Modeling the Impacts

of Glen Canyon Dam Operations

on Colorado River Resources

Prepared as part of Interagency Agreement 

R24PG00010 (USBR) – NEPA Modeling Project

April 2024
Modeling the Impacts

of Glen Canyon Dam Operations

on Colorado River Resources

Prepared in cooperation with the 

U.S. Geological Survey 

as part of Interagency Agreement 

R24PG00010 (USBR) – NEPA Modeling Project
Suggested citation

Entire Report

Yackulic, C.B., Bair, L.S., Eppehimer, D.E., Salter, G.L., Deemer, B.R., Butterfield, B.J., Kasprak, A., 

Caster, J.J., Fairley, H.C., Grams, P.E., Mihalevich, B., Palmquist, E.C., and Sankey, J.B., 2024, Modeling 

the impacts of Glen Canyon Dam operations on Colorado River resources: Phoenix, Ariz., U.S. 

Department of Interior, Bureau of Reclamation, cooperator publication prepared by U.S. Geological 

Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, 

Flagstaff, Ariz., April 2024, 133 p.

Example Chapter within Report

Eppehimer, D.E., and Yackulic, C.B., 2024, Modeling impacts of different reservoir management 

scenarios on smallmouth bass (Micropterus dolomieu) entrainment and population growth rates, in 

Yackulic, C.B., and others, 2024, Modeling the impacts of Glen Canyon Dam operations on Colorado 

River resources: Phoenix, Ariz., U.S. Department of the Interior, Bureau of Reclamation, cooperator 

publication prepared by U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon 

Monitoring and Research Center, Flagstaff, Ariz., April 2024, p. 72-83. 

Authors (listed in order of contribution)

*U.S Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research 

Center (USGS, SBSC, GCMRC)

**Authors listed in alphabetical order following J. Caster

• Charles Yackulic: USGS, SBSC, GCMRC* 

• Lucas Bair: USGS, SBSC, GCMRC

• Drew Eppehimer: USGS, SBSC, GCMRC

• Gerard Salter: USGS, SBSC, GCMRC

• Bridget Deemer: USGS, SBSC, GCMRC

• Bradley Butterfield, Northern Arizona University 

• Alan Kasprak, U.S. Environmental Protection Agency

• Joshua Caster: USGS, SBSC, GCMRC**

• Helen Fairley: USGS, SBSC, GCMRC

• Paul Grams: USGS, SBSC, GCMRC

• Bryce Mihalevich, Bureau of Reclamation (formerly USGS, SBSC, GCMRC) 

• Emily Palmquist: USGS, SBSC, GCMRC

• Joel Sankey: USGS, SBSC, GCMRC

Meredith Hartwell: USGS, SBSC, editor

Photo credits, cover

Glen Canyon Dam, 2008: Anne Phillips, USGS (retired); Lees Ferry beach, 2021: Lucas Bair, USGS; 

Colorado River Mile ~212 looking upstream below Fall Canyon, 2007: USGS public domain. 

Disclaimer

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.
Table of Contents

Abbreviations _____________________________________________________________________ i

Introduction ______________________________________________________________________ 1

References ____________________________________________________________________ 2

I. Modeling of Monthly Hydrology and Designer Flow Implementation under Different Reservoir 

Management Scenarios__________________________________________________________ 3

Background – Interim Guidelines SEIS _______________________________________________ 3

Background – LTEMP SEIS ________________________________________________________ 3

Workflow and Modeling Details to Produce LTEMP SEIS Monthly Hydrologies _______________ 7

References ___________________________________________________________________ 11

II. Modeling the Energy Generation and Economic Value of Glen Canyon Dam Releases _______ 12

Introduction __________________________________________________________________ 12

Methods _____________________________________________________________________ 12

Results ______________________________________________________________________ 14

References ___________________________________________________________________ 15

III. Modeling Impacts of Different Reservoir Management Scenarios on Sediment Resources ___ 16

Background and Methods _______________________________________________________ 16

Sand Routing Model Methods and Assumptions______________________________________ 16

Sandbar Model Methods and Assumptions__________________________________________ 18

Methods for Interim Guidelines SEIS _______________________________________________ 20

Results of Analyzing Interim Guideline SEIS Alternatives________________________________ 22

Methods for LTEMP SEIS ________________________________________________________ 28

Results of Analyzing LTEMP SEIS Alternatives ________________________________________ 32

Appendix for Chapter III: Additional Figures for LTEMP SEIS Modeling_____________________ 44

References ___________________________________________________________________ 69

IV. Modeling Impacts of Different Reservoir Management Scenarios on Smallmouth Bass 

(Micropterus dolomieu) Entrainment and Population Growth Rates _____________________ 72

Background & Methods _________________________________________________________ 72

General modeling ______________________________________________________________ 73

Interim Guideline SEIS modeling __________________________________________________ 74

Results of Analyzing Interim Guideline SEIS Alternatives________________________________ 74

LTEMP SEIS Modeling ___________________________________________________________ 78
Results of Analyzing LTEMP SEIS Alternatives ________________________________________ 79

References ___________________________________________________________________ 81

V. Modeling Impacts of Glen Canyon Dam Operations Scenarios of the Interim Guidelines SEIS and 

LTEMP SEIS on Sand Exposure for Aeolian Landscape and Cultural Site Resources __________ 84

Background & Methods _________________________________________________________ 84

Model Assumptions ____________________________________________________________ 85

Results of Analyzing Interim Guideline SEIS Alternatives________________________________ 85

Results of Analyzing LTEMP SEIS Alternatives ________________________________________ 87

References ___________________________________________________________________ 88

VI. Modeling Recreation Impacts of Glen Canyon Dam Releases ___________________________ 90

Introduction __________________________________________________________________ 90

Methods _____________________________________________________________________ 90

Results ______________________________________________________________________ 91

References ___________________________________________________________________ 93

VII. Modeling Impacts of Different Reservoir Management Scenarios on Riparian Plant 

Communities and Vegetation Resources ___________________________________________ 95

Background & Methods _________________________________________________________ 95

Species and Training Data _______________________________________________________ 96

Environmental Variables ________________________________________________________ 98

Model Predictions _____________________________________________________________ 99

Model Limitations_____________________________________________________________ 100

Results of Analyzing Interim Guideline SEIS Alternatives_______________________________ 101

Results of Analyzing LTEMP SEIS Alternatives _______________________________________ 105

References __________________________________________________________________ 121

VIII. Modeling Impacts of Different Reservoir Management Scenarios on Dissolved Oxygen 

Concentrations in Glen Canyon Dam Releases______________________________________ 125

Background & Methods ________________________________________________________ 125

Modeling Methods and Results for the Interim Guidelines SEIS _________________________ 126

Results of Analyzing Interim Guideline SEIS Alternatives_______________________________ 127

Modeling Methods and Results for the LTEMP SEIS __________________________________ 127

Results of Analyzing LTEMP SEIS Alternatives _______________________________________ 127

References __________________________________________________________________ 132
i |

Abbreviations

• Bureau of Reclamation (Reclamation)

• cfs: cubic feet per second (ft3

/s) 

• CM: Cool Mix Alternative

• CM_FS: Cool Mix with Flow Spikes Alternative

• CM_FS_RM15: Cool Mix with Flow Spikes triggered by River Mile (RM) 15 water 

temperatures 

• CM_FS_RM61: Cool Mix with Flow Spikes triggered by River Mile (RM) 61 water 

temperatures 

• CM_RM15: Cool Mix triggered by River Mile (RM) 15 water temperatures 

• CM_RM61: Cool Mix triggered by River Mile (RM) 61 water temperatures 

• CS: Cold Shock Alternative

• CS_FS: Cold Shock with Flow Spikes Alternative

• CS_FS_RM15: Cold Shock with Flow Spikes triggered by River Mile (RM) 15 water 

temperatures 

• CS_FS_RM61: Cold Shock with Flow Spikes triggered by River Mile (RM) 61 water 

temperatures 

• CS_RM15: Cold Shock triggered by River Mile (RM) 15 water temperatures scenario

• CS_RM61: Cold Shock triggered by River Mile (RM) 61 water temperatures scenario

• CPUE: catch-per-unit-effort 

• CR: Colorado River

• CRe: Colorado River ecosystem

• CRFS: Colorado River Flow and Sediment

• CRMMS: Colorado River Midterm Modeling System

• CRSS: Colorado River Simulation Study

• DO: dissolved oxygen

• EGC: Eastern Grand Canyon

• EIS: Environmental Impact Statement

• ESP: ensemble streamflow prediction

• Fasl: feet above sea level

• ft3

/s: cubic feet per second (cfs)

• GCD: Glen Canyon Dam

• GCDAMP: Glen Canyon Dam Adaptive Management Program

• GCMRC: Grand Canyon Monitoring and Research Center

• GCNP: Grand Canyon National Park

• HBC: humpback chub (Gila cypha) 

• HFE: High Flow Experiment
ii |

• Interim Guidelines SEIS: Near-term Colorado River Operations Supplemental 

Environmental Impact Statement

• kaf: thousands of acre feet

• LCR: Little Colorado River

• LMC: Lower Marble Canyon

• LTEMP EIS: Long-term Experimental and Management Plan Environmental Impact 

Statement 

• LTEMP EIS ROD: Long-term Experimental and Management Plan Environmental Impact 

Statement Record of Decision

• LTEMP SEIS: Long-term Experimental and Management Plan Supplemental 

Environmental Impact Statement

• maf: million-acre feet

• m/s: meters per second

• MWh: megawatt hours

• NA: No Action Alternative

• NB: Non-Bypass Alternative

• NED: National Elevation Database

• NW: new HFE window only alternative

• PA: Proposed Action Alternative

• RM: River Mile (used in the Colorado River, by convention)1

• Rkm: river kilometers (used in the Little Colorado River, by convention)

• SMB: smallmouth bass (Micropterus dolomieu) 

• SEIS: Supplement to an Environmental Impact Statement

• SRM: Sand Routing Model

• Tmt: thousand metric tons

• UMC: Upper Marble Canyon

• WAPA: Western Area Power Administration

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

1

River miles (RM) for the Colorado River are negative from Glen Canyon Dam (begins at -15 RM) downstream to 

Lees Ferry (0 RM) and then positive thereafter to upper Lake Mead (about RM 305).
1 | Page

Introduction

This introduction is largely taken from that provided in Interagency Agreement R24PG00010 

(Yackulic and others, 2024). 

The Bureau of Reclamation (Reclamation) has published a final supplemental Environmental 

Impact Statement (SEIS) for near-term Colorado River operations (Bureau of Reclamation, 

2024a), and has published a draft and is writing a final SEIS for the Long-Term Experimental and 

Management Plan (LTEMP; Bureau of Reclamation, 2024b). These actions have the potential to 

affect downstream resources, including threatened and endangered species, in the Grand 

Canyon, Arizona, USA. 

This report covers modeling support provided for the two SEIS by the Grand Canyon Monitoring 

and Research Center (GCMRC; U.S. Geological Survey, Southwest Biological Science Center).2

The first SEIS, the Near-term Colorado River Operations Supplemental Environmental Impact 

Statement (Interim Guidelines SEIS; Bureau of Reclamation, 2024a) modifies the U.S. Department 

of the Interior’s 2007 Colorado River Interim Guidelines for Lower Basin Shortages and 

Coordinated Operations for Lake Powell and Lake Mead (U.S. Department of the Interior,, 2007) 

that determines annual water releases from GCD based on inflow to Lake Powell, the power 

generating requirements of GCD, and relative reservoir levels of Lake Powell and Lake Mead

(Bureau of Reclamation, 2024a). Drought conditions have lowered the elevation of Lake Powell 

and may require GCD to release less water than was analyzed in the Interim EIS (7 million acrefeet [maf]/year). The effect of less water released, as well as lower reservoir levels and associated 

water quality concerns, on downstream resources was not analyzed in the 2007 Interim 

Guidelines EIS. Reclamation requested GCMRC support to provide models predicting the effects 

to resources of water releases lower than 7 maf/year, including models predicting effects to 

threatened and endangered species for use in a Biological Assessment.

The second SEIS, the Glen Canyon Dam Long-Term Experimental and Management Plan 

Supplemental Environmental Impact Statement (LTEMP SEIS; Reclamation, 2024b) modifies 

Reclamation’s GCD Long-Term Experimental and Management Plan Environmental Impact 

Statement (U.S. Department of the Interior, 2016). This SEIS provides an adaptive management 

framework for GCD operations through 2036 that includes dam operations (monthly, daily, and 

hourly release patterns), non-flow actions, and experimental and management actions (U.S. 

Department of the Interior, 2016). The LTEMP SEIS modifies the existing analysis to 1) review and 

develop flow options to disadvantage smallmouth bass (including modeling hydropower 

2 Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government. 
2 | Page

revenue under differing flow models), and 2) change the sediment accounting period and 

triggers for High Flow Experiments (HFEs) in the existing EIS (Bureau of Reclamation, 2024b). 

These two changes may alter effects to downstream resources and were not adequately 

modeled in the original LTEMP EIS and Biological Assessment (Bureau of Reclamation, 2024b). 

References 

Bureau of Reclamation, 2024a, Near-term Colorado River operations—Final Supplemental 

Environmental Impact Statement: U.S. Department of the Interior, Bureau of Reclamation, 

Upper and Lower Colorado Basins, Interior Regions 7 and 8, 

https://www.usbr.gov/ColoradoRiverBasin/documents/NearTermColoradoRiverOperations/2

0240300-Near-termColoradoRiverOperations-FinalSEIS-508.pdf. 

Bureau of Reclamation, 2024b, Glen Canyon Dam Long-Term Experimental and Management 

Plan—Draft Supplemental Environmental Impact Statement: U.S. Department of the Interior, 

Bureau of Reclamation, Upper Colorado Basins, Interior Region 7, 

https://www.usbr.gov/uc/DocLibrary/EnvironmentalImpactStatements/GlenCanyonDamLong

-TermExperimentalManagementPlan/20240207-Draft-GCDLTEMP-SEIS_508.pdf. 

U.S. Department of the Interior, 2007, Record of Decision—Colorado River interim guidelines for 

lower basin shortages and the coordinated operations for Lake Powell and Lake Mead: 

Washington, D.C., Office of the Secretary of the Interior, 59 p., 

https://www.usbr.gov/lc/region/programs/strategies/RecordofDecision.pdf. 

U.S. Department of Interior, 2016, Glen Canyon Dam Long-Term Experimental and Management 

Plan final Environmental Impact Statement (LTEMP FEIS): U.S. Department of the Interior, 

Bureau of Reclamation, Upper Colorado Region, National Park Service, Intermountain 

Region, online, http://ltempeis.anl.gov/documents/final-eis/. 

Yackulic, C.B., Bair, L.S., Eppehimer, D.E., Salter, G.L., Butterfield, B.J., Caster, J.J., Deemer, B.R., 

Fairley, H., Grams, P.E., Kasprak, A., Palmquist, E.C., and Sankey, J.B., 2024, Modeling the 

impacts of Glen Canyon Dam operations on Colorado River resources [virtual presentation]: 

Flagstaff, Ariz., LTEMP SEIS meeting, US Geological Survey, Southwest Biological Science 

Center, Grand Canyon Monitoring and Research Center, January 31, 2024, 

https://www.usgs.gov/centers/southwest-biological-science-center/science/modelingimpacts-glen-canyon-dam-operations. 

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.
3 | Page

I. Modeling of Monthly Hydrology and Designer Flow Implementation 

under Different Reservoir Management Scenarios

 

Background – Interim Guidelines SEIS

The Interim guidelines supplemental Environmental Impact Statement (SEIS) considered two 

alternatives (Bureau of Reclamation, 2024a). Under the No Action Alternative, the existing 

agreements that controlled operations of Glen Canyon and Hoover Dams continued to 

guide releases through the 2026 operation year. The Proposed Action alternative included

increased water conservation tied to Lake Mead reservoir elevations and changes to Glen 

Canyon Dam (GCD) releases under certain conditions that could lower annual volume to as 

low as 6.0-million-acre feet (maf; see Interim Guidelines SEIS for more details). The Bureau of 

Reclamation produced 90 hydrologic traces under each alternative to allow for modeling of 

potential impacts (Bureau of Reclamation, 2024a). These 90 hydrologic traces were based on 

the June 2023 ensemble streamflow predictions (ESP) forecast (a set of 30 traces based on 

the last 30 years of observed hydrology) and included the official 100 percent ESP, along 

with 90 percent ESP and 80 percent ESP to represent the potential for drier conditions that 

have been observed over the last 30 years (Bureau of Reclamation, 2024a). 

Background – LTEMP SEIS 

The remainder of this section of the report describes the process used to forecast monthly 

reservoir elevations and hydrologic conditions for 30 hydrologic traces under 11 flow 

scenarios representing 7 flow alternatives considered by Reclamation as part of their LongTerm Experimental and Management Plan Supplemental Environmental Impact Statement

(LTEMP SEIS). As with the Interim Guidelines SEIS analysis, we relied on the June 2023 

ensemble streamflow predictions (ESP). At the time of modeling, no decision had been made 

regarding the Interim Guidelines SEIS so we assumed the No Action hydrologic conditions

from the Interim Guidelines hydrologic modeling as a baseline for reservoir elevations and 

annual volume releases. Furthermore, as the LTEMP SEIS considers several alternatives, we 

focused analyses on the 30 traces under the official 100 percent ESP to allow for efficient 

comparison of alternatives in a timely fashion. Understanding these flow alternatives (as well 

as scenarios within flow alternatives) requires some basic understanding of the geography of 

the Grand Canyon and of the infrastructure within Glen Canyon Dam, which we describe 

before detailing the alternatives.
4 | Page

GCD releases water from Lake Powell into a segment of the Colorado River colloquially 

referred to as the Lees Ferry reach that has been managed for most of the last half-century 

as a blue-ribbon rainbow trout fishery. This river segment extends approximately 15.6 river 

miles (RM; 25 river kilometers) from the base of GCD to the confluence of the Colorado River 

with the Paria River just below Lees Ferry that demarcates the division between the Upper 

and Lower basins of the Colorado River according to the Colorado River compact (Schmidt 

and others, 2022). The location Lees Ferry is commonly defined as River Mile 0 (RM 0), with 

negative river miles referring to distance upstream from Lees Ferry and positive river miles 

referring to distance downstream. Many of the flow alternatives proposed by Reclamation 

are designed to have flexibility to be triggered by water temperatures anywhere from RM 15 

(i.e., ~50 river kilometers [rkm] downstream from the dam) to near the confluence with the 

Little Colorado River near RM 61 (~125 rkm downstream from the dam) (Bureau of 

Reclamation, 2024b). High Flow Experiments (HFEs) are triggered based on the sand mass 

balance in the river segment between the Colorado-Paria confluence and the Colorado-Little 

Colorado River confluence (often referred to as Marble Canyon) (Bureau of Reclamation, 

2024b). 

Since completion, GCD has released most water through penstocks centered on an elevation 

of 3470 feet above sea level (fasl), with occasional releases through river outlet tubes located 

at 3370 fasl, primarily during HFEs; however, many of the flow alternatives being considered 

by Reclamation involve increased releases through the river outlet tubes to cool the overall 

temperature of reservoir releases (Bureau of Reclamation, 2024b). During the late Spring 

through Fall months, water in the upper layers of Lake Powell warms creating a temperature 

gradient from the warm epilimnion layer through a transition zone known as the 

metalimnion, reaching the cold hypolimnion layer at deeper depths, which shows minimal 

seasonal variation in water temperature. As reservoir elevations have declined over the 21st

century, water drawn through the penstocks has increasingly pulled from shallower depths 

within the reservoir leading to substantial warming of releases from Lake Powell (Dibble and 

others, 2021; Eppehimer and others, 2024). Water drawn through the river outlet tubes, 

located 100 feet deeper, is typically much colder during summer and fall months. Some of 

the alternatives being considered manage water temperature through manipulating the 

proportion of water being released from penstocks versus river outlet tubes.

Proposed flow alternatives are triggered by a temperature threshold of 15.5 °C at the target 

location. A 16 °C daily average water temperature spawning initiation threshold is typically 

observed for smallmouth bass (SMB [Micropterus dolomieu]; Eppehimer and others, 2024), 

and is assumed for this modeling (see Chapter IV for more details). The target of 15.5 °C was 

chosen to account for variation in water temperature releases and warming rates and 

increases the likelihood that water temperature would remain near or below 16 °C at the 
5 | Page

target river mile. The alternatives are designed to be flexible to have target locations 

between RM 15 and RM 61. For each of these alternatives we analyzed two scenarios – one 

in which the target location was located at RM 15 and another where the target location was 

located at RM 61.

Reclamation is analyzing seven flow alternatives in the LTEMP SEIS (Bureau of Reclamation, 

2024b): 

1) The first flow option is a No Action Alternative that includes no changes to the 

sediment accounting window (i.e., continuation of separate Fall and Spring sediment 

accounting windows) and no flows specifically designed to prevent warmwater 

nonnative fish establishment. Under this alternative, Fall HFE triggers and duration 

are based on the sand mass balance over the period of July 1st to November 30th, and 

Spring HFE trigger and duration are based on the sand mass balance over the period 

December 1st to June 30th. If HFEs are triggered, but not implemented there is no 

rollover of sediment mass balance.

2) The remaining six flow alternatives all include a change to a one-year sediment 

accounting window, however the second flow alternative, change to sediment 

accounting window only, only includes this change (i.e., it does not include any 

flows specifically designed to prevent warmwater nonnative fish establishment). 

The Fall and Spring sediment accounting windows are merged into a single one-year 

window, allowing decision-makers the flexibility to delay an HFE from Fall until 

Spring. HFE trigger/duration is selected based on modeled sand mass balance 

between July 1st and the end of the HFE (Fall or Spring). If an HFE is triggered but 

not implemented, a positive sand mass balance is carried forward into the next 

accounting period. Modeling assumptions include: 1) Spring HFE implementation is 

preferred to Fall, and an HFE would be delayed to Spring if duration is within one 

duration tier (i.e., 12, 24, 36, 48, 60, 72, 96, 144, 192, or 250 hours), and 2) no 

HFEs implemented below 3500 fasl, and water volume may be borrowed from other 

months to the implementation month to avoid violating LTEMP minimum flow 

constraints.

3) The third flow alternative, Cool Mix, is triggered when predicted daily water 

temperatures at a target location are greater than or equal to 15.5 °C without the 

action and involves mixing releases from penstock and river outlet tubes to obtain a 

predicted average daily water temperature less than 15.5 °C at the target location. 

The target of 15.5 °C was chosen to account for variation in water temperature 

releases and warming rates and increase the likelihood that water temperature would 

remain near or below 16 °C at the target river mile. This alternative, as well as the 
6 | Page

fourth, fifth, and sixth alternatives, are designed to be flexible to have target locations 

between RM 15 and RM 61 and for each of these alternatives we analyzed two 

scenarios – one in which the target location was located at RM 15 and another where 

the target location was located at RM 61.

4) The fourth alternative, Cool Mix with Flow Spikes, is also triggered when predicted 

daily water temperatures at a target location are greater than or equal to 15.5° C 

without the action. The target of 15.5 °C was chosen to account for variation in water 

temperature releases and warming rates and increase the likelihood that water 

temperature would remain near or below 16 °C at the target river mile. In addition to 

a cool mix as described for the third alternative, the alternative also uses flow spikes, 

that is, short duration increases to the maximum attainable discharge through both 

river outlet and penstocks with up and down ramp rates within LTEMP guidelines and 

eight hours at the peak discharge. Flow spikes are intended to disturb marginal side 

habitats (e.g., backwaters or the feature at ~RM -12 often referred to as the slough) 

by increasing velocities to sweep larvae from underneath male SMB and disrupt 

spawning behavior. Flow spikes are only triggered in the months of May through 

August when the potential for warming in marginal side habitats is greatest. A flow 

spike could be replaced by an HFE if doing so would maximize benefits to sediment 

and is timed appropriately to affect SMB spawning. The first flow spike is replaced by 

an HFE if an HFE would be triggered by sediment conditions. 

5) The fifth alternative, Cold Shock, attempts to disrupt spawning by dropping water 

temperature to below 12° C for a 48-hr period or as cold as possible if 12° C is not 

attainable; however, a minimum of 2,000 cubic feet per second (cfs) hydropower 

would be maintained during the cold shock. The 48-hr cold shocks would occur when 

there are lowest impacts to hydropower, and therefore would likely fall on weekends. 

Cold shocks are triggered when the predicted average daily water temperature is 

greater than or equal to 15.5° C at the target location and occur once a week for the 

first 12 weeks after triggering. The target of 15.5 °C was chosen to account for 

variation in water temperature releases and warming rates. Within a month, the 

amount of bypass calculated for cold shocks was the minimum required (tested in 

half-tube increments) to lower temperature below 12 °C at the targeted river mile in 

all weekends or 12,600 cfs if a lesser volume did not meet this condition. 

Hydropower releases were always assumed to be 2,000 cfs during the Cold Shock.

6) The sixth alternative, Cold Shock with Flow Spikes, includes cold shocks as in the 

fifth alternative, but also adds flow spikes directly after the cold shocks. Up to three 

8-hour flow spikes might occur. These were modeled as two in first month and one in 

subsequent month (in the first, third and fifth weeks after cold shocks are triggered). 
7 | Page

Flow spikes occur as described above in the fourth alternative. Flow spikes were only 

modeled to occur in May, June, July, or August. In weeks with flow spikes, the cold 

shock would occur before the flow spike.

7) The seventh flow alternative, the Non-Bypass Alternative, only uses bypass during 

HFEs. When predicted average daily water temperature is greater than or equal to 

15.5 °C at RM 61, this alternative creates once a week fluctuation in discharge using 

only hydropower releases. The target of 15.5 °C was chosen to account for variation 

in water temperature releases and warming rates. Specifically, flow is first dropped to 

2,000 cfs for four hours and then increased to the maximal attainable discharge 

through the hydropower outlets (based on Lake Powell elevation) for four hours 

before returning to normal operations. Note that the Non-Bypass Alternative 

specifically refers to management of SMB. Bypass tubes may still be used for HFEs if 

an HFE trigger occurs. 

One important feature to note is that modeling of each alternative involves some simplifying 

assumptions. HFEs can occur on any day of the month, but for modeling purposes they 

occur on the 15th of the month. Similarly, if flows designed for SMB are implemented in the 

future, it is likely they will be designed at a weekly scale, however they were modeled at a 

monthly scale here. Differences among weeks in a month are typically greatest during June 

and early July when the temperature profile in Lake Powell is developing. Whereas it is 

straightforward for the SMB model to predict bypass at a daily or weekly scale that would 

require running multiple instances of hydropower maximization within each month in 

Chapter II. Therefore, we instead chose to post-process daily bypass estimates from the SMB 

model such that flows were simulated to occur all month long if SMB flows were triggered 

before or at the halfway mark of a month and simulated to start in the subsequent month if 

SMB flows were triggered after the hallway mark of a month. Furthermore, all days within a 

month were simulated to have the same bypass which was calculated as equal to the median 

of the month (rounded up to the higher value if a month had 28 or 30 days and exactly half 

of days were at one value and the other half at another value). Comparison of total bypass 

between raw and post-processed output suggested minimal change to overall bypass across 

all 30 traces with the amount of bypass subtly increasing in some traces and decreasing in 

other traces.

Workflow and Modeling Details to Produce LTEMP SEIS Monthly Hydrologies

Generating monthly reservoir elevations and releases under different management scenarios 

and hydrologic conditions required adopting a workflow that accounted for the 

interdependencies between different decisions. 
8 | Page

Results of this workflow were tracked in spreadsheets that were later converted to csv files 

for the data release associated with this report (Yackulic and others, 2024). 

An important feature of the workflow is that the 11 scenarios can be viewed as the 

consequence of a series of model decisions such that multiple scenarios may be represented 

by a single spreadsheet during early steps of the workflow (Figure 1-1).

Our workflow began by selecting a set of hydrologic traces. Specifically, we used a set of 30 

ensemble streamflow predictions (ESP; 100% ESP set) at a monthly time step over 4 years to 

characterize a range of potential hydrologic conditions (Yackulic and others, 2024). These 30 

traces are a subset of the traces analyzed in the Interim Guidelines SEIS under the No Action 

Alternative (Bureau of Reclamation, 2024a) and include monthly elevations for Lake Powell in 

units of feet above sea level, as well as monthly inflows and monthly outflows in units of 

thousands of acre feet (kaf). Some of our models required additional months of elevations, 

inflows, and outflows beyond the data originally provided by Reclamation (Bureau of 

Reclamation, 2024a only includes hydrologic data through September 2027), so we made 

the same assumptions regarding inflow and outflow in the months between October 2027 – 

April 2028 regardless of the hydrologic trace and management scenarios. Specifically, we 

assumed 8 maf annual inflows into Lake Powell following monthly volumes determined by a 

log transformed linear model fit to 2000-2021 historic inflows, and we assumed 7.48 maf 

annual outflows from Lake Powell with monthly volumes determined by LTEMP EIS 

guidelines. Based on the elevation specific to each hydrologic trace in September 2027, we 

then calculated monthly elevations for October 2027 to April 2028 using the Colorado River 

Simulation Study (CRSS: Schuster, 1998; Wheeler and others, 2019) water balance equation. 
9 | Page

Figure 1-1. Overview of workflow used to represent eleven management scenarios (boxes on the right-hand side of figure) 

as a consequence of a series of modeling steps. The grey boxes represent the No Action Alternative. The white boxes 

represent the change to sediment accounting window only alternative. All alternatives that involve flows designed for SMB 

include color. Scenarios in which temperatures at RM 61 triggered flows are colored purple if they include bypass and red if 

they do not. Scenarios in which temperatures at RM 15 triggered flows are colored green. Scenarios that include flows 

intended to disturb SMB spawning have bold edges. Cool mix scenarios include a color ramp, while cold shock scenarios 

include vertical lines.

Next, in step 2 of our workflow, we determined for each hydrologic trace whether a flow 

spike or the non-bypass flow fluctuations would be triggered based on water temperature 

predictions from the water temperature model embedded in the SMB model (see Chapter

IV) and described in Eppehimer and others (2024). This model is implemented in R statistical 

software (R Core Team, 2021). When flow spikes were triggered was modeled separately 

based on predicted water temperatures at RM 15 and RM 61. Results of this analysis were 

stored in a worksheet with integer values indicating whether 2, 1, or 0 flow spikes were 

predicted to occur in a particular trace and month. Non-Bypass flow fluctuations were 

modelled based only on water temperatures at RM 61. Results of this analysis were also 

stored in an output file. 
10 | Page

We choose these two sets of fluctuating flows first because the changes in hourly releases 

associated with these flows are large enough to significantly impact sand mass balance 

and/or to require adjustments to monthly release volumes. 

In step 3, we then modeled the triggering of HFEs in Matlab (The MathWorks Inc., 2022) 

using models previously developed for the Grand Canyon and described in Chapter III. The 

New HFE protocol (with a single sediment accounting window) was applied to each of the 

four spreadsheets developed in step 2, however, the current (old HFE in Figure 1-1) was only 

applied to the scenario without any flows designed specifically for SMB (i.e., no flow spike in 

Figure 1-1). Output from Step 3 included the magnitude of both HFEs and flow spikes (which 

depend on reservoir elevations) in cfs, as well as the duration of the HFEs in units of hours 

and revised monthly release volumes in units of thousands of acre feet of water (kaf). 

Next, in step 4, we used water mass balance equations to adjust monthly reservoir elevations 

to align with the revised monthly volumes. These analyses were implemented in R statistical 

software (R Core Team, 2021) and results were stored in a new worksheet. The No Action, 

change to sediment accounting window only, and Non-Bypass Alternatives were complete 

after this step. 

For cold shock and cool mix scenarios, an additional step was required in which the SMB 

model (see Chapter IV) was run in R statistical software (R Core Team, 2021) to determine 

the amount of bypass (flow through the river outlet works) required to meet the 

specifications of the flow alternatives at the target river mile. For modeling of alternatives 

that used cool mix or cold shock, we assumed river outlet tubes could be operated at 1⁄2 

tube increments and that each tube had a capacity of 3,150 cfs. Furthermore, the code was 

written to choose the minimum amount of bypass to meet the requirements of the particular 

flow alternative in each month in which the flow alternative was triggered. The amount of 

bypass was reported in units of cfs which apply either to all days and hours in a month (in 

the case of the cool mix scenarios) or to the 48-hour period of the cold shock scenarios. The 

amount of bypass discharge required under cold shock or cool mix scenarios were reported 

in a worksheet in units of cfs. 

Data Availability Statement:

Data generated during this study are published and available (Yackulic and others, 2024).

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.
11 | Page

References

Bureau of Reclamation, 2024a, Near-term Colorado River operations—Final Supplemental 

Environmental Impact Statement: U.S. Department of the Interior, Bureau of Reclamation, 

Upper and Lower Colorado Basins, Interior Regions 7 and 8, 

https://www.usbr.gov/ColoradoRiverBasin/documents/NearTermColoradoRiverOperation

s/20240300-Near-termColoradoRiverOperations-FinalSEIS-508.pdf. 

Bureau of Reclamation, 2024b, Draft supplemental Environmental Impact Statement for the 

Glen Canyon Dam Long-Term Experimental and Management Plan: Salt Lake City, Utah, 

U.S. Department of the Interior, Bureau of Reclamation, Upper Colorado Basins, Interior 

Region 7, 374 p., 

https://www.usbr.gov/uc/DocLibrary/EnvironmentalImpactStatements/GlenCanyonDamL

ong-TermExperimentalManagementPlan/20240207-Draft-GCDLTEMP-SEIS_508.pdf.

Dibble, K.L., Yackulic, C.B., Kennedy, T.A., Bestgen, K.R., and Schmidt, J.C., 2021, Water 

storage decisions will determine the distribution and persistence of imperiled river 

fishes: Ecological Applications, v. 31, no. 2, e02279, p. 1-9, 

https://doi.org/10.1002/eap.2279. 

Eppehimer, D.E., Yackulic, C.B., Bruckerhoff, L.A., Wang, J., Young, K.L., Bestgen, K.R., 

Mihalevich, B.A., and Schmidt, J.C., 2024, Declining reservoir elevations following a twodecade drought increase water temperatures and non-native fish passage facilitating a 

downstream invasion: bioRxiv, https://doi.org/10.1101/2024.01.23.576966. 

R Core Team, 2021, R: A language and environment for statistical computing: Vienna, 

Austria, R Foundation for Statistical Computing, https://www.R-project.org/. 

Schuster, R.J., 1998, CRSSez—Annual Colorado River simulation system documentation—

Colorado River simulation model overview and user's manual: Boulder City, Nev., U.S. 

Department of the Interior, Bureau of Reclamation, Boulder Canyon Operations Office, 

Lower Colorado Region.

Schmidt, J.C., Bruckerhoff, L.A., Wang, J., and Yackulic, C.B., 2022, The Colorado River—The 

science-policy interface, in Robison, J.A., ed., Cornerstone at the confluence—Navigating 

the Colorado River compact's next century: Tucson, The University of Arizona Press, p. 

238-263. 

The MathWorks Inc., 2022, MATLAB version: 9.13.0 (R2022b): Natick, Massachusetts, The 

MathWorks Inc., https://www.mathworks.com. 

Wheeler, K.G., Rosenberg, D.E., and Schmidt, J.C., 2019, Water resource modeling of the 

Colorado River—Present and future strategies—White paper no. 2: Logan, Utah, The 

Future of the Colorado River Project, Center for Colorado River Studies, Utah State 

University, 46 p., https://qcnr.usu.edu/coloradoriver/files/news/White-Paper-2.pdf. 

Yackulic, C.B., Eppehimer, D.E., and Salter, G., 2024, Modelled Lake Powell releases and 

reservoir elevations under different alternative management scenarios: U.S. Geological 

Survey data release, https://doi.org/10.5066/P1CTKSPP.
12 | Page

II. Modeling the Energy Generation and Economic Value of Glen 

Canyon Dam Releases

Introduction

The Glen Canyon Dam (GCD) powerplant is connected to the Western Power Grid through a 

regional transmission system and provides electricity to utilities in a 15-state region of the 

western United States. The objective of modeling energy costs of GCD Releases is to 

produce hourly flow, generation, and economic value estimates over the planning horizon 

for each alternative and hydrologic traces in the Long-term Experimental and Management 

Plan Supplemental Environmental Impact Statement (LTEMP SEIS). The results are used to 

quantitatively analyze the economic impacts from each alternative in the LTEMP SEIS. The 

models are based on standard energy economic analysis methods (Harpman, 1999).

The proposed alternatives in the LTEMP SEIS include a new sediment accounting window 

and managing Colorado River water temperature downstream of GCD from May through 

October. Therefore, altered flows would not be implemented during the winter months, and 

there would be little effect on energy generation during this timeframe. The following 

analysis focuses on warm-weather months when flows could be implemented under the 

proposed action with options. 

Compared with current conditions, five of the seven flow options would include passing 

more water through the bypass tubes where energy is not generated. Energy generation 

effects would vary, depending on the flow option implemented. Each flow would impact 

hydropower by reducing the energy generation and increasing the amount of replacement 

energy required to meet demand in the interconnected electricity sector. 

Methods

The estimated costs of changes in energy generation at GCD were developed using a 

standard constrained optimization model. The constrained optimization model optimizes 

electricity production based on a specified objective, water availability, and operating 

constraints. Modeling was conducted for the planning horizon, October 2023 through 

November 2027. 

Monthly operating priorities are based on average historic hourly releases at GCD from 

September 2020 through August 2023. The assumption is made that the recent operation at 

GCD is a reasonable representation of Western Area Power Administration’s attempt to meet 

near-term scheduling requests by utilities. 
13 | Page

Operation at GCD was optimized using these release data (Bureau of Reclamation, 2024) to 

prioritize hourly operation within a representative week, constrained by the operational 

constraints in the Long-term Experimental and Management Plan Record of Decision (LTEMP 

ROD; U.S. Department of Interior, 2016).

Our hydropower optimization model closely follows Harpman (1999). The hydropower

objective is to identify the load following path that maximizes the opportunity to meet 

scheduling of hydropower generation: 

max

vv1,...,vvTT

∑ wwtt ∗ eeeeeeeeeeee(vvtt, eeeeee tt), TT

tt=1 (1)

where vvtt is the fixed flow cubic feet per second (cfs) through GCD over an hour (tt), wtt is the 

weighted historic scheduling of energy generation at time tt, eeeeee tt is the end of month 

reservoir elevation (feet above mean sea level) at time tt, T is total time, and eeeeeeeeeeee(⋅) 

denotes the energy production function, specified in equation 2.

Hydropower production in megawatt hours (MWh) generated at the GCD is a function of 

flow vvtt through the turbines and reservoir elevation eeeeee tt, both of which are assumed to be 

constant over an hour time step tt, and αα and ββ are estimated coefficients (Waldo and 

others, 2021):

eeeeeeeeeeee(vvtt, eeeeee tt) = −αα ∗ ββ(eeeeee tt) ∗ vvtt, (2)

Hydropower production is subject to several operational constraints, such as the amount of 

water available for release, maximum and minimum flow constraints, and ramp constraints. 

Our optimization model is subject to the following constraints, as specified in the LTEMP 

ROD (U.S. Department of Interior, 2016)

∑ vvtttt∈mm ≤ max monthly volume (cfs) for mm ∈ {month}

 vvtt=jj ≥ min off-peak flow for jj ∈ off-peak hours

 vvtt=ii ≤ min on-peak flow for ii ∈ on-peak hours

 vvtt ≤ max flow (3)

vvtt−1 – vvtt ≤ max down ramp

vvtt – vvtt−1 ≤ max up ramp

vvtt – vvtt−h ≤ max flow change in 24-hours for h ∈ 24-hour period

We post process the optimal hydrograph based on flow specifications in the LTEMP SEIS 

alternatives. For example, if flow spikes are implemented for a month in an alternative and 

hydrologic trace, those flow constraints are imposed on the baseline hydrograph, staying 

true to the constraints specified in the LTEMP ROD (U.S. Department of the Interior, 2016).
14 | Page

To forecast the economic value of energy generated at GCD, we developed models that

predict marginal prices given industry forecasts of price (i.e., ARGUS Forward Mid-Market 

Power Curves: Argus Media, 2024). While these industry forecasts include important 

information on changing energy markets, they also include a risk premium that leads to 

systematic overestimation of future marginal prices (Benth and others, 2008; Office of 

Management and Budget, 2023) so use of these raw forecasts is likely to lead to systematic 

overestimation of differences among alternatives. We treated observed hourly historic 

locational marginal price at the Palo Verde Hub from February 2020 to August 2023 

(California Independent System Operator, 2024) as data since many (but not all) users of 

power from Glen Canyon trade at this hub. We assumed that the relationship between 

ARGUS Forward Mid-Market Power curves (forecasts) and observed data would vary by 

month, day of week, and hour of the day such that each month of the year should have 168 

independent models (one for each hour in each day of the week). Each of these models was 

a linear regression of the form: Y = a + b*X, where X was the forecasted off-peak power price 

and Y was the observed location marginal price and a and b were estimated coefficients. We 

also tested using on-peak forecasts as a predictor but found that they did a poorer job of 

predicting relative changes in observed prices than off-peak forecasts (based on comparison 

of competing models via Akaike Information Criterion). While our X was off-peak, forecasts 

of the value of b varied dramatically based on the hour and day of the week allowing for 

accurate predictions. Values of a and b for each month, day, and hour were then combined 

with Argus Forward Mid-Market Power Curves for October 2023 through November 2027 

(Argus Media, 2024) to predict prices over the period of LTEMP SEIS. 

The economic cost of foregone energy generation of implementing an alternative is the 

difference between hydroelectricity economic value under the optimal load following path 

(No Action Alternative) and hydroelectricity economic value under an action alternative. 

Results

Results are the hourly releases from GCD (cfs), generation (megawatt hours [MWh]) and 

economic value (nominal dollars) for each month, alternative, and hydrologic trace in the 

LTEMP SEIS (Bair and Yackulic, 2024).

Data Availability Statement: 

Data generated during this study are published and available (Bair and Yackulic, 2024).

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.
15 | Page

References

Argus Media, 2024, Argus North American electricity forward curves: accessed October 15, 

2023, at https://www.argusmedia.com/en/solutions. 

Bair, L.S., and Yackulic, C.B., 2024, Predicted hydropower impacts of different management 

scenarios for Lake Powell releases: U.S. Geological Survey data release, 

https://doi.org/10.5066/P135BOD8. 

Benth, F.E., Cartea, Á., and Kiesel, R., 2008, Pricing forward contracts in power markets by the 

certainty equivalence principle—Explaining the sign of the market risk premium: Journal 

of Banking and Finance, v. 32, no. 10, p. 2006-2021, 

https://doi.org/10.1016/j.jbankfin.2007.12.022. 

Bureau of Reclamation, 2024, Hydrologic database (HDB) data query: accessed January 15, 

2024, at https://www.usbr.gov/lc/region/g4000/riverops/_HdbWebQuery.html. 

California Independent System Operator (CISO), 2024, Open access same-time information 

system (OASIS), California ISO: accessed October 15, 2023, at

http://oasis.caiso.com/mrioasis/logon.do. 

Harpman, D.A., 1999, Assessing the short-run economic cost of environmental constraints on 

hydropower operations at Glen Canyon Dam: Land Economics, v. 75, no. 3, p. 390-401, 

https://doi.org/10.2307/3147185. 

Office of Management and Budget (OMB), 2023, Guidelines and discount rates for benefitcost analysis of federal programs—Memorandum for heads of executive departments 

and establishments: Washington, D.C., The White House, OMB Circular A-94, Regulatory 

Analysis, revised Nov. 9, 2023, https://www.whitehouse.gov/wpcontent/uploads/2023/11/CircularA-94.pdf. 

U.S. Department of Interior, 2016, Record of Decision for the Glen Canyon Dam Long-Term 

Experimental and Management Plan final Environmental Impact Statement (LTEMP ROD): 

Salt Lake City, Utah, U.S. Department of the Interior, Bureau of Reclamation, Upper 

Colorado Region, National Park Service, Intermountain Region, 196 p., 

http://ltempeis.anl.gov/documents/docs/LTEMP_ROD.pdf. 

Waldo, S., Deemer, B.R., Bair, L.S., and Beaulieu, J.J., 2021, Greenhouse gas emissions from an 

arid-zone reservoir and their environmental policy significance—Results from existing 

global models and an exploratory dataset: Environmental Science and Policy, v. 120, p. 

53-62, https://doi.org/10.1016/j.envsci.2021.02.006.
16 | Page

III. Modeling Impacts of Different Reservoir Management Scenarios on

Sediment Resources

Background and Methods 

Sandbars are a natural feature of the Colorado River and are important for recreation, 

habitat, and preservation of cultural resources. The construction of Glen Canyon Dam (GCD) 

is estimated to have reduced the sand load at the upstream end of Grand Canyon National 

Park by ~95%, and by the early 1970s was recognized to have caused net-erosion of 

sandbars in Grand Canyon (Topping and others, 2021). Decades of work led to the current 

protocol for sediment management in Grand Canyon, which involves the use of controlled 

floods known as High Flow Experiments (HFEs) to build and maintain sandbars. Under the 

Long-Term Experimental and Management Plan final Environmental Impact Statement

(LTEMP EIS; U.S. Department of Interior, 2016), the implementation of HFEs is specifically 

linked to modeled sand mass balance over specified sediment accounting windows (July 1st

to November 30th, and December 1st to June 30th) to allow HFEs to be implemented at times 

when they will be most effective, and avoid long-term depletion of the amount of sand in 

the system. 

We modeled the potential effects on sediment resources associated with alternatives under 

consideration for the Interim Guidelines Supplemental Environmental Impact Statement

(Interim Guidelines SEIS; Bureau of Reclamation, 2024a) and the Long-Term Experimental 

and Management Plan Supplemental Environmental Impact Statement (LTEMP SEIS; Bureau 

of Reclamation, 2024b). We generated predictions for sand mass balance in Marble Canyon 

using the Wright and others (2010) Sand Routing Model, and predictions for sandbar 

volume using the Mueller and Grams (2021) sandbar model. The Sand Routing Model was 

also the basis for determining when HFEs could be implemented, and for what duration.

Model results are published and available (Salter and Grams, 2024).

Sand Routing Model Methods and Assumptions

The Wright and others (2010) Sand Routing Model (SRM) is used to calculate sand mass 

balance for reaches downstream of GCD. The SRM computes sand loads at River Mile (RM) 

30, 61, and 87, chosen because these are locations of sediment-monitoring gaging stations

(U.S. Geological Survey, 2024). The model divides the Colorado River into three reaches: 

Upper Marble Canyon (UMC), between the Paria River and RM 30, Lower Marble Canyon 

(LMC), between RM 30 and RM 61 (just upstream from the Little Colorado River confluence), 
17 | Page

and Eastern Grand Canyon (EGC), between RM 61 and RM 87 (Figure 3-1). Required inputs 

are an initial condition (bed grain size distribution in each reach and bed sediment 

thickness), discharge time series at each gage location, and time series of tributary sediment 

inputs from the Paria River and Little Colorado River (LCR). 

The model computes sediment loads via a shifting rating curve: for a given bed grain size, 

sediment loads are a power law function of discharge, but a coarser bed results in less 

sediment transport for the same discharge, and a finer bed results in more sediment 

transport for a given discharge. HFE implementation is based on Marble Canyon sand mass 

balance (i.e., the sum of UMC and LMC mass balances), so for our modeling we ignored EGC 

and did not need to account for LCR sand loads.

Figure 3-1. Region assessed in sediment modeling. Segments of the mainstem Colorado River are indicated in colors. Key 

tributaries (Paria River and Little Colorado River) are labeled. Triangles indicate the location of Glen Canyon Dam and 

referenced gages (U.S. Geological Survey, 2024). 
18 | Page

The SRM requires water discharges at each gage location. We used observed discharges at 

the three gages up until October 2022 (U.S. Geological Survey, 2024) to generate the initial 

condition (bed, grain size) used in the model, and beyond that we used the projected 

releases associated with each scenario (Chapter 1). 

The timestep for the SRM is 15 minutes; we had hourly hydrographs available for our studies 

which we linearly interpolated. Typically, when running the Sand Routing Model for future 

scenarios, the Colorado River Flow and Sediment (CRFS) model (Ecometric Research, Inc., 

v.1.0.1.0, 2012), which implements the Wiele and Griffin (1998) kinematic wave model, is 

used to generate the gage discharges, given dam releases. However, running the CRFS 

model is a time-consuming process as it involves use of a graphical user interface, and 

running it for all of the scenarios and traces included in the SEIS would be prohibitive. 

Therefore, we chose to use dam releases at each gage location (U.S. Geological Survey, 2024) 

, rather than calculating the hydrograph at each gage location. This simplification means that 

attenuation of discharge waves as they travel downstream is not accounted for in the SRM 

results. We tested this simplification by comparing SRM results using Lees Ferry discharges 

at each gage site vs. actual gage discharges at each site and found that the error over 1-year 

is typically less than 5-10% of the mean annual Paria River sediment load. Based on 

modeling tests for the amount of sand exported for HFEs of different durations, this error 

would not typically change the duration of the selected HFE, and would at worse result in a 

shift up or down of one duration tier.

The initial condition for SRM (bed thicknesses and bed grain size distribution) is based on an 

SRM model run started from September 1, 2002, using sediment inputs and gage discharges 

downloaded from the U.S. Geological Survey Grand Canyon Monitoring and Research Center

(GCMRC) website (U.S. Geological Survey, 2024). Specific details of setting the initial 

conditions for the modeling runs differ between the Interim Guidelines SEIS modeling and 

the LTEMP SEIS modeling because the LTEMP SEIS modeling was completed later, when 

more data were available. The other key input to the SRM is the sand load time series from 

the Paria River. To account for the potential variability in Paria River sand loads, we ran 

multiple sediment traces resampled from the 1996-2023 historical sand loads (U.S. 

Geological Survey, 2024). Specific details differed between our Interim Guidelines SEIS 

modeling and the LTEMP SEIS modeling , and are described in chapters “Methods for 

Interim Guidelines SEIS” and “Methods for LTEMP SEIS.”

Sandbar Model Methods and Assumptions

The Mueller and Grams (2021) sandbar model was calibrated to a set of the nine most 

dynamic sandbars out of the 45 sandbars that are monitored long-term (Grams and others,

2020; Hazel and others, 2022) . The calibration period was 2015-2022. 
19 | Page

The calibrated parameters were the initial bar volume, the base eddy exchange coefficient, 

and the bar erosion rate parameter. Inputs to the model are discharge, suspended sand 

concentration, and suspended sand median grain size, all at RM 30. 

These are obtained from the RM 30-gage for post-hoc modeling of the past (2015-2023), 

and from SRM for modeling future scenarios. The model output is sandbar volume above 

the 8000 cubic feet per second (cfs) reference stage.

Given that dam operations often substantially exceed the 8000 cfs reference threshold, the 

sandbar volumes do not necessarily represent usable sand (e.g., for camping). This caveat is 

particularly important when considering some traces which result in elevated discharges (i.e., 

sustained monthly releases at 20,000 cfs or greater). Sandbar fluvial deposition can only 

occur at and below river stage. Under some scenarios, the model predicts sandbar building 

above the 8,000 cfs stage associated with these elevated releases, but a significant 

proportion of the predicted sandbar volume would be unusable. Caution should be used in 

interpreting results from the sandbar model for these elevated sustained releases because 

they are not included in the calibration dataset. The sandbar model assumes a constant 

exponential erosion rate (i.e., erosion rate proportional to sandbar size), which is 

independent of discharge. It is therefore unable to capture enhanced erosion rates which 

would likely result from elevated flows. 

Hazel and others (2022) analyzed the effect of peak flow magnitude on sandbar deposition 

and concluded that releases of 34,000 cfs or greater were required to result in significant 

deposition at reattachment bars and upper-pool deposits and that discharges of 37,000 cfs 

or greater were required to result in significant deposition at separation and undifferentiated 

eddy sandbar types. They defined "significant" as an increase in mean sandbar thickness of 

more than 5 cm.

Additionally, the sandbar model did not include any short-duration high-magnitude 

discharge fluctuations over the calibration period, which are included in some of the LTEMP 

SEIS modeling alternatives. Although it is possible that these types of fluctuations could 

produce some sandbar building, particularly if they occur under sediment-enriched 

conditions, previous studies have also shown that repeated flow cycles (fluctuations of any 

magnitude) cause sandbar erosion (Alvarez and Schmeeckle, 2013). Similarly, 1-hr HFEs were 

implemented in the Interim Guidelines SEIS modeling, but HFEs of such a short duration 

have never been tested. We did not allow 1-hr HFEs to be triggered for the LTEMP SEIS 

modeling because at such a short duration they would not provide much sandbar building, 

but could cause erosion due to the stage fluctuations.
20 | Page

Methods for Interim Guidelines SEIS 

We used the Sand Routing Model and additional constraints described below to determine 

when HFEs would be triggered and when they could be implemented, and we compiled 

statistics of trigger/implementation probability and timing. Then we used the Sandbar 

Model to determine how (if) sandbar size differed between alternatives. 

We used the projected hourly releases from the Western Area Power Administration (WAPA) 

hydropower model (GTMax; Veselka and North, 2001) as the starting point for constructing 

model input hydrographs. 

In cases where Lake Powell declined in elevation below powerpool, we assumed that the 

monthly volume was released as a constant, steady discharge based on the monthly volume 

in the Colorado River Midterm Modeling System (CRMMS) model output provided by 

Reclamation (Chapter I; Shuster, 1998; Bureau of Reclamation 2024a). 

A sediment-triggered HFE is defined as the longest-duration HFE that can be performed 

without causing the Marble Canyon sand mass balance to become negative over the course 

of an HFE. In the LTEMP EIS HFE protocol, this mass balance constraint is applied separately 

over Fall (July 1 through November 30) and Spring (December 1 through June 30) sediment 

accounting windows.

We assumed an HFE magnitude of 40,000 cfs. While full capacity releases (45,000 cfs) would 

be more effective for rebuilding sandbars (Hazel and others. 2022), low reservoir levels are 

likely to limit the maximum capacity that can be released. We assumed that HFEs could only 

be implemented within the specified monthly volumes for each alternative; in other words, 

we did not borrow water from other months. We additionally did not allow the GCD releases 

to dip below the LTEMP-specified 8000 cfs during the day and 5000 cfs at night, with ramp 

rates of 4000 cfs/hr up and 2500 cfs/hr down, unless the GTMax hydrograph already did so 

(in which case the release was not allowed to go any lower). The LTEMP minimums result in a 

set of minimum releases (Table 3-1). 

Table 3-1. Monthly minimum volume in thousand acre-feet (kaf) to accommodate an HFE of the 

specified duration without causing flows to drop below LTEMP minimums.
21 | Page

In addition to volume release constraints, it is impossible to run an HFE if the Lake Powell 

elevation goes below power pool, because turbine capacity decreases significantly and 

bypass flow capacity is insufficient. We therefore did not allow HFEs to occur if the Lake 

Powell elevation was below 3490 ft. Finally, we did not include extended-duration HFEs (i.e., 

HFEs longer than 96 hours in duration).

To account for the potential variability in Paria River sand loads, for the Sand Routing Model

we ran each trace with 22 possible traces of Paria River sediment loads, each associated with 

a 5-year period (1996-2001, 1997-2002, etc.) and then analyzed the results statistically. 

The HFE implementation probability plots are based on the full set of 22 Paria River 

sediment traces, whereas the sandbar model runs are based on a subset of 4 traces: 1999-

2003, 2006-2010, 2011-2015, and 2017 to 2021, representing a high, a low, and two medium 

input scenarios (Figure 3-2). 

We initialized our model runs by using observed inputs for a period starting in October 2022 

for the Sand Routing Model, and January 2015 for the Sandbar model. For the months of 

August and September, 2023, we used projected volumes from the 24-month study and a 

fluctuation pattern based on July 2023 with a scaling factor to match the projected release 

volume. At the time the models were run in early August 2023, discharge records for RMs 30 

and 61, used in the Sand Routing Model, ended in early June/late May of 2023, so we filled 

the missing data by using the Wiele and Griffin (1998) Flow Routing Model. Similarly, for the 

Sandbar Model, we used observed data where available, up until June 2023, and then used 

the Sand Routing Model to provide input data beyond that. We used observed Paria River

sand loads through August 10, 2023, assumed zero sediment input for the remainder of 

August, and then started the historical Paria River traces on September 1, 2023. Due to the 

large amount of sand export since July 1, 2023, small observed inputs prior to August 10, 

2023, and assumption for simplicity of no sediment inputs for the remainder of August, the 

probability of a sediment trigger in fall 2023 is lower than for fall of subsequent years.
22 | Page

Figure 3-2. Paria River sediment load for 22 traces in thousand metric tons (Tmt) beginning with 1996-2000, used in Sand 

Routing Model to determine HFE implementation probability. The four labeled traces are the subset used in the Sandbar 

Model runs (U.S. Geological Survey, 2024). 

For the HFE implementation statistics, we ran an initial SRM simulation to determine the 

cumulative mass balance within each sediment accounting window, and used a heuristic 

relationship we derived from 1200 simulations with varying bed conditions and durations to 

determine the sand mass balance that was necessary to trigger an HFE of a particular 

duration (Figure 3-A36). This allowed us to run all 90 hydrology traces for 22 Paria River 

sediment traces, which would have been unwieldy if HFE implementation was included 

explicitly. For the sandbar volume results, we calculated the HFE durations iteratively rather 

than using the heuristic approach, but only used a subset of four Paria River sediment traces.

Results of Analyzing Interim Guideline SEIS Alternatives 

We produced plots of HFE implementation and triggering statistics for each alternative and 

for all possible implementation windows (i.e., fall and spring of each year in the time series). 

These plots were based on all 22 Paria River sediment traces and used the mass balance 

heuristic described in the methods. 
23 | Page

Then, we used a subset of four Paria River traces to produce plots of sandbar volume vs. 

time for each alternative, showing the traces individually as well as their medians and means. 

As shown in Figures 3-3 through 3-5, there is little to no difference in sediment-triggering of 

HFEs between the alternatives. However, differences in monthly volumes and reservoir 

elevations between the alternatives contribute to slight differences in HFE implementation 

probability when volume and reservoir elevation constraints are considered. In general, the 

monthly volume constraint affects HFE implementation more than the reservoir elevation 

constraint for all alternatives; however, in November 2026 there is a slight risk of going 

below the Lake Powell elevation of 3490 ft under the No Action Alternative (5 out of 90 

traces), and smaller risk under Proposed Action Alternative (1 out of 90 traces), which would 

preclude an HFE of any duration. In general, November monthly releases are slightly lower 

under the Proposed Action Alternative relative to No Action, and this results in a slight 

(~5%) reduction in HFE implementation probability for HFE’s between 36 and 72 hours in 

November 2024 and 2025. In November 2026 this difference is partially compensated for by 

the reservoir elevation constraint. Overall, the difference in HFE implementation probability 

between the alternatives is small, but can be important in some traces.

We also obtained model predictions of sandbar volume for the two alternatives (Figures 3-6 

through 3-8). Although differences between the alternatives can be significant for individual 

traces, we find that overall, there is little difference in sandbar size between the two 

alternatives, with almost no median difference in sandbar volume, and a slight mean 

difference in favor of the No Action Alternative (Figure 3-8); however, the mean difference in 

final sandbar volume amounts to only 0.3% of the mean sandbar volume.
24 | Page

Figure 3-3. Probability of HFE implementation under the Proposed Action Alternative for each potential implementation 

window, based on 22 Paria River sediment traces x 90 hydrology traces. Note that the “volume constraint” and “volume 

constraint + Powell >3490 ft” lines overlap considerably (Salter and Grams, 2024).
25 | Page

Figure 3-4. Probability of HFE implementation under No Action Alternative for each potential implementation window, based 

on 22 Paria River sediment traces x 90 hydrology traces (Salter and Grams, 2024). 
26 | Page

Figure 3-5. Difference in probability of HFE implementation between Proposed Action Alternative and No Action Alternative 

for each potential implementation window, based on 22 Paria River sediment traces x 90 hydrology traces (Salter and 

Grams, 2024). 
27 | Page

Figure 3-6. Modeled sandbar size under Proposed Action Alternative for all evaluated traces (4 Paria River sediment traces 

x 90 hydrology traces), with median and mean sandbar sizes superimposed. Lines have transparency, so dark red indicates 

multiple overlapping lines (Salter and Grams, 2024). 

Figure 3-7. Modeled sandbar size under No Action Alternative for all evaluated traces (4 Paria River sediment traces x 90 

hydrology traces), with median and mean sandbar sizes superimposed. Lines have transparency, so dark red indicates 

multiple overlapping lines (Salter and Grams, 2024). 
28 | Page

Figure 3-8. Difference in modeled sandbar size between Proposed Action Alternative and the No Action Alternative, with 

the median difference and mean difference superimposed. Positive values indicate larger sandbars under Proposed Action 

Alternative relative to No Action; negative values indicate smaller sandbars under Proposed Action Alternative relative to No 

Action. Lines have transparency, so dark red indicates multiple overlapping lines (Salter and Grams, 2024). 

Methods for LTEMP SEIS 

As with the previous chapter, model outputs for the figures presented in this chapter are 

available in Salter and Grams (2024). We used the Wright and others (2010) Sand Routing 

Model (SRM) to calculate sand mass balance. The HFE magnitude and duration, were 

selected via iteration according to the sand mass balance. Monthly volumes were distributed 

if necessary. We used the 100% ensemble streamflow predictions (ESP) hydrology traces 

from the Interim Guidelines SEIS modeling (Chapter I). For alternatives including flow spikes, 

we obtained the months that flow spikes would be triggered from the initial run of the 

smallmouth bass model (Chapter IV this report). For the Non-Bypass Flow Alternative, we 

assumed that Non-Bypass fluctuations would be triggered in any month for which bypass 

flows were required under the Cool Mix Alternative with a RM 61 target. Our workflow was 

to then determine months in which HFEs would be triggered (and their duration and 

magnitude), if necessary shift monthly volumes to accommodate the HFEs (unlike in our 

Interim Guidelines SEIS modeling), and also determine the magnitude of flow spikes if they 

occurred. No modifications to the HFEs occurred after this point in the workflow. For traces 

in which monthly volumes were modified to accommodate high flow events or flow spikes, 

we updated Lake Powell elevations during the intervening months based on the same rules 

implemented in the Colorado River Simulation System (CRSS) Model (Chapter I; Shuster, 

1998). This information was passed off for hydropower modeling (Section II of this report) to 

determine the hourly release patterns with greater realism. We then took those hourly 

release hydrographs and reran both SRM and the Sandbar Model to obtain our final results. 
29 | Page

For each trace of each alternative, we completed an initial simulation with the Sand Routing 

Model (SRM) to determine the duration and timing of HFEs, as well as their magnitude. The 

HFE magnitude was based on a penstock capacity vs. Lake Powell elevation curve derived 

from the CRSS Model, and bypass capacity was based on guidance provided by Reclamation

on maximum releases to avoid cavitation risk (written communication from William Stewart 

and Nick Williams, Bureau of Reclamation, November 14, 2023). 

Modeling of HFEs used the combined river outlet works capacity for short duration flows 

(<72 hours), regardless of the length of an HFE. Actual implementation may involve slightly 

different magnitudes. For the initial set of modeling, we generated synthetic hydrographs 

from the monthly release volumes by assuming the maximum discharge fluctuations under 

LTEMP, with a cap at 25,000 cfs or the max penstock release, whichever is lower. We 

assumed the daily pattern was 12 hours on a steady daily minimum release, with a 4000 

cfs/hr ramp up and 2500 cfs/hr ramp down to a steady daily maximum release. 

If, however, the minimum release based on the above was below 8000 cfs, we assumed 12 

hours on maximum release, with again 4000 cfs/hr ramp up and 2500 cfs/hr ramp down to a 

steady daily minimum release; this is to avoid going below the LTEMP EIS minimum daily 

releases, which must be at minimum 8000 cfs for a full 12 hours. 

HFEs were implemented in November and/or April, depending on the alternative, with the 

possibility of delaying until May or June under alternatives with flow spikes. Under the 1-yr 

sediment accounting window, decision-makers can choose to implement an HFE in fall or 

spring depending on the information available at that point. For modeling purposes, we 

assumed that a spring HFE is preferred to a fall HFE and would be selected if modeling as of 

November 1st indicates that it would be equal to or one duration tier lower in duration than 

the fall HFE. We emphasize that this is strictly a modeling assumption, and that the 1-yr 

accounting window alternatives retain flexibility when choosing between implementation 

windows. 

We additionally tested the 1-yr alternative with a slightly different set of assumptions: in this 

case, we delayed an HFE to spring if the spring HFE duration was projected to be within two 

tiers of fall duration, or greater than or equal to 60 hours. For the alternatives that include 

flow spikes, if a spring HFE had been selected and flow spikes occur in May or June, we 

compared implementing the HFE on April 15th (default) vs. in place of the first flow spike, 

using sediment inputs up to April 1st. If the durations were equal or within one duration tier, 

the HFE was implemented in place of the first flow spike. 

We assumed no HFEs would be implemented below a Lake Powell elevation of 3500 ft, as 

HFE magnitude would be below 37,000 cfs, and it could increase the risk of going below 

power pool elevation of 3490 ft. 
30 | Page

Hazel and others (2022) concluded that discharges of 37,000 cfs or greater were required to 

result in significant deposition at separation and undifferentiated sandbar types (with a 

34,000 cfs threshold for reattachment and upper-pool bar types). 

Under the 1-yr window, if an HFE were triggered but not implemented due to this constraint, 

and there were no other HFE’s in the accounting window, a positive sand mass balance is 

carried over into the next accounting window. When monthly volumes were altered, CRSS 

equations for Lake Powell were rerun between the first and last modified month to create as 

accurate as possible elevations, however, for all months after the last modified month CRSS 

was not rerun. HFE and flow spike magnitudes were based on the original rather than the 

revised elevation, however, differences are generally small.

The LTEMP EIS does not provide specific details on how monthly volumes are to be shifted 

to accommodate HFEs, but assumptions are necessary for our modeling. If HFE or flow spike 

implementation plus base releases of 16 thousand acre-feet (kaf)/day result in a monthly 

volume higher than the initially-specified monthly release volume, volume was borrowed 

from other months and added to the implementation month. 

For the months being borrowed from, flow was reduced to a minimum of 16 kaf/day. For a 

fall HFE, if reservoir elevation at the end of the implementation month was 3530’ or greater, 

the order in which volumes were borrowed from other months was: April, March, May, 

February, December, and January. If the elevation was less than 3530 ft, the order was the 

same but May is excluded, as borrowing from May to release water sooner could diminish 

the April end-of-month elevation. 

If, after going through all borrowing months, the implementation month still did not have 

sufficient volume, the adjustment process was repeated, using LTEMP minimum flows of 

approximately 13.1 kaf/day. If there were still not sufficient volume, the HFE duration was 

reduced to the next lower tier as a last resort. For an April HFE implementation, the 

borrowing-month order was April, March, May, June, September, August, then July. For HFEs 

or flow spikes implemented in May or June, or flow spikes implemented in July, August, or 

September, the order was the same as above, except the implementation month was 

borrowed from before any other month. 

Each of the 30 hydrology traces was randomly assigned a trace of Paria River sediment 

inputs derived from the October 1996 to September 2023 record (U.S. Geological Survey, 

2024). Assuming that on October 1, 2023 the trace loops back around to October 1996, for 

the thirty hydrology traces starting in 1991, the Paria River trace starting years are as follows:

1. 1998, 2. 2010, 3. 2022, 4. 2001, 5. 2014, 6. 2000, 7. 2002, 8. 2015, 9. 2008, 10. 1999, 11. 

2018, 12. 2003, 13. 2004, 14. 1996, 15. 2012, 16. 2006, 17. 2005, 18. 2013, 19. 2011, 20. 2007, 

21. 2019, 22. 1997, 23. 2016, 24. 2020, 25. 2009, 26. 2021, 27. 2017, 28. 1997, 29. 2000, 
31 | Page

30. 2019. Hence, differences between traces are due to a combination of the hydrology and 

the specific trace of sediment inputs. Additionally, for our modeling to determine HFE 

statistics, we assigned three random Paria River traces to each hydrology trace for a total of 

90 traces. This improved the robustness of our statistics. 

The initial condition for SRM (bed thicknesses and bed grain size distribution) was based on 

an SRM model run from September 1, 2002 to October 1, 2023 using sediment inputs and 

gage discharges (U.S. Geological Survey, 2024). 

For our modeling, the possible HFE durations were 12, 24, 36, 48, 60, 72, 96, 144, 192, and 

250 hrs. In our report we will refer to these as “duration tiers”. We neglected the possibility 

of 1-hour HFE’s, because such a short duration is unlikely to be sufficient for sandbar 

building, and could result in adverse erosion. Following LTEMP, the 250-hr option was not 

allowed to occur until a 192 or 144 hr HFE had been run previously, and if an HFE longer 

than 96 hours were run in fall, no spring HFE’s could be run. The HFE with the longest 

possible duration resulting in a positive sand mass balance for Marble Canyon for the 

accounting period is the selected HFE. Under ‘No Action’, the accounting periods run July 1st

to November 30thth, and December 1st to June 30th. For the one-year sediment accounting 

window, the mass balance between July 1st and the termination of the HFE was used when 

selecting HFE duration, with the possibility of sediment carryover from the previous year(s) if 

an HFE was triggered but not implemented (e.g., due to low reservoir elevation). 

For alternatives that do not include flow spikes, fall HFEs were assumed to be implemented 

on November 15, and spring HFEs were implemented on April 15th. However, if flow spikes 

occurred in May or June and a spring HFE had been triggered, the HFE could be delayed 

until the first month of flow spike implementation, if the duration for the later 

implementation date was within one duration tier of the earlier date. When selecting HFE 

duration, Paria River sand inputs up to the 1st of the implementation month were 

considered, and a 90% multiplier was used on sand inputs to reflect the ‘lower bound’ 

estimate. For the one-year accounting window, the initial decision to implement a fall vs. 

spring HFE was assumed to occur on November 1st based on sediment inputs to that point. 

If a spring HFE were selected, the duration was revised based on inputs up to the 1st of the 

implementation month. After the appropriate HFE duration is selected, the SRM is rerun with 

the full sediment inputs. 

After having completed our initial run of modeling to determine HFE timing, magnitude, and 

duration, our results were passed off to the next step in the workflow as described 

previously. We reran SRM with the new hourly hydrographs generated by a hydropower 

optimization model, and the same Pacria River traces as in the previous round of modeling. 
32 | Page

Changes to the mass balance were minimal and we did not make any modifications to HFE 

durations. We then used the SRM output to provide the concentration and suspended sand 

median grain size, which along with discharge served as inputs for the Mueller and Grams 

(2021) sandbar model. The sandbar model was recalibrated to the 2015-2023 period, 

including data from October 2023 (not included in the version used in the Interim Guidelines 

SEIS modeling, as that modeling took place before that date). We then ran the sandbar 

model for each trace, initialized using the October 1, 2023 volume computed in the previous 

step.

In addition to the above, we ran a single trace of the Non-Bypass Alternative through the 

Wiele and Griffin (1998) model, implemented in the software CRFS. We generated 

predictions of discharge at various downstream locations in order to assess minimum and 

maximum flows associated with the alternative. We used the 2002 hydrology trace. We 

assumed zero tributary inputs or groundwater exchange (losses or gains). Base flow for the 

LCR is around 200 cfs, but this is not included in the model. Because the hourly discharges 

produced by the hydropower model represent average discharge for the hour (as opposed 

to instantaneous discharge at each time), simple linear interpolation of the time series would 

result in 3-hour minimum and maximum flow durations at the dam. Therefore, instead of 

linear interpolation, we used stepwise interpolation (i.e., assuming a constant discharge for 

each hour in the time series.) This results in an unrealistic staircase pattern at the GCD, but 

has the benefit of preserving the appropriate minimum and maximum flow durations. By the 

time the discharge wave arrives at Lees Ferry, the staircase artifact is no longer present. After 

interpolating the hourly time series, we assumed a 0.1 hr timestep for the model run. We 

verified that results using a 0.25 hr timestep were nearly identical.

Results of Analyzing LTEMP SEIS Alternatives 

HFE likelihood and duration statistics

The 1-yr accounting period provides the flexibility to defer a triggered fall HFE to spring 

provided that the projected sediment mass balance would allow for a spring HFE. Under this 

alternative, deferring an HFE would be a decision that depends on the specific circumstances 

of that year. For modeling purposes, we had to make some assumptions about when a fall 

HFE would be deferred or not, and throughout this analysis we assumed that a fall HFE 

would be deferred to spring if doing so would result in an equal duration or one duration 

tier lower. We term this a “weak” spring preference. Additionally, we tested how modifying 

this assumption affects HFE statistics, by introducing a “strong” spring preference option, 

under which we assume that an HFE would be delayed to spring if spring HFE duration is 

projected to be within two duration tiers of fall duration, or greater than or equal to 60 

hours. 
33 | Page

For flow spike scenarios we assumed the one-year window with “weak” preference for 

spring. For this chapter only, we used three different initializations of random Paria River 

traces for the 30 hydro traces, for a total of 90 unique hydro+Paria River traces. This was to 

ensure that the statistics are robust. The rest of our LTEMP SEIS analysis assumed a weak 

spring preference, but we included a limited analysis of the strong spring preference 

assumption to illustrate how results would differ under a different set of assumptions.

Table 3-2. HFE probability and duration statistics for different alternatives and HFE fall to spring 

deferral assumptions. Note that values are based on all 30 hydrology traces, even though only 6 traces 

have flow spikes under the River Mile (RM) 15 target scenario, 12 have flow spikes when using a RM 

61 target, and 13 have non-bypass fluctuations, meaning that traces that are identical between 

alternatives/strategies are included in the averaging. Tables 3-A1, 3-A2, and 3-A3 Tables 3-3, 3-4, and 

3-5 in the appendix have versions of this table analyzing only the traces which include flow spikes or 

non-bypass fluctuations.
34 | Page

As shown in Table 3-2, for fall HFE probability, unsurprisingly under the one-year window 

(No Flow Spikes) there are fewer fall HFE’s relative to No Action, because some are deferred 

to spring. Conversely, there are more spring HFE’s under the No Action Alternative, with a 

33% probability of a spring HFE vs. 4% under No Action. The discrepancy is slightly larger 

under a “strong” spring preference, as expected. The probability of at least one HFE in a year 

is 60% under both the 1-yr window and No Action, and the average number of HFE’s per 

year is 0.64 for both, regardless of strong or weak preference for spring HFE’s under the 1-yr 

window. The difference between probability and average number per year is because very 

rarely it’s possible to have two HFEs in one year under either alternative. Therefore, the 1-yr 

window alternative changes the timing of HFEs, but doesn’t actually change the number of 

HFEs that would occur. Under the 1-yr window, we find shorter fall HFEs but longer spring 

HFEs. This indicates that long-duration HFEs are more likely to be deferred to spring than 

shorter ones. The last row shows that the overall mean HFE duration is slightly shorter for 

the 1-yr window than for the No Action Alternative. 

This is consistent with the assumption that decision makers would defer an HFE from fall to 

spring even if it means a slightly shorter duration. Under the “strong” preference 

assumption, the mean duration is even shorter than under the “weak” preference, although 

the difference between “strong” and “weak” is minor relative to the difference between 

either and No Action. We find that the flow spike alternatives across the board result in 

similar or slightly lower HFE probabilities and durations relative to the 1-yr window (weak 

preference). A RM 61 target results in more flow spikes than the RM 15 target, and this 

results in lower HFE probabilities. However, the effect of target river mile on HFE duration is 

mixed/minimal, likely due to shorter HFEs being eliminated from the averaging sample when 

comparing the RM 61 target to RM 15. HFE statistics for the Non-Bypass Alternative are 

similar to those for the Flow Spike Alternatives. Probability of at least one HFE in a year is 

0.58, which is less than the 0.60 under the 1-yr window, and in between the 0.59 and 0.56 

found for Flow Spikes Alternatives with a RM 15 and RM 61 target, respectively. Similarly, the 

probability of at least one HFE greater than 60-hrs in a given year is 0.49, slightly less than 

the 0.50 or the 1-yr window, and in between the 0.50 and 0.48 found for the Flow Spike 

Alternatives. Overall mean HFE duration is 84.3 hrs for the Non-Bypass Alternative, which is 

less than the 84.8 hrs under the 1-yr alternative, but greater than either Flow Spike 

Alternative (83.2 and 83.3 hrs).

The following plots (Figures 3-9 through 3-11) show the probability of an HFE of at least a 

certain duration (i.e., the 1-cumulative distribution function). These results are again based 

on 90 Paria River+hydro traces. We verified that results with 30 traces are similar to results 

with 90, indicating that the traces are representative. 
35 | Page

Each plot contains five lines: No Action, No Flow Spikes (i.e., 1-yr window, change in 

sediment accounting window with no small mouth bass flows), flow spikes with a target of 

RM 15, flow spikes with a target of RM 61, and the Non-Bypass Flow Alternative. Cool Mix 

and Cold Spike Alternatives have identical HFE statistics to “No Flow Spikes”, and the 

alternatives Cool Mix with Flow Spikes and Cold Shock with Flow Spikes have identical HFE 

statistics for the same target river mile. The main differences are between “No Action” and 

“No Flow Spikes,” with fewer/shorter Fall HFE’s and more/longer spring HFE’s for the latter. 

Results are similar between “No Flow Spikes” and the Flow Spike Alternatives, but in some 

years, flow spikes cause sand export during the fall HFE accounting window in the lead up to 

HFE implementation, and therefore reduce the final HFE duration. Overall, we find a slight 

reduction in HFE probabilities for both Flow Spike Alternatives relative to No Flow Spikes. 

Additionally, we find that HFE probabilities/durations for the Non-Bypass Alternative fall 

roughly in between the two Flow Spike Alternatives. We note that the triggering under the 

Non-Bypass Alternative is based on an RM 61 target.

Figure 3-9. Fall HFE probability. Statistics reflect average of 30 traces. Figures 3-A1 through 3-A3 in the appendix have 

versions of this figure analyzing only the traces which include flow spikes or non-bypass fluctuations (Salter and Grams, 

2024). 

0 50 100 150 200 250

HFE duration (hrs)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Probability that HFE is of indicated duration or longer

Fall HFE probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
36 | Page

Figure 3-10. Spring HFE probability. Statistics reflect average of 30 traces. Figures 3-A4 through 3-A6 in the appendix have 

versions of this figure analyzing only the traces which include flow spikes or non-bypass fluctuations (Salter and Grams, 

2024.

Figure 3-11. Sum of fall and spring HFE probabilities. Statistics reflect average of 30 traces. Figures 3-A7 through 3-A9 in 

the appendix have versions of this figure analyzing only the traces which include flow spikes or non-bypass fluctuations

(Salter and Grams, 2024). 

0 50 100 150 200 250

HFE duration (hrs)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Probability that HFE is of indicated duration or longer

Spring HFE probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative

0 50 100 150 200 250

HFE duration (hrs)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Probability that HFE is of indicated duration or longer

Fall And Spring HFE Probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
37 | Page

Sand Routing Model Results

We obtained sand mass balance results using the SRM. Among all alternatives, the mean 

long-term mass balance for Marble Canyon trends negative, but is influenced by a few traces 

with extreme erosion resulting from high monthly releases due to equalization. Figures 3-

A10 through 3-A19 show all traces and their means for each alternative. Figure 3-12 shows 

just the means, superimposed to allow for comparison. For plotting purposes, the 

alternatives are grouped into five groups: No Action, “No Flow Spikes” (which includes the 1-

yr window alternative and Cool Mix and Cold Shock Alternatives without flow spikes), flow 

spikes with RM 15 target (both cool mix and cold shock), and flow spikes with RM 61 target 

(both cool mix and cold shock). Within each group, the mass balances are indistinguishable 

on the scale of Figure 3-12. Although the hydrographs are not identical within groups, the 

resulting differences in the sand mass balance are miniscule.

Comparing “No Action” to “No Flow Spikes” we find that the 1-yr window results in a slightly 

higher mass balance on average. This is the result of slightly shorter average HFE duration 

under the 1-yr window. Alternatives with flow spikes result in a decrease in sand mass 

balance. A RM 61 target results in more erosion than RM 15 target, because flow spikes are 

triggered more frequently. Although the differences in mass balance between the Flow Spike 

Alternatives and those with the 1-yr window without flow spikes are not dramatic on the 

scale of Figure 3-12, we note that among individual traces that include flow spikes the mass 

balance difference can be significant. The difference in the means is reduced because only 6 

of 30 traces contain flow spikes for the RM 15 target, 12 of 30 traces contain flow spikes for 

the RM 61 target, and 13 out of 30 traces contain non-bypass fluctuations under the NonBypass Alternative. Additionally, flow spikes that occur in July or later are compensated for 

by shorter duration HFE’s. We additionally find that the Non-Bypass Alternative results in 

more sand export relative to the base “No Flow Spikes” Alternatives, and is generally similar 

to or slightly below the “Flow Spikes RM 61” Alternatives. 
38 | Page

Figure 3-12. Comparison of mean Marble Canyon mass balance Sand Routing Model results for the various Alternatives. 

Each line represents the mean of 30 traces. Alternatives are grouped into “No Action”, “No Flow Spikes”, which includes 

Cold Shock and Cool Mix Alternatives with any target river mile, “Flow Spikes RM 15”, which includes both Cool Mix and 

Cold Shock Alternatives with Flow Spikes and a target of RM 15, “Flow Spikes RM 61”, which includes both Cool Mix and 

Cold Shock Alternatives with Flow Spikes and a target near the Little Colorado River, and “Non-Bypass alternative”. Results 

within each group are non-identical due to subtle differences in release patterns, but would be indistinguishable on the scale 

of the figure. Figures 3-A20 through 3-A22 in the appendix have versions of this figure analyzing only the traces which 

include flow spikes or non-bypass fluctuations (Salter and Grams, 2024). 

Jul 2023 Jan 2024 Jul 2024 Jan 2025 Jul 2025 Jan 2026 Jul 2026 Jan 2027 Jul 2027 Jan 2028

time

-800

-600

-400

-200

0

200

400

600

Mean Marble Canyon Mass Balance (Tmt)

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
39 | Page

Sandbar Model Results

Figures 3-A23 through 3-A32 show sandbar model results for each alternative and each 

trace, and Figure 3-13 is a summary plot showing just the mean sandbar volumes for the 

four groups of alternatives, analogous to Figure 3-12. Figure 3-14 shows an example trace. 

The primary differences are between the No Action Alternative and the 1-yr accounting 

window (“No Flow Spikes”) Alternatives. The increased probability of spring HFEs is reflected 

in more frequent but smaller increases in the mean sandbar volume associated with HFEs. 

The 1-yr window results in smaller sandbars than under No Action, but under either 

alternative the mean sandbar size grows through time. Smaller sandbar size results primarily 

from shorter HFE duration, and secondarily from decreased HFE magnitude (April reservoir

elevations tend to be lower than in November, hence reduced bypass and penstock 

capacities), and coarser bed grain size, which reduces suspended sand concentrations and 

hence sandbar deposition rate. 

Flow spike Alternatives are found to produce slightly larger sandbars than the base 1-yr 

window alternatives. The model predicts modest sandbar growth during flow spikes. We find 

sandbar volumes for the Non-Bypass Alternative that are similar to the “No Flow Spikes” 

Alternative, or slightly larger. The sandbar volume predicts slightly smaller volumes for the 

Non-Bypass Alternative than under the Flow Spike Alternatives, which is because the 

maximum releases under the “Non-Bypass Alternative” are lower. We note that the Mueller 

and Grams (2021) model does not include similar flow operations in its calibration dataset 

(see caveats in the Sandbar Methods and Assumptions chapter), therefore, these results 

should be interpreted cautiously.

The sandbar model results show that sandbar volume is maximized by implementation of 

HFEs in fall when sediment retention is greatest and indicates that strategy also results in the 

greatest potential for long-term increases in sandbar volume. However, if for any reason a 

Fall HFE is not implemented despite a sediment trigger, as was the case in 2015, 2022, and 

2023, the fall-only HFE strategy will result in lower projected sandbar volume than the 

flexible strategy that allows HFE implementation in either fall or spring.
40 | Page

Figure 3-13. Mean sandbar model results for all alternatives. Each line represents the mean of 30 traces. Alternatives are 

grouped into “No Action”, “No Flow Spikes”, which includes Cold Shock and Cool Mix Alternatives with any target river mile, 

“Flow Spikes RM 15”, which includes both Cool mix and Cold Shock Alternatives with flow spikes and a target of RM 15, 

“Flow Spikes RM 61”, which includes both Cool Mix and Cold Shock Alternatives with Flow Spikes and a target near the 

Little Colorado River, and “Non-Bypass Alternative”. Results within each group are non-identical due to subtle differences in 

release patterns but would be indistinguishable on the scale of the figure. Under “No Action”, upward steps in fall are much 

larger than those in spring, since Fall HFE’s are much more likely than Spring HFE’s. For the other three alternatives, fall 

and spring HFE’s occur with similar probability, resulting in smaller but more frequent upward steps. Note that only 13 of the 

traces have non-bypass fluctuations, 12 traces have flow spikes with a RM 61 target, and 6 have flow spikes with a RM 15 

target. Figures 3-A33 through 3-A35 in the appendix have versions of this figure analyzing only the traces which include flow 

spikes or non-bypass fluctuations (Salter and Grams, 2024). 

Jul 2023 Jan 2024 Jul 2024 Jan 2025 Jul 2025 Jan 2026 Jul 2026 Jan 2027 Jul 2027 Jan 2028

time

2200

2400

2600

2800

3000

3200

3400

Mean Sandbar volume (m 3

)

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
41 | Page

Figure 3-14. Example trace showing a) sandbar volume, b) Marble Canyon sand mass balance, and c) Lake Powell 

reservoir elevation. Flow spikes in the summers of 2024 and 2025 result in slight sandbar building and sand export from 

Marble Canyon. All five alternatives trigger HFE’s in November of 2025; delay to spring was not possible due to reservoir 

elevations dropping below the threshold of 3500’ (dashed black line in subfigure c). In November 2026, an HFE is run under 

“No Action”, but is delayed to spring under the 1-yr window. Under “Flow Spikes RM 61”, the Spring HFE is further delayed 

to June in order to replace the first flow spike. Although “No Action” results in a slightly larger sandbar than “No Flow 

Spikes,” we note that if the November 2026 were not possible to run due to hypothetical circumstances such as hydrologic 

conditions, the flexibility under the 1-yr alternative to run a Spring HFE instead would result in much larger sandbars. 

Reservoir elevations below 3550 ft, as occur in subfigure c for most of the modeled period, have in the past precluded Fall 

HFEs (Salter and Grams, 2024.
42 | Page

Flow Routing Model Results

We used the Wiele and Griffin (1998) flow routing model to produce results under the NonBypass Alternative to determine minimum and maximum flows at different locations in the 

river. Model results for flows near the Colorado River confluence with the LCR are important 

for assessing potential effects on humpback chub (Gila cypha). As can be seen from Figures 

3-15 and 3-16, minimum and maximum flows are at their greatest range at and near GCD, 

and as the discharge wave travels downstream they attenuate. Therefore, even though 

minimum flows are 2000 cfs under the Non-Bypass Alternative, by the time the discharge 

wave reaches the LCR these are substantially higher, close to 5000 cfs. We note that tributary 

inflows are not included in these results, but their effect could be approximated by adding 

their discharge to the hydrograph for all points downstream. 

Figure 3-15. Discharge at various locations based on Wiele and Griffin (1998) flow routing model (Salter and Grams, 2024). 
43 | Page

Figure 3-16. Modeled minimum and maximum discharge as a function of distance downstream, based on the time period of 

May through December 2026 for the Non-Bypass flow Alternative and the 2002 hydrology trace (Salter and Grams, 2024). 
44 | Page

Appendix for Chapter III: Additional Figures for LTEMP SEIS Modeling

Table 3-A1. HFE statistics for traces that under the Non-Bypass Alternative include non-bypass 

fluctuations (Salter and Grams, 2024).
45 | Page

Table 3-A2. HFE statistics for traces that under the Flow Spikes RM 61 scenario include flow spikes (Salter 

and Grams, 2024).
46 | Page

Table 3-A3. HFE statistics for traces that under the Flow Spikes RM 15 scenario include flow spikes (Salter 

and Grams, 2024).
47 | Page

Figure 3-A1. Fall HFE probability for traces that under the Non-Bypass Alternative include non-bypass fluctuations (Salter 

and Grams, 2024). 

0 50 100 150 200 250

HFE duration (hrs)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Probability that HFE is of indicated duration or longer

Fall HFE probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
48 | Page

Figure 3-A2. Fall HFE probability for traces that under the Flow Spikes RM 61 scenarios include flow spikes (Salter and 

Grams, 2024). 

0 50 100 150 200 250

HFE duration (hrs)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Probability that HFE is of indicated duration or longer

Fall HFE probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
49 | Page

Figure 3-A3. Fall HFE probability for traces that under the Flow Spikes RM 15 scenarios include flow spikes (Salter and 

Grams, 2024). 

0 50 100 150 200 250

HFE duration (hrs)

0

0.1

0.2

0.3

0.4

0.5

0.6

Probability that HFE is of indicated duration or longer

Fall HFE probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
50 | Page

Figure 3-A4. Spring HFE probability for traces that under the Non-Bypass Alternative include non-bypass fluctuations

(Salter and Grams, 2024). 

0 50 100 150 200 250

HFE duration (hrs)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Probability that HFE is of indicated duration or longer

Spring HFE probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
51 | Page

Figure 3-A5. Spring HFE probability for traces that under the Flow Spikes RM 61 scenarios include flow spikes (Salter and 

Grams, 2024). 

0 50 100 150 200 250

HFE duration (hrs)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Probability that HFE is of indicated duration or longer

Spring HFE probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
52 | Page

Figure 3-A6. Spring HFE probability for traces that under the Flow Spikes RM 15 scenarios include flow spikes (Salter and 

Grams, 2024). 

0 50 100 150 200 250

HFE duration (hrs)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Probability that HFE is of indicated duration or longer

Spring HFE probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
53 | Page

Figure 3-A7. Sum of Fall and Spring HFE probabilities for traces that under the Non-Bypass Alternative include non-bypass 

fluctuations (Salter and Grams, 2024). 

0 50 100 150 200 250

HFE duration (hrs)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Probability that HFE is of indicated duration or longer

Fall And Spring HFE Probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
54 | Page

Figure 3-A8. Sum of Fall and Spring HFE probabilities for traces that under the Flow Spikes RM 61 scenarios include flow 

spikes (Salter and Grams, 2024).

0 50 100 150 200 250

HFE duration (hrs)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Probability that HFE is of indicated duration or longer

Fall And Spring HFE Probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
55 | Page

Figure 3-A9. Sum of Fall and Spring HFE probabilities for traces that under the Flow Spikes RM 15 scenarios include flow 

spikes (Salter and Grams, 2024).

0 50 100 150 200 250

HFE duration (hrs)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Probability that HFE is of indicated duration or longer

Fall And Spring HFE Probability

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
56 | Page

Figure 3-A10. Time series of Marble Canyon mass balance generated by the Sand Routing Model under the “No Action” 

Alternative. Red lines are individual traces, and the dashed black line is the mean of all traces (Salter and Grams, 2024). 

Figure 3-A11. Time series of Marble Canyon mass balance generated by the Sand Routing Model under the “Cool Mix, RM

15” Alternative. Red lines are individual traces, and the dashed black line is the mean of all traces (Salter and Grams, 

2024). 
57 | Page

Figure 3-A12. Time series of Marble Canyon mass balance generated by the Sand Routing Model under the “Cool Mix, RM 

61” Alternative. Red lines are individual traces, and the dashed black line is the mean of all traces (Salter and Grams, 

2024). 

Figure 3-A13. Time series of Marble Canyon mass balance generated by the Sand Routing Model under the “Cold Shock, 

RM 15” Alternative. Red lines are individual traces, and the dashed black line is the mean of all traces (Salter and Grams, 

2024).
58 | Page

Figure 3-A14. Time series of Marble Canyon mass balance generated by the Sand Routing Model under the “Cold Shock, 

RM 61” Alternative. Red lines are individual traces, and the dashed black line is the mean of all traces (Salter and Grams, 

2024).

Figure 3-A15. Time series of Marble Canyon mass balance generated by the Sand Routing Model under the “Cool Mix with 

Flow Spikes, RM 15” Alternative. Red lines are individual traces, and the dashed black line is the mean of all traces (Salter 

and Grams, 2024).
59 | Page

Figure 3-A16. Time series of Marble Canyon mass balance generated by the Sand Routing Model under the “Cool Mix with 

Flow Spikes, RM 61” Alternative. Red lines are individual traces, and the dashed black line is the mean of all traces (Salter 

and Grams, 2024).

Figure 3-A17. Time series of Marble Canyon mass balance generated by the Sand Routing Model under the “Cold Shock 

with Flow Spikes, RM 15” Alternative. Red lines are individual traces, and the dashed black line is the mean of all traces

(Salter and Grams, 2024). 
60 | Page

Figure 3-A18. Time series of Marble Canyon mass balance generated by the Sand Routing Model under the “Cold Shock 

with Flow Spikes, RM 61” Alternative. Red lines are individual traces, and the dashed black line is the mean of all traces

(Salter and Grams, 2024).

Figure 3-A19. Time series of Marble Canyon mass balance generated by the Sand Routing Model under the Non-Bypass 

Alternative. Red lines are individual traces, and the dashed black line is the mean of all traces (Salter and Grams, 2024). 
61 | Page

Figure 3-A20. Average Marble Canyon Sand Mass Balance for traces that under the Non-Bypass Alternative include nonbypass fluctuations (Salter and Grams, 2024). 

Figure 3-A21. Average Marble Canyon Sand Mass Balance for traces that under the Flow Spikes RM 61 scenarios include 

flow spikes (Salter and Grams, 2024). 

Jul 2023 Jan 2024 Jul 2024 Jan 2025 Jul 2025 Jan 2026 Jul 2026 Jan 2027 Jul 2027 Jan 2028

time

-600

-500

-400

-300

-200

-100

0

100

200

300

400

Mean Marble Canyon Mass Balance (Tmt)

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative

Jul 2023 Jan 2024 Jul 2024 Jan 2025 Jul 2025 Jan 2026 Jul 2026 Jan 2027 Jul 2027 Jan 2028

time

-600

-500

-400

-300

-200

-100

0

100

200

300

400

Mean Marble Canyon Mass Balance (Tmt)

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
62 | Page

Figure 3-A22. Average Marble Canyon Sand Mass Balance for traces that under the Flow Spikes RM 15 scenarios include 

flow spikes (Salter and Grams, 2024). 

Figure 3-A23. Sandbar Model results for “No Action” Alternative. Red lines are individual traces, and the dashed black line 

is the mean of all traces. Distinct upward steps in the mean sandbar size occur in the Fall, because this is when most HFEs 

are implemented under the No Action Alternative (Salter and Grams, 2024). 

Jul 2023 Jan 2024 Jul 2024 Jan 2025 Jul 2025 Jan 2026 Jul 2026 Jan 2027 Jul 2027 Jan 2028

time

-500

-400

-300

-200

-100

0

100

200

300

400

500

Mean Marble Canyon Mass Balance (Tmt)

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative

2016 2018 2020 2022 2024 2026

time

1500

2000

2500

3000

3500

4000

4500

5000

Sandbar volume (m3

)

No Action Alternative

Calibration period

Observations

Predicted (all traces)

Mean
63 | Page

Figure 3-A24. Sandbar Model results for “Cool Mix, RM 15” Alternative. Red lines are individual traces, and the dashed 

black line is the mean of all traces. The mean reflects HFE’s implemented in both fall and spring, but individual traces with

both in a single year are rare (Salter and Grams, 2024). 

Figure 3-A25. Sandbar Model results for “Cool Mix, RM 61” Alternative. Red lines are individual traces, and the dashed 

black line is the mean of all traces. The mean reflects HFEs implemented in both fall and spring, but individual traces with 

both in a single year are rare (Salter and Grams, 2024). 

2016 2018 2020 2022 2024 2026

time

1500

2000

2500

3000

3500

4000

4500

5000

Sandbar volume (m3

)

Cool Mix, RM 15

Calibration period

Observations

Predicted (all traces)

Mean

2016 2018 2020 2022 2024 2026

time

1500

2000

2500

3000

3500

4000

4500

5000

Sandbar volume (m3

)

Cool Mix, RM 61

Calibration period

Observations

Predicted (all traces)

Mean
64 | Page

Figure 3-A26. Sandbar Model results for “Cold Shock, RM 15” Alternative. Red lines are individual traces, and the dashed 

black line is the mean of all traces. The mean reflects HFEs implemented in both fall and spring, but individual traces with 

both in a single year are rare (Salter and Grams, 2024).

Figure 3-A27. Sandbar Model results for “Cold Shock, RM 61” Alternative. Red lines are individual traces, and the dashed 

black line is the mean of all traces. The mean reflects HFEs implemented in both fall and spring, but individual traces with 

both in a single year are rare (Salter and Grams, 2024).

2016 2018 2020 2022 2024 2026

time

1500

2000

2500

3000

3500

4000

4500

5000

Sandbar volume (m3

)

Cold Shock, RM 61

Calibration period

Observations

Predicted (all traces)

Mean
65 | Page

Figure 3-A28. Sandbar Model results for “Cool Mix with Flow Spikes, RM15” Alternative. Red lines are individual traces, 

and the dashed black line is the mean of all traces. The mean reflects HFEs implemented in both fall and spring, but 

individual traces with both in a single year are rare (Salter and Grams, 2024).

Figure 3-A29. Sandbar Model results for “Cool Mix with Flow Spikes, RM 61” Alternative. Red lines are individual traces, 

and the dashed black line is the mean of all traces. The mean reflects HFEs implemented in both fall and spring, but 

individual traces with both in a single year are rare (Salter and Grams, 2024). 

2016 2018 2020 2022 2024 2026

time

1500

2000

2500

3000

3500

4000

4500

5000

Sandbar volume (m3

)

Cool Mix with Flow Spikes, RM 15

Calibration period

Observations

Predicted (all traces)

Mean

2016 2018 2020 2022 2024 2026

time

1500

2000

2500

3000

3500

4000

4500

5000

Sandbar volume (m3

)

Cool Mix with Flow Spikes, RM 61

Calibration period

Observations

Predicted (all traces)

Mean
66 | Page

Figure 3-A30. Sandbar Model results for “Cold Shock with Flow Spikes, RM 15” Alternative. Red lines are individual traces, 

and the dashed black line is the mean of all traces. The mean reflects HFEs implemented in both fall and spring, but 

individual traces with both in a single year are rare (Salter and Grams, 2024). 

Figure 3-A31. Sandbar Model results for “Cold Shock with Flow Spikes, RM 61” Alternative. Red lines are individual traces, 

and the dashed black line is the mean of all traces. The mean reflects HFEs implemented in both fall and spring, but 

individual traces with both in a single year are rare (Salter and Grams, 2024). 

2016 2018 2020 2022 2024 2026

time

1500

2000

2500

3000

3500

4000

4500

5000

Sandbar volume (m3

)

Cold Shock with Flow Spikes, RM 15

Calibration period

Observations

Predicted (all traces)

Mean

2016 2018 2020 2022 2024 2026

time

1500

2000

2500

3000

3500

4000

4500

5000

Sandbar volume (m3

)

Cold Shock with Flow Spikes, RM 61

Calibration period

Observations

Predicted (all traces)

Mean
67 | Page

Figure 3-A32. Sandbar Model results for “Non-Bypass” Alternative. Red lines are individual traces, and the dashed black 

line is the mean of all traces. The mean reflects HFEs implemented in both fall and spring, but individual traces with both in 

a single year are rare (Salter and Grams, 2024). 

Figure 3-A33. Mean sandbar volume for traces that under the Non-Bypass Alternative include non-bypass fluctuations

(Salter and Grams, 2024). 

2016 2018 2020 2022 2024 2026

time

1500

2000

2500

3000

3500

4000

4500

5000

Sandbar volume (m3

)

Non-bypass Alternative

Calibration period

Observations

Predicted (all traces)

Mean

Jul 2023 Jan 2024 Jul 2024 Jan 2025 Jul 2025 Jan 2026 Jul 2026 Jan 2027 Jul 2027 Jan 2028

time

2300

2400

2500

2600

2700

2800

2900

3000

Mean Sandbar volume (m 3

)

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
68 | Page

Figure 3-A34. Mean sandbar volume for traces that under the Flow Spikes RM 61 scenarios include flow spikes (Salter and 

Grams, 2024).

Figure 3-A35. Mean sandbar volume for traces that under the Flow Spikes RM 15 scenarios include flow spikes (Salter and 

Grams, 2024). 

Jul 2023 Jan 2024 Jul 2024 Jan 2025 Jul 2025 Jan 2026 Jul 2026 Jan 2027 Jul 2027 Jan 2028

time

2300

2400

2500

2600

2700

2800

2900

3000

Mean Sandbar volume (m 3

)

No Action

No Flow Spikes

Flow Spikes RM15

Flow Spikes RM61

Non-bypass Alternative
69 | Page

Figure 3-A36. Relationship between Marble Canyon mass balance and triggered HFE duration according to the Sand 

Routing Model. Dashed line is the heuristic relationship for predicting HFE duration on the basis of 1200 simulations.

Data Availability Statement:

Data generated during this study are published and available (Salter and Grams, 2024).

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

References

Alvarez, L.V., and Schmeeckle, M.W., 2012, Erosion of river sandbars by diurnal stage 

fluctuations in the Colorado River in the Marble and Grand Canyons—Full-scale 

laboratory experiments: River Research and Applications, v. 29, no. 2, p. 839-954, 

https://doi.org/10.1002/rra.2576. 

Bureau of Reclamation, 2024a, Near-term Colorado River operations—Final Supplemental 

Environmental Impact Statement: U.S. Department of the Interior, Bureau of Reclamation, 

Upper and Lower Colorado Basins, Interior Regions 7 and 8, 

https://www.usbr.gov/ColoradoRiverBasin/documents/NearTermColoradoRiverOperation

s/20240300-Near-termColoradoRiverOperations-FinalSEIS-508.pdf.

Bureau of Reclamation, 2024b, Glen Canyon Dam Long-Term Experimental and Management 

Plan—Draft Supplemental Environmental Impact Statement: U.S. Department of the 
70 | Page

Interior, Bureau of Reclamation, Upper Colorado Basins, Interior Region 7, 

https://www.usbr.gov/uc/DocLibrary/EnvironmentalImpactStatements/GlenCanyonDamL

ong-TermExperimentalManagementPlan/20240207-Draft-GCDLTEMP-SEIS_508.pdf. 

Ecometric Research, Inc., 2012, Colorado River Flow and Sediment v.1.0.1.0, unpublished 

computer software (available upon request): Vancouver, British Columbia, Canada, 

https://ecometric.com/services/. 

Hazel, J.E., Kaplinski, M.A., Hamill, D., Buscombe, D., Mueller, E.R., Ross, R.P., Kohl, K., and 

Grams, P.E., 2022, Multi-decadal sandbar response to flow management downstream 

from a large dam—The Glen Canyon Dam on the Colorado River in Marble and Grand 

Canyons, Arizona: U.S. Geological Survey Professional Paper 1873, prepared in 

cooperation with Northern Arizona University, 104 p., https://doi.org/10.3133/pp1873. 

Grams, P.E., Hazel, J.E., Jr., Kaplinski, M., Ross, R.P., Hamill, D., Hensleigh, J., and Gushue, T., 

2020, Long-term sandbar monitoring data along the Colorado River in Marble and Grand 

Canyons, Arizona: U.S. Geological Survey data release, https://doi.org/10.5066/P93F8JJK.

Mueller, E.R., and Grams, P.E., 2021, A morphodynamic model to evaluate long-term sandbar 

rebuilding using controlled floods in the Grand Canyon: Geophysical Research Letters, v. 

48, no. 9, e2021GL093007, . 

Salter, G., and Grams, P., 2024, Predicting sediment responses to different management 

scenarios for Lake Powell releases: U.S. Geological Survey data release, 

https://doi.org/10.5066/P135PNWT.

Schuster, R.J., 1998, CRSSez—Annual Colorado River simulation system documentation—

Colorado River simulation model overview and user's manual: Boulder City, Nev., U.S. 

Department of the Interior, Bureau of Reclamation, Boulder Canyon Operations Office, 

Lower Colorado Region.

Topping, D.J., Grams, P.E., Griffiths, R.E., Dean, D.J., Wright, S.A., and Unema, J.A., 2021, Selflimitation of sand storage in a bedrock-canyon river arising from the interaction of flow 

and grain size: Journal of Geophysical Research: Earth Surface, v. 126, no. 5, 

e2020JF005565, .

U.S. Department of Interior, 2016, Record of Decision for the Glen Canyon Dam Long-Term 

Experimental and Management Plan final Environmental Impact Statement (LTEMP ROD):

Salt Lake City, Utah, U.S. Department of the Interior, Bureau of Reclamation, Upper 

Colorado Region, National Park Service, Intermountain Region, 196 p., 

http://ltempeis.anl.gov/documents/docs/LTEMP_ROD.pdf. 

U.S. Geological Survey, 2024, Discharge, sediment, and water quality monitoring data: 

Flagstaff, Ariz., U.S. Geological Survey, Southwest Biological Science Center, Grand 

Canyon Monitoring and Research Center, retrieved February 16, 2024, 

http://www.gcmrc.gov/discharge_qw_sediment/. 

Veselka, T.D. and North, M., 2001. Generation and transmission maximization model (No. 

GTMAX; 001354IBMPC00). Argonne National Laboratory (ANL), Argonne, IL (United 

States).
71 | Page

Wiele, S.M., and Griffin, E.R., 1998, Modifications to a one-dimensional model of unsteady 

flow in the Colorado River through Grand Canyon, Arizona: U.S. Geological Survey 

Water-Resources Investigations Report 97-4046, 17 p., 

https://doi.org/10.3133/wri974046. 

Wright, S.A., Topping, D.J., Rubin, D.M., and Melis, T.S., 2010, An approach for modeling 

sediment budgets in supply-limited rivers: Water Resources Research, v. 46, no. 10, p. 1-

18, https://doi.org/10.1029/2009WR008600. 
72 | Page

IV. Modeling Impacts of Different Reservoir Management Scenarios on 

Smallmouth Bass (Micropterus dolomieu) Entrainment and 

Population Growth Rates 

DREW EPPEHIMER, U.S. GEOLOGICAL SURVEY, CHARLES YACKULIC, U.S. GEOLOGICAL SURVEY

Background and Methods 

Smallmouth bass (SMB; Micropterus dolomieu) are a highly valued sport fish that have been 

introduced throughout the globe and have often spread extensively beyond their initial 

point of introduction (Loppnow and others, 2013). Within the Colorado River basin, SMB 

were historically introduced into many reservoirs, including Lake Powell, and have spread 

into many segments of river network above Lake Powell (Breton and others, 2015; Bestgen 

and Hill, 2016; Dibble and others, 2021). SMB invasion into rivers throughout the globe have 

been associated with substantial population declines, and in many instances, extirpations of 

native fish species (Brown and others, 2009; Loppnow and others, 2013). SMB are extremely 

capable predators able to consume many size classes of native fish of the Colorado River 

basin, including federally listed humpback chub (HBC [Gila cypha]; Johnson and others, 2008; 

Arena and others, 2012; Schake and others, 2014; Fernando and others, 2018; Ward and 

Vaage, 2019). In the upper Colorado River basin, SMB are considered the greatest threat to 

the persistence of threatened and endangered fish species (Johnson and others, 2008). SMB 

are also fecund and adaptable to a substantial range of environmental conditions (Edwards 

and others, 1983; Loppnow and others, 2013) and have successfully invaded rivers across 

multiple continents (Loppnow and others, 2013). 

SMB were rarely observed in the Colorado River ecosystem below Glen Canyon Dam (GCD) 

prior to 2022 (Interagency SMB Taskforce presentation to the Technical Working Group, 

2022); however, likely reproduction was identified for the first time in 2022 (Eppehimer and 

others, 2024; National Park Service, unpublished data). Low reservoir elevations in Lake 

Powell have likely led to modest increases in the entrainment of SMB, which, like most fish

species, typically occupy the shallower parts of the water column in Lake Powell (Eppehimer 

and others, 2024). Lower reservoir elevations have also contributed to dramatic warming of 

water release temperatures making the river more suitable for SMB reproduction (Eppehimer 

and others, 2024), which likely occurred in 2022 and 2023.
73 | Page

General modeling

We used models from Eppehimer and others (2024) to analyze different reservoir operation 

alternatives developed by Reclamation as part of the Interim Guidelines Supplemental 

Environmental Impact Statement (SEIS; Bureau of Reclamation, 2024a) and LTEMP Long-term 

Experimental and Management Plan Supplemental Environmental Impact Statement (Bureau 

of Reclamation, 2024b). Outputs from these models include forecasts of daily water 

temperature, the SMB asymptotic population growth rate (i.e., lambda), and SMB 

entrainment rate from Lake Powell. 

In the SMB population growth model, the GCD release temperatures (penstock or bypass jet 

tubes) used are estimated for every day of the year using a model that relies on spring 

inflow (April-July) into Lake Powell, day of year, and depth as predictors and was fitted to 

225 Lake Powell temperature profiles from 2000-2021 (Andrews and Deemer, 2022; see 

Eppehimer and others, 2024). Downriver warming of water released from GCD is estimated 

using a model developed by Dibble and others, (2021) adapted from monthly to daily scale 

by calculating average daily solar insolation and daily air temperatures from the Page 

weather station (see Eppehimer and others, 2024). Daily average water temperatures are for 

the mainstem Colorado River only and do not account for backwaters that will be warmer or 

colder than the mainstem depending on seasonality. The amount of water that needs to be 

released through the river outlets and the penstocks will vary based on the elevation of the 

lake and the distribution of water temperatures through the water column (these factors 

determine the temperature of the water being released), the time of year (air temperature 

and solar radiation) and the daily discharge, all of which determines how quickly a given 

amount of water warms as it travels downriver (Mihalevich and others, 2020; Dibble and 

others, 2021). 

The SMB population growth rate model is based on thermal suitability and assumes that the 

thermal regime is the factor that limits SMB recruitment (see Eppehimer and others, 2024 for 

modeling details). This model relies on a 16-month window beginning in January to calculate 

lambda. It requires more than 12 months because winter conditions are needed to estimate 

overwinter survival. It assumes a 16 °C daily average water temperature spawning initiation 

threshold, when the temperature doesn’t drop below 13.9 °C in the following week 

(Eppehimer and others, 2024). SMB have been observed laying eggs at water temperatures 

as low as 15 °C in some systems; however, water temperatures of 16 °C or greater are 

typically required for SMB to lay eggs (see Eppehimer and others, 2024). For example, in the 

Green and Yampa rivers, the earliest observed hatch was always after the first day when 

temperatures increased above 16 °C daily average during a seven-year study across three 

river reaches (Bestgen and Hill, 2016). 
74 | Page

In addition, a thermal regime >16 °C is needed for young of year to measurably grow during 

the growing season, since SMB in rivers ~16 °C would be unlikely to grow large enough to 

survive the winter (Shuter and others, 1980; Dudley and Trial, 2014). 

The SMB entrainment model estimates the expected number of adult SMB propagules per 

year that are entrained through GCD from Lake Powell and survive (see Eppehimer and 

others, 2024 for modeling details). The SMB entrainment model is run on 12-month time 

step beginning in January.

Interim Guideline SEIS modeling

To estimate the potential changes in the number of entrained SMB through GCD and 

released into Lees Ferry, and the potential population growth rate of SMB in both Lees Ferry 

(River Mile [RM] 0) and the Colorado River at RM 61 (just upstream from the Colorado-Little 

Colorado River [LCR] confluence), we applied models (see Eppehimer and others, 2024) to 

simulate expectations under the 90 Colorado River Mid-term Modeling System (CRMMS) 

hydrologic traces (30 traces modeled at 80, 90, and 100% of the ensemble streamflow 

predictions (ESP), 90 total) to which the No Action and Proposed Action rules for water 

storage and release from Lake Powell in the Interim Guideline SEIS were applied. 

Results of Analyzing Interim Guideline SEIS Alternatives 

Glen Canyon Dam smallmouth bass entrainment 

On average, SMB entrainment and passage through GCD into the Lees Ferry tailwater reach 

is expected to be less than 50 propagules per year across both SEIS scenarios from 2024-

2026, but more extreme entrainment rates (>100 propagules per year) are possible in 2025 

and 2026 (Figure 4-1; Eppehimer and others, 2024). SMB entrainment rates are expected to 

be similar between No Action and Proposed Action Alternatives under most, but not all 

hydrologic traces (Figure 4-2; Eppehimer and others, 2024). For example, under dry 

hydrologic conditions, the Action Alternative is expected to increase SMB entrainment 

relative to No Action (Figure 4-2).

Smallmouth bass population growth rate 

Forecasted SMB population growth rate (lambda) was similar, on average, between No 

Action and Proposed Action under the SEIS from 2024-2026, whether forecasted RM 0 or 

RM 61 (Figure 4-3; Eppehimer and others, 2024). The number of traces in which SMB 

population growth (lambda) was predicted to occur is very similar between No Action and 

Proposed Action (Table 4-1). Over the years analyzed, 2-18% of the hydrologic traces predict 

population growth at RM 0 with the majority of those traces predicting high growth rates 

(lambda > 2; Table 4-1; Figure 4-3). 
75 | Page

Over the years analyzed, 10-26% of the hydrologic traces predict population growth at RM 

61 with the majority of those traces predicting high growth rates (lambda > 2; Table 4-1; 

Figure 4-3). Forecasted population growth rates at RM 61 are higher than those at RM 0 due 

to downriver warming (Figure 4-3). Under most hydrologic traces, there was very little 

relative difference in SMB population growth between No Action and Proposed Action 

Alternatives (Figure 4-4; Eppehimer and others, 2024).

Figure 4-1. Forecasts of the median number of SMB entrained per year under the SEIS No Action (green; presented first 

from left to right) and Proposed Action (blue; presented second from left to right). For each of the 90 hydrologic traces, we

calculated the median forecasted entrainment and summarized these results using a box and whisker plot in which the dark 

line represents the median, the boxes represent the upper and lower 25% quantiles, and the whiskers extended to twice the 

interquartile range with dots representing traces with more extreme values.
76 | Page

Figure 4-2. Ratio in forecasted smallmouth bass propagules between Proposed Action and No Action Alternatives. For 

each of the 90 hydrologic traces, we calculated the ratio of forecasted median propagules entrained from 2024-2026 and 

plotted it versus the mean annual inflow from those years.

Figure 4-3. Forecasts of the potential annual SMB population growth rate (lambda) at RM 0 and RM 61 under No Action 

(green; presented first from left to right) and Proposed Action (blue; presented second from left to right) Alternatives. 

Lambda >1 indicates population growth. For each of the 90 hydrologic traces, we estimated population growth rate based 

on forecasted daily water temperature and summarized these results using a box and whisker plot in which the dark line 

represents the median, the boxes represent the upper and lower 25% quantiles, and the whiskers extended to twice the 

interquartile range with dots representing traces with more extreme values. Note: although the interquartile range for RM 61 

Proposed Action 2026 decreased relative to No Action, the mean, median, and number of traces with lambda >1 are still 

very similar. 
77 | Page

Figure 4-4. Difference in forecasted potential annual SMB population growth rate (lambda) at RM 0 and Rm 61 under 

Proposed Action and No Action Alternatives. For each of the 90 hydrologic traces, we calculated the ratio of the expected 

three-year (2024-2026) population growth rates and plotted it versus the mean annual inflow from those years.

Table 4-1. Percent of traces (rounded to the nearest %, out of 90 traces total) with predicted SMB population growth 

(lambda >1) for No Action and Proposed Action Alternatives by target location (RM 0 and RM 61) and year (2024-2026). 

% of Traces with SMB Lambda >1

Location Year No Action Proposed Action

RM 0 2024 2 2

RM 0 2025 13 13

RM 0 2026 18 18

RM 61 2024 16 10

RM 61 2025 26 26

RM 61 2026 26 24
78 | Page

LTEMP SEIS Modeling

To estimate the potential population growth rate of SMB at RM 15 and RM 61, we applied 

models (see Eppehimer and others, 2024) to simulate expectations under the 30 CRMMS 

hydrologic traces (30 traces at 100% ensemble streamflow prediction [ESP]) to which the No 

Action and SMB Alternative rules for water storage and release from Lake Powell in the 

LTEMP SEIS were applied. 

To run the model for 2027 we had to add inflow, outflow, and elevation for October 2027 – 

April 2028. For water year 2028, we assumed 8-million-acre feet (maf) annual inflows 

following monthly volumes determined by a log transformed linear model fit to 2000-2021 

historic inflows. For water year 2028, we assumed 7.48 maf annual outflows with monthly 

volumes determined by LTEMP EIS guidelines. Elevations were calculated using the CRSS 

water balance equation for Lake Powell given a starting elevation (September 2027) and 

subsequent monthly inflows and outflows. Given the minimal variation in monthly inflow and 

outflow during the October to April intervals and the fact that this period is primarily used to 

calculate starvation days, (Eppehimer and others, 2024) which are primarily a function of 

reservoir elevations, these assumptions have minimal impacts on lambda estimates and 

would not be expected to change the bypass required under a SMB Alternative. 

Analysis of flow disturbance on SMB lambda was estimated for both alternatives with flow 

spikes and the Non-Bypass Alternative. This analysis used a Lees Ferry tailwater dischargevelocity model with 5x5 m resolution (Nelson and others, 2016; Kaplinski and others, 2022a, 

b; Wright and others, 2024). No such model exists for downriver sections of the Colorado 

River in Grand Canyon, so for a given GCD discharge we assumed the proportions of river 

wetted area and proportions of water velocities were the same for all reaches. 

We estimated SMB spawning habitat disturbance under GCD flow scenarios assuming 

nesting in habitat with water velocity ≤0.1 meters per second (m/s; Winemiller and Taylor, 

1982; Lukas and Orth, 1995; Miller and Brewer, 2021) and assuming drying or velocities >0.3 

m/s (Lukas and Orth, 1995; Miller and Brewer, 2021) would cause nest abandonment by 

guarding males and subsequent nest failure, assuming 100% mortality of offspring 

(Winemiller and Taylor, 1982; Lukas and Orth, 1995; Knotek and Orth, 1998). 

This model used habitat available at baseflow conditions (incorporating load following 

discharges) and habitat disturbance by subsequent increases or decreases in discharge. SMB 

can renest multiple times during a spawning season (Lukas and Orth, 1995), so we assumed 

three nesting opportunities per spawning season allowing SMB to renest if their nest was 

disturbed by flows. 
79 | Page

Flow disturbance effect was estimated using an equation for proportion of offspring 

remaining for a given flow: ((1-x)+x*(1-x)*2/3+x*x*(1-x)*1/3), where x represents estimated 

proportion of spawning habitat disturbed. This was then multiplied by fecundity. 

Results of Analyzing LTEMP SEIS Alternatives 

Smallmouth bass population growth rate 

The model predicts that SMB population growth at RM 15 is less than 1 in all four years in 5 

out of 30 traces under the No Action Alternative. Across 30 traces, each lasting 4 years, there 

were only 10 total years under the No Action Alternative in which SMB populations were 

predicted to increase (Figure 4-5A). The Cool Mix Alternatives resulted in no predicted 

population growth for 100% of traces and years. The Cold Shock Alternative is expected to 

allow for population growth in 1 trace in 2026 and 1 trace in 2027. The addition of flow 

spikes reduced these estimated lambdas but did not stop population growth. Similarly, the 

within powerplant capacity flow fluctuations (Non-Bypass) reduced estimated lambdas when 

compared to No Action but did not stop population growth.

The model predicts that SMB population growth at RM 61 is less than 1 in all four years in 7 

out of 30 traces under the No Action Alternative. Due to downriver warming of water 

temperatures, more traces (and more years within traces) have population growth at this 

location than at RM 15. Across 30 traces, each lasting 4 years, there were only 13 total years 

under the No Action Alternative in which SMB populations were predicted to increase at RM 

61 (Figure 4-5B). The Cool Mix Alternatives resulted in no predicted population growth for 

100% of traces and years. Under the Cold Shock Alternative there were 6 years in which SMB 

populations were predicted to increase. The addition of a flow spikes reduced these 

estimated lambdas but did not stop population growth. Similarly, the within powerplant 

capacity flow fluctuations (Non-Bypass) reduced estimated lambdas when compared to No 

Action but did not stop population growth.
80 | Page

Figure 4-5. Forecasts of the potential annual SMB population growth rate (lambda) at A) RM 15 and B) RM 61 of the 

Colorado River under No Action, Cool Mix, Cool Mix with flow spike, Cold Shock, Cold Shock with flow spike, and NonBypass Alternatives. Grey, horizontal dashed line denotes lambda = 1. Lambda >1 indicates population growth. For each of 

the 30 hydrologic traces, we estimated population growth rate based on forecasted daily water temperature and 

summarized these results using a box and whisker plot in which the dark line represents the median, the boxes represent 

the upper and lower 25% quantiles, and the whiskers extend to twice the interquartile range with dots representing traces 

with more extreme values. All box and whisker plots’ interquartile ranges are below 1, and therefore appear compressed. 

Only extreme values (dots) are above 1. The numbers above the dashed, horizontal 1 line represent the percent of traces 

(rounded to the nearest percent; out of 30 traces) in which lambda was predicted to be >1 for that year and scenario.

Data Availability Statement:

Data generated during this study are published and available (Eppehimer and Yackulic, 

2024).

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.
81 | Page

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Bestgen, K.R., and Hill, A.A., 2016, River regulation affects reproduction, early growth, and 

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Breton, A., Winkelman, D.L., Bestgen, K.R., and Hawkins, J.A., 2015, Population dynamics 

modeling of introduced smallmouth bass in the upper Colorado River basin: Denver, 

Colo., final report to the Upper Colorado River Endangered Fish Recovery Program 

Project 161, Larval Fish Laboratory Contribution 186, 150 p., 

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Brown, T.G., Runciman, B., Pollard, S., Grand, A.D.A., and Bradford, M.J., 2009, Biological 

synopsis of smallmouth bass (Micropterus dolomieu): Nanaimo, B.C., Canadian 

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h_Bass_Micropterus_dolomieu. 

Bureau of Reclamation, 2024a, Near-term Colorado River operations—Final Supplemental 

Environmental Impact Statement: U.S. Department of the Interior, Bureau of Reclamation, 

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https://www.usbr.gov/ColoradoRiverBasin/documents/NearTermColoradoRiverOperation

s/20240300-Near-termColoradoRiverOperations-FinalSEIS-508.pdf. 

Bureau of Reclamation, 2024b, Glen Canyon Dam Long-Term Experimental and Management 

Plan—Draft Supplemental Environmental Impact Statement: U.S. Department of the 

Interior, Bureau of Reclamation, Upper Colorado Basins, Interior Region 7, 

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ong-TermExperimentalManagementPlan/20240207-Draft-GCDLTEMP-SEIS_508.pdf. 

Dibble, K.L., Yackulic, C.B., Kennedy, T.A., Bestgen, K.R., and Schmidt, J.C., 2021, Water storage 

decisions will determine the distribution and persistence of imperiled river fishes: 

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smallmouth bass in Spednic Lake, from 1970 through 2008: U.S. Geological Survey 
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Eppehimer, D.E., and Yackulic, C.B., 2024, Forecasting smallmouth bass invasion under 

different management scenarios for Lake Powell releases, 2024-2027: U.S. Geological 

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Eppehimer, D.E., Yackulic, C.B., Bruckerhoff, L.A., Wang, J., Young, K.L., Bestgen, K.R., 

Mihalevich, B.A., and Schmidt, J.C., 2024, Declining reservoir elevations following a twodecade drought increase water temperatures and non-native fish passage facilitating a 

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Channel mapping Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation 

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Channel mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen 

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smallmouth bass: The Prairie Naturalist, South Dakota State University, Natural Resource 

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vulnerability for juvenile Colorado River native fishes?: Journal of Fish and Wildlife 

Management, v. 10, no. 1, p. 196-205, https://doi.org/10.3996/042018-JFWM-031. 

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selection in a small Ohio stream: Ohio Journal of Science, v. 82, no. 5, p. 266-273, 

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https://doi.org/10.5066/P1QTRNEB. 
84 | Page

V. Modeling Impacts of Glen Canyon Dam Operations Scenarios of the 

Interim Guidelines SEIS and LTEMP SEIS on Sand Exposure for 

Aeolian Landscape and Cultural Site Resources 

ALAN KASPRAK, U.S. ENVIRONMENTAL PROTECTION AGENCY*, JOEL SANKEY, U.S. GEOLOGICAL 

SURVEY, JOSHUA CASTER, U.S. GEOLOGICAL SURVEY, HELEN FAIRLEY, U.S. GEOLOGICAL SURVEY

*The views expressed in this publication are those of the author(s) and do not necessarily represent the views 

or policies of the U.S. Environmental Protection Agency.

Background and Methods

The movement of river-sourced sediment by wind is an important process in the Colorado 

River Ecosystem (CRe) because windblown deposition of sediment provides favorable 

habitat for endemic vegetative species, such as sand dropseed (Sporobolus cryptandrus) and 

dunebroom (Parryella filifolia), and results in in-situ burial and preservation of archaeological 

sites that line the river corridor and which are above the elevation of regularly occurring 

post-dam floods of the Colorado River (East and others, 2016; Cook and others, 2019). In the 

absence of such windblown sediment deposition, these archaeological sites are more 

susceptible to loss from gully erosion and overland rainfall runoff. To be mobilized by wind, 

river sand must be both bare (i.e., not covered in vegetation) and dry (i.e., exposed for an 

appreciable amount of time since last being inundated by the Colorado River). 

Cultural resource preservation potential in Grand Canyon National Park (GCNP) increases 

with larger values of exposed, dry Colorado River sand that is susceptible to windblown 

(aeolian) transport. We conducted modeling to determine the area of exposed, dry river 

sand between Glen Canyon Dam (GCD) and Bright Angel Creek within the CRe. The model 

used here predicts the area of bare, dry sand for the upper ~103 miles of Grand Canyon 

between GCD and Phantom Ranch/Bright Angel Creek. It uses a timeseries of daily maximum 

discharge from GCD, field-derived maps of bare sand from a combination of multi- and 

single-beam sonar, total station, and aerial photo interpretation, in combination with 

hydraulic models of inundation extent to derive the total exposed bare sand area for a given 

discharge from GCD. This initial bare sand area is modified to incorporate its exposure time, 

as longer exposure times result in progressively drier, and thus more transportable, sand. 

The model is detailed in Kasprak and others (2021) and the modifications based on drying 

time follow Sankey and others (2022). See Kasprak and others (2024) for the associated data. 
85 | Page

Model Assumptions 

The model used here was developed with, and makes predictions of, dry bare sand for the 

reach of the CRe between GCD (River Mile [RM] -16) and Bright Angel Creek (RM 87). It was 

not developed to also predict bare sand extent in areas downstream from Bright Angel 

Creek. 

Additionally, the drying time component of the model is based on empirical field data of 

effects of sand drying on aeolian sediment transport collected during March 2021 at Lees 

Ferry. Locationally-specific sand drying rates depend on numerous other factors that may 

vary spatially and seasonally, including temperature, solar insolation intensity, and 

precipitation, among others.

The model does not consider effects of implementing High Flow Experiments (HFEs; 

controlled floods) on the availability of river sand for aeolian transport. HFEs that rebuild 

sandbars will increase the supply of windblown sand for archaeological sites that are 

downwind from river sandbars (Sankey and others, 2018). 

At present, this model also does not consider effects of variability in wind. Aeolian sediment 

transport rates increase with frequency of winds above threshold transport velocities, which 

are more common from March through July of each year (Caster and others, 2014). Longer 

sand exposure times during the spring and summer seasons combined with HFEs may 

additionally increase the supply of sand for downwind archaeological sites.

Results of Analyzing Interim Guideline SEIS Alternatives 

Figure 5-1 shows the relative percent difference for implementing the Proposed Action 

Alternative vs. No Action Alternative. Across 90 modeled ensemble streamflow predictions, 

the mean and median exposed sand areas are very similar between the Proposed Action

Alternative and No Action Alternative (Table 5-1). 
86 | Page

Figure 5-1. Percent difference in exposed sand area (%), Proposed Action Alternative vs. No Action Alternative. Positive 

values indicate more, and negative values indicated less, river sourced sand available for wind transport to high elevation 

sand deposits to help preserve archaeological sites in situ. Percent difference in exposed sand area is calculated for 

individual ensemble streamflow prediction (ESP) traces as Percent Change = [(Proposed Action Alternative - No Action 

Alternative)/(Proposed Action Alternative + No Action Alternative)/2)] * 100.

Table 5-1. Results of exposed sand area modeling under the three bins of ensemble streamflow 

predictions. 

Ensemble 

Streamflow 

Bin

Mean Exposed 

Sand Area (m2)

No Action

alternative

Median Exposed 

Sand Area (m2)

No Action

alternative

Mean Exposed 

Sand Area (m2)

Proposed Action

alternative

Median Exposed 

Sand Area (m2)

Proposed Action

alternative

80th

Percentile 1,572,250 1,518,037 1,581,239 1,541,849

90th

Percentile 1,527,976 1,507,151 1,534,520 1,507,151

100th

Percentile 1,489,311 1,507,151 1,506,440 1,507,151
87 | Page

Results of Analyzing LTEMP SEIS Alternatives 

Figure 5-2 shows box and whisker plots summarizing the median daily exposed sand area 

(m2

) predicted for each of the alternative scenarios. The median daily exposed sand area is 

predicted from hourly releases (generated as input for resource models) for each of the 

alternative scenarios for 30 hydrological traces (ensemble streamflow prediction (ESP) 100%).

Figure 5-3 shows box and whisker plots summarizing the percent difference in exposed sand 

area (%) for each of the alternative scenarios relative to the No Action Alternative. In general, 

results suggest that the daily exposed sand area is not predicted to differ substantially from 

the No Action alternative for each of the alternative scenarios examined here.

Figure 5-2. Median daily exposed sand area (m2) for each of the alternative scenarios. Larger values indicate more river 

sourced sand available for wind transport to high elevation sand deposits to help preserve archaeological sites in situ. 
88 | Page

Figure 5-3. Percent difference in exposed sand area (%) for each of the alternative scenarios relative to the No Action 

Alternative. Positive values indicate more, and negative values indicate less, river sourced sand available for wind transport 

to high elevation sand deposits to help preserve archaeological sites in situ. Percent difference in exposed sand area is 

calculated for individual ESP traces as Percent Change = [(Alternative scenario X - No Action Alternative)/(Alternative 

scenario X + No Action Alternative)/2)] * 100.

Data Availability Statement:

Data generated during this study are published and available (Kasprak and others, 2024).

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

References

Caster, J.J., Dealy, T.P., Andrews, T., Fairley, H., East, A., and Sankey, J.B., 2014, Meteorological 

data for selected sites along the Colorado River corridor, Arizona, 2011-13: U.S. 

Geological Survey Open-File Report 2014-1247, 56 p., 

https://doi.org/10.3133/ofr20141247. 

Cook, T., East, A., Fairley, H., and Sankey, J.B., 2019, Managing sand along the Colorado River 

to protect cultural sites downstream of Glen Canyon Dam: U.S. Geological Survey Fact 

Sheet 2019–3054, 6 p., https://doi.org/10.3133/fs20193054. 

East, A.E., Collins, B.D., Sankey, J.B., Corbett, S.C., Fairley, H.C., and Caster, J.J., 2016, 

Conditions and processes affecting sand resources at archeological sites in the Colorado 

River corridor below Glen Canyon Dam, Arizona: U.S. Geological Survey Professional 

Paper 1825, 104 p., https://doi.org/10.3133/pp1825. 

Kasprak, A., Bransky, N., and Sankey, J.B., 2024, Subaerially exposed river sand and reservoir 

shoreline modelling for Lake Powell releases and changes in Lake Powell and Lake Mead 

storage: U.S. Geological Survey data release, https://doi.org/10.5066/P1VDGSXE.
89 | Page

Kasprak, A., Sankey, J.B., and Butterfield, B.J., 2021, Future regulated flows of the Colorado 

River in Grand Canyon foretell decreased areal extent of sediment and increases in 

riparian vegetation: Environmental Research Letters, v. 16, no. 1, p. 1-15, 

https://doi.org/10.1088/1748-9326/abc9e4. 

Sankey, J.B., Caster, J.J., Kasprak, A., and East, A.E., 2018, The response of source-bordering 

aeolian dunefields to sediment-supply changes 2—Controlled floods of the Colorado 

River in Grand Canyon, Arizona, USA: Aeolian Research, v. 32, p. 154-169, 

https://doi.org/10.1016/j.aeolia.2018.02.004. 

Sankey, J.B., Caster, J., Kasprak, A., and Fairley, H., 2022, The influence of drying on the 

aeolian transport of river-sourced sand: Journal of Geophysical Research: Earth Surface, v. 

127, no. 12, e2022JF006816, p. 1-24, https://doi.org/10.1029/2022JF006816. 
90 | Page

VI. Modeling Recreation Impacts of Glen Canyon Dam Releases

LUCAS BAIR, U.S. GEOLOGICAL SURVEY

Introduction

We estimate the net economic value of recreational angling and whitewater rafting trips in 

Glen and Grand Canyons, respectively, in support of the Interim Guidelines (Supplemental 

Environmental Impact Statement) SEIS and Long-term Experimental and Management Plan 

Supplemental Environmental Impact Statement (LTEMP SEIS). This analysis is similar to the 

recreation economic analysis for the LTEMP EIS (Gaston and others, 2015). Models were 

informed from past survey research and used to project the change in net economic value 

for angling in Glen Canyon and whitewater rafting in Grand Canyon comparing the Action 

and No Action Alternative scenarios. No analysis was completed for reservoir levels, waterbased day use in Glen Canyon, and recreational rafting in the Lower Grand Canyon below 

Diamond Creek. Water-based day use in Glen Canyon has historically not been impacted by 

river flow (Bishop and others, 1987). 

Methods

For Glen Canyon anglers, a flow function (Equation 1) was estimated from angler surveys 

(Bishop and others, 1987). The angler surveys use different river flow scenarios to estimate 

the net economic value of an individual trip, as a function of river flow. The function used to 

estimate net economic value are for conditions where within-day fluctuations are less than 

10,000 cubic feet per second (cfs), consistent with the evaluated alternatives. 

WWWWWW = eeeeeeeeee ee vvvv vv (1985$ pp tt ) 

FF = rr ff GG CC DD ii cc ff pp ss ( ) 

Equation 1 is used to then estimate the economic value for angling in Glen Canyon on an 

individual trip basis. This is accomplished by estimating the average river flow within a 

month for each alternative and hydrology provided (e.g., the 80%, 90% and 100% of 

streamflow levels predicted by the ensemble streamflow prediction (ESP) forecast). The 

average monthly river flow is the mean flow across all hours within a month. The average 

flow is used in Equation 1 to estimate the net economic value of an individual angler on a 

single trip within a month. 
91 | Page

The estimated angler trips per month (D. Rogowski, Arizona Game and Fish Department, 

written communication, February 2023) are then multiplied by the net economic value to 

obtain the aggregate net economic value for angling. 

Net economic value is indexed to 2022 dollars using the consumer pricing index (U.S. Bureau 

of Labor Statistics, 2023). 

For Grand Canyon whitewater rafters, a flow function (Equation 2) was estimated from 

whitewater rafter surveys (Bishop and others, 1987; Neher and others, 2017). The whitewater 

surveys use different river flow scenarios to estimate the net economic value of an individual 

trip, as a function of river flow. The function used to estimate net economic value are for 

conditions where within day fluctuations are less than 10,000 cfs, consistent with the Action 

and No Action Alternatives. In the absence of an updated primary study, the relationship 

between flow and economic value for private whitewater rafting was used for all trips in 

Grand Canyon. 

WWWWWW = eeeeeeeeee ee vvvv vv (2015$ pp tt ) 

FF = rr ff GG CC DD ii 

Equation 2 is used to then estimate the economic value for whitewater rafting in Grand 

Canyon on an individual trip basis. This is accomplished by estimating the average river flow 

within a month for each alternative and hydrology provided (e.g., the 80%, 90% and 100% of 

streamflow levels predicted by the ESP forecast). The average monthly river flow is the mean 

flow across all hours within a month. The average monthly flow is used in Equation 2 to 

estimate the net economic value of an individual whitewater trip within a month. The 

estimated individual whitewater trips per month (National Park Service, 2006) are then 

multiplied by the net economic value to obtain the aggregate net economic value for 

whitewater rafting. Net economic value is indexed to 2022 dollars using the consumer 

pricing index (U.S. Bureau of Labor Statistics, 2015). 

Results

Results are the net economic value in 2022 nominal dollars for recreational angling in Glen 

Canyon and whitewater rafting on Grand Canyon by month for each alternative and 

hydrologic traces in the LTEMP SEIS and Interim Guidelines SEIS (Bair, 2024). We illustrate 

results for the estimated net changes in economic value across months for each ESP when 

comparing the proposed Action and No Action Alternatives in the Interim Guidelines SEIS. 
92 | Page

Figures 6.1 and 6.2 illustrate this for angler and whitewater trips, respectively, and highlight 

the 10th, 50th, and 90th percentile. The data publication that accompanies this chapter

includes the monthly aggregate net economic values for angling and whitewater trips for 

each alternative and hydrology provided for each alternative (Bair, 2024).

Figure 6.1. Difference in monthly aggregate angling trip net economic value comparing the proposed action and the No 

Action Alternatives in the Interim Guidelines SEIS. The 10th, 50th and 90th percentiles are reported for the 100% ESP 

scenario (top panel), 90% ESP scenario (middle panel) and 80% ESP scenario (bottom panel).
93 | Page

Figure 6.2. Difference in monthly aggregate whitewater trip net economic value comparing the proposed action and the No 

Action Alternatives in the Interim Guidelines SEIS. The 10th, 50th and 90th percentile are reported for the 100% ESP 

scenario (top panel), 90% ESP scenario (middle panel) and 80% ESP scenario (bottom panel).

Data Availability Statement:

Data generated during this study are published and available (Bair, 2024).

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

References

Bair, L.S., 2024, Predicted recreation economics under different management scenarios for 

Lake Powell releases: U.S. Geological Survey data release, 

https://doi.org/10.5066/P13Q8ZVC. 

Bishop, R.C., Boyle, K.J., Welsh, M.P., Baumgartner, R.M., and Rathbun, P.R., 1987, Glen 

Canyon Dam releases and downstream recreation—An analysis of user preferences and 
94 | Page

economic values: Madison, Wisc., Huberlein-Baumgartner Research Service, submitted to 

Bureau of Reclamation, National Technical Information Service, Springfield, VA, NTIS 

report PB88-182546/AS, Glen Canyon Environmental Studies report no. GCES/27/87, 188 

p.

Gaston, T.D., Harpman, D.A., Platt, J., and Piper, S., 2015, Recreation economic analysis for the 

Long-Term Experimental and Management Plan Environmental Impact Statement: 

Bureau of Reclamation, Upper Colorado River Basin, Technical report EC-2014-03, August 

2015.

National Park Service, 2006, Colorado River management plan: U.S. Department of the 

Interior, National Park Service, Grand Canyon National Park, 35 p., 

http://www.nps.gov/grca/learn/management/upload/CRMPIF_s.pdf. 

Neher, C., Duffield, J., Bair, L.S., Patterson, D., and Neher, K., 2017, Testing the limits of 

temporal stability—Willingness to pay values among Grand Canyon whitewater boaters 

across decades: Water Resource Research, v. 53, no. 12, p. 10108-10120, 

https://doi.org/10.1002/2017WR020729. 

U.S. Bureau of Labor Statistics, 2015, Consumer price index for all urban consumers—All 

items in U.S. city average [CPIAUCSL]: St. Louis, MO, retrieved from Federal Reserve Bank 

of St. Louis (FRED), February 8, 2023, https://fred.stlouisfed.org/series/CPIAUCSL.
95 | Page

VII. Modeling Impacts of Different Reservoir Management Scenarios on 

Riparian Plant Communities and Vegetation Resources 

BRADLEY BUTTERFIELD, NORTHERN ARIZONA UNIVERSITY, EMILY PALMQUIST, U.S. GEOLOGICAL SURVEY

Background and Methods 

Native riparian vegetation is a key aspect of functioning riparian ecosystems. Riparian plant 

communities increase regional biodiversity (Sabo and others, 2005), harbor unique and 

culturally important species (Roberts and others, 1995; Fairley, 2005), support wildlife 

communities (Holmes and others, 2005; Ralston, 2005), alter sediment fluvial and aeolian 

sediment transport (Butterfield and others, 2020; Sankey and others, 2023), and provide 

shade and wind breaks for recreationists (Stewart and others, 2003). Along the Colorado 

River downstream from Glen Canyon Dam (GCD), expansion of woody riparian species has 

reduced already limited camping area (Hadley and others, 2018; Hazel and others, 2022). 

The Long-term Experimental and Management Plan (LTEMP) goal for riparian plant 

communities is to support riparian vegetation that is diverse and primarily composed of 

native species (U.S. Department of Interior, 2016). While not included in the riparian 

vegetation goal, concerns over further reduction of camping areas due to plant 

encroachment is undesirable, so total cover of vegetation is a key consideration in this 

ecosystem (U.S. Department of Interior, 2016). 

Under current dam operations, native plant species richness and cover are greater than 

nonnative species richness and cover, but woody plant encroachment continues (Durning 

and others, 2021; Palmquist and others, 2023).

Riparian plant communities are structured by both river flow patterns and air temperature 

(Tabbachi and others, 1998; Palmquist and others, 2018a; Butterfield and others, 2018). 

Across broad regions, air temperature determines which floristic groups occur along a river 

(Tabbachi and others, 1998; Palmquist and others, 2018a). Within climate gradients, the 

magnitude, timing, duration, and frequency of high and low flow patterns determines which 

plant species establish and grow (Poff and others, 1997). Air temperature, humidity, and 

precipitation can mediate plant water needs and thus plant responses to flow patterns, 

resulting in interactive effects between climate and flow patterns on riparian plant responses 

(Butterfield and others, 2018; Butterfield and others, 2023; Moran and others, 2023).

Along the Colorado River ecosystem (CRe), a steep temperature gradient of approximately 5

°C drives broader floristic patterns. 
96 | Page

Four distinct floristic groups occur longitudinally and are associated with Glen Canyon (River 

Mile [RM] -15.5 – 0), Marble Canyon (RM 0-61), eastern Grand Canyon (RM 61-160), and 

western Grand Canyon (RM 160-240) (Palmquist and others, 2018a; Palmquist and others, 

2023). 

Within those floristic groups, plant communities are structured by hydrological zones related 

to dam operations (Palmquist and others, 2023). Communities differ among the areas 

inundated by daily river fluctuations (inundated by flows up to 25,000 cubic feet per second

[cfs]), High Flow Experiments (HFEs, between 25,000 and 45,000 cfs), and exceptional and 

rare releases over 45,000 cfs (Palmquist and others, 2023). There are indications that the 

hotter temperatures of eastern and western Grand Canyon modify preferred hydrological 

conditions for some plant species, particularly on large sandbars (Butterfield and others, 

2018). The prevalence of native species in this system is related to the timing of both high 

and low flow periods, where high flows during the summer maximize native plant richness 

and low flows in the winter are associated with greater proportions of native species relative 

to nonnative species (Butterfield and others, 2023). Thus, air temperature, the minimum 

discharge of the daily fluctuations during the lowest streamflow period, the peak discharge 

of daily fluctuations during the highest streamflow period, and the maximum discharge of 

the year (larger for HFE years) are key variables shaping riparian plant communities in the 

CRe. 

Species and Training Data

To estimate the predicted response of riparian plant communities to operational alternatives, 

we conducted hydrological niche modeling of 47 common riparian plant species (33 native, 

14 nonnative) growing on sandbars in the CRe (Table 7-1; Butterfield and Palmquist, 2024). 

The modeling methods were the same for the Interim Guidelines SEIS and LTEMP SEIS but 

differed in the modifications to the dam operations (see Chapter I) and resolution of the 

hydrological traces used (described below). The general framework for these analyses was 

developed in Butterfield and others (2018) and Kasprak and others (2021). Habitat suitability 

was estimated for the No Action scenario and all alternatives under consideration using 

Maximum Entropy (Maxent) algorithms (Phillips and others, 2006). Riparian plant community 

data from 44 long-term monitoring sandbar sites collected in 2014-2019 (Palmquist and 

others, 2018b; Palmquist and others, 2022), coupled with the digital elevation models (DEMs) 

of those sandbars (Grams and others, 2020), were used to train the models. These sandbars 

are all between Lees Ferry, AZ and the confluence of the Colorado River and Diamond Creek, 

AZ and represent a somewhat different plant community than is supported by other 

geomorphic types (Hazel and others, 2022; Palmquist and others, 2023). 
97 | Page

The plant communities represented by these analyses, then, are the floristic communities of 

Marble Canyon, eastern Grand Canyon, and western Grand Canyon on large sandbars of 

considerable recreational and ecological value (Hazel and others, 2022; Palmquist and 

others, 2022; Palmquist and others, 2023). Models and results were developed for each 

floristic region independently.

Table 7-1. List of species used for hydrological niche modeling.
98 | Page

Environmental Variables

Three hydrological variables, minimum temperature of the coldest month (January), mean 

annual precipitation, and solar insolation, were used as predictor variables in the habitat 

suitability models for each plant species. 
99 | Page

Approximately 1km resolution average climate data were acquired from WorldClim version 2 

(Fick and Hijmans, 2017). This resolution is too coarse to account for the steep elevation 

gradients within the CRe and therefore produce inaccurate climate estimates along the 

Colorado River. Temperature exhibits a strong, linear dependence on elevation, so we used 

the 10m USGS National Elevation Database (NED; Gesch and others, 2018) to statistically 

downscale air temperature. Precipitation is less dependent on elevation but exhibits strong 

spatial autocorrelation (Caster and Sankey, 2016; Palmquist and others, 2018a); thus, we 

used a spatial smoothing function to downscale precipitation (‘fields’ package, Nychka and 

others, 2021). The 10m NED was also used to estimate insolation using the ‘terra’ package in 

R (R Core Team, 2022; Hijmans, 2023).

For the Interim Guidelines SEIS, monthly traces provided by Reclamation (see Chapter I), 

including multiple traces with 80%, 90% and 100% of the ensemble streamflow predictions 

(ESPs), were used. These data were downscaled to 15-minute resolution by assuming 

maximum daily fluctuations by month outlined in LTEMP (U.S Department of Interior, 2016). 

The No Action Alternative was modeled without HFEs. The Proposed Action Alternative

scenario was modeled two ways, one without HFEs and one with a 96 hour, ~40,000 cfs HFE. 

For the LTEMP SEIS, 30 hourly traces were used to represent each hydrological scenario (see 

Chapter II). 

For both the Interim Guidelines SEIS and the LTEMP SEIS modeling, the elevation of each 

plant community monitoring plot above river stage was calculated at 15-minute intervals 

during the year prior to data collection. These elevations were used as estimates of depth to 

groundwater and inundation experienced by the plants in those plots. The hydrological 

variables were extracted from the distribution of elevations above river stage: the 95th

percentile, 5th percentile, and minimum elevation. These variables reflect the trough of daily 

fluctuations during the lowest streamflow month of the year, the peak of daily fluctuations 

during the highest streamflow month of the year, and the peak of the HFE.

Model Predictions

The Maxent models for each species were spatially projected onto each sandbar using the 

2019 DEMs and each of the traces under all scenarios. The results of the alternative flow 

scenarios were compared against the No Action scenario, such that results indicate changes 

in suitable habitat relative to No Action. 

For each trace, the habitat suitability maps for each species were compared between the No 

Action and alternative scenarios to identify the amount of predicted habitat for each species 

that (1) switched from suitable to unsuitable or (2) switched from unsuitable to suitable, and 

identified the original amount of suitable habitat. 
100 | Page

Habitat suitability thresholds varied among species and optimized the balance between 

errors of omission and commission based on metrics of model fit (Butterfield and others, 

2023). These data were used to quantify the change in predicted habitat suitability between 

the No Action and alternative scenarios. 

Rather than elaborating on predicted responses for all species, regions, and traces, we 

focused on three metrics of interest to stakeholders: species richness, proportion of 

vegetation cover that is native, and total vegetation cover. Analyses were subdivided into 

groups of traces with similar lake elevations: 0-10%; 10-25%; 25-50%; and 50-100%. 

Both the absolute changes in habitat (m2

) and relative changes (%) are provided for context. 

Since the 44 long-term monitoring sandbars are a subset of sandbars in the CRe, we scaled 

up the absolute changes in habitat from the 44 long-term monitoring sandbar sites to the 

total estimated area changed. Total vegetated area in 2024 estimated from the models for 

the 44 monitoring sites (196,384 m2

) was approximately one order of magnitude smaller 

than the total vegetated sandbar area estimated from remote sensing (~1,400,000 – 

2,300,000 m2

, depending on the definition of the riparian zone) by Durning and others

(2021). Thus, all area estimates presented here are multiplied by 10 as an approximate 

attempt to scale up to sandbars across the entire river system. This is still an underestimate, 

given that sandbars only make up approximately 1/3 of the riparian zone in the CRe 

(Durning and others, 2021).

Model Limitations

This modeling approach can address shifts in the highest flows and lowest flows (for 

example, no HFEs versus having HFEs), but does not account for flow frequency or timing. 

Thus, changes in plant species habitat suitability due to alterations in the frequency, timing, 

or duration of HFEs would not be reflected by these analyses. 

Plant composition and trends differ on sandbars relative to other geomorphic surfaces (like 

debris fans), so these analyses represent changes to a subset of the plant communities in the 

CRe (Palmquist and others, 2023).

These model results are for changes in habitat suitability, not plant occurrence or cover. Loss 

of habitat suitability can result in plant death but gain of habitat suitability does not 

guarantee plant colonization and growth. Further, we present absolute changes in habitat 

suitability, but this does not account for the location of that habitat. 

For example, an equivalent amount of suitable habitat may be available in both scenarios, 

but that suitable habitat may not be located in the same place. If this were to happen in 

reality, plant death would occur in the newly unsuitable habitat and there may or may not be 

recolonization in the newly suitable habitat.
101 | Page

Results of Analyzing Interim Guideline SEIS Alternatives

The probabilities of HFEs under the No Action and Alternative three scenarios are nearly 

identical (see Chapter III). Thus, we compared No Action to the Action Alternative without 

HFEs, and No Action to Action Alternative with HFEs. Either with or without HFEs, the small 

differences in monthly volumes between No Action and Action Alternative had little to no 

effect on the hydrological variables that we have demonstrated as driving habitat suitability 

for riparian plant species (Butterfield and others, 2018). Thus, the majority of traces resulted 

in zero predicted difference in vegetation metrics between the No Action and Action 

Alternative scenarios, either with or without HFEs (Tables 7-2 through 7-4). The largest 

predicted effect of Action Alternative was a 1.7% change in proportion of native cover in one 

region of the CRe, and the largest effects were observed under the driest 10% of traces. 

Overall, the predicted effects of the Action Alternative versus No Action on riparian 

vegetation metrics are negligible. Model results are available in Butterfield and Palmquist 

(2024).
102 | Page

Table 7-2. Predicted net change in the proportion of vegetation native cover for the Interim Guidelines 

SEIS, calculated as the difference between cumulative habitat gains and losses comparing the Action 

Alternative to the No Action scenario, with or without HFEs, across all sandbars and years 2025-2027. 

Suitable habitat totals under the No Action scenario in 2024 were used to calculate the relative (%) change 

values. Different trace classes represent lowest (0-10%) to highest (50-100%) lake level quantiles. Marble 

Canyon – RM 0-61, Eastern Grand Canyon – RM 61-160, Western Grand Canyon – RM 160-226. 
103 | Page

Table 7-3. Predicted net change in vegetation species richness for the Interim Guidelines SEIS, calculated 

as the difference between species gains and losses comparing the Action Alternative to the No Action 

scenario, with or without HFEs, across all sandbars and years 2025-2027. Suitable habitat totals under the 

No Action scenario in 2024 were used to calculate the relative (%) change values. Different trace classes 

represent lowest (0-10%) to highest (50-100%) lake level quantiles. Marble Canyon – RM 0-61, Eastern 

Grand Canyon – RM 61-160, Western Grand Canyon – RM 160-226.
104 | Page

Table 7-4. Predicted net change in suitable habitat for vegetation for the Interim Guidelines SEIS, 

calculated as the difference between cumulative habitat gains and losses comparing the Action Alternative 

to the No Action scenario, with or without HFEs, across all sandbars and years 2025-2027. Suitable habitat 

totals under the No Action scenario in 2024 were used to calculate the relative (%) change values. Different 

trace classes represent lowest (0-10%) to highest (50-100%) lake level quantiles. Marble Canyon – RM 0-

61, Eastern Grand Canyon – RM 61-160, Western Grand Canyon – RM 160-226.
105 | Page

Results of Analyzing LTEMP SEIS Alternatives 

The only alternative that resulted in substantial differences in plant community habitat suitability 

when compared to the No Action scenario was the Non-Bypass Alternative. While results varied 

quantitatively among regions of the CRe, and among traces, overall the proportion of native-tonon-native cover increased (Figure 7-1, Table 7-5), species richness increased (Figure 7-2, Table 

7-6), and total vegetation cover decreased (Figure 7-3, Table 7-7) relative to the No Action 

scenario. 

Impacts of the other LTEMP SEIS alternatives (those including bypass options) on vegetation 

resources did not differ significantly from the No Action Alternative, with nearly all changes 

below 1%. This is due to the minor changes in monthly and HFE volumes associated with these 

alternatives. Model results are available in Butterfield and Palmquist (2024).
106 | Page

Figure 7-1. Predicted net change in proportion native cover, calculated as the difference between cumulative habitat gains 

and losses comparing each alternative to the No Action scenario, represented as percent change. Marble Canyon – RM 0-

61, Eastern Grand Canyon – RM 61-160, Western Grand Canyon – RM 160-226. Acronyms are: NW – new sediment 

accounting window, CM_15 – cool mix targeting RM 15, FS_CM_15 – cool mix with flow spike targeting RM 15, CM_61 – 

cool mix targeting RM 61, the confluence with Little Colorado River, FS_CM_61 – cool mix with flow spike targeting RM 61, 

CS_15 – cold shock targeting RM 15, FS_CS_15 – cold shock with flow spikes targeting RM 15, CS_61 – cold shock 

targeting RM 61, FS_CS_61 – cold shock with flow spikes targeting RM 61.
107 | Page

Figure 7-2. Predicted net change in species richness, calculated as the difference between cumulative habitat gains and 

losses comparing each alternative to the No Action scenario, represented as percent change. Marble Canyon – RM 0-61, 

Eastern Grand Canyon – RM 61-160, Western Grand Canyon – RM 160-226. Acronyms are: NW – new sediment 

accounting window, CM_15 – cool mix targeting RM 15, FS_CM_15 – cool mix with flow spike targeting RM 15, CM_61 – 

cool mix targeting RM 61, the confluence with Little Colorado River, FS_CM_61 – cool mix with flow spike targeting RM 61, 

CS_15 – cold shock targeting RM 15, FS_CS_15 – cold shock with flow spikes targeting RM 15, CS_61 – cold shock 

targeting RM 61, FS_CS_61 – cold shock with flow spikes targeting RM 61.
108 | Page

Figure 7-3. Predicted net change in total vegetation cover, calculated as the difference between cumulative habitat gains 

and losses comparing each alternative to the No Action scenario, represented as percent change. Marble Canyon – RM 0-

61, Eastern Grand Canyon – RM 61-160, Western Grand Canyon – RM 160-226. Acronyms are: NW – new sediment 

accounting window, CM_15 – cool mix targeting RM 15, FS_CM_15 – cool mix with flow spike targeting RM 15, CM_61 – 

cool mix targeting RM 61, the confluence with Little Colorado River, FS_CM_61 – cool mix with flow spike targeting RM 61, 

CS_15 – cold shock targeting RM 15, FS_CS_15 – cold shock with flow spikes targeting RM 15, CS_61 – cold shock 

targeting RM 61, FS_CS_61 – cold shock with flow spikes targeting RM 61
109 | Page

Table 7-5. Predicted net change in the proportion of vegetation native cover for the LTEMP SEIS, calculated as the difference between cumulative 

habitat gains and losses comparing each alternative to the No Action scenario, across all sandbars and years 2025-2027. Suitable habitat totals 

under the No Action scenario in 2024 were used to calculate the relative (%) change values. Different trace classes represent lowest (0-10%) to 

highest (50-100%) lake level quantiles. Marble Canyon – RM 0-61, Eastern Grand Canyon – RM 61-160, Western Grand Canyon – RM 160-226. 

Acronyms are: CM_15 – cool mix targeting RM 15, CM_61 – cool mix targeting RM 61, the confluence with Little Colorado River, CS_15 – cold 

shock targeting RM 15, CS_61 – cold shock targeting RM 61, FS_CM_15 – cool mix with flow spike targeting RM 15, FS_CM_61 – cool mix with 

flow spike targeting RM 61, FS_CS_15 – cold shock with flow spikes targeting RM 15, FS_CS_61 – cold shock with flow spikes targeting RM 61, 

New Accounting Window – new sediment accounting window.
110 | Page
111 | Page
112 | Page
113 | Page

Table 7-6. Predicted net change in vegetation species richness for the LTEMP SEIS, calculated as the difference between cumulative habitat gains 

and losses comparing each alternative to the No Action scenario, across all sandbars and years 2025-2027. Suitable habitat totals under the No 

Action scenario in 2024 were used to calculate the relative (%) change values. Different trace classes represent lowest (0-10%) to highest (50-100%) 

lake level quantiles. Marble Canyon – RM 0-61, Eastern Grand Canyon – RM 61-160, Western Grand Canyon – RM 160-226. Acronyms are: CM_15 

– cool mix targeting RM 15, CM_61 – cool mix targeting RM 61, the confluence with Little Colorado River, CS_15 – cold shock targeting RM 15, 

CS_61 – cold shock targeting RM 61, FS_CM_15 – cool mix with flow spike targeting RM 15, FS_CM_61 – cool mix with flow spike targeting RM 61, 

FS_CS_15 – cold shock with flow spikes targeting RM 15, FS_CS_61 – cold shock with flow spikes targeting RM 61, New Accounting Window – new 

sediment accounting window.
114 | Page
115 | Page
116 | Page
117 | Page

Table 7-7. Predicted net change in total vegetation cover for the LTEMP SEIS, calculated as the difference between cumulative habitat gains and 

losses comparing each alternative to the No Action scenario, across all sandbars and years 2025-2027. Suitable habitat totals under the No Action 

scenario in 2024 were used to calculate the relative (%) change values. Different trace classes represent lowest (0-10%) to highest (50-100%) lake 

level quantiles. Marble Canyon – RM 0-61, Eastern Grand Canyon – RM 61-160, Western Grand Canyon – RM 160-226. Acronyms are: CM_15 – 

cool mix targeting RM 15, CM_61 – cool mix targeting RM 61, the confluence with Little Colorado River, CS_15 – cold shock targeting RM 15, 

CS_61 – cold shock targeting RM 61, FS_CM_15 – cool mix with flow spike targeting RM 15, FS_CM_61 – cool mix with flow spike targeting RM 61, 

FS_CS_15 – cold shock with flow spikes targeting RM 15, FS_CS_61 – cold shock with flow spikes targeting RM 61, New Accounting Window – new 

sediment accounting window.
118 | Page
119 | Page
120 | Page
121 | Page

Data Availability Statement:

Data generated during this study are published and available (Butterfield and Palmquist, 

2024).

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

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dynamics along the regulated Colorado River in Grand Canyon: River Research and 

Applications, v. 36, no. 3, p. 410-421, https://doi.org/10.1002/rra.3589. 

Butterfield, B.J., Palmquist, E.C., and Ralston, B.E., 2018, Hydrological regime and climate 

interactively shape riparian vegetation composition along the Colorado River, Grand 

Canyon: Applied Vegetation Science, v. 21, no. 4, p. 572-583, 

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River in Grand Canyon, Arizona under different management scenarios for Lake Powell 

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Butterfield, B.J., Palmquist, E.C., and Yackulic, C.B., 2023, The hydroclimate niche—A tool for 

predicting and managing riparian plant community responses to streamflow seasonality: 

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Durning, L.E., Sankey, J.B., Yackulic, C.B., Grams, P.E., Butterfield, B.J., and Sankey, T.T., 2021, 

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encroachment—Remote sensing of 360 km of the Colorado River in Grand Canyon: 

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125 | Page

VIII. Modeling Impacts of Different Reservoir Management Scenarios on 

Dissolved Oxygen Concentrations in Glen Canyon Dam Releases

BRIDGET DEEMER, U.S. GEOLOGICAL SURVEY, CHARLES YACKULIC, U.S. GEOLOGICAL SURVEY,

BRYCE MIHALEVICH, BUREAU OF RECLAMATION

Background and Methods 

Metalimnion low dissolved oxygen (DO) zones can develop in reservoirs when large inflows 

(such as occur with floods) create an interflow (density current) from which oxygen is 

consumed faster than it is re-introduced from other water layers (Thornton and others, 

1991). In Lake Powell, these metalimnion low DO zones develop each year, generally 

reaching their maximum development in September (Johnson and Page, 1981). Low oxygen 

is often most pronounced in the heads of side bays but is advected into the metalimnion 

(Johnson and Page, 1981). Ongoing analysis of historic profiles suggests that the magnitude 

of these low oxygen events increases when the reservoir inflow is low and spring snowmelt 

inflows are large (Deemer, 2023). These conditions can mobilize deltaic sediments, carrying 

limiting nutrients and organic carbon into the reservoir and promoting high rates of water 

column decomposition (Deemer, 2023). These low DO zones usually reach Glen Canyon Dam

(GCD) in September or early October, although under lower water levels and large spring 

inflows such as were observed in 2023, low DO concentrations arrived at the forebay in July 

(DO below 5 mg/L persisted from 10 July to 3 November 2023, a total of 116 days; (U.S. 

Geological Survey, 2024)). A previous low DO event (in 2005) coincided with much lower 

recruitment and growth in the Lees Ferry Rainbow Trout fishery (Korman and others, 2012), 

so the low DO observed in Glen Canyon is of concern. 

Low DO levels can pose both chronic and acute threats that are of concern to the Rainbow 

Trout fishery below GCD. Specifically, dissolved oxygen DO concentrations below 5.2 mg/L 

often have growth and reproductive effects for cold water fish and concentrations below 3.8 

mg/L are estimated to cause ≥ 50% growth impairment and mark the onset of fish mortality 

events in rainbow trout (Saari and others, 2018). This document describes DO modeling that 

was conducted to estimate late summer and early fall dissolved oxygen concentrations in 

GCD releases under different management scenarios. We report the fraction of traces that 

are above or below the 5.2 mg/L DO threshold, which likely represents an upper end of 

concentrations that begin to be stressful for trout and aquatic invertebrates.
126 | Page

Modeling Methods and Results for the Interim Guidelines SEIS 

We used a long-term record of DO profiles from the reservoir forebay (site name LPCR0024; 

Deemer and others, 2023; Andrews and Deemer, 2023) to model and predict DO

concentration within a 10 m envelope of the penstock depth for 30 historical reconstructions

of hydrology at either 80%, 90%, or 100% of ensemble streamflow prediction (ESP), for a 

total of 90 hydrological traces under each of two alternatives (No Action and Proposed 

Action), generated as part of the Interim Guidelines Supplemental Environmental Impact 

Statement (SEIS). 

While the Lake Powell CE-QUAL-W2 does have a DO module (Williams, 2007), recent 

observations suggest the need for its recalibration to improve performance under low water 

levels and with the aging of the reservoir. 

We used a total of 132 water quality profiles from the months of August, September, and 

October (1967-2022) to calculate annual mean late-summer/early fall DO concentrations in 

six 10-m layers of the Lake Powell water column that represent the depths from which water 

could be drawn through the penstocks under the various SEIS hydrological traces being 

examined here (6 to <16 m, 16 to <26 m, 26 to <36 m, 36 to <46 m, 46 to <56 m, and 56 to 

<66 m). Water quality profile data are available as part of a ScienceBase data release 

(Andrews and Deemer, 2023). We then built linear models to predict these water-layerspecific DO concentrations as a function of minimum spring reservoir elevation in that year, 

the volume of the spring inflow (calculated as the inflow from April-July), and the years since 

the reservoir was filled. Modeling was done using the “lm” function in R version 4.3.0 (R Core 

Team, 2019) and models of 10m depth bins had the structure: DO ~ Inflow + Minimum 

Elevation * Age. This model was based on the best model for predicting whole-metalimnion 

mean DO in the late summer and fall (Deemer, 2023). The models were then used to predict 

DO based on spring inflow volumes and minimum spring reservoir elevations in the 

hydrologic traces generated from each scenario. Predictions were made from the models in 

R using the predict.lm function in the stats package of base R (R Core Team, 2019). 

In cases where the reservoir elevation was <3490 feet, we predicted a high-end 8 mg/L DO

concentration would pass downstream. This assumption was based on the aeration that has 

been observed when water is spilled through the bypass tubes (Hueftle and Stevens, 2001; 

Vernieu, 2010). Bypass releases of 15,000 ft3/s during the 2008 high flow experiment 

resulted in supersaturated DO concentrations (12.6 milligrams per liter [mg/L]; Vernieu, 

2010) below the dam, so we consider 8 mg/L DO a conservative estimate for spills under 

lower lake elevations (with bypass spill rates of 14,620 cubic feet per second (cfs) at lake 

elevations of 3490 feet, and spill rates dropping at lower elevations). 
127 | Page

Results of Analyzing Interim Guideline SEIS Alternatives 

Across both the No Action and Proposed Action examined in this SEIS, 79% of the year by 

trace combinations are predicted to have mean DO concentrations <5 mg/L in the late 

summer and early fall (709 out of 900; Figure 8-1; Deemer and others, 2024). Tailwater low 

DO events (<5 mg/L) were only very slightly more probable under the Proposed Action 

Alternative (80% of years) than under the No Action Alternative (78% of years). Across both 

the No Action and Proposed Action Alternatives, reduced hydrological inputs had higher 

probabilities of low DO events (83% and 90% for No Action and Proposed Action 80% 

hydrology respectively) than traces with 100% of historical hydrology (72% and 70% for No 

Action and Proposed Action respectively). This may be due to the lower Lake Powell 

elevations that result from reduced hydrology (but that are not low enough to trigger 

bypass releases; Figure 8-2). Model outputs from this exercise are available in Deemer and 

others (2024).

Modeling Methods and Results for the LTEMP SEIS 

Similar to the Interim Guidelines SEIS, we used a long-term record of DO profiles from the 

reservoir forebay (site name LPCR0024; Deemer and others, 2023) to model and predict DO

concentration within a 10 m envelope of the penstock depth, this time for 300 traces 

generated as part of the LTEMP SEIS. None of the LTEMP hydrologic traces resulted in 

minimum spring reservoir elevations <3490 feet, so the only bypass releases predicted were 

those associated with HFEs, flow spikes, cool mix, or cold shock treatments. For days when 

any amount of bypass spill was used, we assigned a daily average dissolved oxygen 

concentration of 8 mg/L. We then calculated a weighted average DO concentration (AugustOctober) by assigning modeled penstock DO concentrations for days without bypass and 8 

mg/L DO for days with bypass. 

Methods Caveats

Future work to constrain the relationship between bypass spill rate and re-aeration would 

help to more accurately model outflow DO concentrations resulting from bypass spill. This 

modeling exercise did not attempt to characterize monsoon-driven low DO events. 

Particularly low oxygen zones have been observed following monsoon storms (e.g., profile 

data from 2021; Andrews and Deemer, 2023), suggesting that monsoons may be another 

driver of low DO events. 

Results of Analyzing LTEMP SEIS Alternatives 

Across all the alternatives examined in this SEIS, 74% of the year by trace combinations are 

predicted to have mean DO concentrations <5 mg/L in the late summer and early fall (1007 

out of 1500). 
128 | Page

The likelihood of low DO releases (with summertime mean <5.2 mg/L) did not differ 

between the No Action, New HFE window only and Non-Bypass (73% of years for both 

Alternatives; Figure 8-3). The Cold Shock Alternatives (both with and without flow spikes) 

also led to similar likelihoods of low DO events (71-73% of years; Figure 8-3). Low DO

releases were less probable under the Cool Mix Alternative scenarios (57-62% of years; 

Figure 8-3). For the Cool Mix Alternative, the scenario that targets River Mile (RM) 61 (57% of 

years) led to fewer years with average DO <5.2 mg/L than the scenario that targets RM 15 

(61% of years). This finding was very similar for the Cool Mix with Flow Spikes Alternative

scenarios. Differences in the target scenarios are more pronounced during some years than 

others (Figure 8-4). Model outputs from this exercise are available in Deemer and others 

(2024).

Figure 8-1. Predictions of mean August-October dissolved oxygen (DO) concentrations in GCD outflows for each prediction 

year under Interim Guidelines SEIS Proposed Action (pink, left-most of pairs) and No Action (blue, right-most of pairs) 

Alternatives. Each point represents one year for a total of 90 points per box whisker (30 historical reconstructions x 100%, 

90%, and 80%). The dashed blue line demarcates 8 mg/L DO which was the modeled concentration for traces that resulted 

in bypass spill. The dashed red line demarcates 5 mg/L, a threshold below which DO concentrations are stressful to trout. 
129 | Page

Figure 8-2. Differences in the number of years within each Interim Guidelines SEIS trace that are likely to have a low 

dissolved oxygen (DO) event as a function of the maximum spring inflow (in millions of acre feet) in the trace. A value of 

zero indicates no difference between the alternatives, whereas a negative value indicates that the No Action Alternative led 

to more years with DO dropping below 5mg/L than the Proposed Action (represented as –1 on the y-axis). 
130 | Page

Figure 8-3. Mean August-October dissolved oxygen (DO) concentrations in Glen Canyon Dam outflows across the entire 

prediction time frame under seven LTEMP SEIS alternatives (and associated river mile target scenarios). Red dots indicate 

the mean release concentration across years and traces. Box plots were constructed with data points representing one year 

for a given hydrologic trace for a total of 150 points per box whisker (30 historical reconstructions and five prediction years). 

The dashed blue line demarcates 8 mg/L DO which was the modeled concentration for days where any amount of bypass 

spill was implemented. The dashed red line demarcates 5.2 mg/L, a threshold below which DO concentrations are stressful 

to trout. The percentage of trace/year combinations where average DO is below 5.2 mg/L is annotated above each 

scenario.
131 | Page

Figure 8-4. Predictions of mean August-October dissolved oxygen (DO) concentrations in Glen Canyon Dam outflows for 

prediction years 2024-2026 under the LTEMP SEIS alternatives and scenarios (unique colors for each alternative). Each 

point represents one year for a total of 30 points per box whisker (30 historical reconstructions). The dashed blue line 

demarcates 8 mg/L DO which was the modeled concentration for days where any amount of bypass spill was implemented. 

The dashed red line demarcates 5.2 mg/L, a threshold below which DO concentrations are stressful to trout. Note that the 

range of possible DO conditions is greater each year due to uncertainty in reservoir surface elevation among traces. 

Data Availability Statement:

Data generated during this study are published and available (Andrews and Deemer, 2023; 

Deemer and others, 2024). Specifically, the data that we used in our modeling exercises is 

available in the “Profiles” .csv file in the Andrews and Deemer data release (2023). The output 

from the modeling exercises described in this document is available in Deemer and others 

(2024).

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.
132 | Page

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