Soil erosion vulnerability under scenarios of climate land-use changes after the development of a large reservoir in a semi-arid area

Climate and land-use/cover changes (LUCC) influence soil erosion vulnerability in the semi-arid region of Alqueva, threatening the reservoir storage capacity and sustainability of the landscape. Considering the effect of these changes in the future, the purpose of this study was to investigate soil erosion scenarios using the Revised Universal Soil Loss Equation (RUSLE) model. A multi-agent system combining Markov cellular automata with multi-criteria evaluation was used to investigate LUCC scenarios according to delineated regional strategies. Forecasting scenarios indicated that the intensive agricultural area as well as the sparse and xerophytic vegetation and rainfall-runoff erosivity would increase, consequently causing the soil erosion to rise from 1.78 Mg ha^?1 to 3.65 Mg ha^?1 by 2100. A backcasting scenario was investigated by considering the application of soil conservation practices that would decrease the soil erosion considerably to an average of 2.27 Mg ha^?1. A decision support system can assist stakeholders in defining restrictive practices and developing conservation plans, contributing to control the reservoir's siltation.     

基于GIS与RUSLE的武陵山区小流域土壤侵蚀评价研究

以长江中下游武陵山区女儿寨小流域为研究区,基于数据观测积累及实地调查采样等方法,计算了研究区降雨侵蚀力、土壤可蚀性等因子,运用GIS与RUSLE评价了流域土壤侵蚀强度并分析了其与土地利用方式、海拔高度的关系。结果表明,流域平均土壤侵蚀强度为78844 t/（km2·a）,属微度侵蚀,流域面积9518%的范围发生轻度以下的侵蚀,强烈以上侵蚀仅占1.19%。从土地利用类型来看,耕地、果园侵蚀强度较大,均达到中度侵蚀,有林地除竹林地为轻度侵蚀外均属微度侵蚀,耕地、果园、竹林地是今后水土流失防治的主要地类。不同海拔高度中,低海拔（200～400 m）区域侵蚀量占到流域侵蚀总量的6442%,是水土流失防治的重点地带。研究为应用修正通用土壤流失方程在武陵山区进行土壤侵蚀评价提供范例,为研究区防治土壤侵蚀和流域管理规划决策提供相应参考     

Predicting RUSLE (Revised Universal Soil Loss Equation) Monthly Erosivity Index from Readily Available Rainfall Data in Mediterranean Area

Summary Seasonal rainerosivity is important in the structure and dynamics of Mediterranean ecosystems. The present paper contributes to the quantitative assessment of RUSLE's monthly erosion index in a data-scarce Mediterranean region. Therefore, a regionalized relationship for estimating monthly erosion index (EI_30-month) from only three rainfall parameters has been obtained. Knowledge of the seasonal and annual distribution of erosivity index, permit soil and water conservationists to make improved designs for erosion control, water harvesting or small hydraulic structures. Although a few long data sets were used in the analysis, validation with established monthly erosivity index values from other Italian locations, suggest that the model presented (r² = 0.973) is robust. It is recommended to monthly erosivity estimates when experimental data-scarce rainfall become available.     

基于BP神经网络的三峡库区土壤侵蚀强度模拟

降雨侵蚀力变化是一复杂过程,其变化存在一定的随机波动性,土壤侵蚀是三峡库区生态环境脆弱最主要的影响因素之一,查明库区土壤侵蚀强度的演化过程及未来趋势是库区生态文明建设过程中急需解决的关键科学问题。论文基于三峡库区1990年侵蚀降雨特征,利用BP神经网络对2010年75个站点降雨侵蚀力进行模拟、验证,预测2030年75个站点降雨侵蚀力。选取2030年预测结果中位于库区周围的27个站点,结合2030年库区自然增长、生态保护情景下土地利用模拟数据,使用RUSLE计算2030年土壤侵蚀强度。结果表明：1）2010年库区降雨侵蚀力模拟相对误差为15%,测试样本数据相对误差为14.67%,预测相对误差为19.65%,NE系数为0.85,说明BP神经网络对库区降雨侵蚀力具有良好模拟效果;2）2010年库区土壤侵蚀强度的Kappa指数为0.75,计算结果能满足模拟与预测需求;3）在土地利用不变情况下,2030年库区轻度、中度侵蚀面积均有所增加,微度及强烈以上侵蚀面积均呈减少趋势,且侵蚀强度转变中的58%来源于相邻侵蚀强度,跨侵蚀等级区的较少;4）在降雨侵蚀力不变情况下,自然增长、生态保护情景下未来土地利用变化所导致的土壤侵蚀均呈下降趋势,后者下降的趋势更为明显;5）在降雨侵蚀力及土地利用均变化的情况下,自然增长、生态保护情景下土壤侵蚀均呈下降趋势。     

基于RUSLE模型的东江源区土壤侵蚀时空变化分析

研究东江源区土壤侵蚀对于加强东江流域的生态环境保护和建设,提高粤港地区的用水安全,保证粤港地区的繁荣、稳定发展意义重大。基于1995—2020年的降雨数据、土壤数据、DEM数据和Landsat影像数据,采用RS、GIS技术以及RUSLE模型定量分析东江源区土壤侵蚀时空特征。结果表明：(1)东江源区土壤侵蚀以微度和轻度侵蚀为主,严重侵蚀区域主要分布在源区东南部,果园和矿区等的开发使得局部区域侵蚀加剧。(2)1995—2020年,土壤侵蚀程度总体有所下降,微度以上侵蚀面积共减少了10.19%,土壤侵蚀模数下降了57.75%。其中2008年土壤侵蚀模数最大,达到2397.13 t · (km² · a)^-1,2020年土壤侵蚀模数最小,为669.47 t · (km² · a)^-1。(3)近26 a以来,东江源区土壤侵蚀改善区域的面积达到16.52%,侵蚀加剧区为4.28%,以矿区侵蚀加剧情况最为明显。(4)源区内土壤侵蚀较严重区域主要发生在矿区、裸地、耕地和果园区,矿产资源的过度开采、不合理的农业耕作方...     

Modelling the effect of support practices (P-factor) on the reduction of soil erosion by water at European scale

The USLE/RUSLE support practice factor (P-factor) is rarely taken into account in soil erosion risk modelling at sub-continental scale, as it is difficult to estimate for large areas. This study attempts to model the P-factor in the European Union. For this, it considers the latest policy developments in the Common Agricultural Policy, and applies the rules set by Member States for contour farming over a certain slope. The impact of stone walls and grass margins is also modelled using the more than 226,000 observations from the Land use/cover area frame statistical survey (LUCAS) carried out in 2012 in the European Union.The mean P-factor considering contour farming, stone walls and grass margins in the European Union is estimated at 0.9702. The support practices accounted for in the P-factor reduce the risk of soil erosion by 3%, with grass margins having the largest impact (57% of the total erosion risk reduction) followed by stone walls (38%). Contour farming contributes very little to the P-factor given its limited application; it is only used as a support practice in eight countries and only on very steep slopes. Support practices have the highest impact in Malta, Portugal, Spain, Italy, Greece, Belgium, The Netherlands and United Kingdom where they reduce soil erosion risk by at least 5%. The P-factor modelling tool can potentially be used by policy makers to run soil-erosion risk scenarios for a wider application of contour farming in areas with slope gradients less than 10%, maintaining stone walls and increasing the number of grass margins under the forthcoming reform of the Common Agricultural Policy.     

Uncertainty analysis of predicted disturbance from off-road vehicular traffic in complex landscapes at fort hood

The US Army Engineering Research Development Center (ERDC) uses a modified form of the Revised Universal Soil Loss Equation (RUSLE) to estimate spatially explicit rates of soil erosion by water across military training facilities. One modification involves the RUSLE support practice factor (P factor), which is used to account for the effect of disturbance by human activities on erosion rates. Since disturbance from off-road military vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) is used to predict the distribution of P factor values across a training facility. This research analyzes the uncertainty in this model's disturbance predictions for the Fort Hood training facility in order to determine both the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. This analysis shows that a three-category vegetation map used by the disturbance model was the greatest source of prediction uncertainty, especially for the map categories shrub and tree. In areas mapped as grass, modeling error (uncertainty associated with the model parameter estimates) was the largest uncertainty source. These results indicate that the use of a high-quality vegetation map that is periodically updated to reflect current vegetation distributions, would produce the greatest reductions in disturbance prediction uncertainty.     

北部湾钦江流域土壤侵蚀及其硒元素流失评估

以钦江流域2015年的气象数据、遥感数据及数字高程模型、土壤类型以及土壤质地等数据为基础,基于修正的通用土壤流失方程（RUSLE）和GIS空间分析技术,定量分析了广西北部湾钦江流域土壤侵蚀及其硒元素流失的空间分布特征.研究结果表明：（1）北部湾钦江流域2015年土壤侵蚀总量为381.64×104t/a,平均土壤侵蚀模数为14.79t/（hm²·a）,小于2010年钦江流域的土壤侵蚀模数,但远大于水利部规定的在南方红壤丘陵区土壤允许流失量;（2）流域土壤侵蚀强度以微度侵蚀为主,侵蚀强度从流域上游到下游依次降低,0~240m之间的高程带以及>15°的坡度带是未来土壤侵蚀防治的重点区域;（3）山地地区的土壤侵蚀模数最高,达23.49t/（hm²·a）,高于流域平均土壤侵蚀模数约1.59倍,丘陵地区次之,而冲积平原最小;（4）流域土壤的硒含量介于0.38~0.72mg/kg之间,平均值0.49mg/kg,高于中国土壤硒元素背景值的1.69倍;（5）不同土地利用类型土壤硒含量随着土壤剖面深度的增加均呈现出减低趋势,硒的含量在不同土地利用类型中的排序为林地 > 园地 > 草地 > 水田 > 旱地,而在不同土壤类型中硒含量大小顺序则为：新积土 > 石灰岩土 > 潜育水稻土 > 淹育水稻土 > 赤红壤 > 潴育水稻土 > 砖红壤 > 滨海沙土 > 紫色土 > 咸酸水稻土.（6）流域土壤硒元素的流失总量为8987.05kg/a,平均流失模数为0.0344kg/（hm²·a）,其中流域中游的硒元素流失量最大.该项研究成果可为钦江市政府开发富硒农产品、发展富硒农业以及提升钦江流域土地利用的价值提供科学依据.     

Terraced fields and Mediterranean landscape structure: An analytical case study from Antikythera, Greece

Terraces are important capital investments in a range of agricultural landscapes worldwide, typically enduring well beyond any single farming cycle and over many human generations. This paper begins by emphasising that, while human population growth may often be loosely linked to terrace construction efforts, the association is by no means a straightforward one. We then argue that the choice of which parts of the landscape to terrace is driven by a range of cultural and environmental priorities that are most usefully explored by a combination of global, local and auto-correlative modelling, as well as via simulation-based methods. The results demonstrate that surficial geology, terrain slope, pre-existing terraces and pre-existing patterns of human habitation are all important structuring features. We also consider terraces as method for soil conservation, question the uncritical use of meso-scale erosion models and argue that patterns of catastrophic soil loss are often overstated in Mediterranean contexts. However, erosion modelling can, if deployed cautiously and comparatively, nonetheless be used to explore ways in which terraces do indeed manage localised soil movement in agriculturally favoured parts of the landscape, with our results suggesting that a substantial proportion of the erosion in this regions is indeed ameliorated by such measures.