Interdisciplinary Modeling for Aquatic Ecosystems

1. Interdisciplinary Modeling forAquatic Ecosystems Curriculum Development Workshop Water Quality Modeling John J. Warwick, Director Division of Hydrologic Sciences Desert Research Institute

2. 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)

3. 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

4. River Conceptualization Point Source Flow Groundwater, Non Point Source

5. Surface Water Quality Modeling • Simple Mass Balance • Mass Flux Rate (MFR) = Mass/time • Change in Mass over time • Losses, Reaction rate coefficients (K) • Zero-order • First-order

6. 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

7. 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

8. Surface Water Quality Modeling Warwick’s Modeling Rules • 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

9. Surface Water Quality Modeling Warwick’s Modeling Realities • 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

10. Surface Water Quality Modeling

11. Surface Water Quality Modeling