Coupling of Engineering and Biological Models for Ecosystem Analysis

Robust ecosystem analysis of water resource systems remains elusive. A principle reason is the difficulty in linking engineering models used to simulate physicochemical processes associated with project design or operation with biological models used to simulate biological population attributes. A retrospective shows that each modeling tradition can be generally assigned (with exceptions) into either an Eulerian or Lagrangian reference framework. Eulerian and Lagrangian reference frameworks can be coupled to create a new synthesis, the Coupled Eulerian-Lagrangian Hybrid Ecological Modeling Concept (CEL Hybrid Concept), capable of simulating different ecosystem processes that range widely in spatial and temporal scale. The foundation of the CEL Hybrid Concept is the coupler, a collection of algorithms based on conservation principles that transform and conserve data in a way that allows the two frameworks to share a common information base. The coupling algorithm allows the simulation to aggregate, disaggregate, and translate information, as required by each framework, so that processes that differ substantially in scale can each be adequately simulated. The coupled system is illustrated by linking a fish swim path selection model with a hydrodynamic and water quality model.

[1]  B. Mandelbrot How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension , 1967, Science.

[2]  Keith M. Reynolds,et al.  Evolving Approaches and Technologies to Enhance the Role of Ecological Modeling in Decision Making , 2003 .

[3]  James P. Hoffmann,et al.  Modeling Phosphorus Dynamics in Ecosystems: Mass Balance and Dynamic Simulation Approaches , 1998 .

[4]  D. Schindler,et al.  STOICHIOMETRY OF FISHES AND THEIR PREY: IMPLICATIONS FOR NUTRIENT RECYCLING , 1997 .

[5]  L. Edelstein-Keshet,et al.  Complexity, pattern, and evolutionary trade-offs in animal aggregation. , 1999, Science.

[6]  D. DeAngelis,et al.  Individual-Based Models and Approaches in Ecology , 1992 .

[7]  W. Shyy,et al.  Elafint: a Mixed Eulerian-Lagrangian Method for Fluid Flows with Complex and Moving Boundaries , 1996 .

[8]  Scott Ferson,et al.  Extreme event risk analysis for age-structured populations , 1989 .

[9]  John M. Nestler,et al.  Describing scales of features in river channels using fractal geometry concepts , 2000 .

[10]  C. Alewell,et al.  Use of objective criteria for the assessment of biogeochemical ecosystem models , 1998 .

[11]  Raymond S. Chapman,et al.  New York Bight Study. Report 3. Three Dimensional Particle Tracking Model for Floatables and Dissolved and Suspended Materials , 1994 .

[12]  James H. Brown,et al.  Abundance and distribution of desert annuals: are spatial and temporal patterns related? , 2000 .

[13]  R. Andrew Goodwin,et al.  Simulating Movement Patterns of Blueback Herring in a Stratified Southern Impoundment , 2002 .

[14]  James L. Martin,et al.  Hydrodynamics and Transport for Water Quality Modeling , 1998 .

[15]  Hin-Fatt Cheong,et al.  A size-based ecosystem model for pelagic waters , 1998 .

[16]  Jahau Lewis Chen,et al.  The conflict-problem-solving CAD software integrating TRIZ into eco-innovation , 2004 .

[17]  David A. Schmetterling,et al.  Montana Anglers' Inability to Identify Bull Trout and other Salmonids , 1999 .

[18]  C. Scott Particle Tracking Simulation of Pollutant Discharges , 1997 .

[19]  S. Levin The problem of pattern and scale in ecology , 1992 .

[20]  P. Cury,et al.  Population viability and spatial fish reproductive strategies in constant and changing environments: an individual-based modelling approach , 1997 .

[21]  Daniel P. Loucks,et al.  Simulating Mobile Populations in Aquatic Ecosystems , 2001 .

[22]  H. Ronald Pulliam,et al.  The Scientific Basis for Ecosystem Management , 1996 .

[23]  Thomas Maxwell,et al.  Development of a general ecosystem model for a range of scales and ecosystems , 1996 .

[24]  R. Thomann,et al.  Principles of surface water quality modeling and control , 1987 .

[25]  J. Parrish,et al.  Animal Groups in Three Dimensions: Individual decisions, traffic rules, and emergent pattern in schooling fish , 1997 .

[26]  Mary T. Bremigan,et al.  Nitrogen and phosphorus excretion by detritivorous gizzard shad in a reservoir ecosystem , 1997 .

[27]  A. Baptista,et al.  A comparison of integration and interpolation Eulerian‐Lagrangian methods , 1995 .

[28]  D. McQueen,et al.  Biomanipulation at Rice Lake, Ontario, Canada , 1994 .

[29]  Andrew Staniforth,et al.  A mass-conserving semi-Lagrangian scheme for the shallow-water equations , 1994 .

[30]  William K. Nuttle Ecosystem managers can learn from past successes , 2000 .

[31]  H. S. Udaykumar,et al.  Rheological modelling of leukocytes , 1998, Medical and Biological Engineering and Computing.

[32]  P. H. Michaletz Population Characteristics of Gizzard Shad in Missouri Reservoirs and Their Relation to Reservoir Productivity, Mean Depth, and Sport Fish Growth , 1998 .

[33]  P. Turchin Animal Groups in Three Dimensions: Quantitative analysis of animal movements in congregations , 1997 .

[34]  Animal Groups in Three Dimensions: Perspectives on sensory integration systems: Problems, opportunities, and predictions , 1997 .

[35]  R. J. Gibson,et al.  Density-dependent habitat selection by juvenile Atlantic salmon (Salmo salar) in experimental riverine habitats , 1999 .

[36]  F. Oliver Gathmann,et al.  Inter-site: a new tool for the simulation of spatially realistic population dynamics , 1998 .

[37]  G. Mathias Kondolf,et al.  Measuring and Modeling the Hydraulic Environment for Assessing Instream Flows , 2000 .