Transcript Interdisciplinary Modeling for Aquatic Ecosystem

Interdisciplinary Modeling for
Aquatic Ecosystems
Curriculum Development Workshop
Water Quality Modeling
John J. Warwick, Director
Division of Hydrologic Sciences
Desert Research Institute
7/18/05
Surface Water Quality Modeling
 What
 Simulate over space and time various
important water quality constituents
(e.g., temperature, dissolved oxygen,
nutrients, metals, toxics, bacteria)
 Analytical or numerical solutions
 Deterministic or Stochastic
 Typical spatial scales = 10 to 100 km
 Typical temporal scales
 Rivers = steady state to years
 Lakes = steady state to centuries
 Why
 Understanding
 Prediction/Regulatory (TMDLs)
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Surface Water Quality Modeling
 How
 First perform flow modeling
 Completely Stirred Tanks Reactor
(CSTR) – Lakes
 Mass (M), Volume (V), Concentration (C)
 M = V*C
 Spatially uniform concentration within
single CSTR (impulse load example)
 Plug Flow Reactor – Streams/Rivers
 No Dispersion (impulse load example)
 With Dispersion (impulse load example)
 Numerical solutions are often for a series
of CSTRs (impulse load example)
 Numerical Dispersion
 All solutions are based upon a relatively
simple mass balance approach
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River Conceptualization
Point Source
Flow
Groundwater, Non Point Source
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Surface Water Quality Modeling
 Simple Mass Balance
 Mass Flux Rate (MFR) = Mass/time
 Change in Mass over time
dM
 MFR in  Creation  Losses  MFR out
dt
dM d V C 
dC
dV

V
C
dt
dt
dt
dt
 Losses, Reaction rate coefficients (K)
 Zero-order
dC
 K Cn
 First-order
dt
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Surface Water Quality Modeling
 Typical Assumptions, Limitations,
and Errors
 Reaction rate coefficients apply
globally (i.e. homogeneous)
 Reaction rate coefficients vary with
temperature but are otherwise
constant with respect to time
 Complex biological systems are
simplified greatly (e.g. abbreviated
foodwebs)
 Incredible LACK OF DATA
 Coffee and Donut Monitoring
 Lagrangian Sampling Example
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Surface Water Quality Modeling
 Uncertainties
 Monitoring
 Errors (e.g. sample labeling)
 Overall lack of data
 Uncertainties in data
 Field sampling error
 Laboratory analysis error
 Modeling
 Errors (e.g. decimal point or units)
 Decision Uncertainty
 Model Simplifications
 Steady state
 Homogeneous
 Single variable for multiple
species
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Surface Water Quality Modeling
Warwick’s Modeling Rules
1.
2.
Data Analysis
(Modeling)
Refinement
of Model and
Data Collection
3.
4.
5.
Data
Collection
6.
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Carefully review existing data (spatial
and temporal resolutions)
Carefully consider the goals of the
modeling project (what is really
needed)
Carefully review existing models and
select the most appropriate for the
data and goals
Do NOT assume that the selected
model is correct or that you can
correctly run the selected model
(model validation)
Develop an integrated monitoring and
modeling approach including both
calibration and verification
Do NOT underestimate the need for
and importance of technology transfer
and public education
Surface Water Quality Modeling
Warwick’s Modeling Realities
1.
2.
3.
4.
5.
6.
Data is VERY limited (get used to it)
Models will never be perfect (get over it)
Model documentation is poor (expect it)
Thoughtfully constructed and applied
models should nonetheless be better
than guessing and are therefore
necessary
The complexities of the system (e.g.
biogeochemical interactions) begs for
multi-disciplinary teams
A successful team will have persons with
strong disciplinary expertise, who
understand how to communicate
effectively, and who appreciate the value
of other’s knowledge
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Surface Water Quality Modeling
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Surface Water Quality Modeling
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