太湖流域典型河流沉积物的反硝化作用

用15N同位素配对法对太湖流域江苏和上海境内主要河流沉积物的反硝化作用及其影响因素进行研究.结果表明,河流沉积物反硝化作用具有显著空间差异.25℃时,河流总反硝化速率Dtot在0～4.03mmol.m-.2d-1之间;10℃时,Dtot在0～2.27mmol.m-.2d-1之间,平均值分别为0.79mmol.m-.2d-1和0.26mmol.m-.2d-1.相关性分析显示,沉积物的反硝化作用与沉积物氮含量和实验过程中的耗氧速率(SOD)呈显著相关关系(p<0.01).以总反硝化速率大小为依据将沉积物分成4组后的相关分析表明,反硝化速率与原位上覆水中的NO3--N含量之间亦存在显著的相关关系(p<0.05),预示着反硝化作用对水体氮负荷起到的氮汇作用.     

水质遥感研究进展

本文简述了遥感技术在水质监测中的意义,总结了可用于遥感监测的水质指标,同时对清澈水体、一类水体、二类水体及污染水体的光谱特征分别给予了介绍。着重讨论了水质遥感的基本方法,并在此基础上从营养盐、有机污染物和营养状态指数三项水质指标出发,对其研究现状进行了阐述,最后进一步提出了水质遥感领域中存在的主要问题,并展望了其未来的发展方向。     

海水痕量营养盐和金属的分子光谱分析方法研究进展

海洋现场观测是获取海洋环境研究数据的重要途径.鉴于海洋的现场环境严酷复杂,以及采集、储存和运输海水样品的困难,海洋化学参数的现场和原位检测一直为海洋学家所追求,成为海水分析技术的研究热点.通过综述近年来海水痕量营养盐和重要痕量金属的分子光谱分析方法的研究进展,从灵敏度和测定范围等方面,总结痕量营养盐（活性磷、亚硝氮、硝氮、铵态氮）和痕量金属（铁、锰、铜、铝）的分析技术,侧重于现场和原位测定方法,聚焦于分光光度法、荧光法和化学发光法等适用于海洋现场的光学检测方法的研究情况,同时展示这些方法及相应仪器在岸基实验室、船基实验室、海洋走航分析以及原位长期观测中的应用实例;并对现存的关键问题及可能的解决方案进行探讨,对本领域今后的发展方向做出展望.     

河道型水库支流库湾营养盐动态补给过程

河道型水库支流库湾营养盐季节变化直接决定支流藻类群落结构的演替及初级生产力变化过程.为分析三峡水库支流香溪河库湾主要营养盐来源对营养盐季节性变化的影响,基于2010~2011年香溪河库湾野外监测数据,选取干、支流差异显著(P0.01)的常量离子Cl-和Na+作为水团混合示踪离子,采用二元线性混合模型计算不同时期营养盐各来源的贡献率.结果表明,在枯水运行期(11月~次年2月)和汛后蓄水期(9~10月)时,长江干流对支流库湾营养盐贡献率超过75%,干流倒灌为库湾营养盐主要来源;在汛前泄水期(3~5月)和汛期(6~8月)时,干流倒灌和上游来水的贡献率差异缩小,库湾营养盐来源需考虑两者共同的影响,但干流倒灌仍占据优势.上游来水磷营养浓度相对较高,而长江干流氮、硅营养盐浓度较高,不同时期香溪河库湾分层异重流模式不同,库湾营养盐各来源的贡献率也随之改变,因此库湾营养盐在水库不同运行时期呈现出规律性变化.     

Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

Statistical Models to Predict and Assess Spatial and Temporal Low‐Flow Variability in New England Rivers and Streams

In the northern hemisphere, summer low flows are a key attribute defining both quantity and quality of aquatic habitat. I developed one set of models for New England streams/rivers predicting July/August median flows averaged across 1985–2015 as a function of weather, slope, % imperviousness, watershed storage, glacial geology, and soils. These models performed better than most United States Geological Survey models for summer flows developed at a statewide scale. I developed a second set of models predicting interannual differences in summer flows as a function of differences in air temperature, precipitation, the North Atlantic Oscillation (NAO) index, and lagged NAO. Use of difference equations eliminated the need for transformations and accounted for serial autocorrelations at lag 1. The models were used in sequence to estimate time series for monthly low flows and for two derived flow metrics (tenth percentile [Q10] and minimum 3‐in‐5 year average flows). The first metric is commonly used in assessing risk to low‐flow conditions over time, while the second has been correlated with increased probability of localized extinctions for brook trout. The flow metrics showed increasing trends across most of New England for 1985–2015. However, application of summer flow models with average and extreme climate projections to the Taunton River, Massachusetts, a sensitive watershed undergoing rapid development, projected that low‐flow metrics will decrease over the next 50 years.     

Implications of Upstream Flow Availability for Watershed Surface Water Supply across the Conterminous United States

Although it is well established that the availability of upstream flow (AUF) affects downstream water supply, its significance has not been rigorously categorized and quantified at fine resolutions. This study aims to fill this gap by providing a nationwide inventory of AUF and local water resource, and assessing their roles in securing water supply across the 2,099 8‐digit hydrologic unit code watersheds in the conterminous United States (CONUS). We investigated the effects of river hydraulic connectivity, climate variability, and water withdrawal, and consumption on water availability and water stress (ratio of demand to supply) in the past three decades (i.e., 1981–2010). The results show that 12% of the CONUS land relied on AUF for adequate freshwater supply, while local water alone was sufficient to meet the demand in another 74% of the area. The remaining 14% highly stressed area was mostly found in headwater areas or watersheds that were isolated from other basins, where stress levels were more sensitive to climate variability. Although the constantly changing water demand was the primary cause of escalating/diminishing stress, AUF variation could be an important driver in the arid south and southwest. This research contributes to better understanding of the significance of upstream–downstream water nexus in regional water availability, and this becomes more crucial under a changing climate and with intensified human activities.     

Activity of Beavers as an Ecological Factor Affecting the Zooplankton of Small Rivers

The role of beavers in the transformation of small rivers is elucidated by analyzing the taxonomic and trophic structure of zooplankton in ponds differing in age, water flow rate, and the degree of overgrowing with higher aquatic plants. The building activity of beavers in the river system promotes the formation of biotopes of the ecotone type, in which the planktonic invertebrate community develops some features similar to or different from those of zooplanktonic communities from lakes and rivers undergoing anthropogenic eutrophication. The main distinctive feature of response to this activity in zooplankton is that its development reaches a plateau at the early and middle stages of succession, compared to that under conditions of anthropogenic eutrophication. For small rivers, the concept of zoogenic eutrophication (in addition to anthropogenic) is proposed.     

Analysis of Daily Peaking and Run‐of‐River Operations with Flow Variability Metrics,Considering Subdaily to Seasonal Time Scales

Environmental flows are an important consideration in licensing hydropower projects as operational flow releases can result in adverse conditions for downstream ecological communities. Flow variability assessments have typically focused on pre‐ and post‐dam conditions using metrics based on daily averaged flow values. This study used subdaily and daily flow data to assess environmental flow response to changes in hydropower operations from daily peaking to run‐of‐river. An analysis tool was developed to quantify flow variability metrics and was applied to four hydropower projects. Significant differences were observed between operations at the 99% confidence level in the median flow values using hourly averaged flow datasets. Median daily rise and fall rates decreased on average 34.5 and 27.9%, respectively, whereas median hourly rise and fall rates decreased on average 50.1 and 50.6%, respectively. Differences in operational flow regimes were more pronounced in the hourly averaged flow datasets and less pronounced or nonexistent in the daily averaged flow datasets. These outcomes have implications for the development of ecology‐flow relationships that quantify effects of flow on processes such as fish stranding and displacement, along with habitat stability. Results indicate that flow variability statistics should be quantified using subdaily datasets to accurately represent the nature of hydropower operations, especially for daily peaking facilities.     

Nitrogen Removal by Stormwater Management Structures: A Data Synthesis

A comprehensive synthesis of data from empirically based published studies and a widely used stormwater best management practice (BMP) database were used to assess the variability in nitrogen (N) removal performance of urban stormwater ponds, wetlands, and swales and to identify factors that may explain this variability. While the data suggest that BMPs were generally effective on average, removal efficiencies of ammonium (NH₄), nitrate (NO₃), and total nitrogen (TN) were highly variable ranging from negative (i.e., BMPs acting as sources of N) to 100%. For example, removal of NO₃ varied from (median ±1 SD) ?15 ± 49% for dry ponds, 32 ± 120% for wet ponds, 58 ± 210% for wetlands, and 37 ± 29% for swales. Across the same BMP types, TN removal was 27 ± 24%, 40 ± 31%, 61 ± 30%, and 50 ± 29%. NH₄ removal was 9 ± 36%, 29 ± 72%, 31 ± 24%, and 45 ± 34%. BMP size, age, and location explained some of the variability. For example, small and shallow ponds and wetlands were more effective than larger, deeper ones in removing N. Despite well‐known intra‐annual variation in N fluxes, most measurements have been made over short time periods using concentrations, not flow‐weighted N fluxes. Urban N export is increasing in some areas as large storms become more frequent. Thus, accounting for the full range of BMP performance under such conditions is crucial. A select number of long‐term flux‐based BMP studies that rigorously measure rainfall, hydrology, and site conditions could improve BMP implementation.