Environmental modelling : new research / Paul N. Findley, editor.

Format:

Book

Contributor:

Findley, Paul N.

Language:

English

Subjects (All):

Environmental sciences--Mathematical models.

Environmental sciences.

Physical Description:

1 online resource (250 p.)

Edition:

1st ed.

Place of Publication:

New York : Nova Science Publishers, c2009.

Language Note:

English

Summary:

Environment models seek to re-create what occurs during some event in nature. It is much easier and practical to create computer models to run certain experiments than it is to go out and do the same experiment again and again. Computer models take equations which were usually formulated through testing under natural conditions, and put them into computer programs where they can be run quickly and easily. A model can then output the results of doing these equations into a form which can be output to a screen for the user to view. The aim is to improve the capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales. This new book presents the latest research from around the globe.

Contents:

Intro

ENVIRONMENTAL MODELLING:NEW RESEARCH

CONTENTS

PREFACE

EXPERT COMMENTARY

ADVANCES IN SPACE-TIME TECHNOLOGY FORASSESSING HUMAN EXPOSURE TO ENVIRONMENTALCONTAMINANTS

Abstract

Introduction

Space-Time Software Systems

Space-Time Datasets for Exposure Assessment

Example Applications of Space-Time Exposure Reconstruction

Conclusion

References

RESEARCH AND REVIEW STUDIES

BAYESIAN BELIEF NETWORKS IN ENVIRONMENTALMODELLING: A REVIEW OF RECENT PROGRESS

Description of a BBN

Literature Search Methods

Domains

Network Structure

Obtaining Conditional Probabilities

Model Testing

Sensitivity Analysis

Use of BBNs to Support Decision-Making

Advantages of BBNs

Limitations of BBNs

Conclusions and Forward Look

Acknowledgements

EOF REGRESSION ANALYTICAL MODELWITH APPLICATIONS TO THE RETRIEVALOF ATMOSPHERIC TEMPERATURE AND GASCONSTITUENTS CONCENTRATION FROM HIGHSPECTRAL RESOLUTION INFRAREDOBSERVATIONS

1. Introduction

2. Mathematical Theory

3. EOF Based Regression Algorithm

3.1. Data and Parameters Space, Training Data Set and Basic Definitions

3.2. How Many Principal Components Do We Need to Extract?

3.3. The System of Regression Coefficients

3.3.1. Bias and Second Order Statistics of the Retrieval

3.3.2. Assessing the Vertical Spatial Resolution of the Retrieval, the Index iD

4. Implementation with Simulated Data and Assessment of theRetrieval Performance

4.1. More on "How Many Components doWe Need to Extract?"

4.2. Values of the Retrieval Interdependency Index, iD

5. Application to Real Observations

5.1. CAMEX/3 Experiment

5.2. EAQUATE Experiment

5.3. IASI Tropical Soundings

6. Conclusion

Acknowledgment

References.

COMOVEMENT AND CYCLICAL PATTERNSOF SOUTHERN PINE BEETLE OUTBREAKS

2. Methods and Data

2.1. Measuring Infestation Risk

2.2. Assessing Comovement

2.3. Assessing Cyclical Patterns

3. Results

3.1. Infestation Risk

3.2. Comovement

3.3. Cyclical Patterns

4. Concluding Remarks

TRENDS IN MODELLING OF RADIONUCLIDESUPTAKE BY PARTICULATE MATTER IN THE MARINEENVIRONMENT USING BOX MODELS

I. Introduction

II. Theory of Ion Exchange between Water and SuspendedParticles

III. Development of Kinetic Box Models

III.1. The One-Step Reversible Reaction

III.2. The Two-Step Model

III.3. The Three-Step Model

IV. Applicability of Box Models

Strontium

Americium

Plutonium

V. Conclusion

SPATIAL DOWN-SCALING AS A TOOL TO IMPROVEMULTIFUNCTIONALITY INDICATORS IN ECONOMICMODELS

2. Methods

2.1. The CAPRI Model1

2.2. Regional Down-Scaling 3

2.3. Meta-model of DNDC

3. Indicators

4. Indicator Performance

5. Technical Solution

ADAPTIVE CONTROL METHODOLOGY AND SOMEAPPLICATIONS IN ENVIRONMENTAL MODELLING

2. Adaptive Control Methodology

2.1. The Methodology

2.2. Environmental Modelling with ACM

3. A Sustainability Case Study

3.1. Background

3.2. The Initial Policies in 'NOW - 5 years'

3.3. Monitoring 'NOW' the Initial Policies

3.4. Revisiting 'NOW' the Initial Policies

4. Conclusion

PREDICTION OF SEDIMENT SOURCE AREASWITHIN WATERSHEDS AS AFFECTED BY SOIL DATARESOLUTION

Introduction.

Description of SWAT

STATSGO versus SSURGO

Study Area within the Elm River Watershed

Study Area within the Cowhouse Creek Watershed

Model Set up

Assessment Method

Results and Discussion

Calibrated Models

Predicted Sediment

Conclusions

Acknowledgement

LANDSLIDE MODELING

2. Landslide Mapping

3. Physically-Based Landslide Models

3.1. Factor of Safety (FS)

3.2. Critical Rainfall Model

4. Statistical Landslide Model

4.1. Bivariate Analysis

4.2. Multivariate Analysis

5. Model Validation

6. Examples of Landslide Models

6.1. A Critical Rainfall Model

6.2. A Certainty Factor Model

6.3. A Logistic Regression Model

7. Conclusion

SPATIAL MODELLING OF GROUNDWATERPOLLUTION USING A GIS

Study Area

Geology and Hydrogeology

Materiel and Methods

Sampling and Analysis

GIS Approaches

Multivariate Analysis

Results

1. Physico-Chemical Parameters

2. Cation Chemistry

3. Anion Chemistry

4. Heavy Metals Distributions in Groundwater Samples

5. Multivariate Analysis

6. GIS Analysis

INDEX.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-61728-411-4

OCLC:

662453081