珠江三角洲地质灾害的模糊综合评价

珠江三角洲地质灾害种类的多样性与其所处的复杂地质环境和多变的气候条件密切相关。文章选用了地震活动、活动断裂、地壳升降运动、软土地基、地面塌陷以及冲、淤积等６个主要因素作为评价指标，并用模糊数学方法对珠江三角洲内１７个小区作了计算，结果表明该区大部分地区属于轻灾害区，只有滨海沿岸地区属于较重灾害区。     

数字测图技术在阜新市地理数据库更新中的应用

采用GPS和全站仪联合作业的数字测图方式进行了外业快速数据采集。并利用南方CASS软件对数据进行内业成图工作,最后利用大型的地理信息系统软件ArcGIS进行地理数据的入库,从而实现了阜新市区地理数据库的更新,此方式具有速度快、效率高和精度高等优点,可以借鉴到同类的地区。     

极端洪水导致的湖泊水位抬升和氮磷输入增加对苦草(Vallisneria natans)的复合影响

据全球气候变化模型预测,未来极端洪水事件将呈现增多和加剧态势。极端洪水对沉水植物功能性状、生长发育状况及整个生态系统均有深远的影响,研究极端洪水对沉水植物生长发育的影响对理解和预测气候变化过程中水生态系统的变化具有重要意义。本文针对极端洪水事件引起的水位急剧上升和营养负荷量增加双重效应,在实验周期90 d内设置对照(水位保持75 cm)、2种极端洪水条件(水位在第1天从75 cm快速上升至150 cm + NP输入、水位从75 cm逐步上升至150 cm + NP输入)以及水位保持75 cm + NP输入4项处理(后3项处理中营养输入总量相同),研究了极端洪水对沉水植物苦草(Vallisneria natans)生长、繁殖对策及生物量分配的影响,实验过程中还同步监测了浮游植物、附着藻类和水体营养状况(TN和TP)。在苦草的17个测定指标中,只有根生物量分配和有性繁殖分配在4个处理下无明显变化。从水位的单独作用看,相对于营养增加且水位恒定的处理,两种洪水条件(水位急剧上升和逐步上升)均降低了苦草的分株数、叶片数、间隔子数、间隔子总长、最大根长、花果数以及各器官生物量和总生物量,促进了人工基质表面附着藻类的生长。水位急剧上升时,植株对间隔子的生物量投资倾向于最小,而株高和植株对叶的生物量投资倾向于最大。从营养负荷的单独作用来看,相对于对照组,营养增加且水位恒定的处理中水体N浓度显著增加,促进浮游植物和苦草表面附着藻类的生长,但是从二者的Chl.a浓度来看,浮游植物遮光作用相对较大,抑制了苦草叶、根和间隔子的生长,但有性繁殖生物量无明显变化。从水位和营养负荷联合作用的模式来看,水位上升和营养负荷两个环境因素的联合作用会使入湖营养负荷加强水位上升对沉水植物生长的影响效应,二者的联合作用使叶、根、间隔子和有性繁殖生物量均大大降低。因此,极端洪水对沉水植物的不利影响较大,一方面水位抬升会直接影响沉水植物,另一方面会通过浮游植物间接影响沉水植物生长发育。     

洞庭湖中枯水期水情变化及其驱动因素

为揭示洞庭湖中枯水期水情变化特征及其驱动因素,采用长短期记忆神经网络模拟洞庭湖出湖流量及湖区水位,通过情境模拟开展水情变化归因分析。洞庭湖1992—2019年9—10月出湖流量大幅减少,主要受长江流量降低的影响。洞庭湖中枯水期水位主要呈下降趋势,其中9—10月平均水位在西洞庭湖、南洞庭湖降幅约1 m,在东洞庭湖降幅约2 m。地形变化对中枯水期水位主要起拉低作用,长江和流域四水流量变化在9—10月起拉低作用、在12月至次年3月起抬升作用,其中对东洞庭湖水位的影响相对更为显著。研究结果可为洞庭湖中枯水期水资源管理和湿地保护提供参考。     

Bearing Capacity of Two-Layered Artificial Ground with Firm Crust and Weak Subgrade Under Circular Pressure

Classical bearing capacity expressions can not afford variant engineering requests, since they were deducted under hypotheses of strip footing on homogeneous soil. An often met case was taken into account that circular footing on two-layered ground, which has firm crust and weak subgrade. Weighted method and layered method were used in capacity deduction, respectively. The former counts layered soils as single layer of weighted structure and material parameters. But it is restricted to conditions with close strength to each layer. The latter is preferred in engineering calculation, which regards the whole bearing capacity as subgrade’s capacity under the influence of the upper layer. Thus the issue is transformed to K. Terzaghi model plus stress reduction and dispersion effects. At last, the expressions gained from both methods and finite element analysis (FEA) were used in an example calculation, respectively. It approves layered calculating method and suggests the corresponding security coefficient based on elastoplastic FEA results.     

贵州省近30a秋风的气候特征分析

利用1981—2010年30 a贵州省78站逐日日平均气温数据和NCEP/NCAR逐日的再分析资料,采用合成、EOF分解、傅立叶滤波等方法,分析了贵州省秋风的气候特征。结果表明:贵州省秋风天气强度有自西向东部或向东南部、东北部递减的分布规律。贵州省异常秋风过程次数的空间分布表现为全省各站呈同位相变化,进入2000年后,秋风过程次数由全省秋风偏弱偏少型转为全省秋风偏多偏强型。在环流场上,纬向西风在不断东移的过程中不断分裂出短波槽引导冷空气南下影响贵州,形成秋风天气过程,因而造成的秋风过程时间较长,强度较大。对秋风过程的环流场进行滤波表明:1波对于秋风过程有着十分显著的影响,而1波、2波在时间上的配合对于秋风过程也非常重要。     

布尔莎模型在GPS时间序列融合中的应用

利用“中国地壳运动观测网络”基准网与全球IGS站的公共站点,采用布尔莎模型求取坐标转换7参数,将地壳观测网络工程基准站点位时间序列与全球解进行绑定.结果显示,转换得到的时间序列N、E向中误差约为2、3mm,与直接融合得到的时间序列的单天误差水平相当,转换得到的站间大地线误差大部分在6mm内,表明该转换方法是可行的.     

武汉东湖不同营养型子湖的水生生物与水域功能

于１９９１－１９９３年研究了武汉东湖４个营养型不同的子湖中水生生物与水域功能,表明各子湖之间各类水生生物的生态特征存在明显差异,茶港湾的ＨＢＣ和ＦＣ的数量,藻类的细胞密度和初级生产量,浮游动物和底栖动物的个体密度等均居４个子湖之首,其水质已达到了超富营养水平,将该湖区的作为污水天然净化区进行管理,可望对东湖的主体湖区起到缓冲和保护作用,郭郑湖的水质状况仅次于茶港湾湖区,处于富营养阶段,该湖区多项生     

Piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events on the Loess Plateau,China

Establishing a universal watershed‐scale erosion and sediment yield prediction model represents a frontier field in erosion and soil/water conservation. The research presented here was conducted on the Chabagou watershed, which is located in the first sub‐region of the hill‐gully area of the Loess Plateau, China. A back‐propagation artificial neural model for watershed‐scale erosion and sediment yield was established, with the accuracy of the model, then compared with that of multiple linear regression. The sensitivity degree of various factors to erosion and sediment yield was quantitatively analysed using the default factor test. On the basis of the sensitive factors and the fractal information dimension, the piecewise prediction model for erosion and sediment yield of individual rainfall events was established and further verified. The results revealed the back‐propagation artificial neural network model to perform better than the multiple linear regression model in terms of predicting the erosion modulus, with the former able to effectively characterize dynamic changes in sediment yield under comprehensive factor conditions. The sensitivity of runoff erosion power and runoff depth to the erosion and sediment yield associated with individual rainfall events was found to be related to the complexity of surface topography. The characteristics of such a hydrological response are thus closely related to topography. When the fractal information dimension is greater than the topographic threshold, the accuracy of prediction using runoff erosion power is higher than that of using runoff depth. In contrast, when the fractal information dimension is smaller than the topographic threshold, the accuracy of prediction using runoff depth is higher than that of using runoff erosion power. The developed piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events, which introduces runoff erosion power and runoff depth using the fractal information dimension as a boundary, can be considered feasible and reliable and has a high prediction accuracy. Copyright © 2013 John Wiley & Sons, Ltd.