Intercomparison of Two Models,ETA and RAMS,with TRACT Field Campaign Data

In this work a model intercomparison between RAMS and ETA models is carried out, with the aim of evaluating the quality and accuracy of these mesoscale models in reproducing the time evolution of the meteorology in real complex terrain. This is of great importance not only for meteorological forecast but also for air quality assessment. Numerical simulations are performed to reproduce the mean variables' fields and to compare them with measurements collected during the field campaign TRACT. The domain covers the Rhine valley and surrounding mountainous region and we consider a time period of two days. Results from simulations are compared to observations relative to ground stations and radiosoundings. A qualitative analysis is joined to a quantitative estimation of some reference statistical indexes. Both RAMS and ETA models performances are satisfactory when compared to the measured data and also their relative agreement is good. The mean variable fields are reproduced with a satisfactory degree of reliability, even if the simulated profiles are not able to describe the largest fluctuations of the variables. At the surface stations, the best agreement between predictions and observations is obtained for the wind velocity, while the quality of the results is lower for temperature and humidity.     

Database-driven models of the world's Large Marine Ecosystems

We present a new methodology for database-driven ecosystem model generation and apply the methodology to the world's 66 currently defined Large Marine Ecosystems. The method relies on a large number of spatial and temporal databases, including FishBase, SeaLifeBase, as well as several other databases developed notably as part of the Sea Around Us project. The models are formulated using the freely available Ecopath with Ecosim (EwE) modeling approach and software. We tune the models by fitting to available time series data, but recognize that the models represent only a first-generation of database-driven ecosystem models. We use the models to obtain a first estimate of fish biomass in the world's LMEs. The biggest hurdles at present to further model development and validation are insufficient time series trend information, and data on spatial fishing effort.     

Plant-trait-based modeling assessment of ecosystem-service sensitivity to land-use change

Evidence is accumulating that the continued provision of essential ecosystem services is vulnerable to land-use change. Yet, we lack a strong scientific basis for this vulnerability as the processes that drive ecosystem-service delivery often remain unclear. In this paper, we use plant traits to assess ecosystem-service sensitivity to land-use change in subalpine grasslands. We use a trait-based plant classification (plant functional types, PFTs) in a landscape modeling platform to model community dynamics under contrasting but internally consistent land-use change scenarios. We then use predictive models of relevant ecosystem attributes, based on quantitative plant traits, to make projections of ecosystem-service delivery. We show that plant traits and PFTs are effective predictors of relevant ecosystem attributes for a range of ecosystem services including provisioning (fodder), cultural (land stewardship), regulating (landslide and avalanche risk), and supporting services (plant diversity). By analyzing the relative effects of the physical environment and land use on relevant ecosystem attributes, we also show that these ecosystem services are most sensitive to changes in grassland management, supporting current agri-environmental policies aimed at maintaining mowing of subalpine grasslands in Europe.     

An integrated modeling framework for performing environmental assessments: Application to ecosystem services in the Albemarle-Pamlico basins (NC and VA, USA)

The U.S. Environmental Protection Agency uses environmental models to inform rulemaking and policy decisions at multiple spatial and temporal scales. As decision-making has moved towards integrated thinking and assessment (e.g. media, site, region, services), the increasing complexity and interdisciplinary nature of modern environmental problems has necessitated a new generation of integrated modeling technologies. Environmental modelers are now faced with the challenge of determining how data from manifold sources, types of process-based and empirical models, and hardware/software computing infrastructure can be reliably integrated and applied to protect human health and the environment.In this study, we demonstrate an Integrated Modeling Framework that allows us to predict the state of freshwater ecosystem services within and across the Albemarle-Pamlico Watershed, North Carolina and Virginia (USA). The Framework consists of three facilitating technologies: Data for Environmental Modeling automates the collection and standardization of input data; the Framework for Risk Assessment of Multimedia Environmental Systems manages the flow of information between linked models; and the Supercomputer for Model Uncertainty and Sensitivity Evaluation is a hardware and software parallel-computing interface with pre/post-processing analysis tools, including parameter estimation, uncertainty and sensitivity analysis. In this application, five environmental models are linked within the Framework to provide multimedia simulation capabilities: the Soil Water Assessment Tool predicts watershed runoff; the Watershed Mercury Model simulates mercury runoff and loading to streams; the Water quality Analysis and Simulation Program predicts water quality within the stream channel; the Habitat Suitability Index model predicts physicochemical habitat quality for individual fish species; and the Bioaccumulation and Aquatic System Simulator predicts fish growth and production, as well as exposure and bioaccumulation of toxic substances (e.g., mercury).Using this Framework, we present a baseline assessment of two freshwater ecosystem services-water quality and fisheries resources-in headwater streams throughout the Albemarle-Pamlico. A stratified random sample of 50 headwater streams is used to draw inferences about the target population of headwater streams across the region. Input data is developed for a twenty-year baseline simulation in each sampled stream using current land use and climate conditions. Monte Carlo sampling (n = 100 iterations per stream) is also used to demonstrate some of the Framework's experimental design and data analysis features. To evaluate model performance and accuracy, we compare initial (i.e., uncalibrated) model predictions (water temperature, dissolved oxygen, fish density, and methylmercury concentration within fish tissue) against empirical field data. Finally, we ‘roll-up’ the results from individual streams, to assess freshwater ecosystem services at the regional scale.     

An integrated ecosystem service assessment in an artificial desert oasis of northwestern China

Spatially explicit integrated assessment of ecosystem services is a new and important research field in landscape ecology. The objective of this paper was to develop an integrated process-based modeling method to simulate changes in multiple ecosystem services in 2000–2009 at pixel and regional scales in the Zhangye oasis of northwestern China. Six ecosystem service indicators were selected and quantified using process-based models, including net primary production (NPP), grain production, net oxygen production (NOP), carbon sequestration (CS), water conservation, and soil conservation. Analytical results were as follows: (1) At the oasis scale, NPP, NOP, CS, water conservation, and soil conservation decreased from 2000 to 2009, whereas grain production increased. (2) At the pixel scale, the spatial changes in NPP were similar to those in NOP and CS, but changes in grain production showed the opposite pattern. Water conservation and soil conservation showed somewhat unintuitive spatial patterns. (3) The impact of land-use forms on ecosystem services showed that grazing and township construction both had negative impacts on all services, but that nature conservation and wetland development had positive impacts on all services. This research showed that the integrated modeling can be proposed as an environmental decision-making tool in similar case studies.     

Turbulence-Based Models for Gas Transfer Analysis with Channel Shape Factor Influence

Gas transfer through surface water of streams is an effective process for the environmental quality of the aquatic ecosystem. Several theoretical approaches have been proposed to estimate gas transfer rate. This paper is devoted to present a turbulence-based model and to compare it with other 3 turbulence-based modeling frameworks that provide an estimation of gas-transfer coefficient K_L at the air-water interface. These models were derived for the reaeration process. In this paper, they have been verified both for reaeration and volatilization using experimental data collected in a laboratory rectangular flume and in a circular sewer reach. These data refer to oxygen absorption and cyclohexane volatilization, respectively. Comparison of results for oxygen shows that the tested models exhibit an average absolute difference between their results and the experimental data ranging from 12.5% and 25.6%. Also, the scaling analysis of the experimental data support both small-eddy based models and the model proposed by the authors. Moreover, volatilization results show that the process is also affected by a channel shape factor, which was, finally, quantified.     

Analysis of neighborhood dynamics of forest ecosystems using likelihood methods and modeling.

Advances in computing power in the past 20 years have led to a proliferation of spatially explicit, individual-based models of population and ecosystem dynamics. In forest ecosystems, the individual-based models encapsulate an emerging theory of "neighborhood" dynamics, in which fine-scale spatial interactions regulate the demography of component tree species. The spatial distribution of component species, in turn, regulates spatial variation in a whole host of community and ecosystem properties, with subsequent feedbacks on component species. The development of these models has been facilitated by development of new methods of analysis of field data, in which critical demographic rates and ecosystem processes are analyzed in terms of the spatial distributions of neighboring trees and physical environmental factors. The analyses are based on likelihood methods and information theory, and they allow a tight linkage between the models and explicit parameterization of the models from field data. Maximum likelihood methods have a long history of use for point and interval estimation in statistics. In contrast, likelihood principles have only more gradually emerged in ecology as the foundation for an alternative to traditional hypothesis testing. The alternative framework stresses the process of identifying and selecting among competing models, or in the simplest case, among competing point estimates of a parameter of a model. There are four general steps involved in a likelihood analysis: (1) model specification, (2) parameter estimation using maximum likelihood methods, (3) model comparison, and (4) model evaluation. Our goal in this paper is to review recent developments in the use of likelihood methods and modeling for the analysis of neighborhood processes in forest ecosystems. We will focus on a single class of processes, seed dispersal and seedling dispersion, because recent papers provide compelling evidence of the potential power of the approach, and illustrate some of the statistical challenges in applying the methods.     

Using the expert model PERPEST to translate measured and predicted pesticide exposure data into ecological risks

An important topic in the registration of pesticides and the interpretation of monitoring data is the estimation of the consequences of a certain concentration of a pesticide for the ecology of aquatic ecosystems. Solving these problems requires predictions of the expected response of the ecosystem to chemical stress. Up until now, a dominant approach to come up with such a prediction is the use of simulation models or safety factors. The disadvantage of the use of safety factors is a crude method that does not provide any insight into the concentration–response relationships at the ecosystem level. On the other hand, simulation models also have serious drawbacks like that they are often very complex, lack transparency, their implementation is expensive and there may be a compilation of errors, due to uncertainties in parameters and processes. In this paper, we present the expert model prediction of the ecological risks of pesticides (PERPEST) that overcomes these problems. It predicts the effects of a given concentration of a pesticide based on the outcome of already performed experiments using experimental ecosystems. This has the great advantage that the outcome is more realistic. The paper especially discusses how this model can be used to translate measured and predicted concentrations of pesticides into ecological risks, by taking data on measured and predicted concentrations of atrazine as an example. It is argued that this model can be of great use to evaluate the outcome of chemical monitoring programmes (e.g. performed in the light of the Water Framework Directive) and can even be used to evaluate the effects of mixtures.     

土壤温室气体产生与排放影响因素研究进展

土壤是温室气体(如CO2、CH4和N2O)产生的重要源,土壤温室气体主要来自于微生物呼吸,植物根呼吸和土壤动物呼吸。土壤温室气体排放机制及其影响因素是研究全球碳氮循环的重要组成部分。研究表明,影响土壤呼吸的因素很多,土壤理化性质如温度、含水量、有机质含量、pH值、氧化还原电位(Eh)、土壤质地等因素都可以直接影响土壤微生物量及其生理生化过程,从而影响温室气体排放。其中,土壤温度,湿度、有机质含量是关键性因素。此外,地域气候、土地利用以及土地覆盖变化也可以通过改变土壤理化性质及呼吸底物来影响温室气体排放。文章重点论述了土壤温室气体排放机制,排放影响因素以及排放的日变化和季节变化规律。认为今后的研究方向应该是土壤微环境碳氮循环机制,土壤呼吸模型在尺度上的推延,以及注重中国陆地与近海生态系统碳固定及减少碳排放的对策和应用技术研究,特别在人工林碳固定及农业固碳减排方面加大研究力度等。     

Using equation discovery to revise an Earth ecosystem model of the carbon net production

Equation discovery approaches to automated modeling from observed data usually derive equation-based models from scratch rather than from an initial model already established in the domain of use. In this paper, we present an approach that uses new or recent observational data to improve an existing equation-based model. The approach is used to reduce the error of the Earth ecosystem model of the net production of carbon in the atmosphere. We revise the initial ecosystem model in two directions. First, we calibrate the values of the constant parameters in the model on new observational data. Second, we allow the use of alternative equation structures for some of the sub-models of the initial model and use our approach to choose among them. Experiments show that both revision of values of the constant parameters and revision of the structures of sub-models can considerably reduce the error of the initial model.     

Development of Lake Ladoga ecosystem models: modeling of the phytoplankton succession in the eutrophication process. I

New models of Lake Ladoga ecosystem and the results of modeling are presented. In the first part the model of phytoplankton succession in the process of anthropogenic eutrophication of the lake is considered under the evolution of the phosphorus loading. The still continued anthropogenic eutrophication of the lake started in 1962 when the phosphorus load began to increase. Since 1962 during the evolution of the lake’s state from oligotrophic to developed mezotrophic one, the structure of phytoplankton community dominating species was significantly changed as well as its total productivity. The system state in the model is described by 14 parameters: nine phytoplankton complexes, zooplankton, dissolved organic matter, detritus, dissolved mineral phosphorus and dissolved oxygen. The number of parameters of this model is noticeably larger than that of previous models created by the authors. The relative dynamics of phytoplankton complexes in the lake’s ecosystem evolution was simulated by the new model. It is shown that the modeling results are adequately corresponding to the observation data. The results of phytoplankton structure modeling allow to estimate the impact of phytoplankton on the water quality as well as give the prediction of the lake’s ecosystem evolution with the changes of the phosphorus loading.     

Implementation of ecological modeling as an effective management and investigation tool: Lake Kinneret as a case study

The need for scientifically based management of lakes, as key water resources, requires the establishment of quantitative relationships between in-lake processes responsible for water quality (WQ) and the intensity of major management measures (MM, e.g. nutrient loading). In this paper, we estimate the impact of potential changes in nutrient loading on the Lake Kinneret ecosystem. Following validation of the model against a comprehensive dataset, we applied an approach that goes beyond scenario testing by linking the lake ecosystem model DYRESM–CAEDYM with a set of ecosystem variables included in a pre-assessed system of water quality indices. The emergent properties of the ecosystem predicted from the model simulations were also compared with lake data as a form of indirect validation of the model. Model output, in good agreement with lake data, indicated differential effects of nitrogen and phosphorus nutrient loading on concentrations, and major in-lake fluxes, of TN and TP, and dynamics and algal community structure. Both model output and lake data indicated a strong relationship between nitrogen loading and in-lake TN values. This relationship is not apparent for phosphorus and only a weak relationship exists between phosphorus loading and in-lake TP. The modeling results, expressed in terms of water quality, allowed establishment of critical/threshold values for the nutrient loads. Implementation of the ecological modeling supplemented with the quantified set of WQ indices allowed us to take a step towards establishment of the association between permissible ranges for water quality and major management measures, i.e. towards sustainable management.     

A new scheme for initializing process-based ecosystem models by scaling soil carbon pools

Process-based ecosystem models are useful tools, not only for understanding the forest carbon cycle, but also for predicting future change. In order to apply a model to simulate a specific time period, model initialization is required. In this study, we propose a new scheme of initialization for forest ecosystem models, which we term a “slow-relaxation scheme”, that entails scaling of the soil carbon and nitrogen pools slowly during the spin-up period. The proposed slow-relation scheme was tested with the CENTURY version 4 ecosystem model. Three different combinations of scaled soil pools were also tested, and compared to the results from a fast-relaxation regime. The fast-relaxation of soil pools produced unstable, transient model behaviour whereas slow-relaxation overcame this instability. This approach holds promise for initializing ecosystem models, and for starting simulations with more realistic initial conditions.     

Isomorphic chain graphs for modeling spatial dependence in ecological data

Graphical models (alternatively, Bayesian belief networks, path analysis models) are increasingly used for modeling complex ecological systems (e.g., Lee, In: Ferson S, Burgman M(eds) Quantative methods for conservation biology. Springer, Berlin Heilin Heideslperk New York, pp.127–147, 2000; Borsuk et al., J Water Res Plann Manage 129:271–282, 2003). Their implementation in this context leverages their utility in modeling interrelationships in multivariate systems, and in a Bayesian implementation, their intuitive appeal of yielding easily interpretable posterior probability estimates. However, methods for incorporating correlational structure to account for observations collected through time and/or space—features of most ecological data—have not been widely studied; Haas et al. (AI Appl 8:15–27, 1994) is one exception. In this paper, an “isomorphic” chain graph (ICG) model is introduced to account for correlation between samples by linking site-specific Bayes network models. Several results show that the ICG preserves many of the Markov properties (conditional and marginal dependencies) of the site-specific models. The ICG model is compared with a model that does not account for spatial correlation. Data from several stream networks in the Willamette River valley, Oregon (USA) are used. Significant correlation between sites within the same stream network is shown with an ICG model.     

Multiple sources of predictive uncertainty in modeled estimates of net ecosystem CO2 exchange

Net ecosystem CO₂ exchange (NEE) is typically measured directly by eddy covariance towers or is estimated by ecosystem process models, yet comparisons between the data obtained by these two methods can show poor correspondence. There are three potential explanations for this discrepancy. First, estimates of NEE as measured by the eddy-covariance technique are laden with uncertainty and can potentially provide a poor baseline for models to be tested against. Second, there could be fundamental problems in model structure that prevent an accurate simulation of NEE. Third, ecosystem process models are dependent on ecophysiological parameter sets derived from field measurements in which a single parameter for a given species can vary considerably. The latter problem suggests that with such broad variation among multiple inputs, any ecosystem modeling scheme must account for the possibility that many combinations of apparently feasible parameter values might not allow the model to emulate the observed NEE dynamics of a terrestrial ecosystem, as well as the possibility that there may be many parameter sets within a particular model structure that can successfully reproduce the observed data. We examined the extent to which these three issues influence estimates of NEE in a widely used ecosystem process model, Biome-BGC, by adapting the generalized likelihood uncertainty estimation (GLUE) methodology. This procedure involved 400,000 model runs, each with randomly generated parameter values from a uniform distribution based on published parameter ranges, resulting in estimates of NEE that were compared to daily NEE data from young and mature Ponderosa pine stands at Metolius, Oregon. Of the 400,000 simulations run with different parameter sets for each age class (800,000 total), over 99% of the simulations underestimated the magnitude of net ecosystem CO₂ exchange, with only 4.07% and 0.045% of all simulations providing satisfactory simulations of the field data for the young and mature stands, even when uncertainties in eddy-covariance measurements are accounted for. Results indicate fundamental shortcomings in the ability of this model to produce realistic carbon flux data over the course of forest development, and we suspect that much of the mismatch derives from an inability to realistically model ecosystem respiration. However, difficulties in estimating historic climate data are also a cause for model-data mismatch, particularly in a highly ecotonal region such as central Oregon. This latter difficulty may be less prevalent in other ecosystems, but it nonetheless highlights a challenge in trying to develop a dynamic representation of the terrestrial biosphere.     

Predicting leaf physiology from simple plant and climate attributes: a global GLOPNET analysis.

Knowledge of leaf chemistry, physiology, and life span is essential for global vegetation modeling, but such data are scarce or lacking for some regions, especially in developing countries. Here we use data from 2021 species at 175 sites around the world from the GLOPNET compilation to show that key physiological traits that are difficult to measure (such as photosynthetic capacity) can be predicted from simple qualitative plant characteristics, climate information, easily measured ("soft") leaf traits, or all of these in combination. The qualitative plant functional type (PFT) attributes examined are phylogeny (angiosperm or gymnosperm), growth form (grass, herb, shrub, or tree), and leaf phenology (deciduous vs. evergreen). These three PFT attributes explain between one-third and two-thirds of the variation in each of five quantitative leaf ecophysiological traits: specific leaf area (SLA), leaf life span, mass-based net photosynthetic capacity (Amass), nitrogen content (N(mass)), and phosphorus content (P(mass)). Alternatively, the combination of four simple, widely available climate metrics (mean annual temperature, mean annual precipitation, mean vapor pressure deficit, and solar irradiance) explain only 5-20% of the variation in those same five leaf traits. Adding the climate metrics to the qualitative PFTs as independent factors in the model increases explanatory power by 3-11% for the five traits. If a single easily measured leaf trait (SLA) is also included in the model along with qualitative plant traits and climate metrics, an additional 5-25% of the variation in the other four other leaf traits is explained, with the models accounting for 62%, 65%, 66%, and 73% of global variation in N(mass), P(mass), A(mass), and leaf life span, respectively. Given the wide availability of the summary climate data and qualitative PFT data used in these analyses, they could be used to explain roughly half of global variation in the less accessible leaf traits (A(mass), leaf life span, N(mass), P(mass)); this can be augmented to two-thirds of all variation if climatic and PFT data are used in combination with the readily measured trait SLA. This shows encouraging possibilities of progress in developing general predictive equations for macro-ecology, global scaling, and global modeling.     

Data assimilation in a simple marine ecosystem model based on spatial biological parameterizations

Assuming that a set of constant parameters fits for marine ecosystem modeling and parameter estimation studies on large space scales is questionable since ecosystem types spanning long distances are quite different. In this study, SeaWiFS chlorophyll-a data are assimilated into a simple NPZD model by the adjoint method in a climatological physical environment provided by FOAM. To improve the assimilation results, different spatial parameterization schemes are utilized. The results show that the values of the selected sensitive parameters are spatially variable and the application of spatial parameterizations can improve the assimilation results significantly.