基于RUSLE的流域土壤侵蚀敏感性评价——以福建省吉溪流域为例

以修正的通用水土流失方程(RUSLE)为核心,在分析流域土壤侵蚀敏感性影响因子的基础上,运用G IS技术对各敏感因子值进行估算,结合已有研究成果探讨了定量化的流域土壤侵蚀敏感性评价指标体系的建立,并以吉溪流域为例进行土壤侵蚀敏感性评价。同时分析了该流域土壤侵蚀敏感性与土壤侵蚀量的关系。最后,针对不同的流域土壤侵蚀敏感等级提出了相应的管理措施和建议。     

基于RUSLE的卧虎山水库流域土壤侵蚀特征分析

通过RUSLE模型对卧虎山水库流域土壤侵蚀进行全面评价验证和总结。结果表明：① 水库流域平均侵蚀模数为462 t/(km²·a),该数值与通过水库淤积等资料推算评估结果基本一致,表明本研究结果具有较高的可信度;水库流域年均侵蚀量达到2.6×10⁶t,其中高于容许土壤流失量的面积为176 km²,占到流域总面积的31.51%。从不同侵蚀级别来看,占流域面积27.77%的轻度侵蚀,对流域侵蚀总量的贡献率为54.64%; 面积占比3.74%的中度及以上侵蚀,侵蚀量贡献率达到30.94%。② 流域内土壤侵蚀空间差异较大,回归分析发现地形因子是导致各子流域土壤侵蚀模数差异的主要因素;就土地利用类型而言,旱地和农村居民点是流域内的主要侵蚀土地利用类型;流域内土壤侵蚀模数随着坡度增加呈现相应增大趋势,8°~25°坡度段面积比例不仅最大,而且侵蚀量占比最高,是水库流域的主要侵蚀坡度段。     

Identifying erosion hotspots and assessing communities' perspectives on the drivers,underlying causes and impacts of soil erosion in Toledo's Rio Grande Watershed: Belize

Erosion in the Rio Grande watershed of Belize, Central America results in widespread ecological impacts and significant economic costs. In this study, quantitative soil loss analysis and qualitative social surveys were integrated to identify erosion vulnerable areas or hotspots, and to analyze varying perspectives between communities near and far from erosion hotspots regarding the causes of erosion. The results of the quantitative analysis suggest that erosion hotspots are located in the upper-mid reaches of the watershed near the communities of Crique Jute, Naluum Ca, San Pedro Columbia and San Miguel. The Mann–Whitney U test identified significant difference in the ranking of erosion drivers (cattle ranching, logging, and clearing of slopes) between communities. Communities far from erosion hotspots (FEH) ranked cattle ranching and logging higher than communities near erosion hotspots as the main drivers of soil erosion (NEH and FEH, mean = 79.02, 105.92, (U) = 3055, p < 0.001 and mean = 84.9, 100.90, (U) = 3560.5 p < 0.05) respectively. On the other hand, communities near erosion hotspots (NEH) ranked clearing and planting on slopes higher than communities far from erosion hotspots as the main driver of soil erosion (NEH and FEH, mean = 107.03, 81.86, (U) = 3136.5, p < 0.001). The logistic regression model depicted that ethnicity, distance, gender, and employment were significant in explaining the data variability on the perceived implementation of erosion prevention techniques in the watershed (2LL = 208.585, X² = 49, df = 8, p < .001). This research provides significant information on the drivers, underlying causes and erosion vulnerable areas that will aid stakeholders to garner community support, develop and implement sustainable soil management practices. Moreover, the study highlights the need to implement cost-effective soil erosion prevention programs and to assess the loss of soil nutrients and agriculture productivity in the study site.     

Prediction of soil loss differences and sediment accumulation at the Nilufer creek watershed,Turkey, using multiyear satellite data in a GIS

This study integrates the RUSLE, remote sensing and GIS to assess soil loss and identify sensitive areas to soil erosion in the Nilufer creek watershed in Bursa province, Turkey. The annual average soil loss was generated separately for years 1984 and 2011, in order to expose possible soil loss differences occurred in 27 years. In addition, sediment accumulation and sediment yield of the studied watershed was also predicted and discussed. The results indicated that very severe erosion risk areas in 1984 was 13.4% of the area, but it was increased to 15.3% by the year 2011, which needs immediate attention from soil conservation point of view. Furthermore, the estimated annual sediment yield of the Nilufer creek watershed was increased from 903 to 979 Mg km^?2 y^?1 in 27 years period. The study also provides useful information for decision-makers and planners to take appropriate land management practices in the area.     

Estimating rainfall erosivity by incorporating seasonal variations in parameters into the Richardson model

Rainfall erosivity is an important climatic factor for predicting soil loss. Through the application of high-resolution pluviograph data at 5 stations in Huangshan City, Anhui Province, China, we analyzed the performance of a modified Richardson model that incorporated the seasonal variations in parameters α and β. The results showed that (1) moderate to high seasonality was presented in the distribution of erosive rainfall, and the seasonality of rainfall erosivity was even stronger; (2) seasonal variations were demonstrated in both parameters α and β of the Richardson model; and (3) incorporating and coordinating the seasonality of parameters α and β greatly improved the predictions at the monthly scale. This newly modified model is therefore highly recommended when monthly rainfall erosivity is required, such as, in planning soil and water conservation practices and calculating the cover-management factor in the Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE).     

基于RUSLE的贵州省红枫湖流域土壤侵蚀时空变化特征

以贵州省红枫湖流域为研究对象,运用GIS和RUSLE模型分析了该流域1960～1986年、1987～1997年、1998～2004年三个时段内的年平均土壤侵蚀量和土壤侵蚀强度,并探讨了40多年来流域土壤侵蚀变化的时空变化特征。结果表明,过去40多年来,流域的土壤侵蚀经历了一个先增强再减弱的过程,土壤侵蚀强度空间分布呈西强东弱的格局,且流域西部呈明显先增强再减弱的特征,东部变化相对较小。     

基于土壤侵蚀控制度的黄土高原水土流失治理潜力研究

以整个黄土高原为研究对象,首先将水土保持措施容量定义为某一区域能容纳的最大适宜水土保持措施量。根据梯田、林地和草地的适宜布设区域,在地理信息系统（GIS）软件的支持下,确定了黄土高原的水土保持措施容量。使用修正通用土壤流失方程（RUSLE）,计算了最小可能土壤侵蚀模数和2010年现状土壤侵蚀模数,并将水土保持措施容量下的最小可能土壤侵蚀模数与现状土壤侵蚀模数之比定义为土壤侵蚀控制度。随后使用土壤侵蚀控制度,对黄土高原水土流失治理潜力进行了研究。结果显示：黄土高原2010年现状土壤侵蚀模数为3355 t·km^-2·a^-1,最小可能土壤侵蚀模数为1921 t·km^-2·a^-1,土壤侵蚀控制度为0.57,属于中等水平。相比于现状条件,在水土保持措施容量条件下,微度侵蚀区比例从50.48%提高至57.71%,林草覆盖率从56.74%增加至69.15%,梯田所占比例由4.36%增加到19.03%,人均粮食产量可从418 kg·a^-1提高至459 kg·a^-1。研究成果对于黄土高原生态文明建设具有一定的指导意义。     

基于遥感和GIS的宣化县水土流失定量空间特征分析

以遥感和GIS技术为支撑,利用通用的土壤流失方程(USLE)的修正模型(RUSLE)定量评估宣化县2000年的水土流失量和土壤侵蚀强度,并对宣化县水土流失空间分布特征进行了分析。结果表明,宣化县2000年土壤侵蚀(轻度侵蚀以上)面积为982.85 km2,占宣化县总面积的39.25%,平均土壤侵蚀模数为13.92 t/hm2.a,属于轻度侵蚀;坡度越大,极强度及剧烈侵蚀越有可能发生,从整体来看,15°~25°是侵蚀比例最大的坡度带。宣化县土壤侵蚀主要集中于灌草地和旱地两种土地类型,两者土壤侵蚀面积占宣化县2000年总土壤侵蚀面积的93.897%。     

六盘水市土壤侵蚀时空特征及影响因素分析

六盘水市是我国生态地位极其重要,水土流失又较为严重的城市。近些年,六盘水市实施了一系列生态工程,为了定量分析六盘水市土壤侵蚀状况及其影响因素,本文基于RUSLE模型,利用降雨数据、遥感影像数据、土地利用数据等,对贵州省六盘水市1990-2015年土壤侵蚀模数和土壤侵蚀量进行定量模拟,分析其时空动态变化,利用地理探测器定量分析影响因素,并进行空间相关性分析,结果表明： ① 六盘水市土壤侵蚀以微度和中度侵蚀为主。土壤侵蚀严重地区主要分布在北盘江流域与南盘江流域交界处以及喀斯特山地地区,煤矿开采使植被覆盖等抑制土壤侵蚀因子减少作用,使局部地区土壤侵蚀程度加剧。② 1990-2010年平均土壤侵蚀模数整体为下降趋势,2010-2015年为上升趋势。其中2000年平均土壤侵蚀模数最大,2010年平均土壤侵蚀模数最小。该变化由降雨可蚀性因子和植被覆盖度因子综合影响所致。③ 植被覆盖度因子和多年平均降雨量因子是影响区域土壤侵蚀空间分布的重要因素。未利用土地、植被覆盖度小于0.3、坡度在25°以上和降雨量在1543~1593 mm之间的地区为高风险侵蚀区域。④ 植被覆盖度与土壤侵蚀在空间上全部呈负相关性,降雨因子与土壤侵蚀在空间上存在负相关性和正相关性。⑤ 土壤侵蚀改善区域大多分布在生态工程区域内,生态工程建设能够改善土壤侵蚀情况,不同生态工程保护侧重点不同导致土壤侵蚀改善程度不同。退耕还林还草工程对六盘水市土壤侵蚀的改善具有重要意义,六盘水市更宜退耕还林。     

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.