Glen Canyon Dam Bug Flow Experiment featuring GAMS optimization models and supporting data for evaluating trade-offs between ecological flow objectives and hydropower generation.

Bugs Pay for Days of Steady Reservoir Releases to Reduce Hydropeaking-Ecosystem Conflict

This study is part of my M.S. degree in Civil and Environmental Engineering at Utah State University, Utah, USA

This research was jointly funded by the Future of the Colorado River Project and the Higher Education Commission (HEC) of Pakistan.

Corresponding Author: Moazzam Ali Rind (moazzamalirind@gmail.com)

Advised by: Dr. David E. Rosenberg (http://rosenberg.usu.edu/; david.rosenberg@usu.edu)

Starting Date: 6/1/2019

Lasted updated: 7/20/2026

Project Summary:

This study quantifies the trade-offs between the number of days with steady reservoir releases and hydropower-peaking objectives. A steady-flow day—during which releases remain constant throughout the day—provides suitable conditions for aquatic invertebrates to lay eggs and for those eggs to hatch. Since 2018, a Bug Flow Experiment has been implemented at Glen Canyon Dam, with summer releases kept low and steady on weekends. The overarching questions are:1) How does hydropeaking value vary as steady flow days expand from weekends to weekdays? 2) How can the tradeoff results be used to inform an ecosystem manager’s budget and commitments regarding the number and timing of Bug Flow days to purchase from hydropower producers? The optimization model, using constraint method, was used to quantify the tradeoffs. The model runs for one month with two sub-daily timesteps and is subjected to reservoir’s physical and managerial constraints. Estimates include scenarios that vary monthly release volume, weekend offset release, energy-pricing structure (market versus contract prices), and the model’s sensitivity to 2014 versus 2024 energy prices. The results help design a program where ecosystem managers can purchase additional days of steady releases from hydropower producers and compensate the producers for the lost hydropower revenue.

Objectives

• Quantify the tradeoffs between ecosystem objectives, represented by the number of low, steady-flow days, and traditional reservoir-management objectives, represented by monthly hydropower-peaking value.

• Evaluate how key factors influence the shape and position of the tradeoff curve, including monthly release volume, offset releases, energy-price type (market versus contract), days template (weekday–weekday versus Saturday–Sunday–weekday), and the use of 2014 versus 2024 energy-pricing datasets.

• Assess monthly variation in the tradeoffs and determine how the results can help hydropower producers and ecosystem managers better understand and address conflicts between hydropower generation and ecosystem objectives.

Features of the study

1. We have transformed a monthly non-linear hydropower objective with 744 hourly release decisions (24 hours *31 days) to a linear problem with only 6 sub-daily decisions i.e: 3 day type (Saturday, Sunday, and Weekday) and 2 periods per day.

2. The model can produce results for scenarios: monthly release volumes, offset release between off-peak weekday and weekend, and price type (market and contract).

3. Only two periodic releases per day and those releases remain constant for the month under similar flowpatterns (Steady and hydropeak).

4. Concept of bugs buying water from hydropower producers by paying the losses. Tradeoffs of the months provide purchase price ($/day) of different day types during months, hence, ecosystem managers make informed purchase decisions.

5. Example of trade-off analysis used for multi-objective decision making.

6. The study is replicable and adaptable to other sites and designer flow experiments (e.g. HFEs)

Model Formulation

**Details of Repository Contents**

There are four distinct folders:

a. Documents

b. EnergyPrices and Supporting Data

c. Results_2014Pricing

d. Results_2024Pricing

a. Documents

Throughout this project, we produced a range of documents, including the research proposal, conference papers, thesis, journal article drafts, and supplementary materials.

The most recent versions of the manuscript, supplemental, and cover letter submitted to Journal of Hydrology in July 2026 appear in this folder (Rind and Rosenberg_July2026_Revised.docx and .pdf; Rind and Rosenberg_Supplementary_July2026_Revised.docx & .pdf; CoverLetterReviewerResponses-Resubmit_Rind&Rosenberg2026). This folder also includes a presentation to the Adaptive Management Program in April 2023 (Rosenberg-BugsPayForSteadyFlows-April2023AMP.pptx) and feedback from that presentation (KeyFeedbackFromTechnicalWorkGrou-April12-2023.docx).

The subfolder labeled "Other" contains the additional files: The initial linear optimization model is documented in "Rind_LinearModel_Final.pdf". The proposal is provided in "Proposal_MS_Rind.pdf", while the complete thesis is available in "Rind_2022_Thesis_USU.pdf". The development of the journal article is represented by multiple versions, including "Final_Draft_JHI_2025_Feburary.pdf", "JHI_Resubmit2025_December_Article.pdf" and current resubmission "Rind and Rosenberg_July2026_Revised.pdf", together with their corresponding supplementary materials. Collectively, these documents provide a comprehensive record of the study’s objectives, methods, model development, results, and evolution over time.

b. EnergyPrices and Supporting Data

There are three subfolders with distinct infromation.

i. 2014Prices

"EnergyRates_2014.xlsx" contains the raw hourly energy prices received from WAPA for 2014. The hourly data were aggregated into two time periods per day, and the average price for each period was used to represent the 2014 energy prices for each month.

ii. 2024Prices

This folder contains all files and code required to generate the 2024 energy-price dataset. The accompanying README provides step-by-step instructions for navigating the folder and executing the complete data-generation workflow. Supplementary Tables S1–S2 and Figures S2–S3 were adopted from the output file "GCDEnergyPrice.pdf".

iii. Observed_Hydrographs

File named "Hydrographs_Observed_Used.xlsx" contains obserevd releases at Lees Ferry below Glen Canyon Dam,Arizona (https://waterdata.usgs.gov/usa/nwis/uv?09380000). There are observed hydrographs from 2013 to 2021 and those excel files have some initial data analysis, visualization, and selections. Figure 2 in the main article and supplementary Figure S4-S6 are prepared using the observed information. "Hydropower_Fluctuations(2018).xlsx" provides an approximate estimate of variations in hydropower generation resulting from changes in reservoir storage levels (Supplementary Table S6).

c. Results_2014Pricing

This folder contains subfolders named by month, followed by “2018” (for example, April 2018 and August 2018). The “2018” designation indicates that the hydrologic conditions used in the analysis—including reservoir storage levels and observed releases—were obtained from 2018 data. The energy prices used in these simulations are from the 2014 pricing dataset.

Each monthly subfolder contains three directories: Contract Price Model, Market-Contract Price Model, and Miscellaneous. For example, "April 2018/Contract Price Model" contains the GAMS code file April18_Sat_Sun-Weekday_Mode.gms and its corresponding output files, Sat-Sun-Weekday_April.gdx and Sat-Sun-Weekday_April.xlsx. Similar code and output files are provided for each month. The "April 2018/Market-Contract Price Model" folder likewise contains the relevant GAMS model code and associated output files for the market-contract pricing scenario. The "April 2018/Miscellaneous" folder contains code and results from an earlier version of the model that used only two day types: Weekday and Weekend. This version was used solely for model validation and was not included in the main analysis.

d. Results_2024Pricing

There are three main subfolders: Contract Price Model, Market Price Model, and Figures_Analysis. The Contract Price Model and Market Price Model folders each contain monthly subfolders corresponding to the months analyzed in this study. For example, "Contract Price Model/April" contains the GAMS model file "April18_Contract_2024.gms" and the associated output files "2024_Contract_April.gdx" and "2024_Contract_April.xlsx". The same folder structure and naming convention are used for the monthly analyses in the Market Price Model folder. The Figures_Analysis folder contains the Excel files used to generate the figures and tables presented in the main article and supplementary document.

Required Softwares

1. General Algebraic Modeling System (GAMS), which can be freely downloaded from (https://www.gams.com/download/). We used GAMS version 30.3 and acquired license to run the model.

2. Microsoft Excel. We used Office 2016 for this analysis.

Directions to Reproduce Results

The following instructions will help users reproduce the results in Rind and Rosenberg_July2026_Revised.pdf and in Rind and Rosenberg_Supplementary_July2026_Revised.pdf

Download the Repository: Download and extract the repository from GitHub into your desired local folder (e.g., E:\GAMS).

Figure 1 This location map was created using ArcMap 10.8 (GIS).

Figure 2 1. Download Data: * Fetch the 15-minute observed hydrograph data for August 2018 from USGS Water Data (Site 09380000). 2. Update Excel Workbook: * Open EnergyPrices and Supporting Data/Observed_Hydrographs/Hydrographs_Observed_Used.xlsx. * Navigate to the August_2018 sheet. * Paste the downloaded release data into the corresponding time slots within the blue-highlighted cells. 3. Verify Visualization: * View the updated chart on the August_2018 (Hydrograph) worksheet.

Figure 3 Schematic created using Microsoft PowerPoint.

Figure 4 Generated using Results_2024Pricing/Contract Price Model/August/August18_Contract_2024.gms. 1. Run gamside.exe. Go to File and save the project at your desired location. It may be convenient to save the project inside the folder where you downloaded the repository. 2. Import the code file:File $\rightarrow$ Open $\rightarrow$ Results_2024Pricing/Contract Price Model/August/August18_Contract_2024.gms. A main window with the model code will appear. You are only required to run the model by either pressing F9 or Run button. (all inputs are defined in the code), and the output files will be generated/updated in the project's folder. 3. Verify the run: Confirm "Status: Normal completion" and check for "Optimal Solution found". Since checking individual log statuses across multiple scenarios is difficult, verify scenario statuses via the .gdx file instead. 4. Check scenario results: Click File $\rightarrow$ Open, set Files of type to GDX files (*.gdx), and open 2024_Contract_August.gdx. Scroll to the symbol ModelResults to view the ModStat and SolStat for each run. A value of 1 indicates an optimal solution. For further details on ModStat and SolStat, visit the GAMS Documentation. 5. Prepare the visualization file: After verifying optimality, open Tradeoffs.xlsx located in Results_2024Pricing/Figures_Analysis/. 6. Locate the update sheet: Navigate to the Tradeoff_Control worksheet. You will update the blue-highlighted cells using data from the output .xlsx file. 7. Extract model outputs: Open 2024_Contract_August.xlsx from your project output folder and go to the Fstore worksheet. Filter the Offset column (column A) to select only H4 (representing a 1000 cfs offset). 8. Update the graph: Copy the filtered values from columns A–D and paste them into the Tradeoff_Control worksheet in Tradeoffs.xlsx. The trade-off plot in the Graph_Tradeoff_2024_August worksheet will update automatically.

Figure 5 This figure compares two model formulations—contract pricing and market pricing—and illustrates how differences in price structure influence the trade-offs. 1. For the contract model, use same data as in Figure 4 (Fstore values from 2024_Contract_August.xlsx). 2. For the price model, you have to run the price model code (Results_2024Pricing/Market Price Model/August/August18_2024MarketPricing.gms). i. Import the code file into gamside:File $\rightarrow$ Open $\rightarrow$ Results_2024Pricing/Market Price Model/August/August18_2024MarketPricing.gms. A main window with the model code will appear. You are only required to run the model (all inputs are defined in the code), and the output files will be generated/updated in the project's folder. ii. Verify the run: Confirm "Status: Normal completion" and check for "Optimal Solution found". Since checking individual log statuses across multiple scenarios is difficult, verify scenario statuses via the .gdx file instead. iii. Check scenario results: Click File $\rightarrow$ Open, set Files of type to GDX files (*.gdx), and open Market_August_2024.gdx. Scroll to the symbol ModelResults to view the ModStat and SolStat for each run. A value of 1 indicates an optimal solution. 3. Open same Tradeoffs.xlsx located in Results_2024Pricing/Figures_Analysis/ and move to 2024price_August_Compare worksheet. 4. Import GDX output files: In the GAMS IDE, open both 2024_Contract_August.gdx and Market_August_2024.gdx. Locate the Fstore variable in each file.

Tip: Review the column layout in the 2024price_August_Compare worksheet first, then adjust/reorder the variable columns in the GDX viewer to match that exact layout for easy copying. 5. Paste results: * Copy the Contract price Fstore values into the 2024price_August_Compare worksheet under the yellow-highlighted Contract cell. * Copy the Market price Fstore values into the same worksheet under the green-highlighted Market cell. 6. Graph in the 2024price_August_Compare worksheet will update automatically.

Table 1 This table summarizes the relative loss in hydropeaking value under 2024 market pricing as steady low-flow days are added, compared with the no-bug-flow baseline of zero steady low-flow days. 1. Run the Market Price Model separately for each month. All required codes are in the monthly subfolders within BugFlowExperiment_Analysis2026/Results_2024Pricing/Market Price Model. 2. As a demonstration, we’ll reproduce the results for August. You can follow the same procedure for other months. i. Import the code file into gamside:File $\rightarrow$ Open $\rightarrow$ Results_2024Pricing/Market Price Model/August/August18_2024MarketPricing.gms. A main window with the model code will appear. You are only required to run the model (all inputs are defined in the code), and the output files will be generated/updated in the project's folder. ii. Verify the run: Confirm "Status: Normal completion" and check for "Optimal Solution found". Since checking individual log statuses across multiple scenarios is difficult, verify scenario statuses via the .gdx file instead. iii. Check scenario results: Click File $\rightarrow$ Open, set Files of type to GDX files (*.gdx), and open Market_August_2024.gdx. Scroll to the symbol ModelResults to view the ModStat and SolStat for each run. A value of 1 indicates an optimal solution. 3. For visulation, open Tables.xlsx available at Results_2024Pricing/Figures_Analysis/. Move to Table1-2024Prices worksheet. 4. Paste GDX output values: Copy the Fstore values from your generated .gdx output file and paste them into the designated green-highlighted cells. 5. Update market price values and verify: * Extract the Fstore values for each month from the market price model. * As you paste the Fstore values into the green cells, the yellow-highlighted cells will automatically recalculate. * Verify that the calculated values in the yellow cells match the target values in the uncolored (no-color) cells in the table.

Tip: Pay close attention to month lengths (30 vs. 31 days) when aligning and updating the data in the table. 6. Configuration parameters: Note that all results in this worksheet correspond to 2024 Market Pricing with: * $5/MWh premium * H4 (1,000 cfs offset release) * V2 (0.83 MAF) monthly volume release 7. Insert values manually for each month, updating the table step-by-step.

Supplemantary Section

Figure S1 We compiled hourly energy prices obtained from WAPA into Energy Rates_2014.xlsx (located at BugFlowExperiment_Analysis2026/EnergyPrices and Supporting Data/2014Prices). Each monthly worksheet (e.g., August) contains the observed hourly prices for that given month.

Table S1 These compiled rates are derived from 2014 WAPA energy pricing and are available in the Pricing 2014 worksheet of BugFlowExperiment_Analysis2026/EnergyPrices and Supporting Data/Price_comparison_2014vs2024.xlsx.

Figures S2 and S3 These figures are adapted directly from GCDEnergyPrice.pdf.

Table S2 These compiled 2024 energy rates are available in the Pricing 2024 worksheet located at BugFlowExperiment_Analysis2026/EnergyPrices and Supporting Data/Price_comparison_2014vs2024.xlsx. Source prices were obtained directly from GCDEnergyPrice.pdf.

Figure S4, S5, and S6 These observed releases were obtained from USGS 09380000 Colorado River at Lees Ferry, AZ USGS Water Data (Site 09380000). Refer Hydrographs_Observed_Used.xlsx (location: BugFlowExperiment_Analysis2026/EnergyPrices and Supporting Data/Observed_Hydrographs) and worksheets March 2016, August 2015 and August 2017.

Figure S7 Model structure flow diagram was created in Microsoft PowerPoint.

Figure S8 These are observed releases from August 2018 Colorado River at Lees Ferry, AZ USGS Water Data (Site 09380000). Refer to Hydrographs_Observed_Used.xlsx (location: BugFlowExperiment_Analysis2026/EnergyPrices and Supporting Data/Observed_Hydrographs/) and then move to August_2018(Hydrograph). The release data acquired was 15min time step (observed), then we averaged that to hourly time step (Hourly) and again averaged over the on and off-peak periods during different daytypes over the month (Saturday-Sunday-Weekday model). We expect the user to only acquire the 15 mins time step release data and paste those releases in August_2018 worksheet (blue-highlighted cells). The updated hydrograph can be seen in the "August_2018 (Hydrograph)" worksheet.

Figure S9 For August 2018, we compared observed daily energy generated from the model using controlled hourly and Saturday–Sunday–Weekday releases from Figure S8.

1. Download the daily energy generated at Glen Canyon Dam from the USBR CRSP Website. You can also find this observed data in BugFlowExperiment_Analysis2026/Validation/Models Results Summary.xlsx within the Data_Summary worksheet.
2. In the Engery_Validation_2018 worksheet, the Observed column directly comes from the Data_Summary worksheet. Look for Aug-18 and the column named Energy (MWh) in Data_Summary.
3. Hourly energy values can be generated using the hourly validation code (BugFlowExperiment_Analysis2026/Validation/August/Weekend-Weekday&Hourly/August2018_Validation(Hourly).gms). The code requires an input file Input_August2018.xlsx located at BugFlowExperiment_Analysis2026/Validation/August/Weekend-Weekday&Hourly/Input_August2018.xlsx. Keep this input file in the project folder for the GAMS IDE to detect it. The file contains observed releases at various time steps, which are fed directly into the model to estimate energy generation and hydropeaking values. Output energy generation values can be found using Valid_August2018(Hourly).gdx or Valid_August2018(Hourly).xlsx under the Energy_Gen worksheet. Note that Energy_Gen values are hourly. You are expected to copy "Value" column from the Energy_Gen worksheet and paste it into the Energy_Validation_2018 worksheet, under the blue highlighted cells. You can also read the instructions above those blue-highlighted cells, which say "Paste only the Value column...". The figure will update automatically.
4. Like step 3, generate energy values for the Saturday–Sunday–Weekday model template. The GAMS code file is at BugFlowExperiment_Analysis2026/Validation/August/Saturday_Sunday_Weekday/August18_Sat_Validation.gms. This file includes predefined releases, so no input file is needed, and it can be run directly in the GAMS IDE. The output files Valid_SatModel_August.xlsx and Valid_SatModel_August.gdx will be generated in your project directory. Pre-generated output files are also available at BugFlowExperiment_Analysis2026/Validation/August/Saturday_Sunday_Weekday/.
5. Copy the three energy generation values in Xstore inside Valid_SatModel_August.gdx and paste them into column O (highlighted in blue) in the Engery_Validation_2018 worksheet. The graph will update automatically.

Table S5: Monthly Energy Validation Instructions

This table summarizes model validation results by comparing modeled vs. observed monthly energy generation, reporting percentage errors, and listing applicable monthly energy prices.

Follow the step-by-step workflow below to reproduce results for August 2018. The same workflow applies to all other months (March–October). Record all final values in the Validation_Results worksheet within:
BugFlowExperiment_Analysis2026/Validation/Models Results Summary.xlsx

a. Locate Model Files All validation scripts are located in BugFlowExperiment_Analysis2026/Validation/.
Navigate to the August directory, which contains two subfolders: Weekend-Weekday&Hourly/ Saturday_Sunday_Weekday/

b. Run Weekend–Weekday & Hourly Models Navigate to BugFlowExperiment_Analysis2026/Validation/August/Weekend-Weekday&Hourly/. You will run two scripts: August2018_Validation(Hourly).gms (Hourly model: inputs hourly releases and prices) August2018_validation(2periods).gms (Weekend–Weekday model: inputs 2 day-types)

Execution Steps: 1. Open the .gms file in GAMS IDE (File > Open). No code edits are required. 2. Confirm that Input_August2018.xlsx is present in the active working directory. (Note: This file contains additional datasets like revenue/pricing that feed the model; no user edits are needed). 3. Click Run on the GAMS toolbar. 4. Verify execution success in the status window: * Look for "Status: Normal completion" * Look for "Optimal Solution found"

Solver Note: Models default to the CPLEX solver (option LP = CPLEX;). This model is linear and CPLEX was well-suited for this analysis. To use a different linear solver, comment out this line by adding an asterisk (*) at the beginning, then select a solver via File > Options > Solvers.

c. Extract Output Metrics Open Valid_August2018(Hourly).xlsx and navigate to the Scalar worksheet:

• Read the Vol_monthlyrelease parameter for 'Released volume (Ac-ft/Month)'.
• Read the FStore parameter for 'Hydropeaking value ($)'.
• For 'Energy Generated (MWh)', move to the Xstore worksheet (within the same file, Valid_August2018(Hourly).xlsx). Sum all values in that worksheet to get the total energy generated (MWh) for the month.
• For 'Percentage Error in Energy (%)', calculate using:

$$\text{Error} = 100 \times \frac{\text{Model} - \text{Observed}}{\text{Observed}}\ \%$$

Example:

$$ 100 \times \frac{\text{Hourly} - \text{Observed}}{\text{Observed}} = 100 \times \frac{409{,}289 - 392{,}938}{392{,}938} = 4.2\%$$

Replicate this exact extraction procedure for the two-period script (August2018_validation(2periods).gms).

d. Run Saturday–Sunday–Weekday Model i. Navigate to BugFlowExperiment_Analysis2026/Validation/August/Saturday_Sunday_Weekday/. ii. Run August18_Sat_Validation.gms following the same GAMS execution steps. iii. Extract output metrics from Valid_SatModel_August.xlsx (or .gdx).

e. Complete Table S5 Repeat Steps a–d for all remaining months (March through October). Input all extracted metrics into the Validation_Results tab of Models Results Summary.xlsx to complete Table S5.

Figure S10 1. Open Hydrograph_August_2024Price.xlsx located at BugFlowExperiment_Analysis2026/Results_2024Pricing/Figures_Analysis/. 2. Locate Market_August_2024.xlsx in the GAMS project directory (hint: results you also used in Figure 4). 3. Open Market_August_2024.xlsx and go to the RStore worksheet. Filter for PriceScen1 and V2, copy the values, and paste them into the corresponding blue-highlighted cells in Hydrograph_August_2024Price.xlsx. 4. The hydrograph will update automatically.

Figure S11 Open Tradeoffs.xlsx located in Results_2024Pricing/Figures_Analysis/ and navigate to the Offset_2024Pricing worksheet. 1. Open 2024_Contract_August.gdx in GAMS IDE and locate the Fstore symbol. The output .gdx file must be generated during model run within the project directory. 2. Copy the Fstore values from the gdx file and paste the values into the blue-highlighted cells in the Offset_2024Pricing worksheet. 3. Verify that the graph in the worksheet updates automatically.

Figure S12 Open Tradeoffs.xlsx located in Results_2024Pricing/Figures_Analysis/ and navigate to the 2024Pricing_August_5to30MWh worksheet. 1. Open Market_August_2024.gdx in GAMS IDE and locate the Fstore symbol. The output .gdx file must be generated during model run within the project directory. 2. Copy the Fstore values from the gdx file and paste the values into the blue-highlighted cells in the 2024Pricing_August_5to30MWh worksheet. 3. Verify that the graph in the worksheet updates automatically.

Table S6 This information is sourced from BugFlowExperiment_Analysis2026/EnergyPrices and Supporting Data/Observed_Hydrographs/Hydropower_Fluctuation(2018).xlsx.

Table S7 This table mirrors Table 1, with the primary difference being that it displays hydropeaking values using 2014 energy prices instead of 2024 energy pricing.

This table summarizes the relative loss in hydropeaking value under 2014 market pricing as steady low-flow days are added, compared with the no-bug-flow baseline of zero steady low-flow days.

1. Run the Market Price Model separately for each month. All required codes are in the monthly subfolders within BugFlowExperiment_Analysis2026/Results_2014Pricing.
2. You will see Months followed by 2018 (e.g., August 2018). For demonstration, we are just using August 2018 but the same process has to be repeated for all other months.
3. Inside August 2018, you will find three folders. Hover to Market-Contract Price Model.
4. Open GAMS IDE. and then import the code and run by following steps: i. Import the code file into gamside:File $\rightarrow$ Open $\rightarrow$ Results_2014Pricing/August 2018/August18_MarketPricing_Updated.gms. A main window with the model code will appear. You are only required to run the model (all inputs are defined in the code), and the output files will be generated/updated in the project's folder. ii. Verify the run: Confirm "Status: Normal completion" and check for "Optimal Solution found". Since checking individual log statuses across multiple scenarios is difficult, verify scenario statuses via the .gdx file instead. iii. Check scenario results: Click File $\rightarrow$ Open, set Files of type to GDX files (*.gdx), and open Pricing_Model_Updated.gdx. Scroll to the symbol ModelResults to view the ModStat and SolStat for each run. A value of 1 indicates an optimal solution.
5. For visulation, open Tables.xlsx available at Results_2024Pricing/Figures_Analysis/. Move to Table1-2014Prices worksheet.
6. Paste GDX output values: Copy the Fstore values from your generated .gdx output file and paste them into the designated green-highlighted cells.
7. Update market price values and verify:
8. Extract the Fstore values for each month from the market price model.
9. As you paste the Fstore values into the green cells, the yellow-highlighted cells will automatically recalculate. Only focus on values in Yellow cells "PriceScen1".
10. Verify that the calculated values in the yellow cells match the target values in the uncolored (no-color) cells in the table.

    Tip: Pay close attention to month lengths (30 vs. 31 days) when aligning and updating the data in the table.

11. Configuration parameters: Note that all results in this worksheet correspond to 2014 Market Pricing with:
12. $5/MWh premium
13. H4 (1,000 cfs offset release)
14. V2 (0.83 MAF) monthly volume release
15. Insert values manually for each month, updating the table step-by-step.

Table S8 This table compiles hydropeaking values from the 2014 and 2024 contract price model runs and calculates the slopes (loss or benefit per additional Saturday, Sunday, or weekday). Location: BugFlowExperiment_Analysis2026/Results_2024Pricing/Figures_Analysis/Tables.xlsx and worksheet Compare2024to2014.