Modeling Stream Channel Characteristics From Drainage‐Enforced DEMs in Puget Sound,Washington, USA1

Abstract: Mapping stream channels and their geomorphic attributes is an important step in many watershed research and management projects. Often insufficient field data exist to map hydromorphologic attributes across entire drainage basins, necessitating the application of hydrologic modeling tools to digital elevation models (DEMs) via a geographic information system (GIS). In this article, we demonstrate methods for deriving synthetic stream networks via GIS across large and diverse basins using drainage‐enforced DEMs, along with techniques for estimating channel widths and gradient on the reach scale. The two‐step drainage enforcement method we used produced synthetic stream networks that displayed a high degree of positional accuracy relative to the input streams. The accuracies of our estimated channel parameters were assessed with field data, and predictions of bankfull width, wetted width and gradient were strongly correlated with measured values (r² = 0.92, r² = 0.95, r² = 0.88, respectively). Classification accuracies of binned channel attributes were also high. Our methodology allows for the relatively rapid mapping of stream channels and associated morphological attributes across large geographic areas. Although initially developed to provide salmon recovery planners with important salmon habitat information, we suggest these methodologies are relevant to a variety of research and management questions.     

区域环评中大气环境容量估算的研究进展

综述了大气污染物区域环境容量的主要影响因素、用于其计算的空气质量模型以及大气环境容量的研究步骤。现阶段应用较广泛的大气环境容量的预测模型有A-P值法模型、箱模型、线性优化模型、ADMS模型及多种模型相结合的复合模式等，通过研究分析，提出了存在的主要问题及其有效的解决途径。     

达州市国内旅游市场回归预测模型研究

旅游市场预测可以帮助旅游地掌握市场需求的变化及其发展趋势，是旅游地制定发展战略和市场营销决策的基本依据。以达州市国内旅游市场为研究对象，主要从统计学的角度，建立达州市国内旅游市场的预测模型，并结合达州市旅游业的发展战略趋势，对达州市国内旅游市场进行预测，这对达州市旅游业的健康发展具有一定的指导意义。     

流域空间统计模型SPARROW及其研究进展

SPARROW(SPAtially Referenced Regressions On Watershed attributes流域属性基于空间的回归模型)是美国地质调查局(USGS)开发的经验统计和地表过程相结合的流域空间统计模型。模型通过对河流水质数据和流域属性建立空间回归实现污染负荷产生和迁移的定量化。模型的最大特色是其空间特性非常显著,可以将上游的营养盐污染源数据和下游的营养盐负荷数据联系起来,同时可以将河流中的水质监测数据或污染物通量数据和流域的空间属性特征(比如土地利用类型、河网、大气沉降等)联系起来。模型除了一般水质模型所具有的水质模拟和流域污染源的分析功能外,还可在模拟过程中对流域中每个污染源、流域属性和污染物迁移过程对水质监测结果的影响进行显著性检验。文章简要介绍了SPARROW模型的结构和原理、功能和应用发展前景。     

巷道内粉尘二次飞扬规律的数值模拟研究

针对巷道中沉积粉尘二次飞扬的现象,利用数值模拟的方法对粉尘二次飞扬进行了研究,分析了巷道内粉尘二次飞扬的极限风速与扬尘粒径之间的关系,从而为矿井通风除尘提供一定参考依据,给矿井工人创造良好的工作环境。     

湍流模型对预测街道峡谷污染物扩散的影响

文章选取标准k-ε湍流模型、RNGk-ε湍流模型、realizablek-ε湍流模型和单方程Spalart-Allmaras涡黏系数模型对街道峡谷附近的流动和汽车尾气污染物扩散进行了模拟,并与风洞试验值进行了比较,结果表明,4种模型对街谷壁面的浓度预测在趋势上与试验值基本一致,Spalart-Allmaras模型的预测效果最好,realizablek-ε模型预测最差,而标准k-ε模型和RNGk-ε模型的预测介于其间;RNGk-ε模型和realizablek-ε模型的修正作用在预测建筑物尖角和顶部附近的流动处有所体现,但对街谷内浓度分布的预测仍不如标准k-ε模型;本文从流场分布的特点对4种模型的浓度预测差别进行了解释,证明了壁面浓度与其附近的速度和湍流黏性系数的分布相对应。     

生活污水典型有机污染物与ASMs模型水质特性参数相关性研究

研究了生活污水中典型有机污染物糖类、蛋白质、油脂以及直链烷基苯磺酸钠（LAS）对生活污水中COD的贡献率,采用硝酸盐利用速率法（NUR）测定了活性污泥数学模型（ASMs）中的有机水质特性参数,分析了单一糖类、蛋白质、油脂、LAS对有机水质特性参数的影响,并给出了生活污水中这4种有机污染物与ASMs有机水质特性参数S_S、X_S、S_I、X_I的相关关系.结果表明,反硝化条件下异养菌产率系数为0.683;蛋白质、糖类、油脂和LAS分别占COD的24%～35%、 17%～35%、 5.78%～10.56%和 3.77%～7.23%,是污水中COD的主要化学组成成分;该污水中的快速生物降解物质占总COD的22%～29%,慢速可生物降解物质占29%～38%;生活污水中糖类、蛋白质、油脂、LAS这4种典型有机物的浓度与ASMs的水质特性参数S_S、X_S、S_I、X_I的相关性较好,相关系数>0.9.     

翡翠评估价的量化探索

由于翡翠评估本身具有现实性、市场性、预测性等特性,传统的评估方法过于依赖评估人员的经验,主观随意性大,难以保证评估结果的客观、科学、公正.针对翡翠价格与其质量因素之间的复杂、不确定关系的特性,我们提出利用特尔斐法来判定各权重之间关系,从而利用数学模型计算出各质量因素之间的权重,以减少由于评估人员主观因素造成评估结果的偏差,使评估人员运用此方法能够迅速、准确、公正的对翡翠估价前期的数据进行量化处理.     

小尺度范围内植被类型对土壤呼吸的影响

2005~2007年用红外气体分析仪在太原天龙山自然保护区对油松、草地和灌木地的土壤呼吸进行了测定.结果表明,3个样地的土壤呼吸具有明显的季节变化特点,夏秋季高、冬春季低.土壤呼吸的年(3~12月)平均值分别为(3.58±2.50)、(3.82±2.75)和(4.42±3.38)μmol.(m2.s)-1(p0.05).3~12月土壤CO2释放量在854.9~1 297.2 g.(m2.a)-1之间,年间和样地之间的土壤CO2释放量均无显著差异.3个样地土壤呼吸与土壤温度的指数方程均为极显著(p0.01),R2值在0.61~0.81之间;Q10值在2.60~4.50之间,R10值在1.70~3.02μmol.(m2.s)-1之间.3种植被条件下土壤呼吸与土壤水分的关系均不显著(p0.05),最大的R2值仅0.12;但是,用土壤温度10℃时的数据进行分析时,土壤呼吸与土壤水分的关系显著(p0.05).4个双变量复合关系方程的R2值大部分在0.7以上,最高达0.91.     

Debt for brands: tracking down a bias in financing photovoltaic projects in Germany

What kinds of PV project configurations do lenders prefer to finance? Recent developments in the field of renewable energy project finance have reinforced the need for investigation, as fundraising has become more challenging and project evaluation by banks more demanding. To contribute to the limited research in this field, we focus on photovoltaic projects and report from an Adaptive Choice-Based Conjoint experiment with German experts in project finance. We find a bias which we call “debt for brands”. Simulations reveal that debt investors prefer projects with premium brand technology (modules, inverters) to low-cost technology. Although we assumed that lenders prefer projects with the highest Debt Service Cover Ratio (DSCR), they favor projects with lower DSCR, as long as those projects include premium brand technology. We find that, if premium brands were engaged, lenders would also choose projects with higher risk. Our findings have implications for renewable energy project finance in practice and research.