漫湾大坝上下游沉积物重金属与营养元素分布特征及环境风险评价

研究了漫湾水电站大坝上下游11个采样断面的沉积物中的有机质(OM)、总氮(TN)、总磷(TP)和金属元素Al、As、Cd、Cr、Cu、Mn、Ni、Pb、Zn的含量及空间分布特征,利用地积累指数法、潜在生态风险指数法对沉积物重金属的环境风险进行了评价.结果表明,As、Cd、Cr、Cu、Mn、Ni、Pb、Zn的平均年含量分别为31.88、0.80、63.26、32.55、607.81、32.11、36.54、132.29 mg·kg-1,与云南省土壤背景值相比,重金属元素均出现一定程度的富集.其中,Cd和As比其他金属元素污染重,处于中-强度污染状态.在大坝上游干流中靠近大坝的断面环境风险最高,支流断面风险水平普遍低于附近的干流断面,大坝下游断面的风险值明显低于大坝上游断面.干流沉积物重金属蓄积明显受大坝建设影响,支流则受其上游区域人类活动和大坝建设的共同影响.营养元素在村庄聚集区和坝前地区含量较高,干流断面含量高于临近的支流断面,表明大坝建设和库区居民生产生活共同影响沉积物营养元素的分布.相关性分析与聚类分析表明,毒性较高的重金属元素Cd、As、Pb可以聚为一类,而且相互间呈显著正相关关系,并与OM呈正相关关系.虽然沉积物的有机污染在大部分地区呈现清洁或较清洁的水平,但是有机质可以吸附Cd和As,对沉积物的重金属污染具有增强效应.     

Sources and Transport of Phosphorus to Rivers in California and Adjacent States,U.S., as Determined by SPARROW Modeling

The SPARROW (SPAtially Referenced Regression on Watershed attributes) model was used to simulate annual phosphorus loads and concentrations in unmonitored stream reaches in California, U.S., and portions of Nevada and Oregon. The model was calibrated using de‐trended streamflow and phosphorus concentration data at 80 locations. The model explained 91% of the variability in loads and 51% of the variability in yields for a base year of 2002. Point sources, geological background, and cultivated land were significant sources. Variables used to explain delivery of phosphorus from land to water were precipitation and soil clay content. Aquatic loss of phosphorus was significant in streams of all sizes, with the greatest decay predicted in small‐ and intermediate‐sized streams. Geological sources, including volcanic rocks and shales, were the principal control on concentrations and loads in many regions. Some localized formations such as the Monterey shale of southern California are important sources of phosphorus and may contribute to elevated stream concentrations. Many of the larger point source facilities were located in downstream areas, near the ocean, and do not affect inland streams except for a few locations. Large areas of cultivated land result in phosphorus load increases, but do not necessarily increase the loads above those of geological background in some cases because of local hydrology, which limits the potential of phosphorus transport from land to streams.     

Spatiotemporal Associations of Reservoir Nutrient Characteristics and the Invasive,Harmful Alga Prymnesium parvum in West Texas

Golden alga (Prymnesium parvum) is a harmful alga that has caused ecological and economic harm in freshwater and marine systems worldwide. In inland systems of North America, toxic blooms have nearly eliminated fish populations in some systems. Modifying nutrient profiles through alterations to land or water use may be a viable alternative for golden alga control in reservoirs. The main objective of this study was to improve our understanding of the nutrient dynamics that influence golden alga bloom formation and toxicity in west Texas reservoirs. We examined eight sites in the Upper Colorado River basin, Texas: three impacted reservoirs that have experienced repeated golden alga blooms; two reference reservoirs where golden alga is present but nontoxic; and three confluence sites downstream of the impacted and reference sites. Total, inorganic, and organic nitrogen and phosphorus and their ratios were quantified monthly along with golden alga abundance and ichthyotoxicity between December 2010 and July 2011. Blooms persisted for several months at the impacted sites, which were characterized by high organic nitrogen and low inorganic nitrogen. At impacted sites, abundance was positively associated with inorganic phosphorus and bloom termination coincided with increases in inorganic nitrogen and decreases in inorganic phosphorus in late spring. Management of both inorganic and organic forms of nutrients may create conditions in reservoirs unfavorable to golden alga.     

Model‐Based Nitrogen and Phosphorus (Nutrient) Criteria for Large Temperate Rivers: 2. Criteria Derivation

Nitrogen and phosphorus criteria were developed for 233 km of the Yellowstone River, one of the first cases where a mechanistic model has been used to derive large river numeric nutrient criteria. A water quality model and a companion model which simulates lateral algal biomass across transects were used to simulate effects of increasing nutrients on five variables (dissolved oxygen, total organic carbon, total dissolved gas, pH, and benthic algal biomass in depths ≤1 m). Incremental increases in nutrients were evaluated relative to their impact on predefined thresholds for each variable; the first variable to exceed a threshold set the nutrient criteria. Simulations were made at a low flow, the 14Q5 (lowest average 14 consecutive day flow, July‐September, recurring one in five years), which was derived using benthic algae growth curves and EPA guidance on excursion frequency. An extant climate dataset with an annual recurrence was used, and tributary water quality and flows were coincident with the river's 10 lowest flow years. The river had different sensitivities to nutrients longitudinally, pH being the most sensitive variable in the upstream reach and algal biomass in the lower. Model‐based criteria for the Yellowstone River are as follows: between the Bighorn and Powder river confluences, 55 μg TP/l and 655 μg TN/l; from the Powder River confluence to Montana state line, 95 μg TP/l and 815 μg TN/l. Pros and cons of using steady‐state models to derive river nutrient criteria are discussed.     

Calibration and Verification of SWMM for Low Impact Development

The Storm Water Management Model was used to simulate runoff and nutrient export from a low impact development (LID) watershed and a watershed using traditional runoff controls. Predictions were compared to observed values. Uncalibrated simulations underpredicted weekly runoff volume and average peak flow rates from the multiple subcatchment LID watershed by over 80%; the single subcatchment traditional watershed had better predictions. Saturated hydraulic conductivity, Manning's n for swales, and initial soil moisture deficit were sensitive parameters. After calibration, prediction of total weekly runoff volume for the LID and traditional watersheds improved to within 12 and 5% of observed values, respectively. For the validation period, predicted total weekly runoff volumes for the LID and traditional watersheds were within 6 and 2% of observed values, respectively. Water quality simulation was less successful, Nash–Sutcliffe coefficients >0.5 for both calibration and validation periods were only achieved for prediction of total nitrogen export from the LID watershed. Simulation of a 100‐year, 24‐h storm resulted in a runoff coefficient of 0.46 for the LID watershed and 0.59 for the traditional watershed. Results suggest either calibration is needed to improve predictions for LID watersheds or expanded look‐up tables for Green–Ampt infiltration parameter values that account for compaction of urban soil and antecedent conditions are needed.     

Temporal and Spatial Trends in Nutrient and Sediment Loading to Lake Tahoe,California‐Nevada,USA

Since 1980, the Lake Tahoe Interagency Monitoring Program (LTIMP) has provided stream‐discharge and water quality data—nitrogen (N), phosphorus (P), and suspended sediment—at more than 20 stations in Lake Tahoe Basin streams. To characterize the temporal and spatial patterns in nutrient and sediment loading to the lake, and improve the usefulness of the program and the existing database, we have (1) identified and corrected for sources of bias in the water quality database; (2) generated synthetic datasets for sediments and nutrients, and resampled to compare the accuracy and precision of different load calculation models; (3) using the best models, recalculated total annual loads over the period of record; (4) regressed total loads against total annual and annual maximum daily discharge, and tested for time trends in the residuals; (5) compared loads for different forms of N and P; and (6) tested constituent loads against land use‐land cover (LULC) variables using multiple regression. The results show (1) N and P loads are dominated by organic N and particulate P; (2) there are significant long‐term downward trends in some constituent loads of some streams; and (3) anthropogenic impervious surface is the most important LULC variable influencing water quality in basin streams. Many of our recommendations for changes in water quality monitoring and load calculation methods have been adopted by the LTIMP.     

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base‐flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite‐nitrate (NO₂‐NO₃), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO₂‐NO₃ concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed‐use and heavily impacted watersheds.     

Emerging Tools for Continuous Nutrient Monitoring Networks: Sensors Advancing Science and Water Resources Protection

Sensors and enabling technologies are becoming increasingly important tools for water quality monitoring and associated water resource management decisions. In particular, nutrient sensors are of interest because of the well‐known adverse effects of nutrient enrichment on coastal hypoxia, harmful algal blooms, and impacts to human health. Accurate and timely information on nutrient concentrations and loads is integral to strategies designed to minimize risk to humans and manage the underlying drivers of water quality impairment. Using nitrate sensors as the primary example, we highlight the types of applications in freshwater and coastal environments that are likely to benefit from continuous, real‐time nutrient data. The concurrent emergence of new tools to integrate, manage, and share large datasets is critical to the successful use of nutrient sensors and has made it possible for the field of continuous monitoring to rapidly move forward. We highlight several near‐term opportunities for federal agencies, as well as the broader scientific and management community, that will help accelerate sensor development, build and leverage sites within a national network, and develop open data standards and data management protocols that are key to realizing the benefits of a large‐scale, integrated monitoring network. Investing in these opportunities will provide new information to guide management and policies designed to protect and restore our nation's water resources.     

Linking Nitrogen Export to Landscape Heterogeneity: The Role of Infrastructure and Storm Flows in a Mediterranean Urban System

Urban ecosystems are often sources of nonpoint source (NPS) nitrogen (N) pollution to aquatic ecosystems. However, N export from urban watersheds is highly variable. Examples of densely urbanized watersheds are not well studied, and these may have comparatively low export rates. Commonly used metrics of landscape heterogeneity may obscure our ability to discern relationships among landscape characteristics that can explain these lower export rates. We expected that differences not often captured by these metrics in the relative cover of vegetation, structures, and impervious surfaces would better explain observed variation in N export. We examined these relationships during storms in residential watersheds. Contrary to expectations, land cover did not directly predict variation in N or water export. Instead, N export was strongly linked to drainage infrastructure density. Our research highlights the role of fine‐scaled landscape attributes, mainly infrastructure, in explaining patterns of N export from densely urbanized watersheds. Changes to hydrologic flow paths by infrastructure explained more variation in N export than land cover. Our findings support further development of landscape ecological models of urban N export that focus on hydrologic modification by infrastructure rather than traditional landscape measures such as land use, as indicators for evaluating patterns of NPS nitrogen pollution in densely urbanized watersheds.     

苏南水库硅藻群落结构特征及其控制因素

为了解我国东南湿润区丘陵山地型水库硅藻的群落结构特征和控制因素,于2015年6月硅藻水华敏感期对苏南地区18座水库的浮游植物群落结构和水质进行调查,分析了营养盐、水深、库容等因素与硅藻及其它浮游生物的关系.结果表明,硅藻达到轻度水华水平(硅藻细胞含量介于100~1 000万cells·L~(-1))的水库有10座,对供水和景观功能产生明显影响;苏南地区水库普遍处于中营养和富营养水平,总氮浓度普遍偏高,磷及营养状态指数与硅藻生物量的关系密切;苏南地区水库中的浮游植物在数量上以蓝藻门中的席藻为主,在生物量上则以硅藻门、绿藻门和蓝藻门为主,其中硅藻门浮游植物平均占总浮游植物生物量的46.8%,是浮游植物异常增殖的主要门类;硅藻门中,主要是针杆藻、小环藻、曲壳藻和直链藻这4个种属占优,特别是针杆藻和小环藻,平均占硅藻总生物量的51.6%和21.4%;较深的水体,利于硅藻成为主要优势藻门;较大的水库流域库容比和较高总磷水平会导致水库营养水平和叶绿素浓度增加,促进浮游植物从硅藻门向绿藻门、蓝藻门演替,增加藻类危害的风险.因此,对于该地区水库,需要加强流域管理,并且针对水库自身的特点,包括水深、流域库容比等,确定其特定的富营养化控制策略,从而减少硅藻等藻类水华发生的风险,提升水源地水质安全保障能力.