Enhancing generic ecological model for short-term prediction of Southern North Sea algal dynamics with remote sensing images

Physically based numerical modelling follows from the basic understanding of the underlying mechanisms and is often represented by a set of (partial differential) equations. It is one of the main approaches in population dynamics modelling. The emphasis of the model introduced in this paper is on the simulation of short-term spatial and temporal dynamics of harmful algal bloom (HAB) events. Total suspended matter (TSM) concentration is one of the dominant factors for harmful algal bloom (HAB) prediction in North Sea. However, the modelling of suspended matter contains a high degree of uncertainty in this area. Therefore, this research aims to achieve a better estimation for the short-term prediction of harmful algal bloom development in both space and time by using spatially distributed TSM retrieved from remotely sensed images as physically based model inputs. In order to supply complete spatially covered datasets for the physically based model instrument: generic ecological model (GEM), this research retrieves TSM information from MERIS images by means of proper estimation techniques including biharmonic splines and self-learning cellular automata. A better estimation of HAB spatial pattern development is achieved by adding spatially distributed TSM data as inputs to original GEM model, and it proved that chlorophyll-a concentration in this area is very sensitive to TSM concentration.     

Sensitivity analysis of the two dimensional application of the Generic Ecological Model (GEM) to algal bloom prediction in the North Sea

Harmful algae can cause damage to co-existing organisms, tourism and farmers. Accurate predictions of algal future composition and abundance as well as when and where algal blooms may occur could help early warning and mitigating. The Generic Ecological Model is an instrument that can be applied to any water system (fresh, transitional or coastal) to calculate the primary production, chlorophyll-a concentration and phytoplankton species composition. It consists of physical, chemical and ecological model components which are coupled together to build one generic and flexible modelling tool. In this paper the model has been analyzed to assess sensitivity of the simulated chlorophyll-a concentration to a subset of ecologically significant input factors. Only a small number of approaches could be considered as suitable for several reasons including the model complexity, engagement of numerous interacting parameters and relatively long time of a single simulation. Thus, sensitivity analysis has been carried out with the use of the Morris method and later enriched by the computation of the correlation ratios of the selected parameters on the model response at more than a few locations in the modelled area. The obtained results are in agreement with expert knowledge of the ecological processes in the North Sea and correspond well with local characteristics.     

Evaluation of biological water purification functions of inland lakes using an aquatic ecosystem model

An effective measure to cope with eutrophication of lakes is to remove nutrients that can cause algal blooming by taking advantage of natural water purification processes. Here the term “purification” is defined, in a wide sense, as the potential role of a water body to contribute to the reduction of pollutants and thus controlling eutrophication. Also regarded as a kind of ecological regulating services, biological purification involves various processes concerning seasonal nutrient fixation, such as uptake by aquatic macrophytes, biofouling onto foliage substrates, feeding by organisms in higher trophic level, and eternal loss or removal of substance from the water. In order to evaluate the water purification ability, a numerical lake ecosystem model highlighting the role of macrophyte colonies in the shore zone was developed and applied to Lakes Suwa, Kasumi and Biwa, as well as five small lakes attached to Lake Biwa.     

滇池水体磷的时空变化与藻类生长的关系

水体磷的时空变化与藻类生长的关系对研究水体富营养化具有十分重要的作用。采用GPS定位,对滇池海埂、斗南、罗家村、新街、昆阳等5个代表性位点监测断面水体总磷、可溶性磷及叶绿素a含量进行了为期1年（2003年5月至2004年5月）的动态研究,并在滇池海埂位点进行了日变化试验,全面分析了滇池不同区域、不同层次、不同时期水体总磷和可溶性磷的年变化、日变化及水体氮/磷比对藻类生长的影响。结果表明,滇池水体磷与藻类生长呈现显著的年变化和日变化特征,显示了滇池全湖水体总磷与叶绿素a周年变化呈显著正相关,水体可溶性磷与叶绿素a呈正相关趋势;海埂位点水体总磷与叶绿素a日变化呈显著正相关,水体可溶性磷与叶绿素a日变化呈显著的负相关,水体氮磷比与叶绿素a呈显著正相关。表明水体磷负荷对藻类生长影响呈现显著的水体区域性和水层差异性和季节性,藻类生长主要吸收水体中的可溶性磷,暗示了滇池水体磷是藻类生长的主要限制因子之一。     

水动力条件对藻类影响的研究进展

水动力过程是影响水体富营养化状态和水华爆发的重要因素,水动力因素对藻类影响的研究对于富营养化水体藻类控制具有重要意义。归纳分析近年来关于流速、流态对藻类生长和种类变化的研究报道;就水动力条件对藻类的影响及其作用机理等详细地进行了文献综述。水动力条件对藻类生长的影响分为流速和流态两个方面,不论是单一藻种还是混合藻类,低流速、小扰动有利于藻类的生长和聚集,流速增大则导致Chla浓度先递增后递减,不同藻类的临界流速并不相同;藻类生长随着湍流程度的增加而逐渐受到抑制,抑制作用与水流流态（层流、过渡流、湍流）无明显相关关系,水体流态的变化造成水流剪应力的变化,藻类种类的差异导致其对水流剪应力的响应变化。水动力条件变化引起的藻类种群结构变化,主要表现为水体混合加剧导致优势种群的转换。水动力条件对藻类影响的作用原理主要是引起了光强的改变、细胞长度的变化、营养盐运送及捕食行为变化等。综观当前的研究成果,水动力能否真正阻止藻类细胞的生长或聚集,影响藻类生长或种类变化的扰动的最低水平以及水动力对藻类影响的作用机理是这一领域未来研究的重点所在。     

磷及环境因子对太湖梅梁湾藻类生长及其群落影响

在太湖梅梁湾进行的围隔试验表明,藻类生长与水华形成受气候(如水温、风浪)与营养因子(如N,P)影响。统计分析发现各点硝态氮均与叶绿素a显著负相关,在不同围隔中水温、总磷、总氮等与叶绿素a显著相关。各围隔中优势种主要为蓝藻,尤其是微囊藻类。加磷对藻类生长与群落结构均有影响,但磷营养不是目前太湖水华爆发的关键因素。     

程海冬季水华暴发期间氮、磷营养元素的形态与分布

氮、磷营养元素是湖泊生态系统中极其重要的生态因子,它们以不同形态存在于湖泊水体中,表现出不同的地球化学行为和生态效应,从而支配着湖泊生态系统的生产力水平和湖泊富营养化进程。通过设置3个断面9个采样站14个采样点,研究了程海湖水体中氮、磷营养元素的形态与分布,结果表明：2009年11月23日—2010年2月20日,以铜绿微囊藻（Microcystis aeruginosa Kutz.）为主的程海冬季水华暴发期间,总氮质量浓度0.540~3.906 mg.L-1,平均0.836 mg.L-1;总磷质量浓度0.036~0.166 mg.L-1,平均0.061 mg.L-1。其中,溶解态氮、溶解态磷分别为61.7%和50.8%。溶解态氮以有机氮为主,溶解态磷则以无机磷为主。水华期间生物可直接利用氮质量浓度0.118 mg.L-1;生物可直接利用磷质量浓度0.021 mg.L-1,分别占总氮、总磷质量浓度的14.1%和34.4%,显示出此特定时期,氮的消耗速度较磷快。氮素、磷素及其赋存形态在程海的时间分布上有不同的节奏;水平分布差异不明显;垂直分布在水表层至亚底层的水柱中差异也不明显,而在湖底层最高。     

A two-dimensional numerical model for eutrophication in Baiyangdian Lake

Hydrodynamic, physical, and biochemical processes in the Baiyangdian Lake water environment were analyzed comprehensively. An eutrophication ecodynamics model including the effects of reed resistance on flow was coupled with the hydrodynamics governing equations. An improvement on the Water Quality Analysis Simulation Program (WASP, a modeling system introduced by the US Environmental Protection Agency) is established, which uses the zooplankton kinetic equation. The model simulates water quality constituents associated with eutrophication in the lake, including phytoplankton, zooplankton, nitrogen, phosphorus, dissolved oxygen, and others. Various kinetic coefficients were calibrated using measured data or information from relevant literature, to study eutrophication in the lake. The values calculated by the calibrated model agree well with field data, including ammonia nitrogen, total nitrogen, total phosphorus and dissolved oxygen. Changes related to nutrition and dissolved oxygen during the processes were simulated. The present model describes the temporal variation of water quality in Baiyangdian Lake with reasonable accuracy. Deviations between model-simulated and observed values are discussed. As an ideal tool for environmental management of the lake, this model can be used to predict its water quality, and be used in research to examine the eutrophication process.     

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

● A machine learning model was used to identify lake nutrient pollution sources. ● XGBoost model showed the best performance for lake water quality prediction. ● Model feature size was reduced by screening the key features with the MIC method. ● TN and TP concentrations of Lake Taihu are mainly affected by endogenous sources. ● Next-month lake TN and TP concentrations were predicted accurately. Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources, for which mathematical modeling is commonly adopted. In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling, we employed an ensemble machine learning (ML) model to identify the key nitrogen and phosphorus sources of lakes. Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality, environmental input, and meteorological conditions, among which the XGBoost model stood out as the best model for total nitrogen (TN) and total phosphorus (TP) prediction. The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality, while the lake TP is predominantly from endogenous sources. The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control. Finally, one-month-ahead prediction of lake TN and TP concentrations (R² of 0.85 and 0.95, respectively) was achieved based on this model with sliding time window lengths of 9 and 6 months, respectively. Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction, which may provide valuable references for early warning and rational control of lake eutrophication.     

蓝藻水华强度的显著相关环境因素识别模型

为识别蓝藻水华强度的显著相关环境因素,克服现有研究中因变量选择不合理、时间与空间精度较低等问题,构建了以蓝藻水华强度等级为因变量,以水质、水文和气象3类监测指标为自变量的多元线性回归模型,并将该模型应用于太湖蓝藻水华研究.基于水华面积和集聚强度数据,用7级量表生成水华强度等级值,使因变量具有宏观性,避免了仅使用叶绿素a浓度等类似指标表示水华强度所体现出的微观性不足.该数据集的时间精度达到每天采样2次,空间精度则达到太湖湖湾内的某个水域空间范围.因此因变量具有适度宏观性,而自变量的值则与因变量的值在较高的时间和空间精度基础上严格对应.模型的分析结果显示,太湖大贡山水域蓝藻水华强度与气温和硝酸盐浓度呈显著正相关,与风速、湿度和电导率呈显著负相关.上述结论与该研究领域的主流结论一致,验证了该模型的有效性.     

太湖蓝藻的时空变化规律及治理方法

利用2009─2012年丰水期和平水期的生物调查获取的环境和生物数据,研究太湖蓝藻的时空分布规律,分析蓝藻分布与其他物理、化学和生物因子（如温度、酸碱性、有机物和营养盐含量、浮游植物与浮游动物密度等）的相关关系。结果表明：太湖水质基本上超出V类地表水指标,主要的超标因子是总氮。总氮在丰水期和平水期的质量浓度分别为3.05 mg·L-1和1.65 mg·L-1,总氮在丰水期质量浓度降低的主要原因可能是丰水期蓝藻迅速生长,吸收了大量的营养盐。蓝藻仍是太湖浮游植物的优势种。2009─2012年太湖蓝藻的密度随年份无明显变化,但随季节和区域存在显著差异：丰水期蓝藻密度均值为4.87×10^7cell·L-1,明显高于平水期蓝藻密度（1.51×10^6 cell·L-1）;太湖东部采样点蓝藻密度明显低于其他湖区。影响蓝藻的非生物因素包括温度、酸碱度和营养盐,高温、偏碱性和高营养盐含量都会增加蓝藻的密度。蓝藻与其他浮游植物和大型水生植物之间存在竞争关系,蓝藻密度增加促进了枝角类的生长。推荐利用机械打捞和大型水生植物修复方法,因为这2种方法可在降低蓝藻密度的同时去除水体中的有机物和营养盐,可以从根本上降低太湖蓝藻水华的风险。增加其他藻类和枝角类控制蓝藻水华方法可行性较差：1）蓝藻暴发时期其它藻类对能量和营养的竞争能力弱于蓝藻,难以抑制蓝藻的生长;2）在太湖中增加枝角类可能降低现有蓝藻的密度,但建立完整的食物链体系降低富营养化程度,防范生物调控中可能存在的生态风险（如其他藻类水华等）较困难。     

太湖重污染湖区和水源地水质概况及藻毒素污染环境风险

近年来太湖流域局部水质状况有所改善,但太湖藻型生境条件还未根本改变,水污染防治任务依然艰巨。确保太湖湖体水质稳定达标,尤其是加强对太湖重点湖区和水源地重点污染物的调查研究十分重要。在此背景下,本文调研了太湖重点湖区和水源地水质概况、藻毒素污染时空分布特征、环境影响因子和迁移转化规律,并总结了藻毒素的环境和健康风险研究的最新进展,指出了太湖西部湖区和饮用水源地的主要环境风险,以及未来太湖藻毒素污染相关研究需解决的关键技术问题,以期为促进太湖流域重点污染物的控制和治理,确保太湖饮用水源地安全提供有益借鉴。     

Eutrophication of Two Lakes in Kinmen Island (Taiwan)

Water quality was monitored for 12 months in Lake Tai and Lake Zon on Kinmen Island, Taiwan, and physico-chemical conditions were analyzed. No vertical temperature stratification was observed in these shallow lakes. pH is neutral to alkaline and associated with vigorous algal growth. Nitrogen levels are high and present in various forms due to progressive nitrification. Green and blue-green algae play an important part in the process of nitrification.

Assessment of lake eutrophication was made by the use of the N:P ratio, the Trophic State Index (TSI) and the US EPA Eutrophic Screening Model. the result of these calculations indicates eutrophic conditions in both lakes. It is advised that lake restoration be initiated and available techniques are listed.     

Analysis of spatial and temporal patterns in a large reservoir using water quality and hydrodynamic modeling

Effective management of reservoir water resources demands a good command of ecological processes in the waterbody. In this work the three-dimensional finite element hydrodynamic model RMA10 was coupled to an eutrophication model. The models were used together with a methodology for loads estimation to foster the understanding of such processes in the largest reservoir in Western Europe—the Alqueva. Nutrient enrichment and eutrophication are water quality concerns in this man-made impoundment. A total phosphorus and nitrogen loads quantification methodology was developed to estimate the inputs in the reservoir, using point and non-point source data.Field data (including water temperature, wind, water elevation, chlorophyll-a, nutrient concentration and dissolved oxygen) and estimated loads were used as forcing for simulations.The analysis of the modeling results shows that spatial and temporal distributions for water temperature, chlorophyll-a, dissolved oxygen and nutrients are consistent with measured in situ data.Modeling results allowed the identification of likely key impact factors on the water quality of the Alqueva reservoir. It is shown that the particular geomorphological and hydrological characteristics of the reservoir together with local climate features are responsible for the existence of distinct ecological regions within the reservoir.     

化感物质的抑藻作用研究、应用及生态安全性评价

水体富营养化日益严重,水华频繁爆发,如何有效控制水华,治理富营养化水体是目前水环境领域的研究热点和前沿。目前湖泊藻类控制技术主要有：物理方法、化学法、生物法,但是这些方法都有其固有的缺点。利用植物化感作用抑制有害藻类生长具有廉价、生态安全等优点近年来备受关注。化感作用就是生物体产生的生物活性物质即化感物质在生物体之间传递信息并导致生物体相互作用。归纳了国内外不同生活型水生植物化感作用研究的主要成果（包括已报道的抑藻水生植物种类、已从水生植物体内和种植水中分离鉴定得到的化感物质）,以及化感物质的联合作用研究,讨论了化感物质的生态安全性。通过化感作用能有效控制引起水体富营养化的各种藻类生长,优化水生生物的组成结构。例如,水体中投放大麦秆可以增加无脊椎动物以及鱼类的数量,从而达到改善水生生态系统的目的。展望了植物化感作用用于水环境治理的发展前景。以期为利用植物化感作用控制水华的发生提供理论基础。     

富营养化水体磷阈值的动态AGP模拟研究

采用流动原水来培养水体混合藻的动态AGP试验,模拟富营养化条件下藻类的生长过程,并以密云水库为实例,结合MATLAB 7.0数值分析软件,拟合了密云水库富营养化发生的磷阈值.结果表明,该水库达到富营养化的磷阈值随水环境条件和水体生态特征变化;蓝藻(Cyanobacteria)在低质量浓度磷酸盐条件下较绿藻(Chlorophyta)更具有生长竞争力;初始藻密度大小影响藻生长达到最大值的时间.在平均温度为28.8、26.5、25.1℃和初始藻密度为9.2×10~6、10.0×10~6、8.0×10~6个条件下,密云水库发生富营养化的总磷阈值分别为0.053、0.062、0.064mg·L~(-1).     

Analysis of the trade-off between economic growth and the reduction of nitrogen and phosphorus emissions in the Poyang Lake Watershed, China

Lake eutrophication leading to water pollution is a major global concern. In recent years, rapid economic growth and the increase in the intensity of resource exploitation in China have caused the influx of nitrogen and phosphorus into lakes. This in turn has led to more severe lake eutrophication, more frequent outbreaks of algal blooms, and the degradation of lake ecosystems. An effective plan balancing economic growth with the reduction of nitrogen and phosphorus emissions is greatly needed. The design and implementation of such a plan requires the collection and analysis of pertinent data. In this paper, we use the environmental computable general equilibrium (ECGE) model to identify the most effective way to balance economic growth with the reduction of nitrogen and phosphorus emissions. For the multiregional analysis, we use social accounting matrices (SAMs) and a provincial trade matrix based on the assumptions of the gravity model. We consider the Poyang Lake Watershed as a case study to illustrate the utility of the model. Based on present conditions in the Poyang Lake Watershed, restricting nitrogen and phosphorus emissions from sectors with the highest emissions is more effective for balancing economic growth and the reduction of nitrogen and phosphorus emissions than restricting nitrogen and phosphorus emissions from all sectors.     

高原深水湖泊程海氮磷形态分布特征及其与叶绿素a的相关性

湖泊水体氮、磷形态分布特征及其与藻类生长的关系是湖泊富营养化研究的重要方面。采用GPS定位,在程海湖设置了3个断面9个采样点,研究了氮、磷形态分布特征,并分析了各形态氮磷与叶绿素a的相关性。结果表明：总氮（TN）质量浓度为0.773 mg.L-1,总磷（TP）质量浓度为0.046 mg.L-1,叶绿素a质量浓度为0.024 mg.L-1。氮素的赋存形态特征是以溶解态总氮（DTN）占大部分,DTN中又以溶解态有机氮（DON）占绝大部分;磷素的存在特点是溶解态无机磷（DIP）含量比重较大。各形态氮、磷都有明显的季节性波动但区域性差异不明显,叶绿素a则有明显的季节节律和时空差异。叶绿素a很好地响应了总氮（TN）、总磷（TP）、溶解态总氮（DTN）、溶解态总磷（DTP）、颗粒态总氮（PTN）、颗粒态总磷（PTP）的变化。程海富营养化受氮和磷共同限制,控制富营养化必须同时削减氮和磷。     

Neural network modelling of coastal algal blooms

An artificial neural network (ANN), a data driven modelling approach, is proposed to predict the algal bloom dynamics of the coastal waters of Hong Kong. The commonly used back-propagation learning algorithm is employed for training the ANN. The modeling is based on (a) comprehensive biweekly water quality data at Tolo Harbour (1982–2000); and (b) 4-year set of weekly phytoplankton abundance data at Lamma Island (1996–2000). Algal biomass is represented as chlorophyll-a and cell concentration of Skeletonema at the two locations, respectively. Analysis of a large number of scenarios shows that the best agreement with observations is obtained by using merely the time-lagged algal dynamics as the network input. In contrast to previous findings with more complicated neural networks of algal blooms in freshwater systems, the present work suggests the algal concentration in the eutrophic sub-tropical coastal water is mainly dependent on the antecedent algal concentrations in the previous 1–2 weeks. This finding is also supported by an interpretation of the neural networks’ weights. Through a systematic analysis of network performance, it is shown that previous reports of predictability of algal dynamics by ANN are erroneous in that ‘future data’ have been used to drive the network prediction. In addition, a novel real time forecast of coastal algal blooms based on weekly data at Lamma is presented. Our study shows that an ANN model with a small number of input variables is able to capture trends of algal dynamics, but data with a minimum sampling interval of 1 week is necessary. However, the sufficiency of the weekly sampling for real time predictions using ANN models needs to be further evaluated against longer weekly data sets as they become available.