Effects of collapsing gully erosion on soil qualities of farm fields in the hilly granitic region of South China

Collapsing gully erosion is a specific form of soil erosion types in the hilly granitic region of tropical and subtropical South China, and can result in extremely rapid water and soil loss. Knowledge of the soil physical and chemical properties of farmland influenced by collapsing gully erosion is important in understanding the development of soil quality. This study was conducted at the Wuli Watershed of the Tongcheng County, south of Hubei Province, China. The aim is to investigate soil physical and chemical properties of three soil layers （0-20, 20-40 and 40-60 cm） for two farmland types （paddy field and upland field） in three regions influenced by collapsing gully erosion. The three regions are described as follows： strongly influenced region （SIR）, weakly influenced region （WIR） and non-influenced region （NIR）. The results show that collapsing gully erosion significantly increased the soil gravel and sand content in paddy and upland fields, especially the surface soil in the SIR and WIR. In the 0-20 cm layer of the paddy field, the highest gravel content （250.94 g kg-1） was in the SIR and the lowest （78.67 g kg-1） was in the NIR, but in the upland filed, the surface soil （0-20 cm） of the SIR and the 40-60 cm soil layer for the NIR had the highest （177.13 g kg-1） and the lowest （59.96 g kg-1） values of gravel content, respectively. The distribution of gravel and sand decreased with depth in the three influenced regions, but silt and clay showed the inverse change. In the paddy field, the average of sand content decreased from 58.6 （in the SIR） to 49.0% （in the NIR）, but the silt content was in a reverse order, increasing from 27.9 to 36.9%, and the average of the clay content of three regions showed no significant variation （P〈0.05）. But in the upland filed, the sand, silt and clay fluctuated in the NIR and the WIR. Soils in the paddy and upland field were highly acidic （pH〈5.2） in the SIR and WIR; moreover lower nutrient contents （soil organic matter （SOM）, total N and available N, P, K） existed in the SIR. In the 0-20 cm soil layer of the paddy field, compared with the NIR and the WIR, collapsing gully erosion caused a very sharp decrease in the SOM and total N of the SIR （5.23 and 0.56 g kg-1, respectively）. But in the surface soil （0-20 cm） of the upland field, the highest SOM, total N, available N, available P and available K occurred in the NIR, and the lowest ones were in the SIR. Compared with the NIR, the cation exchange capacity （CEC） in the SIR and WIR was found to be relatively lower. These results suggest that collapsing gully erosion seriously affect the soil physical and chemical properties of farmland, lead to coarse particles accumulation in the field and decrease pH and nutrient levels.

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process

This investigation assessed the efficacy of 10 widely used machine learning algorithms(MLA)comprising the least absolute shrinkage and selection operator(LASSO),generalized linear model(GLM),stepwise generalized linear model(SGLM),elastic net(ENET),partial least square(PLS),ridge regression,support vector machine(SVM),classification and regression trees(CART),bagged CART,and random forest(RF)for gully erosion susceptibility mapping(GESM)in Iran.The location of 462 previously existing gully erosion sites were mapped through widespread field investigations,of which 70%(323)and 30%(139)of observations were arbitrarily divided for algorithm calibration and validation.Twelve controlling factors for gully erosion,namely,soil texture,annual mean rainfall,digital elevation model(DEM),drainage density,slope,lithology,topographic wetness index(TWI),distance from rivers,aspect,distance from roads,plan curvature,and profile curvature were ranked in terms of their importance using each MLA.The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE(root mean square error),MAE(mean absolute error),and R-squared.Based on the comparisons among MLA,the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared,and was therefore selected as the best model.The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance.According to the GESM generated using RF,most of the study area is predicted to have a low(53.72%)or moderate(29.65%)susceptibility to gully erosion,whereas only a small area is identified to have a high(12.56%)or very high(4.07%)susceptibility.The outcome generated by RF model is validated using the ROC(Receiver Operating Characteristics)curve approach,which returned an area under the curve(AUC)of 0.985,proving the excellent forecasting ability of the model.The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.

Identifying ephemeral gullies from high-resolution images and DEMs using flow-directional detection

Ephemeral gullies,which are widely developed worldwide and threaten farmlands,have aroused a growing concern.Identifying and mapping gullies are generally considered prerequisites of gully erosion assessment.However,ephemeral gully mapping remains a challenge.In this study,we proposed a flow-directional detection for identifying ephemeral gullies from high-resolution images and digital elevation models(DEMs).Ephemeral gullies exhibit clear linear features in high-resolution images.An edge detection operator was initially used to identify linear features from high-resolution images.Then,according to gully erosion mechanism,the flow-directional detection was designed.Edge images obtained from edge detection and flow directions obtained from DEMs were used to implement the flow-directional detection that detects ephemeral gullies along the flow direction.Results from ten study areas in the Loess Plateau of China showed that ranges of precision,recall,and Fmeasure are 6 o.66%-90.47%,65.74%-94.98%,and63.10%-91.93%,respectively.The proposed method is flexible and can be used with various images and DEMs.However,analysis of the effect of DEM resolution and accuracy showed that DEM resolution only demonstrates a minor effect on the detection results.Conversely,DEM accuracy influences the detection result and is more important than the DEM resolution.The worse the vertical accuracy of DEM,the lower the performance of the flow-directional detection will be.This work is beneficial to research related to monitoring gully erosion and assessing soil loss.

Two-dimensional hydrodynamic robust numerical model of soil erosion based on slopes and river basins

Erosion is an important issue in soil science and is related to many environmental problems,such as soil erosion and sediment transport.Establishing a simulation model suitable for soil erosion prediction is of great significance not only to accurately predict the process of soil separation by runoff,but also improve the physical model of soil erosion.In this study,we develop a graphic processing unit(GPU)-based numerical model that combines two-dimensional(2D)hydrodynamic and Green-Ampt(G-A)infiltration modelling to simulate soil erosion.A Godunov-type scheme on a uniform and structured square grid is then generated to solve the relevant shallow water equations(SWEs).The highlight of this study is the use of GPU-based acceleration technology to enable numerical models to simulate slope and watershed erosion in an efficient and high-resolution manner.The results show that the hydrodynamic model performs well in simulating soil erosion process.Soil erosion is studied by conducting calculation verification at the slope and basin scales.The first case involves simulating soil erosion process of a slope surface under indoor artificial rainfall conditions from 0 to 1000 s,and there is a good agreement between the simulated values and the measured values for the runoff velocity.The second case is a river basin experiment(Coquet River Basin)that involves watershed erosion.Simulations of the erosion depth change and erosion cumulative amount of the basin during a period of 1-40 h show an elevation difference of erosion at 0.5-3.0 m,especially during the period of 20-30 h.Nine cross sections in the basin are selected for simulation and the results reveal that the depth of erosion change value ranges from-0.86 to-2.79 m and the depth of deposition change value varies from 0.38 to 1.02 m.The findings indicate that the developed GPU-based hydrogeomorphological model can reproduce soil erosion processes.These results are valuable for rainfall runoff and soil erosion predictions on rilled hillslopes and river basins.

Cause Analysis of Gully Erosion in Yuanmou Basin of Jinshajiang Valley

Some factors （i.e. lithology, topography, climate, the change of population as well as land use during the past 50 years） that could have great influence on the development of gully in the arid-hot basin of Jinshajiang valley were investigated. The results show that the factors leading to the strong gully erosion in this area include： the widely distributed Yuanmou group stratum, which promotes the development of gully erosion; the unique geomorphologic configuration that is prone to rock fall and gully erosion; the strong and time-concentrated rainfall; the arid-humid alternate climate characteristics that prepares the ground for the development of fissures in soils; the arid-hot climate that goes against the growth and recovery of vegetation; and the unreasonable and abusive human activities.

Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)techniques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model’s result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.

Assessment of gully erosion susceptibility using different DEM-derived topographic factors in the black soil region of Northeast China

As a primary sediment source,gully erosion leads to severe land degradation and poses a threat to food and ecological security.Therefore,identification of susceptible areas is critical to the prevention and control of gully erosion.This study aimed to identify areas prone to gully erosion using four machine learning methods with derived topographic attributes.Eight topographic attributes(elevation,slope aspect,slope degree,catchment area,plan curvature,profile curvature,stream power index,and topo-graphic wetness index)were derived as feature variables controlling gully occurrence from digital elevation models with four different pixel sizes(5.0 m,12.5 m,20.0 m,and 30.0 m).A gully inventory map of a small agricultural catchment in Heilongjiang,China,was prepared through a combination of field surveys and satellite imagery.Each topographic attribute dataset was randomly divided into two portions of 70%and 30%for calibrating and validating four machine learning methods,namely random forest(RF),support vector machines(SVM),artificial neural network(ANN),and generalized linear models(GLM).Accuracy(ACC),area under the receiver operating characteristic curve(AUC),root mean square error(RMSE),and mean absolute error(MAE)were calculated to assess the performance of the four machine learning methods in predicting spatial distribution of gully erosion susceptibility(GES).The results suggested that the selected topographic attributes were capable of predicting GES in the study catchment area.A pixel size of 20.0 m was optimal for all four machine learning methods.The RF method described the spatial relationship between the feature variables and gully occurrence with the greatest accuracy,as it returned the highest values of ACC(0.917)and AUC(0.905)at a 20.0 m resolution.The RF was also the least sensitive to resolutions,followed by SVM(ACC=0.781-0.891,AUC=0.724-0.861)and ANN(ACC=0.744-0.808,AUC=0.649-0.847).GLM performed poorly in this study(ACC=0.693-0.757,AUC=0.608-0.703).Based on the spatial distribution of GES determined using the optimal method(RF+pixel size of 20.0 m),16%of the study area has very high level susceptibility classes,whereas areas with high,moderate,and low levels of susceptibility make up approximately 24%,30%,and 31%of the study area,respectively.Our results demonstrate that GES assessment with machine learning methods can successfuly identify areas prone to gully erosion,providing reference information for future soil conservation plans and land management.In addition,pixel size(resolution)is the key consideration when preparing suitable datasets of feature variables for GES assessment.

Gully internal erosion triggered by a prolonged heavy rainfall event in the tableland region of China's Loess Plateau

Gully erosion is a severe form of soil erosion,but gully internal erosion processes are poorly understood,especially at the event scale.To investigate gully internal erosion intensity and understand the related gully development mechanism in an agricultural environment with gully head stabilization and vege-tation restoration efforts,two successive field investigations were carried out just before and after a prolonged rainfall event in 2021 in the tableland region of China's Loess Plateau.Thirteen gullies were investigated and all experienced gully internal erosion,while most gully boundaries were stable during the heavy rainfall event based on the comparison of the UAV digital orthograph maps(DOMs acquired with Unmanned Aerial Vehicle)before and after the rainfall event.The proportion of gully internal erosion area to gully internal area of the 13 investigated gullies ranged from 3 to 55%,with average areal erosion proportion of the gully sidewall and gully bed of 21%and 36%,respectively.The erosion area of subdrainage units(SDUs)on the gully sidewall was positively correlated to the SDU area,average SDU slope gradient and vegetation type,while the erosion area on the gully bed was positively correlated to the gully area,gully depth and gully bed slope gradient.Gully internal erosion was not significantly correlated with gully drainage area because the connectivity between the upslope and gully areas was interrupted and the effective drainage area of the gully was obviously reduced by soil erosion conser-vation measures,including terraces on the upslope drainage area,shrub belts,and water barriers.Thus,gully internal erosion is still active under the heavy rainfall storm against the background of the'Grain for Green'and'Gully Stabilization and Tableland Protection'programs,and integrated measures for preventing both gully expansion and gully internal erosion must be further enhanced in the context of climate change.

中国南方崩岗侵蚀驱动因素的空间尺度效应

Gully erosion,an integrated result of various social and environmental factors,is a severe problem for sustainable development and ecology security in southern China.Cur-rently,the dominant driving forces on gully distribution are shown to vary at different spatial scales.However,few systematic studies have been performed on spatial scaling effects in identifying driving forces for gully erosion.In this study,we quantitatively identified the de-terminants of gully distribution and their relative importance at four different spatial scales(southern China,Jiangxi province,Ganxian county,and Tiancun township,respectively)based on the Boruta algorithm.The optimal performance of gully susceptibility mapping was investigated by comparing the performance of the multinomial logistic regression(MLR),lo-gistic model tree(LMT),and random forest(RF).Across the four spatial scales,the total contributions of gully determinants were classified as lithology and soil(32.65%)≥topogra-phy(22.40%)>human activities(22.31%)>climate(11.32%)>vegetation(11.31%).Among these factors,precipitation(7.82%),land use and land cover(6.16%),rainfall erosivity(10.15%),and elevation(11.59%)were shown to be the predominant factors for gully erosion at the individual scale of southern China,province,county,and township,respectively.In addition,contrary to climatic factors,the relative importance of soil properties and vegetation increased with the decrease of spatial scale.Moreover,the RF model outperformed MLR and LMT at all the investigated spatial scales.This study provided a reference for factor selection in gully susceptibility modeling and facilitated the development of gully erosion management strategies suitable for different spatial scales.

Reconstructing pre-erosion topography using spatial interpolation techniques： A validation-based approach

Understanding the topographic context preceding the development of erosive landforms is of major relevance in geomorphic research, as topography is an important factor on both water and mass movement-related erosion, and knowledge of the original surface is a condition for quantifying the volume of eroded material. Although any reconstruction implies assuming that the resulting surface reflects the original topography, past works have been dominated by linear interpolation methods, incapable of generating curved surfaces in areas with no data or values out- side the range of variation of inputs. In spite of these limitations, impossibility of validation has led to the assumption of surface representativity never being challenged. In this paper, a validation-based method is applied in order to define the optimal interpolation technique for reconstructing pre-erosion topography in a given study area. In spite of the absence of the original surface, different techniques can be nonetheless evaluated by quantifying their ca- pacity to reproduce known topography in unincised locations within the same geomorphic contexts of existing erosive landforms. A linear method （Triangulated Irregular Network, TIN） and 23 parameterizations of three distinct Spline interpolation techniques were compared using 50 test areas in a context of research on large gully dynamics in the South of Portugal. Results show that almost all Spline methods produced smaller errors than the TIN, and that the latter produced a mean absolute error 61.4% higher than the best Spline method, clearly establishing both the better adjustment of Splines to the geomorphic context considered and the limitations of linear approaches. The proposed method can easily be applied to different interpolation techniques and topographic contexts, enabling better calculations of eroded volumes and denudation rates as well as the investigation of controls by antecedent topographic form over erosive processes.

Evaluation of WEPP versus EGEM and empirical model efficiencies in predicting ephemeral gully erosion around Mubi area,Northeast Nigeria

Evaluation of prediction models is crucial to achieving valid information on erosion processes and their management choices.WEPP model efficiency in predicting ephemeral gully(EG)erosion was recently tested and compared with both EGEM and empirical models.The models abilities to predict EG erosion were validated using measured estimates at the 6 eroding locations around Mubi area in Northeast Nigeria between April 2008 and October 2009.Each location consisted of 3 watersheds where data on soils,climate,slope,management practices,EG shapes and dimensions were collected.Data on relevant soil properties were collected in the field and then analyzed in laboratory.The mass of soil loss(MSL)predicted by empirical,EGEM and WEPP models were compared with the measured using paired T-test,regression graphs(r^(2)-values),error analysis,and analysis of variance(ANOVA)in a completely ran-domized design.The EG erosion losses varied significantly(P≤0.05)between sites and years.No sig-nificant(P≤0.05)differences were observed between measured and the empirically predicted aggregate MSL.The measured aggregate MSL strongly correlated with those predicted by empirical(r^(2)=0.67),than with EGEM(r^(2)=0.57),and WEPP(r^(2)=0.53)models.Slight over and under-prediction instances against the measured erosion were noted with all the models.The WEPP model was found to slightly over-predict MSL when compared to either the empirical or EGEM model.The prediction quality of the models was generally impressive.Future works should focus more on local inputs such as climate,plants,management,and tillage data for use with WEPP.

Efficiency test of modeled empirical equations in predicting soil loss from ephemeral gully erosion around Mubi,Northeast Nigeria

A field study was carried out to assess soil loss from ephemeral gully(EG)erosion at 6 different locations(Digil,Vimtim,Muvur,Gella,Lamorde and Madanya)around the Mubi area between April,2008 and October,2009.Each location consisted of 3 watershed sites from where data was collected.EG shape,land use,and conservation practices were noted,while EG length,width,and depth were measured.Physico-chemical properties of the soils were studied in the field and laboratory.Soil loss was both measured and predicted using modeled empirical equations.Results showed that the soils are heterogeneous and lying on flat to hilly topographies with few grasses,shrubs and tree vegetations.The soils comprised of sand fractions that predominated the texture,with considerable silt and clay contents.The empirical soil loss was generally related with the measured soil loss and the predictions were widely reliable at all sites,regardless of season.The measured and empirical aggregate soil loss were more related in terms of volume of soil loss(VSL)(r^(2)=0.93)and mass of soil loss(MSL)(r^(2)=0.92),than area of soil loss(ASL)(r^(2)=0.27).The empirical estimates of VSL and MSL were consistently higher at Muvur(less vegetation)and lower at Madanya and Gella(denser vegetations)in both years.The maximum efficiency(M_(se))of the empirical equation in predicting ASL was between 1.41(Digil)and 89.07(Lamorde),while the M_(se) was higher at Madanya(2.56)and lowest at Vimtim(15.66)in terms of VSL prediction efficiencies.The M_(se) also ranged from 1.84(Madanya)to 15.74(Vimtim)in respect of MSL predictions.These results led to the recommendation that soil conservationists,farmers,private and/or government agencies should implement the empirical model in erosion studies around Mubi area.

黄土高原沟蚀区地表蚕食度空间分布特征和影响因素研究

In China’s Loess Plateau severe gully erosion(LPGE)region,the shoulder-line is the most intuitive and unique manifestation of the loess landform,which divides a landform into positive and negative terrains(PNTs).The spatial combination model of PNTs is of great significance for revealing the evolution of the loess landform.This study modeled and proposed the Surface Nibble Degree(SND),which is a new index that reflects the comparison of the areas of PNTs.Based on 5 m DEMs and matched high-resolution remote sensing images,the PNTs of 164 complete watersheds in the LPGE were extracted accurately,and the SND index was calculated.The spatial distribution trend of SND was discussed,and the relationship between SND and the factors that affect the evolution mechanism of regional landform was explored further.Results show that:(1)The SND can be calculated formally.It can quantify the development of the loess landform well.(2)The SND of the LPGE has evident spatial differentiation that increases from southwest to northeast.High values appear in Shenmu of Shaanxi,Shilou of Shanxi,and northern Yanhe River,whereas the low values are mainly distributed in the southern loess tableland and the inclined elongated ridge area of Pingliang in Gansu and Guyuan in Ningxia.(3)In the Wuding River and Yanhe River,the SND decreases with the increase in flow length(FL).In the North-Luohe River and Jinghe River,the SND increases with FL.(4)SND is significantly correlated with gully density and sediment modulus and moderately correlated with hypsometric integral.As for the mechanism factors analysis,the relationship between loess thickness and SND is not obvious,but SND increased first and then decreased with the increase of precipitation and vegetation in each geographical division,and we found that the land use type of low coverage grassland has greater erosion potential.

Temporal variations in runoff and soil loss in relation to soil conservation practices in catchments in Shiwaliks of lower Himalayas

The soil conservation strategies adopted in the catchments of Shiwaliks,the most fragile region in the Himalayan ecosystem,failed to serve their purpose after a few years of their execution.A study was carried out in four differentially-treated catchments to monitor the variation in runoff and soil loss.The treatments imposed during 1988 included fencing,planting native vegetation and engineering structures in catchment I;planting native vegetation and fencing in catchment II;fencing alone in catchment III in addition to an untreated catchment IV.The soil loss during the initial years(1989–1995)of imposition of the treatments was lowest(25.2 t ha^(-1))in catchment I,treated to the maximum extent and highest(43.3 t ha^(-1))in untreated catchment IV.During the later period(1996–2006)the trends reversed,i.e.,catchment IV recorded the lowest(14.1 t ha^(-1))soil loss whereas catchment I recorded the highest(23.4 t ha^(-1))soil loss despite the fact that there was no change in the status of soil conservation or the characteristics of the catchments.The runoff was 71%higher in untreated catchment than in treated catchments initially and this difference decreased to 27%during the later period.