洋河水库富营养化发展趋势及其关键影响因素

洋河水库作为秦皇岛和北戴河暑期办公的重要水源地,近年来富营养化逐年加重,水质恶化.通过收集历史资料和现场连续监测,综合分析了洋河水库富营养化发展趋势及其关键影响因素,结果表明1990-2011年期间,总氮、总磷常年超过国际公认的发生富营养化的浓度水平,每年8月是水华集中暴发期.以8月为例,自1990年以来,水体总氮、总磷和叶绿素a浓度逐年上升,透明度则呈明显下降趋势,富营养化状态从1990年的中营养状态变成2011年的超富营养状态.对2011年5-10月监测数据进行相关关系分析发现水温、透明度、溶解氧、pH值、总磷、正磷酸盐、高锰酸盐指数和总有机碳都与叶绿素a浓度明显相关,其中透明度、溶解氧、pH值是水华暴发的结果而非原因,表明透明度、溶解氧和pH值是水华暴发影响水质的主要表现因素;既然洋河水库地处北温带,四季分明,冬季存在2-3个月的冰封期,因此水温是水库水华暴发的驱动因子之一.2011年7月中旬至8月底洋河水库暴发了全库水域的蓝藻水华,优势种为铜绿微囊藻(Microystis aeruginosa)和鱼害微囊藻(M.ichthyoblabe),密度分别达到3.5×106和1.4×106个/mL.     

在不同蓄水位下三峡库区春季水华特征及趋势分析

基于2004—2015年三峡库区支流春季水华的监测数据,开展了三峡库区不同蓄水位下春季水华特征及趋势分析。结果表明:总共有26条支流发生春季水华,香溪河为水华发生概率最高的支流。水华藻类优势种主要是硅藻和甲藻,甲藻水华出现的概率随着蓄水位的增加而增加,但是在持续几年175 m蓄水之后,甲藻水华出现的频率下降,硅藻水华出现的频率增加。在175 m蓄水之前,水华发生期藻类密度、叶绿素a含量及水体营养盐水平相对较低,此后都有了较高的提升,但是在持续几年175 m蓄水之后,藻类密度、叶绿素a浓度以及营养盐浓度都有下降趋势。对藻密度及叶绿素a含量与主要环境因子进行主成分分析(Principal components analysis)发现,藻密度和叶绿素a与总磷、总氮、高锰酸盐指数聚类一簇,藻密度、叶绿素a浓度与总磷、溶解氧、高锰酸盐指数显著正相关。在蓄水之后,三峡库区支流营养水平有所增加,随着蓄水位的增加,三峡库区支流断面的平均流速降低,在合适的温度下,三峡库区支流水华易发生。     

三峡水库香溪河库湾夏季水华调查

以三峡水库支流香溪河为研究对象,以2011年夏季的采样数据为基础,比较分析了水华前后支流库湾营养盐和叶绿素a的时空分布规律及其相关性。结果表明,2011年6月21—29日,香溪河暴发了显著的藻类水华,叶绿素a峰值达到125.8 mg·m-3。相关分析表明,水华过程显著影响可溶性无机N、P的空间分布,营养盐空间分布从中游高、河口低,转变为从河口至上游递减,并且在水华区域锐减;长江干流较高的营养盐本底值增大了支流库湾水华的风险;高营养盐背景值的长江干流水体在香溪河库湾的潜出位置是水华最严重的区域;干流水体对库湾表层的营养盐补给作用是影响库湾水华生消的关键因素。     

引黄水库藻类变化特征及其影响因素研究

以济南鹊山引黄水库为研究对象,系统研究了2008-2012 年间其水中浮游藻类的种类和数量变化特征以及藻类细胞密度与总氮、总磷浓度的相关性,并在实验室模拟条件下分析了温度、光照、氮磷营养盐等条件对其藻类生长影响。结果表明,该水库水藻密度年际、年内变化较大,年平均值均在500 万个/L 以上,藻种共7 个门,37 个属,蓝藻和硅藻为水库的优势藻种。该水库藻类生长的最佳温度为25℃,光强为3 000 lx,氮磷营养盐对藻类生长影响排序为硝酸盐>磷酸盐>亚硝酸盐>氨氮。磷可能是藻生长的限制性因子,在夏、秋季及水库磷浓度大幅变化时易发生水华。     

基于ETM+影像的千岛湖叶绿素a浓度卫星遥感反演研究

以千岛湖为研究对象,利用Landsat 7 ETM+遥感影像与野外实测数据,建立叶绿素a浓度的遥感定量反演模型。将叶绿素a浓度与波段反射率组合进行Pearson相关性分析,选择(B4+B2)/B3波段组合构建叶绿素a反演模型,并得到千岛湖叶绿素a浓度的时空分布。结果表明:(1)2007年千岛湖叶绿素a浓度低于4μg/L的水体面积占水体总面积达到99%以上,整体水质优良;(2)千岛湖叶绿素a浓度随季节变化特征明显,夏季容易出现局地高值,秋季平均浓度整体升高;(3)通过反演不同时期的叶绿素a浓度分布,可以刻画出千岛湖藻类的消长过程,从空间上发现易爆发富营养化的区域。该反演模型能较为精确地估算千岛湖的叶绿素a浓度,对今后水体富营养化的监测和预警具有重要意义。     

巢湖夏、秋季浮游植物叶绿素a及蓝藻水华影响因素分析

2007年6-11月份,对设置在巢湖全湖的23个样点水体的理化指标水温(WT)、pH值、溶解氧(DO)、总氮(TN)、总磷(TP)、活性磷(RP)以及浮游植物的种类组成和叶绿素a(Chl-a)浓度进行了调查分析。结果表明:在巢湖夏秋季温差变化不大的环境中,温度依然是影响藻类生物量的重要因素。夏、秋季蓝藻为最主要的藻类类群(其平均值占藻类总生物量的63.36%);藻类生物量与所测理化因子均有显著正相关。在夏、秋季各月份,蓝藻生物量呈前高后低状M型波动,其中7月份湖水中蓝藻浓度最低。夏、秋季湖水中叶绿素的浓度没有太大变化,维持在一个较高的水平(>10mg/m3),遇到合适的气象条件有形成大面积水华的可能。     

钱塘江干流杭州段水体叶绿素a浓度及与环境因子的关系

2006年1月至2007年12月,对钱塘江干流杭州段水体的叶绿素a时空分布及其与环境因子的关系进行研究.结果表明钱塘江干流杭州段的叶绿素a浓度时间差异显著,空间差异不显著.叶绿素a浓度呈现夏秋季节高、冬春季节低的规律.叶绿素a浓度与温度呈显著正相关,叶绿素a与透明度在不同范围内表现出不同的相关关系,叶绿素a与TN、TP之间的相关关系在不同江段有所差异.钱塘江干流杭州段总氮和总磷浓度均很高,足够满足藻类生长需要;氮磷比较低,基本在8～30之间,说明氮磷含量可能不是钱塘江藻类生长的限制因子.     

香溪河库湾春季水华期间可溶性碳动力学研究

2005年2月23日—4月28日,对三峡水库香溪河库湾内一样点进行每天采样,监测水体中叶绿素a与可溶性碳(DOC)浓度的变化,研究在春季水华暴发期间DOC的动力学特性。监测结果表明,随着时间的推移,叶绿素a有逐渐升高的趋势。其间共暴发了两次水华,其中第一次历时较短,第二次历时较长。DOC的变化趋势与叶绿素a基本吻合。整个暴发过程中的叶绿素a与DOC的回归分析表明,二者具有较好的相关性(R2=0.62),但第一次暴发过程中的相关性很高(R2=0.72),而第二次暴发过程中的相关性较低(R2=0.30)。根据天然水体中DOC来源的主要途径,推测在第一次暴发过程中DOC的来源主要是水体中藻类的光合作用的代谢产物,而第二次暴发过程中DOC的来源主要是水体中藻类死亡腐烂而产生的有机物质。     

三峡水库香溪河库湾夏季藻类水华的时空动态及其影响因素

2008年6-8月,三峡水库香溪河库湾相继暴发蓝藻和绿藻水华.依据香溪河库湾夏季的每周监测,对研究区2次水华分别进行聚类和判别分析,研究了2次水华的时空动态及其影响因素.结果表明：研究区2次水华过程均可划分为无水华组、过渡组和水华组;2次水华的暴发对可溶性硅(DSi)、硝态氮与亚硝态氮(NO₃--N+NO₂^--N)和磷酸盐(PO₄^3--P)3种营养盐的吸收程度不同;蓝藻水华暴发期间的DSi、总氮/总磷(TN/TP)、DSi/TN和DSi/TP值均低于绿藻水华;判别蓝藻水华暴发的参数为叶绿素a(Chl a)、TN和PO₄^3--P,而Chl a和DSi则是绿藻水华暴发的判别因子,将2次水华过程划分为水华组和无水华组的判别效果更好;判断蓝藻和绿藻水华暴发的叶绿素a临界浓度分别为40和20 μg·L^-1.     

香溪河水系附石藻类的时空动态

香溪河系长江三峡水库湖北库区内第一大支流.对香溪河2005年7月—2006年6月干流及主要支流上12个样点的附石藻类进行调查,共观察到藻类218种,其中硅藻183种、绿藻24种、蓝藻10种、黄藻1种,硅藻门的线形曲壳藻为绝对优势种.其物种丰富度和Shannon-Wiener多样性指数时空动态差异显著（或接近显著）,总平均值分别为32和154.附石藻类密度和叶绿素a含量年总平均值分别为8.75×10.9 cells·m^-2和14.62 mg·m^-2.不同样点的藻类密度和叶绿素a含量差异显著,其中古夫河支流最高,九冲河支流最低,两者相差一个数量级; 不同季节附石藻类密度和叶绿素a含量差异不显著(P>0.05),但均表现出冬春季高、夏秋季低的趋势.附石藻类密度及叶绿素a含量与海拔及水流流速呈显著负相关,而与总氮呈正相关.     

太湖水华程度及其生态环境因子的时空分布特征

湖泊水华是全世界面临的严重生态环境问题之一,对人类和生态系统健康都有重大影响。由于湖泊水华受流域面源、点源污染、气候、水文因子以及湖泊生态系统自身特征等众多因素影响,水华是否爆发、其严重程度及时空分布特征呈现明显的复杂性。以我国太湖为研究区域,基于近年的水华及水环境的监测数据,用自组织特征映射神经网络对太湖不同监测点的水华程度进行了自动聚类分析。结果表明,太湖水华程度呈现为明显的无水华、轻度、中度和重度水华4类。不同程度水华的叶绿素a、水温、COD_(Mn)、营养盐、浮游植物生物量以及藻种(蓝藻、绿藻、硅藻)结构的时空差异显著,不同变量间的关系复杂,有助于深入认识太湖近年水华发生的时空变异特性。     

蓝藻水华预报模型及基于遗传算法的参数优化

蓝藻水华预报是应对水危机,保障水资源供给的一项重要工作。以太湖北部三湾(竺山湖、梅梁湾、贡湖)为研究对象,采用动态空间环境建模技术,构建了蓝藻水华预报模型,并通过实地观测建立了模拟的初始参数集。利用2008年04-09月太湖水环境、气象等实测数据,采用遗传算法优化叶绿素a浓度预报模型中敏感度较高的4个参数。研究结果表明,该模型在蓝藻水华空间分布的预报上达到了一定的精度;采用遗传算法能全面、高效地进行参数优化,降低了模拟结果的相对残差,提高了模型预报精度。     

Forecasting of environmental risk maps of coastal algal blooms

We present the elements of an algal bloom risk forecast system which aims to provide a scientific prognosis of the likelihood of an algal bloom occurrence as a function of: (a) the nutrient concentration; and (b) the forecasted wind and tide-induced vertical mixing relative to the critical value defined by the environmental and algal growth conditions. The model is validated with high frequency field observations of algal blooms in recent years and only requires the input of readily available field measurements of water column transparency, nutrient concentration, representative maximum algal growth rate, and a simple estimate of vertical mixing as a function of tidal range, wind speed, and density stratification. The forecasted region-wide risk maps successfully predicted the observed algal bloom occurrences in Hong Kong waters over the past 20 years, with a correct prognosis rate of 87%. It is shown that algal blooms are to a large extent controlled by the interaction of physical and biological processes. This work provides a general framework to interpret the complex spatial and temporal dynamics of observed algal blooms, and paves the way for the development of real time algal bloom risk forecast systems.     

Spatial patterns of diatom diversity and community structure in ancient Lake Ohrid

For effective lakes’ management, high-frequent water quality data on a synoptic scale are essential. The aim of this study is to test the suitability of the latest generation of satellite sensors to provide information on lake water quality parameters for the five largest Italian subalpine lakes. In situ data of phytoplankton composition, chlorophyll-a (chl-a) concentration and water reflectance were used in synergy with satellite observations to map some algal blooms in 2016. Chl-a concentration maps were derived from satellite data by applying a bio-optical model to satellite data, previously corrected for atmospheric effects. Results were compared with in situ data, showing good agreement. The shape and magnitude of water reflectance from different satellite data were consistent. Output chl-a concentration maps, show the distribution within each lake during blooming events, suggesting a synoptic view is required for these events monitoring. Maps show the dynamic of bloom events with concentration increasing from 2 up to 7 mg m^?3 and dropping again to initial value in less than 20 days. Latest generation sensors were shown to be valuable tools for lakes monitoring, thanks to frequent, free of charge data availability over long time periods.     

In situ and satellite observations of a harmful algal bloom and water condition at the Pearl River estuary in late autumn 1998

Harmful algal blooms (HABs) have posed a serious threat to the aquaculture and fisheries industries in recent years, especially in Asia. During 1998 there were several particularly serious blooms in the coastal waters of south China, which caused a serious damage to aquaculture. We report a massive dinoflagellate bloom near the mouth of Pearl River in November 1998 with analyses of data from both in situ sea water measurements and satellites. A multi-parameter environmental mapping system was used to obtain real-time measurements of water quality properties and wind data through the algal bloom area, which allow us to compare water measurements from inside and outside of the bloom areas. This bloom with high concentrations of algal cells was evident as a series of red colored parallel bands of surface water that were 100–300 m long and 10–30 m wide with a total area of about 20–30 km² by visual. The algal density reached 3.8×10⁷ cells l⁻¹ and the surface chlorophyll-a (Chl-a) concentration was high. The algal species has been identified as Gymnodinium cf. catenatum Graham. The water column in the bloom area was stratified, where the surface temperature was 24–25 °C, the salinity was 18–20%, and the northern wind was about 3–4 m s⁻¹ in the bloom area. The SeaWiFS image has shown high Chl-a area coinciding with the bloom area. The sea surface temperature (SST) image of the Pearl River estuary combined with the in situ measurements indicated that the bloom occurred along a mixing front between cooler lower salinity river water and warmer higher saline South China Sea (SCS) water.     

Phytoplankton–zooplankton dynamics in periodic environments taking into account eutrophication

In this paper, we derive and analyze a mathematical model for the interactions between phytoplankton and zooplankton in a periodic environment, in which the growth rate and the intrinsic carrying-capacity of phytoplankton are changing with respect to time and nutrient concentration. A threshold value: “Predator’s average growth rate” is introduced and it is proved that the phytoplankton–zooplankton ecosystem is permanent (both populations survive cronically) and possesses a periodic solution if and only if the value is positive. We use TP (Total Phosphorus) concentration to mark the degree of eutrophication. Based on experimental data, we fit the growth rate function and the environmental carrying capacity function with temperature and nutrient concentration as independent variables. Using measured data of temperature on water bodies we fit a periodic temperature function of time, and this leads the growth rate and intrinsic carrying-capacity of phytoplankton to be periodic functions of time. Thus we establish a periodic system with TP concentration as parameter. The simulation results reveal a high diversity of population levels of the ecosystem that are mainly sensitive to TP concentration and the death-rate of zooplankton. It illustrates that the eruption of algal bloom is mainly resulted from the increasing of nutrient concentration while zooplankton only plays a role to alleviate the scale of algal bloom, which might be used to explain the mechanism of algal bloom occurrence in many natural waters. What is more, our results provide a better understanding of the traditional manipulation method.     

有害藻华预警预测技术研究进展

近年来有害藻华频繁发生且危害严重,对有害藻华预警预测技术的研究可为有害藻华的预警预报、生态学防治及防灾减灾提供借鉴.本文从有害藻华的运动预测预警、指标临界值预警、数据驱动模型和生态数学模型4个方面介绍了国内外有害藻华的预警技术研究进展,分析了各类预警技术的优劣,并提出了基于细胞特征预测蓝藻生长速率以及基于藻类群落特征预警蓝藻水华的新思路.     

滇池外海蓝藻水华爆发反演及规律探讨

气象条件和营养盐浓度一直被认为是导致蓝藻水华爆发的两个重要因素。通过滇池外海Chla浓度时空分异性分析,得出晖湾中测点最易爆发蓝藻水华且爆发时间集中在每年6—9月。同时,采用基于缺失数据多重插补的EMB算法将气象条件和蓝藻水华爆发的不完全数据集进行反演,建立了滇池外海2004—2008年4—10月完整的气象、营养盐及蓝藻爆发的基础数据集,解决了表观蓝藻水华爆发研究中观测数据缺失的问题。据此,探讨了滇池外海晖湾中测点Chla、TN和TP与蓝藻水华爆发关系,进而提出了控制滇池外海蓝藻水华的一种新思路。     

A Catastrophe Model for Water Bloom Prediction: A Case Study of China's Lake Chaohu

Using a case study of Lake Chaohu, the fifth largest lake in China, we constructed a cusp model for water bloom prediction that used TP (total phosphorus), T (temperature), Chla (chlorophyll-a), and DO (dissolved oxygen). These four parameters were assumed to be the most important factors in eutrophication and water bloom of the lake. The model was found to be accurate, because its relative error was around 10%. What is more convincing, according to the catastrophe discriminant of the cusp model, it could be judged that a discontinuous jump of the aquatic ecosystem occurred in July 2004, in Lake Chaohu. This conclusion is consistent with the fact that water blooms arose in August 2004. The cusp model also showed satisfactory precision when applied to forecast the eutrophication trend and prediction of water bloom in Lake Chaohu in 2005. The case study found that water bloom brought on by eutrophication can be fit and predicted by a catastrophe model. We suggest that catastrophe models would be a constructive approach to forecast and judge the outbreak of water bloom in lakes. In addition, by constructing and studying such catastrophe models, lake managers would be able to simulate the effects of different protection and mitigation projects and enrich the scientific basis for the optimization of these projects as well.