modeling process further and enhance predictions. The generated digital thematic maps offer valuable insights for understanding the spatial dynamics of soil erosion within the study area, analyzing the factors driving the process and developing effective mitigation strategies.

Enhancing wind erosion risk assessment through remote sensing techniques.

Preventing wind erosion and dust storms has always been a major concern in arid and semi-arid areas because of their negative effects on the environment. This study aims to utilize remote sensing and machine learning techniques to model, monitor, and predict the risk of wind erosion in Northeast Iran. Through an examination of relevant studies, a comprehensive review was conducted, leading to the identification of eight remote sensing indicators that exhibited the highest correlation with field data. These indicators were subsequently employed to model the risk of wind erosion in the study area. Various methods including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Generalized Linear Models (GLM) were employed to carry out the modeling process. The final method utilized a weighted average of the model, and the SDM statistical package was used to combine different approaches to decrease uncertainty when modeling and monitoring wind erosion in the area. The modeling results indicated that in 2008, the RF model performed the best (AUC = 0.92, TSS = 0.82, and Kappa = 0.96), while in 2023, the GBM model showed superior performance (AUC = 0.95, TSS = 0.79, and Kappa = 0.95). Therefore, the utilization of an ensemble model emerged as an effective approach to reduce uncertainty during the modeling process. By employing the ensemble model, the outcomes obtained accurately depicted an elevated intensity of wind erosion in the northeastern regions of the study area by 2023. Furthermore, considering the climatic scenarios and projected land use changes, it is anticipated that wind erosion intensity will experience a 23% increase in the central and southern parts of the study area by 2038. By taking into account the reliable results of the ensemble model, which offers reduced uncertainty, it becomes feasible to implement effective planning, optimal management, and appropriate measures to mitigate the progression of wind erosion.

A GIS-toolbox for a landscape structure based Wind Erosion Risk Assessment (WERA)

The landscape structure influences the local wind field by lowering the wind speed and thus reducing the wind erosion risk. An important parameter is the height of each landscape element, as this determines the length of wind protection behind it. Further determining parameters are the wind speeds above a threshold value for initiating wind erosion and the corresponding wind directions. The presented method combines heights of landscape elements and the directional transport capacities of erosive wind speeds to derive a map of the spatial wind speed reduction by landscape structures. This map can be combined with the soil-derived erodibility map to get finally a wind erosion risk map which includes landscape effects.•The ArcGIS toolbox “WERA” allows a detailed analysis of the landscape structure on wind erosion processes,.•The method allows both the identification of risk areas and needs for additional plantings of LE.•The area-specific query determines the wind erosion risk for field blocks, districts or municipalities.

Assessment of soil erosion risk and vulnerability in the transboundary Sio-Malaba-Malakisi watershed in Kenya and Uganda

Persistent soil erosion poses a significant threat to water quality, ecosystem viability and soil health in many regions of the world. Addressing this challenge requires a comprehensive understanding of local soil erosion rates, including the identification of vulnerable areas, to facilitate effective and integrated environmental management. In East Africa, however, many affected regions are data poor and lack measured hydrological data from which soil erosion estimates can be derived. This study used the physically based Revised Universal Soil Loss Equation (RUSLE) model to estimate the annual rate of soil erosion in the transboundary Sio-Malaba-Malakisi catchment, straddling Kenya and Uganda. Soil erosion events were quantified by integrating input factors derived from physical datasets using a Geographical Information System (GIS). The Analytical Hierarchy Process (AHP) technique was then used to assess the importance of selected physical factors influencing the soil erosion process in order to identify regions of increasing environmental vulnerability. The results obtained indicate a wide range of soil erosion rates across the region, with an estimated mean annual rate of 250 t ha−1 yr−1 for the whole catchment. Upstream areas characterized by intensive agricultural activity had erosion rates of up to 2000 t ha−1 yr−1, while downstream areas recorded erosion rates below 600 t ha−1 yr−1. Areas with intensive agriculture, unprotected soils, and a soil loss tolerance value above 12 t ha−1 yr−1 were found to be most vulnerable. Rainfall and vegetation cover were identified as key factors influencing erosion susceptibility. Regions with soil erosion rates above 50 t ha−1 yr−1 were identified as high priority regions for targeted soil and water conservation measures. The identification of vulnerability zones, including the classification of their severity, provides an opportunity to prioritize sustainable environmental management interventions; a process that can be replicated for such affected regions elsewhere.

Exploring the inclusion of soil management practices in erosion models towards the improvement of post-fire predictions

Wildfires are recognized for having a strong impact on forest soils, a situation aggravated by inadequate pre-fire land management practices. Land management operations, such as plowing, are routinely carried out for cultural reasons and can impact soils for decades after their implementation. Therefore, it is crucial to take into account the pre-fire land management history when predicting post-fire sediment losses in burnt areas. This consideration is critical for a realistic assessment of soil erosion risk and, consequently, for effectively implementing emergency stabilization and/or rehabilitation measures.The aim of the study was to integrate pre-fire land management practices into erosion models, to enhance post-fire sediment losses predictions at slope scale. To accomplish this goal, both Multiple Linear Regression (MLR) and the revised-Morgan-Morgan-Finney model (revised-MMF) were applied in the Colmeal burnt area (Central Portugal). These models were adapted to account the impacts of different management options, specifically no plowing, downslope-plowing and contour-plowing, on the erosive response following a wildfire.The results revealed fluctuations in the performance of both models across different soil management, and over time since the wildfire. Despite the observed variability, it is important to highlight the positive outcomes achieved with the revised-MMF model over the three monitoring years where contour-plowing was applied. These results demonstrate that the best model performances are achieved when soil management is individualized and analyzed independently. Similarly, the MLR model exhibited improved performance when incorporating management practices into its predictions. This study confirms that disturbances on topsoil, whether caused by wildfires or soil management operations, play key roles in driving change in soil erosion. Hence, integrating these factors into models is essential for providing relevant information for the development of mitigation and/or restoration strategies in areas at high risk of erosion.

A Probabilistic Statistical Risk Assessment Method for Soil Erosion Using Remote Sensing Data: A Case Study of the Dali River Basin

Soil erosion risk assessment enables the identification of areas requiring priority treatment and avoids wasting human and material resources. The factor scoring method used in existing studies has high subjectivity, and the method of expressing erosion risk according to the soil erosion intensity ignores the random nature of the occurrence of erosion; therefore, neither method accurately reflects the risk of soil erosion. In order to address this issue, this study proposes a soil erosion risk assessment method that integrates the outcome and the probability of occurrence of soil erosion by means of a probabilistic statistical model. Subsequently, experimental research is conducted in the Dali River Basin. On the basis of long time-series data, using mathematical statistics as a tool and drawing on the empirical frequency formula, the probabilistic statistical risk assessment model is combined with the Modified Universal Soil Loss Equation (RUSLE) model to account for the probability of regional soil erosion at different intensity levels in the long time-series, which is combined with the intensity of erosion to carry out soil erosion risk assessment. The results of our study show the following: (1) The central and southwestern regions of the Dali River Basin (DRB) present medium and high levels of soil erosion risk, with the proportion of low-risk areas increasing annually, accounting for 78.97% of the DRB in 2020, while extremely high-risk areas account for only 0.40% of the DRB. (2) The major components impacting soil erosion risk in the DRB, as revealed by the geodetector, are the normalized difference vegetation index (NDVI) and slope, where the interaction between the two dominated the spatial variation in soil erosion risk. (3) Comparing the soil erosion risk and its status in the coming years, the proposed assessment method based on the occurrence probability can reveal the future soil erosion risk better than the traditional assessment method.

A large-scale riverbank erosion risk assessment model integrating multi-source data and explainable artificial intelligence (XAI)

The impact of riverbank erosion poses serious threat to the environment, socio-economics and human safety. Due to the extremely complex mechanisms of erosion, assessing the risk of riverbank erosion is challenging. To address this, we propose an interpretable intelligent model framework to accurately assess large-scale riverbank erosion risk. Firstly, we constructed a multi-source dataset that encompasses 29 riverbank erosion influencing factors. Subsequently, by employing an adaptive feature weighting method, a comprehensive water level factor was synthesized, unifying data dimensions. The Relief algorithm was used to identify influential features for riverbank erosion, and an adaptive feature weighting SMOTE (AFW-SMOTE) algorithm was developed to balance the riverbank erosion dataset. Additionally, an ELM and BiGRU autoencoder was designed to effectively capture and learn key information from static and dynamic features. Finally, the outputs of the two autoencoders were integrated using the XGBoost algorithm to produce riverbank erosion risk assessment results, and the risks were visualized. This model not only performs excellently across multiple evaluation metrics but also significantly surpasses 22 other machine learning models. By integrating the Shapley value method, it enhances the model’s interpretability. This provides policymakers and relevant environmental management agencies with a powerful tool to scientifically assess and manage the risk of riverbank erosion.

Erosion Risk Assessment for Prioritization of Soil and Water Conservation Measures in the Semi-Arid Region: A Remote Sensing and GIS-Based Approach

Erosion Risk Assessment for Prioritization of Soil and Water Conservation Measures in the Semi-Arid Region: A Remote Sensing and GIS-Based Approach

Estimating erosion vulnerability within agricultural fields by downscaling the Daily Erosion Project (DEP): the OFEtool

AbstractAgriculture continues to be one of the most important sources of nonpoint source pollution to surface water bodies. Consequently, it is critical to identify and prioritize high‐contributing agricultural fields and sub‐field areas for reducing soil erosion and sediment delivery by implementing best management practices (BMPs). Current erosion risk assessment tools are either complex modelling approaches or rely on a simplified reality and generalized assumption. The Daily Erosion Project (DEP) is a daily estimator of precipitation, hillslope runoff, detachment and soil loss covering ~630 000 km2 across the Midwest United States. These estimations are reported daily and publicly at the hydrologic unit code 12 watershed resolution (approximately 100 km2). The main objective of this study was to develop a new tool (named Overland Flow Element tool [OFEtool]) that downscales the watershed scale of DEP to estimate average runoff and soil displacement within a field, helping to locate erosive hotspots at multiple scales. We also demonstrated the applicability of OFEtool in Bennet Creek‐Sugar Creek in East Central Iowa (the United States) and compared its results with other erosion vulnerability tools such as the Soil Vulnerability Index for Cultivated Cropland (SVI‐cc) and a GIS‐based Revised Universal Soil Loss Equation (RUSLE). The same erosion risk classes and ranges (low, moderate, moderately high and high) were implemented for all indexes. The advantages of the OFEtool compared to the SVI‐cc and RUSLE models are related to the use of an event‐based modelling approach, such as DEP, with updated soil loss estimates based on temporal changes in climate inputs and land use and management. The OFEtool uses a 6‐year time frame and a more up‐to‐date field inputs, while RUSLE provides a long‐term average and SVI‐cc only considers soil and topographical factors for risk assessment. Results indicated that the spatial distribution of vulnerable fields (and parts of the fields) followed a similar trend as other tested indices. However, the risk level associated with each tool differed (SVI‐cc > RUSLE > OFEtool). These differences could arise from intrinsic disparities within the tools (inputs, timing, processes considered, assumptions). While currently limited to the DEP domain and relying on the DEP random sampling scheme, further research is warranted to validate the tool at other Midwest locations and ensure it captures the watershed's landscape variability (combination of terrain, soil, land use and management) required to identifying critical erosion hotspots.

Dental care staff’s experience with risk assessment of dental erosion: a qualitative study

BackgroundThe risk assessment of dental erosion among children and adolescents is an important aspect of dental care, as dental erosion constitutes a rapidly growing, global problem. Dental professionals rely solely on their own perception, as the current risk assessment process is not completely automatized, which affects the risk assessment reliability.AimTo explore dental professionals’ experiences with risk assessment of dental erosion among children and adolescents.MethodIn-depth interview was used as data collection method. A total of 11 dental professionals were interviewed. The interviews were analyzed using qualitative content analysis.ResultsThe findings were summarized in the categories Professionals’ responsibility, Systematic approach , and Collaboration and communication. Dental staff perceived that their basic knowledge regarding erosion should be improved, and skills development was desired to reduce the knowledge gaps around the risk assessment of dental erosion. They alleged that the systematic approach could be improved by reducing workplace stress, implementing a universal dental erosion index, improving the existing risk assessment software, and automating the risk assessment of the condition. Dental professionals also experienced a need to calibrate and collaborate with each other and with other healthcare professionals to improve patient care.ConclusionDental professionals experienced their basic knowledge of dental erosion and their risk assessment as good, but a more advanced skill development was required. Furthermore, they experienced the risk assessment software as a good tool that should be improved to compile more objective risk assessment. A universal erosion index was also requested.

Nine months of daily LiDAR, orthophotos and MetOcean data from the eroding soft cliff coast at Happisburgh, UK

The dynamic interaction between cliff, beach and shore-platform is key to assessing the sediment balance for coastal erosion risk assessments, but this is poorly understood. We present a dataset containing daily, 3D,colour LiDAR scans of a 450 m coastal section at Happisburgh, Norfolk, UK. This previously para-glaciated region comprises mixed sand-gravel sediments, which are less well-understood and well-studied than sandy beaches. From Apr-Dec 2019, 236 daily surveys were carried out. The dataset presented includes: survey areas, transects LiDAR scans, georeferenced orthophotos, meteorological- and oceanographical conditions during the Apr-Dec observation period. Full LiDAR point-clouds are available for 67 scans (Oct-Dec). Hourly time-series of offshore sea-state parameters (significant wave height, mean propagation direction, selected spectral periods) were obtained by downscaling the ERA5 global reanalysis data (global atmosphere, land surface and ocean waves) using the numerical model Simulating Waves Nearshore (SWAN). We indicate how to obtain hourly precipitation time-series by interpolating ERA5 data. This dataset is important for researchers understanding the interaction between cliff, beach and shore-platform in open-coast mixed-sand-gravel environments.

Soil erosion risk assessment of the Lakhmess watershed (northwestern Tunisia) via the SEAGIS model: Inferred prioritization of risky sub-watersheds

Water erosion in Tunisian semi-arid regions causes harmful effects by silting reservoirs and reducing agricultural lands and soil fertility. Several factors are involved in the erosion process: rain erosivity, soil fragility, and degraded land cover on steep slopes associated with the intensification of inappropriate human practices. Thus, identifying erosion vulnerable sub-watersheds based on the assessed soil loss rate is very important to apply suitable conservation measures. The current research aimed to prioritize risky areas in the Lakhmess watershed, north-west Tunisia via the Soil Erosion Assessment using Geographical Information System (SEAGIS) model. To prioritize sub-watersheds vulnerable to soil erosion and sediment yield, the Lakhmess watershed, covering an area of 162 km2, was divided into 16 sub-watersheds (L1–L16), according to the hydrographic network. Then, the mean annual soil erosion rate and the mean annual sediment yield in the watershed were estimated by integrating the Revised Universal Soil Loss Equation (RUSLE) and the Sediment Delivery Ratio (SDR) in the SEAGIS model and their spatial distribution was determined. The obtained results indicate that the estimated average annual soil erosion rate is 4.2 t/ha/y and the annual sediment yield is 2.6 t/ha/y. Maner's SDR model was selected as the best model for estimating SY, with standard error, standard deviation, and coefficient of variation values of 0.75%, 0.01, and 0.45%, respectively. The prioritization of the Lakhmess sub-watersheds based on the estimated soil loss rate reveals that among the 16 sub-watersheds, three sub-watersheds (L10, L12, and L15) were identified as being in a very high priority soil erosion class. The high soil erosion rate and sediment yield in these sub-watersheds is explained by the steep slope and a high rainfall erosivity factor. Six sub-watersheds (L2, L4, L5, L6, L7, and L16) were found to belong to a very low priority soil erosion class, as they are characterized by a very gentle slope, which appears to be an extremely determining factor. These findings constitute a basis for decision makers to plan effective conservation measures to conserve agricultural lands, soil, and water resources in northwestern Tunisia.

Geotechnical Stability Analysis of the Tiga Dam, Nigeria on the Assessment of Downstream Soil Properties, Erosion Risk, and Seasonal Expansion

The Tiga Dam, a primary hydraulic structure in northern Nigeria, is subjected to intense hydrological stress during the rainy season, posing potential risks to its structural integrity. This study investigates the geotechnical properties and stability of the Tiga Dam in Kano State, Nigeria. Twelve soil samples from the downstream area were analyzed for specific gravity, grain size distribution, Atterberg limits, compaction parameters, permeability, and shear strength. The dam’s stability was assessed using Plaxis 2D under various reservoir conditions. Soil erodibility was evaluated using the Revised Universal Soil Loss Equation (RUSLE), and a linear regression model with noise was developed to predict soil expansion rates. The results showed heterogeneous soil properties, with specific gravity ranging from 2.11 to 2.63 and permeability from 3.40 × 10−9 to 1.49 × 10−7 m/s. Stability analysis revealed factors of safety of 1.322, 1.006, 1.002, and 1.147 for high reservoir, rapid drawdown, slow drawdown, and low reservoir conditions, respectively. The RUSLE K factor ranged from 0.055 to 0.145, indicating low to moderate soil erodibility. The expansion rate model demonstrated high accuracy (R2 = 0.989) in predicting seasonal and long-term soil expansion trends, with peak rates increasing from 16.94 mm/month in 2010–2013 to 19.45 mm/month in 2017–2020. This comprehensive analysis provides crucial insights into the Tiga Dam’s geotechnical behavior, highlighting potential vulnerabilities and the need for targeted management strategies to ensure long-term stability and safety.

Assessment of Morphology and Soil Erosion Risk in Agrarian Watershed of Jharkhand India Using RUSLE, GIS and MCDA-AHP

Assessment of Morphology and Soil Erosion Risk in Agrarian Watershed of Jharkhand India Using RUSLE, GIS and MCDA-AHP

Assessment of soil erosion risk in a hilly zone sub-watershed of Karnataka using geospatial technologies and the RUSLE model

ABSTRACT Human activities and climate change have sped up soil erosion, leading to annual losses of fertile soil and declining crop yields. The biggest concerns in land and water conservation are modeling and monitoring soil loss, as well as measuring the vulnerability and danger of active erosional processes. In the present study, a comprehensive approach used to evaluate the risk of soil erosion in the Ganjigatti sub-watershed of Karnataka, India, using the RUSLE model and GIS techniques. The annual average soil loss estimated using the RUSLE model ranged from 0 to 122.73 t ha−1 yr−1, with an average of 3.48 t ha−1 yr−1 in the Ganjigatti sub-watershed. The quantity of erosion was found to be greatly influenced by topography (LS factor) and land use and land cover (C factor). The results showed that a larger part of the study area, 98.34%, accounted for slight erosion (0–5 t ha−1 yr−1). The spatial pattern of erosion revealed high to very severe risk zones in the main stream of sub-watershed. The total soil loss was calculated to be 15,046.96 t yr−1 in the sub-watershed. The study’s results aid decision-makers, land administrators, environmentalists, and others in implementing effective land and water conservation measures in the Ganjigatti sub-watershed to control soil erosion.

Assessment of Soil Wind Erosion and Population Exposure Risk in Central Asia’s Terminal Lake Basins

Assessment of Soil Wind Erosion and Population Exposure Risk in Central Asia’s Terminal Lake Basins

Assessment of Soil Erosion Risk at Carrascal, Surigao del Sur, Philippines using Soil and Water Assessment Tool (SWAT+)

Erosion causes the disruption of soil health brought about by the removal of rich topsoil. Erosion risk in Carrascal, Surigao del Sur, Philippines was assessed using the process-based modeling SWAT+. The hydrologic model of the watershed was created and characterized in SWAT based on DEM, land use, and soil thematic maps. Individual combinations of hydrologic response units (HRUs) and landscape units (LSUs) of the watershed per dataset were generated and together with climate data, average annual sediment yields for different channels and subbasins were then estimated. Sediment yield was observed to have increased in specific areas within the catchment in the year 2003, 2010, 2015, and 2017 simulations. Relatively high sediment yield was observed in the northwestern and south sections of the catchment corresponding to the steep slopes of the Surigao mountains, while no significant sedimentation occurred in the coastal and eastern sections. Two land use change scenarios, a conversion of 15% forested area to agricultural area and a 10% increase in forest area, were introduced in the simulation to assess the variation of sediment yields in each subbasin. Results show only moderate variation in sedimentation between low-yield and relatively high-yield channels subjected to the same land use changes indicating that erosion in the area is influenced not solely by land use but a combination of both land use and slope. Sediment yield simulations and scenario analysis can serve as an additional tool for the formulation of conservation policies in the locality.

Spatio-Temporal Analysis of Erosion Risk Assessment Using GIS-Based AHP Method: A Case Study of Doğancı Dam Watershed in Bursa (Türkiye)

Soil erosion, one of the most serious phenomena in watershed management, can be estimated based on various criteria. Land use change is one of the most important factors affecting the susceptibility of soil erosion. In this study, the effect of land use change on soil erosion risk in two plan periods (2005 and 2017) was investigated using Analytical Hierarchy Process (AHP) and Geographic Information Systems (GIS) for the forest planning units in the Doğancı Dam Watershed, located in Bursa, Türkiye. Eight criteria were evaluated including erosion-related slope, bedrock type, land use/land cover, precipitation, relative relief, aspect, drainage frequency, and density. According to the results, the most effective factor in soil erosion was slope (0.29), while bedrock type and land use/land cover ranked second with 0.19. It was found that full closure forests were characterized by high erosion resistance (0.3), while bare land was characterized as the most sensitive area to erosion (0.39). In terms of spatio-temporal changes in a 12-year period, the areas in the medium and high erosion risk decreased, while low and very low-risk areas increased. The ROC method showed a satisfactory accuracy of 72.8% and 80.2% for the 2005 and 2017 erosion risk maps, respectively.