1. Introduction

5. The Hanford Site

13. What does in, must come out….

14. recharge Other losses..including external water exchanges

21. K-U1 Hanford (Unit 1) Hydraulic Conductivity Multiplier K-U5 Ringold (Unit 5) Hydraulic Conductivity Multiplier K-U7 Ringold (Unit 7) Hydraulic Conductivity Multiplier K-U9 Ringold (Unit 9) Hydraulic Conductivity Multiplier SY-H Hanford (Unit 1) Specific Yield Multiplier SY-RU5 Ringold (Unit 5) Specific Yield Multiplier F-CC Cold Creek Valley Flux Multiplier F-DC Dry Creek Valley Flux Multiplier F-NR Natural Recharge Multiplier F-RH Rattlesnake Hills Flux Multiplier. Model parameters Application of “principal of parsimony”

23. Hillside and Piezometers

24. System Properties Transmissivity = 100 m 2 /day Creek conductance is very high Recharge = 30 mm/yr

25. Groundwater levels

26. Transmissivity distribution - I 100 m 2 /day

27. 12 m 2 /day 360 m 2 /day Transmissivity distribution - II

28. Chesapeake Bay

29. Watershed Model by Major Basin

30. P4 Potomac Segmentation

31. Simulated with HSPF

32. Loading Sources in Watershed Model PastureHay Imp Urb Cons. Till Perv UrbForest Conv. TillManure RIVER REACH Mixed Point Source Septic

33. Crop Simulation MeteorologyPrecipitation Runoff and Groundwater Land Morphology Nitrogen Cycle Sediment Export Phosphorus Cycle Nutrient Inputs

34. Crop Simulation Water Sediment AGCHEM PQUAL Water Sediment

35. Water simulation Ground Water Surface Interflow Lower Zone

36. Sediment Simulation Detached Sediment Soil Matrix (unlimited) Wash off Detachment Attachment f(rain intensity) f(time)

37. Trees Roots Leaves Particulate Refractory Organic N Particulate Labile Organic N Solution Ammonia Nitrate Solution Labile Organic N Adsorbed Ammonia Solution Refractory Organic N Nitrogen Cycle in Watershed Model Forest Atmospheric Deposition Denitrification

38. River Sediment Simulation Suspended Sediment Bed Storage (unlimited) Outflow Scour Deposition in Phase IV Watershed Model Inflow

39. N River Simulation Algae ORGN NO3 } Sediment NH3

40. SNOW section of the PERLND module of HSPF

41. PWATER section of the PERLND module of HSPF

42. (continued)

43. P4 Potomac Segmentation Gauging station

47. Daily Flow

48. Monthly Volume

49. Exceedence fraction

50. lzsn 0.2732 0.8083 infilt 0.1599 0.0704 agwrc 0.9500 0.9837 deepfr 2.0000E-03 1.0000E-03 basetp 2.9457E-02 0.1719 agwetp 1.5590E-03 3.4537E-03 uzsn 1.5798 0.7269 intfw 2046.0 9.0670 irc 0.8671 0.8931 lzetp 0.6000 0.6000 Parameter values

51. lzsn 0.2732 0.8083 0.0460 infilt 0.1599 0.0704 0.0159 agwrc 0.9500 0.9837 0.9979 deepfr 2.0000E-03 1.0000E-03 5.0000e-2 basetp 2.9457E-02 0.1719 1.6641e-3 agwetp 1.5590E-03 3.4537E-03 1.0000e-3 uzsn 1.5798 0.7269 2.4509 intfw 2046.0 9.0670 18.950 irc 0.8671 0.8931 0.9203 lzetp 0.6000 0.6000 0.6000 More parameter values

52. The common thread A model parameterised on the basis of “outside measurements” alone will probably fit field data poorly. Hence it must be “calibrated”.

53. The common thread No matter how many parameters a model has (ie. no matter how complex it is) normally only a handful of parameters will be adjusted during the calibration process. The model’s complexity then becomes a veneer.

54. The common thread Even after a model has been calibrated, it is still very rare for model to have the ability to simulate every nuance of a system’s behaviour.

55. The common thread Even if it could, a multiplicity of parameters would normally provide an identically good fit between model outcomes and field measurements. The more parameters that a model has (ie. the more complex that it is, the greater the extent of parameter nonuniqueness).

56. Soooooooo…. When we make a prediction using our model, especially one that involves fine detail, to what extent can we believe the model?

57. Soooooooo…. And which of the many possible parameter sets that calibrate the model do we use in making this prediction?