洞庭湖区不同利用方式下农田土壤有机碳含量特征

土地利用方式是影响土壤有机碳含量和动态的重要因子之一。其利用方式的改变必将引起土壤有机碳含量发生相应的变化。在洞庭湖腹地选取典型样区,通过调查走访和密集取样,分析了不同土地利用方式（旱地、水旱轮作地、一季稻水田和双季稻水田）下623个农田耕层土样有机碳含量。结果表明,研究区内土壤有机碳含量高低顺序为双季稻水田（28.12 g/kg） > 一季稻水田（27.03 g/kg） > 水旱轮作地（24.79 g/kg） > 旱地（17.96 g/kg）,其差异均达到极显著水平（P < 0.01）。土地生产力、秸秆还田量和土壤水文状态是导致不同利用方式下耕层土壤有机碳含量差异的主要原因。进一步分析表明：加强作物秸秆还田（土）、提高土地复种指数、增加地表覆盖是维持和提高洞庭湖区耕作土壤有机碳含量的可行措施,尤其是旱作土壤。     

鄱阳湖湿地细菌群落多样性和可培养细菌功能基因丰度

鄱阳湖湿地中蕴含着丰富的微生物资源,参与并维持着湿地生态系统的物质循环和稳定.为探明鄱阳湖湿地土壤细菌分布规律,分析可培养细菌群落结构和功能特征,采用Illumina MiSeq高通量测序技术分析了鄱阳湖湿地土壤全部细菌（AW）和湿地外围周边土壤全部细菌（AS）群落结构多样性特征;又结合可培养方法和qPCR功能基因芯片技术,分析了湿地可培养细菌（CW）和湿地外围周边土壤可培养细菌（CS）功能基因丰度差异.研究发现鄱阳湖湿地土壤细菌多样性低于湿地外围周边土壤细菌,变形菌门和酸杆菌门是两种土壤环境中共有的细菌优势菌门,放线菌门、厚壁菌门、绿弯菌门和热脱硫杆菌门也是存在于两种土壤中的主要菌门,但相对丰度均差异显著;网络分析显示湿地土壤较周边土壤细菌网络结构更简单.采用可培养方法获得的不同采样点土壤细菌群落多样性差异不明显.但湿地土壤可培养细菌功能基因的拷贝数高于周边土壤,其中参与氮循环的UreC基因和参与碳固定的acsE基因在两种土壤可培养细菌中相对丰度较高.研究结果可为深入挖掘调控、维持湿地土壤养分循环和促进鄱阳湖湿地生态系统功能稳定性的潜在有益微生物提供理论依据.     

农药施用对兴凯湖水中农药残留的影响及其风险评价

主要研究了中国东北兴凯湖周围水稻种植区域农药残留以及对湖水水质的影响.分别于水稻抽穗期和水稻成熟期在兴凯湖地区对稻田、排干和大小兴凯湖地表水进行采集并对27种农药及其降解产物进行分析.结果表明,在水稻抽穗期所分析的27种农药及其降解产物全部检出,总浓度范围为247.97~6 094.49 ng·L^-1;水稻成熟期检出25种,总浓度范围为485.36~796.23 ng·L^-1,两时期对比,抽穗期的农药及其降解产物检出数量更多且总浓度更高;在水稻抽穗期阿特拉津、西草净和多效唑为主要检出农药,在水稻成熟期阿特拉津和稻瘟灵为主要检出农药;大小兴凯湖水中农药的分布特征与排干水相似,表明大小兴凯湖水中农药及其降解产物很可能来源于稻田排干的输入;在排干和大小兴凯湖水中,阿特拉津、西草净、扑草净、丁草胺、稻瘟灵和草酮RQ均值大于0.1,对水生生物存在潜在生态风险.     

黄土高原半干旱区侧柏林群落土壤呼吸日变化及季节动态变化特征

Desertification is one of the most serious eco-environmental problems around Qinghai Lake. Based on GIS and RS, GIS analysis was used to obtain the information of spatial-temporal change and different pattern of desertification in each county around Qinghai Lake in four phases of 1976, 1987, 1995 and 2006. Using the function of intersect and overlay supported by ARCGIS, with analyzing attributes of vector RS images, we obtained transition data and established transfer matrix of land-use types in recent 30 years, besides, protracted land-use transition maps of all desertification classes. In further, we analyzed transition quantity and direction of desertification in each county, moreover, reconstructed the process of spatial-temporal evolution of desertification. The results are showed below: (1) it′s the transition between desertified land and others but not in the desertified that played the main role in Gangcha and Gonghe County, while the transfer between desertified lands were greater than that with other land-use types in Haiyan County. (2) Extremely severe desertified land mainly distributes in Ketu and Sand Islands that belongs to Haiyan County, and moderate desertified land in riversides of Gangcha County, also, the light desertification expanded too rapidly to become the most desertified land-use type in the surrounding of Qinghai Lake since 1976. (3) By using relative change rate of desertified land, the regional variability rank from top to toe was Gangcha, Haiyan and Gonghe.     

传统文化与现代旅游景观的契合 ——重庆龙水湖规划与设计

文章通过对重庆龙水湖景区规划设计构思的分析，阐述了传统文化与现代旅游景观融合的规划设计手法。     

湿地生态脆弱性评价及其恢复与重建研究——以洞庭湖区为例

洞庭湖区是中国具有代表性和典型意义的湿地分布区，又是中国重要的商品粮基地，其良好的生态环境质量是实现区域经济社会持续发展的重要保证。在分析洞庭湖区湿地主要生态环境问题的基础上，建立了湿地生态脆弱性评价的相关指标体系，对洞庭湖区17个县市进行了湿地生态脆弱度的量化评价，最后有针对性地提出了洞庭湖区湿地生态恢复与重建的基本对策。     

Distribution of Redox-sensitive Elements in Bottom Waters,Porewaters and Sediments of Rogoznica Lake (Croatia) in Both Oxic and Anoxic Conditions

Geochemical, mineralogical and sedimentological analyses were carried out to contrast two different sites (respectively characterized by permanently oxic and anoxic conditions) in a small, meromictic, seawater lake. In fact, due to relatively high organic matter content, and reduced water exchange, the Rogoznica Lake has almost permanent anoxic conditions below the depth of 12 m, where sediment can be considered an anoxic–sulphidic sedimentary environment. Different water column and sediments redox conditions affect the distribution and speciation of major redox-sensitive metals (Fe, Mn, Mo), reduced sulphur species (RSS) and dissolved organic C (DOC). Trace metals, especially those that accumulate in anoxic–sulphidic environments (Fe, Mo) showed a marked enrichment in the solid phase, whereas the low solubility of sulphides leads to low porewater concentrations. The relatively high sedimentary enrichment of Mo (up to 81 mg/kg) also confirms highly anoxic conditions within the Rogoznica Lake sediments. Results clearly show that chemical species within the sediments will tend towards equilibrium between porewater and solid phase according the prevailing environment conditions such as redox, pH, salinity, DOC.     

太湖流域水资源综合规划数模研究--水质模型的建立与率定

太湖流域水资源综合规划数模研究建立了一个覆盖全流域的，包括污染负荷模型、河网湖泊相耦合的水量水质模型的数字流域系统。该系统实现了流域的耦合求解，已在流域水资源规划中发挥了重要的作用。本文建宴了流域水质模型，并在水量模型率定的基础上进行了水质模型的率定。率定结果与实测数据吻合较好，相对误差多在30％之内，一定程度上反映了太湖流域的水质概况。     

奥运湖不同补水方案营养状态趋势分析

规划中的奥运湖位于奥林匹克公园，运行中将采用人工补水方式。为保证未来奥运湖的正常景观生态功能，源水经奥运公园中人工湿地处理后再引入湖中。在规划设计阶段，提出三种补水方案：（1）清河再生水厂出水经湿地处理后入湖，（2）清河河水经湿地处理后入湖，（3）经湿地处理达地表水环境标准中的Ⅳ类水要求后入湖。为了考察不同水源补水方案对奥运湖的水质保障程度，本研究对不同方案下奥运公园湖水质的时间、空间变化过程予以模拟，分析评价奥运湖可能出现的富营养化状况，并对各个补水方案带来的富营养化风险和基本成因予以推断，为奥运湖的设计运行提供技术依据。结果表明，方案3的富营养化风险最小、水质保障程度最高。结合三种补水方案水质特点，推断磷是主要限制因子，所以控制磷的浓度是保证水质安全、降低富营养化风险的重要途径。     

MULTITEMPORAL SCALE HYDROGRAPH PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

Abstract: An artificial neural network (ANN) provides a mathematically flexible structure to identify complex nonlinear relationship between inputs and outputs. A multilayer perceptron ANN technique with an error back propagation algorithm was applied to a multitime-scale prediction of the stage of a hydro-logically closed lake, Devils Lake (DL), and discharge of the Red River of the North at Grand Forks station (RR-GF) in North Dakota. The modeling exercise used 1 year (2002), 5 years (1998–2002), and 27 years (1975–2002) of data for the daily, weekly, and monthly predictions, respectively. The hydrometeorological data (precipitations P_(t), P_(t-1), P_(t-2), P_(t-3), antecedent runoff/lake stage R_(t-1) and air temperature T_(t) were partitioned for training and for testing to predict the current hydro-graph at the selected DL and RR-GF stations. Performance of ANN was evaluated using three combinations of daily datasets (Input I = P_(t)), P_(t-l), P_(t-2), P_(t-3), T_(t) and R_(t-l); Input II = Input-l less P_(t) P_(t-l), P_(t-2), P_(t-3); and Input III = Input-II less T_(t)). Comparison of the model output using Input I data with the observed values showed average testing prediction efficiency (E) of 86 percent for DL basin and 46 percent for RR-GF basin, and higher efficiency for the daily than monthly simulations.