Lessons for successful participatory watershed modeling: A perspective from modeling practitioners

Participatory modeling is the process of incorporating stakeholders, often including the public, and decision makers into an otherwise purely analytic modeling process to support decisions involving complex natural resources questions. Participatory modeling is particularly compatible with the rising focus on integrated water resources management, which incorporates systems theory and aims to protect and improve water resources while considering economic and social concerns in the community. In this article, we present a series of lessons based on experience working with stakeholder groups to develop watershed and water quality models to address water resource issues in Maryland, Vermont, Utah, and Virginia. We believe these lessons in participatory modeling, discussed from our perspective as scientists and modelers engaged in applied watershed issues, can help to achieve successful participatory modeling efforts elsewhere. The lessons relate to stakeholder engagement, modeling tools, model development and calibration, scenario testing, and applying results to management decisions.     

基于UML的学籍管理系统分析与设计

在当前的计算机软件设计中，大型软件系统基本上是采用面向对象方法来进行设计的，统一建模语言UML是面向对象分析和设计过程中重要的建模工具．本文以学籍管理系统的开发为实例，介绍UML如何辅助完成用例建模、静态建模和动态建模．     

Some comments on computer simulators for plant growth—1975

The status of plant growth modeling activities is reviewed for a general audience. Recently, excellent textbooks and review papers have been published which summarize past efforts in modeling plant physiological processes. Anyone interested in plant-growth modeling should be aware of this background material which describes the modeling tradition in the plant sciences. Presently, we need better critical evaluations of such models, including experimental techniques necessary for generating associated data. Such critiques should be aimed at users of large plant-growth models in other disciplines. Recent developments in the construction of cotton growth models are described. Modeling philosophy is discussed in relation to the above.     

Introduction to the special issue of Ecological Modelling: “Advances in Modeling Estuarine and Coastal Ecosystems: Approaches, Validation, and Applications”

The development and application of ecosystem models in estuarine and coastal systems has grown exponentially over the past four decades. Models have become ensconced as major tools for both heuristic study of ecosystem structure and function as well as for informing management decisions, particularly with respect to cultural eutrophication. In recent years an ever-expanding toolbox of modeling approaches is being offered to complement traditional methods. This expansion of modeling in estuarine and coastal science was exemplified by four sessions devoted to modeling at the 2007 biennial conference of the Estuarine Research Federation in Providence, RI. We felt the time was right to propose a special session of Ecological Modelling to synthesize talks from these sessions to present the state of the art in coastal and estuarine modeling. The collection of papers contained in this special issue presents a diversity of traditional and novel modeling approaches, methods for assessing model validity and predictability, and the utility of models in management applications. We believe that together these papers provide an excellent overview of current approaches to modeling estuarine hydrodynamics, water quality, and ecosystem/food web dynamics, applications of complex and relatively simple modeling approaches, applications in both deep and shallow coastal systems, goals relevant for both heuristic and management applications, and perspectives based on traditional mechanistic model development as well as more recent alternative approaches.     

An enhanced environmental multimedia modeling system based on fuzzy-set approach: II. Model validation and application

Part I of this study develops an enhanced environmental multimedia modeling system (EMMS) based on fuzzy-set approach. Once the model development is complete, the composite module and the entire modeling system need to be tested and validated to ensure that the model can simulate natural phenomena with reasonable and reliable accuracy. The developed EMMS is first tested in a complete case study. And then verification results are conducted to compare with extensively researched literature data. In the third step, the data from an experimental landfill site is used for a pilot-scale validation. The comparisons between EMMS outputs and the literature data indicate that the EMMS can perform accurate modeling simulation. The modules of EMMS could support the entire environmental multimedia modeling system. Further field-scale validation is finished. The results are satisfactory. Most of the modeling yields closely match the monitoring data collected from sites. In addition, with the aid of fuzzy-set approach, EMMS can be a reliable and powerful tool to address the complex environmental multimedia pollution problems and provide an extensive support for decision makers in managing the contaminated environmental systems.     

Refining logistic regression models for wildlife habitat suitability modeling—A case study with muntjak and goral in the Central Himalayas, India

High quality habitat suitability maps are indispensable for the management and planning of wildlife reserves. This is particularly important for megadiverse developing countries where shortages in skilled manpower and funding may preclude the use of mathematically complex modeling techniques and resource-intensive field surveys. In this study, we propose a simulation based k-fold partitioning and re-substitution approach to refine and update logistic regression models that are widely used for habitat suitability assessment and modeling. We test the modeling strategy using data from a rapid field survey conducted for habitat suitability assessment for muntjak (Muntiacus muntjak) and goral (Naemorrhaedus goral) in the central Himalayas, India. Results obtained from simulations match expectations in terms of model behavior and in terms of published habitat associations of the investigated species. Qualitative comparisons with predictions from the GARP, MaxEnt and Bioclimatic Envelopes modeling systems also show broad agreement with predictions obtained from the proposed technique. The proposed technique is suggested as a rapid-assessment precursor to detailed habitat studies such as patch occupancy modeling in situations where funds or trained manpower are not available.     

Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?

An important decision in presence-only species distribution modeling is how to select background (or pseudo-absence) localities for model parameterization. The selection of such localities may influence model parameterization and thus, can influence the appropriateness and accuracy of the model prediction when extrapolating the species distribution across time and space. We used 12 species from the Australian Wet Tropics (AWT) to evaluate the relationship between the geographic extent from which pseudo-absences are taken and model performance, and shape and importance of predictor variables using the MAXENT modeling method. Model performance is lower when pseudo-absence points are taken from either a restricted or broad region with respect to species occurrence data than from an intermediate region. Furthermore, variable importance (i.e., contribution to the model) changed such that, models became increasingly simplified, dominated by just two variables, as the area from which pseudo-absence points were drawn increased. Our results suggest that it is important to consider the spatial extent from which pseudo-absence data are taken. We suggest species distribution modeling exercises should begin with exploratory analyses evaluating what extent might provide both the most accurate results and biologically meaningful fit between species occurrence and predictor variables. This is especially important when modeling across space or time—a growing application for species distributional modeling.     

A new synergetic paradigm in environmental numerical modeling: Hybrid models combining deterministic and machine learning components

A new type of environmental numerical models, hybrid environmental numerical models (HEMs) based on combining deterministic modeling and machine learning components, is introduced and formulated. Conceptual and practical possibilities of developing HEM, as an optimal synergetic combination of the traditional deterministic/first principles modeling (like that used for solving PDEs on the sphere representing model dynamics of global climate models) and machine learning components (like accurate and fast neural network emulations of model physics or chemistry processes), are discussed. Examples of developed HEMs (hybrid climate models and a hybrid wind–wave ocean model) illustrate the feasibility and efficiency of the new approach for modeling extremely complex multidimensional systems.     

An enhanced environmental multimedia modeling system based on fuzzy-set approach: I. Theoretical framework and model development

Multimedia environmental modeling is extremely complex due to the intricacy of the systems with the consideration of many related factors. Traditional environmental multimedia models (EMMs) are usually based on one-dimensional and first-order assumptions, which may cause numerical errors in the simulation results. In this study, a new user-friendly fuzzy-set enhanced environmental multimedia modeling system (FEEMMS) is developed, and includes four key modules: an air dispersion module, a polluting source module, an unsaturated zone module, and a groundwater module. Many improvements over previous EMMs have been achieved through dynamically quantifying the intermedia mass flux; incorporating fuzzy-set approach into environmental multimedia modeling system (EMMS); and designing a user-friendly graphic user interface (GUI). The developed FEEMMS can be a useful tool in estimating the time-varying and spatial-varying chemical concentrations in air, soil, and groundwater; characterizing the potential risk to human health presented by contaminants released from a contaminated site; and quantifying the uncertainties associated with modeling systems and subsequently providing robustness and flexibility for the remediation-related decision making.     

PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python

• PyLUR comprises four modules for developing and applying a LUR model. • It considers both conventional and novel potential predictor variables. • GDAL/OGR libraries are used to do spatial analysis in the modeling and prediction. • Developed on Python platform, PyLUR is rather efficient in data processing. Land use regression (LUR) models have been widely used in air pollution modeling. This regression-based approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment. Although conceptually quite simple, its successful implementation requires detailed knowledge of the area, expertise in GIS, statistics, and programming skills, which makes this modeling approach relatively inaccessible to novice users. In this contribution, we present a LUR modeling and pollution-mapping software named PyLUR. It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently. This self-developed software comprises four modules: a potential predictor variable generation module, a regression modeling module, a model validation module, and a prediction and mapping module. The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR (with similar functions implemented on R language platform) in terms of model accuracy, processing efficiency and software stability. The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined. Furthermore, PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.     

离心模型试验技术在环境岩土工程中的应用现状与展望

首先简单介绍了离心模型试验技术的发展状况,然后探讨了其在环境岩土工程中的应用,研究了模型和原型的相似规律,提出了环境岩土工程离心模型试验的发展方向.     

3S与在线监测技术在环境模型研究中的应用

结合环境信息系统的研究现状和研究实例，首先分析了3S和在线监测技术与环境模型研究结合的必要性，并分析指出了3S、在线监测技术在环境模型研究中潜在的应用范围，然后剖析了3S技术与环境模型研究结合的三阶水平，最后介绍了3S在环境规划模型研究中的理想结合框架。     

Nano-QSAR: 纳米毒理学领域的新方法

随着纳米毒理学实验研究的不断深入,反映纳米材料生物毒性效应的数据也不断丰富,以这些数据为基础建立的定量结构活性关系(QSAR)模型开始发挥其在纳米材料潜在毒性研究和预测方面的作用。纳米材料的QSAR(Nano-QSAR)研究以经典QSAR模型为指导,结合纳米材料特殊的物理化学性质,提供了一种对纳米材料快速筛选和优先测试的新途径。本文就Nano-QSAR的前期研究现状,从纳米材料结构描述符、毒性效应数据和建模方法3个方面分析了模型的构建流程和框架;通过列举部分研究成果和主要的模型指标,初步探讨了建模方法的选择和结构描述符的识别;最后指出目前Nano-QSAR研究面临的挑战和今后努力的方向。     

Efficient statistical mapping of avian count data

We develop a spatial modeling framework for count data that is efficient to implement in high-dimensional prediction problems. We consider spectral parameterizations for the spatially varying mean of a Poisson model. The spectral parameterization of the spatial process is very computationally efficient, enabling effective estimation and prediction in large problems using Markov chain Monte Carlo techniques. We apply this model to creating avian relative abundance maps from North American Breeding Bird Survey (BBS) data. Variation in the ability of observers to count birds is modeled as spatially independent noise, resulting in over-dispersion relative to the Poisson assumption. This approach represents an improvement over existing approaches used for spatial modeling of BBS data which are either inefficient for continental scale modeling and prediction or fail to accommodate important distributional features of count data thus leading to inaccurate accounting of prediction uncertainty.     

化学反应模式计算程序的自动生成

在化学模式，尤其是大气化学模式中，常常需要求解涉及许多化学反应的大型刚性常微分方程组，其中书写算法所需的右函数子程序及雅可比矩阵子程序是一相当繁琐费时的工作，本文讨论并给出了可自动书写这些子程序的实用程序的开发过程，使用该实用程序时，用户仅需输入符合格式规定的化学动力学反应文本即可得到相应的化学反应模式的计算程序，文中涉及的程序和子过程用Ｃ语言并在微机上编译运行通过，可以满足绝大部分化学模式的需要     

Comparison of Two Types of Metapopulation Models in Real and Artificial Landscapes

Abstract: Application of metapopulation models is becoming increasingly widespread in the conservation of species in fragmented landscapes. We provide one of the first detailed comparisons of two of the most common modeling techniques, incidence function models and stage-based matrix models, and test their accuracy in predicting patch occupancy for a real metapopulation. We measured patch occupancies and demographic rates for regional populations of the Florida scrub lizard (   Sceloporus woodi ) and compared the observed occupancies with those predicted by each model. Both modeling strategies predicted patch occupancies with good accuracy ( 77–80%) and gave similar results when we compared hypothetical management scenarios involving removal of key habitat patches and degradation of habitat quality. To compare the two modeling approaches over a broader set of conditions, we simulated metapopulation dynamics for 150 artificial landscapes composed of equal-sized patches (2–1024 ha) spaced at equal distances (50–750 m). Differences in predicted patch occupancy were small to moderate (<20%) for about 74% of all simulations, but 22% of the landscapes had differences openface> 50%. Incidence function models and stage-based matrix models differ in their approaches, assumptions, and requirements for empirical data, and our findings provide evidence that the two models can produce different results. We encourage researchers to use both techniques and further examine potential differences in model output. The feasibility of obtaining data for population modeling varies widely among species and limits the modeling approaches appropriate for each species. Understanding different modeling approaches will become increasingly important as conservation programs undertake the challenge of managing for multiple species in a landscape context.     

Maximum entropy modeling of species geographic distributions

The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In order to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical mammals: a lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP). We made predictions on 10 random subsets of the occurrence records for both species, and then used the remaining localities for testing. Both algorithms provided reasonable estimates of the species’ range, far superior to the shaded outline maps available in field guides. All models were significantly better than random in both binomial tests of omission and receiver operating characteristic (ROC) analyses. The area under the ROC curve (AUC) was almost always higher for Maxent, indicating better discrimination of suitable versus unsuitable areas for the species. The Maxent modeling approach can be used in its present form for many applications with presence-only datasets, and merits further research and development.     

Space-time modeling of environmental monitoring data

This study describes and applies statistical methods for space-time modeling of data from environmental monitoring programs, e.g., within areas such as climate change, air pollution and aquatic environment. Such data are often characterized by sparse sampling in both the temporal and spatial dimensions. In order to improve the amount of information on the physical system in question we suggest using statistical modeling methods for monitoring data. Model predictions combined with observations could be analyzed directly to assess the environmental state or as forcing functions for time series models and deterministic, hydrodynamic models. To illustrate the approach we applied the proposed modeling methods to data from the Danish and Swedish marine monitoring programs. Time series with a weekly resolution were predicted from observations of dissolved inorganic nitrogen (DIN) from the Kattegat basin (1993–1997). DIN observations were sparse, irregularly distributed and comprised approximately 10% of the generated time series.     

Integrated modeling of flood flows and tidal hydrodynamics over a coastal floodplain

The interactions of physical processes between estuaries and upstream river floodplains are of great importance to the fish habitats and ecosystems in coastal regions. Traditionally, a hydraulic analysis of floodplains has used one- or two-dimensional models. While this approach may be sufficient for planning the engineering design for flood protection, it is inadequate when floodwaters inundate the floodplain in a complex manner. Similarly, typical estuarine and coastal modeling studies do not consider the effect of upstream river floodplains because of the technical challenge of modeling wetting and drying processes in floodplains and higher bottom elevations in the upstream river domain. While various multi-scale model frameworks have been proposed for modeling the coastal oceans, estuaries, and rivers with a combination of different models, this paper presents a modeling approach for simulating the hydrodynamics in the estuary and river floodplains, which provides a smooth transition between the two regimes using an unstructured-grid, coastal ocean model. This approach was applied to the Skagit River estuary and its upstream river floodplain of Puget Sound along the northwest coast of North America. The model was calibrated with observed data for water levels and velocities under low-flow and high-flood conditions. This study successfully demonstrated that a three-dimensional estuarine and coastal ocean model with an unstructured-grid framework and wetting-drying capability can be extended much further upstream to simulate the inundation processes and the dynamic interactions between the estuarine and river floodplain regimes.