RECLAMATION 


Managing Water in the West 


Colorado River Basin 

Water Supply and Demand Study 

Technical R epor t B-Water Supp ly Assessmen t 

U.S. Department of the lnlerior 

Bureau of Reclamation December 2012 
Mission Statements

The U.S. Department of the Interior protects America’s natural 

resources and heritage, honors our cultures and tribal communities, 

and supplies the energy to power our future. 

The mission of the Bureau of Reclamation is to manage, develop, 

and protect water and related resources in an environmentally and 

economically sound manner in the interest of the American public.
U.S. Department of the Interior

Bureau of Reclamation December 2012

Colorado River Basin 

Water Supply and Demand Study

Technical Report B — Water 

Supply Assessment 

December 2012 B-iii

Contents 

Page

1.0 Introduction .............................................................................................................. B-1

2.0 Approach to Water Supply Scenario Development .............................................. B-2

2.1 Identify Characteristics within each Critical Uncertainty .............................. B-4

2.2 Water Supply Scenarios ................................................................................. B-4

3.0 Summary of the Water Supply Assessment Approach ........................................ B-5

3.1 Tools and Methods ......................................................................................... B-5

3.2 Sources of Data and Information ................................................................... B-8

3.2.1 Literature Review............................................................................... B-8

3.2.2 Data Sources ...................................................................................... B-9

4.0 Historical Supply ...................................................................................................... B-10

4.1 Methods.......................................................................................................... B-10

4.2 Results ............................................................................................................ B-12

4.2.1 Climate ............................................................................................... B-12

4.2.2 Hydrologic Processes ......................................................................... B-17

4.2.3 Climate Teleconnections .................................................................... B-20

4.2.4 Streamflow ......................................................................................... B-22

4.3 Paleo Reconstruction of Streamflow ............................................................. B-28

5.0 Future Supply under the Observed Resampled Scenario .................................... B-30

5.1 Methods.......................................................................................................... B-30

5.2 Results ............................................................................................................ B-31

6.0 Future Supply under the Paleo Resampled Scenario ........................................... B-35

6.1 Methods.......................................................................................................... B-35

6.2 Results ............................................................................................................ B-35

7.0 Future Supply under Paleo Conditioned Scenario ............................................... B-39

7.1 Methods.......................................................................................................... B-39

7.2 Results ............................................................................................................ B-40

8.0 Future Supply under the Downscaled GCM Projected Scenario ........................ B-43

8.1 Methods.......................................................................................................... B-43

8.1.1 Emission Scenarios ............................................................................ B-45

8.1.2 GCMs ................................................................................................. B-46

8.1.3 Bias Correction and Spatial Downscaling ......................................... B-47

8.1.4 Daily Weather Generation (Temporal Dissaggregation) ................... B-47

8.1.5 Hydrologic Modeling ......................................................................... B-48

8.1.6 Systems Modeling .............................................................................. B-49

8.2 Uncertainty ..................................................................................................... B-49

8.3 Results ............................................................................................................ B-50

8.3.1 Climate ............................................................................................... B-51

8.3.2 Summary of Changes in Climate ....................................................... B-56
Colorado River Basin 

Water Supply and Demand Study 

B-iv December 2012 

Page 

8.3.3 Hydrologic Processes ......................................................................... B-57

8.3.4 Summary of Changes in Hydrologic Processes ................................. B-62

8.3.5 Climate Teleconnections .................................................................... B-64

8.3.6 Streamflow ......................................................................................... B-65

9.0 Comparison of Future Supply Scenarios ............................................................... B-74

10.0 Summary and Limitations ...................................................................................... B-82

11.0 References ................................................................................................................. B-83

Disclaimer ............................................................................................................................. B-91

Tables 

B-1 Percentage Change in Mean Flow with Respect to Historical Mean 

 (1950–1999) at the Colorado River at Lees Ferry, Arizona ...................................... B-70

B-2 Summary of Key Streamflow Statistics for Each Water Supply Scenario 

 for the Period 2011–2060........................................................................................... B-81

B-3 Summary of Annual and Monthly Streamflow Statistics for the Downscaled 

 GCM Projected Scenario for the 3 Future 30 Year Time Periods: 2011–2040 

 (2025), 2041–2070 (2055), and 2066–2095 (2080) ................................................... B-82

Figures 

B-1 Scenario Development Process ........................................................................................B-3 

B-2 Water Supply Indicator Groups Used in the Study ..........................................................B-5 

B-3 Colorado River Basin and 29 Natural Flow Locations (Reclamation, 2011a) ................B-7 

B-4 Colorado River Basin Elevation (feet above mean sea level) ........................................B-13 

B-5 Average Annual Temperature (°C) and Average Annual Precipitation for the 

Period 1971–2000 ..........................................................................................................B-14 

B-6 Monthly Average Temperature and Precipitation for Three Representative 

Locations in the Colorado River Basin ..........................................................................B-15 

B-7 Annual Average Surface Air Temperature for the Colorado River Basin, 1895–

2005 (top); and Annual Water Year Average Precipitation for the Colorado 

River Basin above Lees Ferry, Arizona (bottom) ..........................................................B-16 

B-8 Estimated Average Annual ET and Runoff, April 1 SWE, and Annual Average 

Runoff Efficiency for 1971–2000 ..................................................................................B-18 
Technical Report B — Water 

Supply Assessment 

B-9 Left panel: Linear Trends in April 1 SWE at 594 Locations in the Western 

United States and Canada, 1950–2000 ..........................................................................B-19 

B-10 Plot of Water-Year Average PDO Values and ENSO Events Defined by SOI 

Averages for the Period June–November ......................................................................B-20 

B-11 Median Change in Flows from Long-term Average for Warm and Cold PDO 

and ENSO Years ............................................................................................................B-21

B-12 Colorado River Basin Average Annual Natural Flow Contribution for each of 

the 29 Natural Flow Locations .......................................................................................B-22 

B-13 Upper Basin Average Annual Total Natural Flows .......................................................B-23 

B-14 Colorado River at Lees Ferry, Arizona Natural Streamflow Snapshot Analysis ..........B-24 

B-15 Green River at Green River, Utah Natural Streamflow Snapshot Analysis ..................B-25 

B-16 Colorado River near Cisco, Utah Natural Streamflow Snapshot Analysis ....................B-25 

B-17 San Juan River near Bluff, Utah Natural Streamflow Snapshot Analysis .....................B-26 

B-18 Cumulative Streamflow Deficits for the Colorado River at Lees Ferry, Arizona .........B-27 

B-19 Colorado River at Lees Ferry, Arizona Paleo Streamflow Snapshot Analysis ..............B-29 

B-20 Comparison of Drought Characteristics between a Segment of the Observed 

Period (1906–2005) and the Paleo Period (762–2005) ..................................................B-30 

B-21 Colorado River at Lees Ferry, Arizona Natural Flow for 102 Sequences for the 

Observed Resampled Scenario ......................................................................................B-32 

B-22 Simulated Annual Colorado River at Lees Ferry, Arizona Natural Flow 

Statistics for 102 Realizations, 2011–2060 ....................................................................B-32 

B-23 Summary Statistics for Annual Colorado River at Lees Ferry, Arizona Natural 

Flows for the Observed Resampled Scenario ................................................................B-33 

B-24 Simulated Monthly Colorado River at Lees Ferry, Arizona Natural Flow 

Statistics for 102 Realizations, 2011–2060 ....................................................................B-33 

B-25 Simulated Deficit and Surplus Spell Length and Magnitude for all 102 

Realizations in the Observed Resampled Scenario ........................................................B-34 

B-26 Colorado River at Lees Ferry, Arizona Natural Flow for 1,244 Sequences for 

the Paleo Resampled Scenario .......................................................................................B-36 

B-27 Simulated Annual Colorado River at Lees Ferry, Arizona Natural Flow 

Statistics for 1,244 Traces, 2011–2060 ..........................................................................B-37 

B-28 Summary Statistics for Annual Colorado River at Lees Ferry, Arizona Natural 

Flows for the Paleo Resampled Scenario.......................................................................B-37 

B-29 Simulated Annual Colorado River at Lees Ferry, Arizona Natural Flow 

Statistics for 1,244 Realizations, 2011–2060 .................................................................B-38 

December 2012 B-v 
Colorado River Basin 

Water Supply and Demand Study 

B-vi December 2012 

B-30 Simulated Deficit and Surplus Spell Length and Magnitude for all 1,244 

Realizations in the Paleo Resampled Scenario ..............................................................B-39 

B-31 Colorado River at Lees Ferry, Arizona Natural Flow for 1,000 Sequences for 

the Paleo Conditioned Scenario .....................................................................................B-41 

B-32 Simulated Annual Colorado River at Lees Ferry, Arizona Natural Flow 

Statistics for 1,000 Realizations, 2011–2060 .................................................................B-41 

B-33 Summary Statistics for Annual Colorado River at Lees Ferry, Arizona Natural 

Flows for the Paleo Conditioned Scenario .....................................................................B-42 

B-34 Simulated Annual Colorado River at Lees Ferry, Arizona Natural Flow 

Statistics for 1,000 Realizations, 2011–2060 .................................................................B-42 

B-35 Simulated Deficit and Surplus Spell Length and Magnitude for all 1,000 

Realizations in the Paleo Conditioned Scenario ............................................................B-43 

B-36 Methodological Approach for the Development of the Downscaled GCM 

Projected Scenario .........................................................................................................B-45 

B-37 Historical and Projected Annual Average Temperature and Projected Annual 

Total Precipitation Smoothed as a 10-year Mean ..........................................................B-53 

B-38 Mean Projected Change in Annual Temperature and Precipitation ...............................B-54 

B-39 Projected Changes in Mean Seasonal and Annual Temperature and Precipitation 

for the Colorado River Basin .........................................................................................B-55 

B-40 Mean Projected Percent Change in Annual ET and Median Projected Percent 

Change in Runoff ...........................................................................................................B-58 

B-41 Mean Projected Percent Change in April 1 SWE and July 1 Soil Moisture ..................B-59 

B-42 Projected Change in Mean Monthly Climatological and Hydrologic Parameters: 

01-Colorado River at Glenwood Springs, Colorado (Upper Basin) ..............................B-61 

B-43 Projected Change in Mean Monthly Climatological and Hydrologic Parameters: 

25-Colorado River at Hoover Dam ................................................................................B-62 

B-44 Colorado River at Lees Ferry, Arizona Natural Flow for 112 Sequences for the 

Downscaled GCM Projected Scenario...........................................................................B-66 

B-45 Colorado River at Lees Ferry, Arizona Natural Flow Statistics for the 

Downscaled GCM Projected Scenario...........................................................................B-66 

B-46 Colorado River at Lees Ferry, Arizona Natural Flow Statistics for the 

Downscaled GCM Projected Scenario as Compared to Observed Flow .......................B-67 

B-47 Cumulative Difference from Simulated Annual Maximum Flow and 25 maf and 

Total No. of Years that Exceed 25 maf for Colorado River at Lees Ferry, 

Arizona for 112 Downscaled Projections for the Period 2011–2060 ............................B-68 
Technical Report B — Water 

Supply Assessment 

B-48 Simulated Relative Change in Mean Annual Flows (Ensemble Mean) for the 

Study Period (2011–2060), and Three Future Periods (2011–2040, 2041–2079, 

and 2066–2095), Compared to 1950–1999 for each of the 29 Natural Flow 

Locations ........................................................................................................................B-69 

B-49 Comparison of Observed and Future Simulated Mean Monthly Flows at 

Colorado River at Lees Ferry, Arizona ..........................................................................B-71 

B-50 Simulated Deficit and Surplus Spell Length and Magnitude for All 112 Climate 

Projections (Threshold Defined as 1906–2007 Mean Annual Flow ~15 maf) .............B-73 

B-51 Simulated Deficit and Surplus Spell Length and Magnitude for all 112 Climate 

Projections (Threshold Defined as Individual Projection Mean for 2011–2060) ..........B-73 

B-52 Annual Colorado River at Lees Ferry, Arizona Natural Flow Time Series for 

Supply Scenarios ............................................................................................................B-75 

B-53 Summary Statistics for Annual Colorado River at Lees Ferry, Arizona Natural 

Flows for Supply Scenarios (for 2011–2060) ................................................................B-76 

B-54 Annual Colorado River at Lees Ferry, Arizona 5-Year Natural Flow Time 

Series and Monthly Variability across Supply Scenarios (for 2011–2060) ...................B-77 

B-55 Monthly Colorado River at Lees Ferry, Arizona Natural Flow Variability for 

Supply Scenarios (for 2011–2060) ................................................................................B-78 

B-56 Frequency, Duration, and Magnitude of Deficit and Surplus Periods for Supply 

Scenarios (for 2011–2060) .............................................................................................B-80

December 2012 B-vii 

Appendices 

B1 Water Supply Sub-Team Members 

B2 Supplemental Water Supply Data and Methods 

B3 Supplemental Analysis of Future Climate Data 

B4 Variable Infiltration Capacity (VIC) Hydrologic Modeling Methods and Simulations 

B5 Supplemental Streamflow Analysis 

B6 Watershed-based Climate and Hydrologic Process Changes 

Technical Report B — Water 

Supply Assessment 

December 2012 B-ix 

Acronyms and Abbreviations 

2007 Interim Colorado River Interim Guidelines for Lower Basin

Guidelines Final Shortages and Coordinated Operations for Lake Powell

EIS and Lake Mead Final Environmental Impact Statement 

°C degrees Celsius 

AMJ April, May, and June 

AMO Atlantic Multi-decadal Oscillation 

Basin Colorado River Basin 

Basin States Colorado River Basin States 

BCSD bias correction and spatial downscaling 

CDF cumulative distribution function 

CMIP3 Coupled Model Intercomparison Project Phase 3 

CRSS Colorado River Simulation System 

EIS Environmental Impact Statement 

ENSO El Nino Southern Oscillation 

ET evapotranspiration 

GCM General Circulation Model 

GHG greenhouse gas 

in/d inches per day 

IPCC Intergovernmental Panel on Climate Change 

ISM Indexed Sequential Method 

JAS July, August, and September 

JFM January, February, and March 

kaf thousand acre-feet 

km kilometer 

maf million acre-feet 

Mexico United Mexican States 

mm millimeters 

mm/d millimeters per day 

netCDF network common data format 

NOAA National Oceanic and Atmospheric Administration 

NRC National Research Council 
Colorado River Basin 

Water Supply and Demand Study 

B-x December 2012 

OND October, November, and December 

PET potential ET 

PDO Pacific Decadal Oscillation 

Reclamation Bureau of Reclamation 

RMSE root mean square error 

SNOTEL snow-telemetry 

SOI Southern Oscillation Index 

SRES Special Report on Emissions Scenarios 

Study Colorado River Basin Water Supply and Demand Study 

SWE Snow water equivalent 

USGS U.S. Geological Survey 

VIC Variable Infiltration Capacity 

WCRP World Climate Research Program 

WWCRA West-Wide Climate Risk Assessment 
December 2012 B-1 

Technical Report B — Water Supply 

Assessment 

1.0 Introduction 

The Colorado River Basin Water Supply and Demand Study (Study), initiated in January 2010, 

was conducted by the Bureau of Reclamation’s (Reclamation) Upper Colorado and Lower 

Colorado regions, and agencies representing the seven Colorado River Basin States (Basin 

States) in collaboration with stakeholders throughout the Colorado River Basin (Basin). The 

purpose of the Study is to define current and future imbalances in water supply and demand in 

the Basin and the adjacent areas of the Basin States that receive Colorado River water over the 

next 50 years (through 2060), and to develop and analyze adaptation and mitigation strategies to 

resolve those imbalances. The Study contains four major phases to accomplish this goal: Water 

Supply Assessment, Water Demand Assessment, System Reliability Analysis, and Development 

and Evaluation of Options and Strategies for Balancing Supply and Demand. 

Spanning parts of the seven states of Arizona, California, Colorado, New Mexico, Nevada, Utah, 

and Wyoming, the Colorado River is one of the most critical sources of water in the western 

United States. The Colorado River is also a vital resource to the United Mexican States 

(Mexico). It is widely known that the Colorado River, based on the inflows observed over the 

last century, is over-allocated and supply and demand imbalances are likely to occur in the 

future. Up to this point, this imbalance has been managed, and demands have largely been met as 

a result of the considerable amount of reservoir storage capacity in the system, the fact that the 

Upper Basin States are still developing into their apportionments, and efforts the Basin States 

have made to reduce their demand for Colorado River water. 

Concerns regarding the reliability of the Colorado River system to meet future needs are even 

more apparent today. The Basin States include some of the fastest growing urban and industrial 

areas in the United States. At the same time, the effects of climate change and variability on the 

Basin water supply has been the focus of many scientific studies which project a decline in the 

future yield of the Colorado River. Increasing demand, coupled with decreasing supplies, will 

certainly exacerbate imbalances throughout the Basin. 

It is against this backdrop that the Study was conducted to establish a common technical 

foundation from which important discussions can begin regarding possible strategies to reduce 

future supply and demand imbalances. The content of this report is a key component of that 

technical foundation and describes the Study’s assessment of water supply. The purpose of the 

Water Supply Assessment is to determine the probable magnitude and variability of historical 

and future natural flows in the Basin. Natural flow represents the flow that would have occurred 

at a location, had depletions and reservoir regulation not been present upstream of that location. 

Because the magnitude and variability of future water supply is uncertain, a set of future water 

supply scenarios were developed to explore that uncertainty, including the potential effects of 

future climate variability and climate change. The water supply projections were used to analyze 

future reliability of the river system to meet water demands, with and without future options and 
Colorado River Basin 

Water Supply and Demand Study

B-2 December 2012

strategies. The Water Supply Assessment drew on the expertise of researchers and analysts 

worldwide who have been investigating the hydrology of the Basin and the dynamics of global 

climate change.

Initially published in June 2011 under Interim Report No. 1 with updates published in February 

2012, this report replaces these earlier publications.

2.0 Approach to Water Supply Scenario Development

A scenario planning process was implemented to examine the uncertainty in future water supply 

and demand and is detailed in Technical Report A – Scenario Development. As noted in that 

report, a collaborative process that engages stakeholders was essential to the successful 

development of future scenarios. Numerous organizations participated in the Water Supply 

Assessment, including representatives of the Reclamation, Reclamation’s Technical Service 

Center, the Basin States, U.S. Fish and Wildlife Service, National Oceanic and Atmospheric 

Administration (NOAA), federally recognized tribes, conservation organizations, and others 

interested in the Basin. This collaboration was accomplished through a variety of means, 

including participation in a Water Supply Sub-Team and direct contact with the organizations 

listed above. The Water Supply Sub-Team members and the points of contact are identified in 

appendix B1 of this report.

A scenario is an alternative view of how the future might unfold. Scenarios are not predictions or 

forecasts of the future. The scenario planning process involved identifying the key driving forces 

(i.e., the factors that will likely have the greatest influence on the future state of the system and 

thereby the performance of the system over time), ranking the driving forces as to their relative 

importance and relative uncertainty, and associating the highly uncertain and highly important 

driving forces, identified as critical uncertainties, with either water supply or water demand. The 

process is shown in figure B-1, which is also presented in Technical Report A – Scenario 

Development. The critical uncertainties that were identified and associated with water supply 

(the step, “Associate Critical Uncertainties with Water Supply and Demand,” shown in 

figure B-1) are:

• Changes in Streamflow Variability and Trends 

• Changes in Climate Variability and Trends 

See Technical Report C – Water Demand Assessment for a discussion of the critical uncertainties 

associated with water demand. 

The subsequent process (shown on the left-hand side of figure B-1 and labeled “Supply”) was 

used by the Water Supply Sub-Team to move from the critical uncertainties to supply scenarios. 

Each step of this process is described in the following sub-sections. 
Technical Report B — Water

Supply Assessment

December 2012 B-3 

FIGURE B-1 

Scenario Development Process

Analysis & Strategy 

Development

Scenario 

Development 

Process Identify Driving Forces

Rank Driving Forces

Identify 

Critical Uncertainties

Frame the Question 

Associate Critical Uncertainties with

Water Supply and Water Demand

Quantify Scenarios

Identify Characteristics within 

each Critical Uncertainty

Analyze Supply 

Scenarios

Identify Parameters within 

each Critical Uncertainty

Develop 

Storylines

Supply Demand

Uncertainty_Flow_Diagram_rev10.ppt

No

Yes

Analyze Demand 

Scenarios

Combine 

Scenarios

No

Yes

Supply 

Scenarios

Document 

Supply

Scenarios

Demand 

Scenarios

Document 

Demand

Scenarios

Do the existing 

scenarios 

represent a 

sufficiently broad 

range of 

plausible 

futures?

Do the existing 

scenarios 

represent a 

sufficiently broad 

range of plausible 

futures?

Identify Parameter Ranges
Colorado River Basin 

Water Supply and Demand Study

B-4 December 2012

2.1 Identify Characteristics within each Critical Uncertainty

Characteristics can be either qualitative or quantitative descriptions of the trend or values over 

time that describe the trajectory of the critical uncertainty. In 2004, Reclamation initiated a 

multi-faceted research and development program to enable the use of methods beyond those that 

use the observed record for projecting possible future inflow sequences for Basin planning 

studies. Through this effort, two additional water supply scenarios were developed and have 

been used in previous Basin planning studies; these scenarios assume that characteristics of the 

water supply critical uncertainties are represented by the observed and paleo-reconstructed 

streamflow records. These scenarios, Paleo Resampled and Paleo Conditioned, have most 

recently been published in appendix N of the Colorado River Interim Guidelines for Lower Basin 

Shortages and Coordinated Operations for Lake Powell and Lake Mead Final Environmental 

Impact Statement (2007 Interim Guidelines Final Environmental Impact Statement [EIS]) 

(Reclamation, 2007). 

For purposes of the Study, it was determined that these previously used scenarios did not 

represent a sufficiently broad range of plausible futures because they did not include the 

consideration of changing climate beyond what has occurred in history. For this reason, a fourth 

scenario was developed that assumes the characteristics of the critical uncertainties Changes in 

Streamflow Variability and Trends, and Changes in Climate Variability and Trends are indicated 

by Downscaled General Circulation Model (GCM) projections and simulated hydrology. 

2.2 Water Supply Scenarios

The following scenarios and associated themes were considered in the Study: 

• Observed Record Trends and Variability (Observed Resampled): Future hydrologic 

trends and variability are similar to the past approximately100 years. 

• Paleo Record Trends and Variability (Paleo Resampled): Future hydrologic trends and 

variability are represented by reconstructions of streamflow for a much longer period in the 

past (nearly 1,250 years) that show expanded variability. 

• Observed Record Trends and Increased Variability (Paleo Conditioned): Future 

hydrologic trends and variability are represented by a blend of the wet-dry states of the 

longer paleo reconstructed period (nearly 1,250 years), but magnitudes are more similar to 

the observed period (about 100 years). 

• Downscaled GCM Projected Trends and Variability (Downscaled GCM Projected):

Future climate will continue to warm with regional precipitation and temperature trends 

represented through an ensemble of future Downscaled GCM Projections and simulated 

hydrology. 

The scenarios each use well-established techniques to represent plausible future water supply 

conditions. The Observed Resampled, Paleo Resampled, and Paleo Conditioned scenarios use

approaches previously developed to represent a range of hydroclimatic variability (annual to 

decadal scales) under a broad retrospective view. These scenarios are considered plausible in that 

they represent the range of hydroclimatic conditions experienced in the past. Future changes in 

climate variability and trends, and their influence on streamflow and Basin water supply, have 

been studied by several researchers in recent years. The Study represents the first time future 
Technical Report B — Water

Supply Assessment

December 2012 B-5 

climate scenarios have been included in Reclamation’s Basin planning studies. For these reasons, 

greater detail is provided for the Downscaled GCM Projected scenario in this report. This 

scenario is considered plausible in that it reflects a growing body of scientific research 

suggesting future changes in hydroclimatic conditions globally and in the Basin. Each of the 

scenarios in the Study is considered a plausible future condition and is informative for future 

Basin planning. 

3.0 Summary of the Water Supply Assessment Approach

A plausible range of future water supply scenarios was considered to analyze the future 

reliability of the system. An assessment of historical supply conditions was performed to 

facilitate an understanding of how the projected future supply conditions under each scenario 

differ from historical supply conditions. This section describes the water supply indicator groups 

analyzed for historical and future conditions and includes a summary of published research 

related to Basin supply. 

3.1 Tools and Methods

The assessment of historical and future supply conditions focused on four main groups of water 

supply indicators, presented in figure B-2. The water supply indicator groups are interrelated: 

climate influences hydrologic processes; hydrologic processes generate streamflow; and 

teleconnections (defined below) influence the oscillation of climate patterns. 

FIGURE B-2 

Water Supply Indicator Groups Used in the Study

Although the primary indicator of water supply in the Basin is streamflow, a fundamental 

understanding of the processes that influence the quantity, location, and timing of streamflow is 

beneficial. Comparisons for each indicator group were made between historical supply and 

future supply under the Downscaled GCM Projected scenario because this scenario assumes a 

changing climate. For the Observed Resampled, Paleo Resampled, and Paleo Conditioned 

scenarios, which assume a climate similar to the past, streamflow is the primary indicator. 

Methods applied to project streamflow under the future supply scenarios are described in their 

respective sections. 

Climate indicators considered in this assessment were temperature and precipitation. Hydrologic 

process indicators were runoff, evapotranspiration (ET), snowpack accumulation (snow water 

equivalent [SWE]), and soil moisture. Climate and hydrologic process indicators were primarily 

Climate

Hydrologic 

Processes

Streamflow

Climate

Teleconnections
Colorado River Basin 

Water Supply and Demand Study

B-6 December 2012

derived from gridded data sets (Maurer et al., 2002; Maurer et al., 2007; Reclamation, 2011a), 

and spatial averaging was performed for selected sub-basins associated with Reclamation’s 

natural flow computation points. The sub-basin averaging of climate and hydrologic process 

information allowed assessment of broader regions of the Basin than the detailed grid cell 

calculations.

Climate teleconnection indicators considered were the El Nino Southern Oscillation (ENSO), 

Pacific Decadal Oscillation (PDO), and Atlantic Multi-decadal Oscillation (AMO) indices. 

Teleconnections refer to the linkage between large-scale, ocean-atmosphere patterns (such as 

ENSO, PDO, and AMO) and weather or climate changes within a separate region of the globe

(e.g., precipitation patterns in the Basin). Finally, streamflow indicators considered were natural 

flows at select locations in the Basin.

Natural flow represents the flow that would have occurred at the location had depletions and 

reservoir regulation not been present upstream of that location. Natural flow has been computed 

historically by Reclamation1 and is currently available for 29 locations throughout the Basin: 

20 locations in the Upper Basin upstream of and including the Lees Ferry gaging station in 

Arizona; and nine additional locations below Lees Ferry, including the Paria River and other 

inflow points in the Lower Basin. These locations are shown in figure B-3. At this time, Basinwide, natural flow estimates extend from 1906 through 20082. Although all gages were not in 

place back to 1906, the existing records were extended back to 1906 using methods described in 

Lee et al. (2006). 

For some tributaries in the Lower Basin (specifically the Little Colorado River, Virgin River, and 

Bill Williams River), U.S. Geological Survey (USGS)-gaged flows at specific locations near the 

confluence of the tributary and the Colorado River mainstream were used in place of natural 

flows. This approach was also taken for the Paria River, which joins the Colorado River just 

downstream of Lees Ferry, Arizona. In addition, the Gila River is not included in the Colorado 

River Simulation System (CRSS) and is therefore not included as one of the 29 locations where 

natural flow is estimated throughout the Basin. See Technical Report C – Water Demand 

Assessment, Appendix C11 – Modeling of Lower Basin Tributaries in the Colorado River 

Simulation System, for further discussion. 

CRSS is Reclamation’s primary Basin-wide simulation model used for long-term planning 

studies and, in its current configuration, requires natural flow inputs at these 29 locations on a 

monthly time step over the Study’s planning horizon. This report describes the specific methods 

used to quantify, and results of, the water supply scenarios considered in the Study. 

Additional information related to water supply data and methods is provided in appendix B2. 

 

1 Additional information, documentation, and the natural flow data are available at 

http://www.usbr.gov/lc/region/g4000/NaturalFlow/index.html. 

2 At the time the analysis for this report was performed, natural flow data were available only through 2008.
Technical Report B — Water

Supply Assessment

December 2012 B-7 

FIGURE B-3 

Colorado River Basin and 29 Natural Flow Locations (Reclamation, 2011a) 

Of the 29 streamflow locations, a subset was used for analysis in this report. Circled stations are used for describing climate and 

streamflow in this report; red dashed circled stations are used to describe climate, orange dashed circled stations are used to 

describe both climate and streamflow, and solid orange circled stations are used to describe only streamflows. 
Colorado River Basin 

Water Supply and Demand Study

B-8 December 2012

3.2 Sources of Data and Information

An extensive review of relevant literature, water supply studies, and hydroclimatic data was 

performed as part of the Water Supply Assessment. The Basin supply has been studied by 

numerous researchers, and a wealth of information is available, including several recent studies 

directly relevant to the Study. Relevant hydroclimate data were collected throughout the Water 

Supply Assessment, with particular emphasis on gridded climate data sets and natural flows for 

the 29 natural flow locations in the Basin. 

3.2.1 Literature Review

Due to its strategic importance as a source of water for the western United States, the Colorado 

River is one of the most studied river systems in the world. The Basin water supply has been 

assessed using a variety of hydrologic analyses for many decades, but efforts accelerated in the 

1990s with the availability of GCMs and observed increased streamflow variability (Pagano and 

Garen, 2005). Reclamation published an extensive literature review of Colorado River climate 

and hydrology studies in appendix U of the 2007 Interim Guidelines Final EIS (Reclamation, 

2007). This appendix summarizes the state of the science in 2007. In 2011, Reclamation’s 

Technical Service Center published a second edition of the Literature Synthesis on Climate 

Change Implications for Water and Environmental Resources (Reclamation, 2011b) that 

summarizes relevant research through the summer of 2010. Provided below is a brief summary 

of past efforts and research that were used to assess Basin supply.

• The following studies: Gleick (1987); Nash and Gleick (1991, 1993); Hamlet and 

Lettenmaier (1999); McCabe and Wolock (1999a, 1999b); and Wilby et al. (1999) discuss 

climate change impacts on the hydrology and water resources of western U.S. river basins. 

All these studies assume or predict increasing temperatures, but disagree about both the 

magnitude and direction of precipitation changes. 

• Nash and Gleick (1991) evaluate prescribed changes of +2 degrees Celsius (°C) and +4 °C, 

coupled with precipitation reductions of 10 and 20 percent. The 2 °C increase per 10 percent 

precipitation decrease resulted in a 20 percent streamflow reduction, while the 4 °C increase

per 20 percent precipitation decrease resulted in a 30 percent runoff decrease.

• Christensen et al. (2004) project average temperature increases of 1.0 °C, 1.7 °C, and 2.4 °C, 

and precipitation decreases of 3, 6, and 3 percent for the Basin for the periods 2010 to 2039, 

2040 to 2069, and 2070 to 2099, respectively, relative to the period 1950 to 1999 means. The 

temperature and precipitation changes lead to reductions of April 1 SWE of 24, 29, and 30 

percent, and runoff reductions of 14, 18, and 17 percent for the three periods. 

• Updated analyses by Christensen and Lettenmaier (2007) using a larger ensemble of 

climate projections, result in smaller mean projected reductions in Lees Ferry flows (less than 

11 percent). 

• Hoerling et al. (2009), in an attempt to reconcile streamflow estimates by several researchers, 

summarize the recent hydroclimatic analyses of the Basin and find that the projections range 

from a 5 to 20 percent reduction in streamflow by 2050. 

• A recently released Colorado River Water Availability Study (Colorado Water Conservation 

Board, 2010) focuses on the State of Colorado’s hydrometeorological contribution to the 
Technical Report B — Water

Supply Assessment

December 2012 B-9 

Colorado River system. The study describes the tools available to simulate river hydrology, 

agricultural demands, water allocation, and decision support. 

• Several papers in a special issue of the Proceedings of the National Academy of Sciences on 

climate change and water in southwestern North America (Sabo et al., 2010) focus on the 

climate and water supply in the Basin. Cayan et al. (2010) provide an analysis of the current 

Colorado River drought and suggest that, although the current drought is exceptional in the 

observed record, future droughts in the Basin may be more severe and longer in duration. 

Woodhouse et al. (2010) provide the 1,200-year perspective on Southwestern drought, draw 

linkages of warming to paleo drought severity, and place the drought in context with the 

medieval period worst-case drought. Seager and Vecchi (2010) attribute the current and 

future Southwest drying to a broader expansion of the Hadley cell that causes storms to track 

farther north. It is important to note that the latter study (Seager and Vecchi, 2010) suggests 

decreases in winter (October through March) precipitation, although many other studies 

(including Cayan et al., 2010) suggest increases during this same period for much of the 

Basin. It is not clear whether this discrepancy is due to the large domain (southwest North 

America, from southern Mexico to the Oregon-California border and from the Pacific Ocean 

to the High Plains) that is being averaged, or due to the lack of regional/local spatial 

resolution of the GCM-based information. 

• Das et al. (2011) further evaluate the effect of seasonal differences in warming on Colorado 

River streamflow changes. The Intergovernmental Panel on Climate Change (IPCC) Fourth 

Assessment Report group of climate models indicates that climate warming over the Basin 

may be greater in summer than in winter. Das et al. (2011) find that annual Colorado River 

streamflow is more sensitive to warm season (April through September) warming than cold 

season warming (October through March), and is the most sensitive of the four western river 

basins evaluated. A 3 °C warming in the warm season results in a 13.3 percent reduction in 

annual flow, while the same warming applied during the cool season results in an annual 

flow reduction of only 3.5 percent. Climate warming, especially if amplified in summer as 

projected, may drive significant reductions in available supply, even if there is no reduction 

in precipitation. 

Common to nearly all this research is the projection of continued and accelerated warming in the 

Basin and very likely increases in the severity of future droughts. However, the research suggests 

continued uncertainty in projections of the magnitude and direction of potential future changes in 

annual precipitation. Effective treatment of this uncertainty is important in making credible 

estimates of future water supply. 

3.2.2 Data Sources

The Water Supply Assessment relied on a variety of peer-reviewed datasets collected by 

Reclamation, other recognized federal sources, and hydrologic modeling results obtained from 

Reclamation’s West-Wide Climate Risk Assessment study (Reclamation, 2011c). The data 

sources and methods are described further in subsequent sections of this report and a complete 

listing is collectively included in appendices B2, B3, and B4. 
Colorado River Basin 

Water Supply and Demand Study

B-10 December 2012

4.0 Historical Supply

An assessment of the Basin’s historical climate and hydrology is critical for a robust 

understanding of the projected changes associated with each of the four future water supply 

scenarios. For this reason, an assessment of the historical supply of the Basin is first presented. 

This presentation begins with a discussion of the methods used to perform the assessment, 

followed by the results for the four groups of water supply indicators: climate, hydrologic 

processes, climate teleconnections, and streamflow. 

4.1 Methods

Historical daily temperature and precipitation data for 1950 to 2005 (Maurer et al., 20023) were 

processed into average temperature and total precipitation for each month and year of the period. 

Monthly, seasonal, and annual statistics were computed for each grid cell (1/8th-degree 

resolution, or about 12 kilometers [km]) of the gridded meteorological dataset for the 1971 to 

2000 historical period to represent the historical climatology and compare to future projected

climates. The historical dataset was derived from individual NOAA Cooperative Observer 

station observations and gridded to the 1/8th-degree using mapping algorithms that account for 

station elevation, orographic effects, and other characteristics (Maurer et al., 2002). 

Climate is defined by the World Meteorological Organization (2011) as the “average weather,” 

or a statistical description in terms of the mean and variability of variables such as temperature, 

precipitation, and wind. Climate change is the shift in the long-term weather statistics, or trend, 

that a region experiences. Thus, climate change cannot be represented by single annual events or 

individual anomalies. That is, a single large flood event or particularly hot summer is not an 

indication of climate change, although a series of floods or warm years that statistically change 

the average precipitation or temperature over time may indicate climate change. The World 

Meteorological Organization recommends the use of a 30-year period for evaluating climate. At 

the time the Study was initiated, the established 30-year climatological period as described by 

NOAA (2011) was the 1971 to 2000 historical period. This period was used in the study to 

define the historical base climate. While NOAA has recently updated its climatological period to 

1981 to 2010, climate and hydrologic information from various sources were not available at the 

time required to support this assessment for the Basin.

The historical climatological period allows for the averaging of individual year and multi-year 

variability over a longer period to capture the average conditions. A longer period could have 

been selected as the historical base period, but ensuring consistency with NOAA’s period 

definition, and establishing a period consistent with tracking future changes (desire to estimate 

future changes for similar 30-year time slices), were considered important to define time-varying 

changes in this analysis. The seasons are defined as follows: Fall (October, November, and 

December); Winter (January, February, and March); Spring (April, May, and June); and Summer

(July, August, and September).

Historical hydrologic parameter data were generated by the Variable Infiltration Capacity (VIC) 

model for the period 1950 to 2005. The VIC model (Liang et al., 1994; Liang et al., 1996; 

Nijssen et al., 1997) is a spatially distributed macro-scale hydrologic model that solves the water 

balance at each model grid cell. The VIC model is populated with the historical temperature and 

 

3 Subsequent to Maurer et al. (2002), the climate dataset was extended to 2005 using identical methods.
Technical Report B — Water

Supply Assessment

December 2012 B-11

precipitation data to simulate historical hydrologic parameters (Maurer et al., 2002). Appendix 

B4 provides details on the VIC model and its application in the Study. The simulated hydrologic 

parameters include ET, runoff (surface runoff), baseflow (subsurface runoff), soil moisture (in 

each of three soil layers), and SWE. Representative statistics describing these parameters were 

generated on monthly, seasonal, and annual bases. The statistical analysis was conducted on both 

grid cell and watershed bases. The results of the grid cell analysis produce the most informative 

map graphics and clearly show spatial variation at the greatest resolution possible, while the 

watershed basis provides an aggregate graphic of the variation across a natural flow station’s 

watersheds.

Climate teleconnections were analyzed first by selecting indices that could have potential 

influence in streamflow changes for the Basin. Published research (such as Redmond and Koch, 

1991; Diaz and Kiladis, 1992; and McCabe et al., 2004) indicates that the strongest correlations 

with Basin flows were observed with the ENSO and PDO indices. For ENSO, data were 

collected for both the ocean component (sea surface temperature anomalies) and the atmospheric 

component (sea level pressure anomalies). The sea surface temperature anomalies indicate the 

relative temperature state of the tropical Pacific Ocean as compared to normal (warm phase 

indicating El Nino conditions), while the sea level pressure anomalies are one measure of largescale fluctuations in air pressure occurring between the western and eastern tropical Pacific. The 

two components are highly correlated and, combined, describe ENSO. The Southern Oscillation 

Index (SOI) was the primary dataset used in the Study to describe ENSO due to the longer period 

of data availability. The PDO indicates the longer-term (about 15 to 25 years) oscillation of the 

north Pacific Ocean sea surface temperatures. The PDO index, which indicates the warm or cool 

phase of the sea surface temperatures, has been linked to decadal-length period of above average 

or below average precipitation and was used directly in this assessment. The quantitative 

teleconnections analysis was based on the SOI and the PDO indices. 

Only a qualitative discussion of the AMO is included in this report. For additional information 

pertaining to indices' choice, refer to appendix B2. 

Annual average values for the SOI were computed using different annual windows. The 

average SOI presented in the Study refers to the June through November period, which was 

identified as a strong indicator of ENSO events (Redmond and Koch, 1991). Once the SOI 

averages were computed, ENSO events were determined by years where the averaged SOI was 

below -1 (classified as an El Niño year) or above 1 (classified as a La Niña year). Annual 

averages of the PDO on a water year basis were calculated and compared with the same water 

year annual flows. A warm PDO was defined as a value greater than or equal to 0.0 and a cold 

PDO was a PDO value less than 0.0. 

Two historical streamflow data sets, the observed record spanning the period 1906 to 2007, and 

the paleo reconstructed record spanning the period 762 to 2005 (Meko et al., 2007), were used in 

the Study to characterize historical streamflow patterns and variability. Period comparisons were

made between the full extent of the data and a more recent period. For the observed dataset 

spanning 1906 to 2007, the second comparison period (1978 to 2007) was selected as the most 

recent 30-year period. For the paleo dataset spanning 762 to 2005, the second comparison period 

was selected as 1906 to 2005 so that direct comparisons could be made of the observed and paleo 

timeframes.
Colorado River Basin 

Water Supply and Demand Study

B-12 December 2012

4.2 Results

4.2.1 Climate

The Basin contains climate zones ranging from alpine to desert and is fundamentally influenced 

by climate variability from seasonal to millennial scales (National Research Council [NRC],

2007). The Basin water supply, as is typical in many western river systems, strongly depends on 

snowmelt from high elevation portions (figure B-4) of the Upper Basin, with about 15 percent of 

the watershed area producing about 85 percent of the entire Basin’s average annual runoff. 

Annual precipitation ranges from 84 millimeters (mm) (less than 4 inches) in southwestern 

Arizona to nearly 1,600 mm (63 inches) in the headwaters of Colorado, Utah, and Wyoming, as 

shown in figure B-5. Average temperatures vary considerably by season, Basin location, and 

elevation, as also shown in figure B-5. Warmest temperatures are seen in the southwestern 

Arizona summer and coolest in the headwaters during the winter. 

The climate of the Basin exhibits important spatial and seasonal variability. To illustrate this 

variability, figure B-6 shows monthly average temperature and precipitation as watershed 

averages for the areas immediately upstream of the Colorado River near Glenwood Springs 

(Colorado), Colorado River at Lees Ferry (Arizona), and Colorado River above Imperial Dam 

(Arizona/California). These three locations reflect a coarse transect of the Basin from the 

headwaters to Imperial Dam. 

As illustrated in figure B-6, the average temperature varies by more than 20 °C seasonally at 

each of the three locations and similarly across the Basin within seasons. Cool winter 

temperatures at the higher elevation portions of the Upper Basin cause much of the precipitation 

to fall in the form of snow. At lower elevations, warmer conditions exist and liquid precipitation 

is the dominant form. For most regions, the majority of the precipitation occurs in the cool 

season (fall and winter). Warmer temperatures in the spring and summer induce snowmelt at the 

higher elevations, and storms tend to be shorter and more intense. The summer precipitation does 

not contribute a significant portion of the Basin annual total. In the southwest portions of the 

Basin (Arizona, California, and Nevada), summer precipitation is locally important. The North 

American monsoon season plays a significant role in bringing moisture from the sub-tropical 

Pacific and Gulf of California and causes intense summer storms in the southwestern desert. The 

monsoon influence extends into Upper Basin states as well and can contribute to significant 

summer precipitation in New Mexico, Utah, and Colorado. 
Technical Report B — Water

Supply Assessment

December 2012 B-13

FIGURE B-4 

Colorado River Basin Elevation (feet above mean sea level)

Derived from National Elevation Dataset, USGS, (HTTP://NED.USGS.GOV). 
Colorado River Basin 

Water Supply and Demand Study

B-14 December 2012

FIGURE B-5 

Average Annual Temperature (°C) and Average Annual Precipitation (mm) for the Period 1971–2000

Derived from Maurer et al., 2002. 
Technical Report B — Water

Supply Assessment

December 2012 B-15

FIGURE B-6 

Monthly Average Temperature and Precipitation for Three Representative Locations in the Colorado River Basin 

Derived from daily gridded observed meteorology (Maurer et al., 2002) and averaged for the local watershed immediately 

upstream of the indicated point. 

Trends in temperature and precipitation for the Basin have been studied by Groisman et al. 

(2001), McCabe et al. (2002), Piechota et al. (2004), Hamlet et al. (2005), Pagano and Garen 

(2005), Regonda et al. (2005), Andreasdis et al. (2006), Fassnacht (2006), Mote (2006), 

Christensen and Lettenmaier (2007), and several others. Long-term trends are summarized in the 

2007 NRC summary report on hydroclimatic variability in the Basin (NRC, 2007). The longterm annual temperatures and precipitation amounts from the period 1895 to 2005 are shown in 

figure B-7. A significant increase in temperature is apparent in this figure, although periods of 

-15

-10

-5

0

5

10

15

20

25

30

35

OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP

Monthly Average Air Temperature (C)

Month

Monthly Average Air Temperature

1950-2005

Colorado River at Glenwood Springs, CO

Colorado River at Lees Ferry, AZ

Colorado River above Imperial Dam

0

10

20

30

40

50

60

70

80

OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP

Monthly Average Precipitation (mm)

Month

Monthly Average Precipitation

1950-2005

Colorado River at Glenwood Springs, CO

Colorado River at Lees Ferry, AZ

Colorado River above Imperial Dam
Colorado River Basin 

Water Supply and Demand Study

B-16 December 2012

cooling have occurred historically. Most important is the warming trend that has occurred since 

the 1970s. This warming trend is also seen in the Upper and Lower Basins and with observed 

North American and global trends. 

Annual precipitation shows substantial variability and periods of dry and wet spells. Most 

notable in the precipitation record is the lack of a significant long-term annual trend, yet the 

annual variability appears to be increasing. Both the highest and lowest annual precipitation 

years appear in the most recent 30-year record. 

FIGURE B-7 

Annual Average Surface Air Temperature for the Colorado River Basin, 1895–2005 (top); and Annual Water Year Average 

Precipitation for the Colorado River Basin above Lees Ferry, Arizona (bottom) 

Red lines show annual values; blue lines show the 11-year running mean. Source: NRC (2007) and Western Regional Climate 

Center. 
Technical Report B — Water

Supply Assessment

December 2012 B-17

A 2008 publication by Miller and Piechota summarizes Basin temperature, precipitation, and 

streamflow trends and also examines the possibility that a “step change” in these parameters 

occurred during the mid 1970s. The step-change time series data were divided into the first 

24 years of data (1951 to 1974) and the later 31 years of data (1975 to 2005) for temperature and 

precipitation datasets. Miller and Piechota (2008) find that increasing temperature trends and 

step changes were observed consistently throughout the year, often times at greater than a 

95 percent confidence level. Temperature trends were most significant in the first quarter of the 

year, January through March. Precipitation trends and step changes were not as evident as those 

for temperature. An increasing precipitation trend was observed January through March, but not 

at all stations and not significant for other months. 

4.2.2 Hydrologic Processes

The hydrologic processes that describe the interaction between climate and the watershed 

landscape are critically important in determining water availability and the manner in which the 

Basin response may change under future climate. The regions of greatest precipitation in the 

Basin are those at high elevation in the headwaters of the Green, Colorado, and San Juan Rivers. 

Due to cold temperatures, these areas accumulate substantial snowpack that is critical to the 

Basin supply. Figure B-8 provides an estimate of the average spatially distributed April 1 SWE 

for the period 1971 to 2000 derived from a historical simulation of the VIC hydrology model. 

Important in this figure is the relatively small portion of the watershed that offers significant 

seasonal water storage in the form of snowpack. Although snow falls in other portions of the 

Basin, temperatures are generally not sufficiently cold to retain the snowpack for any great 

length of time. The remainder of this lower elevation portion of the watershed is primarily 

dominated by rainfall. 

One way to synthesize many complex hydrologic processes at the watershed scale is to introduce 

the concept of runoff efficiency. Runoff efficiency is a measure of the effectiveness of a 

particular watershed in converting precipitation into runoff. Watersheds with very high runoff 

efficiencies dominate the overall contribution toward streamflow and have relatively lower 

losses. Watersheds with low runoff efficiencies have high losses and tend to be dominated by 

infiltration to soil moisture and consumptive use through ET. ET is the sum of evaporation from 

the land surface and plant transpiration. As can be seen in figure B-8, the watersheds with the 

highest efficiencies are the headwaters of the Colorado, Green, and San Juan Rivers. These 

watersheds are able to convert about 20 to 30 percent of the precipitation into runoff and 

baseflow; however, even in the headwater regions there is considerable variability in runoff 

efficiencies, with some values less than 10 percent. In the Lower Basin, average runoff 

efficiencies are all less than 10 percent and many watersheds have runoff efficiencies less than 

5 percent. The runoff efficiency Basin-wide is about 12 percent. 

ET is the dominant hydrologic flux on the annual scale, consuming more than 70 percent of the 

precipitation supply. As can be seen in figure B-8, ET is highest in regions with greatest 

precipitation. This is not to say that the ET demand is highest in these regions, but rather that ET 

tends to be supply-limited in the Basin. The ET demand (potential ET [PET]) is actually higher 

in the warmer climate of the Lower Basin, but water supply in the form of soil moisture is less 

and what is available is depleted earlier than in the Upper Basin watersheds. 
Colorado River Basin 

Water Supply and Demand Study

B-18 December 2012

FIGURE B-8 

Estimated Average Annual ET and Runoff (mm), April 1 SWE (mm), and Annual Average Runoff Efficiency (fraction of 

precipitation converted into runoff) for 1971–2000

Derived from historical VIC simulations. 
Technical Report B — Water

Supply Assessment

December 2012 B-19

Previously published research was relied on to assess observed snowpack trends in the Basin. 

Research by Mote (2003), Mote et al. (2008), Clark et al. (2001), Cayan et al. (2001), and 

Pederson et al. (2011) indicate a general decline in April 1 SWE for Pacific Northwest and 

northern Rocky Mountain locations, and increases in parts of the Great Basin and southern 

Rockies, as shown in figure B-9. 

FIGURE B-9 

Left panel: Linear Trends in April 1 SWE at 594 Locations in the Western United States and Canada, 1950–2000 

(Mote et al., 2008) (Negative trends are shown by open circles, positive by solid circles.) 

Right panel: April 1 SWE Trends (1950–2000) Plotted against Elevation of Snow Course (Mote et al., 2008) 

(Units on y-axis are incorrectly labeled by author as mm and should be meters.)

Widespread decreases in springtime snowpack are observed with consistent results across the 

lower elevation northern latitudes of the western United States. The high-elevation and thus 

cooler Rockies do not consistently produce decreasing trends for SWE. To assess the vertical 

characteristics of SWE, Mote plotted April 1 SWE trends (1950 to 2000) against elevation of 

snow course (figure B-9). Losses of SWE tend to be largest at low elevations and strongly 

suggest a temperature-related effect. 

Finally, Mote et al. (2008) used the VIC model to simulate SWE accumulation and depletion for 

western U.S. basins. From this analysis, it was clear that changes in SWE are not simply linear, 

but fluctuate on decadal time scales. SWE was estimated to have declined from 1915 to the 

1930s, rebounded in the 1940s and 1950s, and despite a peak in the 1970s, declined since midcentury. 

Additionally, recent research demonstrates dust-on-snow events have the ability to alter the 

timing and magnitude of runoff (Painter et al., 2010). Dust-on-snow events reduce snow albedo, 
Colorado River Basin 

Water Supply and Demand Study

B-20 December 2012

or reflectivity, thereby increasing the solar radiation that reaches and warms the snow, 

potentially contributing to changes in timing of snowmelt and seasonal streamflows. 

4.2.3 Climate Teleconnections

Research indicates a relationship between Pacific Ocean climate indices and Basin streamflow

(Redmond and Koch, 1991; Webb and Betancourt, 1992; Cayan et al., 1999; Mo et al., 2009; and 

others). The June through November SOI is identified by Redmond and Koch (1991) as a strong 

indicator of ENSO events. For the Study, relationships between the PDO and ENSO and natural 

flows in the Upper Basin were examined. Figure B-10 presents the annual PDO index and 

indicates when June through1 November SOI average values are below -1.0 or above 1.0. The 

solid red bars indicate a positive PDO index, or warm PDO phase, while the solid blue bars 

indicate the cold PDO phase. The light red and blue shading indicate the SOI condition. Evident 

in this figure is the low frequency phasing of the PDO (multi-decadal scales) and the significant 

year-to-year variability in the ENSO events. Indicated by the line on this figure is the 11-year, 

center-weighted annual flow departure from long-term mean for the Colorado River at Lees 

Ferry, Arizona. Correlation between the low frequency PDO and decadal scale Colorado River 

flows appears prominent since the mid-1940s with lower decadal-scale flows during cool 

PDO phases and higher flows during warm PDO phases. However, significant variability exists 

even at these scales and prior to the mid-1940s, the correlation is poor. 

There are other climate teleconnections that appear to influence multi-decadal variations in 

precipitation patterns (e.g., AMO) and others that can modify the characteristics of seasonal 

precipitation (e.g., Madden-Julian Oscillation and Arctic Oscillation) (Becker et al., 2011; Bond 

and Vecchi, 2003; Hu and Feng, 2010). The understanding of the influence of these 

teleconnections on the Colorado River precipitation, and their usefulness as an indicator, is still 

evolving. 

FIGURE B-10

Plot of Water-Year Average PDO Values and ENSO Events Defined by SOI Averages for the Period June–November

Figure B-11 illustrates water year departure from median streamflows in percent during warm 

and cold PDO and ENSO periods sampled from the period 1906 to 2007 for Upper Basin natural 

flow locations. The red bars indicate the streamflow departures for the warm phase of PDO (top) 

and ENSO (bottom), while the blue bars reflect the departures during the cool phases. Although 

significant streamflow variability exists from year to year, the majority of the flows are higher 1900 1902 1904 1906 1908 1910 1912 1914 1916 1918 1920 1922 1924 1926 1928 1930 1932 1934 1936 1938 1940 1942 1944 1946 1948 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

-4

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190019021904

190619081910191219141916

19181920192219241926192819301932

19341936193819401942

194419461948195019521954195619581960

19621964196619681970

19721974197619781980198219841986

198819901992199419961998

20002002200420062008

Departure from Mean Flows (MAF)

PDO index

PDO/ENSO Events and Colorado River at Lees Ferry Annual Flow Departures

La Nina Years El Nino Years Cold PDO Phase Warm PDO Phase 11-yr center-weighted flow departure
Technical Report B — Water

Supply Assessment

December 2012 B-21

than normal during the warm PDO and ENSO (El Niño) phases. Conversely, the majority of the 

flows are lower than normal during the cool PDO and ENSO (La Niña) phases. It should be 

noted that the PDO and ENSO relationship is essentially inverted for the northern Basin in 

Wyoming (Green River Basin) where flows tend to be higher during the cool PDO and ENSO 

(La Niña) phase. The dividing line separating typical ENSO influence varies considerably from 

year to year, but is often referred to as a line from San Francisco to Cheyenne (Edwards and 

Redmond, 2005). 

Overall, the natural inter-annual variability in streamflow tends to be more dominant than the 

relationships to either ENSO or PDO. ENSO has considerably more skill (strength as a predictor 

of seasonal precipitation or streamflow) in the coastal watersheds of the Pacific, than over the 

Basin. PDO, on the other hand, is a low-frequency signal (multi-decadal scale) that limits the 

number of events that could be correlated. However, it is important to note that in 2011 to 2012 

the climate was entering a strong combined cool phase of both ENSO and PDO. The alignment 

of both signals in the cool phase suggests a propensity for continued drying trends in the coming 

years. The ability to predict the future state of PDO, however, is limited at this time. 

FIGURE B-11 

Median Change in Flows from Long-term Average for Warm and Cold PDO (top) and ENSO (bottom) Years
Colorado River Basin 

Water Supply and Demand Study

B-22 December 2012

4.2.4 Streamflow

Analysis of streamflow records for the 29 natural flow locations indicated that about 92 percent of 

the total Colorado River at Imperial Dam, Arizona natural flow is contributed by runoff upstream 

of Lees Ferry, Arizona (figure B-12). As shown graphically in figure B-12, the Green River 

contributes about 33 percent of the total natural flow, the Colorado River at Cisco, Utah about 

42 percent, and the San Juan River about 13 percent based on long-term annual natural flows 

from 1906 to 2007. Due to the importance of these rivers to the overall supply, they were selected 

as key locations for historical assessment. In addition, the Colorado River at Lees Ferry, Arizona 

is used because approximately 92 percent of the Basin natural flow (measured at Imperial Dam, 

Arizona) has accumulated there.

FIGURE B-12 

Colorado River Basin Average Annual Natural Flow Contribution (% of total) for each of the 29 Natural Flow Locations

Streamflow derived from the observed period (1906–2007). See figure B-2 for names of locations.

The mean annual flows for 1906 to 2007 at each of the 20 Upper Basin natural flow locations are 

shown in figure B-13. Also shown is the variability of annual flows as “box-whisker” ranges. 

The mean annual flow of the Colorado River at Lees Ferry, Arizona (location 20) is 

approximately 15.0 million acre-feet (maf), but ranged from 5.6 maf (1977) to 25.2 maf (1984) 

over this period. The upper Colorado River at Cisco, Utah (location 8), Green River at Green 

River, Utah (location 16), and San Juan River at Bluff, Utah (location 19) have mean annual 

flows of 6.8 maf (ranging from 2.6 to 12.6 maf), 5.4 maf (ranging from 1.9 to 5.3 maf), and 

2.1 maf (ranging from 0.5 to 4.5 maf), respectively. 
Technical Report B — Water

Supply Assessment

December 2012 B-23

FIGURE B-13 

Upper Basin Average Annual Total Natural Flows

kaf/yr = thousand acre-feet per year

Box represents the 25th, 50th, and 75th percentiles; whiskers represent the maximum (max) and minimum (min), and triangle 

represents the mean flow. Streamflow derived from the observed period (1906–2007). 

Streamflow analysis summaries (snapshots) were prepared for all 29 natural flow locations to 

evaluate the trends and variability of flows. Four snapshot summaries are presented in this report 

for the following key locations: Colorado River near Cisco, Utah (location 8); Green River at 

Green River, Utah (location 16); San Juan River near Bluff, Utah (location 19); and Colorado 

River at Lees Ferry, Arizona (location 20). Additional streamflow analysis figures for each of the 

major contributing flow locations are included in appendix B5. This supplemental material

includes a table reporting specific monthly streamflow averages, annual averages including 

minimum and maximum values with the years they occurred, and a more-detailed analysis of 

deficit/surplus periods. 

The snapshot results were developed from the natural flows dataset using data for water years 

1906 to 2007 (figures B-14 to B-17). The top plot in each figure shows the annual flow volumes 

and the moving averages for 3, 5, and 10 years. This plot provides a visual assessment of 

streamflow variability, minimum and maximum flows, and long-term trends. For most selected 

locations, greater variability and more frequent events of greater magnitude are observed after 

1976. Generally lower flows are observed from the mid 1930s to mid 1960s and a slightly 

downward trend in flows is observed in all locations for this time period. As an example, the 

Colorado River at Lees Ferry, Arizona plot (figure B-14) shows a period of generally below 

average streamflow and a period of moderate variability for the period 1930 to 1976. Beginning 

in 1977, streamflow amplitude and variability increased, with a decrease in streamflows in the 

most recent two decades. These recent changes in streamflow are attributed, in part, to shifts in 

the atmospheric-oceanic conditions as represented by PDO and ENSO and hydrologic response 

to recent warming.
Colorado River Basin 

Water Supply and Demand Study

B-24 December 2012

FIGURE B-14 

Colorado River at Lees Ferry, Arizona Natural Streamflow Snapshot Analysis

The bottom left plot shows a two-period comparison of monthly average streamflow. The first 

period spans 1906 to 2007, while the second period captures the more recent 30-year period, 

1978 to 2007. For the period 1978 to 2007, all selected locations exhibit a reduction in late 

spring streamflows and a slight increase in winter streamflows when compared to the long-term 

(1906 to 2007) averages. The annual mean flow was slightly lower at most of the Upper Basin 

locations during the 1978 to 2007 period, while annual variability, based on the inter-quartile 

(25th to 75th percentile) range of flows, was higher during this period. The mean annual flow for 

the 1978 to 2007 period is 14.6 maf—about 3 percent lower than the 1906 to 2007 period mean 

annual flow of 15.0 maf. The increase in variability can be explained largely by the two 

significant high-flow periods (the early-mid 1980s and the late 1990s) and the recent extended 

drought conditions during this period. The two periods show similar maximums and minimums 

for the 1-, 3-, and 5-year averages because the annual flow extremes (both high and low) have 

mostly occurred in the most recent 30-year period and are thus represented in both periods (the 

most recent period is also included in the long-term period).This finding is consistent with 

precipitation trends that show increased variability in the recent period. However, these changes 

are not universal. For example, the Colorado River at Cisco, Utah station shows an increase in 

variability in the more recent period, and also a slight increase in annual mean flow. Conversely, 

the San Juan River near Bluff, Utah station shows a lower mean flow, but a slightly lower 

variability in the recent period as compared to the longer 1906 to 2007 period. The two highest 

flows at this location occurred in 1941 and 1973. 

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Annual Flow Statistics

Box = 25th to 75th percentile range; Dash is median value (50th percentile); and whiskers 

represent min and max range
Technical Report B — Water

Supply Assessment

December 2012 B-25

FIGURE B-15 

Green River at Green River, Utah Natural Streamflow Snapshot Analysis

FIGURE B-16 

Colorado River near Cisco, Utah Natural Streamflow Snapshot Analysis

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Box = 25th to 75th percentile range; Dash is median value (50th percentile); and whiskers 

represent min and max range

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1906-2007 1978-2007

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KAF/yr

Annual Flow Statistics

Box = 25th to 75th percentile range; Dash is median value (50th percentile); and whiskers 

represent min and max range
Colorado River Basin 

Water Supply and Demand Study

B-26 December 2012

FIGURE B-17 

San Juan River near Bluff, Utah Natural Streamflow Snapshot Analysis 

As with temperature and precipitation, Miller and Piechota (2008) also evaluated streamflow 

trends and explored the significance of a step change in streamflow, which occurred during the 

mid 1970s. The step change time series data were divided into the first 69 years of data (1906 to 

1974) and the latter 31 years of data (1975 to 2005). Increasing streamflow trends in January 

through March and decreasing streamflow trends during peak runoff months (April through July) 

were reported in the authors’ study. The authors also note that decreasing streamflow trends were 

apparent at the 99 percent confidence level throughout the Basin during the traditional peak flow 

months, despite the high variability of streamflow rates that historically occurred in the Basin 

(e.g., Pagano and Garen, 2005; Woodhouse and Lukas, 2006). Because streamflow trends are 

more apparent than precipitation trends, the authors speculate that it is possible that the form of 

precipitation (rain or snow) and other components of the water budget (e.g., evaporation and 

seepage losses) are changing. Based on these studies, a general warming in the Basin is shifting 

winter precipitation to a higher rain-snow ratio when compared to historical data. These changes

are consistent with earlier peak streamflows in the spring.

The inter-annual variability of climate and hydrology within the Basin produces frequent periods 

when the mean flow during that period is below the long-term mean. These occurrences are 

referred to as periods of streamflow deficit or deficits for the purpose of this report. As part of 

the analysis conducted for this report, different averaging periods for determining and measuring 

deficits were considered. The use of a 1-year averaging period was discarded because it implied 

that any 1 year above 15 maf of natural flow at Lees Ferry, Arizona, would break a multi-year 

deficit. The use of a 2-year averaging period implies that it may take 2 consecutive, abovenormal years (or 1 extremely wet year) to end a deficit. The definition used in the remainder of 

0

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Box = 25th to 75th percentile range; Dash is median value (50th percentile); and whiskers 

represent min and max range
Technical Report B — Water

Supply Assessment

December 2012 B-27

this report is the following: a deficit occurs whenever the 2-year average flow falls below 

15 maf, the long-term mean annual flow of the 1906 to 2007 period. 

Applying this definition, figure B-18 presents the severity of 2-year deficits in the observed 

record. For each year of the 1906 to 2010 period4, the 2-year running average annual flow was 

calculated. The difference between the 2-year running average flow and the long-term mean 

annual flow was computed. If the difference was negative, it was labeled “deficit” and the 

volumes were accumulated until the difference was once again positive. The deficit length and 

cumulative amount were recorded for each year. Three significant deficit spells that occurred in 

the observed period beginning in 1931 (7-year deficit), 1959 (7-year deficit), and 2000 (9-year 

deficit) are shown on the figure in green, orange, and red, respectively. As can be seen from the 

figure, the deficit that began in 2000 accumulated a 9-year deficit of more than 28 maf. This 

recent deficit is more severe than any other deficit in the observed period. 

FIGURE B-18 

Cumulative Streamflow Deficits (defined as 2-year running mean below 15 maf) for the Colorado River at Lees Ferry, Arizona

2008–2010 natural flows are provisional. 

 

4 The natural flow at Lees Ferry, Arizona extended to 2010, based on provisional natural flow estimates is used here to better reflect the current 

state of streamflow deficit. 

1931

1932 1933

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1936 1937

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Cumulative Deficit (kaf)

Deficit Length (Years)

Cumulative Streamflow Deficits in Observed Natural Flow Records

Colorado River at Lees Ferry "Natural Flows", 1906-2010

Deficit Defined as 2-yr Running Mean below Long-Term Mean

All Years 1931-1937 1959-1965 2000-2008
Colorado River Basin 

Water Supply and Demand Study

B-28 December 2012

4.3 Paleo Reconstruction of Streamflow

A summary of the snapshot results for Colorado River at Lees Ferry, Arizona from the paleoreconstructed 762 to 2005 period is shown in figure B-19. The top plot shows the annual flow 

volumes and the moving averages for 3, 5, 10, 20, and 30 years for the period of record. This plot 

provides a visual assessment of streamflow variability, minimum and maximum flows, and longterm trends. Period comparisons between long-term paleo reconstruction (762 to 2005) and a 

segment of the observed record (1906 to 2005) are shown. The annual flow box plot shows the 

minimum, 75th, 50th, and 25th percentiles, and maximum annual streamflows for the two 

analysis periods. The minimum, 25th percentile, median, and 75th percentile are all slightly less 

in the paleo reconstructed record, indicating that the paleo reconstructed streamflows are lower 

than the observed record. Variability is increased in the paleo reconstructed record, as illustrated 

by the broader inter-quartile range and minimum/ maximum values. Finally, the bottom panel 

shows the annual (left axis) and cumulative (right axis) deviations from the mean annual flow to 

illustrate the wet and dry periods in this long-term record. 

Streamflow deficits using the same methods as described in the previous section were 

similarly computed for the 762 to 2005 period and the 1906 to 2005 period, and statistics 

are presented in three exceedance plots (duration, magnitude, and intensity) in figure B-20. 

The 762 to 2005 period contains deficits that are longer in duration (16 years) and larger (as 

much as 35 maf) than those in the 1906 to 2005 period. Thus, the sequences of wet-dry from 

the much longer paleo record suggest that deficits of greater severity than the recent deficit are 

possible. Interestingly, the deficit intensity (defined as the cumulative deficit divided by the 

duration of the deficit, which can give an indication of the annual severity of deficits) is similar 

between the two periods, suggesting that the paleo record produces longer deficits, but that they 

may not be any more intense on an annual basis than the observed record. 

In summary, the trends over the observed period and over the recent climatological regime 

suggest declining streamflows, increases in variability, and seasonal shifts in streamflow that are 

likely linked to warming. The paleo reconstruction indicates a slightly lower mean than the 

observed record. The paleo reconstruction suggests the annual and inter-annual flows have been 

more variable in terms of both wet and dry sequences, as compared with the observed record 

period. Deficits of longer duration and greater magnitude can be expected based on the paleo 

record, although the paleo record shows that past deficits were not significantly more intense 

than the observed record. 
Technical Report B — Water

Supply Assessment

December 2012 B-29

FIGURE B-19 

Colorado River at Lees Ferry, Arizona Paleo Streamflow Snapshot Analysis

cum=cumulative

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762 862 962 1062 1162 1262 1362 1462 1562 1662 1762 1862 1962

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Annual Flow Box Plot
Colorado River Basin 

Water Supply and Demand Study

B-30 December 2012

FIGURE B-20

Comparison of Drought Characteristics between a Segment of the Observed Period (1906–2005) and the Paleo Period (762–

2005)

Exceedance Probability (%)

5.0 Future Supply under the Observed Resampled 

Scenario

5.1 Methods

Used by Reclamation in several past planning studies (such as Reclamation, 2007), the Observed 

Resampled5 scenario is quantified by applying the Indexed Sequential Method (ISM) (Ouarda 

et al., 1997) to the 1906 to 2007 observed natural flow record to generate 102 sequences, each 

50 years in length. ISM is a stochastic resampling method that creates a number of different 

future hydrologic sequences (or realizations). The length of the hydrologic sequence is

determined by the simulation horizon (2011 to 2060, or 50 years in the Study) and the number 

 

5 The analysis of the Direct Natural Flow, Direct Paleo, and Nonparametric Paleo Conditioning scenarios discussed in appendix N of the 2007 

Interim Guidelines Final EIS (Reclamation, 2007) are synonymous with the analysis of the Observed Resampled, Paleo Resampled, and Paleo 

Conditioned scenarios discussed in this report, respectively. 
Technical Report B — Water

Supply Assessment

December 2012 B-31

of sequences is determined by the length of the record that is being resampled (1906 to 2007, or 

102 years in this scenario). The ISM cycles through the observed record generating 

102 hydrologic sequences, based on the assumption that the record “wraps around” at the 

end (i.e., 1960 to 2007, followed by 1906, 1907, and 1908). 

Strengths of this method are that it is based on the best available measured data, provides the 

basis for a quantification of the uncertainty and an assessment of risk with respect to future 

inflows, and is widely accepted by Basin stakeholders. The major drawback of this approach is 

that future scenarios are limited to the magnitudes and sequencing that occurred in the observed 

record, with the exception of new sequences generated as a result of the wrap. Therefore, a wider 

range of plausible future streamflows (including flow magnitudes and wet and dry sequences not 

seen in the observed record) are not possible in the Observed Resampled scenario.

5.2 Results

The results for the Observed Resampled scenario are presented as summary figures for annual 

and monthly flows at Colorado River at Lees Ferry, Arizona in figures B-21 through B-24. 

Because each supply scenario included multiple hydrologic sequences, there is a range 

associated with the flow statistics. Figure B-21 displays all of the individual 102 sequences in the 

Observed Resampled scenario. The sequence bolded in figure B-21 also appears in figure B-22, 

which is a representative trace of the 102 sequences for illustration purposes. Figure B-22 also

depicts the annual range of natural flows when applying the ISM technique, and figure B-23 

provides the annual statistics. 

Annual natural flows are generally in the range of 5 to 25 maf, with a mean of approximately 

15 maf. The standard deviation is almost one-third of the mean annual flow, providing a 

representation of the inter-annual variability of this flow record. Skew is a measure of the shape 

of the annual flow distribution. A skew of zero implies a normal distribution in which wetter 

years and magnitudes are evenly balanced with drier years. The skew and backward lag 

correlation indicate that the flows are slightly biased to the lower side of the distribution (more 

dry years than wet years) and that year-to-year correlation of flows (indicated by the backward 

lag correlation) is relatively high.
Colorado River Basin 

Water Supply and Demand Study

B-32 December 2012

FIGURE B-21 

Colorado River at Lees Ferry, Arizona Natural Flow for 102 Sequences for the Observed Resampled Scenario

The bolded line indicates a representative trace. 

FIGURE B-22 

Simulated Annual Colorado River at Lees Ferry, Arizona Natural Flow Statistics for 102 Realizations, 2011–2060

Figure shows the median (line), 25th–75th percentile band (dark shading), 10th–90th percentile band (light shading), max/min 

(whiskers), and 1906–2007 observed min and max (dashed lines). The blue line indicates a representative trace.

0

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Annual Natural Flow at Lees Ferry (kaf)

Range of Lees Ferry Natural Flow Sequences in the Observed Resampled 

Scenario

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2011201620212026203120362041204620512056

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Annual Natural Streamflow - Observed Resampled
Technical Report B — Water

Supply Assessment

December 2012 B-33

FIGURE B-23 

Summary Statistics for Annual Colorado River at Lees Ferry, Arizona Natural Flows for the Observed Resampled Scenario

Figure shows the median (dash), 25th–75th percentile band (box), and max/min (whiskers). 

River flow peaks in late spring due to delayed snowmelt from the higher elevation upstream 

watersheds, with May, June, and July exhibiting the highest flows (figure B-24). June flows are 

both the highest and most variable with mean monthly flows averaging about 4 maf per month 

and ranging from about 1 to 9 maf per month. Late summer and fall flows are considerably lower 

and exhibit significantly less variability. 

FIGURE B-24 

Simulated Monthly Colorado River at Lees Ferry, Arizona Natural Flow Statistics for 102 Realizations, 2011–2060 

Figure shows the median (dash), 25th–75th percentile band (shading), and max/min (whiskers).

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Colorado River Basin 

Water Supply and Demand Study

B-34 December 2012

Another measure of the inter-annual variability and persistence of streamflow states (wet 

and dry) is characterized by determining the frequency, duration, and magnitude of deficit and 

surplus periods. Recall that for the purpose of this report, “deficit” is defined as a consecutive 

2-year period when the mean is less than the observed long-term mean of 15.0 maf. Similarly, 

“surplus” is defined as a consecutive 2-year period when the mean is above 15.0 maf.

Figure B-25 illustrates four characteristics of deficit and surplus spells throughout the Study 

period (2011 to 2060): spell length, spell magnitude, the frequency of specific spell lengths 

occurring, and the relationship between deficits and surpluses in the scenario. Box plots 

displaying spell length are shown in the left figure (deficit, below the x-axis, and surplus, above 

the x-axis). The exceedance plot shown in the right figure displays the exceedance probabilities 

for spell lengths. Probabilities for deficit spells are shown in the bottom half of the plot. 

Probabilities for surplus spells are shown in the top half of the plot. 

FIGURE B-25 

Simulated Deficit and Surplus Spell Length and Magnitude for all 102 Realizations in the Observed Resampled Scenario

Box plots show the median (dash), 25th–75th percentile band (shading), and max/min (whiskers).

Spell length: the maximum deficit is 8 years (note that this length would be 9 years if the 

observed record extended through 2010), and the maximum surplus is 7 years. This information 

is provided in both the box plots and the exceedance plot. 

Spell magnitude: referring to the box plots, the magnitude of the maximum deficit and surplus is 

about 27 maf and 22 maf, respectively. Deficit or surplus intensity can be computed by dividing 

the spell magnitude by the spell length. 

Frequency of specific spell lengths occurring: the exceedance plot inset provides information 

regarding the frequency of the length of deficit and surplus spells. As such, the median 

exceedance probability of a deficit spell of 5 years is about 70 percent, meaning there is about a 
Technical Report B — Water

Supply Assessment

December 2012 B-35

30 percent chance of a deficit longer than 5 years. Similarly, at the 30 percent median 

exceedance probability is a surplus spell of 3 years, meaning there is about a 30 percent chance 

of a surplus period lasting more than 3 years. 

Relationship between deficits and surpluses in the scenario: the median (50 percent exceedance 

probability) corresponds to a deficit of 3 years. This result indicates that under the Observed 

Resampled scenario, there is a greater probability of being in a deficit (lasting at least 3 years) 

than in a surplus period. 

6.0 Future Supply under the Paleo Resampled Scenario

6.1 Methods

The Paleo Resampled scenario is generated by applying the ISM to paleo reconstructed

streamflow data (762 to 2005) to develop 1,244 traces, each 50 years in length. The major 

strength of this method is the ability to produce sequences with magnitudes and deficit/surplus 

spells not found in the Observed Resampled scenario. In addition, as is true for the Observed 

Resampled scenario, this method is based on relationships to measured data. Although there is a 

wealth of literature documenting the strong link between streamflow and tree-ring growth in 

moisture limited regions, the exact magnitudes of a paleo reconstruction are not as reliable as 

historical flow data, particularly at the extremes (Woodhouse and Brown, 2001). This is 

attributed to a variety of factors in the reconstruction process, such as model selection to relate 

tree-ring width to streamflow. Furthermore, because ISM sequentially resamples the paleo record 

to generate hydrologic sequences, the sequences will only consist of flow magnitudes and 

sequences that are present in the paleo record, with the exception of the sequences created as a 

result of the wrap. The inclusion of the Paleo Conditioned scenario addresses this issue and the 

weakness of the paleo record in capturing magnitudes at the extremes. 

Because the paleo flow data are only available at the annual time step for a single location 

(Colorado River at Lees Ferry, Arizona), annual flows at this location were disaggregated, 

spatially and temporally, throughout the Upper Basin natural flow locations using a nonparametric disaggregation method (Nowak et al., 2010). The disaggregation method relies on the 

observed record to model the spatial and temporal distribution properties of the monthly and 

annual flow. Disaggregated flows at the Lower Basin natural flow locations are generated by 

selecting an “analog” year from the observed record. These methods have been demonstrated to 

be appropriate and effective for the Basin and time step. For a more detailed explanation of these

methods, please see Nowak et al., 2010, and appendix N of the 2007 Interim Guidelines Final 

EIS (Reclamation, 2007). 

6.2 Results

The results for the Paleo Resampled scenario are presented as summary figures for annual and 

monthly flows for the Colorado River at Lees Ferry, Arizona in figures B-26 through B-29. As 

with the Observed Resampled scenario, multiple realizations are simulated, producing a range 

associated with the flow statistics. Figure B-26 displays all of the individual 1,244 sequences in 

the Paleo Resampled scenario. The sequence bolded in figure B-26 also appears in figure B-27, 

which is a representative trace of the 1,244 sequences for illustration purposes. Figure B-27 also

depicts the annual range of natural flows, while figure B-28 provides the annual statistics. 
Colorado River Basin 

Water Supply and Demand Study

B-36 December 2012

Annual natural flows are generally in the range of 3 to 25 maf, with a mean of approximately 

14.7 maf. The minimum annual flow is much lower than the Observed Resampled scenario, 

while the maximum annual flow is similar. Conversely, the standard deviation is smaller than the 

Observed Resampled scenario, suggesting that a greater number of traces are closer to the mean 

value. In the Paleo Resampled scenario, the skew is slightly negative (compared to slightly 

positive in the Observed Resampled scenario), suggesting a greater frequency of wet years than 

dry years (compared to the Observed Resampled scenario). Finally, the backward lag correlation 

is slightly higher than the Observed Resampled scenario, suggesting a greater year-to-year 

correlation than in the observed record. The latter likely results from the reconstruction 

techniques and relatively few chronologies in the distant past. 

FIGURE B-26 

Colorado River at Lees Ferry, Arizona Natural Flow for 1,244 Sequences for the Paleo Resampled Scenario

The bolded line indicates a representative trace.

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Annual Lees Ferry Natural Flow (kaf)

Range of Lees Ferry Natural Flow Sequences in the Paleo Resampled 

Scenario
Technical Report B — Water

Supply Assessment

December 2012 B-37

FIGURE B-27 

Simulated Annual Colorado River at Lees Ferry, Arizona Natural Flow Statistics for 1,244 Traces, 2011–2060 

Figure shows the median (line), 25th–75th percentile band (dark shading), 10th–90th percentile band (light shading), max/min 

(whiskers), and 1906–2007 observed min and max (dashed lines). The orange line indicates a representative trace. 

FIGURE B-28 

Summary Statistics for Annual Colorado River at Lees Ferry, Arizona Natural Flows for the Paleo Resampled Scenario

Figure shows the median (dash), 25th–75th percentile band (box), and max/min (whiskers).

0

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2011201620212026203120362041204620512056

KAF

Annual Natural Streamflow - Paleo Resampled
Colorado River Basin 

Water Supply and Demand Study

B-38 December 2012

Monthly river flows suggest no significant change from the Observed Resampled scenario. Peak 

flows occur in late spring, with May, June, and July exhibiting the highest flows (figure B-29).

As in the Observed Resampled scenario, June flows are both the highest and most extreme, with 

mean monthly flows averaging about 4 maf per month and ranging from about 1 to 9 maf per 

month. This was expected because the disaggregation applied to the annual paleo reconstruction

was trained on the observed natural flow data. Also similar to the Observed Resampled scenario, 

late summer and fall flows are considerably lower and exhibit significantly less variability. 

FIGURE B-29 

Simulated Annual Colorado River at Lees Ferry, Arizona Natural Flow Statistics for 1,244 Realizations, 2011–2060 

Figure shows the median (dash), 25th–75th percentile band (shading), and max/min (whiskers).

Figure B-30 illustrates the length, magnitude, and frequency of deficit and surplus spells. Under 

the Paleo Resampled scenario, maximum deficit and surplus periods are significantly longer in 

duration than those in the Observed Resampled scenario. Maximum deficit spell length under the 

Paleo Resampled scenario is about 17 years, and the maximum surplus spell length is about 

15 years. The 17-year deficit period contains approximately 35 maf of total deficit. For 

comparison, the recent deficit persisted for 9 years (through 2008) with an accumulated deficit of 

about 29 maf. Thus, from a measure of deficit intensity, although the deficit is sustained longer 

in the Paleo Resampled scenario, the annual deficits are not dissimilar from the Observed

Resampled scenario. 

0

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14,000

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

KAF/month
Technical Report B — Water

Supply Assessment

December 2012 B-39

FIGURE B-30

Simulated Deficit and Surplus Spell Length and Magnitude for all 1,244 Realizations in the Paleo Resampled Scenario

Box plots show the median (dash), 25th–75th percentile band (shading), and max/min (whiskers).

7.0 Future Supply under Paleo Conditioned Scenario

7.1 Methods

The Paleo Conditioned scenario is generated by applying a non-parametric technique to “blend” 

the observed historical and paleo reconstructed records to generate 1,000 traces, each 50 years in 

length. Flow magnitudes vary significantly across multiple reconstructions for a particular site 

(Stockton and Jacoby, 1976; Hildalgo et al., 2009; Hirschboeck and Meko, 2005; Woodhouse 

et al., 2006). However, the paleo hydrologic state agreement (i.e., wet or dry) is quite reliable 

across different reconstructions (Woodhouse et al., 2006). 

The paleo conditioned technique blends the rich variety of drought/surplus found in the paleo

reconstruction with reliable magnitudes from the observed natural flow data by first extracting a 

sequence of years represented simply as wet or dry from the streamflow reconstruction. Flow 

magnitudes are then conditionally resampled from the observed record for each year in the 

sequence, based on the current and previous hydrologic state. Thus, any underlying relationship 

between magnitude and sequencing is preserved while circumventing issues associated with 

magnitude reliability. For example, if an observed flow value occurred as the first year of a 

drought, it can only be assigned to a “dry state year” that was preceded by a “wet state year” as 

part of a paleo conditioned trace. Similarly, if an observed flow magnitude was the second year 

of a multi-year surplus period, that value can only be assigned to a “wet state year” that was 

preceded by another “wet state year.” This logic holds true for all wet/dry sequencing 

combinations. Following this method, a wealth of traces can be generated (at least 1,000 are 

recommended to limit sample variability) by simply changing the initial wet/dry sequence 

information extracted from the paleo data. Different from the ISM technique, the number of 
Colorado River Basin 

Water Supply and Demand Study

B-40 December 2012

sequences is not limited to the length of the streamflow record being resampled. For a more 

detailed explanation of the method, see appendix N of the 2007 Interim Guidelines Final EIS

(Reclamation, 2007) and Prairie et al., 2008. As was the case with the Paleo Resampled scenario, 

the Paleo Conditioned scenario introduces considerable variability when compared with the 

observed data, yet maintains the reliability of the observed magnitudes. Paleo conditioned traces 

were also generated at the annual time scale for Colorado River at Lees Ferry, Arizona and 

required the same disaggregation process employed for the Paleo Resampled scenario in order to 

produce monthly data at multiple locations. 

7.2 Results

The results for the Paleo Conditioned scenario are presented as summary figures for annual 

and monthly flows at Colorado River at Lees Ferry, Arizona in figures B-31 through B-34. 

Figure B-31 displays all of the individual 1,000 sequences in the Paleo Conditioned scenario. 

The sequence bolded in figure B-31 also appears in figure B-32, which is a representative 

trace from the 1,000 sequences. Figure B-32 depicts the annual range of natural flows, and 

figure B-33 provides the annual flow statistics. 

Annual natural flows are generally in the range of 5 to 25 maf, with a mean of approximately 

14.9 maf. The annual statistics are similar to the Observed Resampled scenario, largely due to 

the paleo conditioned technique that borrows the magnitudes from the observed record when 

combining with state information from the paleo reconstructions. Similarly, the standard 

deviation, skew, and backward lag correlation indicate that the annual flow statistics are similar 

to the Observed Resampled scenario. Monthly flows are also similar in pattern and magnitude to 

the Observed Resampled and Paleo Resampled scenarios, as shown in figure B-34. 

The most significant difference between the Paleo Conditioned scenario and the Observed 

Resampled and Paleo Resampled scenarios is in the inter-annual variability and persistence of 

streamflow states (wet and dry). Figure B-35 illustrates the frequency, length, and magnitude of 

deficit and surplus spells. Deficit periods of 15 years or longer are observed in this scenario and 

produce accumulated deficits greater than 60 maf. Similarly, extended surplus periods of similar 

length produce surpluses greater than 60 maf. Under this scenario, deficit periods could persist 

considerably longer than the Observed Resampled and produce deficits almost twice as large. 

However, interestingly, the median probability of exceeding a deficit spell of greater than 7 years 

is only 20 percent, and there is only a 10 percent likelihood of exceeding a 5-year surplus period. 
Technical Report B — Water

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December 2012 B-41

FIGURE B-31 

Colorado River at Lees Ferry, Arizona Natural Flow for 1,000 Sequences for the Paleo Conditioned Scenario

The bolded line indicates a representative trace.

FIGURE B-32 

Simulated Annual Colorado River at Lees Ferry, Arizona Natural Flow Statistics for 1,000 Realizations, 2011–2060 

Figure shows the median (line), 25th–75th percentile band (dark shading), 10th–90th percentile band (light shading), max/min 

(whiskers), and 1906–2007 observed min and max (dashed lines). The bolded line indicates a representative trace.

0

5,000

10,000

15,000

20,000

25,000

30,000

Annual Lees Ferry Natural Flow (kaf)

Range of Lees Ferry Natural Flow Sequences in the Paleo-Conditioned 

Scenario

0

5,000

10,000

15,000

20,000

25,000

30,000

2011201620212026203120362041204620512056

KAF

Annual Natural Streamflow - Paleo Conditioned
Colorado River Basin 

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B-42 December 2012

FIGURE B-33 

Summary Statistics for Annual Colorado River at Lees Ferry, Arizona Natural Flows for the Paleo Conditioned Scenario

Figure shows the median (dash), 25th–75th percentile band (box), and max/min (whiskers). 

FIGURE B-34 

Simulated Annual Colorado River at Lees Ferry, Arizona Natural Flow Statistics for 1,000 Realizations, 2011–2060 

Figure shows the median (dash), 25th–75th percentile band (shading), and max/min (whiskers).

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

KAF/month
Technical Report B — Water

Supply Assessment

December 2012 B-43

FIGURE B-35 

Simulated Deficit and Surplus Spell Length and Magnitude for all 1,000 Realizations in the Paleo Conditioned Scenario

Box plots show the median (dash), 25th–75th percentile band (shading), and max/min (whiskers).

8.0 Future Supply under the Downscaled GCM Projected 

Scenario

8.1 Methods

Future changes in climate variability and trends, and their influence on streamflow and Basin 

water supply, have been studied by several researchers in recent years, and GCM future 

projections indicate that the climate may exhibit trends and variability over the next 50 years

beyond what has occurred historically. The Downscaled GCM Projected scenario is one 

representation of this plausible future condition. 

A number of methods for incorporating climate information in planning studies are available and 

have been summarized by Reclamation (2007) and others (Hamlet et al., 2010). Methods range 

from simple adjustments to the temperature and precipitation inputs (Delta method), to 

application of regional climate models for weather generation, to bottom-up risk-based 

approaches targeting system vulnerabilities. No one approach is better than the other; rather, 

each serves a specific planning purpose and consists of a set of analysis tools. The approach 

taken in the Study incorporates future climate information from GCMs, subsequently bias 

corrected and statistically downscaled, to drive a hydrologic model of the Basin. The hydrologic 

model simulates the effects of future climate on hydrologic processes in the Basin and provides 

information relating to streamflow at all major inflow points to the Colorado River and 

tributaries. The streamflow and ET information is then used as input into CRSS, Reclamation’s 

primary Basin-wide simulation model used for long-term planning studies. This approach is 

shown graphically in figure B-36. Using this approach of linking global and regional climate 
Colorado River Basin 

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B-44 December 2012

information, physically based hydrologic processes, streamflow routing, and systems modeling 

allows for a consistent linkage between climate and system responses that are desired as part of 

this scenario and the overall study of future Basin reliability. The methodological approach to 

develop the Downscaled GCM Projected scenario consists of five major elements depicted 

graphically in figure B-36. A total of 112 future climate projections used in the IPCC Fourth 

Assessment Report (2007), subsequently bias corrected and statistically downscaled, were

obtained from the Lawrence Livermore National Laboratory under the World Climate Research 

Program’s (WCRP) Coupled Model Intercomparison Project Phase 3 (CMIP3) (Maurer et al., 

2007)6. These data were incorporated in the first three elements of the approach in figure B-36. 

Each of the 112 downscaled climate projections was then used as input into the VIC hydrology 

model. The VIC hydrology model used the climate projections along with land cover, soils, 

elevation, and other watershed information to simulate hydrologic fluxes. The hydrologic fluxes 

were then routed to each of the 29 natural flow locations using a routing network derived from 

the topography (Lohmann et al., 1996; Lohmann et al., 1998). The result of this approach was 

112 unique sequences of natural flow under future climate projections. Notably, the simulated 

natural flows can contain significant monthly and annual biases when compared to the natural 

flows of the historical period. These biases are generally small for mainstem Colorado River 

locations, but can be large for smaller watersheds and in areas where the VIC model was not 

specifically calibrated. To account and compensate for these biases, the VIC-simulated 

streamflows for both the historical and future periods were first adjusted for biases before 

incorporating into systems modeling. Details on the methods used to correct for biases are 

included in appendix B4. 

The same Downscaled GCM Projected scenario was also employed to develop the results 

described in the SECURE Water Act Section 9503(c) – Reclamation Climate Change and Water 

2011 (Reclamation, 2011c) Report. The SECURE Report was prepared by Reclamation’s Office 

of Policy and Administration and includes projections of how climate change may impact the 

water supply on the Basin. The SECURE Report was prepared by Reclamation to provide 

consistent, reconnaissance-level information focused on the future risks to water supply 

throughout the eight Reclamation basins. 

While the results are consistent between this report and the SECURE Report, the SECURE 

Report was limited to the evaluation of the meteorological and hydrologic changes under 

projected climate change. The Study also considered how hydrological changes may impact the 

performance of the Colorado River system through CRSS modeling. The differences in study 

objectives led to some differences in approach. The methodological differences consist primarily 

of the application of a streamflow bias correction method before using the simulated natural 

flows in the CRSS model. Reporting differences between this report and the SECURE Report 

consist of the selection of baseline climate conditions and the future analysis periods. 

Specifically, the SECURE Report computed future decadal changes from a 1991 to 2000 

baseline condition, whereas the streamflow change statistics reported here were computed 

between the long-term historical record (1906 to 2007) and the Study period of 2011 to 2060. 

This period provides a long-term record consistent with that used in the Observed Resampled

 

6 These data are available via the website, Bias Corrected and Downscaled World Climate Research Program Coupled Model Intercomparison 

Project Phase 3 Climate Projections (http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/), which is jointly hosted by the Green Data 

Oasis, Santa Clara University, Reclamation, and Lawrence Livermore National Laboratory. 
Technical Report B — Water

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December 2012 B-45

scenario, captures a sufficiently long period necessary to describe drought and surplus statistics, 

and represents a mean annual flow of importance to Colorado River management. The 1906 to 

2007 mean annual flow for the Colorado River at Lees Ferry, Arizona is 15.0 maf; the mean 

annual flow is 15.5 maf for the 1971 to 2000 period, 15.0 maf for the 1978 to 2007 period, 15.3 

maf for the 1991 to 2000 period, and 14.6 maf for the 1950 to 1999 period. The 1950 to 1975 

period contained lower annual flows and lower interannual variability than many of the other 

periods, likely influenced by conditions associated with the cold phase of PDO. To capture the 

projected future trends in streamflow changes associated with the Downscaled GCM Projected 

scenario, additional information has been provided in this report for three future 30-year time 

periods (2011 to 2040, 2041 to 2070, and 2066 to 2095). While the last of these periods extends 

beyond the Study period, it provides an important reference for understanding the potential 

direction of the future Basin hydrology. Therefore, results between the Study and the SECURE 

Report are not identical; however, work from the Study will be used to inform future reports 

under the SECURE Water Act.

FIGURE B-36 

Methodological Approach for the Development of the Downscaled GCM Projected Scenario

8.1.1 Emission Scenarios 

As discussed previously, the downscaled climate projections were obtained from the World 

Climate Research Program’s CMIP3 database. This database includes downscaled climate 

projections from 16 different GCMs simulated with three different IPCC emission scenarios 

(IPCC, 2000). The emission scenarios are those from the Special Report on Emissions Scenarios

(SRES) (IPCC, 2000), emission scenarios A2 (high), A1B (medium), and B1 (low), and reflect a 
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B-46 December 2012

range of future greenhouse gas (GHG) emissions. The A2 scenario is representative of high 

population growth, slow economic development, and slow technological change. It is 

characterized by a continuously increasing rate of GHG emissions, and features the highest 

annual emissions rates of any scenario by the end of the 21st Century. The A1B scenario features 

a global population that peaks mid-century and rapid introduction of new and more efficient 

technologies balanced across both fossil- and non-fossil intensive energy sources. As a result, 

GHG emissions in the A1B scenario peak around mid-century. Last, the B1 scenario describes a 

world with rapid changes in economic structures toward a service and information economy, 

with reductions in material intensity, and the introduction of clean and resource-efficient 

technologies. GHG emission rates in this scenario peak prior to mid-century and are generally 

the lowest of the scenarios. 

SRES emission scenarios exist that have both higher (A1FI) and lower (A1T) GHG emissions 

than those considered in the Study (see appendix B2). However, the three scenarios included in 

the analysis span the widest range available for which consistent, comprehensive GCM modeling 

has been performed and for which downscaled climate information is available. Furthermore, 

while it is possible that higher rates of warming and resulting effects on streamflows are 

possible, it should be noted that the atmospheric response to emission increases is not immediate. 

Climate response to increases in GHG emissions is on the decadal scale. Uncertainty in the 

projected climate system response due to increased emissions (GCM uncertainty) tends to be a 

greater determinant of the range of climate conditions through mid-century than the uncertainty 

associated with future emission scenarios themselves. 

Assumptions related to parameter characteristics included in the SRES emission scenarios (such 

as high population growth and slow economic development) are not related to parameter 

characteristics of the Water Demand scenarios (see Technical Report C – Water Demand 

Assessment) because they describe a global set of drivers rather than those directly associated 

with the Colorado River. When considering water demand scenarios combined with water supply 

scenarios that incorporate climate change, outdoor water demands and reservoir evaporation 

rates were modified to reflect estimates of changes in ET and open water surface evaporation 

rates consistent with the assumptions for water supply. 

8.1.2 GCMs

Sixteen GCMs were coupled with the three emissions scenarios to simulate the global 

atmosphere and oceans and provide projections of specific climatological forcings (principally 

temperature and precipitation) during the period 1950 to 2099. Many of the GCMs were 

simulated multiple times for the same emission scenario due to differences in starting climate 

system state (initial oceanic and atmospheric conditions); thus, the number of available 

projections (112) is greater than simply the product of the number of GCMs and emission

scenarios. Appendix B2 provides a summary of the GCMs, initial conditions, and emissions

scenario combinations featured in the Study. Recent research (Pierce et al., 2009; Gleckler et al., 

2008) has shown the importance of incorporating multiple climate projections (even when 

derived from the same GCM) and the superiority of the multi-model ensemble for a wide array 

of climate metrics. The subsequent results presented on future climate (primarily temperature 

and precipitation) and, indirectly, streamflow rely on the data generated by these GCMs.
Technical Report B — Water

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December 2012 B-47

8.1.3 Bias Correction and Spatial Downscaling 

Due to the coarseness of the GCM grids and inherent biases in their results, the GCM results 

were transformed to a local scale (~12 km) through a process called bias correction and spatial 

downscaling (BCSD). The methods of this process are described in detail in Wood et al., 2002; 

Wood et al., 2004; and Maurer, 2007. 

The purpose of bias correction was to adjust a given climate projection for inconsistencies 

between the simulated historical climate data and observed historical climate data. In the 

BCSD approach, GCM projections were bias corrected at a 2-degree resolution using a quantile 

mapping technique which corrects the simulated historical monthly temperature and precipitation 

projections to be consistent with the observed distributions at the same resolution. Following bias 

correction, the adjusted climate projection data were statistically consistent (monthly cumulative 

distribution functions were identical) with the observed climate data for the historical overlap 

period of 1950 to 1999. The bias correction quantile maps derived from the historical overlap 

period were then used to adjust the GCM projections for the future period. Note that this method 

assumes that the GCM biases have the same structure during the 20th and 21st Centuries’ 

simulations. 

Downscaling spatially translated bias corrected climate data from the coarse, 2-degree 

(~200 km), spatial resolution typical of climate models to a Basin-relevant resolution of 

1/8th-degree (~12 km), which is more useful for hydrology and other applications. The spatial 

downscaling process generally preserves observed spatial relationships between large- and finescale climates. This approach assumes that the topographic and climatic features that determine 

the fine-scale distribution of large-scale climate will be the same in the future as in the historical 

period. 

8.1.4 Daily Weather Generation (Temporal Dissaggregation)

The resulting BCSD climate projections provided a representation of future monthly temperature 

and precipitation through 2099. However, to be useful for hydrologic modeling, this information 

was required on a daily temporal scale. The monthly downscaled data was temporally 

disaggregated to a daily temporal scale to create realistic weather patterns using the sampling 

methods described in Wood et al. (2002). To generate daily values, for each month in the 

simulation a month was randomly selected from the historic record for the same month (e.g., for 

the month of January, a January is selected from the 1950 to 1999 period). The daily 

precipitation and temperature data from the historic record were then adjusted (rescaled 

precipitation and shifted temperature) such that the monthly average matches the simulated 

monthly value. The same historic month was used throughout the domain to preserve plausible 

spatial structure to daily storms. The results of the temporal disaggregation were daily weather 

sequences that preserve the monthly values from the downscaled climate projections. Some 

uncertainties were introduced depending on the method employed to produce the daily data from 

the monthly climate values. A comparison of two available methods (Wood et al., 2002, and 

extension of Wood et al., 2002, described in Salathé, 2005) to generate daily weather patterns for 

the Study favored the use of the Salathé, 2005, method employed in the SECURE Report to 

produce the daily downscaled data. An additional description of the comparative analysis is 

presented in appendix B3. 
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B-48 December 2012

8.1.5 Hydrologic Modeling

The daily weather sequences were used as input to the VIC hydrologic model to generate 

estimates of hydrologic fluxes and streamflow under various climate futures. For each of the 

112 climate projections, the VIC hydrologic model produced a distinct trace of natural flows at 

each of the 29 natural flow locations. 

Developed at the University of Washington, the VIC model is a semi-distributed, macro-scale 

hydrologic model that solves the water balance at each model grid cell. A VIC model of the 

Basin was previously developed by the University of Washington (Christensen and Lettenmaier, 

2007), and was provided to Reclamation for the Study. The model has not been further calibrated 

or refined as part of the Study, but the model performance and bias correction has been evaluated 

and is discussed in the next section. A thorough description of the VIC model is provided in 

appendix B4. 

Analysis shows (presented in appendix B4) that there are some biases in the VIC streamflows as 

driven by historical observed and downscaled climate model simulated historical meteorological 

forcings in comparison with the natural flows for the Basin for the overlapping period of 1950 to 

1999. These biases are generally small for mainstem Colorado River locations, but can be large 

for smaller watersheds and in areas where the VIC model was not specifically calibrated. The 

mean annual flow bias for the Colorado River at Lees Ferry, Arizona, is positive 1.1 percent. 

Moving upstream to the three largest contributors to flow at Lees Ferry, the bias is negative 

3 percent for the Green River near Green River, Utah, less than 1 percent for the Colorado River 

near Cisco, Utah, and negative 6 percent for the San Juan River near Bluff, Utah. The VIC model 

appears to have higher biases in the upper watersheds and lower biases farther downstream as 

more of the watershed contributes to the flow. In general, the upper Colorado River locations 

exhibited a positive bias, while the Green River and San Juan River locations exhibited a 

negative bias. 

These biases are due to differences between the GCM-simulated historical climate and observed 

climate data, differences in hydrology model inputs and parameterization, and differences 

between the VIC-simulated hydrologic responses and observed watershed responses implied in 

the natural flows. The lack of calibration of the VIC model for lower order streams within the 

Basin is believed to be a significant source of the bias at these scales. 

A streamflow bias correction method was developed and applied to the “raw” VIC-simulated 

flows to account for any systematic bias in the hydrology model and/or climate data sets. 

The method corrected for monthly and annual biases, while ensuring that the corrected flows 

maintained the system and local mass balance. The raw VIC-simulated streamflows for both 

the historical and future periods were first adjusted for biases before incorporating into the 

CRSS modeling. The streamflow bias correction step was an important component for the use 

of climate-driven hydrologic modeling and results in subsequent systems modeling. Without this 

step, the VIC-simulated historical flow biases would be carried forward into future assessments 

and the potential existed for misattribution of some streamflow changes to changes in climate, 

while these may be partially associated with model/data bias. Details on the methods used to 

correct for biases are included in appendix B4. 
Technical Report B — Water

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December 2012 B-49

In addition to producing routed natural flows, the VIC model also provided output for other 

water supply indicators, including precipitation, runoff, baseflow, ET, soil moisture, and SWE. 

The subsequent results presented on hydrologic processes relied on these parameters generated 

by the VIC model. 

Additional detail on VIC and its application for the Study can be found in appendix B4. 

8.1.6 Systems Modeling

A total of 112 realizations at the 29 natural flow locations were taken from the VIC model 

simulations and subsequently corrected for streamflow biases. Differing from the three other 

future water supply scenarios, which do not address changes in climate, each Downscaled 

GCM Projection hydrologic sequence of streamflow exhibits a long-term future trend and 

increased variability beyond what occurred historically. For the Study, no differentiation was 

applied for each of the sequences, based on emission scenario or historical GCM skill. In 

essence, each of the 112 sequences was treated as equally likely when applied in CRSS in later 

phases of the Study. Included in this report is an evaluation of the relative sensitivity of 

streamflows to emission scenarios. From a mechanical standpoint, the Downscaled GCM 

Projected scenario was implemented as 112 distinct projections of the future, each starting in the 

year aligned with the Study period start year of 2011. 

8.2 Uncertainty 

The process outlined above and shown graphically in figure B-36, in which climate projections 

are used to generate projections of future streamflow, contains a number of areas of uncertainty. 

Each step in the process contains uncertainty, and it is important to recognize these in the 

interpretation of results. First, emission scenarios describing the global emissions of GHGs over 

the century were used as the primary input to GCMs. The SRES emission scenarios were used to 

project a range of future global development pathways. Each emission scenario was considered 

plausible, but the fact that the range may not be sufficiently broad cannot be ruled out. In 

addition, the climate system responds to a number of factors that contribute to radiative forcings 

affecting the warming of the earth’s surface. Factors such as aerosols, solar activity, surface 

albedo, and variations in the earth’s orbit, all influence the earth’s energy balance. These 

mechanisms are included in the climate models to the degree they are understood and can be 

projected into the future, but represent an inherent uncertainty in attempting to simulate the 

global climate system on decadal and century scales. Anthropogenic carbon dioxide emissions, 

which are directly represented in the emission scenarios, are believed to represent the largest 

component of the estimated radiative forcing (IPCC, 2007). 

Second, GCMs are used to simulate global climate patterns resulting from atmospheric forcings 

and feedbacks throughout the land, ocean, and atmosphere interactions. The GCMs were applied 

at relatively coarse scales (~150- to 200-km resolution) in relation to what is required for 

watershed assessments, and therefore are not likely to capture important regional phenomena. 

Because of the atmospheric lag from GHG emissions, much of the uncertainty in climate 

projections through mid-century is associated with the structure and application of the many 

different GCMs themselves, rather than the emission scenarios driving them. The GCM results 

were necessarily bias corrected and spatially downscaled to be useful at the watershed scale. 

These bias correction and downscaling processes, while necessary, removed some of the physical 

linkages from the climate projections and introduced an aspect of further uncertainty. High-
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B-50 December 2012

resolution regional climate modeling may help resolve some of the scale mismatch (both 

spatially and temporally) in the future, but the availability of these simulations over a broad 

ensemble of models and emission scenarios is limited. The statistical downscaling method 

employed in the Study preserves monthly observed precipitation and temperature statistics for 

the overlapping period at the 2-degree spatial scale. However, the statistics at finer spatial scales 

(i.e., 1/8th-degree scale) or longer temporal scales (seasonal, annual, and longer scales) are not 

necessarily preserved. Analysis included in appendix B3 provides further information on this 

topic. 

Finally, hydrologic models are approximations of the complex physical processes that occur on 

the watershed scale. The VIC model is considered a strong, physically based hydrology model, 

but simulates hydrologic processes at the macro scale. The model necessarily needs to 

parameterize certain aspects of the hydrologic cycle to capture the effects at smaller scales.

Several assumptions in the VIC modeling approach carry considerable uncertainty. First, the 

VIC modeling assumes that land use and vegetative cover are fixed throughout the simulation 

period. Future assumptions of land use that are consistent with the socioeconomic assumptions in 

the water demand scenarios were not integrated into the water supply scenarios. Changes in 

climate are likely to drive changes in native and invasive species (vegetative, terrestrial, avian,

and aquatic) distribution and these will influence the physical watershed and future hydrologic 

processes and streamflow. The magnitude of these impacts is believed to be relatively small 

compared to the effects of changes in direct temperature and precipitation; however, the 

magnitude has not yet been quantified. 

In addition, the VIC model, as described in this report, has been adopted without re-calibration. 

Results appear reasonable at the larger watershed scale, but there is observed bias in particular 

watersheds and at the sub-watershed scale. A bias correction method has been applied to 

compensate for some of the biases, but in doing so it necessarily introduces assumptions on the 

linkages between past and future climates that are not yet known. 

8.3 Results

The results of the 112 future climate projections are presented in this section for climate, 

hydrologic processes, and streamflow. Climate teleconnections are discussed primarily in a 

qualitative manner due to the broad uncertainty in projecting future states of coupled oceanatmosphere conditions. For climate, results are presented in terms of annual precipitation and 

temperature trends, followed by an analysis of seasonal trends. For hydrologic processes, results 

are presented for ET, snowpack, soil moisture, and runoff. Both annual and seasonal analyses are 

presented. The last section of the results focuses on projected changes in streamflow, both annual 

and seasonal, and predominately at the Colorado River at Lees Ferry, Arizona location. 

Climate and hydrologic process results are presented as changes from the 30-year historical 

climatological period (1971 to 2000) to three future 30-year periods (2011 to 2040, 2041 to 

2070, and 2066 to 2095). Thirty-year periods were chosen to span the almost 90-year future 

projection period (2011 to 2099). In addition, due to the difference in initial atmospheric-ocean 

conditions between the GCMs, a 30-year period is sufficient to separate projected average 

conditions from individual and multi-year variability. For simplicity, these periods are referred to 

as the year in which they are centered: i.e., 1985, 2025, 2055, and 2080. 
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December 2012 B-51

Although the Study period is through 2060, the 112 future climate projections extend through 

2099. The additional approximate 40 years of projections have been included in the analyses for 

the climate and hydrologic processes results because they offer additional insight into the 

projected changes of these parameters. To facilitate a more direct comparison with the projected 

streamflow from the other three scenarios (Observed Resampled, Paleo Resampled, and Paleo

Conditioned), streamflow results are presented through 2060. 

Under the scenario planning approach employed in the Study, each future climate projection was 

viewed as a plausible future. The probability or likelihood of each future projection is unknown, 

hence summary statistics of the resulting projections such as mean or median of the ensemble 

projection is not the most likely future, but simply the central tendency of the ensemble. For the 

Observed Resampled, Paleo Resampled, and Paleo Conditioned scenarios summary statistics are 

grounded by a stationary hydroclimate assumption. Given the increasing debate concerning the 

validity of a stationary hydroclimate assumption, it is tenuous to assert that the past record is 

predictive of future conditions. Thus, while summary statistics such as mean and median are 

used in part to present these data, it is important to consider the full range of outcomes, which are 

also provided throughout these results. 

In the case of the Downscaled GCM Projected scenario, this consideration is further complicated 

by the ensemble of results. Each of these traces is a unique combination of initial conditions, 

GCM choice, and future emission scenario. The resulting streamflows are all considered 

plausible futures. However, summary statistics can be computed by various approaches, which 

influence the outcome of these values. Thus, understanding the full range of results is even more 

paramount in this supply scenario. Recent literature on the topic has found that an ensemble of 

results with multiple realizations from a single GCM can inadvertently bias the ensemble 

statistics favoring the GCM with multiple realizations. Hence, in some cases it may be prudent to 

combine realizations from each GCM such that each contributes equally to the ensemble and 

associated statistics. The alternate perspective suggests that with more runs from a particular 

GCM, there is greater confidence and understanding of the GCM’s tendencies. This is desirable 

and might merit greater weight than a GCM with only one realization. The latter introduces 

sizable uncertainty to the ensemble as it is unknown if additional realizations from the GCM 

with a single realization would yield similar results or vary widely. In practice, the most prudent 

approach is a case by case consideration of these and other methods to determine the most 

appropriate path forward. In the Study, the results were found to be insensitive to the method by 

which summary statistics were computed (ensemble mean streamflow projections were within 

1 percent of each other under both approaches). Results presented throughout the following 

sections weight all GCM realizations equally simply describing summary statistics based on the 

112 available projections. Acknowledging that a summary statistic alone cannot capture the 

complexity of these results, ranges, percentiles and other distributional measures are also 

provided. 

8.3.1 Climate 

Climate projections from the 112-projection ensemble indicate a strong continued warming 

throughout the Basin. Figure B-37 shows the Basin average temperature and precipitation 

projections for 1950 to 2099 (the length of the GCM projection period) in relation to the 1950 to 

2005 (the length of the observed climate period) historical observed climate. The projection 

ensemble indicates substantial warming, with a median increase in annual temperature of about 
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B-52 December 2012

1.3 oC by 2025, 2.4 oC by 2055, and 3.3 oC by 2080. All projections are consistent in the 

direction of the temperature change, but vary in terms of climate sensitivity. Annual precipitation 

trends are not apparent in this Basin-wide analysis. Roughly half of the projections indicate a 

wetter future, while the other half indicate drier conditions. The uncertainty in future annual 

precipitation appears to be increasing with time, while the median of the projections is relatively 

unchanged. 

Figure B-38 presents the change in mean annual temperature (absolute change) and precipitation 

(percent change) for three future periods: 2011 to 2040 (2025), 2041 to 2070 (2055), and 2066 to 

2095 (2080), relative to the 30-year historical period 1971 to 2000 (1985). For most of the Basin, 

temperature increases are within 1.0 °C to 1.5 °C, 2.0 °C to 2.5 °C, and 3.0 °C to 4.0 °C for 

2025, 2055, and 2080, respectively. The Upper Basin is projected to warm more than the Lower 

Basin. Projected precipitation changes are relatively modest in 2025. However, by the 2055 and 

2080 periods, precipitation decreases by up to 10 percent in much of Lower Basin. In contrast, 

precipitation increases by up to 10 percent in the Upper Basin at higher elevation and toward the 

north (Green River Basin). 

Maps of seasonal changes in temperature and precipitation for the three future 30-year periods 

are included in appendix B6 and are summarized here.

The seasonal analysis shows that 2055 projected seasonal temperature changes exhibit minimal 

geographic variation in the fall. Winter and summer temperatures in the Upper Basin increase 

slightly more than those in the Lower Basin. Projected temperature increases are lowest in 

winter, ranging from 1.5 °C to 2.5 °C. The largest projected temperature increases occur in 

summer, and range from 2.5 °C to 3.0 °C. 

The 2055 change in projected mean winter precipitation is highly varied throughout the Basin, 

with values in the Lower Basin decreasing from 0 to 15 percent and the values in the Upper 

Basin increasing from 0 to 15 percent. However, it should be noted that on an absolute basis, the 

Upper Basin receives considerably more rainfall than the Lower Basin, such that a 15 percent 

change is substantially more total precipitation in that region. During spring, precipitation is 

projected to decrease throughout the Basin. The most severe reductions (on a percentage basis) 

occur in the southwestern region, where the decline is up to 30 percent. Summer is the only 

season in which projected precipitation shows a decrease in the Upper Basin and an increase or 

no change in the Lower Basin. Trends in fall precipitation closely resemble those of the winter 

season, but the projected percent changes for fall are lower in magnitude. 

Figure B-39 summarizes projected changes in climate conditions on a watershed basis, as indicated 

by the 112-projection ensemble for the three future 30-year periods. Each point represents a single 

watershed (one for each contributing area). The location of a point in the figure is determined by 

the mean projected change in temperature between the future periods and the simulated historical 

period 1971 to 2000, and the mean projected change in precipitation between the future periods 

relative to the simulated historical period. Change in temperature is measured in oC, while change 

in precipitation is measured as a percentage. 
Technical Report B — Water

Supply Assessment

December 2012 B-53

FIGURE B-37 

Historical (line series with markers) and Projected Annual Average Temperature (top) and Projected Annual Total Precipitation 

(bottom) Smoothed as a 10-year Mean 

Shading represents a range of projections and the solid line represents a median of projections.

0

2

4

6

8

10

12

14

16

18

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090

Annual Air Temperature (C)

Water Year

Annual Colorado River Basin Average Air Temperature

1950 - 2099

0

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700

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090

Annual Total (mm)

Water Year

Annual Colorado River Basin Average Precipitation

1950 - 2099
Colorado River Basin 

Water Supply and Demand Study FIGURE B-38 Mean Projected Change in Annual Temperature and Precipitation 2025 (2011–2040) versus 1985 (1971–2000), 2055 (2041–2070) versus 1985 (1971–2000), and 2080 (2066–2095) versus 1985 (1971–2000). 

Temperature (

∆C) Precipitation (%∆) 2025 2055 2025 2055 2080 2080

B-54 
Technical Report B — Water

Supply Assessment

December 2012 B-55

FIGURE B-39 

Projected Changes in Mean Seasonal and Annual Temperature and Precipitation for the Colorado River Basin 

Periods are 2025 (2011–2040); 2055 (2041–2070); and 2080 (2066–2095), compared to the 1985 (1971–2000) historical period

(hollow symbols represent Lower Basin locations, while solid symbols indicate Upper Basin locations). 

For a given season and future time period, projected changes in temperature are relatively 

consistent across all watersheds, with little variation throughout the Basin. By 2025, temperatures 

are projected to increase at least 1.0 °C in nearly all watersheds for all four seasons. Spring and 

summer show the greatest warming, with seasonal temperatures in most watersheds increasing 

3 °C to 4 °C by 2080. Annual temperature increases are projected at 1.0 °C to 1.5 °C, 2.0 °C to 

2.5 °C, and 3.0 °C to 3.5 °C for 2025, 2055, and 2080, respectively. 

0.0

0.5

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-40% -20% 0% 20% 40%

Change in Temperature (deg C)

Change in Precipitation (%)

2025 2055 2080

Fall (OND)

0.0

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Change in Temperature (deg C)

Change in Precipitation (%)

2025 2055 2080

Winter (JFM)

0.0

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Change in Temperature (deg C)

Change in Precipitation (%)

2025 2055 2080

Spring (AMJ) 0.0

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Change in Temperature (deg C)

Change in Precipitation (%)

2025 2055 2080

Summer (JAS)

0.0

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Change in Temperature (deg C)

Change in Precipitation (%)

2025 2055 2080

Annual
Colorado River Basin 

Water Supply and Demand Study

B-56 December 2012

Projected changes in seasonal precipitation vary widely across watersheds and seasons. In 

general, relative precipitation variability increases with time. On an annual basis, projected 

precipitation through 2080 is generally within 5 percent of historical precipitation, with half of 

the Basin’s watersheds exhibiting positive change and half exhibiting negative change. The most 

significant and monotonic change in precipitation occurs in spring, during which all watersheds 

show a decrease in precipitation for each of the future time periods. By 2080, the decrease in 

spring precipitation ranges from 0 to 40 percent, with the values well distributed within the 

range. During fall and winter, small increases (less than 10 percent) are projected for 2025, but 

bimodal patterns of increases in the Upper Basin (about 20 percent for winter in 2080) and 

decreases or neutral changes in the Lower Basin (about 10 percent for winter in 2080) begin to 

appear in the 2055 and 2080 time periods. Summer is the only season in which the bimodal 

pattern is reversed with decreases in precipitation projected in the Upper Basin and increases in 

the Lower Basin (see appendix B6 for projected seasonal precipitation maps). The summer 

pattern is likely due to a more active monsoon and increased moisture flow from the Gulf of 

California during this season simulated in the GCMs, although the summer precipitation 

associated with the monsoon is poorly simulated in most climate models (Lin et al. 2008; Gutzler 

et al. 2005). 

8.3.2 Summary of Changes in Climate

• Warming is projected to increase across the Basin, with the largest changes in spring and 

summer and larger changes in the Upper Basin than in the Lower Basin. Annual Basin-wide 

median temperature increases are projected to be approximately 1.3 °C, 2.4 °C, and 3.3 °C 

for 2025, 2055, and 2080, respectively, with less warming in winter and higher warming in 

summer. 

• Precipitation patterns continue to be spatially and temporally complex, but projected seasonal 

trends toward drying are significant in certain regions. Precipitation patterns are complex due 

to influence of oceans, storm tracks, changes in atmospheric circulation patterns (e.g., Hadley 

cell expansion), and the interplay with mountainous regions (orographic considerations). A 

general trend toward drying is present in the Basin, although increases are projected in the 

higher elevation and most hydrologically productive regions. Consistent and expansive 

drying conditions are projected for the spring throughout the Basin. For much of the Basin, 

drying conditions are projected in the summer, although some areas of the Lower Basin are 

expected to experience slight increases in precipitation which may be due to the monsoonal 

influence in this region. Upper Basin precipitation is projected to increase in the fall and 

winter, while the Lower Basin is expected to experience a decrease. Despite drying spring 

conditions in the Upper Basin, annual precipitation is projected to increase in the higher 

elevations due to higher winter precipitation increases in these regions. Projections 

demonstrate a bi-modal pattern of precipitation changes in fall and winter, with the Upper 

Basin projected to experience increases and the Lower Basin projected to experience 

decreases. The division of wetter versus drier conditions in the winter moves northward with 

continued warming through time, consistent with an expansion of the Hadley cell and more 

northerly storm tracks (Seager and Vechhi, 2010). 
Technical Report B — Water

Supply Assessment

December 2012 B-57

8.3.3 Hydrologic Processes 

Figures B-40 and B-41 present grid cell-based VIC model output via Basin-wide spatial plots for 

ET, runoff, soil moisture, and SWE. For each future time period and for each parameter, the 

mean projected annual changes are presented. Projected seasonal changes for these parameters 

can be found in appendix B6. 

Figure B-40 shows the percent change in mean annual ET and mean annual runoff. ET is 

projected to increase in most high elevation and northerly areas of the Upper Basin and is 

strongly related to the availability of soil moisture. In the Lower Basin, where decreased 

precipitation is projected (and subsequently reduced soil moisture), ET is projected to decrease 

substantially, particularly in the 2055 and 2080 periods. Runoff is projected to decrease 

substantially (up to 30 percent) across large areas of the Basin, with greatest reductions in the 

south and at high elevation. Elsewhere, projected decreases are generally within 15 percent of the 

historical period through 2080. Runoff is projected to increase for small areas in the northeastern

portion of the Basin (Green River Basin primarily). The increases in the northern portion of the 

Basin are an important finding and contribute significantly to mitigate reduced runoff trends for 

much of the rest of the Basin.

Figure B-41 shows the mean percent change in April 1 SWE and July 1 soil moisture. With few 

exceptions, April SWE is projected to decline by up to 30 percent throughout the Basin by 2025 

as more precipitation falls as rain and as warmer conditions lead to earlier snowmelt. This 

process becomes more pronounced in the 2055 and 2080 periods. July 1 soil moisture is 

projected to decrease by 5 to 10 percent throughout the Basin for the three future time periods. 

The loss of soil moisture is primarily the result of the greater moisture availability for ET earlier 

in the year (more rain less snow and earlier melt of snowpack) in the higher elevation Upper 

Basin and reduced overall precipitation in the Lower Basin. The most substantial decline occurs 

in the northeast portion of the Basin. 

Maps of seasonal changes in ET, runoff, soil moisture, and SWE for the three future 30-year 

periods are included in appendix B6 and are summarized here.

Projected 2055 changes in ET vary substantially throughout the Basin. In general, ET is projected 

to increase during fall and winter, but decrease during summer for the majority of the Basin. 

Projected ET changes exhibit considerable geographic variability and range in magnitude during 

spring, when portions of the Upper Basin have ET increases of up to 30 percent and portions of 

the Lower Basin have ET decreases of up to 30 percent. Both phenomena are related to soil 

moisture availability. Increases in Upper Basin ET are due to greater soil moisture availability, 

while Lower Basin decreases are due to reduced available soil moisture. During summer, 

projected 2055 changes in ET range from -5 to -10 percent in most locations. 

Projected 2055 changes in runoff also vary substantially throughout the Basin. In most seasons, 

runoff declines throughout the Basin. However, increases are projected for portions of the Upper 

Basin in fall and winter, and for the extreme southwestern portion of the Basin for all seasons. 

The projected decline in runoff is most substantial during spring, when several areas in both the 

Upper and Lower Basins feature a runoff reduction of up to 30 percent. Portions of the Upper 

Basin exhibit a reduction of similar magnitude during both summer and fall. 
Colorado River Basin 

Water Supply and Demand Study

B-58 December 2012

FIGURE B-40

Mean Projected Percent Change in Annual ET and Median Projected Percent Change in Runoff1

2025 (2011–2040) versus 1985 (1971–2000), 2055 (2041–2070) versus 1985 (1971–2000), and 2080 (2066–2095) versus 1985 (1971–2000). 

1 Median is used for runoff percent change, rather than mean because regions of extreme low historical precipitation may show disproportionately high percentage changes.
Technical Report B — Water

Supply Assessment

December 2012 B-59

FIGURE B-41 

Mean Projected Percent Change in April 1 SWE and July 1 Soil Moisture

2025 (2011–2040) versus 1985 (1971–2000); 2055 (2041–2070) versus 1985 (1971–2000); and 2080 (2066–2095) versus 1985 (1971–2000).
Colorado River Basin 

Water Supply and Demand Study

B-60 December 2012

The results of the watershed-based statistical analysis of VIC model output (climatological and 

hydrologic parameters) are presented for two representative Basin watersheds. The selected 

watersheds are those immediately upstream of the Colorado River at Glenwood Springs, 

Colorado, and the Colorado River at Hoover Dam. These select watersheds represent a high 

elevation headwaters region in the Upper Basin and a lower elevation, warmer region in the 

Lower Basin. Additional locations representing a more-robust cross-section of the Basin are 

included in appendix B6. 

Figures B-42 and B-43 each present the changes in six hydrologic parameters (precipitation, 

temperature, ET, runoff, SWE, and soil moisture) from the 30-year historical period (1971 to 

2000) to three future 30-year periods: 2011 to 2040 (2025); 2041 to 2070 (2055); and 2066 to 

2095 (2080). Figure B-42 presents these hydrologic parameter changes for the Colorado River at 

Glenwood Springs, Colorado. The results for this watershed are representative of those for other 

watersheds in the high elevation Upper Basin: 

• Precipitation: In the three future time periods, the Upper Basin watersheds experience a shift 

in the timing of precipitation; more precipitation occurs in fall and winter (November

through March) and less occurs in spring (April through June) relative to historical 

conditions. For this watershed, the increases in precipitation in fall/winter are greater than the 

reductions in spring/summer, resulting in a net increase. 

• Temperature: Monthly temperatures increase from 1.0 °C to 1.5 °C by 2025, and by 2.5 °C to 

4.0 °C by 2080 relative to the 30-year historical period of 1971 to 2000. 

• ET: Although ET is relatively unchanged from September through March, spring months 

(April through June) feature a marked increase. 

• SWE: Snowpack, as indicated by SWE, is consistently less in the future than in the historical 

period, particularly from March through June. Shifts in both runoff and soil moisture indicate 

that some portion of the reduction in spring SWE may be related to earlier snowmelt. 

• Runoff: Runoff is projected to increase in March, April, and May, while both precipitation 

and SWE are reduced during April and May. This suggests an earlier snowmelt that supplies 

the increased runoff from March through May and contributes to a reduction in snowpack. 

This is further supported as runoff is substantially reduced in June and July, suggesting that 

the melting snowpack, which historically supplied runoff during these months, has been 

substantially reduced by this time. 

• Soil moisture: Soil moisture is increased from February through April in conjunction with 

increased snowmelt infiltration. However, relative to historical conditions, the projected soil 

moisture is lower for the remainder of the year, exhibiting the most substantial reduction in 

June. 

Figure B-43 presents these plots for the Colorado River at Hoover Dam. The results for this 

watershed are representative of those for other watersheds in the Lower Basin. Due to the limited 

snowpack in the Lower Basin and its resulting limited role in the hydrologic processes of this 

region, the SWE results for the Lower Basin are not considered in these plots. Relative to the 
Technical Report B — Water

Supply Assessment

December 2012 B-61

FIGURE B-42 

Projected Change in Mean Monthly Climatological and Hydrologic Parameters: 01-Colorado River at Glenwood Springs, 

Colorado (Upper Basin)
Colorado River Basin 

Water Supply and Demand Study

B-62 December 2012

FIGURE B-43 

Projected Change in Mean Monthly Climatological and Hydrologic Parameters: 25-Colorado River at Hoover Dam

Upper Basin, the changes projected for the Lower Basin are smaller in magnitude on an absolute 

scale (e.g., change in mm rather than change in percentage): 

• Precipitation: In the Lower Basin, precipitation is projected to decrease during half of the 

year, with spring exhibiting the most notable decline. 

• Temperature: Monthly temperature increases are projected throughout the year ranging from 

1.5 °C by 2025 to 4.0 °C by 2080. 
Technical Report B — Water

Supply Assessment

December 2012 B-63

• Evapotranspiration: ET is noticeably reduced in late spring and early summer, though modest 

increases are projected for winter. The marked reductions in late spring and early summer are 

likely due to the reductions in precipitation, runoff, and soil moisture that occur during these 

times.

• Runoff: Runoff is reduced during all seasons at this location and is more pronounced in the 

2055 and 2080 time periods. Reductions result from the compounding effects of decreased 

precipitation and increased winter and early spring ET. It should be noted that runoff in this 

watershed (and similar watersheds in the Lower Basin) is very small and contributes little to 

the overall flow in the Colorado River. Runoff is usually less than 5 to 10 percent of the 

precipitation in this region. 

• Soil Moisture: Soil moisture is projected to be lower year round, with the largest reductions 

occurring in the spring. 

In the future, the Lower Basin is generally projected to have less water in the form of 

precipitation and soil moisture year round, and especially during winter and spring. However, the 

magnitude of these reductions is modest. 

8.3.4 Summary of Changes in Hydrologic Processes

• PET generally increases with warmer conditions and suggests a theoretical increase in 

ET demand. Actual ET, which is limited by soil moisture availability, is projected to increase 

across the Basin during seasons of highest available soil moisture. ET increases are projected 

in the Upper Basin (at lower elevations) and the Lower Basin in fall and winter as snowpack 

is not significant and warmer temperatures exist. Substantial ET decreases in the Upper and 

Lower Basin are projected in summer as soil moisture is depleted earlier than under historical 

conditions. During spring, peak increases in ET are projected in the Upper Basin (at higher 

elevations) as higher winter precipitation and earlier snowmelt allow a higher percentage of 

PET to be satisfied. Conversely, in the Lower Basin, the largest decreases are projected 

during the spring as precipitation, runoff, and soil moisture are reduced during this time. 

ET changes described here are from natural watershed and non-irrigated areas. ET effects on 

irrigated areas and water demand are discussed in Technical Report C – Water Demand 

Assessment. 

• Snowpack is projected to decrease as more precipitation falls as rain rather than snow and 

warmer temperatures cause an earlier melt. Decreases in snowpack in the fall and early 

winter are expected in areas where precipitation is not changed or is increased, and is caused 

by a greater liquid form of precipitation due to warming. Substantial decreases in spring 

snowpack are expected and projected to be widespread, due to earlier melt or sublimation of 

snowpack. 

• Soil moisture represents a significant portion of the seasonal watershed storage and buffers 

monthly changes in water availability and consumptive use. The interplay among 

precipitation, snowpack, ET, and runoff causes changes in soil moisture conditions. In 

general, soil moisture is depleted earlier in the year, and deficits persist longer into the late 

fall and early winter compared to historical conditions. In regions with overlying snowpack, 

earlier melt implies earlier contribution to soil moisture storage and an earlier opportunity for 

ET to consumptively use this stored water. In all regions, there is projected to be increased 
Colorado River Basin 

Water Supply and Demand Study

B-64 December 2012

PET due to warming. However, actual ET is governed by water availability; and when such 

soil moisture storage is depleted actual ET is curtailed. Reductions in soil moisture at the 

beginning of summer (approximated as July 1) are modest but consistent throughout the 

Basin. Larger reductions are projected in the higher elevation portions of the Basin where 

moisture persists longer. Overall, the watershed enters the winter season with larger soil 

moisture deficits and greater opportunity to store and consume winter precipitation. 

• Runoff (both direct and baseflow), the balance of hydrologic processes affecting the supply 

and demand at the local grid-scale, is spatially diverse, but is generally projected to decrease, 

except in the northern Rockies. As with precipitation, runoff is projected to increase 

significantly in the higher elevation Upper Basin during winter, but exhibits decreases during 

spring and summer. Increases in runoff in the summer across the southwestern portion of the 

Basin are consistent with higher precipitation rates, possibly associated with a more active 

monsoon. However, the increases from an absolute change perspective are small (generally 

less than 5 mm [0.15 inch] per year) and do not contribute to substantial net supply to the 

Colorado River. Due to the minimal amount of annual rainfall in this region, however, 

caution should be taken in interpreting a percentage increase (a small increase from near zero 

is a large percentage increase). 

8.3.5 Climate Teleconnections

Climate change projections of ENSO characteristics for the balance of this century are modeldependent and inconclusive. Not all the GCMs used in the Study simulate the dynamics of 

ENSO and other longer-term indices with fidelity. AchutaRao and Sperber (2002) evaluated 

ENSO simulations using 80-year control runs from 17 GCMs that participated in the CMIP3. 

They found that only a subset of the GCMs produce realistic amplitudes of NINO3 (index of the 

sea surface temperatures in the Pacific Ocean) and SOI, but ENSO often tends to occur at higher 

than observed frequency. In their recent study (AchutaRao and Sperber, 2006), though, they find

the next generation GCMs that participated in the IPCC AR4 tend to be more realistic in 

representing the frequency with which ENSO occurs. The GCMs are better at locating enhanced 

temperature variability over the eastern Pacific Ocean. They suggest multi-century integrations 

of GCMs may be required to statistically assess model improvement of ENSO. 

ENSO has an important role in western U.S. climate. Whether the frequency and characteristics 

of ENSO will be changed in a changing climate has strong practical importance. A few of the 

recent studies analyzed GCM simulations to address these questions (Yeh et al. 2009; Collins et 

al., 2010). However, there is no common consensus yet in the scientific community. Collins et al. 

(2010) argue that despite considerable progress in the understanding of the impact of climate 

change on many of the processes that contribute to El Niño variability, it is not yet possible to 

say whether ENSO activity will be enhanced or damped, or if the frequency of events will 

change. Yeh and Kirtman (2007) investigate two coupled GCMs—the Meteorological Research 

Institute’s model, and the Geophysical Fluid Dynamics Laboratory’s model—to analyze 

projected ENSO amplitude changes using a four times carbon dioxide emission scenario. They 

determine that despite the large changes in the tropical Pacific mean state, the changes in 

ENSO amplitude are highly model-dependent. Results suggest that the understanding of changes 

in ENSO statistics among various climate change projections is highly dependent on whether the 

model ENSO is in the linear or nonlinear regime. ENSO and PDO provide only limited skill in 

determining basin precipitation; thus, even improved simulation results for these indices may be 
Technical Report B — Water

Supply Assessment

December 2012 B-65

of limited value in making assessments of future supply conditions. Further research is needed to 

investigate the teleconnections and the direction of these teleconnections in the future. 

8.3.6 Streamflow

Natural streamflows were simulated at the 29 flow locations for each of the 112 climate 

projections. Figure B-44 displays all of the individual 112 sequences in the Downscaled 

GCM Projected scenario. The sequence bolded in figure B-44 also appears in figure B-45, which 

is a representative trace of the 112 sequences. In figure B-45, the mean annual flow of the 

112 sequences at this location declines substantially over time due to changes in hydrologic 

processes. Mean annual flows for Colorado River at Lees Ferry, Arizona, for the 50-year period 

of the Study (2011 to 2060) are approximately 13.7 maf. This represents a reduction in 

streamflow of approximately 6 percent compared to the period 1950 to 1999 (14.6 maf), or 

approximately 9 percent compared to the long-term period 1906 to 2007 (15.0 maf). It should be 

noted that the median of the projections is nearly 1.0 maf lower (annual flow of around 12.7 maf) 

than the mean, indicating that the projection ensemble exhibits a strong drying trend but that 

some wetter projections are compensating in the mean statistic. A few projections (less than 

10 percent) show considerably more annual variability than the observed record. Although 

simulated future minimum flows are similar to those in the observed record, the maximum 

annual flows are significantly higher. 

Finally, figure B-46 shows the range of Colorado River flow projections under the Downscaled 

GCM Projected scenario as compared to the historical observed flows. Observed natural flows 

span from 1906 to 2007 while the projections begin in 1950 and extend through 2099. During the 

overlapping period of 1950 to 2007, the projection reflects the range of natural flows from the 

observed record. Interestingly, the projection ensemble indicates a declining trend starting in the 

1990s and a significant expansion in variability starting in the late 2000s. A number of sequences 

in the Downscaled GCM Projected scenario exhibit occasional annual runoff conditions that far 

exceed the maximum in the observed or paleo records. Although it is possible that future climate 

will expand the magnitude and frequency of extreme events, it is also possible that some 

projections are simply extreme outliers from the ensemble. As shown in figure B-46, 5 to 

10 percent of the projections show annual flows in excess of the maximum observed natural flow 

of 25 maf for any given year. 

To better understand the issue of simulated extreme high flows and to determine whether specific 

GCMs or emission scenarios were driving this result, further analysis was conducted. For each 

downscaled climate projection, the cumulative difference from the simulated maximum flow to 

the maximum observed annual value of 25 maf over the study period 2011 to 2060 was 

computed. In addition, the total number of years in which the simulated flow exceeded 25 maf 

was counted for the 112 VIC model simulations. The results of this analysis are shown in figure 

B-47. Each of the 112 projections are listed and colored by the emission scenario (blue for A1B, 

red for A2, and green for B1). The corresponding dot represents the number of years in which 

that projection had simulated flows greater than 25 maf. The bar represents the cumulative flow 

above 25 maf. More than half of all GCM projections produced at least one event greater than 

25 maf and these occurred regardless of emission scenario. None of the projections had more 

than 4 years in which they exceeded 25 maf. In addition, the GCMs with the largest deviations 

under one emission scenario produced fewer or no deviations under another emission scenario. 
Colorado River Basin 

Water Supply and Demand Study

B-66 December 2012

FIGURE B-44 

Colorado River at Lees Ferry, Arizona Natural Flow for 112 Sequences for the Downscaled GCM Projected Scenario

The bolded line indicates a representative trace.

FIGURE B-45 

Colorado River at Lees Ferry, Arizona Natural Flow Statistics for the Downscaled GCM Projected Scenario 

Median (line), 25th–75th percentile band (dark shading), 10th–90th percentile band (light shading), max/min (whiskers), and 

1906–2007 observed min and max (dashed lines). The red bolded line indicates a representative trace.

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

KAF

Range of Lees Ferry Natural Flow Sequences used in the Downscaled 

GCM Projected Scenario

0

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10,000

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20,000

25,000

30,000

35,000

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45,000

2011201620212026203120362041204620512056

KAF

Annual Natural Streamflow - Downscaled GCM Projected
Technical Report B — Water

Supply Assessment

December 2012 B-67

FIGURE B-46 

Colorado River at Lees Ferry, Arizona Natural Flow Statistics for the Downscaled GCM Projected Scenario as Compared to 

Observed Flow

Median (line), 25th–75th percentile band (dark shading), 10th–90th percentile band (light shading), max/min (whiskers), and 

1906–2007 observed (blue line).
Colorado River Basin 

Water Supply and Demand Study

B-68 December 2012

FIGURE B-47

Cumulative Difference from Simulated Annual Maximum Flow and 25 maf (bar) and Total No. of Years that Exceed 25 maf (dot) 

for Colorado River at Lees Ferry, Arizona for 112 Downscaled Projections for the Period 2011–2060
Technical Report B — Water

Supply Assessment

December 2012 B-69

The analysis concluded that it is a rare occurrence for a projection to exceed 25 maf, but the 

potential is prevalent among the ensemble members regardless of emission scenario. The GCMs 

ECHAM5, CCSM, MIROC, and Geophysical Fluid Dynamics Laboratory, however, stood out in 

producing the fewest of these events (see appendix B2). 

Figure B-48 shows the mean annual percent change in natural flow for all 29 locations for four 

future periods as compared to the 50-year historical period of 1950 to 1999. The comparison here 

is not made to the observed 1906 to 2007 period, but rather to the historical VIC-simulated 

period such that any inherent model biases are incorporated in both VIC-simulated periods. The 

future periods reflect the Study period (2011 to 2060) and three 30-year periods extending 

throughout the 21st Century (2011 to 2040, 2041 to 2070, and 2066 to 2095) to provide the time 

evolution of the projected flow changes. 

FIGURE B-48

Simulated Relative Change in Mean Annual Flows (Ensemble Mean) for the Study Period (2011–2060), and Three Future 

Periods (2011–2040, 2041–2079, and 2066–2095), Compared to 1950–1999 for each of the 29 Natural Flow Locations

 

All locations except the Yampa River, Virgin River, and Bill Williams River are projected to 

experience decreasing annual flows. The Dolores River, White River, San Rafael River, Little 

Colorado River, and San Juan River are projected to experience the largest percentage decrease 

in annual flows (greater than 10 percent). The Green River and upper watershed of the Colorado 

River are projected to experience smaller reductions in streamflow (less than 5 percent). These 

spatial differences in streamflow changes appear to be largely related to the location of the 

watershed in relation to the precipitation pattern changes (more northerly) and the relative Colorado River At Glenwood Springs, CO Colorado River Near Cameo, CO Taylor River Below Taylor Park Reservoir, CO Gunnision River At Blue Mesa Reservoir,CO Gunnison River At Crystal Reservoir,CO Gunnison River Near Grand Junction, CO Dolores River Near Cisco, UT Colorado River Near Cisco UT Green R Bel Fontenelle Res WY Green R. Nr Green River, WY Green River Near Greendale, UT Yampa River Near Maybell, CO Little Snake River Near Lily, CO Duchesne River Near Randlett, UT White River Near Watson, UT Green River At Green River, UT San Rafael River Near Green River, UT San Juan River Near Archuleta,NM San Juan River Near Bluff, UT Colorado R At Lees Ferry, AZ Paria R At Lees Ferry, AZ Little Colorado River Near Cameron, AZ Colorado River Near Grand Canyon, AZ Virgin River At Littlefield, AZ Colorado River Below Hoover Dam, AZ-NV Colorado River Below Davis Dam, AZ-NV Bill Williams River Below Alamo Dam, AZ Colorado River Below Parker Dam, AZ-CA Colorado River Above Imperial Dam, AZ

-30%

-20%

-10%

0%

10%

20%

30%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Change in Period Mean Annual Flows Compared to 1950-1999 Period Mean (%)

Natural Flow Station Number

Change in Natural Flow under Downscaled Climate Projected Scenario

2011-2040 2041-2070 2066-2095 2011-2060
Colorado River Basin 

Water Supply and Demand Study

B-70 December 2012

elevation differences among watersheds (higher elevation). In general, smaller sub-basins that 

are farther north and at higher elevations (such as the Yampa River) may be expected to have 

increasing flows given projected increases in precipitation. Although precipitation is projected to 

increase in some larger sub-basins at lower elevations (such as the Green River), a decrease in 

flow is projected, possibly a result of the dominant role of increased temperature in these 

regions. 

The implicit assumption made with respect to the SRES emission scenarios used to drive the 

GCMs in the Study is that they are equally likely (or unlikely) and that they can be used in a 

multi-model ensemble. Climate projections through mid-century are dominated by the choice of 

GCM rather than individual emission scenarios. Table B-1 presents the range of projected 

change in Colorado River at Lees Ferry, Arizona streamflow associated with specific SRES 

emission scenarios, as compared to the ensemble of all emission scenarios. VIC simulations 

indicate streamflow reductions for all three SRES emission scenarios, and difference of less than 

2 percent between emission scenario groupings for time periods covered in the Study. These 

results are expected since the climate system responds relatively slowly to changes in emissions. 

By late century, the differences between simulated streamflows across emission scenarios 

become substantially larger, reflecting both the response time of the climate system and the 

higher emissions. During the first period indicated in table B-1, it is noteworthy that the greatest 

streamflow reduction occurs under the A1B scenario. While the GHG emissions in this scenario 

fall between the A2 and B1 scenarios in the distant future, they actually represent the highest 

emission scenario (of the three) through about 2020 (see appendix B2). By the second and third 

periods (mid-century and beyond) the streamflow changes between emission scenarios are more 

intuitive with the general emission pathways. 

TABLE B-1 

Percentage Change in Mean Flow with Respect to Historical Mean (1950–1999) at the Colorado River at Lees Ferry, Arizona 

2011–2040 2041–2070 2066–2095 2011–2060

All Projections -5.6 -9.1 -10.5 -6.8 

SRESB1 -5.2 -7.9 -8.0 -6.0

SRESA1B -6.7 -9.1 -10.5 -7.7

SRESA2 -4.9 -10.3 -13.2 -6.5

While annual flows show decreases and likely some expansion in variability, monthly flows 

exhibit a significant shift in timing. Figure B-49 shows the simulated mean monthly flows 

from the climate projections for the Colorado River at Lees Ferry, Arizona compared to the 

observed monthly flows. Commensurate with the seasonal changes in temperature, precipitation, 

and hydrologic processes, after 2025, the peak streamflow occurs about one month earlier 

(from June through May) and is approximately 500 thousand acre-feet (kaf) lower. In addition, 

increases occur in winter streamflow, while substantial reductions occur in spring and summer. 

The wintertime increases are likely associated with increased precipitation in the Upper Basin, 

while spring and summer decreases are likely associated with earlier melt of the snowpack 

and reduced precipitation patterns. Prior to the mid-century it appears that the transition to 
Technical Report B — Water

Supply Assessment

December 2012 B-71

FIGURE B-49 

Comparison of Observed and Future Simulated Mean Monthly Flows at Colorado River at Lees Ferry, Arizona 

J F M A M J J A S O N D

0

1000

2000

3000

4000

Monthly Flow(kaf/mo)

1950-1999

2011-2040

2041-2070

2066-2099

All Projections

J F M A M J J A S O N D

0

1000

2000

3000

4000

Monthly Flow(kaf/mo)

1950-1999

2011-2040

2041-2070

2066-2099

J F M A M J J A S O N D

0

1000

2000

3000

4000

Monthly Flow(kaf/mo)

J F M A M J J A S O N D

0

1000

2000

3000

4000

Monthly Flow(kaf/mo)

SRESA1B

SRESA2

SRESB1
Colorado River Basin 

Water Supply and Demand Study

B-72 December 2012

earlier runoff is underway with increasing May flows and decreases in June flows, but the 

full monthly shift has not yet occurred. The lower panels of figure B-49 also indicate that there 

is no substantial difference in the monthly timing trends between emission scenarios. 

The inter-annual variability in streamflow is another important component of water supply. 

Deficit statistics using the identical methods as those described for the historical supply were 

computed for each of the 112 climate projections. The 1906 to 2007 observed mean of 15.0 maf 

was used to set the threshold for determining whether the system was in a deficit or surplus. For 

the purpose of this report, “deficit” is defined as a consecutive 2-year period when the mean is 

less than the observed long-term mean of 15.0 maf. Similarly, “surplus” is defined as a 

consecutive 2-year period when the mean is above 15.0 maf. 

Figure B-50 illustrates the frequency and magnitude of both deficit and surplus spells. The inset 

figure shows the frequency occurrence of a specific spell length across all projections. The 

median exceedance probability of a surplus spell longer than 0 years is 30 percent, indicating 

that, when as measured against the 1906 to 2007 mean annual flow of 15 maf, about a third of 

the years in the future would be considered to not be a deficit. In addition, deficit length may 

extend greater than 20 years (indicated by the 90th percentile deficit length), as compared to the 

recent 9-year deficit. Under the Downscaled GCM Projected scenario (at the ensemble median) a 

9-year deficit may occur up to 20 percent of the time and result in a cumulative deficit of 30 to 

40 maf. The recent 9-year deficit is estimated to have a cumulative deficit of more than 28 maf. 

The results also suggest that under some climate projections, sustained periods of dryness will 

occur (deficit lengths greater than 50 years). Most projections result in long-term mean annual 

flows that are less than the 15 maf observed mean. The future climate essentially arrives at a new 

mean state. Thus deficits may need to be evaluated against the projection-specific, long-term 

mean to reflect this new inter-annual variability about the new mean. 

Figure B-51 is identical to figure B-50 except that the threshold for deficit and surplus is 

determined from the projection-specific, long-term mean, rather than the observed mean. The 

drought depiction is considerably different under these conditions. As expected, deficit and 

surplus frequencies are roughly equal. In addition, deficit spell lengths do not exceed 17 years 

and are a maximum of 8 years at the median of the projections. Deficit magnitudes at the 9-year 

deficit remain in the 18 to 40 maf range. Under this perspective, the inter-annual variability is not 

substantially different than the recent observed period, but rather the Downscaled GCM 

Projected means are significantly reduced, leading to the perspective of relatively sustained 

deficit when measured against recent observed flows. There is no absolute correct perspective; 

thus, both methods are presented here.
Technical Report B — Water

Supply Assessment

December 2012 B-73

FIGURE B-50

Simulated Deficit and Surplus Spell Length and Magnitude for All 112 Climate Projections 

(Threshold Defined as 1906–2007 Mean Annual Flow ~15 maf)

Box plots show the median (dash), 25th–75th percentile band (shading), and max/min (whiskers).

FIGURE B-51 

Simulated Deficit and Surplus Spell Length and Magnitude for all 112 Climate Projections (Threshold Defined as Individual 

Projection Mean for 2011–2060)

Box plots show the median (dash), 25th–75th percentile band (shading), and max/min (whiskers).
Colorado River Basin 

Water Supply and Demand Study

B-74 December 2012

9.0 Comparison of Future Supply Scenarios

The water supply assessment described in this report includes four distinct supply scenarios that 

attempt to bracket the range of conditions that might be experienced over the next 50 years. The 

scenarios include direct use of the observed record (Observed Resampled scenario), direct use of 

the paleo reconstructions (Paleo Resampled scenario), blends of observed and paleo sequences 

(Paleo Conditioned scenario), and use of future climate projections and hydrologic modeling 

(Downscaled GCM Projected scenario). Figure B-52 shows the range of annual flows for the 

Colorado River at Lees Ferry, Arizona for each of the scenarios in a four-panel series. 

The Observed Resampled, Paleo Resampled, and Paleo Conditioned scenarios have similar mean 

annual flows and a similar range of annual variability. The Paleo Resampled scenario contains 

individual years of flows lower than Observed Resampled, but a narrower band of variability 

within the inter-quartile range. The Paleo Conditioned scenario, by design, includes a similar 

range of annual flows as the Observed Resampled. The Downscaled GCM Projected scenario 

reflects possible changes in climate beyond what occurred historically and has lower mean 

annual flows while expanding the annual variability range through increased maximum annual 

flows. Mean annual natural flows for the Colorado River at Lees Ferry, Arizona range from 

14.7 to 15.0 maf for the Observed Resampled, Paleo Resampled, and Paleo Conditioned 

scenarios. The Downscaled GCM Projected scenario results in mean annual flows of 

approximately 13.7 maf. 

Each supply scenario includes multiple realizations, resulting in a range of flow statistics. Figure 

B-53 graphically depicts these annual flow statistics. The range of mean flows is greatest under 

the Downscaled GCM Projected scenario, with the inter-quartile range spanning roughly 12.5 to 

15 maf and the absolute range covering 10 to 17 maf. Especially with respect to the use of 

climate projections, the ensemble mean or median should be considered more useful than any 

individual projections. This ensemble mean or median has been shown to perform better than 

any individual projection against a range of historical climate metrics and variability and trend 

significance, largely due to the cancelling out of natural internal GCM model variability and 

cancelling out of individual model errors (see Gleckler et al. [2008], and Pierce et al. [2009], for 

a more-complete discussion of this topic). The Paleo Resampled scenario, despite the large 

absolute range, has a smaller standard deviation than the other scenarios due to the tightness of 

the bulk of the realizations. Skew is a measure of the shape of the annual flow distribution. A 

skew of zero implies a normal distribution, in which wetter years and magnitudes are evenly 

balanced with drier years. Most scenarios have a positive skew, suggesting a bias to the drier side 

of the distribution. This is particularly noticeable in the Downscaled GCM Projected scenario. 

The Paleo Resampled scenario has the highest year-to-year correlation as measured by the 

backward lag-1 correlation. This high degree of correlation is attributable in part to the method 

used to develop the reconstructions. The minimum annual flows are fairly consistent across the 

scenarios, with the Paleo Resampled scenario exhibiting the most extreme low-flow condition. 

The Downscaled GCM Projected scenario exhibits a range of maximum annual flows well 

beyond those seen in any of the other scenarios. 
Technical Report B — Water

Supply Assessment

December 2012 B-75

FIGURE B-52 

Annual Colorado River at Lees Ferry, Arizona Natural Flow Time Series for Supply Scenarios 

Median in bold black line, inter-quartile range in dark shading, 10th–90th percentile range in light shading, selected individual sequence in bold colored line, max/min as whiskers, 

and 1906–2007 observed max/min (dashed lines).

0

5,000

10,000

15,000

20,000

25,000

30,000

2011201620212026203120362041204620512056

KAF

Annual Natural Streamflow - Observed Resampled

0

5,000

10,000

15,000

20,000

25,000

30,000

2011201620212026203120362041204620512056

KAF

Annual Natural Streamflow - Paleo Resampled

0

5,000

10,000

15,000

20,000

25,000

30,000

2011201620212026203120362041204620512056

KAF

Annual Natural Streamflow - Paleo Conditioned

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

2011201620212026203120362041204620512056

KAF

Annual Natural Streamflow - Downscaled GCM Projected
Colorado River Basin 

Water Supply and Demand Study

B-76 December 2012

FIGURE B-53 

Summary Statistics for Annual Colorado River at Lees Ferry, Arizona Natural Flows for Supply Scenarios (for 2011–2060) 

Figure shows the median (dash), 25th–75th percentile band (box), and max/min (whiskers).
Technical Report B — Water

Supply Assessment

December 2012 B-77

Figure B-54 provides a side-by-side comparison of each of the scenarios over the study horizon 

and the monthly flow range. Again, the Downscaled GCM Projected scenario demonstrates both 

higher high flows and lower low flows, measured as a 5-year average. This range, combined with 

the reduced mean annual natural flows in this scenario, makes the Downscaled GCM Projected

scenario likely the most challenging supply condition within which to manage the Basin. The 

figure also shows that the monthly variability of the Downscaled GCM Projected scenario is 

significantly larger than any other scenario. This is particularly true in the winter and spring, 

when the Upper Basin hydrologic processes are most active and subject to change under climate 

warming. The shift in peak flow timing from June through May is apparent in figure B-55, and 

becomes more pronounced when analyzing results for the later 30-year time periods (table B-3). 

FIGURE B-54 

Annual Colorado River at Lees Ferry, Arizona 5-Year Natural Flow Time Series (top) and Monthly Variability across Supply 

Scenarios (bottom) (for 2011–2060)

Figure shows the median (dash), 25th–75th percentile band (box), and max/min (whiskers). 
Colorado River Basin 

Water Supply and Demand Study

B-78 December 2012

FIGURE B-55 

Monthly Colorado River at Lees Ferry, Arizona Natural Flow Variability for Supply Scenarios (for 2011–2060)

Figure shows the median (dash), 25th–75th percentile band (shading), and max/min (whiskers).
Technical Report B — Water

Supply Assessment

December 2012 B-79

The inter-annual variability of streamflow across the scenarios is characterized by determining 

the frequency, duration, and magnitude of deficit and surplus periods. Figure B-56 is a fourpanel figure showing the length and magnitude of such spells. For example, the maximum length 

of sustained deficit through 2007 in the Observed Resampled scenario was 8 years (note that this 

length would be 9 years if the observed record extended through 2010), while the maximum 

sustained surplus is 7 years. In comparison, the Paleo Resampled, Paleo Conditioned, and 

Downscaled GCM Projected scenarios all produce deficit periods of 15 years or longer. The 

maximum deficit accumulated is approximately 60 maf over the 15 years of deficit (both Paleo

Conditioned and Downscaled GCM Projected scenarios). However, the reduced mean annual 

flow in the Downscaled GCM Projected scenario causes many of the realizations to be in a 

sustained deficit using the recent observed flows as the measure.
Colorado River Basin

Water Supply and Demand Study

B-80 December 2012

FIGURE B-56 

Frequency, Duration, and Magnitude of Deficit and Surplus Periods for Supply Scenarios (for 2011–2060) 

Top figures (left to right) are the Observed Resampled and Paleo Resampled scenarios. Bottom figures (left to right) are the Paleo Conditioned and Downscaled GCM Projected 

scenarios. Box plots show the median (dash), 25th–75th percentile band (shading), and max/min (whiskers).
Technical Report B — Water

Supply Assessment

December 2012 B-81

Table B-2 summarizes the key statistics for each water supply scenario and generally provides a 

tabular presentation of the information presented in the figures in this section. Similarly, table B3 summarizes the annual and monthly statistics for the Downscaled GCM Projected scenario for 

three distinct future periods (2011 to 2040, 2041 to 2070, and 2066 to 2095) to assist in the 

evaluation of temporal trends. It should be noted that the last of these three periods is beyond the 

Study period, but is shown to assist in understanding trajectory of projected changes. Under this 

scenario, mean annual flows are projected to continue to decrease over time (from -7.5 percent

around 2025 to -10.9 percent around 2055, to -12.4 percent around 2080) as compared to the 

1906 to 2007 mean. At the same time, the shift in peak streamflow timing evolves from a current 

peak in June to an eventual peak in May due to earlier snowmelt and increased rain-to-snow 

ratios in response to warming. 

TABLE B-2 

Summary of Key Streamflow Statistics for Each Water Supply Scenario for the Period 2011–2060

Statistic

Scenario

Observed

Resampled

Paleo

Resampled

Paleo

Conditioned

Downscaled

GCM

Projected

Annual 

(Water 

Year)

Average Annual Flow (maf) 15.0 14.7 14.9 13.7

Percent Change from Long-Term 

Mean (1906–2007)

0% -2% -1% -8.7% 

Median (maf) 15.0 14.7 15.0 13.6

25th Percentile (maf) 14.5 14.3 14.2 12.6

75th Percentile (maf) 15.5 15.0 15.6 14.9

Minimum Year Flow (maf) 5.6 2.3 5.6 4.2

Maximum Year Flow (maf) 25.2 24.3 25.2 44.3

Monthly Peak Month June June June June 

Peak Month Mean Flow (kaf) 4,007 3,914 4,000 3,393

Peak Month Maximum Flow (kaf) 8,467 8,531 8,678 14,693

Month at Which Half of Annual Flow 

(Water Year) is Exceeded

June June June June 

Deficit 

Periods1 Maximum Deficit (maf) 28.2 38.4 98.5 246.1

Maximum Spell Length (years) 8 17 24 50

Intensity (Deficit/Length) (maf/year) 

[median]

3.5 2.3 4.1 7.4

Frequency of 5+ Year Spell Length 

(Percent) [median]

22% 30% 25% 48%

Maximum 8-year Deficit (longest in 

1906–2007 observed record, maf) 

28.2 29.8 50 48.6

Surplus

Periods2 Maximum Surplus (maf) 22.2 36.2 88 74.7

Maximum Spell Length (years) 7 15 25 19 

Intensity (Surplus/Length) (maf/year) 3.2 2.4 3.5 13.2

Frequency of 5+ Year Spell Length 

(Percent) 

28% 15% 18% <1%

Maximum 7-year Surplus (longest in 

1906–2007 observed record, maf) 

22.2 29.2 44 39.2

1 A deficit period occurs whenever the 2-year running average flow is below the observed average from 1906–2007 of 15.0 maf.

2 A surplus period occurs whenever the 2-year running average flow is above the observed average from 1906–2007 of 15.0 maf.
Colorado River Basin 

Water Supply and Demand Study

B-82 December 2012

TABLE B-3 

Summary of Annual and Monthly Streamflow Statistics for the Downscaled GCM Projected Scenario for the 3 Future 30 Year 

Time Periods: 2011–2040 (2025), 2041–2070 (2055), and 2066–2095 (2080) 

Statistic 

Downscaled

GCM

Projected

2011–2040

(2025)

Downscaled

GCM

Projected

2041–2070

(2055)

Downscaled

GCM

Projected

2066–2095

(2080) 

Annual 

(Water 

Year)

Average Annual Flow (maf) 13.9 13.4 13.1

Percent Change from Long-Term Mean 

(1906–2007) -7.5% -10.9% -12.4%

Median (maf) 13.8 13.3 13.4

25th Percentile (maf) 12.8 12.0 11.2

75th Percentile (maf) 15.1 14.6 14.5

Minimum Year Flow (maf) 4.4 3.9 3.7

Maximum Year Flow (maf) 43.8 44.3 44.3

Monthly Peak Month June May May 

Peak Month Mean Flow (kaf) 3,535 3,388 3,495

Peak Month Maximum Flow (kaf) 14,693 10,830 12,991

Month at Which Half of Annual Flow 

(Water Year) is Exceeded June May May 

The last time period is beyond the Study period, but is shown for informational purposes. 

10.0 Summary and Limitations 

This report documents the current and future water supply assessment for the Study. The 

research and development program initiated by Reclamation in 2004 resulted in the development 

of the Paleo Resampled and Paleo Conditioned scenarios. These scenarios are described in 

appendix N of the 2007 Interim Guidelines Final EIS (Reclamation, 2007), as is the Observed 

Resampled scenario. The Downscaled GCM Projected scenario is the newest addition to the set 

of scenarios and has not been previously used in any Reclamation long-term planning activities. 

The VIC modeling associated with the projected climate forcings suggests changes in 

streamflows resulting from this scenario are consistent with past efforts, particularly that of 

Christensen and Lettenmaier (2007). 

The streamflow bias correction that has been included in this report compensates for biases in 

climate and hydrologic data as well as for biases in the hydrology model (VIC) structure. This 

step is important for the use of results other than “change” metrics in subsequent analyses.

However, in evaluating biases and VIC model performance, the need for model calibration at 

finer resolutions is found to be a necessary next step and will reduce the level of bias correction 

needed in the future. Care should be taken in attempting to apply the “raw” VIC results for 

smaller watersheds in the Basin. The improvements in hydrologic model calibration and 

application should parallel progress in climate modeling and methods to develop higherresolution climate information.
Technical Report B — Water

Supply Assessment

December 2012 B-83

The role of snowpack development and melt, ET, and soil moisture are found to be very 

important in determining the available supply in the Basin. However, limited Basin-wide data are 

available to better understand these dynamics. The elevational sensitivity of snowpack, effect of 

warming and increased carbon dioxide on ET, and the role of summer and fall soil moisture on 

water supply are areas in need of further study. 

In addition, newer GCMs and downscaling techniques will soon be available under the IPCC 

Fifth Assessment Report. The improved resolution and model physics of some of the new GCMs 

may refine patterns of precipitation changes (such as the increases in the Green River Basin and 

Upper Colorado, and monsoonal effects in the Lower Basin). 

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Technical Report B — Water

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December 2012 B-91

Disclaimer 

The Colorado River Basin Water Supply and Demand Study (Study) is funded jointly by the Bureau of 

Reclamation (Reclamation) and the seven Colorado River Basin States (Basin States). The purpose of 

the Study is to analyze water supply and demand imbalances throughout the Colorado River Basin and 

those adjacent areas of the Basin States that receive Colorado River water through 2060; and develop, 

assess, and evaluate options and strategies to address the current and projected imbalances. 

Reclamation and the Basin States intend that the Study will promote and facilitate cooperation and 

communication throughout the Basin regarding the reliability of the system to continue to meet Basin 

needs and the strategies that may be considered to ensure that reliability. Reclamation and the Basin 

States recognize the Study was constrained by funding, timing, and technological and other limitations, 

and in some cases presented specific policy questions and issues, particularly related to modeling 

and interpretation of the provisions of the Law of the River during the course of the Study. In such 

cases, Reclamation and the Basin States developed and incorporated assumptions to further complete 

the Study. Where possible, a range of assumptions was typically used to identify the sensitivity of the 

results to those assumptions.

Nothing in the Study, however, is intended for use against any Basin State, any federally recognized 

tribe, the federal government or the Upper Colorado River Commission in administrative, judicial or 

other proceedings to evidence legal interpretations of the Law of the River. As such, assumptions 

contained in the Study or any reports generated during the Study do not, and shall not, represent a legal 

position or interpretation by the Basin States, any federally recognized tribe, federal government or 

Upper Colorado River Commission as it relates to the Law of the River. Furthermore, nothing in the 

Study is intended to, nor shall the Study be construed so as to, interpret, diminish or modify the rights 

of any Basin State, any federally recognized tribe, the federal government, or the Upper Colorado River 

Commission under federal or state law or administrative rule, regulation or guideline, including without 

limitation the Colorado River Compact (45 Stat. 1057), the Upper Colorado River Basin Compact (63 

Stat. 31), the Utilization of Waters of the Colorado and Tijuana Rivers and of the Rio Grande, Treaty 

Between the United States of America and Mexico (Treaty Series 994, 59 Stat. 1219), the United 

States/Mexico agreement in Minute No. 242 of August 30, 1973 (Treaty Series 7708; 24 UST 1968), or 

Minute No. 314 of November 26, 2008, or Minute No. 318 of December 17, 2010, or Minute No. 319 

of November 20, 2012, the Consolidated Decree entered by the Supreme Court of the United States in 

Arizona v. California (547 U.S 150 (2006)), the Boulder Canyon Project Act (45 Stat. 1057), the 

Boulder Canyon Project Adjustment Act (54 Stat. 774; 43 U.S.C. 618a), the Colorado River Storage 

Project Act of 1956 (70 Stat. 105; 43 U.S.C. 620), the Colorado River Basin Project Act of 1968 

(82 Stat. 885; 43 U.S.C. 1501), the Colorado River Basin Salinity Control Act (88 Stat. 266; 43 

U.S.C. 1951) as amended, the Hoover Power Plant Act of 1984 (98 Stat. 1333), the Colorado River 

Floodway Protection Act (100 Stat. 1129; 43 U.S.C. 1600), the Grand Canyon Protection Act of 1992 

(Title XVIII of Public Law 102-575, 106 Stat. 4669), or the Hoover Power Allocation Act of 2011 

(Public Law 112-72). In addition, nothing in the Study is intended to, nor shall the Study be construed 

so as to, interpret, diminish or modify the rights of any federally recognized tribe, pursuant to 

federal court decrees, state court decrees, treaties, agreements, executive orders and federal trust 

responsibility. Reclamation and the Basin States continue to recognize the entitlement and right of 

each State and any federally recognized tribe under existing law, to use and develop the water of the 

Colorado River system.
Appendix B1

Water Supply Sub-Team Members

December 2012 B4-1

Appendix B1 — Water Supply Sub-Team 

Members

The information presented in Technical Report B – Water Supply Assessment is the outcome of a 

collaborative process involving representatives of numerous organizations. 

A list of Water Supply Sub-Team members and their affiliations is presented below. 

• Carly Jerla, Bureau of Reclamation

• Chuck Cullom, Central Arizona Project 

• Tapash Das, CH2M HILL

• Armin Munévar, CH2M HILL

• Jerry Zimmerman, Colorado River Board of California

• Robert Kirk, Navajo Nation 

• John Whipple, New Mexico Interstate Stream Commission

• Mike Roberts, The Nature Conservancy

• John Gerstle, Trout Unlimited

• Steve Cullinan, U.S. Fish and Wildlife Service

• Robert King, Utah Division of Natural Resources

Additional support in the form of supplemental analysis, review, and information was provided 

by those listed below. 

• Ben Harding, AMEC Earth & Environmental

• Ken Nowak, Bureau of Reclamation

• Jim Prairie, Bureau of Reclamation

• Levi Brekke, Bureau of Reclamation’s Technical Service Center

• Subhrendu Gangopadhyay, Bureau of Reclamation’s Technical Service Center 

• Tom Pruitt, Bureau of Reclamation’s Technical Service Center

• Joe Barsugli, University of Colorado and the National Oceanic and Atmospheric 

Administration
Appendix B2

Supplemental Water Supply Data and Methods

December 2012 B2-1 

Appendix B2 — Supplemental 

Water Supply Data and Methods

This appendix provides supplemental information related to the water supply data and methods 

discussed in the Technical Report. As discussed in the Technical Report, the assessment of 

historical and future supply conditions focused on four main groups of water supply indicators: 

climate, hydrologic processes, climate teleconnections, and streamflow. Although the primary 

indicator of water supply in the Colorado River Basin (Basin) is streamflow, a fundamental 

understanding of the processes that influence the quantity, location, and timing of streamflow is 

beneficial. Additional detail on the methods used to assess these indicators for water supply is 

supplied in this appendix. 

Table B2-1 summarizes the water supply indicators evaluated as part of the Water Supply 

Assessment. In addition, the table provides the relevance of the particular parameter for the 

Colorado River Basin Water Supply and Demand Study (Study), temporal and spatial scales 

considered, and analysis methods. Table B2-2 summarizes the data sources considered in the 

evaluation of each of the water supply indicators. The subsequent sections provide further detail 

on the data and methods under each of the four water supply indicator groups. 

TABLE B2-1 

Summary of the Water Supply Indicators for the Water Supply Assessment

Water Supply 

Indicator Relevance

Temporal 

Scale Spatial Scale

Method of 

Analysis

Method of 

Display

CLIMATE

Temperature Identification of trends in 

climate patterns

Monthly, 

Seasonal, 

Annual, 

Decadal 

Grid cell, Select 

Watersheds, 

and Basin-wide

Statistical 

analysis of 

trends and 

variability

Spatial 

analysis and 

visualization

Precipitation Identification of trends in 

climate patterns

Monthly, 

Seasonal, 

Annual, 

Decadal 

Grid cell, Select 

Watersheds, 

and Basin-wide

Statistical 

analysis of 

trends and 

variability

Spatial 

analysis and 

visualization

HYDROLOGIC PROCESSES

Runoff Identification of changes 

in runoff processes; 

identification of 

"productive" watersheds

Monthly, 

Seasonal, 

Annual, 

Decadal 

Grid cell, Select 

Watersheds, 

and Basin-wide

Calculated as 

unit runoff; 

statistics to be 

generated

Spatial 

analysis and 

visualization

Evapotranspiration (ET) Identification of changes 

in natural losses; 

identification of "water 

stressed" watersheds

Monthly, 

Seasonal, 

Annual, 

Decadal 

Grid cell, Select 

Watersheds, 

and Basin-wide

Calculated as 

unit actual ET; 

statistics to be 

generated

Spatial 

analysis and 

visualization
Colorado River Basin

Water Supply and Demand Study

B2-2 December 2012

TABLE B2-1 

Summary of the Water Supply Indicators for the Water Supply Assessment

Water Supply 

Indicator Relevance

Temporal 

Scale Spatial Scale

Method of 

Analysis

Method of 

Display

Snowpack 

Accumulation 

and Snowmelt

Identification of spatial 

changes in snowpack 

development and timing 

of melt

Monthly, 

Seasonal, 

Annual, 

Decadal 

Grid cell, Select 

Watersheds, 

and Basin-wide

Calculated as 

unit snow 

water 

equivalent 

(SWE); peak 

and timing

Spatial 

analysis and 

visualization

Soil Moisture Identification of causes of 

drought and severe drying 

conditions; identification 

of watersheds most 

impacted

Monthly, 

Seasonal, 

Annual, 

Decadal 

Grid cell, Select 

Watersheds, 

and Basin-wide

Calculated as 

percentage of

maximum

Spatial 

analysis and 

visualization

CLIMATE TELECONNECTIONS

El Niño – 

Southern 

Oscillation 

(ENSO) 

Identify changes in 

teleconnections and 

influence on regional 

climate; identify 

relationship between longterm and shorter-term 

climate indices

Season, 

Annual, 

Decadal

Global/

Regional 

Statistical 

analysis of 

correlation 

between 

indicator and 

streamflow

Correlation 

plots and 

statistics

Pacific Decadal 

Oscillation 

(PDO) 

Identify changes in 

teleconnections and 

influence on regional 

climate; identify 

relationship between longterm and shorter-term 

climate indices

Annual, 

Decadal

Global/

Regional 

Statistical 

analysis of 

correlation 

between 

indicator and 

streamflow

Correlation 

plots and 

statistics

Atlantic Multidecadal 

Oscillation 

(AMO) 

Identify changes in 

teleconnections and 

influence on regional 

climate; identify 

relationship between longterm and shorter-term 

climate indices

Annual, 

Decadal

Global/

Regional 

Qualitative 

discussion

Qualitative 

discussion

STREAMFLOW

Intervening and 

Total Natural 

Flows at 29 

Basin Locations

Identification of changes 

in streamflow trends and 

variability

Monthly; 

Annual; 1-, 

3-, 5-, 10-

year; and 

multidecadal 

Accumulated 

Flow at Point

Statistical 

analysis of 

trends and 

variability; 

drought and 

surplus 

statistics

Table and 

box-whisker 

of statistics, 

Basin-scale 

maps
Appendix B2 — Supplemental

Water Supply Data and Methods

December 2012 B2-3 

TABLE B2-2

Sources of Data Used for the Water Supply Assessment

Parameter Description Data Source

CLIMATE INDICATORS

Historical Temperature 

and Precipitation

Historical gridded temperature and 

precipitation at 1/8th-degree resolution for 

the period of 1950–1999. Extension through 

2005 was not documented.

Maurer et al., 2002 

(http://www.engr.scu.edu/~emaurer/ 

data.shtml)

Future Temperature and 

Precipitation Projections

A total of 112 future monthly temperature 

and precipitation projections based on 

Intergovernmental Panel on Climate Change 

(IPCC) Fourth Assessment Report (IPCC, 

2007) emission scenarios, subsequently bias 

corrected, and statistically downscaled to 

1/8th-degree resolution for the period of 

1950–2099. 

Maurer et al., 2007 

(http://gdo-dcp.ucllnlRUJ

downscaled_cmip3_projections/) 

HYDROLOGIC PROCESS INDICATORS

ET, Runoff, SWE, Soil 

Moisture

Variable Infiltration Capacity (VIC)-simulated 

hydrologic fluxes and grid cell storage terms 

driven by observed climatology (1950–2005) 

and 112 future climate projections (1950–

2099). 

Bureau of Reclamation (Reclamation), 

2011

Snowpack Point snow water equivalent from late 1970s 

to present from the snow-telemetry 

(SNOTEL) network. 

National Resources Conservation 

Service, 2011 

(http://www.wcc.nrcs.usda.gov/snow/)

TELECONNECTION INDICATORS

ENSO Monthly Southern Oscillation Index (SOI) for 

January 1866 through March 2010. 

University of East Anglia Climatic 

Research Unit , 2010 

(http://www.cru.uea.ac.uk/cru/data/soi/)

PDO Monthly PDO indices for January 1900 

through January 2010. 

Joint Institute for the Study of the 

Atmosphere and Ocean, 2010 

(http://jisao.washington.edu/pdo/) 

STREAMFLOW INDICATORS

Observed Streamflow 

used in the Observed 

Resampled Scenario

Natural streamflow for the period of 1906–

2007 for the 29 streamflow locations 

commonly used for Reclamation planning. 

Prairie and Callejo, 2005; Reclamation, 

2010

Paleo Reconstructed 

Streamflow used in the 

Paleo Resampled 

Scenario

Reconstructed natural streamflows for the 

period 762–2005 at 29 locations derived 

from ecologically contrasting tree-ring sites 

in the southern Colorado Plateau during the 

past 2 millennia. 

Reclamation, 2010; Meko et al., 2007

Paleo Conditioned 

Streamflow used in the 

Paleo Conditioned 

Scenario

Blended paleo streamflow states with 

observed streamflow magnitudes at 29 

locations. 

Prairie et al., 2008

Future Streamflow 

Projections used in the 

Downscaled General 

Circulation Model (GCM) 

Projected Scenario

VIC-simulated runoff and routed streamflow 

at 29 locations driven by 112 future climate 

projections for the period 1950–2099. 

Reclamation, 2011
Colorado River Basin

Water Supply and Demand Study

B2-4 December 2012

1.0 Climate 

1.1 Historical Climate

Gridded observed climate data for the period from 1950 to 1999, as developed by Maurer et al. 

(2002), were downloaded via the Internet from Santa Clara University 

(http://www.engr.scu.edu/~emaurer/data.shtml). The data are stored in network common data 

format (netCDF) at 1/8th-degree resolution and contain daily temperature (minimum and 

maximum), precipitation, and wind speed values for the contiguous United States. Subsequent to 

the Maurer et al. (2002) data, the gridded dataset was extended to 2005 using identical methods. 

The temperature and precipitation data were processed into monthly average temperature and 

monthly total precipitation to facilitate comparisons. The monthly, seasonal, and annual statistics 

were computed for each parameter and for each grid cell for the period 1971 to 2000 to facilitate 

comparisons to projected future conditions. This 1971 to 2000 historical base period was selected 

as the most current 30-year climatological period at the time of the Study, as described by the 

National Oceanic and Atmospheric Administration (NOAA) (2010), and was used as the basis 

for comparing to future climate projections1. 

1.2 Projections of Future Climate 

Future climate change projections are made primarily on the basis of General Circulation Model 

(GCM) simulations under a range of future emission scenarios. A total of 112 future climate 

projections used in the IPCC Fourth Assessment Report, subsequently transformed to a local scale 

through bias correction and spatial downscaling (BCSD), were obtained from the Lawrence 

Livermore National Laboratory under the World Climate Research Program’s (WCRP) Coupled 

Model Intercomparison Project Phase 3 (CMIP3). This archive contains climate projections 

generated from 16 different GCMs developed by national climate centers and for Special Report 

on Emissions Scenarios emission scenarios A2, A1B, and B1. These projections have been bias 

corrected and spatially downscaled to 1/8th-degree (~12-kilometer) resolution over the contiguous 

United States through methods described in detail in Wood et al. (2002; 2004) and Maurer (2007). 

1.2.1 Emission Scenarios 

In 2000, IPCC published the SRES scenarios that described a family of six emission scenarios to 

condition GCMs (IPCC, 2000). The emissions scenarios are defined by alternative future 

development pathways, covering a wide range of demographic, economic, and technological 

driving forces and resulting greenhouse gas (GHG) emissions. The GHG emissions associated 

with each scenario are shown in figure B2-1. 

 

1 A new 30-year historical base period (1981 to 2010) was issued by NOAA on July 1, 2011. 
Appendix B2 — Supplemental

Water Supply Data and Methods

December 2012 B2-5 

FIGURE B2-1

Scenarios for GHG Emissions from 2000–2100 in the Absence of Additional Climate Policies

Units on the y-axis are billon tons of total annual emissions in equivalent carbon dioxide units.

Source: (IPCC, 2007) 

Of the six emission scenarios included in the IPCC Fourth Assessment Report (IPCC, 2007), 

three were selected to drive the CMIP3 multi-model dataset—A2 (high), A1B (medium), and B1 

(low). The A2 scenario is representative of high population growth, slow economic development, 

and slow technological change. It is characterized by a continuously increasing rate of GHG 

emissions and features the highest annual emissions rates of any scenario by the end of the 21st 

Century. The A1B scenario features a global population that peaks mid-century and rapid 

introduction of new and more-efficient technologies balanced across both fossil- and non-fossilintensive energy sources. As a result, GHG emissions in the A1B scenario peak around midcentury. Lastly, the B1 scenario describes a world with rapid changes in economic structures 

toward a service and information economy. GHG emission rates in this scenario peak prior to 

mid-century and are generally the lowest of the scenarios. The best estimates of global 

temperature change during the 21st Century for each of the A2, A1B, and B1 scenarios are 3.4, 

2.8, and 1.8 degrees Celsius (°C), respectively2 (IPCC, 2007) as shown in Figure B2-2. 

 

2 Temperature change reflects the difference between the global average in the 2090 to 2099 period relative to the global average in the 1980 to 

1999 period.
Colorado River Basin

Water Supply and Demand Study

B2-6 December 2012

FIGURE B2-2

Projections of Surface Temperatures for the Selected GHG Emissions Scenarios from 2000–2100

Source: (IPCC, 2007) 

1.2.2 General Circulation Models 

The CMIP3 multi-model dataset consists of 112 unique climate projections. Sixteen GCMs were 

coupled with the three emissions scenarios described previously to generate these projections. 

Many of the GCMs were simulated multiple times for the same emission scenario due to 

differences in starting climate system state or initial conditions, so the number of available 

projections is greater than simply the product of GCMs and emission scenarios. Table B2-3 

summarizes the GCMs, initial conditions (specified by the run numbers in the A2, A1B, and B1 

columns), and emissions scenario combinations (A2, A1B, and B1) featured in the CMIP3 

dataset. Initial conditions (initial atmosphere and ocean conditions used in a GCM simulation) for 

the 21st Century are defined by the 20th Century “control” simulation. A description of the 

20th Century “control” simulations corresponding to each GCM simulation in table B2-3 can be 

found at http://www-pcmdi.llnl.gov/ipcc/standard_output.html#Experiments. 
Appendix B2 — Supplemental

Water Supply Data and Methods

December 2012 B2-7 

TABLE B2-3 

WCRP CMIP3 Multi-Model Dataset GCMs, Initial Conditions, and Emissions Scenarios

Modeling Group, Country 

WCRP 

CMIP3 I.D. A2 A1B B1

Primary 

Reference 

Bjerknes Center for Climate Research, Norway BCCRBCM2.01 1 1 Furevik et al., 

2003

Canadian Center for Climate Modeling and Analysis, 

Canada

CGCM3.1 

(T47)

1...5 1...5 1...5 Flato and Boer, 

2001

Meteo-France/Center National de Recherches 

Meteorologiques, France

CNRM-CM3 1 1 1 Salas-Melia et 

al., 2005

CSIRO Atmospheric Research, Australia CSIROMk3.01 1 1 Gordon et al., 

2000

U.S. Dept. of Commerce/NOAA/Geophysical Fluid 

Dynamics Laboratory, United States

GFDLCM2.01 1 1 Delworth et al., 

2006

U.S. Dept. of Commerce/NOAA/Geophysical Fluid 

Dynamics Laboratory, United States

GFDLCM2.11 1 1 Delworth et al., 

2006

National Aeronautics and Space 

Administration/Goddard Institute for Space Studies, 

United States

GISS-ER 1 2, 4 1 Russell et al., 

2000

Institute for Numerical Mathematics, Russia INM-CM3.0 1 1 1 Diansky and 

Volodin, 2002

Institut Pierre Simon Laplace, France IPSL-CM4 1 1 1 Institut Pierre 

Simon Laplace, 

2005

Center for Climate System Research (The University 

of Tokyo), National Institute for Environmental 

Studies, and Frontier Research Center for Global 

Change, Japan

MIROC3.2 

(medres) 

1...3 1...3 1...3 K-1 Model 

Developers, 

2004

Meteorological Institute of the University of Bonn, 

Germany and Institute of Korea Meteorological 

Administration, Korea 

ECHO-G 1...3 1...3 1...3 Legutke and 

Voss, 1999

Max Planck Institute for Meteorology, Germany ECHAM5/ 

MPI-OM

1...3 1...3 1...3 Jungclaus et 

al., 2006

Meteorological Research Institute, Japan MRICGCM2.3.21...5 1...5 1...5 Yukimoto et al., 

2001

National Center for Atmospheric Research, United 

States

CCSM3 1...4 1...3, 

5...7

1...7 Collins et al., 

2006

National Center for Atmospheric Research, United 

States

PCM 1...4 1...4 2...3 Washington et 

al., 2000

Hadley Center for Climate Prediction and 

Research/Met Office, United Kingdom

UKMOHadCM31 1 1 Gordon et al., 

2000

Total Number of Climate Projections 36 39 37

Source: (Maurer et al., 2007)
Colorado River Basin

Water Supply and Demand Study

B2-8 December 2012

1.2.3 Bias Correction and Spatial Downscaling

The CMIP3 climate projections have undergone BCSD to 1/8th-degree (~12-kilometer) 

resolution through methods described in detail in Wood et al. (2002; 2004) and Maurer (2007). 

The purpose of this bias correction is to adjust a given climate projection for inconsistencies 

between the simulated historical climate data and observed historical climate data, which are the 

result of GCM bias. In the BCSD approach, projections are bias corrected using a quantile 

mapping technique at 2-degree (~200-kilometer) spatial resolution. Following bias correction, 

the adjusted climate projection data are statistically consistent on a monthly basis with the 

observed climate data for the historical overlap period, which was 1950 to 1999 in the Study. 

Beyond the historical overlap period (2000 to 2099), the adjusted climate projection data reflect 

the same relative changes in mean, variance, and other statistics between the projected (2000 to 

2099) and historical periods (1950 to 1999) as were present in the unadjusted dataset, but the 

adjusted climate projection data are mapped onto the observed dataset variance. This 

methodology assumes that the GCM biases have the same structure during the 20th and 21st 

Century simulations.

Downscaling spatially translates bias corrected climate data from the coarse, 2-degree

(~200-kilometer), spatial resolution typical of climate models to a basin-relevant resolution of 

1/8th-degree (12 kilometers), which is more useful for hydrology and other applications. The 

spatial downscaling process generally preserves observed spatial relationships between largeand fine-scale climates. This approach assumes that the topographic and climatic features that 

determine the fine-scale distribution of the large-scale climate will be the same in the future as in 

the historical period. 

1.2.4 Weather Generation (Temporal Disaggregation)

The resulting BCSD climate projections provide a representation of future monthly temperature 

and precipitation through 2099. However, to be useful for hydrologic modeling, this information 

is required on a daily temporal scale. The monthly downscaled data were temporally 

disaggregated to a daily temporal scale to create realistic weather patterns using the sampling 

methods described in Wood et al. (2002) with extensions of this approach as applied by Salathé 

(2005) and Mote and Salathé (2010). To generate daily values, for each month in the simulation 

a month is randomly selected from the historic record for the same month (e.g., for the month of 

January, a January from the 1950 to 1999 period is selected). The daily precipitation and 

temperature from the historic record are then adjusted (rescaled precipitation and shifted 

temperature) such that the monthly average matches the simulated monthly value. The same 

historic month is used throughout the domain to preserve plausible spatial structure to daily 

storms (Mote and Salathé, 2010). The results of the temporal disaggregation are daily weather 

sequences that preserve the monthly values from the downscaled climate projections. Some 

uncertainties can be introduced depending on the method employed to produce the daily data 

from the monthly climate values. A comparative analysis of two available methods to generate 

daily weather patterns for the Study favored the use of the method employed by Salathé (2005) 

and incorporated in the SECURE Report (Reclamation, 2011) to produce the daily downscaled 

data. Additional detail of the comparative analysis of two daily weather generation (temporal 

disaggregation) methods is presented in appendix B3 under Comparison of Daily Weather 

Generation (Temporal Disaggregation) Methods.
Appendix B2 — Supplemental

Water Supply Data and Methods

December 2012 B2-9 

2.0 Hydrologic Processes 

The primary sources for hydrologic process data are derived from the VIC-simulated conditions 

driven by either observed historical climatology (1950 to 2005) or projected climate (1950 to 

2099). VIC simulates all major moisture fluxes at the grid cell using physically based methods. 

These moisture fluxes are not generally measured at the spatial resolution necessary for Basin 

assessments; thus the VIC-derived patterns are considered the most suitable source. For example, 

although station-specific SWE, precipitation, and temperature are available from the National 

Resources Conservation Service SNOTEL network at 800 stations in 11 western states and 

Alaska (http://www.wcc.nrcs.usda.gov/snow/), the spatial representativeness of the SNOTEL 

data is uncertain (Daly et al., 2000). In preliminary results, Molotch et al. (2001) showed that 

SWE can begin to vary significantly beyond 500 meters from a SNOTEL site, due to terrain 

impacts on snow ablation, as well as small-scale depositional variations. A variety of methods 

have been used to distribute point measurements to spatial grids. The methods used are complex 

and beyond the scope of the Study; therefore, site-specific SNOTEL data were not processed to 

independently validate the SWE fields derived from the VIC model for the Study. However, 

Mote et al. (2008) found correlation of better than 0.75 between VIC-simulated SWE and 

measured SWE for the Rockies. Other parameters, such as ET and soil moisture, are not 

routinely measured, nor are they measured at scales that permit validation with the VICsimulated fields. Thus, the use of VIC-simulated historical fluxes enables a consistent 

comparison of change when considering simulated fluxes under future climate. 

Both the climate and hydrologic data from VIC simulations are stored in formatted text files 

known as “flux files.” One flux file is produced for every grid cell of the model domain, and 

each file contains values for the specified parameters at every time step of the simulation. 

Gridded climate and hydrologic parameter data generated by the VIC model for the historical 

and projected periods were converted from daily to monthly values and stored in a specialized 

format (netCDF). This data conversion allows for statistical and spatial analysis of the data and 

enables a better understanding of the primary factors, both climatological and hydrological, that 

drive projected changes in streamflows relative to historical conditions. In addition to the 

primary VIC outputs of air temperature, precipitation, ET, runoff, and baseflow, total runoff 

(sum of baseflow and runoff) and runoff efficiency were computed at each grid cell and added to 

the netCDF files. Runoff efficiency is defined as the fraction of total runoff to the total 

precipitation. The complete list of hydroclimatic variables compiled is included in table B2-4. 

One netCDF file was produced for each climate projection and for the historical observed data, 

for a total of 113 netCDF files. As with the climate data, monthly, seasonal, and annual statistics 

were derived for the hydrologic process information for the historical period 1971 to 2000 and 

three future 30-year climatological periods: 2011 to 2040, 2041 to 2070, and 2066 to 2095. The 

historical period 1971 to 2000 was selected as the reference climate because it was the most 

current 30-year climatological period described by NOAA (2010) at the time the Study was 

initiated. Representative statistics were generated on monthly, seasonal, and annual bases. In this 

analysis, the seasons are defined as follows: Fall: October, November, and December; Winter: 

January, February, and March; Spring: April, May, and June; and Summer: July, August, and 

September.
Colorado River Basin

Water Supply and Demand Study

B2-10 December 2012

TABLE B2-4 

Climate and Hydrologic Parameters

VIC Parameter Units

Average air temperature °C 

Precipitation millimeters (mm)

ET mm 

Runoff (surface) mm 

Baseflow (subsurface) mm 

Total runoff mm 

Soil moisture (in each of three soil layers) mm 

Soil moisture fraction percent

SWE mm 

Runoff efficiency (total runoff/total precipitation) fraction

The statistical analysis was conducted on both grid cell and watershed bases. The results of the 

grid cell analysis produce the most informative map graphics and clearly show spatial variation 

at the greatest resolution possible. At this spatial scale, the statistics for each grid cell are 

developed independently. The resulting statistics are stored in netCDF files. Monthly time series 

data were extracted from these files to characterize patterns of change in hydrologic parameters. 

Finally, “change metrics” are generated for each parameter, in which the difference between 

future period statistics and historical period statistics are calculated on both absolute and percent 

change bases. These results are again stored in netCDF files, with two files generated for each 

future period—one for grid cell data and one for watershed data. The format of these files is 

identical to those containing the results of the statistical analysis.

3.0 Climate Teleconnections 

During the past 30 years, the understanding of the climatic importance of the oceans, particularly 

ocean temperature, has steadily improved (U.S. Department of Interior, 2004). Initial research 

focused on the distant effects of the recurrent warming of the equatorial Pacific Ocean referred to 

as El Niño, which South American fishermen have long known to have an adverse effect on the 

coastal fisheries in Peru. El Niño is the warm phase of the sea-surface temperature component of 

a coupled ocean-atmosphere process, ENSO, which spans the equatorial Pacific Ocean. The 

atmospheric component, the Southern Oscillation, refers to a “seesaw” effect in sea-level 

pressure between the tropical Pacific and Indian Oceans. Reduced sea-level pressure in the 

Pacific Ocean, combined with increased sea-level pressure in the Indian Ocean, leads to a 

weakening in the trade winds over the eastern Pacific. This weakening enables warm water from 

the central equatorial Pacific to spread eastward and southward along the west coast of South 

America, creating the classic El Niño condition. Conversely, and about as frequently, the sealevel pressure in the Pacific Ocean increases while pressure in the Indian Ocean decreases, which 

causes trade winds to intensify over the eastern Pacific. When this occurs, equatorial upwelling 
Appendix B2 — Supplemental

Water Supply Data and Methods

December 2012 B2-11

of deep, cold water, as well as cold water from the West Coast of South America, are pulled 

northward and westward from the coast into the eastern and central Pacific, producing La Niña. 

Thus, El Niño and La Niña are, respectively, the warm and cold phases of the coupled ENSO 

system.

ENSO events typically last from 6 to 18 months and, therefore, are the single most important 

factor affecting inter-annual climatic variability on a global scale (Diaz and Kiladis, 1992). 

ENSO has been linked to the occurrence of flooding in the Lower Basin (Webb and Betancourt, 

1992) and to both floods and droughts across the western United States (Cayan et al., 1999). 

Warm winter storms have been enhanced during El Niño, causing above-average runoff and 

floods in the Southwest, such as during 1982 and 1983. However, not all El Niño events lead to 

increased runoff in the Southwest. For example, during the 2002 to 2003 warm episode, runoff 

was below average in the Basin. Similarly, La Niña is frequently, though not always, associated 

with below-average flow in the Colorado River. As a result, although ENSO exerts a strong 

influence in modulating wet versus dry conditions in many parts of the United States, the effect 

is not always the same in any given region. Some condition other than ENSO must also be 

influencing weather and climate patterns affecting the Colorado River.

In the mid-1990s, scientists identified another ocean temperature pattern, this one occurring in 

the extratropical Pacific Ocean north of 20 oN (Mantua and Hare, 2002), the PDO. The PDO 

varies or oscillates on a decadal scale of 30 to 50 years for the total cycle; that is, much of the 

North Pacific Ocean is predominantly, though not uniformly, warm (or cool) for periods of about 

15 to 25 years. During the 20th Century, the PDO exhibited several phases−warmer along coastal 

southeastern Alaska from 1923 to 1943 and again from 1976 to 1998, and cooler from 1944 to 

1975. Since 1999, the PDO has exhibited higher-frequency fluctuations, varying from cool (1999 

to 2001) to warm (2002 to 2004). Currently, the causes of the variations in the PDO are unknown 

and its potential predictability is uncertain. Recent research indicates that the PDO phase may be 

associated with decadal-length periods of above- and below-average precipitation and 

streamflow in the Basin (Hidalgo, 2004) but, as with ENSO, such associations are not always 

consistent. 

Climate teleconnections were first analyzed by selecting indices that could have potential 

influence in streamflow changes for the Basin. Published research (Redmond and Koch, 1991; 

Webb and Betancourt, 1992; Cayan et al.; 1999; Mo et al., 2009; and others) indicates that the 

strongest correlations with Basin flows were observed with the PDO and ENSO indices. For 

ENSO, data were collected for both the ocean component (sea surface temperature anomalies) 

and the atmospheric component. The two components are highly correlated and combined 

describe ENSO. The SOI, the atmospheric component, was the primary dataset used in the Study 

due to the longer availability of information. Therefore, the quantitative teleconnections analysis 

was based on the PDO index and the SOI. Only a qualitative discussion of the AMO is included 

in the Technical Report.

Annual averages of the PDO on a water-year basis were calculated and compared with the same 

water year annual flows. Annual average values for the SOI were computed, using different 

annual windows. The average SOI index presented in the Study refers to the June to November 

period, which was identified as a strong indicator of ENSO events (Redmond and Koch, 1991). 

Once the SOI averages were computed, ENSO events were determined by years when the 

averaged SOI was below -1 (classified as an El Niño year) or above 1 (classified as a La Niña 

year). A warm PDO was defined as a PDO value greater than or equal to 0.0, and a cold PDO 
Colorado River Basin

Water Supply and Demand Study

B2-12 December 2012

was a PDO value less than 0.0. AMO research by Mo et al. (2009) indicates that the direct 

influence of the AMO on drought is small. The major influence of the AMO is to modulate the 

impact of ENSO on drought. The influence is large when the sea surface temperature anomalies 

in the tropical Pacific and in the North Atlantic are opposite in phase. A cold (warm) event in a 

positive (negative) AMO phase amplifies the impact of the cold (warm) ENSO on drought. The 

ENSO influence on drought is much weaker when the sea surface temperature anomalies in the 

tropical Pacific and in the North Atlantic are in phase. Because the AMO cycle is approximately 

70 years, AMO research is constrained by the observed data record of approximately 150 years. 

AMO research continues in this area using indirect observations of tree rings and sedimentary 

layers.

There are also other climate teleconnections that appear to influence the characteristics of 

seasonal precipitation (e.g., Madden-Julian Oscillation and Arctic Oscillation) (Becker et al., 

2011; Bond and Vecchi, 2003; Hu and Feng, 2010). However, the understanding of the influence 

of these teleconnections on the Colorado River precipitation, and their usefulness as an indicator, 

is still evolving.

4.0 Streamflow

Streamflow was analyzed through the use of two historical data sets (observed period and a 

longer paleo-reconstructed period) and projections of future streamflow based on climate 

models. Using information from the recent past, more distant past, and projections of the future 

enabled a robust assessment of plausible future conditions. 

Two historical streamflow data sets—the observed record spanning the period 1906 to 2007 and 

the paleo-reconstructed record spanning the period 762 to 2005—were used in the Study to 

characterize historical streamflow patterns and variability. Period comparisons are made between 

the full extent of the data and a more recent period. For the observed dataset spanning 1906 to 

2007, the second comparison period (1978 to 2007) was selected as the most recent (based on 

available natural flow records) 30-year period because it captures the recent drought period and 

the apparent climate shift after 1977 (IPCC, 2007). For the Paleo dataset spanning 762 to 2005, 

the second comparison period selected was 1906 to 2005 so that direct comparisons could be 

made of the observed and paleo timeframes. Annual flows and moving averages for 3, 5, 10, 20, 

and 30 years were computed for the two time periods so that differences in mean flows and 

variability of flows could be accessed. Annual flows and moving averages were also used to 

evaluate minimum and maximum streamflows. Exceedance probability plots were used to 

evaluate the likelihood of annual flows to exceed a specified streamflow value. 

One future streamflow projection data set was represented in the Downscaled GCM Projected 

scenario. In this scenario, the routed streamflow from the VIC simulations driven by 112 climate 

projections for the period 1950 to 2099 were used to characterize natural flows at each of the 

29 flow locations. VIC-simulated runoff from each grid cell was routed to the outlet of each 

watershed (the 29 flow locations) using VIC’s offline routing tool (Lohmann et al., 1996; 1998). 

The routing tool processed individual cell runoff and baseflow terms and routed the flow 

based on flow direction and flow accumulation inputs derived from digital elevation models. 

Flows were output in both daily and monthly time steps. Only the monthly flows were used 

in the analysis for the Study. VIC routed flows are considered “natural flows” in that they 

do not include effects of diversions, imports, storage, or other human management of 
Appendix B2 — Supplemental

Water Supply Data and Methods

December 2012 B2-13

the water resource. Bias-correction was applied to the VIC-simulated flows to account 

for any systematic bias in the hydrology model or data sets. 

Annual streamflows for both the historical analysis and future water supply scenarios were 

analyzed to provide an estimate of the inter-annual variability, or deficit and surplus conditions. 

Definitions of “drought” are often subjective in water planning. In general, droughts are defined 

as periods of prolonged dryness. The inter-annual variability of the climate and hydrology of the 

Southwest imply basins may be in frequent states of drought. As part of the analysis conducted 

for this report, different averaging periods for determining and measuring deficits (cumulative 

volume below some reference) were considered. The definition used in the Technical Report is 

the following: a deficit occurs whenever the 2-year average flow falls below the long-term mean 

annual flow of 1906 to 2007. The use of a 1-year averaging period was discarded because it 

implied that any 1 year above the 15-million-acre-feet Lees Ferry natural flow would break a 

multi-year deficit. The use of a 2-year averaging period implies that it may take 2 consecutive 

above-normal years (or 1 extreme wet year) to end a drought. For a basin with sizable reservoir 

storage in comparison to its mean flow such as the Colorado River, it may take several years to 

alleviate storage deficits. Averaging periods of 1 to 10 years were evaluated, following research 

by Timilsena et al. (2009). The 2-year averaging period appeared to produce similar deficits as 

the longer-averaging periods, and was thus selected as a useful indicator.

A summary of the streamflow data sources used in each of the water supply scenarios is included 

below. 

4.1 Observed Natural Streamflows used in the Observed Resampled Scenario

The natural streamflows were obtained for the 1906 to 2007 period at the 29 flow locations 

commonly used by the Reclamation for planning. Reclamation uses data collected from the 

U.S. Geological Survey (USGS) and other gage sites, consumptive use records, records of 

reservoir releases, and other data to compute monthly natural flows at 29 locations throughout 

the Basin: 20 locations upstream of and including the Lees Ferry gaging station in Arizona, and 

9 locations below the Lees Ferry gaging station (Prairie and Callejo, 2005). 

Natural flow for the Upper Basin is computed as follows: 

Natural Flow = Historic Flow + Consumptive Uses and Losses+/- Reservoir Regulation 

Historical streamflow data were obtained from USGS Web pages. Total depletions in the form of 

consumptive uses and losses include the following: irrigated agriculture, reservoir evaporation, 

stockponds, livestock, thermal power, minerals, municipal and industrial, and exports/imports. 

Reservoir regulation includes mainstem reservoirs and non-mainstem reservoirs. 

Natural flows for the Lower Basin comprise computed gains and losses (on the mainstem) 

and historical flows (on the tributaries). Computed gains and losses consider the following 

consumptive uses and losses: decree accounting reports (http://www.usbr.gov/lc/region/ 

g4000/wtracct.html), evaporation (from Lakes Mead, Mohave, and Havasu), and phreatophytes. 

Reservoir regulation includes change in reservoir storage and change in bank storage. Historical 

flows on the tributaries (Paria, Virgin, Little Colorado, and Bill Williams Rivers) have not had 

the historical depletions added back to the gaged flow due to the state of current methods and 

processes. Thus, most Lower Basin flows should not be considered natural. For more detail on 
Colorado River Basin

Water Supply and Demand Study

B2-14 December 2012

the treatment of the Lower Basin tributaries see Technical Report C – Water Demand 

Assessment, Appendix C5 – Modeling of Lower Basin Tributaries in the Colorado River 

Simulation System. 

Monthly intervening and total natural flow for the 29 locations are available. “Intervening” flows 

represent the flow generated between two locations, but do not include the cumulative 

contribution of the locations upstream. “Total” flows, on the other hand, include the local 

intervening flow and all upstream flows from that location. 

Additional information, documentation, and the natural flow data are available 

at http://www.usbr.gov/lc/region/g4000/NaturalFlow/Index.html.

4.2 Paleo Reconstructed Streamflow used in the Paleo Resampled Scenario

The natural streamflows in the Paleo Resampled scenario were derived from streamflow 

reconstructions at Lees Ferry from tree-ring chronologies for the period of 762 to 2005. The 

reconstructed streamflows at Lees Ferry were derived from ecologically contrasting tree-ring 

sites in the southern Colorado Plateau during the past 2 millennia (Meko et al., 2007). 

Streamflow values were disaggregated, spatially, and temporally, to the 29 locations by 

Reclamation (Prairie and Rajagopalan, 2007; Prairie et al., 2008). 

4.3 Paleo Conditioned Streamflow used in the Paleo Conditioned Scenario

The Paleo Conditioned scenario blends the observed historical record and Paleo-reconstructed 

record to generate future inflow scenarios that comprise magnitudes of the historical record and 

state information from the Paleo record provided by Reclamation (Prairie and Rajagopalan, 

2007; Prairie et al., 2008). 

4.4 Future Streamflow Projections used in the Downscaled GCM Projected

Scenario

The Downscaled GCM Projected scenario includes VIC hydrologic model traces of future 

streamflows for the 1950 to 2099 period from 112 GCM realizations for the 29 streamflow 

locations within the Basin. VIC model results were provided by Reclamation from work 

conducted for the West-Wide Climate Risk Assessment study (Reclamation, 2011). 
Appendix B2 — Supplemental

Water Supply Data and Methods

December 2012 B2-15

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Bond, N. A., and G.A. Vecchi. 2003. “The influence of the Madden–Julian oscillation on 

precipitation in Oregon and Washington.” Wea. Forecasting, 18, 600–613. 

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to be coupled to land surface parameterization schemes.” Tellus, 48: 708-721. 

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I. Formulation of the VIC-2L Model Coupled to a Routing Model.” Hydrological 

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California under two emissions scenarios.” Climatic Change, Vol. 82, No. 3-4, 309-325, 

DOI: 10.1007/s10584-006-9180-9. 

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Enhance Regional Climate Change Impact Studies.” Eos, Transactions, American 

Geophysical Union, 88 (47), 504. 

Maurer, E.P., A.W. Wood, J.C. Adam, D.P. Lettenmaier, and B. Nijssen. 2002. “A Long-Term 

Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous 

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Meko, D.M., C.A. Woodhouse, C.A. Baisan, T. Knight, J.J. Lukas, M.K. Hughs, and 

M.W. Salzer. 2007. “Medieval Drought in the Upper Colorado River Basin.” 

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DOI:10.1175/2009JCLI2966.1. 
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Water Supply Data and Methods

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Appendix B3

Supplemental Analysis of Future Climate Data

December 2012 B3-1 

Appendix B3 — Supplemental Analysis of 

Future Climate Data

During the development of the hydrologic simulations under historical and projected climate 

forcings as part of the Water Supply Assessment, biases were observed for the overlapping 

period of 1950 to 1999 as compared to the natural flow data set. These biases are due to 

differences between the General Circulation Model (GCM)-simulated historical climate and 

observed climate data, differences in hydrology model inputs and parameterization, and 

differences between the Variable Infiltration Capacity (VIC)-simulated hydrologic responses and 

observed watershed responses implied in the natural flows. This appendix describes analysis that 

was conducted to determine the effect of bias in climate forcings used to simulate streamflows 

and whether choice of the daily weather generation (temporal disaggregation) method 

significantly affects this bias. 

Although it was expected that biases would exist due to the hydrology model and historical 

gridded climate, it was believed that these biases would be similar (same magnitude and 

direction) when comparing to simulations of GCM-simulated historical climate. However, 

the biases were found to be substantially different when comparing three representations of 

the historical period (1950 to 1999) streamflow: 1) natural flows derived from gage 

measurements; 2) VIC-simulated flows when forced with observed (derived) historical 

climate; and 3) VIC-simulated flows when forced with GCM-simulated historical climate. 

For example, the VIC simulation using observed historical climate for 1950 to 1999 suggested 

an over-estimation of flows in the Colorado River at Lees Ferry, Arizona. However, the same 

VIC model, when forced with GCM-simulated historical climate, produced an under-estimation 

of flow. Without a robust streamflow bias correction method, it is possible that the effects of 

climate change could be overstated. 

Several potential causes of streamflow bias were investigated to support the use of the 

downscaled climate projections on a daily scale and to support the development of a streamflow 

bias correction method. The biases were investigated through various separate analyses using the 

historical climate forcings and VIC model simulations for the period of 1950 to 1999. The 

following areas related to climate forcing bias were investigated:

1. Bias due to 2-degree climate forcings. The projected climate forcings are bias corrected

through the bias correction and spatial downscaling (BCSD) process at a common 2-degree

scale. The forcings are corrected for each month, but residual bias at seasonal, annual, and 

multi-year scales are possible. 

2. Bias due to 1/8th-degree spatial downscaling. Because the BCSD process corrects for 

month-specific bias at the 2-degree scale, it is possible that residual bias exists after 

performing spatial downscaling to the 1/8th-degree scale. 

3. Bias due to daily weather generation method. Two data sets were available using slightly 

different methods to temporally disaggregate monthly climate data into daily weather inputs. 

It is possible that the choice of method could affect the resulting streamflow bias.

The evaluation of each of the potential causes of bias is discussed further in the following 

sections. In each of these evaluations, GCM-simulated historical climate was compared to 
Colorado River Basin

Water Supply and Demand Study

B3-2 December 2012

historical observed climate from Maurer et al. (2002) for the period of 1950 to 1999. Although 

any of the 112 downscaled climate projections could have been used, one particular projection 

(Trace 44 – sresa2.ccma_cgcm3_1.4) was selected for presentation of results. Biases were found 

to be relatively consistent across the range of projections.

Analyses were performed for precipitation at representative grid cells at the locations in the 

Colorado River Basin (Basin) shown in table B3-1. However, results are shown for the grid cell 

at the Colorado River at the Glenwood Springs, Colorado, location. 

TABLE B3-1 

Locations where Evaluation of Biases Was Performed (decimal latitude and longitude)

No. Location

Nearest Grid Cell 

(Latitude, Longitude) 

1 Colorado River at Lees Ferry, Arizona 36.4375, -112.0625

2 Green River at Green River, Utah 38.8125, -111.3125

3 San Juan River near Bluff, Utah 35.5625, -110.6875

4 Colorado River near Cisco, Utah 38.6875, -109.6875

5 Colorado River above Imperial Dam, Arizona 32.9375, -114.8125

6 Colorado River at Glenwood Springs, Colorado 39.3125, -107.5625

7 Colorado River below Fontenelle Reservoir, Wyoming 42.0625, -110.8125

8 San Juan River near Archuleta, New Mexico 36.6875, -107.8125

9 Colorado River below Davis Dam, Arizona-Nevada 35.1875, -115.0625

10 Taylor River below Taylor Park Reservoir, Colorado 38.8125, -106.5625

1.0 Bias Due to 2-degree Climate Forcings 

The BCSD method adjusts monthly biases in climate projections at the 2-degree spatial scale. By 

construction, the method preserves monthly precipitation and temperature statistics to the 

observed for the overlapping 1950 to 1999 period at the 2-degree spatial scale. However, because 

hydrologic responses are dependent on seasonal, annual, and sometimes multi-year sequences of 

precipitation and temperature, the bias was evaluated for longer temporal scales. 

Figures B3-1A and B3-1B show the observed, raw GCM, and the bias corrected GCM monthly 

precipitation for grid cell at the Colorado River at Glenwood Springs location. As can be seen 

from the figures, the raw GCM results need to be bias corrected to achieve similar statistics to 

the observed in the overlapping period. The raw GCM biases appear to be largest in the 

December and January months. However, after bias correction, the monthly statistics are 

preserved for all months as compared to the observed (bias corrected [BC] line is same as 

observed line in figures).
Appendix B3 — Supplemental Analysis

of Future Climate Data

December 2012 B3-3 

FIGURE B3-1A 

Comparison of Monthly Precipitation Non-exceedance Probability Using 2-degree Raw GCM (Raw), Bias Corrected GCM (BC), 

and Observation (Obs) Data, January–June 

2-degree grid cell near Colorado River at Glenwood Springs, Colorado location. GCM-simulated from 

Trace 44 – sresa2.cccma_cgcm3_1.4; Observation data from 2-degree spatially aggregated precipitation from Maurer et al. (2002). 

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100
Colorado River Basin

Water Supply and Demand Study

B3-4 December 2012

FIGURE B3-1B 

Comparison of Monthly Precipitation Non-exceedance Probability Using 2-degree Raw GCM (Raw), Bias Corrected GCM (BC), 

and Observation (Obs) Data, July–December 

2-degree grid cell near Colorado River at Glenwood Springs, Colorado location. GCM-simulated from 

Trace 44 – sresa2.cccma_cgcm3_1.4; Observation data from 2-degree spatially aggregated precipitation from Maurer et al. (2002). 

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Appendix B3 — Supplemental Analysis

of Future Climate Data

December 2012 B3-5 

Figure B3-2 shows the same information for the seasonal and annual time scales. As shown in 

this figure, despite monthly BC, residual bias exists at seasonal and annual scales as compared to 

the observed. The 2-degree bias corrected GCM precipitation appears to underestimate the 

periods of high seasonal precipitation. The underestimation of high seasonal precipitation 

appears to be caused by differences in sequences of wet months within the season between the 

GCM-simulated historical climate and the observed climate. The seasonal bias is largest during 

the winter (January, February, and March) and fall (October, November, and December) and 

relatively small in other seasons. However, small bias continues to persist at annual scales as 

shown in the bottom panel of the figure. Figure B3-3 also indicates that GCM-simulated 

historical climate (after bias correction) retains bias at multi-year scales. In almost all multi-year 

averaging periods, the observed precipitation is larger than the bias corrected GCM precipitation, 

although the magnitude of this impact has not been isolated. 

The temperature biases (not shown) are significantly less than precipitation biases at all time 

scales and are not believed to represent a significant source of bias to streamflow assessments.

2.0 Bias Due to 1/8th-degree Spatial Downscaling 

The BCSD method adjusts for monthly biases in climate projections at 2-degree spatial scale. By 

construction, the method preserves monthly precipitation and temperature statistics to the 

observed for the overlapping 1950 to 1999 period at the 2-degree spatial scale. However, to be 

useful for most watershed assessments, the climate information is needed at finer spatial scales. 

The spatial downscaling transforms the climate information to the 1/8th-degree scale. The 

1/8th-degree spatial scale climate data were used as inputs into the VIC hydrologic model. 

Analyses were performed to investigate bias after downscaling to this finer spatial scale. 

As shown in figures B3-4A and B3-4B, although there is generally agreement between the 

observed and simulated historical climate statistics at the 1/8th-degree scale, bias exists even at 

the monthly scale. As with the 2-degree climate information, the biases are largest in winter; 

particularly December and January. Biases continue to exist at the seasonal, annual, and multiyear scales (figure B3-5 and B3-6). These longer time-scale biases are larger at the 1/8th-degree 

than at the 2-degree spatial scales. 
Colorado River Basin

Water Supply and Demand Study

B3-6 December 2012

FIGURE B3-2 

Comparison of Seasonal and Annual Precipitation Non-exceedance Probability Using 2-degree Raw GCM (Raw), Bias Corrected

GCM (BC), and Observation (Obs) Data

2-degree grid cell near Colorado River at Glenwood Springs, Colorado location. GCM-simulated from 

Trace 44 – sresa2.cccma_cgcm3_1.4; Observation data from 2-degree spatially aggregated precipitation from Maurer et al. (2002). 

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Appendix B3 — Supplemental Analysis

of Future Climate Data

December 2012 B3-7 

FIGURE B3-3 

Comparison of Non-exceedance Probability for Precipitation Averaged over 2-year, 3-year, 5-year, and 10-year Periods, Using 

2-degree Raw GCM (Raw), Bias Corrected GCM (BC), and Observation (Obs) Data

2-degree grid cell near Colorado River at Glenwood Springs, Colorado location. GMC-simulated from 

Trace 44 – sresa2.cccma_cgcm3_1.4; Observation data from 2-degree spatially aggregated precipitation from Maurer et al. (2002). 

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Colorado River Basin

Water Supply and Demand Study

B3-8 December 2012

FIGURE B3-4A 

Comparison of Monthly Precipitation Non-exceedance Probability Using 1/8th-degree BCSD (sresa2.cccma_cgcm3_1.4) and 

Observed (Obs) Data, January–June

1/8th-degree grid cell near Colorado River at Glenwood Springs, Colorado location. GMC-simulated from 

Trace 44 – sresa2.cccma_cgcm3_1.4; Observation data from Maurer et al. (2002).

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Appendix B3 — Supplemental Analysis

of Future Climate Data

December 2012 B3-9 

FIGURE B3-4B 

Comparison of Monthly Precipitation Non-exceedance Probability Using 1/8th-degree BCSD (sresa2.cccma_cgcm3_1.4) and 

Observed (Obs) Data, July–December

1/8th-degree grid cell near Colorado River at Glenwood Springs, Colorado location. GMC-simulated from 

Trace 44 – sresa2.cccma_cgcm3_1.4; Observation data from Maurer et al. (2002).

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2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

0 20 40 60 80 100

Precipitation (mm/d)

Non-Exceedence Probability (%)

Monthly P - Nov

SRESA2.CCCMA_CGCM3_1.4

Obs

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

0 20 40 60 80 100

Precipitation (mm/d)

Non-Exceedence Probability (%)

Monthly P - Dec

SRESA2.CCCMA_CGCM3_1.4

Obs
Colorado River Basin

Water Supply and Demand Study

B3-10 December 2012

FIGURE B3-5 

Comparison of Seasonal Precipitation Non-exceedance Probability Using 1/8th-degree BCSD (sresa2.cccma_cgcm3_1.4) and 

Observed (Obs) Data

1/8th-degree grid cell near Colorado River at Glenwood Springs, Colorado location. GMC-simulated from 

Trace 44 – sresa2.cccma_cgcm3_1.4; Observation data from Maurer et al. (2002).

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 20 40 60 80 100

Precipitation (mm/d)

Non-Exceedence Probability (%)

Seasonal P - JFM

SRESA2.CCCMA_CGCM3_1.4

Obs

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 20 40 60 80 100

Precipitation (mm/d)

Non-Exceedence Probability (%)

Seasonal P - AMJ

SRESA2.CCCMA_CGCM3_1.4

Obs

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 20 40 60 80 100

Precipitation (mm/d)

Non-Exceedence Probability (%)

Seasonal P - JAS

SRESA2.CCCMA_CGCM3_1.4

Obs

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 20 40 60 80 100

Precipitation (mm/d)

Non-Exceedence Probability (%)

Seasonal P - OND

SRESA2.CCCMA_CGCM3_1.4

Obs
Appendix B3 — Supplemental Analysis

of Future Climate Data

December 2012 B3-11

FIGURE B3-6 

Comparison of Seasonal and Averaged over 2-year, 3-year, 5-year, and 10-year Periods, Precipitation Non-exceedance 

Probability Using 1/8th-degree BCSD (sresa2.cccma_cgcm3_1.4) and Observed (Obs) Data

1/8th-degree grid cell near Colorado River at Glenwood Springs, Colorado location. GMC-simulated from 

Trace 44 – sresa2.cccma_cgcm3_1.4; Observation data from Maurer et al. (2002).

Figure B3-7 shows the annual time history of the observed precipitation and the simulated 

historical period precipitation for one particular GCM projection for 1950 to 1999. The GCMs 

are not expected to reproduce the identical sequences of observed precipitation due to differences 

between actual and simulated initial ocean and climate states, differences between actual and 

simulated emissions and other radiative forcings, and other model limitations. As shown in the 

figure, multi-year wet periods such as that observed in 1983 to 1986 are not expected to occur at 

the same time in the historical simulations, but are expected to be reproduced over some 

historical period. However, the magnitude of this wet persistence was not reproduced in the 

simulated climate (see figure B3-7). This under-representation of wet persistence appears to be 

common across all 112 projections. 

0.0

0.5

1.0

1.5

2.0

2.5

0 20 40 60 80 100

Precipitation (mm/d)

Non-Exceedence Probability (%)

2-yrs running mean

SRESA2.CCCMA_CGCM3_1.4

Obs

0.0

0.5

1.0

1.5

2.0

2.5

0 20 40 60 80 100

Precipitation (mm/d)

Non-Exceedence Probability (%)

3-yrs running mean

SRESA2.CCCMA_CGCM3_1.4

Obs

0.0

0.5

1.0

1.5

2.0

2.5

0 20 40 60 80 100

Precipitation (mm/d)

Non-Exceedence Probability (%)

5-yrs running mean

SRESA2.CCCMA_CGCM3_1.4

Obs

0.0

0.5

1.0

1.5

2.0

2.5

0 20 40 60 80 100

Precipitation (mm/d)

Non-Exceedence Probability (%)

10-yrs running mean

SRESA2.CCCMA_CGCM3_1.4

Obs
Colorado River Basin

Water Supply and Demand Study

B3-12 December 2012

FIGURE B3-7 

Comparison of Annual Precipitation Non-exceedance Probability Using 1/8th-degree BCSD (sresa2.cccma_cgcm3_1.4) and 

Observed (Obs) Data, July–December

1/8th-degree grid cell near Colorado River at Glenwood Springs, Colorado location. GMC-simulated from 

Trace 44 – sresa2.cccma_cgcm3_1.4; Observation data from Maurer et al. (2002).

0.0

0.5

1.0

1.5

2.0

2.5

Precipitation (mm/d)

Annual Precipitation Time Series

OBS SRESA2.CCCMA_CGCM3_1.4

3.0 Comparison of Daily Weather Generation (Temporal 

Disaggregation) Methods

As part of the assessment of future climate data and their impact on streamflow, two different 

daily weather datasets were available for the Colorado River Basin Water Supply and Demand 

Study (Study). The two methods used to develop these datasets are: 1) a method developed by 

the Climate Impacts Group at the University of Washington (Salathé, 2005) and that used in the 

Bureau of Reclamation’s (Reclamation) West-Wide Climate Risk Assessment (WWCRA) 

(Reclamation, 2011), and 2) the method developed by Wood et al. (2002) and used in previous 

Colorado River VIC assessments (Christensen and Lettenmaier, 2007). Both daily weather 

generation methods preserve monthly total precipitation from the downscaled climate projections 

and use the historical database to develop realistic daily storm patterns through a temporal 

disaggregation method. The differences between the two approaches are relatively subtle, but it 

was found that VIC hydrologic model results were sensitive to the choice of method. 

Analysis of the precipitation statistics between the two methods indicates no significant 

differences at the monthly scale. The observational data set was derived from Maurer et al. 

(2002). Comparisons have been prepared for one downscaled climate projection: Trace 44 – 

sresa2.cccma_cgcm3_1.4 under the two different daily weather generation methods. Figure B3-8 

illustrates a graphical comparison of the monthly precipitation for January and July between the 

two methods and the observed. The differences between simulated and observed are generally 

zero, as can be seen from the bottom plots. However, some small differences occur in the 

extreme southwest of the Basin under the Wood methodology. 
Appendix B3 — Supplemental Analysis

of Future Climate Data

December 2012 B3-13

FIGURE B3-8 

Comparison of Monthly Precipitation between Observational Data (Maurer et al., 2002) and Downscaled Precipitation 

(Wood et al. 2002; Salathe, 2005), January and July 

Only January and July monthly averaged values in millimeters per day [mm/d] are shown. Downscaled climate data for 

Wood et al. (2002) and Salathé (2005) are from Trace 44 – sresa2.cccma_cgcm3_1.4. Maps are shown with decimal latitude 

and longitude coordinates.

Wood Maurer Salathé

Wood-Maurer Salathé-Maurer 

Wood Maurer Salathé

Wood - Maurer Salathé-Maurer

January

July
Colorado River Basin

Water Supply and Demand Study

B3-14 December 2012

To better understand the differences in storm patterns generated under each weather generation 

method, analyses of precipitation events greater than certain thresholds were conducted. 

Figure B3-9 shows the comparison for 2 mm/d (0.08 inches per day [in/d]) and 20 mm/d 

(0.8 in/d) precipitation events. Figure B3-10 shows the comparison for 50 mm/d (2 in/d) and 

100 mm/d (4 in/d) precipitation events. In general, the method (Salathé, 2005) applied in the 

WWCRA produces precipitation events more similar to those in the observed record, although 

differences exist at all precipitation thresholds. 
Appendix B3 — Supplemental Analysis

of Future Climate Data

December 2012 B3-15

FIGURE B3-9 

Comparison of Number of Days (percent) with Precipitation Greater than 2 mm/d (top) and 20 mm/d (bottom) between 

Maurer et al. (2002) Observed Precipitation and GCM Downscaled Precipitation Using Two Methods (Wood et al., 2002; Salathé, 

2005)

GCM downscaled precipitation from Trace 44 – sresa2.cccma_cgcm3_1.4. Maps are shown with decimal latitude and longitude 

coordinates.

Wood Maurer Salathé

Wood - Maurer Salathé - Maurer

Wood Maurer Salathé

Wood - Maurer Salathé - Maurer

P > 2 mm/d

P > 20 mm/d
Colorado River Basin

Water Supply and Demand Study

B3-16 December 2012

FIGURE B3-10 

Comparison of Number of Days (percent) with Precipitation Greater than 50 mm/d (top) and 100 mm/d (bottom) between 

Maurer et al. (2002) Observed Precipitation and GCM Downscaled Precipitation Using Two Methods (Wood et al., 2002; Salathé, 

2005)

GCM downscaled precipitation from Trace 44 – sresa2.cccma_cgcm3_1.4. Maps are shown with decimal latitude and longitude 

coordinates.

Wood Maurer Salathé

Wood - Maurer Salathé - Maurer

Wood Maurer Salathé

Wood - Maurer Salathé - Maurer

P > 50 mm/d

P > 100 mm/d
Appendix B3 — Supplemental Analysis

of Future Climate Data

December 2012 B3-17

The analysis shown in the spatial figures was performed for each grid cell independently and did 

not reflect spatial correlation during storm events. In figure B3-11, the spatially averaged 

precipitation for all grid cells above Lees Ferry was analyzed for thresholds likely to produce 

runoff (2 mm/d, 5 mm/d, and 10 mm/d). The method employed by Salathé (2005) and 

incorporated in the WWCRA appeared to more faithfully reflect observed precipitation 

frequencies for this spatial area. This method produced significantly more representative 

precipitation frequencies to the observed than that used in the previous VIC simulations, 

particularly at the 2–mm/d and 5–mm/d thresholds. At the 10–mm/d threshold, both methods 

overestimated the frequency of occurrence; however, the observed frequency was already low. 

For the area above Glenwood Springs (figure B3-11), the method applied by Salathé was

significantly better at all precipitation frequencies considered.

FIGURE B3-11 

Number of Days per Year (averaged over the 1950–1999 period) with Precipitation Larger than Selected Thresholds (2 mm/d, 

5 mm/d, and 10 mm/d)

Computed from the daily precipitation over the period 1950–1999 using spatially averaged precipitation for all grid cells above 

Colorado River at Lees Ferry contributing area (top) and above Colorado River at Glenwood Springs (bottom). Wood et al.(2002)

and Salathé (2005) are from downscaled data from Trace 44 – sresa2.cccma_cgcm3_1.4. Values are also shown from 

Maurer et al. (2002) observed daily forcing for comparison. 

Above Lees Ferry

Above Glenwood Springs

Finally, VIC simulations were prepared using the two methods of daily weather generation for 

the historical period 1950 to 1999 using identical GCM-simulated monthly climate. These 

simulations were compared to the VIC simulation using historical observed climate; and the 

natural flow estimates for the Colorado River at Lees Ferry, Arizona. The VIC historical 

validation (VIC simulation using the historical observed methodology) suggests an 

overestimation of mean annual flows by about 4 percent. Of the two daily weather generation 

methods, the VIC simulation using the Salathé method is closest to this historical validation 

simulation (table B3-2); 2.8 percent compared to 5.8 percent using the Wood et al. (2002) 

method. Although the differences between methods appear to be relatively small in percentage 

terms, the difference in mean annual flows is nearly 500,000 acre-feet between methods. 

O N D J F M A M J J A S 3

4

5

6

7

 No. of Days with P>2 mmd-1

(Day Yr-1)

Wood et al. 2002

Maurer et al. 2002

Salathé, 2005

O N D J F M A M J J A S 0

0.5

1

1.5

2

2.5

 No. of Days with P>5 mmd-1

(Day Yr-1)

Wood et al. 2002

Maurer et al. 2002

Salathé, 2005

O N D J F M A M J J A S 0

0.1

0.2

0.3

0.4

 No. of Days with P>10 mmd-1

(Day Yr-1)

Wood et al. 2002

Maurer et al. 2002

Salathé, 2005

O N D J F M A M J J A S 4

6

8

10

12

14

 No. of Days with P>2mmd-1

(Day Yr-1)

Wood et al. 2002

Maurer et al. 2002

Salathé, 2005

O N D J F M A M J J A S 0

1

2

3

4

5

 No. of Days with P>5mmd-1

(Day Yr-1)

Wood et al. 2002

Maurer et al. 2002

Salathé, 2005

O N D J F M A M J J A S 0

0.5

1

1.5

 No. of Days with P>10mmd-1

(Day Yr-1)

Wood et al. 2002

Maurer et al. 2002

Salathé, 2005
Colorado River Basin

Water Supply and Demand Study

B3-18 December 2012

TABLE B3-2 

Annual Average Streamflows at Colorado River at Lees Ferry Computed from the Period 1950–1999

Colorado River at Lees Ferry Estimate 

(1950–1999)

Mean Annual Flow 

(million acre-feet) 

% Difference from Natural 

Flow Estimate

(% Difference from 

Validation)

Reclamation Natural Flow Estimate 14.673 – 

VIC Historical Validation 15.248 3.9% 

VIC Historical Simulation (Trace 44; Wood et al., 2002) 14.362 -2.1% (-5.8%)

VIC Historical Simulation (Trace 44; Salathé, 2005) 14.839 1.1% (-2.8%)

4.0 Conclusions

Based on the analysis of climate data, biases, and weather generation methods, several 

conclusions can be drawn. First, although the bias correction of GCM-simulated climate 

occurs to preserve monthly statistics, biases for seasonal, annual, and multi-year exist even at the 

2-degree spatial resolution. Second, spatial downscaling of climate data to the 1/8th-degree

resolution, required for hydrologic analysis, introduces small biases at the monthly scale that do 

not exist in the 2-degree data. Finally, even under identical monthly climate forcings, the method 

for developing daily patterns of precipitation is important and can contribute to substantially 

different streamflow results. The analysis included in the Study addresses these findings by 

adopting the Salathé approach of daily weather generation because it produced smaller overall 

biases as compared to the historical validation simulations. In addition, the analysis indicates that 

biases in climate data and hydrologic simulation will continue to be present, and that a final 

adjustment to VIC-simulated streamflows is necessary to use these flows in comparable fashion 

in systems modeling. For these reasons, a method for bias correction of resulting VIC-simulated 

flows is incorporated and discussed in appendix B4. 

5.0 References 

Bureau of Reclamation (Reclamation). 2011. West-Wide Climate Risk Assessments: BiasCorrected and Spatially Downscaled Surface Water Projections. Technical Memorandum 

No. 86-68210-2011-01. 

Christensen, N.S., and D.P. Lettenmaier. 2007. “A Multimodel Ensemble Approach to 

Assessment of Climate Change Impacts on the Hydrology and Water Resources of the 

Colorado River Basin.” Hydrology and Earth System Sciences, 11, 1417–1434. 

Maurer, E.P., A.W. Wood, J.C. Adam, D.P. Lettenmaier, and B. Nijssen. 2002. “A Long-Term 

Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous 

United States.” Journal of Climate, 15(22), 3237–3251. 

Salathé E.P. 2005. “Downscaling simulations of future global climate with application to 

hydrologic modeling.” Int J Climatol, 25:419–436. 

Wood, A.W., E.P. Maurer, A. Kumar, and D.P. Lettenmaier. 2002. “Long-range experimental 

hydrologic forecasting for the Eastern United States.” J. Geophysical ResearchAtmospheres, 107(D20), 4429, DOI:10.1029/ 2001JD000659. 
Appendix B4

Variable Infiltration Capacity (VIC)

Hydrologic Modeling Methods and Simulations

December 2012 B4-1

Appendix B4 — Variable Infiltration 

Capacity (VIC) Hydrologic Modeling 

Methods and Simulations

The Variable Infiltration Capacity (VIC) model (Liang et al., 1994; Liang et al., 1996) is the 

hydrology model used in the Colorado River Basin Water Supply and Demand Study (Study)

to simulate the hydrologic response of the Colorado River Basin (Basin) to historical and 

future climate. The results from VIC simulations were used to describe the range of streamflows

under the Downscaled General Circulation Model (GCM) Projected scenario. Each of the 

112 downscaled climate projections was used as input into the VIC hydrology model. The 

VIC hydrology model uses the climate projections along with land cover, soils, elevation, and 

other watershed information to simulate hydrologic fluxes. The hydrologic fluxes were then 

routed to each of the 29 natural flow locations using a routing network derived from the 

topography. The result of this approach is 112 unique sequences of natural flow under future 

climate projections. However, the simulated natural flows can contain significant monthly and 

annual biases when compared to the natural flows of the historical period. These biases are 

generally small for mainstem Colorado River locations, but can be large for smaller watersheds 

and in areas where the VIC model was not specifically calibrated. To account and compensate 

for these biases, the VIC-simulated streamflows for both the historical and future periods were 

first adjusted for biases before incorporating into systems modeling. This appendix describes the 

VIC hydrology model, methods, and simulations included in the Study.

1.0 General Description of VIC

The VIC model (Liang et al., 1994; Liang et al., 1996) is a spatially distributed hydrologic model 

that solves the water balance at each model grid cell. It incorporates spatially distributed 

parameters describing topography, soils, land use, and vegetation classes. VIC is considered a 

macro-scale hydrologic model in that it is designed for larger basins with fairly coarse grids. In 

this manner, it accepts input meteorological data directly from global or national gridded 

databases or from GCM projections. To compensate for the coarseness of the discretization, VIC 

is unique in its incorporation of sub-grid variability to describe variations in the land parameters 

as well as precipitation distribution. Parameterization within VIC is performed primarily through 

adjustments to parameters describing the rates of infiltration and baseflow as a function of soil 

properties, as well as the soil layers’ depths. When simulating in water balance mode, VIC is 

driven by daily inputs of precipitation, maximum and minimum temperature, and wind speed. 

The model internally calculates additional meteorological forcings such as short- and long-wave 

radiation, relative humidity, vapor pressure, and vapor pressure deficits. Rainfall, snow, 

infiltration, evapotranspiration (ET), runoff, soil moisture, and baseflow are computed over each 

grid cell on a daily basis for the entire period of simulation. An offline routing tool then 

processes the individual cell runoff and baseflow terms and routes the flow to develop 

streamflow at various locations in the watershed. Figure B4-1 shows the hydrologic processes 

included in the VIC model.
Colorado River Basin

Water Supply and Demand Study

B4-2 December 2012

FIGURE B4-1

Hydrologic Processes Included in the VIC Model

Source: http://www.hydro.washington.edu/Lettenmaier/Models/VIC/Overview/ModelOverview.shtml

The VIC model has been applied to many major basins in the United States, including large-scale 

applications to California’s Central Valley (Maurer et al., 2002; Brekke et al., 2008; Cayan et al., 

2010), Colorado River Basin (Christensen and Lettenmaier, 2007), Columbia River Basin 

(Hamlet et al., 2010), and for several basins in Texas (Maurer et al., 2002; CH2M HILL, 2008). 

The VIC model has a number of favorable attributes for the Study, but VIC’s three most 

significant advantages are that it has a reliable, physically based model of ET, it has a physically 

based model of snow dynamics, and it has been used for two studies of climate change in the 

Basin for which calibrated parameters are available.

2.0 VIC Modeling Methods Specific to the Colorado 

River Basin

2.1 Model Inputs

The VIC model was driven by meteorological forcing data. Although the model has some 

flexibility in what variables are required, forcing files typically include daily values for 

precipitation, maximum temperature, minimum temperature, and wind speed. The VIC model 

required that the forcing files be in either American Standard Code for Information Interchange 

or binary format, with one file for each grid cell of the simulation domain. The model grid for the 

Basin consists of approximately 4,500 grid cells at a 1/8th-degree latitude by longitude spatial 

resolution. 

Daily gridded observed meteorology data were obtained from Santa Clara University (Maurer et 

al., 2002) for the period 1950 to 1999. Projections of monthly future climate data were obtained 
Appendix B4 — Variable Infiltration Capacity (VIC)

Hydrologic Modeling Methods and Simulations

December 2012 B4-3

from the Lawrence Livermore National Laboratory under the World Climate Research 

Program’s Coupled Model Intercomparison Project Phase 3 and using the weather general 

(temporal disaggregation) methods described in appendix B3. Wind speed in the future 

projections was not adjusted in these analyses because downscaling of this parameter was not 

available, nor well translated from global climate models to local scales.

2.2 VIC Model Processes and Output

The VIC model was simulated in water balance mode. In this mode, a complete land surface 

water balance is computed for each grid cell on a daily basis for the entire model domain. Unique 

to the VIC model is its characterization of sub-grid variability. Sub-grid elevation bands enable 

more-detailed characterization of snow-related processes. Five elevation bands are included for 

each grid cell. In addition, VIC also includes a sub-daily (1-hour) computation to resolve 

transients in the snow model. The soil column is represented by three soil zones extending 

downward from the land surface to capture the vertical distribution of soil moisture. The 

VIC model represents multiple vegetation types using the National Atmospheric and Space 

Administration’s Land Data Assimilation System databases as the primary input data set. 

For the simulations performed for the Basin, the following water balance parameters were

produced as output on a daily and monthly time step: precipitation, runoff, baseflow, ET, soil 

moisture, and snow water equivalent. The runoff simulated from each grid cell was routed to 

various river flow locations using VIC’s offline routing tool. The routing tool processes 

individual cell runoff and baseflow terms and routes the flow based on flow direction and flow 

accumulation inputs derived from digital elevation models. For the simulations performed for the 

Basin, intervening streamflow was routed to 29 locations that align with the 29 natural flow 

locations in the Colorado River Simulation System (CRSS), the Bureau of Reclamation’s 

(Reclamation) long-term planning model and the primary modeling tool used in the Study. Flows 

are output in both daily and monthly time steps. Only the monthly flows were used in subsequent 

analyses. It is important to note that VIC routed flows are considered “naturalized” in that they 

do not include effects of diversions, imports, storage, or other human management of the water 

resource. 

3.0 Colorado River Basin VIC Model Validation

A VIC model of the Basin was previously developed by the University of Washington 

(Christensen and Lettenmaier, 2007), and was provided to Reclamation for the Study. The VIC 

model was not further calibrated or refined as part of the Study, but the model performance over 

the 1950 to 1999 validation period is described in this section. 

The VIC historical validation run was simulated on a daily time step over the 1950 to 1999

period. Historical observed climate inputs are from Maurer et al. (2002). Streamflow was routed 

to each of the 29 natural flow locations used by Reclamation in Basin planning. Figure B4-2 

shows the validation results for the Colorado River at Lees Ferry, Arizona location. The VIC 

simulation results in an overestimation of mean annual flows of about 3.9 percent when 

compared to the Reclamation natural flow estimate. The validation run captured the low and 

moderate annual flows, but has a slight overestimation of the high annual flows. Simulated flows 

in April and May flows are higher than Reclamation calculated historical natural flows, while 

July and August flows are slightly lower. Simulated flows for Colorado River at Cisco, Green 

River at Green River, Utah, and the San Juan River near Bluff, Utah, are shown in figures B4-3 
Colorado River Basin

Water Supply and Demand Study

B4-4 December 2012

through B4-5. The simulated flows show a slight overestimation for the Colorado River at Cisco 

and Green River at Green River stations when compared to the Reclamation natural flow 

estimates, while an underestimation is apparent for the San Juan River near Bluff station. 

Pearson's linear correlation coefficient, bias, and root mean square error (RMSE) are computed 

using the observed naturalized and VIC-simulated streamflows as driven by Maurer et al. (2002) 

over the 1950 to 1999 validation period for all 20 locations in the Upper Basin. These results are 

summarized in table B4-1. In general, the VIC model appears to have relatively small biases for 

the larger watersheds as compared to the Reclamation natural flow estimates, but can be larger 

for smaller watersheds and in areas where the VIC model was not specifically calibrated. The 

VIC model appears to have higher biases in the upper watersheds and lower biases farther 

downstream as more watershed contributes to the flow. 

FIGURE B4-2 

VIC Validation Summary for Colorado River at Lees Ferry, Arizona
Appendix B4 — Variable Infiltration Capacity (VIC)

Hydrologic Modeling Methods and Simulations

December 2012 B4-5 

FIGURE B4-3 

VIC Validation Summary for Colorado River at Cisco, Utah

FIGURE B4-4 

VIC Validation Summary for Green River at Green River, Utah
Colorado River Basin

Water Supply and Demand Study

B4-6 December 2012

FIGURE B4-5 

VIC Validation Summary for San Juan River near Bluff, Utah
Appendix B4 — Variable Infiltration Capacity (VIC)

Hydrologic Modeling Methods and Simulations

December 2012 B4-7

TABLE B4-1

Observed Annual Naturalized Streamflow and VIC-simulated Streamflow (with Maurer et. al [2002] historical meteorology)

Comparison Statistics (1950–1999)

ID Location

Obs. Nat.

Flow

(thousand 

acre-feet 

[kaf])

VIC Nat.

Flow

(kaf)

Bias

(%)

Pearson's 

Linear

Correl.

Coef.

RMSE

(kaf)

RMSE 

(% of 

mean 

flow)

1 Colorado River at Glenwood 

Springs, Colorado

2,071 2,192 5.8% 0.9 360.0 17.4%

2 Colorado River near Cameo, 

Colorado

3,489 3,741 7.2% 0.9 546.4 15.7%

3 Taylor River Below Taylor 

Park Reservoir, Colorado

148 172 15.9% 0.8 48.2 32.5%

4 Gunnison River at Blue Mesa 

Reservoir, Colorado

1,045 1,316 26.0% 0.9 332.3 31.8%

5 Gunnison River at Crystal 

Reservoir, Colorado

1,273 1,494 17.4% 0.9 325.5 25.6%

6 Gunnison River near Grand 

Junction, Colorado

2,304 2,336 1.4% 0.9 295.2 12.8%

7 Dolores River near Cisco, 

Utah

789 554 -29.7% 0.9 307.0 38.9%

8 Colorado River near Cisco, 

Utah

6,647 6,829 2.7% 1.0 640.4 9.6%

9 Green River below Fontenelle 

Reservoir, Wyoming

1,364 1,079 -20.9% 0.8 396.8 29.1%

10 Green R. near Green River, 

Wyoming

1,469 1,226 -16.5% 0.8 359.1 24.5%

11 Green River near Greendale, 

Utah

2,009 1,971 -1.9% 0.8 392.3 19.5%

12 Yampa River near Maybell, 

Colorado

1,210 1,086 -10.2% 0.9 196.4 16.2%

13 Little Snake River near Lily, 

Colorado

466 580 24.3% 0.8 173.1 37.1%

14 Duchesne River near Randlett, 

Utah

778 920 18.2% 0.9 291.1 37.4%

15 White River near Watson, 

Utah

557 525 -5.7% 0.8 167.1 30.0%

16 Green River at Green River, 

Utah

5,397 5,440 0.8% 0.9 785.7 14.6%

17 San Rafael River near Green 

River, Utah

161 273 69.1% 0.7 152.8 94.8%

18 San Juan River near 

Archuleta, New Mexico

1,028 869 -15.5% 0.9 268.2 26.1%

19 San Juan River Bluff, Utah 1,953 1,856 -5.0% 0.9 292.6 15.0%

20 Colorado River at Lees Ferry, 

Arizona

14,673 15,248 3.9% 1.0 1550.9 10.6%
Colorado River Basin

Water Supply and Demand Study

B4-8 December 2012

4.0 Application of Streamflow Bias Correction

The analysis presented in appendix B3 shows that there are some biases in the VIC streamflows 

as driven by GMC-simulated historic meteorological forcings in comparison with the naturalized 

streamflows for the Basin for the overlapping period 1950 to 1999. These biases result from 

several factors, including spatial and temporal errors in downscaled climate model forcings, 

complex groundwater interactions, and other complexities normally inherent to VIC hydrologic 

model parameter calibration. The analysis showed there are some uncertainties in the daily 

disaggregation method that was used to produce daily meteorological forcings from the monthly 

downscaled meteorology (see appendix B3). Daily meteorological data are required to drive the 

VIC. Moreover, there are uncertainties related to VIC model processes and parameter calibration 

demonstrated through comparisons of VIC-simulated historical streamflows with the naturalized 

streamflows for the Basin. Bias corrections of the downscaled climate model simulated 

VIC streamflows are performed to better reflect the statistics of the observed streamflows for the 

historical simulation period. This document describes the method developed to bias-correct the 

streamflows for the Basin. The method has been implemented for all 29 river locations for the 

period 1950 to 1999 for VIC simulation for each of the 112 projections. Results are presented for 

one particular projection (Trace 44 – sresa2.cccma_cgcm3_1.4) to demonstrate the process. 

VIC streamflows generated under future climate projections incorporate the same bias correction

process before determining the flow projections for use in systems modeling. 

The streamflow bias correction accounts for monthly and annual statistical bias at each of the 29 

flow locations. Following the station-specific adjustments, the total Basin mass balance is again 

checked and adjustments are made such that flow continuity is maintained throughout the Basin. 

The streamflow bias correction involves the following steps:

1. Evaluate the monthly and annual bias in VIC-simulated streamflows as compared to the 

observed natural flows for each of the 29 locations. See Figure B4-6.

FIGURE B4-6

Comparisons of the January Cumulative Distribution Function (CDF) (left) and Mean Monthly (right) Streamflow Developed from 

VIC-simulated and Natural Streamflow

Colorado River at Parker Dam, Arizona location. Simulated streamflow from VIC simulation is driven by downscaled climate 

model forcings from Trace 44. 

0 200 400 600 800 1000 1200 0

0.2

0.4

0.6

0.8

1

Streamflow (kaf)

F(x)

NaturalFlows

VICraw

J F M A M J J A S O N D

0

1000

2000

3000

4000

Streamflow (kaf)

NaturalFlows

VICraw
Appendix B4 — Variable Infiltration Capacity (VIC)

Hydrologic Modeling Methods and Simulations

December 2012 B4-9

2. Develop a quantile map that aligns the observed CDF with the simulated CDF for each 

simulated month for the period 1950 to 1999 at each location. For each simulated value, 

determine the simulated percentile and adjust to be equal to the observed flow at the same 

percentile. This method preserves the mean and variance of the observed flows. See figure 

B4-7.

FIGURE B4-7

Comparisons of the January Cumulative Distribution Function (CDF) (left) and Mean Monthly (right) Streamflow Developed from 

VIC-simulated Streamflow, VIC Monthly Bias-Corrected, and Natural Streamflow

Colorado River at Parker Dam, Arizona location. Simulated streamflow from VIC simulation as driven by downscaled climate 

model forcings from Trace 44.

3. Re-scale the monthly values (if needed) to ensure that the annual simulated CDF aligns with 

the observed CDF. For each simulated annual flow value from step 2, determine the 

percentile and adjust to be equal to the observed flow at the same percentile. This step 

ensures that the adjusted streamflows are consistent at the annual scale. See figure B4-8.

FIGURE B4-8 

Comparisons of the January Cumulative Distribution Function (CDF) (left) and Mean Monthly (right) Streamflow Developed from 

VIC-simulated Streamflow, VIC Monthly Bias-Corrected, VIC Annual Bias-Corrected, and Natural Streamflow

Colorado River at Parker Dam, Arizona location. Simulated streamflow from VIC simulation as driven by downscaled climate 

model forcings from Trace 44.

Because the bias correction is performed for each station independently, this can create 

discrepancies in spatial mass balance. Additional steps described below are performed to remove 

any spatial mass balance inconsistencies. The procedure begins from the most downstream 

location and moving upstream, as described below: 

0 200 400 600 800 1000 1200 0

0.2

0.4

0.6

0.8

1

Streamflows(KAF)

F(x)

NaturalFlows

VICraw

Qbcmonthly

J F M A M J J A S O N D

0

1000

2000

3000

4000

Streamflows(KAF)

NaturalFlows

VICraw

Qbcmonthly

0 200 400 600 800 1000 1200 0

0.2

0.4

0.6

0.8

1

Streamflow (kaf)

F(x)

NaturalFlows

VICraw

Qbcmonthly

Qbcannual

J F M A M J J A S O N D

0

1000

2000

3000

4000

Streamflow (kaf)

NaturalFlows

VICraw

Qbcmonthly

Qbcannual
Colorado River Basin

Water Supply and Demand Study

B4-10 December 2012

4. Anchor the calculations at the most downstream location (i.e., bias corrected streamflows a

the Imperial Dam are unaltered). 

Compare bias corrected flows at upstream locations (including incremental flows) with the 

downstream location. Compute the difference (Deltamon) as the downstream-computed 

monthly flow (Qds) minus the upstream-computed monthly flow (Qus), then adjust all 

upstream flows based on their relative flow contribution. 

t 

Q1

Q2 

5.

Deltamon = Qds - Qus or (e.g. Q3 – (Q1+Q2) )

Adji, mon = Deltamon * |Qi|/sum(|Qi..n|) or 

[e.g. Adj1,mon = Delta,mon * |Q1|/(|Q1|+|Q2|) ] Q3 

This process results in consistent mass balance on monthly scales (i.e., Q3=Q1+Q2). See 

figure B4-9. 

FIGURE B4-9 

Comparisons of the January Cumulative Distribution Function (CDF) (left) and Mean Monthly (right) Streamflow Developed from 

VIC-simulated Streamflow, VIC Monthly Bias-Corrected, VIC Annual Bias-Corrected, VIC Monthly Spatial Mass Balance 

Corrected and Natural Streamflow

Colorado River at Parker Dam, Arizona location. Simulated streamflow from VIC simulation as driven by downscaled climate 

model forcings from Trace 44. 

0 200 400 600 800 1000 1200 0

0.2

0.4

0.6

0.8

1

Streamflow (kaf)

F(x)

NaturalFlows

VICraw

Qbcmonthly

Qbcannual

Qbcmonthlyspatial

J F M A M J J A S O N D

0

1000

2000

3000

4000

Streamflow (kaf)

NaturalFlows

VICraw

Qbcmonthly

Qbcannual

Qbcmonthlyspatial

4. Finally, a verification check is performed based on the annual flows to ensure that all mass 

balance and corrections have been implemented correctly. See figure B4-10. 

A summary of the biases for each step in the bias correction process is shown for one climate 

projection simulation (table B4-2). The process is automated such that each Downscaled GCM 

Projection streamflow is bias corrected independently. The results from the VIC simulation 

presented in table B4-2 are different than those presented in table B4-1 because the VIC 

simulation is driven by two different meteorological datasets. Table B4-2 shows the results when 

simulated over the historical period with one GMC-simulated historical climate. The bias thus 

represents both hydrologic and meteorologic bias. The “station” bias correction column shows 

the resulting biases after conducting steps 1 through 3 in the streamflow bias correction above. 

The “spatial balance” bias correction column shows the resulting biases after conducting steps 1 

through 6, and represents the final residual bias in the model results. 
Appendix B4 — Variable Infiltration Capacity (VIC)

Hydrologic Modeling Methods and Simulations

December 2012 B4-11

FIGURE B4-10 

Comparisons of the January Cumulative Distribution Function (CDF) (left) and Mean Monthly (right) Streamflow Developed from 

VIC-simulated Streamflow, VIC Monthly Bias-Corrected, VIC Annual Bias-Corrected, VIC Monthly Spatial Mass Balance 

Corrected, VIC Annual Spatial Mass Balance Corrected, and Natural Streamflow

Colorado River at Parker Dam, Arizona location. Simulated streamflow from VIC simulation as driven by downscaled climate 

model forcings from Trace 44. 

0 200 400 600 800 1000 1200 0

0.2

0.4

0.6

0.8

1

Streamflow (kaf)

F(x)

NaturalFlows

VICraw

Qbcmonthly

Qbcannual

Qbcmonthlyspatial

Qbcannualspatial

J F M A M J J A S O N D

0

1000

2000

3000

4000

Streamflow (kaf)

NaturalFlows

VICraw

Qbcmonthly

Qbcannual

Qbcmonthlyspatial

Qbcannualspatial

TABLE B4-2 

Summary of Biases at the 20 Upper Basin Natural Flow Stations at Each Step in the Bias Correction Process

% Differences of Streamflows

ID Location Obs Nat Flow VIC Nat Flow % Bias Station Bias-correctionSpatial Balance Bias-correction

Stn01 Colorado River at Glenwood Springs, CO 2,071 2,181 5.3% 0.0% 1.3%

Stn02 Colorado River near Cameo, CO 3,489 3,701 6.1% 0.0% 0.9%

Stn03 Taylor River below Taylor Park Reservoir, CO 148 174 17.0% 0.0% 2.4%

Stn04 Gunnision River at Blue Mesa Reservoir, CO 1,045 1,314 25.8% 0.0% 1.3%

Stn05 Gunnison River at Crystal Reservoir, CO 1,273 1,486 16.7% 0.0% 0.6%

Stn06 Gunnison River near Grand Junction, CO 2,304 2,293 -0.5% 0.0% -0.3%

Stn07 Dolores River near Cisco, UT 789 537 -32.0% 0.0% -2.5%

Stn08 Colorado River near Cisco UT 6,647 6,699 0.8% 0.0% 0.2%

Stn09 Green R Bel Fontenelle Res, WY 1,364 1,062 -22.1% 0.0% 2.1%

Stn10 Green R. near Green River, WY 1,469 1,198 -18.5% 0.0% 1.7%

Stn11 Green River near Greendale, UT 2,009 1,881 -6.4% 0.0% 1.6%

Stn12 Yampa River near Maybell, CO 1,210 1,078 -10.9% 0.0% 0.9%

Stn13 Little Snake River near Lily, CO 466 558 19.6% 0.0% 0.2%

Stn14 Duchesne River near Randlett, UT 778 872 12.1% 0.0% 1.3%

Stn15 White River near Watson, UT 557 516 -7.2% 0.0% 1.0%

Stn16 Green River at Green River, UT 5,397 5,234 -3.0% 0.0% 1.0%

Stn17 San Rafael River near Green River, UT 161 262 62.2% 0.0% -1.3%

Stn18 San Juan River near Archuleta, NM 1,028 867 -15.7% 0.0% -0.8%

Stn19 San Juan River near Bluff, UT 1,953 1,835 -6.0% 0.0% -0.7%

Stn20 Colorado R at Lees Ferry, AZ 14,673 14,839 1.1% 0.0% 0.3%

5.0 VIC-simulated Hydrologic Fluxes

Although the primary result of the VIC modeling is streamflow for use in Colorado River system 

modeling, the model also produces hydrologic fluxes that are important in describing the causes 

of changes in streamflows. This section provides details on the methods and use of such 

hydrologic fluxes. 

5.1 Climate and Gridded Hydrologic Process Analysis Methods

Gridded climate and hydrologic process data were generated by the VIC model for the historical 

and the 112 climate projection scenarios. These data were converted to a specialized format, 

allowing for statistical analysis and visualization via spatial mapping. This analysis was 

performed to better understand the primary factors, both climatological and hydrological, that 

drive projected changes in streamflows relative to historical conditions. 
Colorado River Basin

Water Supply and Demand Study

B4-12 December 2012

5.2 Production of Gridded Data Sets

In addition to streamflows, the VIC model exports climate and hydrologic data for each 

simulation. The climate data include average air temperature (degrees Celsius [°C]) generated 

during the model simulations and precipitation (millimeters [mm]), which is consistent with the 

data provided in the model input files. Hydrologic parameters include ET, runoff (surface 

runoff), baseflow (subsurface runoff), soil moisture (in each of three soil layers), and snow water 

equivalent (SWE). Both the climate and hydrologic data are stored in American Standard Code 

for Information Interchange-formatted text files known as “flux files.” One flux file is produced 

for every grid cell of the Study Area, and each file contains values for the specified parameters at 

every time step of the simulation.

The flux file output generated by the VIC model was converted to network common data format 

(netCDF) to more readily evaluate and visualize the data. Developed by the staff at the Unidata 

Program Center in Boulder, Colorado, netCDF is a machine-independent data format for arrayoriented (i.e., multi-dimensional) scientific data. In particular, netCDF is well suited to spatially 

gridded time series data, such as gridded climate data. Unidata has developed a variety of 

software libraries and tools that support the creation, manipulation, and analysis of multidimensional data. Unidata’s netCDF-Java library was used to develop an application-specific 

Java program to convert the VIC flux files from American Standard Code for Information 

Interchange format to netCDF format. 

The resulting netCDF files are each three-dimensional, defined by latitude, longitude, and time. 

The spatial extent of the hydrologic basin spans from latitude 31.3125° to 43.4375° North and 

from longitude 115.6875° to 105.6875° West. Given a grid cell size of 1/8th-degree, the latitude 

dimension spans 98 grid cells and the longitude dimension spans 81 grid cells, for a total 7,938 

grid cells. The temporal extent of the data is from 1950 to 2099. Given a monthly time step, the 

time dimension consists of 1,800 values. 

The complete list of parameters included in the netCDF files is as follows:

• Average air temperature (°C)

• Precipitation (mm)

• ET (mm)

• Potential ET (mm)

• ET Efficiency (percent)

• Runoff (surface) (mm)

• Baseflow (subsurface) (mm)

• Total Runoff (mm)

• Total Runoff Efficiency (percent) 

• Soil Moisture Sum (mm)

• Maximum Soil Moisture (mm)

• Soil Moisture Fraction (percent)

• SWE (mm)

One netCDF file was produced for each climate projection and for the historic scenario, for a 

total of 113 netCDF files. 
Appendix B4 — Variable Infiltration Capacity (VIC)

Hydrologic Modeling Methods and Simulations

December 2012 B4-13

5.3 Statistical Analysis

To quantify potential changes between historical and future time periods, the VIC output data 

were statistically evaluated. For each historical and future time period of interest, statistics were 

developed for the consolidated dataset consisting of all 112 projections, such that the resulting 

statistics are representative of the 112-member ensemble. Statistics were generated for a subset 

of the VIC output parameters and derived parameters described previously. The eight parameters 

evaluated are as follows:

• Average air temperature (°C)

• Precipitation (mm)

• ET (mm)

• ET Efficiency (percent)

• Total Runoff (mm)

• Total Runoff Efficiency (percent) 

• Soil Moisture Fraction (percent)

• SWE (mm)

A Java program was developed to process the VIC model output data stored in the netCDF files 

described previously. The Java program relies heavily on the netCDF-Java library, and on the 

Descriptive Statistics package of the Apache Commons math library. The statistics generated for 

each parameter include the mean, standard deviation, variance, skew, minimum, and maximum. 

In addition, the CDF for each time period was produced. A CDF describes the probability that a 

data point will be found at a value less than or equal to some value, “x.” For this analysis, “x” 

values corresponding to all integer percentiles from 1 to 100 (inclusive) were generated for each 

cumulative distribution function. 

5.3.1 Analysis Time Periods

Three future periods were selected for comparison to the historical period. Each period, 

including the historical, consists of 30 years and is identified by the representative middle value

that defines that period. For example, the historical period consists of the years 1971 to 2000,

and is represented by the year 1985. The historical period of 1971 to 2000 was selected as the 

reference climate because it was the established climate normal used by the National Oceanic 

and Atmospheric Administration at the onset of the Study. The three future periods selected for 

analysis were 2011 to 2040 (represented by the year 2025), 2041 to 2070 (represented by the 

year 2055), and 2066 to 2095 (represented by the year 2080). Because the last year of the climate 

projections is 2099, which is 1 year short of a 30-year period starting in 2071, the end year 

selected for the 2080 period was 2095. Therefore, the 2080 period includes 5 years of overlap 

(2066 to 2070) with the 2055 period. For each of the four time periods specified, the 

representative statistics described previously were generated on a monthly, seasonal, and annual 

basis. In this analysis, the seasons are defined as follows:

• Fall: October, November, and December

• Winter: January, February, and March

• Spring: April, May, and June

• Summer: July, August, and September
Colorado River Basin

Water Supply and Demand Study

B4-14 December 2012

5.3.2 Analysis Spatial Scale

The statistical analysis described previously was conducted on both a grid cell and watershed 

basis. The results of the grid cell analysis produce the most informative map graphics and clearly 

show spatial variation at the greatest resolution possible. At this spatial scale, the statistics for 

each grid cell are developed independently. 

In contrast, watershed statistics are developed concurrently for all grid cells that are members of 

a watershed unit. In this case, a time series of watershed data was generated for each parameter 

prior to conducting the statistical analysis. For a given watershed, this was done by averaging the 

values of all member grid cells for each time step of the simulation period. The statistical 

analysis was then applied to the watershed time series, such that the resulting values are 

representative of the watershed as a whole. The watershed analysis results in a more manageable 

set of outputs and is useful for evaluating trends in different regions of the basin. 

5.3.3 Statistical Analysis Output

The resulting statistics were stored in four-dimensional netCDF files, which are defined by 

latitude, longitude, time, and statistic. The spatial extent of the Study Area spans from latitude 

31.3125 ° to 43.4375 °North and from longitutde 115.6875 ° to 105.6875 °West. Given a grid 

cell size of 1/8th-degree, the latitude dimension spans 98 grid cells and the longitude dimension 

spans 81 grid cells, for a total 7,938 grid cells. The temporal extent of the data consisted of 

17 values, each of which represents a monthly (1 to 12), annual (13), or seasonal (14 to 17) 

analysis time. The “statistic” dimension contains 111 values. The first 100 values are integer 

percentiles corresponding to the CDF distribution. The last 11 values represent the general 

statistics—mean, standard deviation, variance, skewness, minimum, P10, P25, P50, P75, P90, 

and maximum. Two netCDF files were produced for each of the four time periods—one for the 

grid cell-based statistics and one for the watershed-based statistics. Each netCDF file contains 

statistics representative of the 112-member projection ensemble for each of the eight 

climatological and hydrologic parameters identified previously. For watershed statistics, text 

files containing the general statistics and CDF values are also produced for each variable and 

time period. This output allows for ready production of spreadsheet charts, such as those 

presented in the results section. 

5.3.4 Change Metrics

Finally, change metrics were generated for each parameter, in which the difference between 

future period statistics and historical period statistics were calculated on both absolute and 

percent change bases. These results are again stored in netCDF files, with two files generated for 

each future period—one for grid cell data and one for watershed data. The format of these files is 

identical to those containing the results of the statistical analysis.

6.0 VIC Model Limitations

The VIC model and simulations described in this appendix include several limitations that 

should be considered: 

• Although the VIC model contains several sub-grid mechanisms, the coarse-grid scale should 

be noted when considering results and analysis of local-scale phenomenon. The VIC model is 

currently best applied for the regional scale hydrologic analyses. 
Appendix B4 — Variable Infiltration Capacity (VIC)

Hydrologic Modeling Methods and Simulations

December 2012 B4-15

• The VIC model has been applied without re-calibration. As the results suggest, the model is 

reasonable for capturing flow changes at the larger watersheds in the Basin, but has 

significant bias at smaller scales. The streamflow bias correction method corrects for much of 

the bias, but improved VIC calibration would limit the extent of these adjustments. 

• The VIC model has been evaluated for monthly and annual time-scales, but daily results have 

not been assessed. Caution should be exercised with the use of any daily results due to issues 

related to daily weather generation of inputs, lack of hydrology model evaluation, and 

inherent limitations with climate bias correction for extreme events. 

• The VIC model is only as good as its inputs. There are several limitations to long-term 

gridded meteorology related to data, spatial-temporal interpolation, and bias correction that 

should be considered. In addition, the inputs to the model do not include any transient trends 

in the vegetation or water management that may affect streamflows; they should only be 

analyzed from a naturalized flow change standpoint. 

• Finally, the VIC model includes three soil zones to capture the vertical movement of soil 

moisture, but does not include groundwater. In areas where groundwater connectivity with 

surface process or streamflow is important, the VIC model may not have sufficient 

subsurface characterization to capture hydrologic responses. 

7.0 References 

Brekke, L.D., M.D. Dettinger, E.P. Maurer, and M. Anderson. 2008. “Significance of Model 

Credibility in Estimating Climate Projection Distributions for Regional 

Hydroclimatological Risk Assessments.” Climatic Change Vol. 89 No. 3-4, 371-394, 

DOI: 10.1007/s10584-007-9388-3. 

Cayan, D.R., T. Das, D.W. Pierce, T.P. Barnett, M. Tyree, and A. Gershunov. 2010. “Future 

dryness in the southwest US and the hydrology of the early 21st century drought.” 

Proceedings of the National Academy of Sciences, 107(50), 21271-21276, DOI: 

10.1073/pnas.0912391107. 

CH2M HILL. 2008. Climate Change Study, Report on Evaluation Methods and Climate 

Scenarios. 

Christensen, N.S., and D.P. Lettenmaier. 2007. “A Multimodel Ensemble Approach to 

Assessment of Climate Change Impacts on the Hydrology and Water Resources of the 

Colorado River Basin.” Hydrology and Earth System Sciences, 11, 1417–1434. 

Hamlet, A.F., P. Carrasco, J. Deems, M.M. Elsner, T. Kamstra, C. Lee, S-Y Lee, G. Mauger, E. 

P. Salathé, I. Tohver, and L. Whitely Binder. 2010. Final Project Report for the 

Columbia Basin Climate Change Scenarios Project. Retrieved from 

http://www.hydro.washington.edu/2860/report/. 

Liang, X., D.P. Lettenmaier, E.F. Wood , and S.J. Burges. 1994. “A Simple Hydrologically 

Based Model of Land Surface Water and Energy Fluxes for General Circulation Models.” 

Journal of Geophysical Research, Vol. 99, pp. 14415-14428. 

Liang, X., D.P. Lettenmaier, and E.F. Wood . 1996. Surface Soil Moisture Parameterization of 

the VIC-2L Model: Evaluation and Modification.
Colorado River Basin

Water Supply and Demand Study

B4-16 December 2012

Maurer, E.P., A.W. Wood, J.C. Adam, D.P. Lettenmaier, and B. Nijssen. 2002. “A Long-Term 

Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous 

United States.” Journal of Climate, 15(22), 3237-3251. 
Appendix B5 

Supplemental Streamflow Analysis 

Appendix B5 — Supplemental Streamflow 

Analysis 

The streamflow analyses presented in this appendix provide additional supporting information 

consistent with that provided in the Technical Report. The streamflow analysis, as described 

here, was based on reconstructed natural flows in the Colorado River Basin (Basin). The data 

consist of two historical datasets. The first dataset (referred to as the observed record) consists of 

monthly observed natural flows for the period October 1905 to September 2007. The second 

dataset (referred as the paleo record) consists of monthly flows reconstructed from tree ring 

analysis for the period October 761 to September 2005. 

The observed record was provided in the total flows format (flows accumulating from upstream 

to downstream locations) and intervening format (single watershed flows). The paleo record was 

provided in an intervening format and had to be accumulated from upstream to downstream 

basins to obtain a total flows format. 

1.0 Streamflow Data Summary 

Streamflow was analyzed for the 29 natural flow stations that serve as the primary inflow 

locations for the Colorado River Simulation System model. A spreadsheet tool was constructed 

to provide an interactive environment to explore the temporal and spatial characteristics of 

streamflow in the Basin, as shown in figure B5-1. The features of this visual summary are 

described as follows: 

A. Table of Statistics: The table includes statistics (Stat1 and Stat2) for two periods in columns 

that represent the absolute and percentage difference between the two time periods. The Stat1 

and Stat2 columns present the long-term water year streamflow average for the two periods. 

The “Annual” statistic block shows the minimum and the maximum observed for the 1-year 

totals and 3-, 5-, 10-, 20-, and 30-year moving averages, followed by the year that the value 

was observed (e.g., the line “3yr Min (WYear) 7370 (1847)” represents a minimum value of 

7,370 thousand acre-feet (kaf) per year for a 3-year moving average time series ending in the 

year of 1847). The “Monthly statistics” section shows the monthly streamflow averages for 

each month, followed by the seasonal statistics (average, standard deviation, and amplitude 

[maximum-minimum]). The amplitude accounts for all seasons; for example, for amplitude 

October, November, and December (OND), the value on the table is computed as the 

maximum flow observed in an OND season minus the minimum flow observed in an OND 

season. The minimum and the maximum do not necessarily occur in the same water year. 

B. Average Monthly Streamflow Graphic: Average monthly streamflow (kaf) is shown for the 

water year over the time periods. The data used for this plot are also presented in the Table of 

Statistics as Stat1 (solid line) and Stat2 (dashed line). This graphic can be used to assess 

monthly and seasonal shifts in streamflow from the comparison periods. 

December 2012 B5-1 
Colorado River Basin 

Water Supply and Demand Study FIGURE B5-1 Summary Graphic for Colorado River at Lees Ferry, Arizona Displaying Streamflow, Annual Exceedance Probabilities, Streamflow Deficits and SurpluMagnitude and Intensity 

B 

A 

C 

E F 

G 

D 

B5-2 
Appendix B5 — Supplemental 

Streamflow Analysis 

December 2012 B5-3 

C. Annual Streamflow Box and Whiskers Graphic: This plot illustrates annual streamflow 

variability for the two time periods. The box represents the range of half of annual observed 

flows (inter-quartile range between 25th and 75th percentile). The triangle represents the 

median, and the horizontal lines at the top of the vertical line represent the period of record 

maximum and minimum annual values. This graphic can be used to assess trends in period 

streamflow variability and volumes. 

D. Table of Statistics: The table includes statistics (Stat1 and Stat2) for two periods in columns 

that represent the absolute and percentage difference between the two time periods. The Stat1 

and Stat2 columns present the long-term water year streamflow average for the two periods. 

The “Annual” statistic block shows the minimum and the maximum observed for the 1-year 

totals and 3-, 5-, 10-, 20-, and 30-year moving averages, followed by the year that the value 

was observed (e.g., the line “3yr Min (WYear) 7370 (1847)” represents a minimum value of 

7,370 kaf per year for a 3-year moving average time series ending in the year of 1847). The 

“Monthly statistics” section shows the monthly streamflow averages for each month, 

followed by the seasonal statistics (average, standard deviation, and amplitude [maximumminimum]). The amplitude accounts for all seasons; for example, for amplitude October, 

November, and December (OND), the value on the table is computed as the maximum flow 

observed in an OND season minus the minimum flow observed in an OND season. The 

minimum and the maximum do not necessarily occur in the same water year. 

E. Average Monthly Streamflow Graphic: Average monthly streamflow (kaf) is shown for the 

water year over the time periods. The data used for this plot are also presented in the Table of 

Statistics as Stat1 (solid line) and Stat2 (dashed line). This graphic can be used to assess 

monthly and seasonal shifts in streamflow from the comparison periods. 

F. Annual Streamflow Box and Whiskers Graphic: This plot illustrates annual streamflow 

variability for the two time periods. The box represents the range of half of annual observed 

flows (inter-quartile range between 25th and 75th percentile). The triangle represents the 

median, and the horizontal lines at the top of the vertical line represent the period of record 

maximum and minimum annual values. This graphic can be used to assess trends in period 

streamflow variability and volumes. 

G. Annual Streamflow Exceedance Graphic: This plot presents the full range of probabilities of 

exceeding a given streamflow for two selected periods. The plot is equivalent to the box and 

whiskers plot but provides probabilities ranging from zero to 100 percent. This graphic can 

be used to assess trends in period streamflow variability and volumes. For example, at the 

Lees Ferry, Arizona, location, 90 percent of the years had streamflows exceeding 10,000 kaf 

for both periods. 

H. Deficit Related Statistics – Exceedance Plots: The deficit statistics are illustrated in three 

charts: duration, magnitude, and intensity. The statistics presented in these charts refer only 

to deficit periods defined as only the years when streamflows were below the specified 

threshold. The “percentage of all years in a deficit” takes into account all years in the time 

period and determines how many were within a “deficit.” Below is a more detailed 

description of the deficit related statistics. 
The average streamflow for each time period is the default threshold to define deficit or 

surplus periods (e.g., a sequence of years with streamflows below the average is considered a 

deficit period). 

Duration: The duration chart presents the exceedance probability of deficit duration in years. 

For example, the chart illustrates that at Lees Ferry, 30 percent of the years defined as deficit 

years (only deficit years) had a deficit that lasted or exceeded 3 years in duration. 

Magnitude: The magnitude of a deficit (kaf) corresponds to the cumulative difference 

between observed streamflows and long-term average streamflows for an uninterrupted 

drought period. The exceedance plot shows the probability of a deficit to exceed a certain 

magnitude based on observed flows. 

Intensity: Deficit intensity is presented as magnitude divided by duration. The chart presents 

the exceedance probabilities for two selected periods. 

2.0 Streamflow Data Summaries 

Sample streamflow data summaries are provided in the following pages for the following natural 

flow stations: 

 Figure B5-2: Colorado River at Lees Ferry, Arizona (Station 20) 

 Figure B5-3: Green River at Green River, Utah (Station 16) 

 Figure B5-4: Colorado River near Cisco, Utah (Station 8) 

 Figure B5-5: San Juan River near Bluff, Utah (Station 19) 

 Figure B5-6: Colorado River above Imperial Dam, Arizona (Station 29) 

Colorado River Basin 

Water Supply and Demand Study 

B5-4 December 2012 
Appen

FIGURE B5-2 Streamflow Data Summary for Colorado River at Lees Ferry, Arizona, Natural Flows Based on historical 1906–2007 data. 

December 2012 
Colorado River Basin 

Water Supply and Demand Study FIGURE B5-3 Streamflow Data Summary for Green River at Green River, Utah, Natural Flows 

Based on historical 1906–2007 data. 

B5-6 
Appen

FIGURE B5-4 Streamflow Data Summary for Colorado River near Cisco, Utah, Natural Flows 

Based on historical 1906–2007 data. 

December 2012 
Colorado River Basin 

Water Supply and Demand Study FIGURE B5-5 Streamflow Data Summary for San Juan River near Bluff, Utah, Natural Flows Based on historical 1906–2007 data. B5-8 
Appen

FIGURE B5-6 Streamflow Data Summary for Colorado River above Imperial Dam, Arizona, Natural Flows Based on historical 1906–2007 data. 

December 2012 

Appendix B6 

Watershed-based Climate and 

Hydrologic Process Changes 

Appendix B6 — Watershed-based Climate 

and Hydrologic Process Changes 

The results of the watershed-based statistical analysis of the Variable Infiltration Capacity model 

output (climatological and hydrologic parameters) are presented for a subset of the Colorado 

River Basin (Basin) watersheds. The selected watersheds span the geographic range of the Basin. 

One group of watersheds was selected from the Upper Basin, and each of these watersheds 

contains the headwaters of a significant river. A second group of watersheds was selected from 

the Lower Basin, and each contains a streamflow station of significance. The selected watersheds 

are as follows: 

Upper Basin 

 01 – Colorado River at Glenwood Springs, Colorado 

 04 – Gunnison River at Blue Mesa Reservoir, Colorado 

 09 – Green River below Fontenelle Reservoir, Wyoming 

 12 – Yampa River near Maybell, Colorado 

 13 – Little Snake River near Lily, Colorado 

 18 – San Juan River near Archuletta, New Mexico 

 20 – Colorado River at Lees Ferry, Arizona 

Lower Basin 

 25 – Colorado River below Hoover Dam, Arizona-Nevada 

 29 – Colorado River Above Imperial Dam, Arizona 

Figures B6-1 through B6-9 depict the relative changes in monthly precipitation, temperature, 

evapotranspiration, runoff, snow water equivalent, and soil moisture for these selected 

watersheds. Separate lines depict the changes for the periods 2011 to 2040 (2025), 2041 to 2070 

(2055), and 2066 to 2094 (2080) as compared to the base period 1971 to 2000 (1985). 

Hydrologic variables were produced using Variable Infiltration Capacity as driven by 

downscaled climate model forcings. The selection of time periods is explained in appendix B5. 

Figures B6-10 through B6-21 are spatial plots of the changes in these parameters for four 

seasons. The seasons are defined as: Fall (October, November, and December [OND]); Winter 

(January, February, and March [JFM]); Spring (April, May, and June [AMJ]); and Summer 

(July, August, and September [JAS]). Separate figures have been provided for the three future 

periods. 

December 2012 B6-1 
 

Colorado River Basin 

Water Supply and Demand Study 

FIGURE B6-1 

Projected Change in Mean Monthly Climatological and Hydrologic Parameters 

01 – Colorado River at Glenwood Springs, Colorado 

B6-2 December 2012 
Appendix B6 — Watershed-based 

Climate and Hydrologic Process Changes 

FIGURE B6-2 

Projected Change in Mean Monthly Climatological and Hydrologic Parameters 

04 – Gunnison River at Blue Mesa Reservoir, Colorado 

December 2012 B6-3 
Colorado River Basin 

Water Supply and Demand Study 

FIGURE B6-3 

Projected Change in Mean Monthly Climatological and Hydrologic Parameters 

09 – Green River below Fontenelle Reservoir, Wyoming 

B6-4 December 2012 
Appendix B6 — Watershed-based 

Climate and Hydrologic Process Changes 

FIGURE B6-4 

Projected Change in Mean Monthly Climatological and Hydrologic Parameters 

12 – Yampa River near Maybell, Colorado 

December 2012 B6-5 
Colorado River Basin 

Water Supply and Demand Study 

FIGURE B6-5 

Projected Change in Mean Monthly Climatological and Hydrologic Parameters 

13 – Little Snake River near Lily, Colorado 

B6-6 December 2012 
Appendix B6 — Watershed-based 

Climate and Hydrologic Process Changes 

FIGURE B6-6 

Projected Change in Mean Monthly Climatological and Hydrologic Parameters 

18 – San Juan River near Archuletta, New Mexico 

December 2012 B6-7 
Colorado River Basin 

Water Supply and Demand Study 

FIGURE B6-7 

Projected Change in Mean Monthly Climatological and Hydrologic Parameters 

20 – Colorado River at Lees Ferry, Arizona 

B6-8 December 2012 
Appendix B6 — Watershed-based 

Climate and Hydrologic Process Changes 

FIGURE B6-8 

Projected Change in Mean Monthly Climatological and Hydrologic Parameters 

25 – Colorado River below Hoover Dam, Arizona-Nevada 

December 2012 B6-9 
Colorado River Basin 

Water Supply and Demand Study 

FIGURE B6-9 

Projected Change in Mean Monthly Climatological and Hydrologic Parameters 

29 – Colorado River Above Imperial Dam, Arizona 

B6-10 December 2012 
Appendix B6 — Watershed-based 

Climate and Hydrologic Process Changes 

FIGURE B6-10 

Projected Percent Change in Mean Seasonal Precipitation 

2025 (2011–2040) versus 1985 (1971–2000). 

OND JFM

AMJ JAS

December 2012 B6-11 
OND JFM

AMJ JAS

Colorado River Basin 

Water Supply and Demand Study 

FIGURE B6-11 

Projected Change in Mean Seasonal Air Temperature 

2025 (2011–2040) versus 1985 (1971–2000). Change shown in degrees Celsius (°C). 

B6-12 December 2012 
Appendix B6 — Watershed-based 

Climate and Hydrologic Process Changes 

FIGURE B6-12 

Projected Percent Change in Mean Seasonal Evapotranspiration 

2025 (2011–2040) versus 1985 (1971–2000). 

OND JFM

AMJ JAS

December 2012 B6-13 
OND

AMJ

JFM

JAS

Colorado River Basin 

Water Supply and Demand Study 

FIGURE B6-13 

Projected Percent Change in Mean Seasonal Runoff 

2025 (2011–2040) versus 1985 (1971–2000). 

B6-14 December 2012 
OND

AMJ

JFM

JAS

Appendix B6 — Watershed-based 

Climate and Hydrologic Process Changes 

FIGURE B6-14 

Projected Percent Change in Mean Seasonal Precipitation 

2055 (2041–2070) versus 1985 (1971–2000). 

December 2012 B6-15 
Colorado River Basin 

Water Supply and Demand Study 

FIGURE B6-15 

Projected Change in Mean Seasonal Air Temperature 

2055 (2041–2070) versus 1985 (1971–2000). Change shown in degrees Celsius (°C). 

OND JFM

AMJ JAS

B6-16 December 2012 
OND

AMJ

JFM

JAS

Appendix B6 — Watershed-based 

Climate and Hydrologic Process Changes 

FIGURE B6-16 

Projected Percent Change in Mean Seasonal Evapotranspiration 

2055 (2041–2070) versus 1985 (1971–2000). 

December 2012 B6-17 
OND

AMJ

JFM

JAS

Colorado River Basin 

Water Supply and Demand Study 

FIGURE B6-17 

Projected Percent Change in Mean Seasonal Runoff 

2055 (2041–2070) versus 1985 (1971–2000). 

B6-18 December 2012 
 

Appendix B6 — Watershed-based 

Climate and Hydrologic Process Changes 

FIGURE B6-18 

Projected Percent Change in Mean Seasonal Precipitation 

2080 (2066–2095) versus 1985 (1971–2000). 

OND JFM

AMJ JAS

December 2012 B6-19 
Colorado River Basin 

Water Supply and Demand Study 

FIGURE B6-19 

Projected Change in Mean Seasonal Air Temperature 

2080 (2066–2095) versus 1985 (1971–2000). Change shown in degrees Celsius (°C). 

OND JFM

AMJ JAS

B6-20 December 2012 
Appendix B6 — Watershed-based 

Climate and Hydrologic Process Changes 

FIGURE B6-20 

Projected Percent Change in Mean Seasonal Evapotranspiration 

2080 (2066–2095) versus 1985 (1971–2000). 

OND JFM

AMJ JAS

December 2012 B6-21 
Colorado River Basin 

Water Supply and Demand Study 

FIGURE B6-21 

Projected Percent Change in Mean Seasonal Runoff 

2080 (2066–2095) versus 1985 (1971–2000). 

OND JFM

AMJ JAS

B6-22 December 2012