Application of sediment rating curves to evaluate efficiency of EPM and MPSIAC using RS and GIS

Erosion potential method (EPM) and Modified Pacific Southwest Interagency Committee (MPSIAC) are two empirical models for estimating soil erosion and sediment delivery. These models use a relatively simple formulation, but they are still applied in various areas with different environmental conditions. However, evaluation of their efficiency is challenging. Accordingly, the main purpose of this study is investigating the performance of EPM and MPSIAC in estimating soil erosion and sediment yield using sediment rating curve (SRC) methods. Talar watershed in Iran was selected as the study area and suspended sediment load (SSL) of two Shirgah–Talar and Valikbon stations were used to assess the output of the models. Remote sensing and geographic information system were utilized in implementing the models. The estimated sediment yield values by the models were evaluated using the results of least square error regression and quantile regression (QR) SRC methods. Then, sediment yield values were obtained from 20-year discharge data (1992–2011). Despite the high uncertainty of QR results, the annual sediment delivery values of the models were achieved in an acceptable range. The most likely (with a probability of 0.5) average annual SSL values were between 713?×?10³ and 840?×?10³ ton for Shirgah–Talar station. Those values for Valikbon station were between 3142?×?10¹ and 3702?×?10¹. Moreover, the estimated average sediment yield in Shirgah–Talar station using MPSIAC and EPM were 591392 and 514054 ton/year, respectively. Those values for Valikbon station were 51881 and 27449 ton/year. Then, the results proved the better performance of MPSIAC in estimating SSL in the study area compared with EPM.     

基于GIS RS与AHP耦合技术的矿山水力侵蚀研究

晋陕蒙(西)地区的水力侵蚀受控于多种因素。在详尽地分析了影响水力侵蚀的各种因子基础上,确定母质类型、植被覆盖、地貌类型、沟壑密度、地形坡度、土地利用类型、矿山开发面积、降雨强度、水土保持、大风强度作为其主控致灾因子。根据遥感(RS)解译成果,应用GIS分别建立了各主控因子的专题层图,利用先进的层次分析方法(AHP),确定影响水力侵蚀的各致灾因子的权重系数。通过GIS、RS与AHP耦合技术的应用,对各子专题层图进行加权复合叠加,利用频率和频数分布直方图,确定出水力侵蚀的分区阈值,构建水力侵蚀的危险度评价的多源地学信息复合叠加模型,并对水力侵蚀危险度进行了分区评价。水力侵蚀模型的建立,为水力侵蚀的分区评价与预测提供了理论依据,使评价结果更加科学、合理、准确。     

基于RS与GIS的麻地沟土壤侵蚀及其生态环境效应研究

以GIS、RS和修正的土壤流失方程(RUSLE)为基础,结合遥感影像解译数据、地形图及降雨资料等,对麻地沟的土壤侵蚀进行了定量分析,并根据土壤侵蚀定量计算结果,结合景观生态学的方法,分析土壤侵蚀引起生态环境效应的因子,包括植被指数和景观指数等,对麻地沟地区的生态环境效应进行了分析。结果表明:研究区土壤侵蚀状况正在逐步好转,坡耕地和坡荒地是发生土壤侵蚀面积相对较高的两种土地利用类型,在保证当地居民所需坡耕地的情况下,修造梯田和植树造林是控制土壤侵蚀最有效的方式。     

遥感与GIS支持下的南桐矿区水土流失评价与区划

以重庆市南桐矿区为研究对象,运用遥感和GIS技术获取对水土流失影响较大的植被覆盖度、地形坡度、土地利用类型等信息并进行空间叠加分析,计算了水土流失类型及面积。研究结果表明,南桐矿区水土流失面积262.91km2,侵蚀模数2281t/(km2?a),水土流失强度以轻度和中度为主,其中轻度流失132.37km2,中度流失108.95km2。根据区域地貌类型以及水土流失特征,将研究区水土流失划分为盆边低山丘陵中强度流失区、北部坪状低山中轻度流失区和盆边中山轻度流失区三个类型区,盆边低山丘陵中强度流失区以中度流失为主;北部坪状低山中轻度流失区以轻度流失为主;盆边中山轻度流失区虽然以轻度流失为主,但微度流失也占有相当部分的比重。      

Evaluation of sediment yield and soil loss by the MPSIAC model using GIS at Golestan watershed, northeast of Iran

Watershed degradation due to soil erosion and sedimentation is considered to be one of the major environmental problems in Iran. In order to address the critical conditions of watershed degradation in arid and semiarid regions, a study based on the Modified Pacific Southwest Inter-Agency Committee (MPSIAC) model was carried out at Golestan watershed, northeast of Iran. The model information layers comprising nine effective factors in erosion and sedimentation at the watershed site were obtained by digitalization and spatial interpolation of the basic information data in a GIS program. These factors are geology, soil, climate, runoff, topography, land cover, land use, channel, and upland erosion. The source data for the model were obtained from available records on rainfall and river discharge and sediment, topography, land use, geology, and soil maps as well as field surveys and laboratory analysis. The results of the MPSIAC model indicated that 60.75 % (194.4 km²) and 54.97 % (175.9 km²) of the total watershed area were classified in the heavy sedimentation and erosion classes, and the total basin sediment yield and erosion were calculated as 4,171.1 and 17,813.4 m³ km^?2 year^?1, respectively. In the sensitivity analysis, it was found that the most sensitive parameters of the model in order of importance were topography (slope), land cover and use, runoff, and channel erosion (R ²?=?0.92–0.94), while geology, climate (rainfall), soil, and upland erosion factors were found to have moderate effect to the model output (R ²?=?0.74–0.59).     

Soil erosion and sediment yield modeling using RS and GIS techniques: a case study, Iran

Water erosion is a serious and continuous environmental problem in many parts of the world. The need to quantify the amount of erosion, sediment delivery, and sediment yield in a spatially distributed form has become essential at the watershed scale and in the implementation of conservation efforts. In this study, an effort to predict potential annual soil loss and sediment yield is conducted by using the Revised Universal Soil Loss Equation (RUSLE) model with adaptation in a geographic information system (GIS). The rainfall erosivity, soil erosivity, slope length, steepness, plant cover, and management practice and conservation support practice factors are among the basic factors that are obtained from monthly and annual rainfall data, soil map of the region, 50-m digital elevation model, remote sensing (RS) techniques (with use of Normalized Difference Vegetation Index), and GIS, respectively. The Ilam dam watershed which is located southeast part of Ilam province in western Iran is considered as study area. The study indicates that the slope length and steepness of the RUSLE model are the most effective factors controlling soil erosion in the region. The mean annual soil loss and sediment yield are also predicted. Moreover, the results indicated that 45.25%, 12.18%, 12.44%, 10.79%, and 19.34% of the study area are under minimal, low, moderate, high, and extreme actual erosion risks, respectively. Since 30.13% of the region is under high and extreme erosion risk, adoption of suitable conservation measures seems to be inevitable. So, the RUSLE model integrated with RS and GIS techniques has a great potential for producing accurate and inexpensive erosion and sediment yield risk maps in Iran.     

Soil erosion modeling of a Himalayan watershed using RS and GIS

Employing the remote sensing (RS) and geographical information system (GIS), an assessment of sediment yield from Dikrong river basin of Arunachal Pradesh (India) has been presented in this paper. For prediction of soil erosion, the Morgan-Morgan and Finney (MMF) model and the universal soil loss equation (USLE) have been utilized at a spatial grid scale of 100 m × 100 m, an operational unit. The average annual soil loss from the Dikrong river basin is estimated as 75.66 and 57.06 t ha⁻¹ year⁻¹ using MMF and USLE models, respectively. The watershed area falling under the identified very high, severe, and very severe zones of soil erosion need immediate attention for soil conservation.     

Spatial estimation of soil erosion risk using RUSLE/GIS techniques and practices conservation suggested for reducing soil erosion in Wadi Mina watershed (northwest,Algeria)

The Wadi Mina Watershed, western area of Algeria is characterized by rare and irregular rains and a fragile and weak vegetable cover. The sediments resulting from erosion are transported and contributed to silting dam Sidi Mhamed Benaouda. The combination of the thematical maps of the various erosive factors according to the Revised Universal Soil Loss Equation (RUSLE) in SIG by ArcGIS 10.2 software provided a reliable forecast of the annual rates of soil loss by delimiting the areas prone to erosive risk in the catchment above mentioned. The estimated potential average annual soil loss is 11.2 t/ha/yr., and the potential erosion rates from recognized erosion classes ranged from 0.0 to plus 100 t/ha/yr. About 50% of the catchment area was predicted to have very low to low erosion risk, with soil loss between 0 and 7.4 t/ha/yr. Erosion risk is moderate over 13.9% of the catchment, where calculated soil loss is between 7.4 and 12 t/ha/yr. Erosion risk is high to dangerous over 36.1% of the catchment, where calculated soil loss is more than 12 t/ha/yr. According to this study, it appeared clearly that we must intervene quickly by using reliable and effective conservation techniques.     

A stochastic model of soil erosion

The erosion model computes the rill and inter-rill flow over a surface with random roughness, and the erosion caused by this flow. The measured roughness of a surface is analysed and used to generate random surfaces for the simulation process. Computations are carried out over a number of time intervals; the steady state condition is assumed for each interval. Changes in the surface geometry due to erosion during an interval are used to revise the surface for the subsequent interval. The model includes simplified mechanisms to simulate ponding, deposition and failure of side slopes of rills.     

遥感、GIS结合与区域天然滑坡调查

以香港大屿山岛中部为试验研究区，探讨了遥感与GIS结合在区域滑坡调查中应用的可能性。在详细分析滑坡与环境因子关系后，将岩土类型、地貌、侵蚀、坡度等环境因子引入到影像中，与影像组成多源复合图像，可使地物的空间属性信息得到补充。同时采用适合高维复合数据的BP神经元网络对研究区天然滑坡进行识别，结果表明，区域滑坡的识别能力有较大提高。该方法的应用，将为大区域滑坡调查与监测奠定基础。     

考虑重力侵蚀影响的分布式土壤侵蚀模型

黄土高原地区的土壤侵蚀具有水力侵蚀和重力侵蚀相伴发生的特点,在大多数针对黄土高原的侵蚀模型研究中未考虑重力侵蚀的影响,使得模型的模拟精度较差。针对黄土高原的侵蚀特点,采用量化影响重力侵蚀发生的主要因素,确定重力侵蚀发生的具体沟道栅格单元的方法,从而考虑了重力侵蚀对产输沙过程的影响。所建立的分布式土壤侵蚀模型以逐网格汇流的水文模型为基础,采用逐网格侵蚀输沙的模拟方式,能够模拟上方来水来沙对侵蚀输沙的影响。经小理河流域实测资料验证,模型具有一定的计算精度。     

Wind erosion hazard assessment of the Mongolian Plateau using FCM and GIS techniques

The most serious environmental problems of the Mongolian Plateau are land degradation and sand storms caused by wind erosion, but the evaluation of wind erosion at regional scales has been a difficult process in wind erosion research. In this study, fuzzy c-means clustering (FCM) was used to assess the spatial pattern of wind erosion hazard on the Mongolian Plateau. By fuzzy clustering four main wind erosion factors (vegetation cover, average degree of land surface relief, degree of soil dryness and intensity of wind energy), wind erosion hazard was classified into six grades. Results show that FCM can effectively integrate related information between wind erosion and environmental factors, which provides the basis for predictive mapping of wind erosion hazard. Spatial patterns of wind erosion hazard indicate a gradual trend of increasing hazard in the Mongolian Plateau from east to west. Similar patterns were also found in NDVI and soil dryness, indicating that soil moisture and vegetation are the most important factors in the formation of wind erosion hazard. In addition, the distribution of different levels of wind erosion hazard is basically consistent with the regional distribution of landscape vegetation types in the Mongolian Plateau.     

土壤侵蚀模型研究现状与展望

土壤侵蚀已成为极为严峻的环境问题之一,对它进行准确的预报具有重要的实践意义.本文对国内外土壤侵蚀模型的研究现状进行了详细的回顾,并对我国土壤侵蚀模型研究中相对薄弱的环节进行了深入的分析.旨在借鉴国外研究经验,促进我国土壤侵蚀模型研究的进展.     

Catchment-wide estimate of single storm interrill soil erosion using an aggregate instability index: a model based on geographic information systems

The main objective of this paper is to estimate interrill erosion after rainfall in the basin of Mourganis river (442?km²; Kalabaka province, Trikala prefecture, Thessaly, Greece). For the estimation of the interrill erosion, the method of Valmis et al. (1988) was used, in combination with Nearing et al. (1989). Input data of the algorithm include the slope angle of the ground surface, the rainfall, the ground cover type, the height of canopy, and the instability of ground of the study area. The spatial data were processed by standard GIS software. Soil samples were collected in the field to calibrate the model. The results comprise soil erosion maps for two specific rainfall scenarios. The first rainfall scenario refers to the most extreme rainfall in this catchment that happened on the 7/21/1959 with 48?mm/h. The second scenario is closer to average as the intensity rainfall is 3.54?mm/h. The total mass of eroded material ranges from 0.048?t/ha (assuming mean rainfall intensity) up to 3.5?t/ha (for the extreme scenario). We note that the western part of the Mourgani basin exhibits higher erosion than the eastern part.     

Catchment-scale soil erosion and sediment yield simulation using a spatially distributed erosion model

Increasing rainfall intensity and frequency due to extreme climate change and haphazard land development are aggravating soil erosion problems in Korea. A quantitative estimate of the amount of sediment from the catchment is essential for soil and water conservation planning and management. Essential to catchment-scale soil erosion modeling is the ability to represent the fluvial transport system associated with the processes of detachment, transport, and deposition of soil particles due to rainfall and surface flow. This study applied a spatially distributed hydrologic model of rainfall–runoff–sediment yield simulation for flood events due to typhoons and then assessed the impact of topographic and climatic factors on erosion and deposition at a catchment scale. Measured versus predicted values of runoff and sediment discharge were acceptable in terms of applied model performance measures despite underestimation of simulated sediment loads near peak concentrations. Erosion occurred widely throughout the catchment, whereas deposition appeared near the channel network grid cells with a short hillslope flow path distance and gentle slope; the critical values of both topographic factors, providing only deposition, were observed at 3.5 (km) (hillslope flow path distance) and 0.2 (m/m) (local slope), respectively. In addition, spatially heterogeneous rainfall intensity, dependent on Thiessen polygons, led to spatially distinct net-erosion patterns; erosion increased gradually as rainfall amount increased, whereas deposition responded irregularly to variations in rainfall.     

Assessment of soil erosion risk using SWAT model

Soil erosion is one of the most serious land degradation problems and the primary environmental issue in Mediterranean regions. Estimation of soil erosion loss in these regions is often difficult due to the complex interplay of many factors such as climate, land uses, topography, and human activities. The purpose of this study is to apply the Soil and Water Assessment Tool (SWAT) model to predict surface runoff generation patterns and soil erosion hazard and to prioritize most degraded sub-catchment in order to adopt the appropriate management intervention. The study area is the Sarrath river catchment (1,491 km²), north of Tunisia. Based on the estimated soil loss rates, the catchment was divided into four priority categories for conservation intervention. Results showed that a larger part of the watershed (90 %) fell under low and moderate soil erosion risk and only 10 % of the watershed was vulnerable to soil erosion with an estimated sediment loss exceeding 10 t?ha^?1?year^?1. Results indicated that spatial differences in erosion rates within the Sarrath catchment are mainly caused by differences in land cover type and gradient slope. Application of the SWAT model demonstrated that the model provides a useful tool to predict surface runoff and soil erosion hazard and can successfully be used for prioritization of vulnerable areas over semi-arid catchments.     

Spatial variability of soil water erosion: Comparing empirical and intelligent techniques

Soil water erosion (SWE) is an important global hazard that affects food availability through soil degradation, a reduction in crop yield, and agricultural land abandonment. A map of soil erosion susceptibility is a first and vital step in land management and soil conservation. Several machine learning (ML) algorithms optimized using the Grey Wolf Optimizer (GWO) metaheuristic algorithm can be used to accurately map SWE susceptibility. These optimized algorithms include Convolutional Neural Networks (CNN and CNN-GWO), Support Vector Machine (SVM and SVM-GWO), and Group Method of Data Handling (GMDH and GMDH-GWO). Results obtained using these algorithms can be compared with the well-known Revised Universal Soil Loss Equation (RUSLE) empirical model and Extreme Gradient Boosting (XGBoost) ML tree-based models. We apply these methods together with the frequency ratio (FR) model and the Information Gain Ratio (IGR) to determine the relationship between historical SWE data and controlling geo-environmental factors at 116 sites in the Noor-Rood watershed in northern Iran. Fourteen SWE geo-environmental factors are classified in topographical, hydro-climatic, land cover, and geological groups. We next divided the SWE sites into two datasets, one for model training (70% of the samples = 81 locations) and the other for model validation (30% of the samples = 35 locations). Finally the model-generated maps were evaluated using the Area under the Receiver Operating Characteristic (AU-ROC) curve. Our results show that elevation and rainfall erosivity have the greatest influence on SWE, while soil texture and hydrology are less important. The CNN-GWO model (AU-ROC = 0.85) outperformed other models, specifically, and in order, SVR-GWO = GMDH-GWO (AUC = 0.82), CNN = GMDH (AUC = 0.81), SVR = XGBoost (AUC = 0.80), and RULSE. Based on the RUSLE model, soil loss in the Noor-Rood watershed ranges from 0 to 2644 t ha^–1yr^?1.     

桂林毛村岩溶地下河流域水土流失遥感动态监测研究

为了给西南岩溶地区石漠化发展演变的研究工作提供一定的科学依据和决策支持,选择桂林毛村岩溶地下河流域作为研究区,利用两期遥感影像对该区影响水土流失的三个主要自然要素,即地形坡度、植被覆盖度和土地利用方式进行信息提取,并以地理信息系统为分析平台,参考水土流失强度分级标准,对该区两个时期的水土流失状况进行对比分析。调查监测结果显示,研究区随着坡度的增大,水土越容易流失,而且水土流失强度级别增大;植被覆盖度对水土流失强度起着控制性作用,为抑制并缩减水土流失面积,应保护植被,退耕还林;研究区土地利用变化比较缓慢,导致水土流失面积变化幅度不大。上述结果表明,利用遥感技术手段,可为当地的经济发展和水土流失的监测、评价、预测及治理提供参考。      

An assessment of soil erosion probability and erosion rate in a tropical mountainous watershed using remote sensing and GIS

Remote sensing data and Geographical Information System (GIS) has been integrated with the weighted index overlay (WIO) method and E ₃₀ model for the identification and delineation of soil erosion susceptibility zones and the assessment of rate of soil erosion in the mountainous sub-watershed of River Manimala in Kerala (India). Soil erosion is identified as the one of the most serious environmental problems in the human altered mountainous environment. The reliability of estimated soil erosion susceptibility and soil loss is based on how accurately the different factors were estimated or prepared. In the present analysis, factors that are considered to be influence the soil erosion are: land use/land cover, NDVI, landform, drainage density, drainage frequency, lineament frequency, slope, and relative relief. By the WIO analysis, the area is divided into zones representing low (33.30%), moderate (33.70%), and high (33%) erosion proneness. The annual soil erosion rate of the area under investigation was calculated by carefully determining its various parameters and erosion for each of the pixels were estimated individually. The spatial pattern thus created for the area indicates that the average annual rate of soil erosion in the area was ranging from 0.04 mm yr⁻¹ to 61.80 mm yr⁻¹. The high soil erosion probability and maximum erosion rate was observed in areas with high terrain alteration, high relief and slopes with the intensity and duration of heavy precipitation during the monsoons.     

Assessing soil erosion in Mediterranean karst landscapes of Lebanon using remote sensing and GIS

Maps showing the potential for soil erosion at 1:100,000 scale are produced in a study area within Lebanon that can be used for evaluating erosion of Mediterranean karstic terrain with two different sets of impact factors built into an erosion model. The first set of factors is: soil erodibility, morphology, land cover/use and rainfall erosivity. The second is obtained by the first adding a fifth factor, rock infiltration. High infiltration can reflect high recharge, therefore decreasing the potential of surface runoff and hence the quantity of transported materials. Infiltration is derived as a function of lithology, lineament density, karstification and drainage density, all of which can be easily extracted from satellite imagery. The influence of these factors is assessed by a weight/rate approach sharing similarities between quantitative and qualitative methods and depending on pair-wise comparison matrix.The main outcome was the production of factorial maps and erosion susceptibility maps (scale 1:100,000). Spatial and attribute comparison of erosion maps indicates that the model that includes a measure of rock infiltration better represents erosion potential. Field investigation of rills and gullies shows 87.5% precision of the model with rock infiltration. This is 17.5% greater than the precision of the model without rock infiltration. These results indicate the necessity and importance of integrating information on infiltration of rock outcrops to assess soil erosion in Mediterranean karst landscapes.