基于GIS的低丘缓坡建设对水土流失的影响--以昆明市西山区花红园区块为例

[目的]研究低丘缓坡建设对水土流矢的影响。[方法]采用地理信息系统(GIS)和RUSLE模型对昆明市西山区花红园低丘缓坡项目建设区开展前、后土壤侵蚀量变化进行量化分析,对项目区建设前、后在不同土地利用类型和不同坡度下的水土流失特征以及建设后水土流失对项目区周围生态环境、地表径流、未做土地平整区域的影响进行了研究。[结果]建成后项目区总体水土流失现象得到极大缓解;保留植被因景观破碎化导致土壤流失加剧;建成后坡度5°的区域地表径流主要是沿着道路用地分布以及向道路的两边蔓延。[结论]低丘缓坡项目建设用地建成后的侵蚀类型为极微度侵蚀。     

近30年三北防护林工程区中阳县土壤侵蚀变化分析

基于遥感和GIS,应用修正的通用土壤流失方程(RUSLE)对比分析中阳县实施三北防护林工程后1978年、1992年和2006年的土壤侵蚀量变化,掌握三北防护林工程建设对水土流失治理的影响,为进一步制定相应的治理措施提供科学依据。结果表明,中阳县土壤侵蚀面积减少,比工程建设初期减少了305.56km2,土壤侵蚀强度降低,从建设初期的极强烈侵蚀成为中度侵蚀。从1978-1992年,中阳县发生轻度以上土壤侵蚀面积减少了145.52km2,平均土壤侵蚀模数降低了1 619.04t.km-2.a-1,尽管土壤侵蚀强度仍然为极强烈,但是整体来说土壤侵蚀治理初步呈现好转。从1992-2006年,中阳县发生轻度以上土壤侵蚀面积减少了160.01km2,平均土壤侵蚀模数降低了5 962.57t.km-2.a-1,土壤侵蚀强度从极强烈降低到中度,土壤侵蚀面积中有588.60km2侵蚀强度降低,占侵蚀面积的75.21%,土壤侵蚀呈现逆转趋势。土壤侵蚀强度降低的土地,主要是由于三北防护林工程实施以来,荒山造林、退耕还林和加强林地管护,以及农田基本建设工程改造坡耕地成为梯田。     

基于GIS和RUSLE的锦州市水土流失定量空间特征分析

以RS和GIS技术为支撑,利用修正的土壤流失方程(RUSLE)定量评估锦州市2010年水土流失量和土壤侵蚀强度,并且对锦州市水土流失空间分布特征进行分析。结果表明,锦州市2010年土壤侵蚀面积为7 284.87 km2,占锦州市总面积的70.72%,平均土壤侵蚀模数为18.27 t/(hm2·年),属于轻度侵蚀;15°~25°和6°~15°2个坡度带是研究区土壤侵蚀的主要发生区域。锦州市土壤侵蚀主要发生在农村居民点和旱地2种土地类型,两者的侵蚀量占锦州市2010年总侵蚀量的60.97%。未来应加大对这2种土地类型的治理力度,将其列为水土保持重点治理对象。以上研究分析可以为政府制定水土保持的相关政策提供科学依据。     

Assessing spatial variability and erosion susceptibility of soils in hilly agricultural areas in Southern Italy

Soil erosion is one of the main environmental problems in the Mediterranean area. This problem is becoming even more important especially in Italy, in the Apennines, where severe erosive processes occur due to the action of concentrated running water. The erodibility (K-Factor) of a soil, estimated using the Revised Universal Soil Loss Equation (RUSLE), is a measure of its susceptibility to erosion and depends on several soil properties such as organic matter, texture and permeability and structure.To assess the spatial variability of soil properties and soil erodibility in hilly agricultural areas and to investigate the relationships between soil features and landscape morphodynamics, a detailed study in Molise region (southern Italy), in a small drainange basin located along its hilly Adriatic flank, was carried out. In this catchment, 63 topsoil samples (A horizons) were collected and 10 soil profiles, forming a catena crossing 3 land units, were sampled. The calculated K-Factors ranges between 0.012 and 0.048 t ha h ha⁻¹ MJ⁻¹ mm⁻¹ indicating a complex spatial distribution, due to the several local pedological and geomorphological factors affecting soil erodibility. The results give clear evidence about the relationships among soil characteristics, soil erodibility and landscape morpho-dynamics (land units).Comparing the soil loss rates estimated for the study area with those reported in literature, a good correspondence can be observed only for the more stable land unit, not characterized by intense erosive processes. The proposed methodology is suitable to highlight areas characterized by similar morphodynamics features, and comparable soil erodibility, for a more effective spatialization of K factor.     

基于GIS和RUSLE的滇池流域土壤侵蚀敏感性评价及其空间格局演变

土壤侵蚀是滇池流域重要的生态问题之一,掌握滇池流域土壤侵蚀敏感性的时空变化特征有助于水土保持工作的实施和改进。以降雨量、DEM、土壤类型和Landsat影像为数据源,选择降雨、土壤、坡度坡长、植被覆盖4个因子建立土壤侵蚀敏感性评价体系,对滇池流域进行土壤侵蚀敏感性评价。结果表明:滇池流域土壤侵蚀敏感性以轻度敏感和中度敏感为主。空间分布上,轻度敏感区主要分布在滇池周边。中度敏感区主要分布在滇池流域山地区域,地形陡峭。时间变化上,1999—2014年滇池流域土壤侵蚀敏感程度呈下降趋势。轻度敏感区域面积增加20.18%,中度敏感区域面积减少20.31%,轻度敏感区的增加来源于中度敏感区的转变,转变区域分布于滇池流域西北部和东南部。在土壤侵蚀敏感性影响因子中,降雨是影响滇池流域土壤侵蚀敏感性的关键因子。研究滇池流域土壤敏感性时空变化,识别滇池流域易发生土壤侵蚀的区域,有助于该区域水土保持措施实施、生态治理和土地利用优化。     

黄土高原森林草原区退耕还林还草土壤保持效应评估

黄土高原地处生态过渡带和环境脆弱区,区内大范围的土壤侵蚀严重影响了当地的生态环境。以黄土高原森林草原区为研究对象,应用修正通用土壤流失方程,根据2000、2005、2010年气象数据及土地利用等数据,从不同坡度、植被覆盖度、土地利用类型评估了黄土高原森林草原区退耕还林还草工程的土壤保持效应。结果表明,(1)随着退耕还林还草工程的实施,林地和草地面积明显增加,分别增加2 219.41 km~2、2 205.27 km~2,研究区植被覆盖度逐渐改善。(2)2000—2010年土壤保持量增加2.41亿t,单位面积土壤保持量由3 033.15 t·km~(-2)·a~(-1)增加至5 114.86 t·km~(-2)·a~(-1),土壤保持效应显著提升。(3)研究区土壤保持效应与植被覆盖度呈正相关关系,在不同土地利用类型中,林地、草地和耕地具有较高的土壤保持效应,单位面积土壤保持量分别为5 405.57、3 598.41、3 078.81 t·km~(-2)·a~(-1)。退耕还林还草工程的实施提升了区域的土壤保持效应,但是,区内东北部由于矿产资源开采导致的植被破坏、地表塌陷以及土壤侵蚀问题亟待解决。     

退耕还林工程建设对土壤侵蚀的影响——以安塞县为例

以黄土丘陵区退耕还林典型县——安塞县为研究对象,基于3S技术与RUSLE土壤侵蚀模型,从县域尺度上分析评价了黄土高原区退耕还林前后土壤侵蚀变化。研究结果表明:与退耕前1999年相比较,2010年土壤侵蚀强度在空间上发生明显改变,总体上,土壤侵蚀强度有明显减小的趋势。极强烈侵蚀下降幅度最大(下降13.73%),主要向强烈转移,占10.45%;中度侵蚀由35.92%增加到59.98%,主要由强烈侵蚀转移而来,占27.08%;微度和轻度变化较小。土壤侵蚀既有增强区域也有减弱区域,总体趋势以减弱为主。侵蚀加强区域主要是由中度和强烈向强烈、极强烈和剧烈转移,转移面积较小;发生增强的区域主要是以草地覆盖为主;其次是原耕地,退耕还林后转化为林地和草地,部分地区出现裸露斑块,呈现部分小面积土壤侵蚀加剧。土壤侵蚀减弱区域主要是极强烈、剧烈向中度、强烈等转移;转移面积主要发生区域沟壑区低盖度草地、沟道以及河道边缘裸地。随着退耕还林还草工程的实施,荒山林草覆盖度增加,林草植被对降水进行截留下渗,缓减洪峰流量,减少降雨洪峰对沟道和河道侧冲刷,降低土壤侵蚀强度。安塞县实施退耕还林11年后,土壤侵蚀以强度侵蚀为主(46.47%)转中度侵蚀为主(59.98%),全县平均土壤侵蚀由1998年的9 780t/(km~2·a)转为2010年的5 460t/(km~2·a),每年约减少土壤侵蚀量1 274万t。退耕还林工程建设对于安塞县控制水土流失和改善生态环境有着重要作用。研究结果将对该区域水土流失治理提供参考依据。     

退耕还林还草工程实施对洛河流域土壤侵蚀的影响

退耕还林还草工程是中国实施的重要生态环境建设与保护工程,对区域植被覆盖及土壤侵蚀产生重要影响。以洛河流域（陕北黄土高原部分）为研究对象,利用流域通用土壤侵蚀方程（RUSLE）,结合流域降雨、土壤类型、DEM、植被覆盖等数据,定量分析了2000—2010年退耕还林还草工程实施对流域土壤侵蚀的影响。结果表明:（1）洛河流域2000—2010年耕地面积减少,林地、草地面积增加,土地利用变化主要发生在2000—2005年;（2）洛河流域2000—2010年土地利用变化导致植被NDVI平均值增大,耕地变化区域植被NDVI值增加幅度高于耕地未变化区域,表明耕地变化区域植被NDVI增加对耕地区域总体植被NDVI值增加贡献较大;（3）降雨侵蚀力和退耕还林还草工程实施对土壤侵蚀具有明显的影响。受降雨侵蚀力增大影响,2000—2010年洛河流域土壤侵蚀呈增加趋势;不考虑降雨侵蚀力变化情况下,洛河流域土壤侵蚀呈减少趋势,反映出退耕还林还草工程实施对土壤侵蚀的减缓作用。     

Using the RUSLE to identify factors controlling erosion rates of mine soils

Observable differences in particle size, smoothness and compaction between cap site (slope 2·8 per cent) and batter site (slope 20·7 per cent) surfaces on the waste rock dump at Ranger Uranium Mine were quantified in terms of revised universal soil loss equation (RUSLE) parameter values. Cap site surface material had a K_m (erodibility corrected for sediment density) of 0·030 and batter site surface material had a K_m of 0·0056. Using these K_m values (derived from particle size distributions), slope length and steepness (LS) factors of 0·36 for the cap site and 3·66 for the batter site, and a cover (C) factor of 0·45 for the cap site and 0·16 for the batter site, the RUSLE predicts an erosion rate from the cap site that is 1·9 times greater than erosion from the much steeper batter site. The RUSLE indicates that the finer particle size and blocky soil structure of the cap site (D₅₀ = 0·91 mm) compared with the looser granular structure of the batter site (D₅₀ = 1·74 mm) strongly influence erosion. The predictions are similar to observed soil losses from erosion plots on these sites under rainfall simulation events, for which the measured erosion rate from the cap site was approximately twice that from the batter site. For the RUSLE to predict the observed erosion rates, the support practice (P) factor for the cap site would have to be approximately 30 per cent greater than the P factor for the batter site. The higher cap site P factor probably results from smoothing and compaction caused by vehicle movement across the surface. Compaction is considered to have greatly reduced infiltration capacity, thus increasing the erodibility of the cap site. Vehicles probably also crushed the surface material at the cap site, creating the observed finer particle size distribution and further increasing the erodibility. Compaction, through its effects on erodibility (K_m) and surface roughness (P), is concluded to be the major cause of higher erosion from the cap site, even though the slope steepness is 10 times less. Parameterisation of the RUSLE quantifies the differences between sites and explains the unexpected erosion rates observed. The results highlight the need for careful management of rehabilitated sites to avoid increases in erosion which may arise from compaction by machinery.     

Modelling soil erodibility in mountain rangelands of southern Kyrgyzstan

Soil erosion in mountain rangelands in Kyrgyzstan is an emerging problem due to vegetation loss caused by overgrazing. It is further exacerbated by mountain terrain and high precipitation values in Fergana range in the south of Kyrgyzstan. The main objective of this study was to map soil erodibility in the mountainous rangelands of Kyrgyzstan. The results of this effort are expected to contribute to the development of soil erodibility modelling approaches for mountainous areas. In this study, we mapped soil erodibility at two sites, both representing grazing rangelands in the mountains of Kyrgyzstan and having potentially different levels of grazing pressure. We collected a total of 232 soil samples evenly distributed in geographical space and feature space. Then we analyzed the samples in laboratory for grain size distribution and calculated soil erodibility values from these data using the Revised Universal Soil Loss Equation (RUSLE) K-factor formula. After that, we derived different terrain indices and ratios of frequency bands from ASTER GDEM and LANDSAT images to use as auxiliary data because they are among the main soil forming factors and widely used for prediction of various soil properties. Soil erodibility was significantly correlated with channel network base level (geographically extrapolated altitude of water channels), remotely sensed indices of short-wave infrared spectral bands, exposition, and slope degree. We applied multiple regression analysis to predict soil erodibility from spatially explicit terrain and remotely sensed indices. The final soil erodibility model was developed using the spatially explicit predictors and the regression equation and then improved by adding the residuals. The spatial resolution of the model was 30 m, and the estimated mean adjusted coefficient of determination was 0.47. The two sites indicated different estimated and predicted means of soil erodibility values (0.035 and 0.039) with a 0.05 significance level, which is attributed mainly to the considerable difference in elevation.