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.     

Linking species- and ecosystem-level impacts of climate change in lakes with a complex and a minimal model

To study the interaction between species- and ecosystem-level impacts of climate change, we focus on the question of how climate-induced shifts in key species affect the positive feedback loops that lock shallow lakes either in a transparent, macrophyte-dominated state or, alternatively, in a turbid, phytoplankton-dominated state. We hypothesize that climate warming will weaken the resilience of the macrophyte-dominated clear state. For the turbid state, we hypothesize that climate warming and climate-induced eutrophication will increase the dominance of cyanobacteria. Climate change will also affect shallow lakes through a changing hydrology and through climate change-induced eutrophication. We study these phenomena using two models, the full ecosystem model PCLake and a minimal dynamic model of lake phosphorus dynamics. Quantitative predictions with the complex model show that changes in nutrient loading, hydraulic loading and climate warming can all lead to shifts in ecosystem state. The minimal model helped in interpreting the non-linear behaviour of the complex model. The main output parameters of interest for water quality managers are the critical nutrient loading at which the system will switch from clear to turbid and the much lower critical nutrient loading – due to hysteresis – at which the system switches back. Another important output parameter is the chlorophyll-a level in the turbid state. For each of these three output parameters we performed a sensitivity analysis to further understand the dynamics of the complex model PCLake. This analysis showed that our model results are most sensitive to changes in temperature-dependence of cyanobacteria, planktivorous fish and zooplankton. We argue that by combining models at various levels of complexity and looking at multiple aspects of climate changes simultaneously we can develop an integrated view of the potential impact of climate change on freshwater ecosystems.     

Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system

A fully non-linear analysis of forcing influences on temperatures is performed in the climate system by means of neural network modelling. Two case studies are investigated, in order to establish the main factors that drove the temperature behaviour at both global and regional scales in the last 140 years. In particular, our neural network model shows the ability to catch non-linear relationships among these variables and to reconstruct temperature records with a high degree of accuracy. In this framework, we clearly show the need of including anthropogenic inputs for explaining the temperature behaviour at global scale and recognise the role of El Niño southern oscillation for catching the inter-annual variability of temperature data. Furthermore, we analyse the relative influence of global forcing and a regional circulation pattern in determining the winter temperatures in Central England, showing that the North Atlantic oscillation represents the driven element in this case study. Our modelling activity and results can be very useful for simple assessments of relationships in the complex climate system and for identifying the fundamental elements leading to a successful downscaling of atmosphere–ocean general circulation models.     

Dynamics of the nitrogen cycle in a lake and its stability

Recent eutrophication of lakes or sea water has caused serious damage to the ecological balance in aquatic systems and to the quality of water for human use. The purpose of this study is to construct a dynamic model of the nitrogen cycle in a lake and to study the dynamic behavior of the various nitrogen forms such as organic, inorganic and plankton in connection with the mechanism of algal blooms in lakes. The dynamics of the system are represented by a system of non-linear differential equations which include the predator-prey relation between plankton types, and are discussed with the stability analysis of the critical points of differential equations. The effects of seasonal changes of environmental parameters are studied with computer simulations.     

乙酸钙不动杆菌对富营养化景观水体的净化作用

利用乙酸钙不动杆菌(Acinetobacter calcoaceticus),在室内采用投菌法对富营养化景观水体进行预处理试验研究.通过不同的投菌量ф=0.05×10-3、0.1×10-3、0.2×10-3,对水体进行处理,结果表明,乙酸钙不动杆菌对富营养化水体中的总氮(TN)、总磷(TP)、CODcr,均有一定的去除效果,其中在投菌量为O.1×10-3时处理效果最好.通过进一步的连续投菌净化试验,水体中的TN、TP、CODcr显著降低,处理后,水体中叶绿素a的质量浓度为19.1 mg·m-3,去除率为83.7%,藻类基本得到控制.由此可见,乙酸钙不动杆菌能够有效性地去除水体中的有机物、氮、磷,因此具有净化富营养化水体的作用.     

Point patterns of forest fire locations

In this paper I demonstrate some of the techniques for the analysis of spatial point patterns that have become available due to recent developments in point process modelling software. These developments permit convenient exploratory data analysis, model fitting, and model assessment. Efficient model fitting, in particular, makes possible the testing of statistical hypotheses of genuine interest, even when interaction between points is present, via Monte Carlo methods. The discussion of these techniques is conducted jointly with and in the context of some preliminary analyses of a collection of data sets which are of considerable interest in their own right. These data sets (which were kindly provided to me by the New Brunswick Department of Natural Resources) consist of the complete records of wildfires which occurred in New Brunswick during the years 1987 through 2003. In treating these data sets I deal with data-cleaning problems, methods of exploratory data analysis, means of detecting interaction, fitting of statistical models, and residual analysis and diagnostics. In addition to demonstrating modelling techniques, I include a discussion on the nature of statistical models for point patterns. This is given with a view to providing an understanding of why, in particular, the Strauss model fails as a model for interpoint attraction and how it has been modified to overcome this difficulty. All actual modelling of the New Brunswick fire data is done only with the intent of illustrating techniques. No substantive conclusions are or can be drawn at this stage. Realistic modelling of these data sets would require incorporation of covariate information which I do not so far have available.

Simulation modeling of material and energy flow through an ecosystem: Methods and documentation

This paper discusses the process of development of large-scale ecological simulation models as it proceeds through steps divided between two major phases — conceptualization and implementation. The activities in these phases are strongly influenced by model objectives. Activities transpiring in each step are discussed and documentation methods both for the modelling process and its product are introduced for each of the steps. Examples are given from CONIFER, a large-scale coniferous forest model developed by the author in cooperation with another investigator. The role of the model documentation, both in aiding model development and in clearly communicating and allowing criticism of the model, is emphasized. Model evaluation, checking that the model satisfies its objectives, is seen as an area of model development which indicates which parts of the model need reworking.     

Sensitivity analysis with a phosphorus diagenesis model (SPIEL)

Sensitivity analysis is an important part of the modelling procedure. In this paper, sensitivity analysis of a phosphorus diagenesis model (SPIEL) was used for determining the most important parameters and forcing functions, as well as the potential influence of technical in-lake measures on phosphorus (P) release from the sediment. The model “Sedimentary Phosphorus In Eutrophic Lakes” (SPIEL) includes sub-modules for calculating the aerobic layer thickness, the iron–sulphur cycle and the free aluminium sorption capacity. The results of a detailed sensitivity analysis for different module combinations were compared. By means of graphical representation of the cause and effect chains, the synergistic and compensatory effects of different processes on P release in Lake Sempach were discussed. The importance of the iron–sulphur cycle and of the aerobic layer thickness for the P release in this lake is demonstrated.     

Assessment of a model of pollution disaster in near-shore coastal waters based on catastrophe theory

In this study, a model for assessing of environmental disasters in near-shore areas was developed using a multi-criteria evaluation method of catastrophe theory. The assessment model involved scenarios of eutrophication, pollution with heavy metals and organic compounds. An evaluation system of the model was composed of seven mesosphere indicators and twenty underlying indicators including water chemistry, water physics, water biology, heavy metals and organic pollutants in water and surface sediments. The model was applied to possibility assessment of environmental disasters in different functional regions of the Dalian Bay in 2001 and 2006. Results showed that the environmental disaster indicators in 2001 were equivalent to the Level 4 standard values of marine functional areas, but the eutrophication disaster indicators were lower than the Level 4 standard values. It is consistent with the occurrence of a large-scale red tide in Dalian Bay in 2001. In 2006, eutrophication remained the dominant problem of the region but organic pollutants, such as oil, were reduced remarkably. This coincided with ongoing local environmental-friendly practices for industries.     

The effect of species response form on species distribution model prediction and inference

Ecological theory and current evidence support the validity of various species response curves according to a variety of environmental gradients. Various methods have been developed for building species distribution models but it is not well known how these methods perform under various assumptions about the form of the underlying species response. It is also not well known how spatial correlation in species occurrence affects model performance. These effects were investigated by applying an environmental envelope method (BIOCLIM) and three regression-based methods: logistic regression (LR), generalized additive modelling (GAM), and classification and regression tree (CART) to simulated species occurrence data. Each simulated species was constructed as a sum of responses with varying weights. Three basic species response curves were assumed: Gaussian (bell-shaped), Beta (skew) and linear. The two non-linear responses conform to standard ecological niche theory. All three responses were applied in turn to three simulated environmental variables, each with varying degrees of spatial autocorrelation. GAM produced the most consistent model performance over all forms of simulated species response. BIOCLIM and CART were inclined to underrate the performance of variables with a linear response. BIOCLIM was less sensitive to data density. LR was susceptible to model misspecification. The use of a linear function in LR underestimated the performance of variables with non-linear species response and contributed to increased spatial autocorrelation in model residuals. Omission of important environmental variables with non-linear species response also contributed to increased spatial autocorrelation in model residuals. Adding a spatial autocovariate term to the LR model (autologistic model) reduced the spatial autocorrelation and improved model performance, but did not correct the misidentification of the dominant environmental determinant. This is to be expected since the autologistic approach was designed primarily for prediction and not for inference. Given that various forms of species response to environmental determinants arise commonly in nature: (1) higher order functions should always be tested when applying LR in modelling species distribution; (2) spatial autocorrelation in species distribution model residuals can indicate that environmental determinants with non-linear response are missing from the model; and (3) deficiencies in LR model performance due to model misspecification can be addressed by adding a spatial autocovariate to the model, but care should be taken when interpreting the coefficients of the model parameters.     

Assessment of the effects of a port expansion on algae appearance in a costal bay through mathematical modelling. Application to San Lorenzo Bay (North Spain)

In the last years several episodes of algae appearance affecting bathing areas have been observed in San Lorenzo Bay (north of Spain). The analysis of the collected algae revealed that they might come from near intertidal or shallow subtidal zones due to eutrophication processes or through drift algae movement by the action of marine currents. In the vicinity of this area, the expansion of the Port of Gijón (now under construction) supposes a significant modification of the coastal geometry. The magnitude of such an expansion could cause changes in the patterns of currents in the bay, with the consequent alteration of the observed algal appearance phenomena. A mathematical modelling study to evaluate the risk of generation of eutrophication processes in the San Lorenzo Bay area and the transport of drift algae from near sea bed areas was developed. This study required the use of different hydrodynamic models in order to characterize the currents caused by tides, winds and waves. The eutrophication processes in the bay were analyzed with a depth-averaged two-dimensional eutrophication model which deals with eight water quality variables. Calibration of model parameters with the observed data from a field survey was performed. A reasonable agreement with the field measurements was achieved. Model results showed that the maximum phytoplankton concentrations were below eutrophic conditions. Although, the port expansion has led to an increment of phytoplankton concentrations, chlorophyll a levels were not representative of eutrophic conditions. To analyse the transport of drift algae, a methodology based on the utilization of a two-dimensional model which solves the depth-averaged advection-diffusion equation considering seaweed as a conservative tracer was developed and applied. Numerical modelling allowed the identification of the coastal areas that seems to be the source of the seaweed found on the beach. It was also proven that port expansion does not significantly affect drift algae transport in the area.     

Integrating empirical and heuristic knowledge in a KBS to approach stream eutrophication

The nutrient enrichment of rivers and its consequences are among the most severe water quality problems in Europe, causing eutrophication and other problems. The decision-making processes involved in the management of these problems require extensive human expertise from people who deal directly with day-to-day stream problems, as well as empirical knowledge based on scientific research. This means that eutrophication is a complex problem, the optimal management of which requires an integrated and multidisciplinary approach. This approach can be taken using a Knowledge-Based System (KBS) built upon the concepts and methods of human reasoning. Accordingly, a KBS was developed within the STREAMES project. In this KBS most of the knowledge needed for managing eutrophication problems was organised and structured in the form of a decision tree (DT). The methodology specially developed to build this KBS, as well as the internal structure of the eutrophication decision tree, is presented here. The good DT obtained led to consider the KBS a suitable tool to support the management of eutrophication.     

A sensitivity analysis of an ecosystem model of estuarine carbon flow

The North Inlet Marsh-Estuarine System Model (NIMES) is a 19-compartment real-time deterministic ecosystem simulation model of intrasystem carbon flow and exchange between an estuary and adjacent coastal water. A complete sensitivity analysis of this model with regard to POM, DOM and nekton annual exchange and annual system net productivity was completed and the functional relationship between these system behaviors and the perturbed parameters were determined by regression techniques. Simulated POM annual exchange between the estuary and the sea was largely controlled by offshore POM concentration, water column respiration and the gross productivity of the marsh and water column flora. Simulated DOM annual estuarine-oceanic exchange was most sensitive to perturbations in the gross productivity and biomass changes in marsh flora and water column microbial DOM uptake. Simulated nekton exchange reflected a sensitivity to migratory behavior and subtidal benthic biomass changes. System annual net productivity as simulated by the model showed a high sensitivity to all model processes which affected component primary production and respiration. From this sensitivity analysis, a scheme is developed to evaluate research needs for further model development for the North Inlet ecosystem.     

Application of a coastal model to simulate present and future inundation and aid coastal management

This paper describes the application of coastal hydro-informatic modelling (using the TELEMAC Modelling System) to address management issues arising from projected hydrodynamical and morphological changes within a shallow, sandy estuarine environment. The model incorporates the complex interaction of ocean, terrestrial and atmospheric processes. The case study of the Dyfi Estuary, on the west coast of Wales, is highlighted here. As sea levels have risen locally and are predicted to rise further, a National Nature Reserve (Borth Bog), which has been reclaimed from tidal waters by embankments, will be at increasing risk from flooding episodes due to overtopping of these embankments at high tide. Present and predicted future tidal-fluvial scenarios have been modelled in the Dyfi Estuary in order to estimate the potential for flooding. In addition, areas of greatest velocity change and potential for sediment erosion/accretion have been identified. A further process that has been investigated is how salt marsh migration is affected by sea-level rise. This case study exemplifies some fundamental and complex physical processes inherent to estuaries, and shows how different management options can be assessed, before their implementation, through a modelling approach.     

Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models

The coherence between different aspects in the environmental system leads to a demand for comprehensive models of this system to explore the effects of different management alternatives. Fuzzy logic has been suggested as a means to extend the application domain of environmental modelling from physical relations to expert knowledge. In such applications the expert describes the system in terms of fuzzy variables and inference rules. The result of the fuzzy reasoning process is a numerical output value. In such a model, as in any other, the model context, structure, technical aspects, parameters and inputs may contribute uncertainties to the model output. Analysis of these contributions in a simplified model for agriculture suitability shows how important information about the accuracy of the expert knowledge in relation to the other uncertainties can be provided. A method for the extensive assessment of uncertainties in compositional fuzzy rule-based models is proposed, combining the evaluation of model structure, input and parameter uncertainties. In an example model, each of these three appear to have the potential to dominate aggregated uncertainty, supporting the relevance of an ample uncertainty approach.     

An ecosystem model for estimating potential shellfish culture production in sheltered coastal waters

A generic ecosystem model has been developed for estimating the potential production of shellfish culture and the effect of that cultivation on the pelagic ecosystem in sheltered coastal waters. The model describes the dynamics of a simple food web, nutrient cycling and growth of shellfish. The design of the model is closely tied to the temporal and spatial scales that are important in determining the sustainable production level for a particular embayment. The pelagic ecosystem, mussel energetics, population dynamics and hydrodynamics are coupled to allow fully dynamic predictions of the effect of the shellfish density. When applied to Beatrix Bay, an intensive culture embayment in the Pelorus Sound of New Zealand, the model successfully captured main features of the observed system behaviour. The hydrodynamic regime of the bay controls mussel growth and production. Although high fluxes of water into the bay suppress nutrient and carbon cycling signals in the system, the model simulations demonstrated that the mussel cultivation can have considerable effects on the ecosystem of the bay including food depletion and nutrient cycling. One of the most obvious effects is nutrient enhancement through mussel excretion at low cultivation densities, which promotes primary production particularly during the N-limitation period in summer. The sensitivity analysis identified uncertainty in some parameters and indicated areas for which experimental studies could lead to model improvement. The modelling exercise has established a primary predictive tool for managing mussel aquaculture of a coastal embayment to estimate relationships between the stock level and the growth rate of mussels, and the potentially achievable harvest and stocking density.