APPENDIX B IMC COMMENTS TO DRAFT REPORT WITH USACE RESPONSES

Transcription

1 APPENDIX B IMC COMMENTS TO DRAFT REPORT WITH USACE RESPONSES

2 This appendix presents the comments from the IMC review of the Draft Production Scenario Report. The comments have been copied verbatim into the left column of this appendix. Comments have been divided into table rows for convenience. The right column of this appendix presents USACE responses to the comments, an indication of updates to the report and explanations where necessary. IMC MSR 324 Task 4c -- Review of Draft Regional Model Production Scenario Report Aquifer Storage and Recovery Regional Modeling Study Interagency Modeling Center (IMC) IMC Comments USACE Responses Purpose The purpose of this document is to provide comments based upon the review of the report submitted in support of the Aquifer Storage and Recovery (ASR) modeling study based on the use of a calibrated regional model to analyze the effects of the proposed Comprehensive Everglades Restoration Plan (CERP) ASR system consisting of 333 ASR wells with the potential to inject or extract 5 mgd each. Documents Reviewed The document shown below was submitted for IMC review: Draft Regional Model Production Scenario Report, Aquifer Storage and Recovery Regional Modeling Study. US Army Corps of Engineers, September, No response necessary. Individual reviewers in some cases reviewed additional documents when certain parts of the report above needed further clarification. The technical comments made by the IMC review team on this document follow. Technical Comments The review was limited to the document above, and therefore does not extend to the model code, model input or model output files. The comments are listed following the review objectives agreed upon by the USACE and the IMC. B 1

3 IMC Comments USACE Responses 1. Are the text and figures clear and understandable? In general, the text and figures are clear and understandable. The box and whisker plots in Figure 2.7 through Figure 2.9 do not seem appropriate to be within Section 2.2 (Choice of Time Period). A more appropriate section or subsection heading should be selected to suit precipitation analysis described in these figures. It would be helpful if the proposed ASR sites are shown in Figure 3.1(as was done in Figure 1.1) to understand better the rock fracture potential maximum total head in relation to the ASR well sites. In Figure 4.20 (Scenario 1 Maximum Drawdown and Drawup by Aquifer) and subsequent figures for the other scenarios, it would be helpful to add the boundary locations of the model to these figures as these boundaries are relevant to the discussions in the appendix. In Figures 4.33, 4.52, and similar figures describing ASR well fluxes for the various scenarios, the legend uses Run1, Run 2, etc. Shouldn t these be Scenario 1, Scenario 2 etc? A correction should be made on the note section of Figure from 10 gpm wells to 10 mgd wells. In Figure A.5 to Figure A.8, show the 0 (zero) line on the flux scale as this is a critical location in understanding the data. No comment necessary The purpose of these box and whisker plots and the associated analysis was to support the decision to shorten the modeled time period from 30 years (like the SFWMM D13R model) to just 13 years. As such, we feel this is the right place to include this analysis and the associated figures. Additional text and explanations have been added to Section 2.2 to clarify this purpose. Figure has been updated. Figures have been updated. Figures have been updated. Figure has been updated. Figures have been redesigned to show zero flux line. B 2

4 IMC Comments Overall, the report is well organized. However, the report would be considered more complete if the model setup section had included setup and discussions of initial TDS/temperature and head conditions, how TDS/temperature was accounted for in the methodology on page 7 for boundary conditions, and detailed discussions of recovery efficiency uncertainty in Section 7. USACE Responses Section 2.7 has been added to the document to describe the setup of the initial conditions (head, TDS and temperature). There was no need to account for TDS and temperature when developing the boundary conditions because SEAWAT allows the user to enter observed heads for the boundary conditions. During calculations, the model determines the effective freshwater head using the water quality and the observed head. SEAWAT does not have the capability to automatically stop extraction when the water quality reaches a certain threshold. In addition, this is a regional model and cannot be expected to accurately calculate nearfield impacts of the wells including water quality of extracted water. Recovery efficiency was an assumption and an input to the model. Assumed recovery efficiencies were based on hydraulic conductivity, existing water quality and experience at the two pilot sites as described in Section The initial conditions set for the model did not impact decisions regarding assumed recovery efficiency. 2. Is the methodology used to convert the calibration model to the time period adequate to meet the goals of the projects? The modelers should state the reasons why it was necessary to develop the regional model production scenarios using the RASRSM-D13R model instead of using the SFWMM-D13R. The reasons should include the limitations of addressing the performance measures using SFWMM-D13R and the difference in the characteristics of the aquifer systems modeled using SFWMM- D13R and RASRSM-D13R. Several new paragraphs have been added to Section 2 to describe the shortcomings of the SFWMM and the need for the RASRSM D13R model. B 3

5 IMC Comments Please explain in more detail why the RASRSM would not be able to cover the 30-year period. It was stated in the document that filesize limitation and run time made it difficult to run the RASRSM for the 30 year period. Was investment on additional computer resources considered? USACE Responses Several new computers were acquired during this project and additional computers were borrowed from other projects during the Monte Carlo analysis. During the course of every model, the desired extent, resolution and detail must be balanced with the available time and computer resources. Because the first 13 years of the 30 year D13R period was considered to be representative, the purchase of even more computer resources was not considered to be likely to provide significantly better results. Additional text has been added to section 2.2 to better explain the constraints of the model. To reduce run-time and file-size a possibility could have been shutting-off the density-dependent and transport part of SEAWAT and run it as a MODFLOW model to assume freshwater conditions since the injected water in the ASR is freshwater and the salinity in the UF might not have an effect on the performance measure analyses to determine the number of wells to be installed in the UF. In the event that reducing the time period to 13 years was the most appropriate way to proceed with the analyses of the performance measures, the methodology used by the modelers to convert the calibrated RASRSM model to RASRM-D13R while considering ASR requirements used in the SFWMM-D13R model is reasonable. However, it is requested that the modelers clarify or explain the following modeling issues: As explained in more detail below, the density dependence was very important to the model. Although the UF is not very deep or salty, the BZ is quite deep and salty and exerts a great force on the upper layers of the model. Without the density dependence, the model would not have yielded accurate results. No comment necessary. B 4

6 IMC Comments a) Please explain the methodology to convert the calibrated RASRSM to an earlier time period for the conversion of all the important parameters. This conversion is to some extent a validation process, since stresses and boundaries are redefined and applied to a calibrated model with calibrated parameters such as geological (i.e., aquifer tops and bottoms), hydrogeological (i.e., hydraulic conductivity, storativity) and transport ( i.e., dispersivity) parameters. The modelers discussed and justified recharge and pumping stresses and head boundary conditions; however, initial conditions of heads and TDS concentrations were not mentioned and discussed in the report. As the modelers know, in a transient flow and transport model, initial conditions, especially TDS concentrations, are crucial. Transport process is not reversible, and initial TDS concentrations should be reasonably defined and discussed before running a model. Please discuss how initial conditions of head and TDS concentrations were converted to the back time period of USACE Responses A new section (2.7) has been added to describe the selection of initial conditions. B 5

7 IMC Comments b) The modelers justified the conversion of model period to The justifications look reasonable for the flow aspects but more information is needed for the contaminant transport aspects of the model. The modelers justified reducing the time period to a short 13-yr period due to file size limitations and computational run-time, choosing 1965 as the start time to minimize impact by previous injection periods, choosing 1977 as end time to include periods covered by SAJ Lake Okeechobee models and to incorporate the entire first cycle of Lake Okeechobee basin wells. Precipitations, annual extracted/injected volumes, boundary conditions, and regional pumping are also redefined and discussed for the shorter model time period Model time steps and stress periods were adjusted to reduce computer run-time as well. Though all this looks reasonable, the reviewers were concerned that when you take into consideration the whole period , was probably a period for which data is least accurate or insufficient in Figure 2.2 showed a return to zero storage volume. With this observation, a later time period for example, could produce more reliable data for ASR design since later times in the model period seem to have better data quality especially for pumping data. Please explain in the report why a later period, for example , was not used. Could the results have been significantly different for the performance measures if the period have been used considering that the aquifer storage volumes shape in Figure 2.2 for and are quite different? Why or why not? USACE Responses Figure 2.2 shows the storage volume only for the Lake Okeechobee ASR basin. Although this represents the largest volume in the CERP ASR system, there are 4 other basins with significant ASR flows which do not return to zero storage in Only the Lake Okeechobee and Central Palm Beach basins have zero storage at any period other than Further, running the RASRSM D13R model from would not have covered the time period used in the SAJ Lake Okeechobee models. The use of the period might have had minor impacts on the performance measure results. The pump pressure limit is affected by the largest injection rate at the ASR wells. The maximum injection rates (5 mgd/well) is achieved in all basins during the period. Some minor impacts might be caused by longer periods of injection or greater volumes injected over long periods of time. The maximum annual injection volume in the Lake Okeechobee basin during the period is approximately 98% of the maximum annual injection volume for the period The maximum annual injection volume in the Caloosahatchee basin during the period is also about 98% of the 30 year maximum. For the Lower East Coast basins, the comparisons range from 81% to 87%. Possibly a small increase in these pump pressures might have been noted, but the difference would be within the error of the model because the maximum rate (5 mgd) was met on numerous occasions in all basins. The APPA performance measure results are affected by the extraction rates in a few sites on the east and northeast shore of Lake Okeechobee, mainly Lakeside Ranch, Port Mayaca, L 63N, Taylor Creek, North Lake Okeechobee and Kissimmee River/Paradise Run. As noted above, all of these sites achieve full extraction rates (5 mgd) during the period. Some additional minor impacts might be felt if the extraction rates continue for long periods of time or if large volumes are extracted. However the maximum annual extraction volume for this basin, over the 30 year period, occurred in 1975, a year included in the shortened 13 year period used in the RASRSM D13R scenarios. For these reasons, we feel that no additional justification is needed for the use of the shortened time period for the RASRSM D13R. B 6

8 IMC Comments d) On Page 19, it was mentioned after the calibrated RASRM had been adjusted to reflect the period Please explain how the adjusted model reflects the hydrological and transport conditions. Are there any comparisons and statistics that show that the results from the adjusted model are reasonable especially for the transport conditions? USACE Responses A new section, 2.8, has been added along with a number of figures showing good correlations between modeled output and available head data. Regarding comparisons to transport conditions, the reader is directed to Section 2.7 which explains the fact that salinity conditions are not expected to change drastically in most areas of the model on a time scale of 40 years. 3. Is the methodology used to calculate the required pump pressure adequate to meet the goals of the project? Are the results reasonable? B 7

9 IMC Comments The methodology is reasonable. It is the Theim equation for confined aquifers under steady state conditions. However, in transient conditions as in this model, it could be assume that after a short period of time, release of water from storage is negligible in the vicinity of the well and the Theim equation will apply. The modelers conducted useful numerical experiments with smaller grid sizes, showing that the pressure is inversely proportional to the distance to the pumping well. Also, the lesser the distance to the pumping well, the higher the pressure, and pressure becomes more similar as the distance from the well increases. However, since grid size is about 2000 ft and it is unrealistic to run a numerical model with a 2 ft grid size, the modelers used the Theim equation to estimate pressure around a pumping well by reducing the grid size to a smaller one (2 ft). The equation also includes the transmissivity of the aquifer, thus high pressure is found in an aquifer with low transmissivity. The report mentioned that the PDT determined that it would be important to keep pressure head below 100 psi but the justification of this pressure was not given. Please site a reference to justify that this pump pressure requirement is realistic for ASR wells or how the pressure limit for an ASR well could be determined. This criterion proved to be very important and is used to optimize the ASR design in each of the scenario runs. USACE Responses An additional sentence has been added to the first paragraph of Section 3.2 to explain that permitted wells in the FAS are normally tested to a level of 100 psi. This includes the pilot study ASR wells at Kissimmee River and Hillsboro. B 8

10 IMC Comments On page 4 of the calibration model report, it states, For the Phase II study, SEAWAT modeling was performed using the variabledensity flow with solute transport mode. On page 13, the modelers state that the head difference is converted to pressure using the density of water which is a constant. But the water in the UF is brackish water and it was stated in Section 2.1 of the ASR calibration report that there are substantial density variations in the groundwater thus requiring the use of a density-dependent groundwater modeling code. If this is so, why was the variability in density not taken into account in the pump pressure calculations? USACE Responses It may have been more correct to take this into account when calculating the pump pressures. However, the impact of the decision not to include TDS in this calculation is minor for a few reasons: Although the variation in TDS in the model is large, the variation in TDS at the proposed UF ASR sites is small. Figure 3.18 in the calibration report shows a range of about ug/l TDS at the proposed sites in the UF. This translates into a change in density of lb/ft3. Temperature variations are smaller: about degrees F (see Figure 3.29 in the calibration report). This equates to an increase in density up to 0.02 (for temperature below 25 degrees F) or a decrease in density up to 0.07 (for temperature above 25 degrees). Thus, if freshwater density is lb/ft3, the maximum groundwater density that would be seen at any UF ASR site would be 62.5 lb/ft3. See equation 2.1 in the calibration report for details on the calculations. At 100 psi, the head difference for the freshwater density would be ft. For a density of 62.5 lb/ft3, the head difference would be ft. This difference of 1.1 feet is within the error of the model. Note that in the APPZ and BZ, the TDS levels are higher and so the densities are also higher. This means there may be a greater error caused by using the freshwater densities in these calculations. However, the APPZ and BZ pressures are well below the 100 psi limit in all scenarios and so any error introduced in this way will not impact the number of wells recommended at each site. B 9

11 IMC Comments USACE Responses The results on the maximum pump pressure computed for each proposed ASR site revealed that all sites would need to be able to overcome pressures much greater than 100 psi. This was a very useful finding as this performance measure revealed to the modelers right away that the 333 ASR wells in the UF proposed in CERP would exceed 100psi and the number of ASR wells in the UF would need to be reduced/. No response necessary. 4. Is the methodology used to calculate the reduction in artesian pressure adequate to meet the goals of the project? Are the results reasonable? The methodology used to evaluate artesian pressure reduction in the UF and the APPZ aquifers in Saint Lucie and Martin counties is reasonable. It basically used the Jacob and Lohman flow equation. The flow equation given in the report has an error in one of its coefficients. It should be 2.25 and not 2.5. The reviewers hope that this is a typo. Please revisit the calculations as an error of this nature might have a significant impact on the artesian pressure calculations. This value of 2.25 was a typo and has been corrected in equation 3.8 and 3.9. However, because the calculation is a comparison between flows with and without ASR, this coefficient and many of the other parameters are canceled out during the subsequent derivation. Note that Equation 3.14, which was used in the analysis does not include the 2.25 coefficient and is not dependent on it. No error was introduced by this typo. B 10

12 IMC Comments USACE Responses The flow reduction is calculated from simulated head pressure before and after the ASR project. The modelers used a similar procedure by Merritt (1997) using the Jacob and Lohman equation to calculate flow from an artesian well in the UF over time. The equation includes aquifer transmissivity, well radius, storage coefficient and difference between aquifer head and ground surface elevation. The flow reduction is presented in two ways by the modelers: 1) maps of the UF and APPZ layer with the cells colored by maximum percent flow reduction across the time period and 2) maps of cells colored by the number of days within 13 years that the 10% rule is exceeded. The methods measured magnitudes and frequencies of flow reduction within most of the two counties. An exception is that a ridge coming from the northwest increases the ground surface elevations, making the Merritt equation invalid when evaluating artesian wells. The performance measure is used to evaluate the effect of ASR wells on the flow reduction of artesian wells in Saint Lucie and Martin counties. The results obtained from applying the methodology are reasonable if the coefficient of 2.25 was used and show that the effect on the artesian conditions in St. Lucie and Martin Counties are significant and that most areas loose over 10% of artesian flow for more than 40% of the model time period when all 333 ASR wells are used in the UF. No response necessary. No response necessary. 5. Are the descriptions of the eight D13R scenarios clear and are their purposes explained well? B 11

13 IMC Comments The descriptions of the eight scenarios are clear and their purposes are also explained though the reviewers didn t see the significance of some of the scenarios. Please explain how the 333 ASR wells were determined by the SFWMM-D13R model. Note that the SFWMM models the unconfined surficial aquifer system (SAS) that is the topmost layer in the aquifer system. A link-node approach was used to simulate the ASR wells assuming an efficiency ratio. ASR wells for CERP purposes are supposed to be placed in the UF which is a confined aquifer below the SAS separated by a confining unit presenting a complete separation between the SAS and the UF. In the RASRSM calibration report Figure 2.2 shows that the model includes five confined aquifers and five unconfined units (see p.7 of calibration report). It is also stated on page 7 of the calibration report The SEAWAT grid also includes the SAS although no calculations are made there. The reason is because of the assumed complete separation between the SAS and the UF. Please explain how the 333 ASR wells were determined using the SFWMM (an SAS unconfined system). Please confirm if the original CERP assumption that 333 ASR wells could be used in the UF aquifer and aquifers below is valid. Nonetheless, the scenarios started from a full D13R design from SFWMM, and then scaled back to meet performance measures such as pump pressure requirement. During model runs, some ASR wells were moved among aquifers and pump rates and recovery efficiency were adjusted in order to meet performance measures. USACE Responses Additional text has been added to Section 2 to explain the determination of the need for 333 ASR wells and to explain the differences in the groundwater calculations made in the SWFMM D13R and the RASRSM D13R models Additional text has been added to Section 2 to explain the determination of the SFWMM D13R of the need for 333 ASR wells. This determination was made solely on the volume of excess water which needed to be stored and the volume of additional water that needed to be supplied during dry seasons. The purpose of this RASRSM D13R model was to determine if these 333 wells could be used from a regional hydrogeologic perspective. No response necessary. 6. Do the results of the eight D13R scenarios make sense conceptually? B 12

14 IMC Comments USACE Responses Before discussing whether the eight RASRSM-D13R scenarios make sense conceptually, the reviewers are recommending that two quick possible preliminary scenarios (Scenario 0a and 0b) should be run using the 30-year period RASRSM calibrated model with all 333 ASR wells in the UF but with important changes in the run mode of SEAWAT to avoid the runtime problems encountered by the modelers. Scenario 0a: Solve the RASRSM model as constant density flow and no solute transport with the additional 333 ASR wells. This simulation would determine the sensitivity to variable-density and solute transport to UF heads when compared to Scenario 1. In SEAWAT, turn off the switch for no density-dependent flow and no solute transport so that the simulation is done using MODFLOW only. Make a note of the UF heads for each cell in the UF layers. Scenario 0b: Solve the RASRSM model as a constant density flow and solute transport with the additional 333 ASR wells. This simulation would determine the density-dependence on UF heads when compared to Scenario 1. In SEAWAT, turn off the switch for the variable density component and solve as coupled flow and transport model. Make a note of the UF heads for each cell in the UF layers. Once these two scenarios are run, compare the difference in heads between each of the two scenarios and Scenario 1 for the UF in a difference map. If the UF heads in Scenario 1 are not significantly different from that of Scenario 0a, then all the analysis and scenarios discussed in this work could be performed using the RASRSM model for scenario 0a as the base model for scenarios 1 through 8. To show the advantage of doing this preliminary scenario analysis, consider the following results obtained with the SFWMD lower east coast (LEC) SEAWAT model. It has similar layers as the RASRSM. Each row and column was 2400 ft by 2400 ft; the active cells per layer were approximately 51,040; the total active cells were 714,520 and the time step was 1 month. A simulation similar to Scenario 0a for the LEC model took 1hr and 10 minutes for a 6 year stress period on a 3 GHz EMG4T processor machine with 8 GB RAM. Scenario 0b took 7hr and 25 minutes and Scenario 1 took 8 hours and 30 minutes. These results show that if variable density and TDS concentration do not affect significantly the heads in the UF, then the performance measures analyses could have been done using SEAWAT using Scenario 0a as the base model. The run time could have been reduced by an order of magnitude of about 8 and possibly the modelers would have had no need to limit the modeling period to Removal of density dependence as suggested by this commenter will result in significant changes to groundwater heads and flows. Note that Scenario 0a and 0b will result in the same head results. The only difference between the two is that a TDS concentration will be calculated for Scenario 0b but not for 0a. As shown in Figures B.1 through B.4, the steady state calibration of the RASRSM is greatly different when density dependence is removed. Note that in Figure B.1, the UF heads in the southern 2/3 of the model are significantly lower when density dependence is removed. Similar effects are seen in all other layers of the model. Figure B.5 shows the comparison between model calculated heads and observed heads. Note that where the heads are high (near the Polk County recharge area) the inclusion of density dependence makes no difference. But in other areas of the model, the difference is stark and many error values are greater than 20 feet. A similar effect is shown in Figures B.6 and B.7 where a few comparison points are shown from the transient calibration run. As expected, when density dependence is removed, points far from the recharge area have much lower heads than the measured heads. The measured heads in the southern portion of the model domain are high because of the pressure exerted by the high TDS, low temperature (high density) water in the ocean, which enters the BZ and pushes upward on the water above it. This cyclical movement of water is not only supported by the results of this model, but also in a number of references (Kohout, 1965, Kohout, 1988, Bittner, et al., 2008). Figures B.8 through B.11 show the impact of removal of density dependence for Scenario 1 as suggested by the reviewer. Note that large sections of both the UF and APPZ have head differences greater than 50 feet between the two runs. When post processing is used to compare the results against the performance measures, the results are shown in Figures B.12 through B.14. Pump pressures estimated for the ASR sites are slightly lower when density dependence is removed (compare Figure B.12 to Figure 4.17 in the main report). The results of the APPA analysis are significantly different since most of St. Lucie and Martin Counties are no longer artesian even without the ASR wells (compare Figure B.13 to Figure 4.18 in the main report). Drawdown and drawup caused by the ASR wells is only slightly different (compare Figure B.14 to Figure 4.20 in the main report). The removal of density dependence affects more than just the head results. It completely changes the flow regime. Figures B.15 through B.29 show the results of a few particle tracking exercises. Note that in many places, the direction of flow is opposite for the two runs. Flow directions are summarized in the next few figures. Figures B.30 through B.32 show the comparison of vertical flow through each of the confining units. The sign of the vertical component of flow is shown with red for downward flow and blue for upward flow. In the LC, MC2 and MC1, the majority of the flow is downward without density dependence and upward with density dependence. Figures B.33 through B.36 show the direction of the horizontal component of flow in the aquifers. There are significant differences in the direction of flow in the two runs, especially in the southern half of the model domain. This shows that the entire flow regime has changed with the removal of the density dependence. The time savings of a simple MODFLOW model without water quality transport equations would have been useful, but the results would not have been accurate. Full SEAWAT calculations were necessary in this case. B 13

15 IMC Comments USACE Responses The following comments below pertain to the scenarios that are modeled and presented in this report. Some of the scenarios are reasonable as they follow a logical sequence after discovering that the 333 ASR wells proposed by CERP for the UF wouldn t meet the requirements of the performance measures. Therefore the task after this discovery was to determine whether wells that could not be placed in the UF could be distributed within the other aquifers. However, it leaves the reviewers with two very important questions shown below. No response necessary. B 14

16 IMC Comments With the decision made to distribute some of the wells among other aquifer units, please explain the rationale of placing wells in the Boulder Zone (BZ), instead of the Lower Floridan aquifer (LF). The LF aquifer is a permeable unit above the Boulder Zone which is separated from the BZ by the Lower Floridan Confining unit. Installing wells in the LF would be a much better alternative to installing wells in the BZ. The Boulder Zone is difficult to drill into, having the same rough, shaking, grabbing effect on the drill system and drilling rig as boulders would. Also, the TDS concentrations in the BZ (that of salt water) and the depth from ground level, makes this a challenging zone to work with. USACE Responses The process of developing the D13R scenarios began with the UF aquifer only, as was envisioned by CERP. When it became clear that the UF could not handle all 333 wells, it was a natural step to begin placing some in the APPZ. However, it was soon obvious that even with the two aquifers, the full injection and extraction volumes could not be achieved. As the PDT discussed the next steps, consideration of the BZ was introduced as a way to dispose of excess water, protecting the estuarine habitats from excess fresh water and reducing flooding in Lake Okeechobee and the canals. The PDT was aware of the expense and difficulty in working with the BZ. These wells were not considered to be true ASR wells because of the low recovery efficiency, but were considered to be disposal wells for the removal of excess water. This run was meant to get as close as possible to the D13R volumes, even though it might eventually turn out to be difficult or expensive to build. Further discussions with the PDT in January 2013 (after the release of the Draft version of this report, but before receipt of IMC comments) resulted in the addition of Scenario 12 which removes the BZ wells from Scenario 11. A section on Scenario 12 (4.12) was added to this report. Unfortunately, scheduling constraints do not allow the repeat of the Monte Carlo analysis. However, because of the high conductivity in the BZ, the results would be very similar. B 15

17 IMC Comments USACE Responses Scenario 1: The modeling run made for this scenario using the RASRSM-D13R followed the injection and extraction schedules used in the SFWMM-D13R model as closely as possible. All the 333 recommended ASR wells were in the UF. The simulated results and calculated performance measures showed maximum pump pressure requirements at all ASR sites are above 100psi, and even approach more than 400 psi at Taylor Creek and 800 psi at the L-63N site. The effects on the artesian conditions in Saint Lucie and Martin counties were significant; maximum drawdown is greater than five feet in most of the modeled area. The regional effects of the system are widespread and significant, because all 333 ASR wells were assigned in the UF. This scenario didn t satisfy the performance measures. Scenario 2: Based on simulated results in Scenarios 1, the modelers developed a strategy by removing wells until the pump pressure requirement of 100 psi was met at each site. It was mentioned that removing some ASR wells is somewhat arbitrary, but we would suggest removing those wells located at areas with relatively low hydraulic conductivity, which will thus significantly reduce maximum pump pressures below 100 psi. As a result, the scenario provided less storage and recovery volume and reduced the total number of ASR wells from 333 to 97. No response necessary. The process of removing the ASR wells was termed arbitrary because it was not based on any specific algorithm. Wells were removed from sites where the pressure was above 100 psi. Removal continued until the pressure was within the limit. Naturally, this would mean removal of most wells from areas of low hydraulic conductivity or areas of high ASR well density. Text has been adjusted to clarify this. B 16

18 IMC Comments USACE Responses Scenario 3: Scenario 3 was developed by having 97 ASR wells in the UF and 236 ASR wells in the APPZ aquifer which consists mainly of the Middle Floridan aquifer (MF). The results showed an increase in pump pressure requirements in the UF but most of the wells that were already in the UF didn t violate the 100 psi pump pressure criteria. The 100 psi criterion for the APPZ was violated in most of the 232 wells in the APPZ. Only 41 wells in the APPZ met the 100 psi requirement. Scenario 4: In this scenario, the modelers ended up removing 191 wells from the APPZ in order to satisfy the 100 psi performance measure criteria for both the UF and the APPZ. They were able to successfully leave 41 wells in the APPZ because these 41 wells were mainly in areas that didn t have the UF as an upper confining unit. The implication of this scenario is that once the 97 ASR wells are already in place in the UF, it would be difficult to add wells in the APPZ without violating the pump pressure performance measure. No response necessary. No response necessary. B 17

19 IMC Comments Scenario 5: A scenario is recommended, referred to as (Scenario 4a) in this review report where the 191 wells removed from the APPZ should have been placed in the Lower Floridan aquifer (LF). It is not clear why this scenario was not tested or reported. Based on the familiarity of the reviewers with the Florida Aquifer System (FAS), this scenario would have ended with violation of the 100 psi requirement in the APPZ and UF in some of the wells that were already meeting this requirement under the previous scenarios because the aquifer is confined and pressure builds quickly under high pumping conditions. Thus, Scenario 5. The relevance of installing ASR wells in the BZ is not sufficiently explained. The BZ permeability is very high because of its cavernous nature and its anomalous extremely high permeability prevents pressure buildup in injection wells. However, the BZ is not suitable for ASR wells because the zone contains saltwater of concentration similar to that of seawater. The BZ has been used for years to store vast quantities of treated sewage injected into it in Miami, Fort Lauderdale, West Palm Beach and Stuart but the reviewers are not aware of anywhere where the BZ is being used to store fresh water. USACE Responses As mentioned in this comment, the addition of wells to the LF would probably not have resulted in a significant increase in available storage because of the hydraulic conditions of the aquifer. Because of the water quality conditions of the aquifer, recovery efficiency would have been low and only a small increase in recovery volumes would have been achieved. Once it was determined that the UF and APPZ together could not achieve the volumes set forth in the SFWMM D13R, the PDT began looking for places to dispose of excess water, without expecting to be able to recover it. The BZ seemed like the best location for this. As explained above, the recovery efficiency in the BZ is so low, that these wells cannot truly be considered ASR wells. These wells were meant to remove excess freshwater from the surface water system but were not expected to be able to store or provide freshwater when needed. B 18

20 IMC Comments Due to the existence of the Floridan confining unit, and the lower middle confining unit which is located between the BZ and upper layers (UF and APPZ); and due to the cavernous nature of the BZ, the effect that ASR wells installed in the BZ may have on the upper layers is very limited as shown in the report. It is difficult to build up pressure in the BZ. Also, since TDS concentration in the BZ is similar to that of seawater, the assumption of 10% recovery efficiency is not reasonable. Thus total annual extraction rates will be even lower than estimated from the scenario. Is it consistent with the ASR objectives to pump freshwater into the BZ with TDS concentration close to or higher than seawater? Scenarios 6-8: not detailed in the report as the PDT decided not to pursue these scenarios. Scenarios 9: In this scenario, the pump capacity was increased to 10 mgd at each ASR wells in the BZ in order to maintain the same total amount of injections. Recovery efficiency was reduced to 0% so that drawdowns decreased slightly. Again, because of the cavernous nature of the BZ increasing the pumping rate from 5 mgd to 10 mgd would have very little impact. The reason to develop this scenario for ASR is not clear. USACE Responses Scenario 5 has been removed from this report since it is so similar to Scenario 9. This removes the estimate of 10% recovery efficiency from the BZ and converts ASR wells in the BZ into disposal wells. No response necessary. With the removal of Scenario 5 (see above), Scenario 9 becomes more important. B 19

21 IMC Comments USACE Responses Scenarios 10: Based on Scenario 9, the run simulated in this scenario further reduced recovery efficiency at each site so that the artesian pressure in the APPA area is less than 10% and maximum drawdown one mile each side is less than or equal to one (1) foot. In addition, recovery storage was reduced to between 1% and 4% of the SFWMM-D13R, so scenario 10 is more like storage system with no retrieval. Scenario 11: This is the final design that was selected by the PDT. According to the modelers, it meets all performance measure requirements other than the one (1) foot drawdown at one (1) mile distance from the well. This allowed to increase recovery efficiency at most sites so that total annual recovery storage increased between 12% and 60%. This scenario resulted in a design that located 94 ASR wells in the UF, 37 ASR wells in the APPZ, and 101 ASR wells in the BZ. The UF and APPZ wells will have 5 mgd each while the BZ wells will have 10 mgd capacity. Please describe what was the criteria to assign recovery efficiencies at each site. Also, based on the discussion, it seems that these efficiencies are different from recovery efficiencies expressed by the ratio of recovered over injected volume of water, please explain these differences. In Figure 4.146, recovery efficiency at Marcy and Port Mayaca (Lake Okeechobee basin) was reduced to 0%. No response necessary. The assignment of recovery efficiencies to each proposed ASR site is described sufficiently in Section The extraction percentages shown in Figure are not recovery efficiencies. These are reductions to extraction rates which were used to reduce the impact to the APPA in St. Lucie and Martin Counties. These percentages were applied directly to the extraction rates and were unrelated to the injection rates or water quality. Although a 0% extraction percentage is equal to a 0% recovery efficiency, the reason for the reduction is different. Recovery efficiency is caused by poor water quality in the recovered water resulting in the need to stop extracting. Extraction percentage is used to minimize drawdown impacts in St. Lucie and Martin Counties. Additional notes have been added to Figures and to clarify this. B 20

22 IMC Comments Nevertheless, it is not clear about the rational of the PDT to select Scenario 11 as the final design. Is it required that 333 ASR wells must be installed as opposed to a number of wells that produce the same performance for CERP? Based on the analyses in this report, the reviewers concluded that ASR wells should only be installed in the UF and possibly the APPZ. USACE Responses This analysis shows that installation of ASR wells at the specified sites in the UF and APPZ will not be able to fully meet the water storage needs of CERP or the water supplementation needs of CERP (as defined by SFWMM D13R). Full water removal during wet periods can be achieved with the use of the BZ. No known number of ASR wells can be installed to produce the performance required for CERP while still meeting the regional hydraulic performance measures. This is explained in Section 8 of the report. 7. Does the analysis in Appendix A adequately address possible concerns with the location of the north, west, and south boundary of the model? The concern with the locations of the north, west, and south boundaries was previously provided to the modelers when the calibrated RASRM model was reviewed by the IMC. The review report from the IMC mentioned that when ASR wells are installed, some of the inland boundaries will be too close to the ASR wells and drawdown caused by ASR wells will extend to those boundaries. As mentioned in the report, in Figures 4.20, 4.39, 4.58, 4.77, 4.96, 4.115, and 4.153, significant head changes extended to the western, southern and northern boundaries, indicating the use of inland specified head boundaries at these locations may be violated. No response necessary. B 21

23 IMC Comments USACE Responses However, as stated in the report, extension of western, southern and northern boundaries is not feasible for several reasons. Instead, the modelers conducted a series of numerical modeling, and calculated flux differences across these boundaries between the non-asr well scenario and ASR well scenarios. The ideal situation would have been that there is no difference between these two conditions. However, Figures A.21 through 25 showed increases of flux across IAS/ICU, UF, APPZ, LF and BZ between the two conditions. This indicates that the inland specified head boundaries along the western, northern and southern boundaries do have effects on the interior heads of the model. The magnitudes of the increase are related to the magnitudes of the recharges and discharges of the ASR wells. No response necessary. B 22

24 IMC Comments As found in the numerical experiments, the flux change (increase / decrease) are larger when the model approaches steady state and larger where closer to ASR wells. Therefore, in addition to calculating flux changes across questionable boundaries, it is suggested cropping a smaller box around the area of interest (i.e., APPA) and calculating flux changes across the box boundaries between non-asr and ASR conditions. Any quantifications and justifications about the magnitude of flux changes would be helpful. USACE Responses When a specified head boundary condition is applied to a model boundary, the assumption is that there is an infinite source/sink of water at that location. The model removes or adds whatever volumes of water are necessary to ensure that the head remains at the specified value. If the boundary is set too close to the ASR wells, the specified head assumption is violated since the head will not remain constant. The purpose of this analysis was to determine the validity of the specified head boundary condition. If the fluxes through the boundary remained constant despite the addition of interior pumping, then we could be assured that the boundaries were set at a sufficient distance that they would not impact the head results inside the model. If the fluxes are calculated across the boundaries of a smaller box around the area of interest, as recommended by the reviewer, the fluxes would definitely be larger than at the model boundary, but this would not tell us anything about the validity of boundary conditions since the boundaries of a smaller box would not coincide with a model boundary. Since the heads at the location of a smaller box are allowed to vary, there is no violation of assumptions in allowing the flux to vary. 8. Is the Monte Carlo methodology (including the calibration checks) adequate for the reasonable analysis of model uncertainty? B 23

25 IMC Comments USACE Responses The Monte Carlo methodology is one of the widely used methods to characterize uncertainties in numerical models. The key point of the method is to use a large number of samples (or to run a number of simulations) so that realizations of the parameter fields using their probability density functions could be used to determined uncertainties in the predictive response of the model. To run a number of simulations, computational time is one of important factor to consider. The modelers did some checks before applying the method to the RASRSM-D13R model. If simulations pass steady state and transient model check (NAP, 2011), then the input files (selected parameters) will be valid for the RASRSM-D13R. Therefore, statistical analysis for uncertainties in the model will be based on these simulations. The reason for the check is that RASRSM-D13R cannot be explicitly calibrated but appears consistent with the calibrated RASRM model through the conversion process. No response necessary. No response necessary. 9. Is the selection of parameters for variation (and their probability distributions) in the Monte Carlo simulation adequate for a reasonable analysis of model uncertainty? There were six (6) groups of parameter combination used in these analyses. a) Uniform distribution: Porosity, longitudinal dispersivity and molecular. However, according to previous studies (Gelhar, 1992), dispersivity is usually represented as a log distribution and not as a uniform distribution as used in the model. No response necessary. The range of acceptable values for longitudinal dispersivity was quite small (0.1 to 2.5), only 1.4 times a log scale. It was felt that the linear distribution would be more appropriate for a small range. Also, a log scale would have put additional weight on small values, which was not desired. B 24

26 IMC Comments USACE Responses b) Log Distribution: ratios of longitudinal to transverse dispersivity, longitudinal to vertical dispersivity and horizontal to vertical conductivity for each aquifer. c) Hydraulic conductivity and specific storage: The modelers included the most sensitive parameters into the calibration model as pilot points, which is reasonable and doable. However, the method to vary hydraulic conductivity is questionable. First, the range shouldn t be too narrow to make them close to calibrated values (only ½ to 2 times was used in the report). Uncertainty from these parameters could be investigated with a wider range. Second, uniform distribution can only be applied to log-transformed hydraulic conductivity and log-transformed specific storage but not to their original values directly. No comment necessary The range was set to be very narrow because there was a great amount of field data used during the calibration of the model. (See Figures , 3.34, 3.36 and 3,38 in the Calibration Report.) The purpose of the Monte Carlo was to determine the uncertainty in the results caused by uncertainty in the input parameters. Thus, the ranges and distributions of the input parameters should reflect their uncertainty. Since there was quite a lot of APT data in each of the aquifers, the uncertainty in the hydraulic conductivity was low compared to many other model parameters. The narrow range ensures that this low uncertainty is communicated to the Monte Carlo analysis. Secondly, although a log transform is generally used for hydraulic conductivities, it was not used in this case because the range of values was less than one log cycle. Log distributions were applied to the specific storage values as explained in Section d) Invertible distribution: horizontal anisotropy of hydraulic conductivity. No response necessary. B 25

27 IMC Comments e) Initial (starting) conditions for TDS and temperature: The initial TDS concentrations and temperatures were not discussed in the model set-up section, but they are considered as one of the uncertainties in the numerical model. As mentioned before, initial TDS and temperature should be carefully determined before running a coupled variable-density transient flow and transport model. During randomizing the initial TDS and temperature, the values only vary between 80% and 120%, which means initial TDS and temperature used in the model are mostly close to their calibrated values. In addition, the model results would be very sensitive to the initial TDS concentration. The narrow range of variation of the value may be reasonable, but the question is how the initial TDS (and temperature) have been determined when running Scenario 1. f) Multiplier (0.5 to2) was applied to change the thickness of the BZ. For a specific model run, there is a combination of all input parameters such as hydraulic conductivity, storage, dispersivity, etc. some of them may be correlated. It is not clear how the modelers selected randomized values from each of the parameters and make a combination to run a simulation. It was mentioned that there were 1485 parameters, but detailed information about combining randomized parameters would be helpful. USACE Responses This comment was made previously and has been discussed above. In addition, a new section, 2.7, has been added to the report to discuss initial conditions, including TDS. No comment necessary. No correlation of the parameters was assumed and no combinations were made. All values were randomized with their own unique random value, generated by FORTAN. B 26

28 IMC Comments As to the number of Monte Carlo simulations, in Figure 6.14, it shows that when the number of Monte Carlo simulations increases, half-width of 95% Confidence Interval decreases, but it approaches a constant when certain number of simulations is run. This is the basis of the Monte Carlo method, which means a large number of simulations may be needed to reasonably characterize uncertainty of the model. From Figure 6.14, a number between 1000 and 10,000 would meet this requirement. So, it seems at least a doubled number (1,650) of simulations are needed if running time allows. USACE Responses The half width of the 95% confidence interval approaches zero without ever arriving at zero. When deciding on the number of Monte Carlo simulations necessary, the required precision must be balanced with the available resources and time. The 825 simulations run for this project brought the half width of the 95% confidence interval within 3.5%, which is likely within the error of the model. Sections 6.3.2, and show that this confidence interval is quite small compared to the results and continued Monte Carlo simulations would lend little additional precision to the results. At 825 simulations, the point of diminishing returns has been reached. Finishing with 10,000 Monte Carlo runs would reduce the half width of the 95% confidence interval to less than 1%, but it would take 3 years with current computer resources. That is a very expensive 2.5% improvement. 10. Are there any sources of uncertainty or error that are not addressed in the report, but which might adversely impact the usefulness of the model results? B 27

29 IMC Comments The modelers discussed a number of uncertainties from model setup, conversion of calibration model to D13R model, ASR pumping data, and performance measures. One of the important uncertainties is the recovery efficiency. Throughout the report, the modelers did a series of numerical experiments to rearrange and renumber the ASR wells within aquifers and to assign various recovery efficiencies at ASR wells, in order to meet performance measures set by the PDT and to let the ASR system function properly. It is noticed that as much as 100% recovery efficiencies were assigned for a few ASR wells in the final scenario, but in reality, this 100% recovery efficiency is unreasonable, and it is likely that the water recharged into the aquifer system cannot be recovered or would disappear in fractures in the aquifer due to uncertainties in characterizing the aquifer system. Please clarify what these efficiencies represent. USACE Responses No ASR wells were assigned 100% recovery efficiency. It is possible that the reviewer is referring either to extraction percentage (Figure 4.146) which is the comparison of actual extraction rates to assigned extraction rates, or to comparisons of SFWMM D13R assigned rates and RASRSM D13R assigned rates as shown on Figures 4.162, and Additional text and notes have been added to clarify this. 11. Does the reviewer agree with the conclusions and recommendations? The assumption of 333 ASR wells is based on CERP modeling with the SFWMM which is greatly limited because it assumes no interaction with the UF. The ASR wells are intended to be installed in the UF which is a confined aquifer system. A confined aquifer with 333 wells pumping at 5 mgd results in a cone of depression and drawdown of the aquifer system that could not be simulated with the SFWMM. No response necessary. B 28

30 IMC Comments No information was presented on the TDS concentration and temperature conversion that was done when the RASRSM-D13R was developed for the time period On page 3 of the ASR calibration report which preceded the report currently under review, the modelers state The regional model will provide planning level information to address large scale issues such as the regional effect of the ASR well clusters on saltwater intrusion and water quality. In the ASR impact report under review, the modelers stated on page 17 that unfortunately, the regional model is not able to address water quality migration and salt water intrusion because it cannot accurately portray TDS transport near wells and that the salinity at locations far from the ASR wells are highly dependent on transport parameters such as dispersion, which could not be calibrated due to lack of TDS time series data. Please explain how TDS concentration and temperature conversion were done in RASRSM-D13R for using data from the RASRSM calibrated model. The reviewers question would like to know details of the parameters of the contaminant transport component of the RASRSM-D13R. USACE Responses Section 5.2 and 5.3 of the calibration report provide transport parameter values used in the calibration report. Tables 6.1 and 6.2 provide ranges and distributions for transport parameters values for the Monte Carlo analysis. A new section (2.7) has been added to this report to clarify the selection of initial TDS conditions. B 29

31 IMC Comments The modelers did a series of numerical experiments on the converted RASRSM-D13R model, based on assumptions especially on recovery efficiency. Recovery efficiency, which is a very important uncertain parameter, is not included into the Monte Carlo analysis. The modelers mentioned in the end that the regional model made a few assumptions such as aquifer conditions and recovery efficiency, so that when predicting local effect of the ASR wells system it is necessary to analyze closely using pilot points at proposed ASR sites and using local scale models. A final design was found with 94 wells in the UF, 37 ASR wells in the APPZ and 101 ASR wells in the BZ. The final design in our view should only include wells in the UF and possibly in the APPZ especially since the ASR wells are needed for water supply from the UF to supplement water from the SAS. USACE Responses The inclusion of recovery efficiency in the model was not as simple as inputting a single value in a text file. A change to the recovery efficiency involved a detailed calculation of stored water volumes with comparisons to flow rates. Inclusion of recovery efficiency in the Monte Carlo was deemed far too complicated. However, recovery efficiency only impacts the length of time that extraction occurs. A small variation (5% 10%) of the recovery efficiency would likely only have impacted Lake Okeechobee and Central Palm Beach basins, since these are the basins that return to zero stored volume during the model period (see Figures 2.1 through 2.5). Further, because recovery efficiency only affects extraction and not injection, it would have only minor impacts on the pump pressure analysis or on the drawup results since these occur during injection periods. Although the loss of artesian pressure in the APPA is a result of extraction rates, all ASR sites near the APPA have had extraction rates dropped significantly below the recovery efficiency in order to meet the performance measure, so a small change to recovery efficiency in the Monte Carlo would not have affected that performance measure. The only measurable impact of a small variation to recovery efficiency would be to the drawdown areas. This performance measure did not impact the selection of ASR wells. As mentioned by the reviewer, recovery efficiency is an issue that should be studied in detail at each individual site using pilot projects and local scale models. Scenario 12 has been added which removes the BZ wells from Scenario 11. B 30

32 IMC Comments Additional Comments: 1. Limiting heads to prevent rock fracturing As mentioned in the report, the method of Nick Geibel of USACE Omaha District was used to analyze and estimate the limiting heads in each aquifer. A concern on the calculations is that the static total head was taken from the February 2004 calibration model. Why simulated heads from one of the scenario runs (e.g., full design run) was not used for the calculation? Adding 333 ASR injection wells will increase head pressure locally and/or globally. 2. Can this model be used to determine the optimum number of ASR wells that can be located in the Upper Floridan aquifer while avoiding locating wells in the boulder zone? USACE Responses This analysis is explained more clearly in the 2012 Geibel report and this document (section 3.1) has been adjusted to more closely resemble that explanation. The term static total head as used in this report is called ambient pre fracture water pressure in the Geibel paper. We discussed this comment with Mr. Geibel and he assured us that this term refers to the aquifer conditions before pumping begins. The increased head caused by the injection from 333 ASR wells is compared to the result of Equation 3.6. Scenario 2 provides one possible answer to this question. B 31

33 Head (ft) At left is the original calibrated model (steady state, February 2004, UF layer) with density dependent flow TDS and temperature transport. At right is the same run made with density dependent flow and TDS and temperature transport turned off. Calibration targets show the comparison to measured heads. Green targets are within 2 feet of the measured head; yellow targets are within 4 feet of the measured head; red targets are more than 4 feet different from the measured head. The direction of the target indicates the sign of the error. Original Feb 2004 Steady State Sensitivity Feb 2004 Steady State Solution (With Density Dependent Solution (Without TDS/Temp TDS/Temp Transport) Transport) Impact of TDS and Temperature Transport on UF Head Solution Figure B.1

34 Head (ft) At left is the original calibrated model (steady state, February 2004, APPZ layer) with density dependent flow TDS and temperature transport. At right is the same run made with density dependent flow and TDS and temperature transport turned off. Calibration targets show the comparison to measured heads. Green targets are within 2 feet of the measured head; yellow targets are within 4 feet of the measured head; red targets are more than 4 feet different from the measured head. The direction of the target indicates the sign of the error. Original Feb 2004 Steady State Sensitivity Feb 2004 Steady State Solution (With TDS and Solution (Without TDS and Temperature Transport) Temperature Transport) Impact of TDS and Temperature Transport on APPZ Head Solution Figure B.2

35 Head (ft) At left is the original calibrated model (steady state, February 2004, LF layer) with density dependent flow TDS and temperature transport. At right is the same run made with density dependent flow and TDS and temperature transport turned off. Calibration targets show the comparison to measured heads. Green targets are within 2 feet of the measured head; yellow targets are within 4 feet of the measured head; red targets are more than 4 feet different from the measured head. The direction of the target indicates the sign of the error. Original Feb 2004 Steady State Sensitivity Feb 2004 Steady State Solution (With TDS and Solution (Without TDS and Temperature Transport) Temperature Transport) Impact of TDS and Temperature Transport on LF Head Solution Figure B.3

36 Head (ft) At left is the original calibrated model (steady state, February 2004, BZ layer) with density dependent flow TDS and temperature transport. At right is the same run made with density dependent flow and TDS and temperature transport turned off. Calibration targets show the comparison to measured heads. Green targets are within 2 feet of the measured head; yellow targets are within 4 feet of the measured head; red targets are more than 4 feet different from the measured head. The direction of the target indicates the sign of the error. Original Feb 2004 Steady State Sensitivity Feb 2004 Steady State Solution (With TDS and Solution (Without Temperature Transport) TDS/Temperature Transport) Impact of TDS and Temperature Transport on BZ Head Solution Figure B.4

37 ) Model Calcu ulated Head (ft) Original Run (with Density Dependent TDS/Temp Transport) UF APPZ LF BZ Sensitivity Run (no Transport) UF APPZ LF BZ Density endence With Depe out sport With Trans ME MAE Observed Head (ft) RMS ME = Mean Error MAE = Mean Absolute Error RMS = Root Mean Square Error Impact of TDS and Temperature Transport on Head Solution (February 2004 Calibration Run) Figure B.5

38 50 Head (ft) Shingle Creek at State Hwy 531A /1/2004 3/2/2004 5/2/2004 7/2/2004 9/1/ /1/2004 1/1/2005 ROMP TR 1 2 SWNN Legend (Plots) Observed Heads Model Result with Density Dependence Model Result without Density Dependence All three example wells are open in the UF aquifer. There are numerous other observation wells which could be shown. All show stark differences between runs with and without density dependence except for a few wells near the Polk County recharge area (near the Shingle Creek well shown). Head (ft) He ead (ft) OKF /1/2004 3/2/2004 5/2/2004 7/2/2004 9/1/ /1/2004 1/1/ /1/2004 3/2/2004 5/2/2004 7/2/2004 9/1/ /1/2004 1/1/2005 Impact of TDS and Temperature Transport on Head Solution (Transient Calibration Run) Figure B.6

39 Head (ft) Head (ft) OSF /1/2004 3/2/2004 5/2/2004 7/2/2004 9/1/ /1/2004 1/1/2005 G /1/2004 3/2/2004 5/2/2004 7/2/2004 9/1/ /1/2004 1/1/2005 He ead (ft) PBF /1/2004 3/2/2004 5/2/2004 7/2/2004 9/1/ /1/2004 1/1/2005 Impact of TDS and Temperature Transport on Head Solution (Transient Calibration Run) Legend (Plots) Observed Heads Model Result with Density Dependence) Model Result without Density Dependence OSF 97 is open in the BZ; G 2617 is open in the APPZ; PBF 12 is open in the LF aquifer. There are numerous other observation wells which could be shown. All show stark differences between runs with and without density dependence except for a few wells near the Polk County recharge area (near OSF 97 shown). Figure B.7

40 Head (ft) Head (ft) Average Head Difference (ft) (Map Contours) < 50 1 to 5 50 to 25 5 to to to 5 25 to 50 5 to 1 > 50 1 to 1 Modeled Head (ft) (Plot Lines) Scenario 1 (With Density Dependence) Scenario 0 ( No Density Dependence) Map in upper right shows the average head difference in the UF for Scenario 1 when run with and without density dependence. The original run (with density dependence) was subtracted from the run without density dependence, so a negative number indicates that the removal of density dependence d caused a drop in heads. The head results at two locations are shown in the two time plots. Impact of TDS and Temperature Transport on UF Head Solution (D13R Scenario 1) Figure B.8

41 Head (ft) Average Head Difference (ft) (Map Contours) < 50 1 to 5 50 to 25 5 to to to 5 25 to 50 5 to 1 > 50 1 to 1 Modeled Head (ft) (Plot Lines) Scenario 1 (With Density Dependence) Scenario 0 ( No Density Dependence) Map in upper right shows the average head difference in the APPZ for Scenario 1 when run with and without density dependence. The original run (with density dependence) was subtracted from the run without density dependence, so a negative number indicates that the removal of density dependence d caused a drop in heads. Head (ft) The head results at two locations are shown in the two time plots Impact of TDS and Temperature Transport on APPZ Head Solution (D13R Scenario 1) Figure B.9

42 Head (ft) Head (ft) Average Head Difference (ft) (Map Contours) < 50 1 to 5 50 to 25 5 to to to 5 25 to 50 5 to 1 > 50 1 to 1 Modeled Head (ft) (Plot Lines) Scenario 1 (With Density Dependence) Scenario 0 ( No Density Dependence) Map in upper right shows the average head difference in the LF for Scenario 1 when run with and without density dependence. The original run (with density dependence) was subtracted from the run without density dependence, so a negative number indicates that the removal of density dependence d caused a drop in heads. The head results at two locations are shown in the two time plots. Impact of TDS and Temperature Transport on LF Head Solution (D13R Scenario 1) Figure B.10

43 Head (ft) Head (ft) Average Head Difference (ft) (Map Contours) < 50 1 to 5 50 to 25 5 to to to 5 25 to 50 5 to 1 > 50 1 to 1 Modeled Head (ft) (Plot Lines) Scenario 1 (With Density Dependence) Scenario 0 ( No Density Dependence) Map in upper right shows the average head difference in the BZ for Scenario 1 when run with and without density dependence. The original run (with density dependence) was subtracted from the run without density dependence, so a negative number indicates that the removal of density dependence d caused a drop in heads. The head results at two locations are shown in the two time plots Impact of TDS and Temperature Transport on BZ Head Solution (D13R Scenario 1) Figure B.11

44 re (psi) Pressu MHV Moorehaven FGH Flaghole RVB Riverbend NIC Nicodemus Slough C41 C 41Canal C40 C 40 Canal NLO North Lake Okeechobee Reservoir KSR Kissimmee River / Paradise Run TCR Taylor Creek Reservoir L63 L 63N Canal LKR Lakeside Ranch PMY Port Mayaca L8 L 8 Basin C51 C STA1E CPB Central Palm Beach HLS Hillsboro (Site 1) Legend Upper Floridan Aquifer Avon Park Permeable Zone Boulder Zone 100 psi Limit Scenario 0a includes all of the 333 ASR wells specified by the SFWMM D13R design but no density dependence. All are fully penetrating in the UFAquifer. This plot shows the highest pressure at each site which the pump would need to overcome in order to inject storage water during the 13 year simulation. The PDT determined that it would be important to keep this pressure below 100 psi (indicated by the heavy black line). Note that maximum pressures are shown for all aquifers and all sites even if ASR pumps are not located there for the current scenario. Compare to Figure 4.17 in the main report MHV FGH RVB NIC C41 C40 NLO KSR TCR L63 LKR PMY L8 C51 CPB HLS Proposed ASR Sites Impact of TDS and Temperature Transport on Pump Pressure Performance Measure (D13R Scenario 0a) Figure B.12

45 Upper Floridan Aquifer Legend Not artesian < 5% 5% 10% 10% 20% 20% 50% 50% 100% Loses artesian condition Scenario 0a includes all of the 333 ASR wells specified by the SFWMM D13R design but does not include density dependence. All are fully penetrating in the UF Aquifer. These plots show the maximum reduction in artesian flow at each model cell as a percentage when compared to the flow expected without the ASR project. Permit rules require that the reduction in Saint Lucie and Martin Counties be less than 10%. Compare to Figure 4.18 in the main report. Avon Park Permeable Zone Impact of TDS and Temperature Transport on APPA Performance Measure (D13R Scenario 0a) Figure B.13

46 IAS 2 IAS 3 IAS 4 Legend Maximum Drawdown 1 foot 5 feet Maximum Drawup 1 foot 5 feet Model ldomain UF APPZ LF BZ Scenario 0 includes all of the 333 ASR wells specified by the SFWMM D13R design but does not include density dependence. All are fully penetrating in the UF Aquifer. These figures show the extent of the areas where the maximum drawdown or drawup is greater than 1 foot or 5 feet. The maximum condition does not necessarily occur at the same time for all regions of the model. Compare to Figure 4.20 in the main report. Impact of TDS and Temperature Transport on APPA Performance Measure (D13R Scenario 0a) Figure B.14

47 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released in the BZ in northern Palm Beach County. When the model was solved with density dependence, the particle moved to the northwest and rose to the upper layers of the model. When the model was solved without density dependence, the particle moved to the east and stayed in the BZ. Impact of Density Dependence on Groundwater Flow Direction Figure B.15

48 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released in the BZ just west of Lake Okeechobee. When the model was solved with density dependence, the particle moved to the northwest and rose to the upper layers of the model. When the model was solved without density dependence, the particle moved to the south and stayed in the BZ. Impact of Density Dependence on Groundwater Flow Direction Figure B.16

49 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released in the BZ in Charlotte County. When the model was solved with density dependence, the particle moved to the northwest and rose to the upper layers of the model, changing directions in the IAS. When the model was solved without density dependence, the particle moved to the south east and stayed in the BZ. Impact of Density Dependence on Groundwater Flow Direction Figure B.17

50 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released in the BZ in northern Okeechobee County. When the model was solved with density dependence, the particle moved to the north and rose to the upper layers of the model. When the model was solved without density dependence, the particle moved to the south east and stayed in the BZ until rising to the UF offshore. Impact of Density Dependence on Groundwater Flow Direction Figure B.18

51 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released in the BZ off the coast of Broward County. When the model was solved with density dependence, the particle moved to the west and rose to the upper layers of the model, turning to the east in the LF. When the model was solved without density dependence, the particle moved to the east, exiting to the ocean from the BZ. Impact of Density Dependence on Groundwater Flow Direction Figure B.19

52 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released in the BZ off the coast of St. Lucie County. When the model was solved with density dependence, the particle moved to the northwest and rose to the upper layers of the model, turning to the east in the APPZ. When the model was solved without density dependence, the particle moved to the east, exiting to the ocean from the BZ. Impact of Density Dependence on Groundwater Flow Direction Figure B.20

53 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released in the BZ off the coast of Palm Beach County. When the model was solved with density dependence, the particle moved to the west and rose to the upper layers of the model, turning to the east in the LF. When the model was solved without density dependence, the particle moved to the east, exiting to the ocean from the BZ. Impact of Density Dependence on Groundwater Flow Direction Figure B.21

54 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released in the BZ in the corner of Collier County. When the model was solved with density dependence, the particle moved to the west then south, remaining in the BZ. When the model was solved without density dependence, the particle moved to the east and then south, exiting to the ocean from the BZ. Impact of Density Dependence on Groundwater Flow Direction Figure B.22

55 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released in the LF in Highlands County. When the model was solved with density dependence, the particle moved to the west, rising to the upper layers of the model and changing direction upon entering the IAS. When the model was solved without density dependence, the particle moved to the south, dropping into the BZ. Impact of Density Dependence on Groundwater Flow Direction Figure B.23

56 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released near the eastern boundary of Lee County. When the model was solved with density dependence, the particle rose quickly to the IAS and then moved to the southwest. When the model was solved without density dependence, the particle dropped to the BZ and moved west and south. Impact of Density Dependence on Groundwater Flow Direction Figure B.24

57 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released just south of Lake Okeechobee in Palm Beach County. When the model was solved with density dependence, the particle rose quickly to the UF, while moving north northeast. The particle then dropped back to the APPZ and changed direction towards the northwest, rising back up and exiting the model from the surface. When the model was solved without density dependence, the particle moved to the south and east, eventually dropping to the BZ. Impact of Density Dependence on Groundwater Flow Direction Figure B.25

58 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released in the southern corner of Okeechobee County. When the model was solved with density dependence, the particle moved northward, eventually turning towards the east and rising to the IAS. When the model was solved without density dependence, the particle moved in a eastsoutheasterly direction, turning slightly to the north as it rose to the UF. Impact of Density Dependence on Groundwater Flow Direction Figure B.26

59 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released near the eastern boundary of Polk County. When the model was solved with density dependence, the particle moved to the northeast, eventually dropping to the BZ before exiting the model boundary. When the model was solved without density dependence, the particle moved towards the southeast, dropping to the BZ and then quickly rising to the UF at the end of the track. Impact of Density Dependence on Groundwater Flow Direction Figure B.27

60 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released near the eastern boundary of Polk County. When the model was solved with density dependence, the initially moved to the west and then rose to the APPZ and changed directions to the northeast, eventually exiting to the ocean at the UF outcrop. When the model was solved without density dependence, the particle moved to the southeast, rising to the UF. Impact of Density Dependence on Groundwater Flow Direction Figure B.28

61 Particle Layer IAS UF APPZ LF BZ Using the February 2004 steady state calibration model and MODPATH, a particle was released near the eastern boundary of Polk County. When the model was solved with density dependence, the initially moved to the west and then rose to the APPZ and changed directions to the northeast, eventually exiting to the ocean at the UF outcrop. When the model was solved without density dependence, the particle moved to the southeast, rising to the UF. Impact of Density Dependence on Groundwater Flow Direction Figure B.29

62 With Density Dependence No Density Dependence Legend Downward Flow Upward Flow This figure shows the locations of upward vs. downward flow in the LC when density dependence is included or removed in the February 2004 steady state calibration model. When density dependence is included, the majority of the model domain has upward flow between the BZ and LF layers. When the model is run without density dependence, the majority of the model domain has downward flow from the LF to the BZ. Impact of Density Dependence on Groundwater Flow Direction LC Figure B.30

63 With Density Dependence No Density Dependence Legend Downward Flow Upward Flow This figure shows the locations of upward vs. downward flow in the MC2 when density dependence is included or removed in the February 2004 steady state calibration model. When density dependence is included, the majority of the model domain has upward flow between the LF and APPZ layers. When the model is run without density dependence, the majority of the model domain has downward flow from the APPZ to the LF. Impact of Density Dependence on Groundwater Flow Direction MC2 Figure B.31

64 With Density Dependence No Density Dependence Legend Downward Flow Upward Flow This figure shows the locations of upward vs. downward flow in the MC1 when density dependence is included or removed in the February 2004 steady state calibration model. When density dependence is included, the majority of the model domain has upward flow between the APPZ and UF layers. When the model is run without density dependence, the majority of the model domain has downward flow from the UF to the APPZ. Impact of Density Dependence on Groundwater Flow Direction MC1 Figure B.32

65 With Density Dependence No Density Dependence Horizontal Flow Directions This figure shows the horizontal flow directions for each cell of the with and without density dependence. The solution is from the February 2004 calibration model. With density dependence, the majority of the model has flow towards the west and northwest, with some significant areas of northward flow. Without density dependence, nearly all the flow is towards the south, southeast and east. Impact of Density Dependence on Groundwater Flow Direction BZ Figure B.33

66 With Density Dependence No Density Dependence Horizontal Flow Directions This figure shows the horizontal flow directions for each cell of the LF (Layer 18) with and without density dependence. The solution is from the February 2004 calibration model. Note that in western Polk County and many surrounding areas, density dependence does not impact flow directions. This is where the source of water is surface recharge and where the salinity is lowest. The area around Lake Okeechobee shows a great difference in flow directions: with density dependence, the flow direction is mostly to the north and northwest; without density dependence it is mostly to the southeast. Note that without density dependence a great majority of the flow is towards the south, southeast and east. Density dependence causes much more variability in flow directions. Impact of Density Dependence on Groundwater Flow Direction LF Figure B.34

67 With Density Dependence No Density Dependence Horizontal Flow Directions This figure shows the horizontal flow directions for each cell of the APPZ (Layer 14) with and without density dependence. The solution is from the February 2004 calibration model. Note that in Polk County and many surrounding areas, density dependence does not impact flow directions. This is where the source of water is surface recharge and where the salinity is lowest. The area around Lake Okeechobee shows a great difference in flow directions: with density dependence, the flow direction is mostly to the north and northwest; without density dependence it is mostly to the southeast. Similarly, the Everglades area, including Collier, Monroe and Miami Dade Counties, the flow directions are mainly to the south and west with density dependence but are towards the north and northeast without density dependence. Impact of Density Dependence on Groundwater Flow Direction APPZ Figure B.35

68 With Density Dependence No Density Dependence Horizontal Flow Directions This figure shows the horizontal flow directions for each cell of the UF (Layer 7) with and without density dependence. The solution is from the February 2004 calibration model. Note that in Polk County and many surrounding areas, density dependence does not impact flow directions. This is where the source of water is surface recharge and where the salinity is lowest. The area around Lake Okeechobee shows a great difference in flow directions: with density dependence, the flow direction is mostly to the north and northwest; without density dependence it is mostly to the southeast. Similarly, the Everglades area, including Collier, Monroe and Miami Dade Counties, the flow directions are mainly to the south and west with density dependence but are towards the north and northeast without density dependence. Impact of Density Dependence on Groundwater Flow Direction UF Figure B.36