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国际水土保持研究（英文）
国际水土保持研究（英文）

季刊

2095-6339

国际水土保持研究（英文）/Journal International Soil and Water Conservation ResearchCSCD北大核心SCI
正式出版
收录年代

    An artificial neural network emulator of the rangeland hydrology and erosion model

    Mahmoud SaeedimoghaddamGrey NearingMariano HernandezMark A.Nearing...
    241-257页
    查看更多>>摘要：Machine learning(ML)is becoming an ever more important tool in hydrologic modeling.Previous studies have shown the higher prediction accuracy of those ML models over traditional process-based ones.However,there is another advantage of ML which is its lower computational demand.This is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer spatial scale.Using traditional models like Rangeland Hydrology and Erosion Model(RHEM)requires too much computation time and resources.In this study,we designed an Artificial Neural Network that is able to recreate the RHEM outputs(annual average runoff,soil loss,and sediment yield and not the daily storm event-based values)with high accuracy(Nash-Sutcliffe Efficiency ≈ 1.0)and a very low compu-tational time(13 billion times faster on average using a GPU).We ran the RHEM for more than a million synthetic scenarios and train the Emulator with them.We also,fine-tuned the trained Emulator with the RHEM runs of the real-world scenarios(more than 32,000)so the Emulator remains comprehensive while it works specifically accurately for the real-world cases.We also showed that the sensitivity of the Emulator to the input variables is similar to the RHEM and it can effectively capture the changes in the RHEM outputs when an input variable varies.Finally,the dynamic prediction behavior of the Emulator is statistically similar to the RHEM.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    A modified RUSLE model to simulate soil erosion under different ecological restoration types in the loess hilly area

    Guangyao GaoYue LiangJianbo LiuDavid Dunkerley...
    258-266页
    查看更多>>摘要：Soil erosion is mainly affected by the rainfall characteristics and land cover conditions,and soil erosion modelling is important for evaluating land degradation status.The revised Universal Soil Loss Equation(RUSLE)have been widely used to simulate soil loss rate.Previous studies usually considered the general rainfall characteristics and direct effect of runoff with the event rainfall erosivity factor(Re)to produce event soil loss(Ae),whereas the fluctuation of rainfall intensity within the natural rainfall profile has rarely been considered.In this study,the relative amplitude of rainfall intensity(Ram)was proposed to generalize the features of rainfall intensity fluctuation under natural rainfall,and it was incorporated in a new Re(Re=RamEI30)to develop the RUSLE model considering the fluctuation of rainfall intensity(RUSLE-F).The simulation performance of RUSLE-F model was compared with RUSLE-M1 model(Re=EI30)and RUSLE-M2 model(Re=QREI30)using observations in field plots of grassland,orchard and shrubland during 2011-2016 in a loess hilly catchment of China.The results indicated that the relationship between Ae and RamEI30 was well described by a power function with higher R2 values(0.82-0.96)compared to QREI30(0.80-0.88)and EI30(0.24-0.28).The RUSLE-F model much improved the accuracy in simulating Ae with higher NSE(0.55-0.79 vs-0.11-0.54)and lower RMSE(0.82-1.67 vs 1.04-2.49)than RUSLE-M1 model.Furthermore,the RUSLE-F model had better simulation performance than RUSLE-M2 model under grassland and orchard,and more importantly the rainfall data in the RUSLE-F model can be easily obtained compared to the measurements or estimations of runoff data required by the RUSLE-M2 model.This study highlighted the paramount importance of rainfall intensity fluctuation in event soil loss prediction,and the RUSLE-F model contributed to the further development of USLE/RUSLE family of models.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Landsat satellite programme potential for soil erosion assessment and monitoring in arid environments:A review of applications and challenges

    Tatenda MusasaTimothy DubeThomas Marambanyika
    267-278页
    查看更多>>摘要：This review article presents a comprehensive overview of the current status of the Landsat program and its applications in soil erosion modelling and assessment within arid environments.Literature for the period between 1972 and 2022 was retrieved using directed search strategies and keywords.A total of 170 journal articles were gathered and analyzed.The literature analysis reveals that 27(16％)of the publications fall within the period from 2007 to 2011,marking the highest occurrence within a five-year interval.The scrutinized literature was classified into ten distinct periods,or"pentades,"to accommodate the evolving applications of the Landsat program in response to advancements in remotely sensed data quality.This review article underscores the substantial contribution of Landsat data to the monitoring and assessment of soil erosion attributed to the action of water.Numerous studies have been conducted to model soil erosion using the Revised Universal Soil Loss Equation(RUSLE)model,facilitated by Geographic Information Systems(GIS)and remote sensing technologies.Nonetheless,the integration of Landsat data does present some challenges.Notably,the limitations of coarse resolution and data loss,particularly the scan line issues affecting Landsat 7,have hindered the full potential of the affected satellite datasets.As a solution,a multi-source approach that amalgamates diverse datasets is advocated to bridge data gaps and address disparities in spatial and temporal resolutions.To conclude,the Landsat mission has indisputably emerged as an indispensable instrument for facilitating the assessment and monitoring of soil erosion in resource-constrained communities.To advance this field,there is need to bolster storage infrastructure to manage large datasets,ensuring continuity for these sensor outputs,presenting a promising path for future research.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Gully erosion mapping susceptibility in a Mediterranean environment:A hybrid decision-making model

    Sliman HitouriMohajane MeriameAli Sk AjimQuevedo Renata Pacheco...
    279-297页
    查看更多>>摘要：Gully erosion is one of the main natural hazards,especially in arid and semi-arid regions,destroying ecosystem service and human well-being.Thus,gully erosion susceptibility maps(GESM)are urgently needed for identifying priority areas on which appropriate measurements should be considered.Here,we proposed four new hybrid Machine learning models,namely weight of evidence-Multilayer Per-ceptron(MLP-WoE),weight of evidence-K Nearest neighbours(KNN-WoE),weight of evidence-Logistic regression(LR-WoE),and weight of evidence-Random Forest(RF-WoE),for mapping gully erosion exploring the opportunities of GIS tools and Remote sensing techniques in the El Ouaar water-shed located in the Souss plain in Morocco.Inputs of the developed models are composed of the dependent(i.e.,gully erosion points)and a set of independent variables.In this study,a total of 314 gully erosion points were randomly split into 70％for the training stage(220 gullies)and 30％for the validation stage(94 gullies)sets were identified in the study area.12 conditioning variables including elevation,slope,plane curvature,rainfall,distance to road,distance to stream,distance to fault,TWI,lithology,NDV1,and LU/LC were used based on their importance for gully erosion susceptibility mapping.We evaluate the performance of the above models based on the following statistical metrics:Accuracy,precision,and Area under curve(AUC)values of receiver operating characteristics(ROC).The results indicate the RF-WoE model showed good accuracy with(AUC=0.8),followed by KNN-WoE(AUC=0.796),then MLP-WoE(AUC=0.729)and LR-WoE(AUC=0.655),respectively.Gully erosion susceptibility maps provide information and valuable tool for decision-makers and planners to identify areas where urgent and appropriate interventions should be applied.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Assessing current and future soil erosion under changing land use based on InVEST and FLUS models in the Yihe River Basin,North China

    Xinru QiaoZijun LiJinkuo LinHaijun Wang...
    298-312页
    查看更多>>摘要：The Yihe River Basin is a key area for water conservation and soil erosion control in northern China.The excessive development of land resources is a major factor causing soil erosion and ecological degrada-tion.However,the impacts of land use change on soil erosion in the basin are not yet clearly.Under-standing the complex relationship between land use and soil erosion is an important way to promote the development of land resources utilization and ecological construction from cognition to decision-making.This study simulated the temporal-spatial changes of soil erosion in the basin from 1956 to 2020 using Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model,and evaluated the changes of soil erosion under different land use scenarios from 2020 to 2050 using Future Land Use Simulation(FLUS)model.From 1956 to 2020,the overall soil erosion intensity showed a slight decreasing trend,and the average annual soil erosion modulus was 38.21 t/ha/year.Soil erosion intensity was higher in the central and northern mountainous areas,while it was lower in the flat alluvial plains in the south.Arable land(4.07 t/ha/year)was the largest contributor to the amount of soil erosion,and land use changes caused the soil erosion intensity to fluctuate and decrease after 1995.From 2020 to 2050,soil erosion varied widely under different land use scenarios,and the land use pattern targeting ecological priority development would effectively mitigate soil erosion.Therefore,optimizing land use patterns and structures are critical initiatives to prevent soil erosion.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Estimation of generalized soil structure index based on differential spectra of different orders by multivariate assessment

    Sha YangZhigang WangChenbo YangChao Wang...
    313-321页
    查看更多>>摘要：Better soil structure promotes extension of plant roots thereby improving plant growth and yield.Dif-ferences in soil structure can be determined by changes in the three phases of soil,which in turn affect soil function and fertility levels.To compare the quality of soil structure under different conditions,we used Generalized Soil Structure Index(GSSI)as an indicator to determine the relationship between the"input"of soil three phases and the"output"of soil structure.To achieve optimum monitoring of comprehensive indicators,we used Successive Projections Algorithm(SPA)for differential processing based on 0.0-2.0 fractional orders and 3.0-10.0 integer orders and select important wavelengths to process soil spectral data.In addition,we also applied multivariate regression learning models including Gaussian Process Regression(GPR)and Artificial Neural Network(ANN),exploring potential capabilities of hyperspectral in predicting GSSI.The results showed that spectral reflection,mainly contributed by long-wave near-infrared radiation had an inverse relationship with GSSI values.The wavelengths be-tween 404-418 nm and 2193-2400 nm were important GSSI wavelengths in fractional differential spectroscopy data,while those ranging from 543 to 999 nm were important GSSI wavelengths in integer differential spectroscopy data.Also,non-linear models were more accurate than linear models.In addition,wide neural networks were best suited for establishing fractional-order differentiation and second-order differentiation models,while fine Gaussian support vector machines were best suited for establishing first-order differentiation models.In terms of preprocessing,a differential order of 0.9 was found as the best choice.From the results,we propose that when constructing optimal prediction models,it is necessary to consider indicators,differential orders,and model adaptability.Above all,this study provided a new method for an in-depth analyses of generalized soil structure.This also fills the gap limiting the detection of soil three phases structural characteristics and their dynamic changes and provides a technical references for quantitative and rapid evaluation of soil structure,function,and quality.
      原文链接:
    • NETL
    • NSTL
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    Temporal sediment source tracing during storm events in the black soil region,Northeast China

    Lin SuDonghao HuangLili ZhouChengjiu Guo...
    322-336页
    查看更多>>摘要：Sediment fingerprinting technology is widely used to differentiate sediment sources.However,despite its long-recognized benefits,there it has been seldom applied to assess the variability of sediment sources during storm events.In this study,sediment fingerprinting is used for four storm events to determine the dynamic changes in sediment sources throughout them in the black soil region in Northeast China.Three potential sediment sources-cultivated land,unpaved roads,and gullies-were effectively differentiated using four geochemical tracers(As,Be,Cs,and Cu),with an accuracy of 100％.The relative sediment contribution from each source was determined using linear and Bayesian mixing models.The mean absolute fit(MAF)values of the linear mixing model(MAFmean=0.976-0.949)were higher than those of the Bayesian mixing model(MAFmean=0.921-0.992),indicating that the first performed better.Cultivated land was the primary source of the sediment load,accounting for 59.03％of it(load-weighted mean=68.29％),followed by the gullies(37.15％,load-weighted mean=28.09％),and unpaved roads(3.90％,load-weighted mean=3.69％)for the four storm events.In addition,a high variability in sediment source contribution was observed during the storm events.Cultivated land was the dominant sediment source during storm events with higher sediment concentrations(logarithmic function,r2=0.878,p＜0.01),discharge(linear function,r2=0.452,p＜0.05),and sediment flux(logarithmic function,r2=0.857,p＜0.01),whereas the reverse was observed for gullies.Contrastingly,the contribution of sediment from unpaved roads remained relatively stable during rainfall events.This provides a potential means to assess dynamic changes in sediment contributions from different erosion units.Moreover,it provides data support for exploring soil erosion mechanisms and effective erosion control in the black soil region in Northeast China.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Assessment of nutrients and conductivity in the Wachusett Reservoir watershed:An investigation of land use contributions and trends

    Amanda Carneiro MarquesCarlos Eduardo VerasEmily KumpelJohn E.Tobiason...
    337-350页
    查看更多>>摘要：The quality of drinking water for the Boston Metropolitan Area,supplied by the Quabbin-Wachusett system,is impacted by environmental trends.The objectives of this study are to increase understand-ing of the role that small streams may play in degradation of reservoir quality by characterizing seasonal constituent patterns from 1998 to 2020 in the Wachusett Reservoir watershed and by developing enhanced modeling frameworks.Previous monitoring(1998-2012)exhibited increased loads due to increasing flows despite declining solute concentration.This present study analyzed seasonal nitrate(NO3)and total phosphorus(TP)concentration and load trends from 2012 to 2020 across 11 tributaries.Specific conductivity(SC)was also assessed to evaluate the impacts of road salt application.From 2012 to 2020,statistical results for mean nutrient concentrations suggest static or declining temporal trends,while SC in all tributaries exhibited increasing trends.Land use data suggest association with altered drainage landscapes as potential sources of increased constituent transport.Subbasins with the highest concentrations of TP,NO3,and SC have the largest percentage of impervious and cultivated areas,two to three times greater than other subbasins.Daily flows were modeled using the airGR hydrological model,subsequently used to calculate loads.Overall,flow magnitude was a more important load driver than long-term nutrient concentrations,thus,showing that stream discharge controlled load variability.On the other hand,persistently high SC levels controlled the increasing SC load trends.Finally,many nutrient reduction management strategies demonstrated an important impact from 1998 to 2020.Despite watershed programs aimed at reducing salt applications,concentrations in streams are increasing,indicating a long-term legacy of salt accumulation.Although smaller tributaries represent a modest portion of the system,addressing these sources has the potential to further reduce the long-term ecological impacts of reservoir constituent loading.
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    • NETL
    • NSTL
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    Effect of microrelief features of tillage methods under different rainfall intensities on runoff and soil erosion in slopes

    Xinkai ZhaoXiaoyu SongLanjun LiDanyang Wang...
    351-364页
    查看更多>>摘要：Tillage methods play a crucial role in controlling rainwater partitioning and soil erosion.This study utilized rainfall simulation experiments to investigate the impact of four tillage methods(manual digging(MD),manual hoeing(MH),traditional ploughing(TP),and ridged ploughing(RP))on runoff and soil erosion at the plot scale.The smooth slope(SS)was used as a benchmark.Rainfall intensities of 30,60,90,and 120 mm h-1 were considered.The study revealed that tillage altered rainwater distribution into depression storage,infiltration,and runoff.Tillage reduces runoff and increases infiltration.The four tillage methods(30-73％)increased the proportion of rainwater converted to infiltration to varying degrees compared to the SS(22-53％).Microrelief features influenced the role of tillage methods in soil erosion.Surface roughness and depression storage accounted for 79％of the variation in sediment yield.The four tillage methods reduced runoff by 2.1-64.7％and sediment yield by 2.5-77.2％.Moreover,increased rainfall intensity weakens the ability of tillage to control soil erosion.When rainfall intensity increased to 120 mm h-1,there was no significant difference in runoff yield among RP,TP,MH,and SS.Therefore,assessing the effectiveness of tillage in reducing soil erosion should consider changes in rainfall intensity.Additionally,the cover management(C)factor of the RUSLE was used to assess the effects of different tillage methods on soil loss.Overall,the C factor values for tilled slopes are in the order MH＞TP＞RP＞MD with a range of 0.23-0.97.As the surface roughness increases,the C factor tends to decrease,and the two are exponential functions(R2=0.86).These studies contribute to our understanding of how different tillage methods impact runoff and soil erosion in sloped farmland and provide guidance for selecting appropriate local manual tillage methods.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Automatic mapping of gully from satellite images using asymmetric non-local LinkNet:A case study in Northeast China

    Panpan ZhuHao XuLigang ZhouPeixin Yu...
    365-378页
    查看更多>>摘要：Gully erosion can lead to the destruction of farmland and the reduction in crop yield.Gully mapping from remote sensing images is critical for quickly obtaining the distribution of gullies at regional scales and arranging corresponding prevention and control measures.The narrow and irregular shapes and similar colors to the surrounding farmland make mapping erosion gullies in sloping farmland from remote sensing images challenging.To implement gully erosion mapping,we developed a small training samples-oriented lightweight deep leaning model,called asymmetric non-local LinkNet(ASNL-LinkNet).The ASNL-LinkNet integrates global context information through an asymmetric non-local operation and conducts multilayer feature fusion to improve the robustness of the extracted features.Experiment re-sults show that the proposed ASNL-LinkNet achieves the best performance when compared with other deep learning methods.The quantitative evaluation results in the three test areas show that the F1-score of erosion gully recognition varies from 0.62 to 0.72.This study provides theoretical reference and practical guidance for monitoring erosion gullies on slope farmland in the black soil region of Northeast China.
      原文链接:
    • NETL
    • NSTL
    • 万方数据