Gullies are primary sediment sources that restrict sustainable agricultural development by reducing the quality of soil and destroying farmlands. In the black soil regions of Northeast China, the intensive exploitation and unreasonable cultivation result in severe soil erosion and malignant expansion of gully erosion. However, few gully erosion susceptibility (GES) assessments have been performed in this area, despite being an effective tool to explain the potential of gully occurrence. In this study, four adjacent catchments were selected in Keshan County, the black soil region of Northeast China. The locations of gullies were identified through extensive field surveys and interpreting remote sensing images. We used the random forest machine learning method to establish the spatial relationship between the gully occurrence and ten topographic factors (slope aspect, catchment area, channel network distance, elevation, LS-Factor, plan curvature, profile curvature, stream power index, surface roughness index, and topographic wetness index) and mapped the spatial distribution of GES. The mean decrease accuracy was calculated to identify the importance of the selected variables. The efficiency of the results was tested using the area under the receiver operating characteristic curve (area under the curve, AUC), accuracy, and kappa coefficient. The results indicate that 35%–42% of the total area in the study region presents high or elevated levels of GES. Although the importance of the topographic factors differed for the four catchments, the LS-Factor and channel network distance were the most important factors that affected gully spatial distribution. The AUC (0.805–0.846), accuracy (0.705–0.754), and kappa coefficient (0.715–0.788) indicated that the random forest model provided a reliable spatial distribution of GES in the study area. Our study demonstrates the potential risk of gully erosion in the black soil region of Northeast China.
AbstractConnectivity has become an important indicator of the sediment transfer potential through sediment sources to catchment sinks and plays a vital role in investigating the rate of soil erosion caused by runoff and sediment output across the watershed landscape. However, there have been few quantitative studies on the spatial changes in sediment connectivity, especially in the black soil region of Northeast China. Seven classic watersheds in the northeast black soil region were selected to discover spatial changes in sediment connectivity and their influencing factors. The connectivity index (IC) was used to study the spatial variation of sediment connectivity, and the weight of IC was calculated using the vegetation cover management factor (C), which reflected the influence of human activities on the soil erosion process caused by cultivated land expansion and encroachment of forest grassland. The redundancy analysis method (RDA) was used to determine the main factors affecting the spatial variation of IC. The results showed that IC decreased from north (0.45) to south (0.12). The spatial variations of watersheds in sediment connectivity showed significant spatial heterogeneity. IC was correlated with elevation, topographic wet index, surface roughness, slope length & steepness, slope length, slope, temperature, precipitation, rainfall erosivity, snowmelt erosivity, soil erodibility, and cover and management factor at 0.01 or 0.05 levels; RDA results showed that topography and human activities were the main factors affecting sediment connectivity. Topographic and climatic factors explained more sediment connectivity changes in the southern watersheds than in the northern watersheds, while the human activity factor explanation was the opposite of topographic factors.
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 topographic 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 successfully 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.
There are significant zonation differentiations on the tectonic,the landform,the flow separation,the wind power and the freeze-thaw action in the Northeast China,which cause the significant zonation differentiations on the erosion intensity and the type of soil erosion.At present,in the Northeast China the water erosion is significant in the southeast,the wind erosion in the west and the freeze-thaw erosion in the north respectively.According to the soil erosion zonation differentiation,the North- east China is classified 9 regions of soil erosion types.Furthermore,the models of soil and water conservation for the 9 regions of soil erosion types have been generalized.
The distributed hydrological model was established by the spatial and attribute database of Puhe river basin and using SWAT model combined with ArcGIS technology.The parameters were cabibrated by using the sensitive analysis of model,adjusts sensitive parameters by combing automatic calibration and manual calibration,and finally the value of the model parameters were determined.Verifying the simulation results by the measured data,the result in the validation period indicated that relative error was Re=-0.0689,correlation coefficient was R2=0.8820,Nash-Sutcliffe efficiency coefficient was Ens=0.8568 and all the indices met the requirements of model,which has a strong applicability.
Frequent freeze–thaw cycles (FTCs) can profoundly affect the chemical properties of soils. We conducted laboratory experiments to explore the effects of freeze–thaw treatments on phosphorus (P) adsorption (Pads) and desorption (Pdes) and to determine the capacity of P retention (Pret) via Pads and Pdes in the fertile black soil of northeastern China. The effects of 0, 1, 3, 6, and 10 FTCs on soil P adsorption and desorption were determined. Each cycle consisted of freezing for 12 h at −10 °C and thawing for 12 h at +7 °C, mimicking a diurnal pattern, at initial soil-moisture contents of 20%, 30%, 40%, and 50% and at amounts of added P of 10, 20, 40, 60, and 80 mg P L−1. Pads increased significantly with increasing amounts of added P. The freeze–thaws of black soils significantly decreased Pads and buffering capacities, which would promote the release of adsorbed P and increase the risk of reducing soil P. High moisture content also affected the behavior of soil P adsorption by reducing adsorbed P as the number of FTCs increased. The P-isotherm data for all soils at equilibrium-P concentrations fit the Langmuir equation well (R2 = 0.93 or higher). Pads had exponential and linear relationships with Pdes capacity and Pret capacity, respectively.
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