1990年以来柴达木盆地小柴旦湖扩张及影响分析

柴达木盆地盐湖对维系区域生态平衡、保障农业安全和国民经济发展具有重要的作用。受青藏高原气候暖湿化影响,盆地湖泊格局发生了一系列变化。为分析盆地盐湖变化特征,本文以小柴旦湖为例,基于长时间序列卫星数据,利用随机森林法提取盐湖水域范围并解构、评估“点—线—面”形态变化,同时构建矿化度指数表征盐湖资源禀赋。结果表明:(1) 1990—2022年小柴旦湖面积呈“缓减—缓增—快增”的阶段性变化特征,岸线长度呈阶段性变化(2016年后增长明显),湖泊质心总体向西北方向迁移,截止2022年湖泊质心累计迁移距离为2363.49 m。(2) 1990—2022年矿化度指数波动下降,呈“淡化”态势。(3)气象数据显示研究区年平均气温升高、年降水量增多、年潜在蒸散量下降,处于暖湿化轻度转型期,降水增多、蒸发下降、增温引起的冰雪融水增加综合叠加效应引起区域水资源增加,进而导致湖泊扩张和“淡化”。(4)小柴旦湖扩张和“淡化”对社会经济和生态平衡影响明显,一方面,影响湖区周边基础设施,近70 km²区域受淹没直接影响,包括德小高速公路、柳格高速公路部分路段;另一方面,作为资源型湖泊,小柴旦湖...     

利用HLA的分布式虚拟森林环境构建

以分布式虚拟森林灭火环境为例,基于HLA/RTI协议,设计和初步实现了地形、树木、林火、灭火工具联邦成员组成的分布式森林灭火仿真系统虚拟场景的三维动态生成与显示。系统将仿真的底层通讯和上层的模型控制框架、三维图形绘制分离开来,形成各个独立的模块,具有扩展性和重用性。     

Changes in Agricultural Biodiversity： Implications for Sustainable Livelihood in the Himalaya

Himalayan mountain system is distinguished globally for a rich biodiversity and for its role in regulating the climate of the South Asia.Traditional crop-livestock mixed farming in the Himalaya is highly dependent on forests for fodder and manure prepared from forest leaf litter and livestock excreta. Apart from sustaining farm production, forests provide a variety of other tangible and intangible benefits, which are critical for sustainable livelihood of not only 115 million mountain people, but also many more people living in the adjoining plains. Extension of agricultural landuse coupled with replacement of traditional staple food crops by cash crops and of multipurpose agroforestry trees by fruit trees are widespread changes. Cultivation of Fagopyrum esculentum,Fagopyrum tataricum, Panicum miliaceum, Setaria italica and Pisum arvense has been almost abandoned. Increasing stress on cash crops is driven by a socio-cultural change from subsistence to market economy facilitated by improvement in accessibility and supply of staple food grains at subsidized price by the government. Farmers have gained substantial economic benefits from cash crops. However, loss of agrobiodiversity implies more risks to local livelihood in the events of downfall in market price/demand of cash crops, termination of supply of staple food grains at subsidized price, pest outbreaks in a cash crop dominated homogeneous landscape and abnormal climate years. Indigenous innovations enabling improvement in farm economy by conserving and/enhancing agrobiodiversity do exist, but are highly localized. The changes in agrobiodiversity are such that soil loss and run-off from the croplands have dramatically increased together with increase in local pressure on forests. As farm productivity is maintained with forest-based inputs, continued depletion of forest resources will result in poor economic returns from agriculture to local people,apart from loss of global benefits from Himalayan forests. Interventions including improvement in traditional manure and management of on-farm trees,participatory development of agroforestry in degraded forest lands and policies favoring economic benefits to local people from non-timber forest products could reduce the risks of decline in agricultural biodiversity and associated threats to livelihoods and Himalayan ecosystems.     

ABSENCE OF PREFERENTIAL FLOW IN THE PERCOLATING WATERS OF A CONIFEROUS FOREST SOIL

Evidence for the functioning of macropores and the presence of preferential flow in forest soils is equivocal. This is partly because many workers use only one diagnostic technique to indicate whether or not macropore flow occurs. In this paper three lines of evidence are used to suggest that preferential flow does not occur in the percolating waters of a coniferous forest soil under the range of hydrological conditions that prevail in the field. To simulate field conditions, realistic rainfall intensities were used in conservative solute transport experiments on four undisturbed soil columns. A method is described in which breakthrough data can be used to calculate the percentage of antecedent water displaced from a soil column during frontal-type breakthrough experiments. Calculations based on this method using the experimental data show that as little as five percent of the antecedent water was immobile. The simple form of the functional advection–dispersion equation, based on a single value for linear velocity and the dispersion coefficient was fitted to two of the breakthrough curves with reasonable accuracy, further suggesting that preferential flow did not occur in the experiments. Finally, soil moisture characteristic curves were determined for replicate soil samples from the forest soil. The operational water contents of the columns during the breakthrough experiments were compared with the soil moisture characteristics and it was found that pores exerting pressure heads greater than −0·5 kPa did not appear to contribute to flow through the columns, again suggesting an absence of preferential flow. © 1997 by John Wiley & Sons, Ltd.     

A spreadsheet-based technique (Lotus 1-2-3) for separating tropical forest storm hydrographs using Hewlett and Hibbert's slope

The inclined line separation technique of Hewlett and Hibbert has been widely adopted to separate delayed flow from the total stream storm runoff. Presented here is the application of the technique to highly responsive storm hydrographs using a personal computer method based on a Lotus 1-2-3 spreadsheet. Using discharge measurements (in m³ s⁻¹), catchment area (in km²) and time (in Julian days), the separation slope is adjusted on the monitor screen until the precise time at which the end of quickflow as storm runoff gives way to delayed flow may be established. The application of the inclined line method is compared with other separation techniques applied to the same dataset. The annual stream quickflow runoff for the study catchment was calculated by the four different separating lines — (i) best-fit curve, (ii) N-day after peak, (iii) inclined line and (iv) horizontal line — was 250, 312, 368, and 588 mm, amounting to 33, 31, 51 and 78 per cent respectively of the annual total stream runoff. Separation of flow by computer spreadsheet methods may be consistently applied throughout a dataset and therefore have a comparative advantage over more arbitrary techniques. © 1997 John Wiley & Sons, Ltd.     

基于金矿规格单元数据的机器学习方法在成矿建模分析中的应用

现代金矿勘察主要是通过综合地球化学和地质测量等数字化方法对深部矿床进行研究,所需要的人力物力成本较高。而通过分析积累的金矿规格单元数据,可以建立金矿成矿情况与相关成矿元素含量之间的非线性关系,从已有的勘查数据中寻找金矿成矿的一般规律。本文基于与金矿相关的成矿元素含量数据,分别采用逻辑斯蒂回归、随机森林和决策树方法对原始数据和重采样数据进行训练,综合运用召回率、精确率和准确率对模型进行评价。通过对比发现,在训练和测试原始数据过程中,由于每组之间数据量的巨大差距,导致成矿数据被淹没;而在训练重采样数据过程中,随机森林在召回率和准确率方面均有较好的表现,分别达到了90.63%和70.78%;并最终分析了随机森林模型中不同分类边界对于金矿成矿情况预测结果的影响。利用不同的测量指标对模型进行评价分析,使模型更适用于金矿成矿预测,可有效地提高金矿勘察的效率。     

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.     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.     

Forests and floods: Using field evidence to reconcile analysis methods

The extent to which forests, relative to shorter vegetation, mitigate flood peak discharges remains controversial and relatively poorly researched, with only a few significant field studies. Considering the effect purely of change of vegetation cover, peak flow magnitude comparisons for paired catchments have suggested that forests do not mitigate large floods, whereas flood frequency comparisons have shown that forests mitigate frequencies over all magnitudes of flood. This study investigates the apparent inconsistency using field-based evidence from four contrasting field programmes at scales of 0.34–3.1 km². Repeated patterns are identified that provide strong evidence of real effects with physical explanations. Magnitude and frequency comparisons are both relevant to the impact of forests on peak discharges but address different questions. Both can show a convergence of response between forested and grassland/logged states at the highest recorded flows but the associated return periods may be quite variable and are subject to estimation uncertainty. For low to moderate events, the forested catchments have a lower peak magnitude for a given frequency than the grassland/logged catchments. Depending on antecedent soil saturation, a given storm may nevertheless generate peak discharges of the same magnitude for both catchment states but these peaks will have different return periods. The effect purely of change in vegetation cover may be modified by additional forestry interventions, such as road networks and drainage ditches which, by effectively increasing the drainage density, may increase peak flows for all event magnitudes. For all the sites, forest cover substantially reduces annual runoff.     

Partition-coordinated control of soil and water loss for chestnut forests in the Yanshan Mountain Region,China

Soil erosion from chestnut forests is one of the most important factors causing land degradation in the Yanshan Mountain Region. A 2-year field study was done to compare the effects on erosion of a control plot(CP), a repaired and maintained horizontal ditch built with an engineering baffle every 6 m(MP 1),and a repaired and maintained horizontal ditch built with an engineering baffle every 8 m(MP 2). The results showed that the slope runoff of chestnut forests was influenced by rainfall characteristic factors.No single rainfall characteristic factor showed dominance for hill slope runoff. The runoff reduction effect of the partition-coordinated erosion control measures(MP 1 and MP 2) was substantial for chestnut forests under high rainfall intensity conditions. However, the runoff reduction efficiency was higher under the conditions of heavy rainfall and low average rainfall intensity than for storms with higher intensity and lower total rainfall. The reduction effect of the partition-coordinated erosion control measures on the runoff and sediment yield of chestnut forest slopes was MP 2 > MP 1 > CP. The runoff reduction rate and erosion reduction rate of MP 2 reached 61.70% and 97.41%, respectively, and that for MP 1 was 54.15% and 85.31%, respectively. Therefore, after a comprehensive comparison, MP 2 was determined to be more effective for soil erosion control for a sloping chestnut forest.