太湖流域上游平原河网污染物综合衰减系数的测定

改善太湖水质需要削减上游河流进入太湖的污染物总量.为了探求太湖流域上游平原河网的自净能力,开展原位实验测定了枯水期高锰酸盐指数、氨氮(NH_4~+-N)、总氮(TN)和总磷(TP)的综合衰减系数,根据河道的水力特征对综合衰减系数进行了修正,并利用一维稳态水质模型对修正前后综合衰减系数的可靠性进行了验证.结果表明,高锰酸盐指数、NH_4~+-N、TN和TP的综合衰减系数分别为:0.0296~0.4106、0.0224~0.3564、0.0137~0.3046和0.0555~0.5725 d~(-1).可靠性验证表明高锰酸盐指数、NH_4~+-N、TN和TP综合衰减系数修正前的平均相对误差分别为8.39%、14.40%、11.43%和19.22%,修正后的平均相对误差分别为10.65%、14.34%、11.37%和19.24%.修正前后高锰酸盐指数、NH_4~+-N、TN和TP的平均相对误差均小于20%且变化不显著,表明综合衰减系数的测定结果能够为太湖流域上游平原河网的污染物总量控制管理提供科学参数;也表明枯水期的水力条件对综合衰减系数的影响较小.     

基于ANN的土壤重金属分布和污染评价研究

农田土壤重金属污染与备受关注的农产品安全问题有密切联系,因此对其进行研究意义重大。以江苏省南通市为研究区,利用采样点实测数据,借助神经网络模型(ANN)并结合3S技术对问题进行研究,从而对土壤重金属的空间动态分布进行描述,并对各个空间位点重金属的污染状况进行评价。结果表明,神经网络模型能够智能地学习各个样点的空间位置与该点各重金属含量之间的映射关系和预先设计好的分类评价模式,并能够稳健地对各个空间插值点处的重金属含量和各个位点的重金属污染状况进行预测和评价。结论显示,南通市大部分农田土壤重金属污染较轻,但也存在局部地区的严重污染。结论与实际情况相符,表明神经网络模型可以为农田土壤重金属的研究提供一个新的思路和方法。     

Status of the Riparian Ecosystem in the Upper San Pedro River, Arizona: Application of an Assessment Model

A portion of Arizona’s San Pedro River is managed as a National Riparian Conservation Area but is potentially affected by ground-water withdrawals beyond the conservation area borders. We applied an assessment model to the Conservation Area as a basis for monitoring long-term changes in riparian ecosystem condition resulting from changes in river water availability, and collected multi-year data on a subset of the most sensitive bioindicators. The assessment model is based on nine vegetation bioindicators that are sensitive to changes in surface water or ground water. Site index scores allow for placement into one of three condition classes, each reflecting particular ranges for site hydrology and vegetation structure. We collected the bioindicator data at 26 sites distributed among 14 reaches that had similar stream flow hydrology (spatial flow intermittency) and geomorphology (channel sinuosity, flood-plain width). Overall, 39% of the riparian corridor fell within condition class 3 (the wettest condition), 55% in condition class 2, and 6% in the driest condition class. Condition class 3 reaches have high cover of herbaceous wetland plants (e.g., Juncus and Schoenoplectus spp.) along the perennial stream channel and dense, multi-aged Populus-Salix woodlands in the flood plain, sustained by shallow ground water in the stream alluvium. In condition class 2, intermittent stream flows result in low cover of streamside wetland herbs, but Populus-Salix remain abundant in the flood plain. Perennial wetland plants are absent from condition class 1, reflecting highly intermittent stream flows; the flood plain is vegetated by Tamarixa small tree that tolerates the deep and fluctuating ground water levels that typify this reach type. Abundance of herbaceous wetland plants and growth rate of Salix gooddingii varied between years with different stream flow rates, indicating utility of these measures for tracking short-term responses to hydrologic change. Repeat measurement of all bioindicators will indicate long-term trends in hydro-vegetational condition.     

巢湖流域河流鱼类群落的时空分布

基于2013年4月和10月对巢湖流域66个河道样点的调查数据,初步研究了巢湖流域河流鱼类群落的时空变化特征。主要研究结果显示,鱼类多样性无显著性的水系间、生态分区间的变化,但随季节和河流级别显著变化:10月份的个体数显著高于4月份,2级河流的物种数和个体数均显著大于1级、3级和4级河流。鱼类群落结构的季节动态显著,随生态分区显著变化,但不受水系、河流级别的显著影响。在二级生态分区水平上,仅西南森林生态亚区的鱼类群落结构与其他5个生态亚区的显著差异;宽鳍鱲、吻虾虎鱼等在西南森林生态亚区具有更高多度,而鲫、鰐、鲤等物种在其他生态亚区多度更高。     

基于BP神经网络的鄱阳湖水位模拟

考虑到鄱阳湖水位受流域五河与长江来水等多因素的共同作用而表现出高度非线性响应,采用典型的三层BPNN神经网络模型来模拟鄱阳湖水位与其主控因子之间的响应关系。分别将湖口、星子、都昌、棠荫和康山水位作为目标变量进行BPNN模型构建和适用性评估。结果显示：综合考虑流域五河及长江来水（汉口或九江）的BPNN水位模型,空间站点水位模拟精度（R2和Ens）可达090以上,各站点的均方根误差（RMSE）变化范围约050～10 m,若忽略长江来水的影响作用,仅将流域五河来水作为湖泊水位的主控影响因子,模型训练期与测试期的纳希效率系数（Ens）和确定性系数（R2）显著降低,且低于050,均方根误差（RMSE）也明显增大（124～288 m）,意味着综合考虑流域五河与长江来水是获取结构合理、精度保证的鄱阳湖水位模型的重要前提。同时建议针对鄱阳湖湖盆变化对水位的影响,尽可能选择一致性较好的长序列数据集来训练和测试BPNN模型。所构建的BPNN神经网络模型可进一步结合流域水文模型,用来预测气候变化与人类活动下流域径流变化对湖泊水位的潜在影响,也可作为一种有效的模型工具来回答当前鄱阳湖一些备受关注的热点问题,如定量区分流域五河与长江来水对湖泊洪枯水位的贡献分量,为湖泊洪涝灾害的防治和对策制定提供科学依据     

Recent Changes in Stream Flashiness and Flooding,and Effects of Flood Management in North Carolina and Virginia

The southeastern United States has undergone anthropogenic changes in landscape structure, with the potential to increase (e.g., urbanization) and decrease (e.g., reservoir construction) stream flashiness and flooding. Assessment of the outcome of such change can provide insight into the efficacy of current strategies and policies to manage water resources. We (1) examined trends in precipitation, floods, and stream flashiness and (2) assessed the relative influence of land cover and flow‐regulating features (e.g., best management practices and artificial water bodies) on stream flashiness from 1991 to 2013. We found mean annual precipitation decreased, which coincided with decreasing trends in floods. In contrast, stream flashiness, overall, showed an increasing trend during the period of study. However, upon closer examination, 20 watersheds showed stable stream flashiness, whereas 5 increased and 6 decreased in flashiness. Urban watersheds were among those that increased or decreased in flashiness. Watersheds that increased in stream flashiness gained more urban cover, lost more forested cover and had fewer best management practices installed than urban watersheds that decreased in stream flashiness. We found best management practices are more effective than artificial water bodies in regulating flashy floods. Flashiness index is a valuable and straightforward metric to characterize changes in streamflow and help to assess the efficacy of management interventions.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

Effects of dredging on benthic diatom assemblages in a lowland stream

The objective of this study was to assess the effects of dredging on the structure and composition of diatom assemblages from a lowland stream and to investigate whether the response of diatom assemblages to the dredging is also influenced by different water quality. Three sampling sites were established in Rodríguez Stream (Argentina); physico-chemical variables and benthic diatom assemblages were sampled weekly in spring 2001. Species composition, cell density, diversity and evenness were estimated. Diatom tolerance to organic pollution and eutrophication were also analyzed. Differences in physico-chemical variables and changes in benthic diatom assemblages were compared between the pre- and post-dredging periods using a t-test. Data were analyzed using Principal Components Analysis (PCA), non-metric multidimensional scaling (MDS) ordination and cluster analysis. The effects of dredging in the stream involve two types of disturbances: (i) in the stream bed, by the removal and destabilization of the substrate and (ii) in the water column, by generating chemical changes and an alteration of the light environment of the stream. Suspended solids, soluble reactive phosphorus and dissolved inorganic nitrogen were significantly higher in post-dredging periods. Physical and chemical modifications in the habitat of benthic diatoms produced changes in the assemblage; diversity and species numbers showed an immediate increase after dredging, decreasing at the end of the study period. Changes in the tolerance of the diatom assemblage to organic pollution and eutrophication were also observed as a consequence of dredging; in the post-dredging period sensitive species were replaced by either tolerant or most tolerant species. These changes were particularly noticeable in site 1 (characterized by its lower amount of nutrients and organic matter previous to dredging), which showed an increase in the amount of nutrients and oxygen demand as a consequence of sediment removal. However, these changes were not so conspicuous in sites 2 and 3, which already presented a marked water quality deterioration before the execution of the dredging works.     

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.     

Assessment Tools for Urban Catchments: Defining Observable Biological Potential1

Abstract: Urbanization represents a strong and increasingly more prevalent impact on stream quality worldwide. One of the characteristic effects of increased urbanization is a consistent decline in biological stream condition. The characterization of this biological degradation with increasing urbanization presents a number of advantages for the study and management of urban streams and catchments. In this paper, the limitation of biological condition with urbanization, called observed biological potential, is characterized. Using an urban intensity index and a biological index developed specifically for urban systems in the Baltimore, Maryland; Cleveland, Ohio; and San Jose, California regions, two principal techniques were compared (quantile regression and bin regression) to define observed biological potential along urban gradients. Quantile regression was selected as the preferable tool for describing observed biological potential given the consistency with which it can be applied and its statistical efficiency, however, bin quantile regression performed similarly. Having identified a numeric approximation of observed biological potential, two methods for identifying factors related to distance from potential as a way of identifying critical environmental factors affecting biological condition in urban areas were explored. The results of this work can be used for identifying benchmarks for urban stream biological condition, identifying limiting catchment characteristics, and prioritizing urban stream management efforts.