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Effects of Nonpoint SourceMarsh Loading on Complex         Estuaries               Edwin A. Roehl, Jr.                John...
South Carolina coastal estuaries                                                Myrtle Beach                              ...
A brief review of tidal dynamics                   Freshwater   Riverine                                                  ...
Difficult to wrestle down nonpointsource effects Difficult to measure and predict NPS impacts on  upland areas    Data s...
Complex forces on a tidal river   Overland flow   from watershed•Smallcontributingwatershed•Little                        ...
Consider alternative approach toNPS modeling Data mining   Transforming data into information   Amalgamation of techniq...
Artificial Neural Networks (ANN) models Mathematical representation of the brain     provides complicated behaviors from...
3D response surfaces for SC, WL, Q Surface created    by ANN model   “Unseen”    variables set to    constant value   M...
ANN model performance forhydrodynamic behavior
Data mining NPS – Consider CooperRiver Estuary case study   Sensitivity of DO to rainfall, water    tidal-level flushing ...
Cooper RiverEstuaryArea of nodevelopmentLittle impactfrom all pointsources
Signal decomposition of water level                       Periodic component                       – Tidal rangeChaotic co...
Dissolved oxygen (DO) dynamics                              Measured DO time                              seriesDissolved-...
Or, in equation form:DO deficit (DOD) =DO [saturated f(T and salinity)]- DO (measured)
Effects of rainfall            Z-axis – DOD on Cooper River                X & Y axis – 1- and 3-                         ...
Cooper River measured and predictedDO-deficit (DOD) as result of rainfall only     RAINAA=2-day moving window average
In addition to rainfall effects, responsesurfaces show effects of WLs on DOD1st response surface shows “Low WL” = higher D...
Data-Driven model’s accuracy, Cooper R.                                                  • Mixing - Tides, Flows from 3 Ri...
Beaufort RiverEstuaryComplex tidal system>9 foot tide rangeNet flow to the northModel developed forTMDL and NPDES permitsM...
Decision Support Systems make “what-ifs”easy for Beaufort River TMDL
Savannah Harbor deepening  Model   hydrodynamics  How far does   salinity intrude   when Harbor is   deepened?  What ha...
Accuracy insights: EFDC vs. ANN model for Savannah River, GA        Salinity, Practical Salinity                          ...
Simulate reduced freshwater flows withEFDC and ANN model and compare                                                  EFDC...
Summary for NPS Estuary Modeling Stormwater and tidal effects (as well as point  source impacts) can be quantified using ...
QuestionsContact:John B. CookAdvanced Data Mining  Intl; Greenville, SCJohn.cook@advdmi.com843.513.2130www.advdmi.com
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  1. 1. Effects of Nonpoint SourceMarsh Loading on Complex Estuaries Edwin A. Roehl, Jr. John B. Cook, PE Advanced Data Mining Intl Greenville, SC
  2. 2. South Carolina coastal estuaries Myrtle Beach Grand Strand Georgetown Charleston Beaufort Savannah
  3. 3. A brief review of tidal dynamics Freshwater Riverine Coastal Inputs Inputs Saltwater Saltwater-Freshwater Interface “…estuaries may never really be steady-state systems; they may be trying to reach a balance they never achieve.” Keith Dyer, from Estuaries – A Physical Introduction (1997)
  4. 4. Difficult to wrestle down nonpointsource effects Difficult to measure and predict NPS impacts on upland areas  Data sets sparse as compared to point source data  Equations and models to estimate loads can have large prediction errors (50-100%) NPS problem compounded on the coast Low-gradient system with little or no slope  Tidal complexities of receiving stream  Poorly defined drainage areas  Limited understanding of runoff process along the coast
  5. 5. Complex forces on a tidal river Overland flow from watershed•Smallcontributingwatershed•Little Tidal forcingfreshwater from oceaninflow connection•Tidallydominated
  6. 6. Consider alternative approach toNPS modeling Data mining  Transforming data into information  Amalgamation of techniques from various disciplines: information theory, signal processing, statistics, machine learning, chaos theory, advanced visualization The physics is manifested in the data Need to extract the information from large data sets of continuous monitoring w/in estuary
  7. 7. Artificial Neural Networks (ANN) models Mathematical representation of the brain  provides complicated behaviors from “simple” components - neurons and synapses x1 x2 y1 inputs x3 outputs y2 x4 x5  models created by training the ANN to learn relationships between variables in example data  form of machine learning from Artificial Intelligence (AI)
  8. 8. 3D response surfaces for SC, WL, Q Surface created by ANN model “Unseen” variables set to constant value Manifestation of historical behavior of system Provides insight into the process dynamics or physics
  9. 9. ANN model performance forhydrodynamic behavior
  10. 10. Data mining NPS – Consider CooperRiver Estuary case study  Sensitivity of DO to rainfall, water tidal-level flushing action and tidal range determined  Model able to simulate rainfall effects/amounts  System had long-term data bases  >3 years of 15-minute WL, DO, SC, WT
  11. 11. Cooper RiverEstuaryArea of nodevelopmentLittle impactfrom all pointsources
  12. 12. Signal decomposition of water level Periodic component – Tidal rangeChaotic component –Filtered water level
  13. 13. Dissolved oxygen (DO) dynamics Measured DO time seriesDissolved-oxygen deficit= difference b/w saturationand measured
  14. 14. Or, in equation form:DO deficit (DOD) =DO [saturated f(T and salinity)]- DO (measured)
  15. 15. Effects of rainfall Z-axis – DOD on Cooper River X & Y axis – 1- and 3- day rainfalls ∆2 mg/LThe sensitivity of DODto rainfall :DOD/inch ≈ 2 mg/L/ 8 in. 2 inchesof rainfall over 2 days 2 in ches= 0.25 mg/L per inch ofrainfall.
  16. 16. Cooper River measured and predictedDO-deficit (DOD) as result of rainfall only RAINAA=2-day moving window average
  17. 17. In addition to rainfall effects, responsesurfaces show effects of WLs on DOD1st response surface shows “Low WL” = higher DOD(range of 3.0 to 4.5 mg/L)2nd response surface shows “High WL” = lower DOD(range of 1.5 to 2.8 mg/L)
  18. 18. Data-Driven model’s accuracy, Cooper R. • Mixing - Tides, Flows from 3 Rivers • Weather (T, P Dew Point) • Point Discharge Wastewater Treatment Plants • Non-Point Discharges - rainfall, 50% overbank storage Measured Neural Network BRANCH/BLTM 10 32 9 30 28 Dissolved oxygen (mg/L) 8 Water temperature (degree Celsius) 26 Temperature 7 24 6 22 5 20 4 18 Dissolved oxygen 3 16 8/21/93 0:30 8/22/93 0:30 8/23/93 0:30 8/24/93 0:30 Date and time
  19. 19. Beaufort RiverEstuaryComplex tidal system>9 foot tide rangeNet flow to the northModel developed forTMDL and NPDES permitsModel simulates 3.5 yearsof historical conditions
  20. 20. Decision Support Systems make “what-ifs”easy for Beaufort River TMDL
  21. 21. Savannah Harbor deepening  Model hydrodynamics  How far does salinity intrude when Harbor is deepened?  What happens when fresh water flows are low?
  22. 22. Accuracy insights: EFDC vs. ANN model for Savannah River, GA Salinity, Practical Salinity Streamflow (cfs) EFDC R2=0.10 M2M R2=0.90 Units EFDC unable toSource: Conrads, P., and Greenfield, J., (2008) predict peaks
  23. 23. Simulate reduced freshwater flows withEFDC and ANN model and compare EFDC R2=0.10 Salinity, Practical Salinity M2M R2=0.90 Streamflow (cfs) UnitsSource: Conrads, P., and Greenfield, J., (2008)
  24. 24. Summary for NPS Estuary Modeling Stormwater and tidal effects (as well as point source impacts) can be quantified using Data Mining techniques 3D visualization gives valuable insight into process physics of the system Data Mining can be used with traditional approaches to minimize errors in load estimates from NPS
  25. 25. QuestionsContact:John B. CookAdvanced Data Mining Intl; Greenville, SCJohn.cook@advdmi.com843.513.2130www.advdmi.com
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