中度污染水资源用于电厂循环冷却系统工艺研究

采用曝气生物滤池与高效混凝沉淀工艺对城市污水厂二级处理出水进行了2 m3/h规模的中试研究,并在此基础上对被污染的地表水进行了600m3/h规模的工程试验研究。研究表明,中度污染水资源通过这一工艺处理,可以满足电厂循环冷却水补给水的水质要求,并可以取得较高的处理效率。工程试验证实,此工艺运行稳定,系统易启动,抗冲击能力强,管理方便,具有推广应用的价值。     

维生素B1制药废水的铁炭微电解-混凝预处理工艺

针对维生素B1制药废水高有机物浓度、高悬浮物、色度高、难降解的特点,采用铁炭微电解-混凝工艺对其进行预处理,效果良好。铁炭微电解-混凝工艺的最佳运行条件为:进水pH值为4,铁炭比为1∶1,曝气量为0.2m3/h的情况下停留时间80min。出水CODCr浓度平均为1600mg/L,去除率约为79%,色度去除率为85%,出水达到了(GB8978-1996)二级排放标准。     

固定化生物活性炭纤维处理洗浴废水的研究

采用混凝-砂滤-固定化生物活性炭纤维的组合处理工艺来处理洗浴废水.利用聚合氯化铝和聚丙烯酰胺为混凝剂对废水进行混凝处理,之后将废水通入砂滤柱,废水在曝气池进行曝气后进入固定化生物活性炭纤维(IBACF)单元.IBACF固定化完成后,连续运行30 d,去除率稳定之后,处理后的浊度、LAS、S0D_(Mn)平均值分别为2.2NTU、0.12 mg·L~(-1)、2.33 mg·L~(-1),平均去除率分别为95.2%、94.7%、84.8%.经处理后的洗浴废水各项指标均可以达到生活饮用水卫生标准或城市供水水质标准,可以直接回用于洗浴用水和其他生活杂用水.     

环洱海地区气候变化特征研究

环洱海地区是云南省具有高原湖泊生态脆弱区、民族文化多元融合区和乡村经济发展活跃区等多重叠合特征的典型区域,是全球气候变化影响的敏感区和脆弱区。以环洱海地区1951~2014年6个基本站点的逐年平均气温、极端最高气温、极端最低气温、降水量、最大日降水量和日降水量≥0.1 mm日数资料为基础。采用线性倾向估计、Mann-Kendall趋势检验、Morlet小波分析和R/S分析等方法,研究了环洱海地区气候变化规律。结果发现:自1951年以来,环洱海地区年均气温和极端最低气温呈现出升高的趋势,而极端最高气温则呈现降低的趋势,变化速率分别为0.07℃/10 a、0.03℃/10 a和–0.14℃/10 a,对于年降水量、最大日降水量和降水日数而言,三者均为减少趋势,速率分别为–12.85 mm/10 a、–1.09mm/10 a和–1.73 d/10 a;环洱海地区年均气温、极端最高和极端最低气温均没有发生突变,年降水量和降水日数在2010年发生了一次减少突变,而最大日降水量则没有检测到突变的年份;环洱海地区年平均气温和年降水量在长时间尺度上的周期性变化最为显著,分别存在30 a和33 a左右的周期变化,并贯穿整个研究时段,而短时间尺度上的周期变化局域性特征突出;从未来演变趋势来看,年平均气温和极端最低气温将维持升温趋势,而极端最高气温则将持续降低趋势,年降水量继续减少的趋势未来将会逆转,但最大日降水量和降水日数两者将持续减少的概率更大。     

降水量时间序列变化的小波特征

利用小波变换对降水量时间序列的多时间尺度变化及突变特征进行了探讨。小波变换不仅能将降水量时间序列的频率特征在时间域上展现出来，清晰地给出各种时间尺度的强弱和分布情况以及早涝变化趋势和突变点，而且还能分析出其主要周期。以新安江流域黄山地区主汛期(5—7月)和年降水量为例，计算表明，其年际及年代际时间尺度在时域中分布不均匀，具有明显的局部化特征；同时分析出主汛期降水具有8年、19年左右的周期，年降水存在6年、19年左右的周期；研究还表明，主汛期降水与年降水的时间尺度变化比较接近。     

一种新的汛期降水集中期划分方法

汛期降水集中期是近期气象学者提出的表征汛期气候的一种新的特征量,它在气候研究中体现了较好的灵活性、客观性,通过对其分析,可为汛期气候的诊断和预测提供依据。但现在普遍使用的降水集中期在计算方法和时间长度上存在缺陷,特别是运用到时间跨度较长时段的气候分析时,特征量表征作用就有所缺失,而且计算方法较为复杂。为更好地使用降水集中期这一特征量,提出了以15天作为时长,用滑动统计来划定汛期降水集中期的新方法,并运用统计方法、天气气候学方法进行了论证,同时在长江下游主雨季降水集中期分析和金华地区汛期分析两个实例中进行了应用检验。结果表明,汛期降水集中期新方法划定的特征量与汛期降水总量存在时间上的相对独立性和总趋势上的显著相关性,且在汛期气候极端灾害事件上有较强的描述能力。因此认为,15天滑动统计新方法划定的汛期降水集中期使用便捷,天气气候意义明确,在实际应用中更为客观有效。     

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.     

Robustness of Meteorological Droughts in Dynamically Downscaled Climate Simulations

We examine the robustness of a suite of regional climate models (RCMs) in simulating meteorological droughts and associated metrics in present‐day climate (1971‐2003) over the conterminous United States (U.S.). The RCMs that are part of North American Regional Climate Change Assessment Program (NARCCAP) simulations are compared with multiple observations over the climatologically homogeneous regions of the U.S. The seasonal precipitation, climatology, drought attributes, and trends have been assessed. The reanalysis‐based multi‐model median RCM reasonably simulates observed statistical attributes of drought and the regional detail due to topographic forcing. However, models fail to simulate significant drying trend over the Southwest and West. Further, reanalysis‐based NARCCAP runs underestimate the observed drought frequency overall, with the exception of the Southwest; whereas they underestimate persistence in the drought‐affected areas over the Southwest and West‐North Central regions. However, global climate model‐driven NARCCAP ensembles tend to overestimate regional drought frequencies. Models exhibit considerable uncertainties while reproducing meteorological drought statistics, as evidenced by a general lack of agreement in the Hurst exponent, which in turn controls drought persistence. Water resources managers need to be aware of the limitations of current climate models, while regional climate modelers may want to fine‐tune their parameters to address impact‐relevant metrics.     

Impacts of Changes in Precipitation Amount and Distribution on Water Resources Studied Using a Model Rainwater Harvesting System

Water supply reliability is expected to be affected by both precipitation amount and distribution changes under recent and future climate change. We compare historical (1951‐2010) changes in annual‐mean and annual‐maximum daily precipitation in the global set of station observations from Global Historical Climatology Network and climate models from the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP), and develop the study to 2011‐2099 for model projections under high radiative forcing scenario (RCP8.5). We develop a simple rainwater harvesting system (RWHS) model and drive it with observational and modeled precipitation. We study the changes in mean and maximum precipitation along with changes in the reliability of the model RWHS as tools to assess the impact of changes in precipitation amount and distribution on reliability of precipitation‐fed water supplies. Results show faster increase in observed maximum precipitation (10.14% per K global warming) than mean precipitation (7.64% per K), and increased reliability of the model RWHS driven by observed precipitation by an average of 0.2% per decade. The ISI‐MIP models show even faster increase in maximum precipitation compared to mean precipitation. However, they imply decreases in mean reliability, for an average 0.15% per decade. Compared to observations, climate models underestimate the increasing trends in mean and maximum precipitation and show the opposite direction of change in reliability of a model water supply system.     

Testing and Improving Temperature Thresholds for Snow and Rain Prediction in the Western United States

The phase of precipitation at the land surface is critical to determine the timing and amount of water available for hydrological and ecological systems. However, there are few techniques to directly observe the precipitation phase and many prediction tools apply a single temperature threshold (e.g., 0°C) to determine phase. In this paper, we asked two questions: (1) what is the accuracy of default and station optimized daily temperature thresholds for predicting precipitation phase and (2) what are the regions and conditions in which typical temperature‐based precipitation phase predictions are most suited. We developed a ground truth dataset of rain vs. snow using an expert decision‐making system based on precipitation, snow depth, and snow water equivalent observations. This dataset was used to evaluate the accuracy of three temperature‐threshold‐based techniques of phase classification. Optimizing the temperature threshold improved the prediction of precipitation phase by 34% compared to using 0°C threshold. Developing a temperature threshold based on station elevation improved the error by 12% compared with using the 0°C temperature threshold. We also found the probability of snow as a function of temperature differed among ecoregions, which suggests a varied response to future climate change. These results highlight a current weakness in our ability to predict the effects of regional warming that could have uneven impacts on water and ecological resources.