Estimation of real-time N load in surface water using dynamic data-driven application system

Agricultural, industrial, and urban activities are the major sources for eutrophication of surface water ecosystems. Currently, determination of nutrients in surface water is primarily accomplished by manually collecting samples for laboratory analysis, which requires at least 24 h. In other words, little to no effort has been devoted to monitoring real-time variations of nutrients in surface water ecosystems due to the lack of suitable and/or cost-effective wireless sensors. However, when considering human health or instantaneous outbreaks such as algal blooms, timely water-quality information is very critical. In this study, we developed a new paradigm of a dynamic data-driven application system (DDDAS) for estimating the real-time loads of nitrogen (N) in a surface water ecosystem. This DDDAS consisted of the following components: (1) a Visual Basic (VB) program for downloading US Geological Survey real-time chlorophyll and discharge data from the internet; (2) a STELLA model for evaluating real-time N loads based on the relationship between chlorophyll and N as well as on river discharge; (3) a batch file for linking the VB program and STELLA model; and (4) a Microsoft Windows Scheduled Task wizard for executing the model and displaying outputs on a computer screen at selected schedules. The DDDAS was validated using field measurements with a very good agreement prior to its applications. Results show that the real-time loads of TN (total N) and NO_x (nitrate and nitrite) varied from positive to negative with the maximums of 1727 kg/h TN and 118 kg/h NO_x and the minimums of −2483 kg/h TN and −168 kg/h NO_x at the selected site. The negative loads occurred because of the back flow of the river in the estuarine environment. Our study suggests that the DDDAS developed in this study was feasible for estimating the real-time variations of TN and NO_x in the surface water ecosystem.     

Design and calibration of a simplified river-water quality model including heterotrophic bacteria and chemical parameters

Abstract The results obtained in the design and calibration of a deterministic water quality model from the Llobregat River (Spain) are presented. The water quality indicators studied are dissolved oxygen, biochemical oxygen demand, ammonia, nitrite, nitrate, phosphate, and heterotrophic bacteria. The factors which describe the rates of the transformation processes are of two types: first-order and Michaelis-Menten kinetics. A decomposition process is applied to the model for calibration. The inclusion in the model of heterotrophic bacteria shows good predictive capacity to describe the behaviour of the biological processes in the river. The proposed model gives a correct evaluation of the indicators studied, and may be used in water quality management.     

Assessing water scarcity by simultaneously considering environmental flow requirements,water quantity,and water quality

Water scarcity is a widespread problem in many parts of the world. Most previous methods of water scarcity assessment only considered water quantity, and ignored water quality. In addition, the environmental flow requirement (EFR) was commonly not explicitly considered in the assessment. In this study, we developed an approach to assess water scarcity by considering both water quantity and quality, while at the same time explicitly considering EFR. We applied this quantity–quality-EFR (QQE) approach for the Huangqihai River Basin in Inner Mongolia, China. We found that to keep the river ecosystem health at a “good” level (i.e., suitable for swimming, fishing, and aquaculture), 26% of the total blue water resources should be allocated to meet the EFR. When such a “good” level is maintained, the quantity- and quality-based water scarcity indicators were 1.3 and 14.2, respectively; both were above the threshold of 1.0. The QQE water scarcity indicator thus can be expressed as 1.3(26%)|14.2, indicating that the basin was suffering from scarcity problems related to both water quantity and water quality for a given rate of EFR. The current water consumption has resulted in degradation of the basin's river ecosystems, and the EFR cannot be met in 3 months of a year. To reverse this situation, future policies should aim to reduce water use and pollution discharge, meet the EFR for maintaining healthy river ecosystems, and substantially improve pollution treatment.     

Progress and opportunities in advancing near-term forecasting of freshwater quality

Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water-related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in the early stages of development (i.e., non-operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end-user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.     

Confidence in ecological indicators: A framework for quantifying uncertainty components from monitoring data

The value of an ecological indicator is no better than the uncertainty associated with its estimate. Nevertheless, indicator uncertainty is seldom estimated, even though legislative frameworks such as the European Water Framework Directive stress that the confidence of an assessment should be quantified. We introduce a general framework for quantifying uncertainties associated with indicators employed to assess ecological status in waterbodies. The framework is illustrated with two examples: eelgrass shoot density and chlorophyll a in coastal ecosystems. Aquatic monitoring data vary over time and space; variations that can only partially be described using fixed parameters, and remaining variations are deemed random. These spatial and temporal variations can be partitioned into uncertainty components operating at different scales. Furthermore, different methods of sampling and analysis as well as people involved in the monitoring introduce additional uncertainty. We have outlined 18 different sources of variation that affect monitoring data to a varying degree and are relevant to consider when quantifying the uncertainty of an indicator calculated from monitoring data. However, in most cases it is not possible to estimate all relevant sources of uncertainty from monitoring data from a single ecosystem, and those uncertainty components that can be quantified will not be well determined due to the lack of replication at different levels of the random variations (e.g. number of stations, number of years, and number of people). For example, spatial variations cannot be determined from datasets with just one station. Therefore, we recommend that random variations are estimated from a larger dataset, by pooling observations from multiple ecosystems with similar characteristics. We also recommend accounting for predictable patterns in time and space using parametric approaches in order to reduce the magnitude of the unpredictable random components and reduce potential bias introduced by heterogeneous monitoring across time. We propose to use robust parameter estimates for both fixed and random variations, determined from a large pooled dataset and assumed common across the range of ecosystems, and estimate a limited subset of parameters from ecosystem-specific data. Partitioning the random variation onto multiple uncertainty components is important to obtain correct estimates of the ecological indicator variance, and the magnitude of the different components provide useful information for improving methods applied and design of monitoring programs. The proposed framework allows comparing different indicators based on their precision relative to the cost of monitoring.     

Effects of ground water and harvest intensity on alkaline grassland ecosystem dynamics – a simulation study

A model for the alkaline grassland ecosystems, MAGE, was applied to plant communities dominated by three species. Field observations on two communities dominated respectively by Puccinellia tenuiflora and Suaeda corniculata were used to parameterize the model for multiple species interaction. The model behaves reasonably in following the seasonal variations of water content, soluble sodium cation and calcium cation in surface soil, as well as biomass of the plant communities.Simulations were run to investigate the effects of ground water quality, ground water table depth, maximum non-capillary porosity in surface soil and harvest intensity, on ecosystem dynamics. The results indicated that ground water sodium concentration and ground water table depth had primary control on soil alkalization and vegetation status. The improvement of soil conditions by vegetation is limited to an extent with moderate ground water depth and sodium concentration. Non-capillary pores are critical for vegetation to affect the soil alkalization/de-alkalization process, but the effect of non-capillary pores tends to saturate when maximum non-capillary porosity is greater than 0.1.     

能源预测预警理论与方法研究进展

能源作为一种稀缺性的战略资源是国民经济增长和社会进步的物质基础,但是随着化石能源耗竭及能源使用造成的环境问题日趋严重,能源安全问题逐渐成为关注的焦点,而能源预测预警也成为能源系统科学领域的新兴学科,其内容包含能源安全理论、基于模型的能源供需预测和基于安全评价指标体系的能源预警等方面内容。通过系统回顾能源安全的理论及其演变的历程,重点综述了自上而下、自下而上和混合建模3种建模思路的能源预测模型,探讨了三类模型的优点和局限性,并根据能源安全预警评价指标浓缩信息的程度,将现有预警评价体系划分单个型指标评价体系和聚合型指标评价体系两大类。通过对以上研究内容的总结分析,明确了当前能源预测预警研究各领域的研究进展,及其在理论和应用方面的优势与不足。在未来研究中,建议从供应链的角度出发,考虑能源系统内部各因素及与外部因素的相互作用,构建基于链式的预警体系,以有效弥补现有研究中的不足。     

Fish community response to the longitudinal environmental gradient in Czech deep-valley reservoirs: Implications for ecological monitoring and management

Ecological quality assessment of non-natural water bodies is, in contrast to natural systems, less developed and requires determining biological indicators that reliably reflect environmental conditions and anthropogenic pressures. This study was motivated to propose fish indicators appropriate for assessment of reservoir ecosystems in central Europe. We analysed changes in water quality, total biomass and the taxonomic, trophic and size composition of fish communities along the longitudinal axes of four elongated, deep-valley reservoirs. Due to high nutrient inputs from their catchments, the reservoirs exhibited pronounced within-system gradients in primary productivity and water transparency. Although fish communities were similar among the reservoirs and dominated by few native species, the community structure and biomass systematically changed along the longitudinal axes of the reservoirs. The biomass and proportion of planktivores/benthivores in the fish community were highest at eutrophic sites near the river inflow and declined substantially towards deep, more oligotrophic sites close to the dam. The biomass and proportion of piscivores significantly increased downstream within the reservoirs alongside improving water quality. At species level, perch Perca fluviatilis and bream Abramis brama responded most sensitively, although in opposite directions, to the longitudinal environmental gradient. The major longitudinal changes in fish community characteristics were found to be consistent between pelagic and benthic habitats. The results of this study suggest that fish communities are appropriate indicators of eutrophication and can be used for ecological quality assessment of non-natural lentic water bodies, such as reservoirs. Moreover, our results underline the necessity to consider within-system gradients in water quality and the fish community when planning sampling programmes for deep-valley reservoirs.     

Simulation study on water quality based on sediment release flume experiment in Lake Taihu, China

Sediments have a significant influence on the overlying water, and nutrient release from sediments is an important source for lake eutrophication, particularly in shallow lakes. Sediment resuspension is primarily driven by wind-induced currents. In this research, the correlation between release rate of suspended sediment and flow velocity was studied, and an experiment on hydrodynamic forces was conducted in a rectangle flume using water and sediments collected from three sites in Lake Taihu, a eutrophic lake in China. It was shown that the starting velocities of sediment in Lake Taihu at three different incipient standards gained from the experiment were 15, 30, and 40 cm s⁻¹ and the release rate of suspended sediment could reach up to 643.4, 5377.1, and 13980.5 g m⁻² d⁻¹, respectively. Based on the experiment, a water quantity and quality numerical model of wind-induced current with sediment pollution for Lake Taihu was developed. The model was calibrated and validated by applying it to the study of the water quality of Lake Taihu. The calculated values were generally in good agreement with field observations, which indicated that the developed model could represent the dynamics of sediment resuspension to a certain extent. This study provides a new approach and a practical tool for planning and management policy and operations to protect the water quality and ecosystems of shallow lakes.     

Combining multivariate statistical techniques and random forests model to assess and diagnose the trophic status of Poyang Lake in China

Floodplain lakes are valuable to humans because of their various functions. An emerging public concern on lake eutrophication has heightened the need to assess and predict the trophic status in floodplain lakes, particularly for those with high spatial heterogeneity. In this study, combined multivariate statistical techniques and random forests model were used to characterize the water quality and trophic status of Poyang Lake. By classifying and characterizing seasonal water samples comprising 11 water quality parameters collected from 13 sampling sites in Poyang Lake between 2008 and 2014, the dataset was divided into the central and northern lake groups, which corresponded to lentic and lotic regions in Poyang Lake, respectively. The spatial water quality variations and underlying patterns were investigated by performing discriminant analysis and principal component analysis (PCA). Lastly, random forests (RF) were used to predict the chlorophyll a (Chl-a) variations of the central and northern lakes. The PCA results indicated that the water quality of the central and northern areas of the lake was controlled by different environmental variables and underlying pollutant sources. The RF model outperformed the artificial neural network and linear regression and was robust with strong predictive capabilities. It was determined that the most important predictors of the Chl-a variations in the northern lake were water temperature (T) and water level, whereas transparency, T, and water level were the most efficient predictors in the central lake. The RF model can also be applied to trophic prediction in other large lakes with considerable spatial variations. This study will have implications on water quality management and eutrophication prevention in floodplain lakes with high spatial heterogeneity.     

旱区流域水土环境质量的综合定量评价模型

现有流域水土环境质量的评价方法大多根据评价区评价指标量化值与评价等级标准建立评价模型.评价区不同,评价模型也不相同,计算工作量较大.本文根据给定的水土环境质量评价等级标准,采用随机技术模拟生成足够数量的评价指标序列,应用人工神经网络模型,以评价指标生成序列和其所属的评价等级值建立一种通用的评价模型,其特点是不需要构造评价指标集和评价等级值间的函数关系和计算权重值,减少了建立模型的工作量.以西北地区水资源开发利用程度最高的石羊河流域进行实例研究,表明该模型可操作性强,可用于流域水土环境质量评价.     

Remote sensing for lake research and monitoring – Recent advances

Lakes are important ecosystems providing various ecosystem services. Stressors such as eutrophication or climate change, however, threaten their ecological functions. National and international legislations address these threats and claim consistent, long-term monitoring schemes. Remote sensing data and products provide synoptic, spatio-temporal views and their integration can lead to a better understanding of lake ecology and water quality. Remote sensing therefore gains increasing awareness for analysing water bodies. Various empirical and semi-analytical algorithms exist to derive remote sensing indicators as proxies for climate change or ecological response variables. Nevertheless, most monitoring networks lack an integration of remote sensing data. This review article therefore provides a comprehensive overview how remote sensing can support lake research and monitoring. We focus on remote sensing indicators of lake properties, i.e. water transparency (suspended particulate matter, coloured dissolved organic matter, Secchi disc depth, diffuse attenuation coefficient, turbidity), biota (phytoplankton, cyanobacteria, submerged and emerged aquatic vegetation), bathymetry, water temperature (surface temperature) and ice phenology (ice cover, ice-on, ice-out). After a brief background introducing principles of lake remote sensing we give a review on available sensors and methods. We categorise case studies on remote sensing indicators with respect to lake properties and processes. We discuss existing challenges and benefits of integrating remote sensing into lake monitoring and ecological research including data availability, ready-to-use tools and accuracies.     

Ecological assessments with algae: a review and synthesis

Algae have been used for a century in environmental assessments of water bodies and are now used in countries around the world. This review synthesizes recent advances in the field around a framework for environmental assessment and management that can guide design of assessments, applications of phycology in assessments, and refinements of those applications to better support management decisions. Algae are critical parts of aquatic ecosystems that power food webs and biogeochemical cycling. Algae are also major sources of problems that threaten many ecosystems goods and services when abundances of nuisance and toxic taxa are high. Thus, algae can be used to indicate ecosystem goods and services, which complements how algal indicators are also used to assess levels of contaminants and habitat alterations (stressors). Understanding environmental managers' use of algal ecology, taxonomy, and physiology can guide our research and improve its application. Environmental assessments involve characterizing ecological condition and diagnosing causes and threats to ecosystems goods and services. Recent advances in characterizing condition include site‐specific models that account for natural variability among habitats to better estimate effects of humans. Relationships between algal assemblages and stressors caused by humans help diagnose stressors and establish targets for protection and restoration. Many algal responses to stressors have thresholds that are particularly important for developing stakeholder consensus for stressor management targets. Future research on the regional‐scale resilience of algal assemblages, the ecosystem goods and services they provide, and methods for monitoring and forecasting change will improve water resource management.     

Predicted range expansion of Chinese tallow tree (Triadica sebifera) in forestlands of the southern United States

Aim We present an integrated approach for predicting future range expansion of an invasive species (Chinese tallow tree) that incorporates statistical forecasting and analytical techniques within a spatially explicit, agent‐based, simulation framework. Location East Texas and Louisiana, USA. Methods We drew upon extensive field data from the US Forest Service and the US Geological Survey to calculate spread rate from 2003 to 2008 and to parameterize logistic regression models estimating habitat quality for Chinese tallow within individual habitat cells. We applied the regression analyses to represent population spread rate as a function of habitat quality, integrated this function into a logistic model representing local spread, and coupled this model with a dispersal model based on a lognormal kernel within the simulation framework. We simulated invasions beginning in 2003 based on several different dispersal velocities and compared the resulting spatial patterns to those observed in 2008 using cross Mantel’s tests. We then used the best dispersal velocity to predict range expansion to the year 2023. Results Chinese tallow invasion is more likely in low and flat areas adjacent to water bodies and roads, and less likely in mature forest stands and in pine plantations where artificial regeneration by planting seedlings is used. Forecasted invasions resulted in a distribution that extended from the Gulf Coast of Texas and Louisiana northward and westward as much as 300 km, representing approximately 1.58 million ha. Main conclusions Our new approach of calculating time series projections of annual range expansion should assist land managers and restoration practitioners plan proactive management strategies and treatments. Also, as field sampling continues on the national array of FIA plots, these new data can be incorporated easily into the present model, as well as being used to develop and/or improve models of other invasive plant species.     

Environmental Health Indicators in New Zealand: Drinking Water—A Case Study

This study examines and quantifies the linkages between population health, environmental risks, and its determinants for drinking water in New Zealand using routinely collected data. It was conducted as part of the national environmental health indicators project in New Zealand. The project is based on the World Health Organization’s (WHO) “Environmental and Health Information System” program. Drinking water quality indicators based on the Driving force–Pressure–State–Exposure–Effect–Action (DPSEEA) framework as part of this program were analyzed to validate the model by quantifying the linkages between the indicators. The results of the model suggested over the study period, the state (drinking water quality) and exposure (water access) indicators are significant independent predictors of the effect indicator (waterborne disease rate). This study suggests that routinely collected data can be structured using the DPSEEA framework and tested quantitatively using standard Poisson regression models, thus, illustrating that the model can be used routinely to provide a basis for consideration of the costs and benefits of any interventions to reduce the burden of waterborne disease. Data quality issues need to be considered if such routinely collected data linkages are to be performed for policy purposes. The online version of this article (doi:) contains electronic supplementary material, which is available to authorized users. Supplemental histograms are available in the online appendix.     

Water Resources in Life-Cycle Impact Assessment: Considerations in Choosing Category Indicators

Water is one of many resources, wastes, and pollutants considered in life-cycle assessment (LCA). The widely used indicator for water resources, the total input of water used, is not adequate to assess water resources from a sustainability perspective. More detailed indicators are proposed for water resources in two areas essential to water sustainability: water quantity and water quality. The governing principles for a consideration of water quantity are that (1) the water sources or LCA inputs are renewable and sustainable and (2) the volume of water released or LCA outputs are returned to humans or ecosystems for further use downstream. The governing principle for a consideration of water quality is that the utility of the returned water is not impaired for either humans or ecosystems downstream. Water quantity indicators are defined for water use, consumption, and depletion to reveal the sustainable or nonsustainable nature of the sources. A flexible set of water quality indicators for various factors that may impair water quality are then discussed, including the LCA study choices, technical challenges, and trade-offs involved with such indicators. Indicator selection from this set involves the underlying concern or endpoint represented by the indicator and the level and accuracy of decision-making information that the indicator must provide. With significant differences in emissions among systems studied using LCA and different purposes of the LCA studies themselves, a single, default set of water quality indicators applicable to all systems studied with LCA is problematic. The proposed water quantity and quality indicators for LCA studies are also intended to be compatible with environmental management and reporting systems so that data needs are not duplicated and interpretation for one does not contradict or sow confusion for the other.     

河流水质的景观组分阈值研究进展

土地利用/覆被变化产生的区域生态环境负面效应已引起国内外研究者的广泛关注,其中,河流水质对景观组分变化的响应已在区域及更大尺度的研究中,成为热点。探讨河流水质的景观组分阈值,可以弥补非点源污染研究在区域尺度上的景观变化影响水质问题研究中的不足,而这是当前流域水环境管理及土地利用规划与管理的主要依据之一。从景观组分指数与水质指标出发,分析了当前研究的常用指标,认为:具有明确物理意义的景观组分指数,如不透水表面指数、植被指数等,受到水质的景观组分阈值研究的青睐;在水质指标中,水化学指标应用最为广泛,同时,表征水生生态系统条件的如生物类指标、综合生物类与非生物类指标,也逐渐受到重视,方兴未艾。尽管河流水质的景观组分阈值是当前的研究重点,但在区域以及更大尺度上,阈值的差异较大。在今后的研究中,水质退化的景观组分阈值还需在研究尺度、水质指标及阈值标准等问题上进一步深化,而景观格局指数的应用将会促进对水质退化受景观组分空间配置影响的研究。对水质的景观组分阈值研究进行综述,可以为区域尺度上开展水质保护、流域水环境管理及土地利用规划提供前沿信息。     

Assessment of soil contamination--a functional perspective

In many industrialized countries the use of land is impeded bysoil pollution from a variety of sources. Decisions on clean-up, management or set-aside ofcontaminated land are based on various considerations, including human health risks, butecological arguments do not have a strong position in such assessments. This paper analyses whythis should be so, and what ecotoxicology and theoretical ecology can improve on thesituation. It seems that soil assessment suffers from a fundamental weakness, which relatesto the absence of a commonly accepted framework that may act as a reference. Soilcontamination can be assessed both from a functional perspective and a structuralperspective. The relationship between structure and function in ecosystems is a fundamentalquestion of ecology which receives a lot of attention in recent literature, however, ageneral concept that may guide ecotoxicological assessments has not yet arisen. On the experimentalside, a good deal of progress has been made in the development and standardized useof terrestrial model ecosystems (TME). In such systems, usually consisting of intactsoil columns incubated in the laboratory under conditions allowing plant growth and drainageof water, a compromise is sought between field relevance and experimental manageability.A great variety of measurements can be made on such systems, including microbiologicalprocesses and activities, but also activities of the decomposer soil fauna.I propose that these TMEs can be useful instruments in ecological soil quality assessments. Inaddition a ``bioinformatics approach' to the analysis of data obtained in TME experimentsis proposed. Soil function should be considered as a multidimensional concept and thevarious measurements can be considered as indicators, whose combined values define the``normal operating range' of the system. Deviations from the normal operating range indicatethat the system is in a condition of stress. It is hoped that more work along this line willimprove the prospects for ecological arguments in soil quality assessment.     

Automatic real-time calibration,assessment, and maintenance of generic Raman models for online monitoring of cell culture processes

Raman spectroscopy is a multipurpose analytical technology that has found great utility in real-time monitoring and control of critical performance parameters of cell culture processes. As a process analytical technology (PAT) tool, the performance of Raman spectroscopy relies on chemometric models that correlate Raman signals to the parameters of interest. The current calibration techniques yield highly specific models that are reliable only on the operating conditions they are calibrated in. Furthermore, once models are calibrated, it is typical for the model performance to degrade over time due to various recipe changes, raw material variability, and process drifts. Maintaining the performance of industrial Raman models is further complicated due to the lack of a systematic approach to assessing the performance of Raman models. In this article, we propose a real-time just-in-time learning (RT-JITL) framework for automatic calibration, assessment, and maintenance of industrial Raman models. Unlike traditional models, RT-JITL calibrates generic models that can be reliably deployed in cell culture experiments involving different modalities, cell lines, media compositions, and operating conditions. RT-JITL is a first fully integrated and fully autonomous platform offering a self-learning approach for calibrating and maintaining industrial Raman models. The efficacy of RT-JITL is demonstrated on experimental studies involving real-time predictions of various cell culture performance parameters, such as metabolite concentrations, viability, and viable cell density. RT-JITL framework introduces a paradigm shift in the way industrial Raman models are calibrated, assessed, and maintained, which to the best of authors' knowledge, have not been done before.