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2007-322
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2007-322
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Last modified
6/24/2016 1:34:12 PM
Creation date
9/30/2015 11:20:06 PM
Metadata
Fields
Template:
Official Documents
Official Document Type
First Amendment
Approved Date
09/18/2007
Control Number
2007-322
Agenda Item Number
11.J.1
Entity Name
Post,Buckley,Schuh & Jernigan
Subject
Amendment #1 to Work Order #2 Well Hydrology Modeling
Well Field Expansions
Area
North and South RO Plants
Supplemental fields
SmeadsoftID
6614
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This SOW does not include conducting site inspections or contacting well owners to verify the presence, <br /> condition, or location of the existing legal uses including domestic supply wells or permitted wells <br /> identified from the District ' s database. <br /> TASK 1 -2 : Hydrogeologic Conceptual Model and Groundwater Model Configuration <br /> Prior to groundwater model construction, PBS&J proposes the design of a hydrogeologic conceptual <br /> model . A hydrogeologic conceptual model of an aquifer system is a simplified, qualitative description of <br /> the physical system. The conceptual model may include , but is not limited to, a description of the <br /> aquifers and confining units that make up the aquifer system, boundary conditions, flow regimes. sources <br /> and sinks of water, and general directions of groundwater flow. The site hydrogeologic conceptual model <br /> is based on information collected for Task 1 - 1 . <br /> To accomplish the second half of Task 1 -2, PBS&J proposes to evaluate the hydrogeologic system with <br /> MODFLOW 2000(Harbaugh et. al, 2000) . MODFLOW 2000 (Harbaugh et. al, 2000) is a three- <br /> dimensional, finite difference-modeling program which is cell or grid based and calculates flow in three- <br /> dimensions using finite difference techniques. Upon completion of the hydrogeologic conceptual model , <br /> a groundwater model grid will be assigned such that the aspects discussed and presented in the <br /> hydrogeologic conceptual model are adequately evaluated. This would require assigning the physical and <br /> hydraulic boundaries of the hydrogeologic conceptual model within MODFLOW 2000 (Harbaugh et. al , <br /> 2000) . Examples of physical boundaries include impermeable layers ( i.e., clay deposits), surface water <br /> bodies, or man made structures. Examples of hydraulic boundaries include groundwater divides and flow <br /> lines. <br /> TASK 1 -3 : Groundwater Model Calibration <br /> PBS&J proposes to calibrate the groundwater model in steady-state and transient conditions. The <br /> difference between the two techniques is that transient simulations are needed for time dependent <br /> analyses. Transient simulations produce a data set of hydraulic heads for every time step ( I through nTn) <br /> while steady-state simulations produce only one hydraulic head data set. For groundwater modeling, <br /> steady-state simulations are less complicated in terms of data management and, for this reason, are the <br /> first calibration step . <br /> The model will be calibrated by altering model input parameters until simulated hydraulic heads <br /> approached hydraulic heads in the unconfined and leaky confined aquifer(s) . This is accomplished <br /> through manual alterations of the parameters and/or the use of inverse modeling codes such as PEST <br /> (Watermark Numerical Computing, 2000), UCODE (Hill et . al , 1998), or MODFLOWP (Hill et. al, <br /> 2000) . The final steady-state calibrated model generates the predicted heads numerically through <br /> MODFLOW 2000 (Harbaugh et. al, 2000) and will be used as the basis for the transient model. If the <br /> transient model does not mimic observed conditions, the model will be re-evaluated and re-calibrated. <br /> TASK 1 -4: Groundwater Model Sensitivity Analysis <br /> PSB&J proposes a sensitivity analysis after model calibration. The analysis involves repeating model <br /> predictions after systematically modifying the model input parameters and calculating the sum of the <br /> squared residuals of the simulated hydraulic heads. This is then compared to the calibrated model to <br /> determine which model input parameters are the most sensitive to change ( Le . , greater uncertainty) and, in <br /> turn, an area of focus for future investigation. <br /> Page 3 of 5 <br />
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