AN ABSTRACT OF THE THESIS OF

Imtiaz-Ali M. Kalyan for the degree of Master of Science in Water Resource Engineering presented on May 21st , 2013 Title: Identifying “At-risk” Regions of Snow Accumulation within California’s Sierra Nevada Mountains, and Assessing Implications on Reservoir Operations. Abstract approved: __________________________________________________________________

Anne W. Nolin

California’s water resources vary throughout the state owing to the regions

varying topography, diverse climate, and the distribution of precipitation. Most of the

state’s precipitation falls over the northern coastal range and the western slopes of the

Sierra Nevada Mountains. Winter snowpack that accumulates within these mountain

basins serves as an efficient means of natural water storage. Moreover, the state’s two

massive water conveyance systems, the State Water Project (SWP) and the Central

Valley Project (CVP), are integrally dependent upon winter snowpack accumulation, and

subsequent spring snowmelt runoff.

The SWP and CVP’s extensive network of reservoirs, pipes, and aqueducts are

engineered to collect and transport water from the snowcapped Sierra Nevada Mountains

where it is plentiful, to farmland and urban communities where it is scarce but in greatest

demand. However, increased warming within these mountain basins is causing a declined

winter snowpack, altering the fraction of precipitation occurring as snow, and changing

the timing of snowmelt derived streamflow. The loss of this immense amount of naturally

occurring stored water, and its earlier arrival at the downstream reservoirs, has profound

implications on the state’s existing water management infrastructure. This work attempts

to address these water management challenges that lie in the foreseeable future.

Using a binary based deterministic approach, and a climatologically record of

temperature and precipitation, “at-risk” snow dominated regions were identified

throughout the Feather River Basin, and nested basins of the San Joaquin Watershed.

These “at-risk” regions represent locations that would be the first to transition from a

snow dominated, to a rain dominated precipitation regime under projected future

warming scenarios. Future warming projections ranging from 1°C to 4°C were analyzed

relative to the 1971-2000 base period.

Results show that if warming trends considered by the IPCC 2007 report to be

highly likely continue, nearly all snow dominated regions existing between 1500 and

2100 m in the San Joaquin Watershed would become rainfall dominated. Within the

Feather River Basin, in the Sacramento Watershed, implications are even more alarming.

A 3°C warming in February would result in approximately 87% of the regions previously

snow covered area (SCA) becoming rainfall dominated; only 12% of the basin would

remain snow covered. The decline of winter snowpack within all six study basins is closely

correlated with elevation and average winter temperatures. Lower elevation, snow dominated

regions near the rain to snow transition zone are highly sensitive to warmer temperatures

relative to higher elevation, colder snow dominated regions. Furthermore, warming during

high precipitation months, from December to February, would yield the largest reductions in

loss of Snow Water Equivalent (or SWE). The loss of this immense amount of naturally

occurring stored water, and its earlier arrival at the downstream reservoirs poses challenges

and opportunities for California’s water managers.

For reservoir managers, adapting to a rapidly changing climate would require

updating rigid flood control rule curves that were established based on hydrological trends

during the first half of the twentieth century. Developing greater flexibility into flood-control

rule curves could allow reservoir managers to store more water in the winter, thereby

mitigating the consequences of snow loss from natural stored water sources. Faced with an

expanding population and increased strains on water resources availability, sustaining

future water demands hinges on developing adaptive water management strategies. By

understanding basin and, at a finer scale, elevation specific vulnerability to snow loss due

to warming, water managers can begin to guide effectual adaptation strategies.

©Copyright by Imtiaz-Ali M. Kalyan

May 21st, 2013

All Rights Reserved

Identifying “At-risk” Regions of Snow Accumulation within California’s Sierra

Nevada Mountains, and Assessing Implications on Reservoir Operations

by

Imtiaz-Ali M. Kalyan

A THESIS

submitted to

OREGON STATE UNIVERSITY

In partial fulfillment of the requirements for the

degree of

Master of Science

Presented May 21st, 2013

Commencement June 2013

Master of Science thesis of Imtiaz-Ali M. Kalyan presented on May 21st, 2013. APPROVED: _________________________________________________________________________________________________ Major Professor, representing Water Resource Engineering _________________________________________________________________________________________________ Director of the Water Resources Graduate Program _________________________________________________________________________________________________ Dean of the Graduate School I understand that my thesis will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request. _________________________________________________________________________________________________

Imtiaz-Ali M. Kalyan, Author

ACKNOWLEDGEMENTS

I would like to begin by expressing my sincere gratitude to my advisor, Dr. Anne

Nolin for her trust, patience, and mentorship through my graduate studies. I would also

like to thank the director of the Water Resources graduate program, Dr. Mary Santelmann

for bringing me into the program, and for providing words of encouragement and support

when needed. I am thankful to Dr. Julia Jones and Dr. Gordon Grant for adding their

breadth of knowledge to my research. I would also like to acknowledge and thank the rest

of my committee members, Dr. Richard Cuenca and Dr. Robert Wheatcroft. I owe a

depth of gratitude to Dr. Jim Graham for his generous time and unwavering assistances

with understanding GIS concepts; fundamental to the creation of this work. Additionally,

Mark Lavery was kind enough to provide the lab space and computational resources that

this work would not have been accomplished without.

I would like to express my thanks to fellow graduate students Eric Sproles, Nick

legg, and Allison Danner for taking an active interest in my research. To my near and

dear friend, Nicole Reid who encouraged me to further pursue my academic interests at

the graduate level. My sincere thanks to Dr. Jim Sickman and Dr. Janet Arey at the

University of California, Riverside. I would also like to thank my closest friends, Ali Al-

Saedi and Andrew Pirrello, for providing moral support, and for making the graduate

school experience incredibly more rewarding.

Finally, I would like to thank my family. Throughout our childhood, my parents,

Mushtaq and Salma Kalyan, have strived to provide my siblings and I with every

opportunity for succeeding in life. I am forever thankful to both of them for nurturing me

into the man I have become. It is through their effort, and support, that I was able to

succeed through graduate school. In addition, I would like to thank my brothers, Asad-Ali

Kalyan, Minhaal Kalyan, and Mohsen Nasroullahi, for always being by my side. And

finally, my deepest thanks to the world’s greatest sister, Zakira Kalyan, who did all the

little and grand things that were needed in helping me come this far.

TABLE OF CONTENTS

Page

Chapter 1 Introduction .........................................................................................................1

1.2 Evidence of a Warming Trend ..................................................................................2

1.21 Rising Snowlines and loss of Snow Covered Area ...........................................3

1.22 Shifts in timing of Streamflow ..........................................................................5

1.3 Vulnerability of California’s Water Supply Infrastructure .......................................7

1.4 Study Objectives ......................................................................................................12

Chapter 2 Study Area and Basin Descriptions ...................................................................14

2.1 The Sacramento Watershed ....................................................................................14

2.11 Feather River Basin .........................................................................................14

2.2 The San Joaquin Watershed ...................................................................................17

2.21 Stanislaus River Basin .....................................................................................17

2.22 Tuolumne River Basin .....................................................................................20

2.23 Merced River Basin .........................................................................................23

2.24 Upper San Joaquin River Basin .......................................................................25

2.25 Kings River Basin ...........................................................................................28

Chapter 3 Methods .............................................................................................................30

3.1 Binary Classification Decision Tree ......................................................................31

3.2 Rain versus Snow Temperature Threshold ............................................................31

3.3 Calculating a Snow Water Equivalent ...................................................................32

3.4 Determining Reservoir Storage Capacity ..............................................................33

Chapter 4 Results ...............................................................................................................37

4.1 Feather River Basin................................................................................................37

4.2 Stanislaus River Basin ...........................................................................................49

TABLE OF CONTENTS (Continued)

Page

4.3 Tuolumne River Basin ...........................................................................................61

4.4 Merced River Basin ...............................................................................................73

4.5 Upper San Joaquin Basin .......................................................................................84

4.6 Kings River Basin ..................................................................................................96

Chapter 5 Discussion .......................................................................................................108

5.1 Elevation Dependency of Temperature ...............................................................108

5.2 Trends in Loss of Inter-monthly Snow Water Equivalent ...................................108

5.3 Warming in High Precipitation Months ...............................................................111

5.4 Future Challenges and Reservoir Adaptation Strategies .....................................112

5.5 Case Study with Existing Reservoirs ...................................................................113

5.6 Error Analysis and Study Assumptions ...............................................................115

Chapter 6 Conclusion .......................................................................................................121

References ..................................................................................................................124

LIST OF FIGURES

Figure Page

2.1 The Feather River Basin ..............................................................................................16

2.2 The Stanislaus River Basin ..........................................................................................19

2.3 Tuolumne and Merced River Basin .............................................................................22

2.4 Hydrograph of the Tuolumne and Merced River Basin ...............................................24

2.5 The Upper San Joaquin River Basin ............................................................................27

2.6 The Kings River Basin .................................................................................................29

3.1 Decision Tree Tool ......................................................................................................34

3.2 Don Pedro Reservoir: Elevation versus Storage Volume ............................................35

3.3 Equation to define Elevation versus Storage Volume .................................................36

4.1 Feather River Basin: Loss of Snow Covered Area .....................................................43

4.2 Feather River Basin: Fractional Loss of Snow Covered Area .....................................44

4.3 Feather River Basin: Volume of SWE Loss ................................................................45

4.4 Hypsometric Curve of the Feather River Basin ...........................................................47

4.5 Feather River Basin: Map of “at-risk” Snow Covered Area ........................................48

4.6 Stanislaus River Basin: Loss of Snow Covered Area .................................................55

4.7 Stanislaus River Basin: Fractional Loss of Snow Covered Area ................................56

4.8 Stanislaus River Basin: Volume of SWE Loss ...........................................................57

4.9 Hypsometric Curve of the Stanislaus River Basin ......................................................59

4.10 Stanislaus River Basin: Map of “at-risk” Snow Covered Area .................................60

4.11 Tuolumne River Basin: Loss of Snow Covered Area ...............................................67

4.12 Tuolumne River Basin: Fractional Loss of Snow Covered ......................................68

4.13 Tuolumne River Basin: Volume of SWE Loss .........................................................69

LIST OF FIGURES (Continued)

Figure Page

4.14 Hypsometric Curve of the Tuolumne River Basin ....................................................71

4.15 Tuolumne River Basin: Map of “at-risk” Snow Covered Area .................................72

4.16 Merced River Basin: Loss of Snow Covered Area ....................................................78

4.17 Merced River Basin: Fractional Loss of Snow Covered Area ..................................79

4.18 Merced River Basin: Volume of SWE Loss .............................................................80

4.19 Hypsometric Curve of the Merced River Basin ........................................................82

4.20 Merced River Basin: Map of “at-risk” Snow Covered Area .....................................83

4.21 Upper San Joaquin River Basin: Loss of Snow Covered Area ..................................90

4.22 Upper San Joaquin River Basin: Fractional Loss of Snow Covered Area ...............91

4.23 Upper San Joaquin River Basin: Volume of SWE Loss ...........................................92

4.24 Hypsometric Curve of the Upper San Joaquin River Basin .....................................94

4.25 Upper San Joaquin River Basin: Map of “at-risk” Snow Covered Area ...................95

4.26 Kings River Basin: Loss of Snow Covered Area.....................................................102

4.27 Kings River Basin: Fractional Loss of Snow Covered Area ..................................103

4.28 Kings River Basin: Volume of SWE Loss ..............................................................104

4.29 Hypsometric Curve of the Kings River Basin ........................................................106

4.30 Kings River Basin: Map of “at-risk” Snow Covered Area ......................................107

5.1 Don Pedro Reservoir: 2006 Flood Control Operations .............................................117

5.2 Don Pedro Reservoir: Historical Storage Elevations and Adjustments ....................118

5.3 Oroville Reservoir: Historical Storage Elevations and Adjustments ........................119

5.4 New Melones Reservoir: Historical Storage Elevations and Adjustments ................120

5.5 Pine Flat Reservoir: Historical Storage Elevations and Adjustments ........................121

LIST OF TABLES

Table Page

4.1 Feather River Basin: Monthly Snowline Elevations, SCA, and Snow Loss

Under Varying Warming Scenarios .............................................................................46

4.2 Stanislaus River Basin: Monthly Snowline Elevations, SCA, and Snow Loss

Under Varying Warming Scenarios .............................................................................58

4.3 Tuolumne River Basin: Monthly Snowline Elevations, SCA, and Snow Loss

Under Varying Warming Scenarios .............................................................................70

4.4 Merced River Basin: Monthly Snowline Elevations, SCA, and Snow Loss

Under Varying Warming Scenarios .............................................................................81

4.5 Upper San Joaquin River Basin: Monthly Snowline Elevations, SCA,

and Snow Loss Under Varying Warming Scenarios ......................................................93

4.6 Kings River Basin: Monthly Snowline Elevations, SCA, and Snow Loss

Under Varying Warming Scenarios ...........................................................................105

DEDICATION

I dedicate this work to the Imam of our time, Imam Al-Mehdi (ATF), and to the Lady of

Light: Fatima Az-Zahra (SA)

1

Chapter 1: Introduction

California’s Central Valley is home to more than 58,600 of the world’s most

productive agricultural farmland. Agriculture within the precipitation limited San Joaquin

and Imperial Valley provides a lifeline to the state’s economy generating more than 29

billion dollars in state revenue (DWR 2006). In addition, majority of the state’s

population of about 37 million people are concentrated in the drier, more precipitation

limited regions of the State. For example, the arid regions of Southern California are

home to half of the state’s population. Due to the state’s Mediterranean climate,

summers are typically hot and dry while winters are cool and wet. And as a result, a

sophisticated system of reservoirs, pipes, and aqueducts have been engineered to insure

winter precipitation is adequately captured, and transported to satisfy downstream

demands for irrigation, urban and industrial uses, and electricity generation in the

summer and fall when demand is greatest.

Most of the state’s winter precipitation occurs as a result of storms moving inland

from the Pacific Ocean. Atmospheric circulation patterns cause these storms to move

eastwards across the coastal range and more northern parts of the State. Due to the rain-

shadow effect, the greatest amounts of precipitation tend to fall on the Sierra Nevada

Mountain’s west facing slopes while areas east of the mountains receive little

precipitation. On average, between 1500 to 2000 mm of precipitation falls annually on

California’s Sierra Nevada range (DWR 2006). At the higher elevations above 1500 m,

winter precipitation is more snowfall dominated. The Sierra snowpack accumulates from

2

November through March while the melt season lasts from April through June.

Contribution from the state’s annual snowpack averages approximately 19 of runoff

each year, about 17 of which is estimated to occur in the Central Valley (DWR

2006). During the winter, reservoirs are operated for flood control. As a result, an empty

flood-control space is maintained for absorbing winter storms and rain on snow events.

The flood-control space is then gradually filled with snowmelt derived streamflow once

the threat posed by large winter storms has elapsed, in the spring. California relies

heavily on natural water storage in the form of snowpack, as well as artificial water

storage using reservoirs, to satisfy demand through the year. Total artificial storage

within the Sacramento-San Joaquin Watershed is approximately (DWR 2006).

1.2 Evidence of a Warming Trend

Over the past 100 years, maximum, average, and minimum air temperatures in

California show a statistically significant increasing trend (Anderson et al., 2008).

Moreover, minimum or lower bound temperatures in the State are moving upwards while

the variability in minimum temperatures is decreasing (Anderson et al., 2008). Between

1949 and 2004, winter-mean daily-minimum wet-day temperatures in western United

State had increased by +1.4°C (Knowles et al., 2006). Naturally occurring climate

oscillations such as the Pacific North American pattern or (PNA), El Nino Southern

Oscillation or (ENSO), and Pacific decadal oscillation or (PDO) have contributed to

some of the observed climate variability over the past 100 years (Mote et al., 2006;

Abatzoglou et al., 2011). However, several studies have shown that the observed

hyroclimatic shifts can only partially be explained by fluctuations in the PNA, ENSO, or

3

the PDO cycle (Knowles et al., 2006; Mote et al., 2006; Barnett et al., 2008; Abatzoglou

et al., 2011). Moreover, Abatzoglou et al. (2011) shows that after accounting for, and

removing the influence of PNA on observed decreases in snowfall accumulation

efficiency, a decrease in the fraction of precipitation occurring as snowfall is still

apparent across the western United States.

In mountainous regions, an increasing trend in minimum air temperatures could

have major effects on the magnitude, form, and timing of precipitation resulting in a host

of social and ecological ramifications. For instance, observational findings show a shift in

the proportion of precipitation falling as rain versus snow (Knowles et al., 2006), a

widespread and declining trend in snow water equivalent or SWE (Mote et al., 2005;

Kapnick and Hall 2012), and an advancement in the timing and volume of snowmelt

derived streamflow (Stewart et al., 2005; Maurer et al., 2007; Dettinger et al., 2011;

Fritze et al., 2011). These observed shifts in the hydrological cycle have significant

implications on the risk of dry season wild fires (Westerling et al., 2006), historically

generated flood risk statistics from rain on snow events (McCabe et al., 2007), flood

control and dry season water supply availability (Willis et al., 2011), and hydropower dam

efficiency (Vicuna et al., 2008).

1.21 Rising snowline and loss of Snow Covered Area

Within the Sierra Nevada Mountains, snowline elevations range from 1370 m in

the northern mountain range to about 1530 m in the southern mountain range (DWR

2006; Lundquist et al., 2008). With increased warming during the winter, the snowline

would recede to higher elevations, resulting in a larger area contributing to direct runoff

4

from winter storms, and a significant decline in the state’s winter snowpack volume. For

example, using a Global Climate Model (PCM) to project future changes in temperature,

Knowles et al. (2004) found that a 1.6 °C rise in average surface temperatures, relative to

the mean monthly values between 1995-2005, could contribute to a 34% decline of the

Sierra Nevada’s April total snow accumulation. Results from different simulations run

under varying amounts of temperature increase show California’s vulnerability to

warming temperatures. For example, Knowles (2002) showed a 5% loss in SWE resulting

from a 0.6 °C temperature increase. Moreover, if temperatures were to further increase by

1.6° to 2.1°C, cumulative SWE losses would increase to between 33% and 50%

(Knowles 2002). In addition, when the rain to snow transition rises to higher elevations,

this enhances the rainfall contributing area of a watershed to direct runoff (Anderson et

al., 2008; Willis et al., 2011; Dettinger et al., 2011). Hydrological simulations of the

Feather River Basin show that following a 3°C rise in temperatures, peak runoff could

increase by 82% relative to the base case due to snow elevations rising from 1370 m to

1830 m (Anderson et al., 2008). Although vulnerability to flooding would vary

depending on basin hypsometry, in general, higher snowpack elevations would result in

increased runoff due to a larger contributing area.

The lower to mid-elevation snowpack in the Sierra Nevada range is highly

sensitive to temperature fluctuations since winter temperatures are close to freezing

within these regions (Knowles et al., 2004; Maurer et al., 2007). Within the Sacramento

Watershed, more than 90% of the Feather and American River Basin lie below 2400 m.

In addition, these headwaters of the northern Sierra Nevada range show a large

5

distribution of area with elevation near 1500 m. Conversely, in the San Joaquin

Watershed, only 50% of Kings and Tuolumne River Basin lie below 2400 m. The

headwaters of the southern Sierra Nevada range are more evenly distributed with

significantly more area above 2000 m (Knowles et al., 2004). Knowles shows elevations

between 1500 and 2000 m would experience the largest reductions in SWE volume. The

northern Sierra Nevada snowpack would therefore be more vulnerable to temperature

increases relative to the southern Sierra Nevada snowpack due to the larger portions of

land within the 1500 m-2000 m elevation range. Results from their study further confirm

the increased vulnerability of northern Sierra Nevada snowpack by showing an 85% loss

of SWE would occur at elevations between 1300 m-2200 m. Conversely, in the southern

Sierra Nevada Mountains, an 85% loss of SWE would occur at elevations between 1800

m-3300 m. Maurer et al. (2007) complements these findings by showing that in the

northern Sierra Nevada range, a 1°C warming above 1961-1990 levels would result in

elevations between 1600 and 2000 m loosing 50% of their snow covered area (SCA).

Conversely, in the southern Sierra Nevada range, a 1°C warming would result in

elevations between 2000 and 2400 m losing only 10% of the regions previously SCA.

1.22 Shifts in Timing of Streamflow

A decline in California’s annual winter snowpack due to warmer winter

temperatures has contributed to observed changes in the timing and volume of snowmelt

derived streamflow (Stewart et al., 2005; Hidalgo et al., 2008). These shifts in streamflow

timing represent a significant water management concern since a larger volume of water

would be arriving earlier in the spring, while a subsequent reduction in streamflow would

6

occur later in the year during the summer and fall season. Annual runoff trends in the

Sacramento Watershed show that since the beginning of the 20th

century, April through

July runoff has experienced a downward trend compared to total annual runoff (Roos,

1989). This trend was confirmed by later studies that extended the analysis to include

more complex statistical measures and additional basins (Fox et al., 1990; Aguado et al.,

1992; Dettinger and Cayan 1995; Cayan et al., 2001; Stewart et al., 2005). Maurer et al.

(2007) showed areas with winter temperatures between 0 and -4°C in the Sacramento and

San Joaquin Watersheds are most sensitivity to temperature induced shifts in streamflow.

This is likely due to the large volume of April 1 SWE stored within this temperature

range at the mid-elevations. Using output form a global climate model (GCM) to drive a

hydrological model, Maurer et al. (2007) showed that relative to the 1961-1990 base

period, a mid to high emission scenario by mid-21st century would contribute to a

significant shift in peak streamflow occurring earlier during year. Results from the study

showed these shifts in streamflow timing would be most pronounced within the Feather,

America, Tuolumne and Kings River Basin (Maurer et al., 2007).

A more recent study by Fritze et al. (2011) showed that higher elevation snowmelt

dominated streams throughout western North America are experiencing earlier trends in

streamflow. Consistent with a warming trend and earlier snowmelt, analysis of

streamflow records between 1948-2008 show a redistribution of flow from late spring

and early summer, towards late winter and early spring (Fritze et al., 2011). The positive

trend in increased streamflow during the late winter and early spring months indicates

more precipitation, and/or more precipitation occurring as rain rather than snow. A

7

decrease in the volume of stremflow during the summer months indicates a smaller

snowpack, earlier snowmelt runoff, and possibly less summer precipitation (Fritze et al.,

2011). In comparing the 1948-1988 period with the 1989-2008 period, Fritze et al. (2011)

showed shifts in streamflow, from a mostly snowmelt dominated to a mostly rainfall

dominated regime, are most pronounced in the Sierra Nevada Mountains, and in New

Mexico. Although the influence of PDO on phase changes may be apparent in some

areas, changes in streamflow timing have persisted beyond the most recent PDO warm

phase that ended in 1999 (Fritze et al., 2011).

A 2006 evaluation by the California Department of Water Resources shows April

through July runoff in the Sacramento Valley has declined by about 9% over the past 100

years (DWR 2006). In the San Joaquin Valley, April through July runoff has declined

about 7% over the past 100 years (DWR 2006). These observed shifts in the hydrologic

cycle require that water managers develop more adaptive management operations.

1.23 Vulnerability of California’s Water Supply Infrastructure

The two largest water conveyance projects in the Central Valley, the federal

Central Valley Project (CVP) and the State Water Project (SWP) provide a combined

average total of about 12 of water annually for urban and agricultural uses (DWR

2006). Roughly 20 million Californians rely on the CVP and SWP for part of their water

supply needs. Furthermore, these two projects irrigate approximately 80,940 of

farmland each year (DWR 2006). The CVP, operated and maintained by the U.S. Bureau

of Reclamation, consists of 20 reservoirs with approximately 14 of storage capacity,

11 power plants, and over 804 km of canals and aqueducts. The SWP is operated by

8

California Department of Water Resources (DWR) and consists of 32 reservoirs, 8 power

plants, and 1,062 km of aqueducts and pipelines. The SWP uses these facilities to provide

urban and agricultural water supply, flood control, recreation, fish and wildlife

enhancement, power generation, and salinity control in the Sacramento-San Joaquin

Delta (DWR 2006).

To assess the impacts of climate change on CVP and SWP operations,

VanRheenen et al. (2004) used warming scenarios derived from three global climate

models (PCM) to perturb historical reservoir inflows. The perturbed reservoir inflows

where then used as input into CalSim II- the current planning and simulation model for

California’s CVP and SWP. As predicted by the climate change scenarios, simulated

shifts in seasonal and annual average runoff resulted in considerable impacts to SWP and

CVP delivery capabilities. Results from the study concluded that physical, regulatory,

and operation flexibilities would need to be incorporated into CVP and SWP operations

to maintain project delivery capabilities (VanRheenen et al., 2004).

Knowles et al. (2004) used a regional hydrological model driven by a global

climate model (PCM) to identify elevations that are most vulnerable to warming, and the

impact of hydrologic changes within these vulnerable elevations on the Sacramento-San

Joaquin River Delta. Their simulations showed that from October through February,

estuarine inflows from the Sacramento-San Joaquin Watershed are projected to increase

by 20% while from March through September, inflows are projected to decline by 20%.

Although total annual flow to the estuary is conserved (winter gains approximately

balanced by spring and summer losses), decreased inflow to the delta during the spring

9

would have devastating implications since lost freshwater would be replaced by seawater.

More importantly, Knowles et al. (2004) showed that irrespective of a wet or dry year, a

warmer climate and associated changes in the seasonality of outflow would result in

increased salinity levels. Furthermore, two thirds of the projected changes in salinity can

be attributed to snow loss at elevations between 1300 and 2200 m in the Sacramento

River Basin (Knowles et al., 2004). To avert the possibility of salinity intrusions within

the Delta, reservoir managers currently maintain scheduled water releases in the spring

and summer season. Nevertheless, these findings indicate that mid-elevation SWE is

highly sensitive to climate warming and is a crucial component to the state’s managed

freshwater system.

Prior to the spring snowmelt runoff pulse, reservoirs in California are operated

under flood protection mode to protect downstream communities and existing

infrastructure. Earlier winter runoff is therefore allowed to pass through the reservoirs

unabated. Once the threat posed by large winter storms and rain on snow has events has

passed, reservoirs switch operations from flood control to water supply storage. However

with increased warming, shifts in peak streamflow occurring earlier during the year

threaten to alter reservoir operations under existing rules: flood control, water supply

storage for agricultural, urban, and industrial uses, hydropower generation, environmental

services, and recreation (Vicuna et al., 2007). Moreover, with more precipitation falling

as rain instead of snow, the magnitude and frequency of winter floods would likely

increase (Knowles et al., 2006; Dettinger et al., 2011). Most of California’s reservoirs

were built during the mid 20th

century. As a result, the hydrological records used to create

10

reservoir flood control rule curves are based on historical climate trends during the early

1900s (Willis et al., 2011). While climate and hydrological trends have changed since

then, many of the currently operated reservoirs have not updated their existing flood

control rule curves (Willis et al., 2011; Georgakakos et al., 2012). A declining trend in

the state’s snowpack and earlier shifts in snowmelt runoff will make it more challenging

for water managers to maintain adequate flood control space during the winter, while

relying on the spring snowmelt runoff to refill reservoir flood space, and bring reservoirs

to storage capacity.

A changing climate, along with changes in floodplain land use, and flood

forecasts pose problems and opportunities for water resource managers (IPCC 2007).

Since California typically receives nominal amounts of precipitation between June and

October, adapting to changes in peak flow timing and snowmelt runoff is crucial to

ensuring adequate water supply for the summer and fall when demand is greatest (Willis

et al., 2011). Several studies have examined the effects of climate warming on reservoir

operations. Anderson et al. (2008) used a regional hydrological model driven by output

from global climate models (GCM) to show greater amounts of winter runoff combined

with static flood control rule curves would result in larger uncontrolled water releases

from reservoirs. On the other hand, reduced snowmelt derived flows in the spring would

result in diminished summer and fall water supply deliveries.

In a similar study, VanRheenen et al. (2004) used output from three PCMs

(Parallel Climate Model) to drive a regional hydrological model. Output from the

hydrological model (as well as historical monthly streamflow records) were then used to

11

drive a third simulation, the California Central Valley water resources system (CVmod)

that simulates monthly response of the major federal and state storage projects in

California’s Central Valley. Under current operational policies, results from the CVmod

simulation showed a decrease in regulated mean inflow to the Sacramento-San Joaquin

Delta. Furthermore, hydropower production and annual mean storage in reservoirs

throughout the Central Valley showed a consistent decline. In a follow-up study,

Medellin-Azuara et al. (2008) reassessed the impacts of a dry-warm (A2 emissions)

scenario on California’s water resources. A water resources economic model was then

used to simulate and optimize the response of California’s water system to future

hydrologic and demand scenarios. Results for a 30-year period centered around 2085

showed a 27% reduction in annual streamflow, and a noticeable shift of peak flows

occurring earlier in the spring. California’s most severely impacted sectors included

agriculture, hydropower generation, environmental flows, and reservoir levels. When

reservoir operation policies were updated and modified to compensate for the different

warming scenario, system performance improved (Medellin-Azuara et al., 2008).

One method for updating reservoir operation involves revising static flood

operation rule curves. Lee et al. (2009) developed optimized rule curves for reservoir

operations based on monthly time step simulations for a 2°C climate warming scenario.

Although results relied on a single climate warming scenario, they showed storage

deficits decreased when current rule curves were updated to reflect climate warming.

Results from the different studies outlined above indicate California’s vulnerability to

increased water-stress in a warmer climate. Moreover, these findings collectively

12

emphasize the urgency in developing an integrated water management approach. One

way this can be achieved is by incorporating future projections in SWE loss, and

elevation specific snowpack vulnerability, with reservoir optimization.

1.4 Study Objectives

This study seeks to gauge the potential consequences of future warming on SWE

loss within California’s Sacramento and San Joaquin Watersheds. Since the hydrological

cycle within these two watersheds is primarily driven by snowmelt, loss of snow due to

warming would have widespread implications on future water demand, availability, and

use. The ability to correctly identify and monitor highly vulnerable, “at-risk” areas of

snow accumulation, at the basin scale, is a crucial step towards effective water

management and planning. The study incorporates a climatologically based classification

of seasonal snow cover to map “at-risk” snow covered regions at the basin scale. “At-

risk” regions are defined as regions that would be first to transition from a snowfall to a

rainfall dominated winter precipitation regime under projected climate warming. These

“at-risk” regions do not represent earlier snowmelt contributing areas in a warmer climate

(Nolin and Daly, 2006).

A climatologically based classification of seasonal snow cover provides a

physically based and widely applicable means of characterizing snow classes (Nolin and

Daly, 2006). This approach uses temperature, precipitation, and wind speed data to

discriminate snow classes across a basin, or watershed (Sturm et al., 1995). The

relationship between these three climatic variables and snow cover can be depicted using

13

a binary classification system. The binary classification system uses these three

parameters to determine if snow exists based on: 1) a set rain versus snow temperature

threshold, 2) A precipitation threshold to distinguish between high versus low

precipitation, and 3) A high or low wind speed environment. By examining where these

classes exist geographically, snow cover can be determined at a macro scale (Brown

2008). Once snow cover is determined, the temperature variable can be perturbed to

identify regions where the snow exists below the rain-snow threshold, but above the

newly adjusted value. This perturbed temperature value represents the degree of

warming. Using this method, one is able to identify specific regions and elevation bands

that are most “at-risk” of transitioning from a snow dominated to a rain dominated regime

under projected warming scenarios.

14

Chapter 2: Study Area and Basin Descriptions

2.1 The Sacramento Watershed

The source of the Sacramento River is the Cascades and northern Sierra Nevada

Mountains. The river flows southwards over a journey of 644 km before emptying out

into the San Francisco Bay. The river drains an area of about 70,000 in the northern

half of the state (DWR 2006). The basin lies between the Sierra Nevada and Cascade

Range on the east and the Coast Range and Klamath Mountains in the west. After the

Colombia River, the Sacramento River drainage is the largest U.S. drainage into the

Pacific (DWR 2006). The basin’s geography ranges from glacier-carved, snowy peaks of

the Sierra Nevada Mountains in the east, to sea-level marshes and agricultural lands near

the Sacramento-San Joaquin Delta. Large dams and levees engineered along the

Sacramento River work to absorb flood flows, store water for use during droughts, and

for navigation and electricity generation. Reservoirs have also been constructed to

regulate flows and for irrigation purposes.

2.11 Feather River Basin

The Feather River is a principal tributary of the Sacramento River, merging into it

roughly 24 km north of the city of Sacramento. The river originates in the northern Sierra

Nevada Mountains and flows westwards draining an area of approximately 16,000

(Figure 2.1). The two main branches of the Feather River, North and Middle Fork,

originate east of the Sierra Range in the Diamond Mountains. As these two forks flow

west, they breach the crest of the Northern Sierra Nevada range on their way to Lake

15

Oroville (George et al., 2007). The North Fork is the larger of the two branches draining

roughly 60% of the entire Upper Feather River Basin. Elevation in the Upper North Fork

ranges from 325 m in the lowlands, to over 2,500 m in the mountains. Annual

precipitation varies from less than 330 mm on the arid east side, to more than 1700 mm

on the western mountain slopes. During the winter months, precipitation above 1500 m

occurs primarily as snow. Vegetation in the basin is diverse and ranges from mixed

conifer and deciduous forests on the west, to sparse pine plant communities in the more

arid east (George et al., 2007). The Feather River Basin is a valuable hydrologic resource

for California and a major contributor to the State Water Project (SWP). Lake Oroville at

the basin’s outlet holds roughly 8 percent of the state’s reservoir capacity and plays an

integral role in flood management, water supply storage, and the health of fisheries.

Reservoir operations rely upon winter snowpack, and the subsequent spring snowmelt

runoff to meet the state’s downstream summer water demands. Average annual yield of

the upstream Feather River Basin at Oroville Reservoir is about 5 , with runoff

primarily occurring between January and June. Summer inflows into Oroville Reservoir

are sustained at roughly 28 cubic meters per second (1000 cfs) by snowmelt, and spring

accretions within the upper watershed (DWR 2007).

16

Figure 2.1: The Feather River Basin within the Sacramento Watershed in

California’s Northern Sierra Nevada.

17

2.2 The San Joaquin Watershed

The San Joaquin flows are generated in the high southern Sierra Nevada

Mountains. The river flows northwards draining an area of about 82,880 km2 before

emptying into the Sacramento-San Joaquin Delta. Approximately 3,900 of highly

productive farmland rely on water from the San Joaquin drainage for irrigation.

Reservoirs within the San Joaquin River Basin serve to store water for domestic and

agriculture use. Freshwater flowing southward from the Sacramento River converges

with the northward flowing San Joaquin River to form the Sacramento-San Joaquin Delta

which is the hub of California’s water supply system (DWR, 2006). Between December

and March, the San Joaquin Watershed receives an average 30–40 of freshwater as

rain and snow. Total storage in the watershed’s major reservoirs is about

35 (Knowles and Cayan, 2002). The majority of California’s population and

millions of acres of farmland rely on water from this delta.

2.21 Stanislaus River Basin

The Stanislaus River drains the western slopes of the Sierra Nevada Mountains in

central California. The river forms the boundary between Calaveras and Tuolumne

County as it flows westwards towards the San Joaquin River, draining an area of

approximately 3,356 (Figure 2.2). Geology in the upper Stanislaus River Basin

consists primarily of glaciated granite with mid-river reaches of metamorphic rock

(NOAA 2009). The basin’s elevation ranges from 3,300 m in the Sierra Nevada

Mountains to roughly 10 m at its confluence with the San Joaquin River. From 1948 to

18

2007, annual precipitation at the mid-elevations has ranged from 560 mm on the more

arid eastern slopes to more than 2700 mm on the western slopes. At the confluence of its

three major tributaries, annual average flow is approximately 1 making it one the

largest tributaries of the San Joaquin River (NOAA 2009). Snowmelt runoff accounts for

the largest contribution of flow to the Stanislaus River, with the highest amount of runoff

occurring during the months of April, May and June (NOAA 2009). The river has been

heavily dammed and diverted to supply water for irrigation, flood control benefits, and

power generation. On the main stem of the Stanislaus River, below the point of

convergence with its three main tributaries, flows are regulated by New Melones

Reservoir. The reservoir’s 3 gross storage capacity, including a flood control

reservation of 0.6 , makes it a critical component of the state’s Central Valley

Project (CVP). Regulated flows in the lower Stanislaus River provide water for irrigation,

municipal, and industrial uses. Regulated flows are also used to insure adequate water

supply for riparian water rights holders, fishery management objectives, and dissolved

oxygen requirements (NOAA 2009).

19

Figure 2.2: The Stanislaus River Basin within the San Joaquin Watershed in

California’s Sierra Nevada.

20

2.22 Tuolumne River Basin

The Tuolumne River begins at the confluence of Dana Fork and Lyell Fork in

Yosemite National Park and flows southwest wards draining an area of approximately

4,900 (Figure 2.3). With its headwaters above 3,000 m, this is one of the largest

rivers in Sierra Nevada range (NOAA 2009). The river traverses roughly 2,600 m of

elevation drop as it flows through high mountain valleys and deep canyons, then through

the foothills of the Sierra Nevada Mountains, before flowing into its confluence with the

San Joaquin River in the Central Valley. Geology in the upper basin consists primarily of

granitic bedrock weathered by glaciers (NOAA 2009). The presence of steep canyons,

mountain meadows, and patchy forests are remnants of historical glacial periods.

Elevation in the basin ranges from 4,000 m at Mt. Dana to roughly 11 m at its confluence

with the San Joaquin River. Annual precipitation ranges from 300 mm in the Central

Valley to more than 1500 mm in the high mountains. Precipitation in the basin’s foothills

occurs mostly in the form of rain during the months of December through April. At the

higher elevations above 1500 m, winter precipitation occurs largely as snowfall. April

through July snowmelt runoff accounts for the largest contribution of flow to the

Tuolumne River. To insure adequate water supply for farms and cities downstream, and

for flood management purposes, the river has been heavily dammed at several locations.

In the upper reaches, flows are regulated by Cherry Lake, Lake Eleanor, and Hetch

Hetchy Reservoir (Don Pedro Relicensing 2011). These three reservoirs are owned and

operated by the City and County of San Francisco and are used to provide water supply

and for electricity generation. Water released from these reservoirs is highly regulated

21

and accounts for most of the inflow to Don Pedro Reservoir located a few kilometers

downstream. Due to the prevalence of flooding from rain-on-snow events, one of the

primary purposes of the Don Pedro Reservoir is for flood control (Don Pedro Relicensing

2011). In addition, the reservoir provides power and water for irrigation to the cities of

Turlock and Modesto in the Central Valley. Downstream of the Don Pedro project, the

Tuolumne River flows are regulated one more time at La Grange Dam before flowing

into its confluence with the San Joaquin River.

22

Figure 2.3: The Tuolumne and Merced River Basin within the San Joaquin

Watershed in California’s Sierra Nevada.

23

2.23 Merced River Basin

The Merced River stretches from its headwaters near the Triple Divide Peak in

Yosemite National Park to its confluence with the San Joaquin River, draining an area of

approximately 4,500 (Figure 2.3). Similar to the Stanislaus and Tuolumne River

Basin, geology in the upper Merced River Basin consists primarily of granitic bedrock

scoured by glacier activity as is evident by the steep walls, mountain meadows, and the

U-shaped Yosemite Valley. The basin’s elevation ranges from 4,000 m at the headwaters

to roughly 15 m at its confluence with the San Joaquin River. Overall climate is

characterized by hot, dry summers and cold, wet winters (Figure 2.4). Most of the basin’s

precipitation occurs between November and April, with the greatest amount occurring

during the months of December, January, and February (NPS 2005). Annual precipitation

ranges from roughly 630 mm in the Central Valley to more than 1700 mm in the high

mountains (NOAA 2009). In the foothills and at the lower elevations, precipitation occurs

mostly as rainfall during the months of December through April. At the higher elevations

above 1600 m, winter precipitation occurs largely as snowfall. Spring and early summer

snowmelt runoff accounts for the largest contribution of flow to the Merced River. The

river is further characterized by winter rainstorm peaks and low summer base flows. Like

its neighbor to the north, the Merced River Basin has been modified by reservoirs and

flow regulations. Amongst the largest of these reservoirs is The New Exchequer project

that regulates majority of the basin’s runoff. The Reservoir provides a host of functions

including dry season water supply for irrigation, power generation, flood control, and

environmental flows. Primary land use in the basin is agricultural and mining. At the

24

basin’s lower elevations, favorable climate and irrigated farmland create ideal growing

conditions for fruit orchards and vineyards.

-5

0

5

10

15

20

25

30

35

0

50

100

150

200

250

300

350

400

450

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

TE

MP

ER

AT

UR

E (

°C)

PR

EC

IPIT

AT

ION

(m

m)

MONTH

Average Total Rainfall (mm) Average Total Snowfall (mm)

Average Min. Temperature (Celcius) Average Max. Temperature (Celsius)

Figure 2.4: Hydrograph of the Merced and Tuolumne River Basin showing average

monthly maximum and minimum temperatures and precipitation amounts at

Yosemite National Park from 1905-2012.

25

2.24 Upper San Joaquin River Basin

The San Joaquin River is one of the principal rivers in the southern part of

California’s Central Valley. The upper reaches of the river consist of three principal

branches each of which has its sources in glacial lakes near the summit of the Southern

Sierra Nevada Mountains (Figure 2.5). Among these three branches, the South Fork

drains the largest area and is considered the head of the main stem (NOAA 2009). The

North Fork rises on the slopes of Mount Lyell and flows in a southward direction to its

confluence with the Middle Fork. The Middle and South forks form the main stem of the

San Joaquin River (Figure 2.5). The river flows southwest through steep canyons

draining an area of approximately 4,600 before flows are impeded by Friat Dam at

the basin’s foothills. The basin’s geology consists primarily of granitic bedrock and

metamorphic rock (NOAA 2009). Vegetation in the region varies from alpine meadows

and coniferous forests at the higher elevations to oak-woodlands and rangelands at the

lower elevations. Elevation ranges from 3,950 m at Rodgers Peak in the Sierra Nevada

Mountains to 18 m at the mouth of the Merced River. Climate in the basin is

characteristic of the Sierra Nevada Mountains, consisting of hot, dry summers and cold,

wet winters. Annual precipitation in the basin ranges from 350 mm within the lowlands,

to more than 1500 mm at the higher elevations. Most of the basin’s precipitation occurs

between November and April. Fed largely by snowmelt derived runoff, the San Joaquin

River has been titled “California’s hardest working and most fought-over waterway” due

to the variety of individuals and communities it benefits (NOAA 2009). Friant Dam,

located in the basin’s foothills, is a principal component of the state’s Central Valley

26

Project (CVP). The primary purpose of the Dam is for flood control and to provide dry

season water supply for irrigation, municipal, and industrial uses. Below Friant Dam, the

river flows southwest towards the Central Valley.

27

Figure 2.5: The Upper San Joaquin River Basin within the San Joaquin Watershed in

California’s Southern Sierra Nevada.

28

2.25 Kings River Basin

The Kings River Basin is one of the most rugged in the entire Sierra Nevada

Mountains (USBR 2003). The basin’s headwaters begin in the high mountains of Kings

Canyon National Park and as the river flows southwest, towards the Central Valley, it

drains an area of approximately 4,300 (Figure 2.6). Geology in the basin consists

primarily of granitic, sedimentary, and volcanic rock. Volcanism and glacial activity have

modified the basin to its present day landscape. Elevation ranges from 4,200 m in the

high snow draped Southern Sierra Nevada Mountains, to roughly 25 m at Tulare Lake

bed in the Central Valley. Similar to the other basins in the Sierra Nevada Mountains,

climate consisting of hot, dry summers and cold, wet winters. Annual precipitation ranges

from 200 mm in the Valley to more than 1500 mm at the higher elevations (Raising Pine

Flat Dam 2003). In the foothills and at the lower elevations, precipitation occurs mostly

in the form of rain from November through April. Winter precipitation at the higher

elevations occurs largely as snowfall. Spring and early summer snowmelt runoff accounts

for the largest contribution of flow to the Kings River. Heavy rains during the winter, and

increased snowmelt runoff during the spring have contributed to an extensive history of

flooding in the basin. Flooding from heavy rains typically occurs from November to

March and is characterized by sharp, high peaks of river flow that last a short duration

(Raising Pine Flat Dam 2003). Flooding from snowmelt runoff, on the other hand,

typically occurs from March to June and is characterized by large volumes of runoff

(UBBR 2003). To adequately manage these floods and for irrigation purposes, the U.S.

Army Corps of Engineers constructed Pine Flat Dam in the basin’s foothills.

29

Figure 2.6: The Kings River Basin within the San Joaquin Watershed in California’s

Southern Sierra Nevada.

30

Chapter 3: Methods

Our project builds upon study methods developed by Nolin and Daly (2006). In

their study of “at-risk” snow, the authors explored the sensitivity of the Pacific Northwest

snowpack to a 2°C warming during the accumulation phase of the winter season (from

December to February). The authors used a thirty year historical average (1971-2000) of

mean temperature and precipitation from the Parameter-elevation Regression on

Independent Slope Model (PRISM) dataset to acquire gridded estimates of temperature

and precipitation within a 4 km by 4 km grid cell. PRISM is a sophisticated analytical

model that distributes point based measurements of monthly temperature and

precipitation data to regularly spaced grid cells. And as a result, the model’s built in

algorithm accounts for the orographic effects of precipitation that exists on mountainous

terrain (Daly, 1992).

We extended methods developed by Nolin and Daly (2006) to the Feather River

Basin, within the Sacramento Watershed, as well as all nested basins within the San

Joaquin Watershed. These basins were specifically selected due to their mountainous

climatology, and their enormous contribution to the state’s annual snowpack. We

incorporated a higher resolution 800 m PRISM dataset, and a wider study period, from

December to April. The PRISM dataset contains values of mean monthly maximum

( ), and mean monthly minimum temperatures ( ). We therefore acquired mean

monthly temperature ( ), by adding to , and dividing the result by two. In

previous studies, vegetation cover fraction has been used as a proxy for wind speed

(Sturm et al., 1995; Nolin and Daly, 2006) However in maritime snow environments such

31

as the Sierra Nevada Mountains, wind distribution has a negligible effect on snowpack

density or depth therefore was neglected in our analysis.

3.1 Binary Classification Decision Tree

To determine if snow cover exists within a gird cell, we combined mean monthly

temperature and precipitation data into a single dataset. A binary classification decision

tree was then used to analyze this dataset based on a rain versus snow temperature

threshold, and the magnitude of warming potential for a given month (Figure 3.1). For a

grid cell to be classified as having snow, it must have a mean monthly temperature less

than the selected rain-snow temperature threshold. Grid cells classified as having snow

are further classified as either having cold or warm snow. Warm snow regions are of

primary interest since they represent the most “at-risk” regions that would be first to

transition from a snowfall dominated, to a rainfall dominated regime with increased

warming. We therefore explored the sensitivity of potential future snow cover to

increased warming using the binary decision tree approach.

3.2 Rain versus Snow Temperature Threshold

Snow course data as well as data from Snow Telemetry stations (SNOTEL) was

used to validate the accuracy of snow cover generated using the PRISM dataset. We used

a rain-snow temperature threshold of 3°C because this value provided the most realistic

representation of snow cover across all basins. Previous studies in the Sierra Nevada

Mountains have shown the rain to snow transition zone exists between 0 and 3°C

(Lundquist et al., 2008). In addition, the Utah Energy Balance (UEB) snow model, which

has been shown to accurately reproduce the Sierra Nevada Mountain snowpack,

32

partitions rain from snow based on near-surface air temperatures. Precipitation is

considered all snow if near surface air temperatures are below -1°C, and considered all

rain if near surface air temperatures are greater than 3°C (Knowles et al., 2004). Since a

varying mix of snow and rain exists between -1°C and 3°C, the selected temperature

threshold seems reasonable.

For each of the six basins, 200 m elevation bands were created to closely analyze

specific areas of snow loss that would occur under a 0.5° to 4°C increase in temperature.

A 2.5-min (800 m) DEM from the PRISM Climate Group was selected to minimize

errors associated with re-projection, or changes in resolution. The fraction of potential

snow loss that would occur within each elevation band was computed by dividing the

total area of “at-risk” snow (within a given elevation band) by the area of snow currently

present.

3.3 Calculating a Snow Water Equivalent

The volume of water loss within the “at-risk” SCA (Snow Covered Area) was

determined using SWE output from NOHRSC’s (National Operational Hydrologic

Remote Sensing Center) SNODAS (SNOw Data Assimilation Systems) model. The

model incorporates satellite, airborne, and ground-based measurements to generate a 1

km gridded estimate of SWE. To acquire accumulated monthly SWE estimates, we took

each year’s end of the month SWE output (from November 1978 to May 2000) and

subtracted this value from the preceding month. For example, accumulated net SWE

within December would be equal to the estimated SWE output on December thirtieth,

minus the estimated SWE output on November thirtieth 1978. We then averaged each

33

month’s accumulated SWE over the twenty two year period (from 1978-2000). A volume

estimate of SWE was then calculated by multiplying the area of “at-risk” snow by the

estimated SWE measurement within that band. Data from Snow Telemetry stations

(SNOTEL) as well as snow course data was use to validate the extent of snow cover, and

cross verify the SNODAS derived SWE output.

3.4 Determining Reservoir Storage Capacity

Flood operation rule curves were created in Microsoft Excel using reservoir

dimensions, volume versus elevation curves (Figure 3.2), and reservoir operation

guidelines. Historical storage volumes and reservoir pool elevations were acquired from

California’s Data Exchange Center (CDEC). The cumulative volume of SWE loss within

a given basin was then interpreted to reflect a reservoir specific water storage elevation,

and possible adjustments. These methods provided a means to examine where water

levels have been historically maintained, during flood control operations, and the extent

of additional storage space available to mitigate the loss of SWE without compromising

flood control space.

34

Figure 3.1: A binary decision tree used for snow classification. The first step in

the classification is to distinguish between rain versus snow. For a grid-cell to be

classified as having snow, the mean monthly temperature should be less than 3°C.

Grid cells classified as having snow are then further classified as either having

cold or warm snow depending on the selected threshold value.

35

Figure 3.2: Graph of the Don Pedro Reservoir in Tuolumne River Basin

showing storage volume (in acre-feet) in relation to the reservoir’s pool

elevation. Source: Don Pedro Relicensing2011

36

y = 66.21x0.1739

R² = 0.9986

290

390

490

590

690

790

890

0 500000 1000000 1500000 2000000 2500000

Res

ervoir

Ele

va

tio

n, fe

et

Storage, acre-feet

Don Pedro Reservoir Storage Volume Versus Elevation Curve

Storage

Figure 3.2: Volume versus elevation curves for the Don Pedro fitted with a

regression trend-line. This equation provides a means of interpreting reservoir

pool elevations in relation to the cumulative volume of water storage.

37

Chapter 4: Results

4.1 Feather River Basin

In the Feather River Basin within the Sacramento Watershed, the rain to snow

transition occurs near 1200 m in elevation during the months of December, January, and

February. Roughly 72% of the basin’s total area is snow covered during these months. In

addition, most of the basin’s seasonal snow zone exists at elevations between 1300 and

2300 m. In March, warmer temperatures and less snowfall result in the rain to snow

transition rising to 1500 m in elevation. Approximately 44% of the basin’s total area is

snow covered in March. In April, snow covered area encompasses less than 25% of the

basin’s total area.

Under a 0.5°C warming scenario, 257.1 or 5% of the basin’s December SCA

would be at-risk of becoming rainfall dominated. Furthermore, approximately 0.02

of SWE would be lost. A 0.5°C warming in January, on the other hand, would result in

176.6 or 3% of the basin’s January SCA becoming rainfall dominated. Volume of

SWE loss in January would equal 0.01 . From December to February, snow loss

under a 0.5°C warming would be confined to the lower elevations between 1300 and

1900 m. Most of the loss in SCA and SWE would occur at elevations between 1500 and

1700 m. Elevations above 1900 m would remain cold and unaffected during these

months.

In March, a 0.5°C increase in temperature would result in 551.0 or 18% of

the basin’s previously SCA becoming rainfall dominated. Elevations between 1500 and

38

1700 m, historically 28% snow covered, would become only 13% snow covered.

Volume of SWE loss in March would equal 0.03 Loss of SCA and SWE during the

month of March would be confined to elevations between 1500 and 2100 m. Elevations

above 2100 m would remain cold and unaffected by a 0.5°C warming.

A further 1°C warming in December, January, and February would result in

increased snow loss at the lower elevations, between 1300 and 1900 m (Figure 4.1a). In

February, at-risk SCA under a 1°C warming would increase from 361.6 to

778.0 (Figure 4.1a). At the 1300 to 1500 m elevation band, 339.8 or 42% of the

region’s previously SCA would become rainfall dominated (Figure 4.1a). Elevations

between 1500 and 1900 m, historically 87% snow dominated in February, would lose

434.5 of SCA becoming 74% snow dominated (Figure 4.1a). Volume of SWE loss

in February, following a 1°C warming, would equal 0.07 . Most of the loss in SCA

and SWE, from December to February, would occur at elevations between 1300 and

1900 m (Figure 4.1a and Figure 4.3a). Snow dominated regions above 1900 m would

remain cold and unaffected by a 1°C warming (Figure 4.1a).

In March, 1337.2 or 43% of the basin’s March SCA would be at-risk of

becoming rainfall dominated under a 1°C warming. Roughly 77% of the snow loss in

March would occur within the lower elevations, between 1500 and 1900 m (Figure 4.1a).

At the 1500 to 1700 m elevation band, at-risk SCA would increase from 239.8 to

407.4 (Figure 4.1a). The 1700 to 1900 m elevation band contains roughly 45% of

the basin’s March SCA and is historically 82% snow covered. Following a 1°C warming,

39

626.9 of previously SCA within the region would become rainfall dominated

(Figure 4.1a); only 45% of the region would still remain snow covered. Volume of SWE

loss in March would equal 0.08 (Table 4.0). Loss of SCA and SWE under a 1°C

warming would be confined to the lower elevations between 1500 and 2100 m (Figure

4.1a and Figure 4.3a).

As temperatures increase from 1° to 2°C in December and January, elevations

between 1300 and 1500 m would lose 239.4 or 25% of the regions previously SCA

(Figure 4.ab). Elevations between 1500 and 1700 m would lose roughly 325.9 of

SCA becoming 76% snow dominated (Figure 4.1b). The greatest amount of loss in SCA

and SWE would occur at elevations between 1300 and 1900 m (Figure 4.1b and Figure

4.3b). Elevations above 1900 m, previously unaffected by a 1°C warming in December

and January, show increased sensitivity to a 2°C warming (Figure 4.1a and Figure 4.1b).

In February under a 2°C warming scenario, 2,773.0 or 56% of the basin’s

previously SCA would be lost, or transition to a rainfall dominated precipitation regime.

Elevations between 1300 and 1500 m would become completely rainfall dominated

(Figure 4.2b). Elevations between 1500 and 1900 m, historically 87% snow covered in

February, would lose 1,710.8 of SCA becoming 35% snow covered (Figure 4.1b).

At-risk SCA at the 1900 to 2100 m elevation band would increase from 3.8 to

249.6 (Figure 4.1a & Figure 4.1b). Volume of SWE loss in February, following a

2°C warming, would equal 0.26 . Roughly 92% of this loss in SWE would occur at

40

elevations between 1300 and 1900 m (Figure 4.3b). Snow dominated regions above 2100

m would remain cold and unaffected by a 2°C warming in February.

A 2°C warming in March would result in 2,812.8 or 91% of the basin’s

previously SCA becoming rainfall dominated. Historically snow dominated regions

existing between 1500 and 1900 m would lose all their SCA (Figure 4.2b). The 2100 to

2300 m elevation band, historically completely snow covered in March, would lose

203.0 of SCA becoming 29% snow covered (Figure 4.1b). Volume of SWE loss in

March would equal 0.18 . Most of this SWE loss (78%) in March would occur at

elevations between 1700 and 2300 m (Figure 4.3b). Elevations above 2300 m would

remain unaffected by a 2°C warming in March (Figure 4.1b)

Following a 3°C warming scenario, 1,928.1 or 37% of the basin’s December

SCA would be at-risk of becoming rainfall dominated. Furthermore, approximately 0.15

of SWE would be lost (Table 4.0). Similarly to December, a 3°C warming in

January would result in 1,721.4 or 34% of the basin’s January SCA becoming

rainfall dominated (Table 4.0). Volume of SWE loss in January would equal 0.15 .

From December to February, snow loss under a 3°C warming would be confined to

elevations between 1300 and 2300 m (Figure 4.1c). Snow covered regions existing above

2300 m would remain unaffected by a 3°C warming in December and January (Figure

4.1c).

In February, all previously snow covered regions between 1500 and 1700 m

would become rainfall dominated following a 3°C warming (Figure 4.2c). The 1700 to

41

1900 m elevation band, historically 94% snow covered, contains 32% of the basin’s

February SCA. Following a 3°C warming scenario, 1,424.4 of previously SCA

would be lost (Figure 4.2c); only 10% of the region would still remain snow covered.

Volume of SWE loss in February would increase from 0.26 to 0.40 (Table 4.0).

Most of the SWE loss in February (65%) would occur at elevations between 1500 and

1900 m (Figure 4.3c).

In March, 3080.4 or 99% of the basin previously SCA would be at-risk of

becoming rainfall dominated (Table 4.0). Volume of SWE loss in March would equal

0.20 with most of this loss (75%) occurring at elevations between 1700 and 2100 m

(Figure 4.3c). Elevations above 2300 m would remain cold and unaffected by a 3°C

warming in March (Figure 4.1c)

An extreme 4°C warming would result in elevations between 1300 and 1500 m

becoming completely rainfall dominated (Figure 4.2d). From December to February, the

1500 to 1700 m elevation band is 82% snow covered. Furthermore, this band contains

26% of the basin’s December to February SCA. Following a 4°C warming during these

months, 1230.5 of previously snow covered area would become rainfall dominated

(Figure 4.1d); only 9% of the region would still remain snow covered.

In December, elevations between 1700 and 2100 m are 98% snow covered.

Following a 4°C warming, this region would lose 1,892.7 of previously SCA

becoming 27% snow covered (Figure 4.1d). Volume of SWE loss in December would

equal 0.25 ; most of the SWE loss (66%) would occur at elevations between 1500

42

and 1900 m (Figure 4.3d). Snow loss in December would be confined to elevations

between 1300 and 2300 m (Figure 4.1d and Figure 4.2d).

In January under a 4°C warming scenario, the 1500 to 1700 m elevation band

would lose 1,151.6 of previously SCA remaining only 17% snow covered (Figure

4.1d). Elevations between 1700 and 2100 m, historically 94% snow covered, would lose

1,570.3 of snow cover becoming 34% snow dominated (Figure 4.1d). At the higher

elevations above 2100 m, at-risk SCA would increase from 58.6 to 195.4

(Figure 4.1c and Figure 4.1d). Volume of SWE loss in January, following a 4°C

warming, would equal 0.35 (Table 4.0). Most of this SWE loss (74%) would be

confined to elevations between 1500 and 1900 m (Figure 4.3d).

A 4°C warming in February would result in 4,895.7 , or 99% of the basin’s

previously snow covered regions becoming rainfall dominated (Figure 4.1d) . All

previously snow covered regions existing between 1700 and 1900 m would be lost

(Figure 4.2d). The 1900 to 2100 m elevation band, historically completely snow covered

in February, contains 19% of the basin’s February SCA. Following a 4°C increase in

temperature, only 4% of this region would still remain snow covered. Volume of SWE

loss in February would increase from 0.40 to 0.44 (Table 4.0). Similarly in

March, all previously snow covered regions existing between 1500 and 2500 m would

become rainfall dominated (Figure 4.1d). Volume of SWE loss in March would equal

0.20 with most of this loss (75%) occurring at elevations between 1700 and 2100 m

(Figure 4.3d).

43

Figure 4.1: Estimated loss of SCA within the Feather River Basin, at the different

elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0

300

600

900

1200

1500

1800

DEC. JAN. FEB. MAR.

SC

A L

oss

(k

m²)

MONTH

a)

0

300

600

900

1200

1500

1800

DEC. JAN. FEB. MAR.

SC

A L

oss

(k

m²)

MONTH

b)

0

300

600

900

1200

1500

1800

DEC. JAN. FEB. MAR.

SC

A L

oss

(k

m²)

MONTH

c)

0

300

600

900

1200

1500

1800

DEC. JAN. FEB. MAR.

SC

A L

0ss

(k

m²)

MONTH

1300 TO 1500

1500 TO 1700

1700 TO 1900

1900 TO 2100

2100 TO 2300

2300 TO 2500

2500 TO 2700

d)

EL

EV

AT

ION

20

0 (

ME

TE

RS

)

44

Figure 4.2: Estimated fractional loss of SCA within the Feather River Basin, at the

different elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

DEC. JAN. FEB. MAR.FR

AC

TIO

NA

L S

CA

MONTH

a)

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

DEC. JAN. FEB. MAR.FR

AC

TIO

NA

L S

CA

MONTH

b)

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

DEC. JAN. FEB. MAR.FR

AC

TIO

NA

L S

CA

MONTH

c)

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

DEC. JAN. FEB. MAR.FR

AC

TIO

NA

L S

CA

MONTH

1300 TO 1500

1500 TO 1700

1700 TO 1900

1900 TO 2100

2100 TO 2300

2300 TO 2500

2700 TO 2900

d)

EL

EV

AT

ION

20

0 (

ME

TE

RS

)

45

Figure 4.3: Estimated volume of SWE within the Feather River Basin, at the different

elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0

0.04

0.08

0.12

0.16

DEC. JAN. FEB. MAR.

SW

E L

oss

(k

m³)

MONTH

a)

0

0.04

0.08

0.12

0.16

DEC. JAN. FEB. MAR.

SW

E L

oss

(k

m³)

MONTH

b)

0

0.04

0.08

0.12

0.16

DEC. JAN. FEB. MAR.

SW

E L

oss

(k

m³)

MONTH

c)

0

0.04

0.08

0.12

0.16

DEC. JAN. FEB. MAR.

SW

E L

oss

(k

m³)

MONTH

1300 TO 1500

1500 TO 1700

1700 TO 1900

1900 TO 2100

2100 TO 2300

2300 TO 2500

2500 TO 2700

d) E

LE

VA

TIO

N 2

00

(M

ET

ER

S)

46

MONTH

SNOWLINE ELEVATION

(meters)

SCA

( )

SCA

(% of basin)

DEGREE

OF WARMING

(°C)

SCA LOST

(% of basin)

SCA LOST

)

SWE LOST

( )

DECEMBER 1250 5248.3 74.2 1 9% 489.3 0.03

2 18% 956.1 0.06

3 37% 1928.1 0.13

4 82% 4321.5 0.30

JANUARY 1275 5126.5 72.5 1 7% 375.1 0.03

2 15% 778.7 0.07

3 34% 1721.4 0.15

4 75% 3865.2 0.35

FEBRUARY 1300 4964.1 70.2 1 16% 778.0 0.07

2 56% 2773.0 0.26

3 87% 4335.0 0.40

4 99% 4895.7 0.40

MARCH 1500 3098.5 43.8 1 43% 1337.2 0.08

2 91% 2812.8 0.18

3 99% 3080.4 0.20

4 100% 3097.7 0.20

Table 4.1: Warming Scenarios considered likely by the IPCC applied to the

Feather River Basin for the months of December to April; area of at risk-snow,

SCA, and volume of SWE loss were then calculated.

47

ELEVATION (METERS) PERCENT OF BASIN

Below 1100 701.33 9.92%

1101 to 1300 624.65 8.83%

1301 to 1500 1147.83 16.23%

1501 to 1700 1634.92 23.12%

1701 to 1900 1698.82 24.02%

1901 to 2100 942.62 13.33%

2101 to 2300 285.64 4.04%

2301 to 2500 28.56 0.40%

2501 to 2700 6.77 0.10%

Rel

ief

(met

ers)

Elev

atio

n (

met

ers)

Z-min (m) = 275 Z-max (m) = 2619

Drainage Area ( ) = 7071.14

Average Elevation (m) = 1582.10

Figure 4.4: Hypsometric curve of the Feather River Basin showing distribution of

area with elevation. Approximately 64% of the basin’s total area exists between

1500 and 2300 m.

48

Figure 4.5: “At-risk” snow covered area within the Feather River Basin under

different warming scenarios.

49

4.2 Stanislaus River Basin

During the months of December and January, the rain to snow transition in the

Stanislaus River Basin occurs near 1500 m in elevation. Snow covered area encompasses

43% of the basin’s total area during these months. In February and March, warmer

temperatures and less snowfall precipitation result in rain to snow transition occurring

near 1623 m and 2165 m respectively. During these months, 91% of the basin’s SCA

exists between 1700 and 2900 m. In April, warmer temperatures result in the snow line

rising to elevations above 2100 m. Only 21% of the basin’s total area is snow dominated

during the month of April.

Under a 0.5°C warming, 74.2 or 5% of the basin’s December to February

SCA would be at-risk of becoming rainfall dominated. Furthermore, approximately

0.01 of SWE would be potentially lost. Loss in SCA and SWE would be confined to

the lower elevations between 1500 and 1900 m. Historically, this elevation band is 68%

snow dominated. A 0.5°C warming would result in the region becoming 49% snow

dominated. Elevations above 1900 m would remain cold and unaffected by a 0.5°C

warming.

In March, under a 0.5°C warming, approximately 93.9 or 8% of the basin’s

SCA would be at-risk of becoming rainfall dominated (Table 4.1). Elevations between

1700 and 2100 m, historically 51% snow covered in March, would lose 92.5 of SCA

becoming 31% snow dominated. Almost all the loss in SCA and SWE would be occur at

elevations between 1700 and 2100 m. In April, a 0.5°C warming would result in

50

100.0 , or 14% of the basin’s snow covered region becoming rainfall dominated.

Most of the snow loss in April would occur at the higher elevations between 2100 and

2500 m.

A further 1°C warming would result in 147.6 or 10% of the basin’s

December to February SCA transition to a rainfall dominated regime (Table 4.1). The

greatest amount of loss in SCA and SWE would occur at elevations between 1700 and

1900 m (Figure 4.6a and Figure 4.8a). Average volume of SWE loss, from December to

February, would equal 0.02 ; roughly 76% of this loss would occur between 1700

and 1900 m (Figure 4.8a). During the early winter, from December to February, snow

dominated regions above 1900 m would remain unaffected by a 1°C warming.

In March, 209.7 or 17% of the basin’s SCA would be at-risk of becoming

rainfall dominated under a 1°C warming. All previously snow covered regions existing

between 1700 and 1900 m would become more rainfall dominated (Figure 4.6a).

Elevations between 1900 and 2100 m, historically 82% snow covered in March, would

lose 136.8 of SCA becoming only 29% snow covered (Figure 4.6a). A 1°C warming

in April would result in 194.7 or 26% of the basin’s previously SCA becoming

rainfall dominated (Table 4.6a). All previously snow covered regions between 2100 and

2300 m would become rainfall dominated (Figure 4.7a). The 2300 to 2500 m elevation

band, historically 95% snow covered in April, contains roughly 32% of the basin April

SCA. Following a 1°C increase in average April temperatures, 133.8 of previously

SCA would be lost (Figure 4.6a); only 41% of the region would still remain snow

51

covered. The greatest amount of snow loss in April would occur at elevations between

2300 and 2500 m (Figure 4.6a). Elevations above 2500 m would remain cold and

unaffected by a 1°C increase in temperature (Figure 4.6a). Cumulative volume of SWE

loss in April would equal 0.01 .

As temperatures rise from 1° to 2°C, all previously snow covered regions between

1500 and 1700 m would become rainfall dominated (Figure 4.7b). The 1700 to 1900 m

elevation band, historically 97% snow covered from December to February, contains

roughly 201.9 or 13% of the basin’s SCA. Following a 2°C increase in temperature,

178.9 of SCA would be lost (Figure 4.6a); only 30% of the region would still remain

snow covered. Average volume of SWE loss from December to February would equal

0.05 . Most of this loss in SWE (58%) would occur within the 1700 to 1900 m

elevation band (Figure 4.8b). Elevations above 2100 m would remain unaffected by a

2°C warming in December, January, or February (Figure 4.6a).

In March, 427.0 or 35% of the basin’s SCA would be at-risk of becoming

rainfall dominated. All previously snow covered regions existing between 1700 and 2100

m would become rainfall dominated (Figure 4.7b). The 2100 to 2300 m elevation band,

historically completely snow covered in March, would lose 306.7 of SCA becoming

38% snow covered (Figure 4.6b). Approximate volume of SWE loss in March would

equal 0.03 . Most of this loss would occur between 1900 and 2300 m (Figure 4.8b).

Elevations above 2300 m would remain unaffected by a 2°C warming in March (Figure

4.6b). In April however, following a 2°C warming, elevations between 2300 to 2500 m

52

would become completely rainfall dominated (Figure 4.6b). At-risk SCA within this

elevation band would increase from 133.8 to 235.3 Elevations between 2500

and 2700 m, historically completely snow covered in April, would lose 103.0 of

previously SCA becoming 54% snow dominated (Figure 4.6b). A 2°C warming in April

would contribute to 0.02 of SWE loss (Table 4.1).

Following a 3°C warming scenario, 527.4 or 35% of the basin’s December to

February SCA would be at-risk of becoming rainfall dominated. Furthermore,

approximately of SWE would be lost. The greatest loss of SCA and SWE, from

December to February, would occur between 1700 and 2100 m (Figure 4.6c and Figure

4.8c). At-risk SCA at the 1700 to 1900 m elevation band would increase from 178.9

to 197.2 (Figure 4.6b and Figure 4.6c). Elevations between 1900 and 2100 m,

historically 95% snow covered from December to February, would lose 204.2 of

SCA becoming 22% snow covered (Figure 4.6c). Elevations between 2100 to 2300 m

that were previously unaffected by a 2°C warming, between December and February,

would lose 82.4 of SCA (Figure 4.6c). Average volume of SWE loss within this

elevation band would equal 0.03 (Figure 4.8c). Elevations above 2300 m would

remain unaffected between December and February (Figure 4.6c).

A 3°C warming in March would result in 623.1 , or 51% of the basin’s

previously snow covered region becoming rainfall dominated. Elevations between 2100

and 2300 m would lose 295.4 of previously SCA becoming only 4% snow covered

(Figure 4.6c). Approximate volume of SWE loss in March would equal 0.06 (Table

53

4.1). Most of the SWE loss (75%) would occur within the basin’s mid-elevations,

between 2100 and 2500 m (Figure 4.8c).

In April, 553.2 or 75% of the basin’s previously SCA would be at-risk of

becoming rainfall dominated. Elevations between 2500 and 2700 m historically contain

31% of the basin’s April SCA. Under a 3°C warming scenario, this region would become

completely rainfall dominated (Figure 4.7c). Higher elevations between 2700 and 2900

m, completely snow covered in April, would lose 28.6 of SCA becoming 78% snow

dominated (Figure 4.7c). Approximate volume of SWE loss in April would equal

0.03 (Table 3-2). Most of this loss in SWE (86%) would occur within the mid-

elevations, between 2300 and 2700 m (Figure 4.8c). Elevations above 2900 m would

remain cold and unaffected by a 3°C warming in April (Figure 4.7c).

An extreme 4°C warming would result in 756.4 or 50% of the basin’s

December to February SCA becoming rainfall dominated (Table 4.1). All previously

snow covered regions existing between 1900 and 2100 m would become rainfall

dominated (Figure 4.7d). The 2100 to 2300 m elevation band, historically completely

snow covered, contains 21% of the basin’s December to March SCA. Following a 4°C

warming, 229.0 or 75% of the region’s previously snow covered area would become

rainfall dominated (Figure 4.6d). Elevations above 2300 m, previously unaffected from

December to February, would lose an average of 23.3 of SCA (Figure 4.6d).

Average volume of SWE loss from December to February would equal 0.14 . Most

of the SWE loss, under a 4°C warming, would occur at elevations between 1700 and

54

2300 m (Figure 4.8d). Snow dominated regions above 2500 m would remain unaffected

by a 4°C increase in temperature (Figure 4.6d).

In March, 855.4 or 69% of the basin’s March SCA would be at-risk of

becoming rainfall dominated. Roughly 88% of the snow loss in March would occur

between 1900 and 2500 m (Figure 4.6d). The basin’s higher snow dominated regions

existing between 2300 and 2700 m would lose 311.9 of SCA becoming 35% snow

covered (Figure 4.6d). Approximate volume of SWE loss in March, following a 4°C

increase in temperature, would equal 0.10 with 70% of this loss occurring between

2100 and 2500 m (Figure 4.8d).

In April, 648.7 or 88% of the basin’s April SCA would be at-risk of

becoming rainfall dominated. Elevations between 2700 and 2900 m, historically

completely snow covered, would lose 123.4 of SCA becoming only 5% snow

covered (Figure 4.6d). Most of the snow loss in April, under a 4°C warming, (54%)

would occur at elevations between 2500 and 2900 m (Figure 4.8d). A 4°C warming in

April would contribute to 0.04 of SWE loss; most of this loss (69%) would occur

within the mid-elevations, between 2300 and 2700 m (Figure 4.8d). Snow dominated

regions above 2900 m would remain unaffected by a 4°C warming in April (Figure 4.6d).

55

Figure 4.6: Estimated loss of SCA within the Stanislaus River Basin, at the different

elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0

50

100

150

200

250

300

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

a)

0

50

100

150

200

250

300

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

b)

0

50

100

150

200

250

300

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

c)

0

50

100

150

200

250

300

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

1500 TO 1700

1700 TO 1900

1900 TO 2100

2100 TO 2300

2300 TO 2500

2500 TO 2700

2700 TO 2900

EL

EV

AT

ION

2

00

(M

ET

ER

S) d)

56

Figure 4.7: Estimated fractional loss of SCA within the Stanislaus River Basin, at the

different elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.

FR

AC

TIO

NA

L

SC

A

MONTH

a)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.FR

AC

TIO

NA

L S

CA

MONTH

b)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.

FR

AC

TIO

NA

L S

CA

MONTH

c)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.

FR

AC

TIO

NA

L

SC

A

MONTH

1500 TO 1700

1700 TO 1900

1900 TO 2100

2100 TO 2300

2300 TO 2500

2500 TO 2700

2700 TO 2900

2900 TO 3100 EL

EV

AT

ION

2

00

(M

ET

ER

S) d)

57

Figure 4.8: Estimated volume of SWE loss within the Stanislaus River Basin, at the

different elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

a)

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

b)

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

c)

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

1500 TO 1700

1700 TO 1900

1900 TO 2100

2100 TO 2300

2300 TO 2500

2500 TO 2700

2700 TO 2900

2900 TO 3100

3100 TO 3300 EL

EV

AT

ION

20

0 (

ME

TE

RS

) d)

58

MONTH

SNOWLINE ELEVATION (meters)

SCA

( )

SCA

(% of basin)

DEGREE

OF WARMING

(°C)

SCA LOST

(% of basin)

SCA LOST

)

SWE LOST

( )

DECEMBER 1518 1513.9 43.8 1 10% 147.3 0.02

2 20% 306.7 0.05

3 34% 520.9 0.09

4 50% 763.7 0.13

JANUARY 1518 1531.9 44.3 1 10% 147.3 0.03

2 19% 297.7 0.05

3 33% 502.9 0.10

4 48% 740.4 0.14

FEBRUARY 1623 1459.0 42.2 1 10% 148.1 0.02

2 22% 318.7 0.05

3 38% 558.5 0.10

4 52% 765.2 0.14

MARCH 1784 1231.3 35.6 1 17% 209.7 0.01

2 35% 427.0 0.03

3 51% 623.1 0.06

4 69% 855.4 0.10

APRIL 2165 735.1 21.3 1 26% 194.7 0.01

2 54% 399.1 0.02

3 75% 553.2 0.03

4 88% 648.7 0.04

Table 4.1: Warming Scenarios considered likely by the IPCC applied to the Stanislaus

River Basin for the months of December to April; area of at risk-snow, SCA, and volume

of SWE loss were then calculated.

MONTH

SNOWLINE ELEVATION

(meters)

SCA

(KM^2)

SCA

(% of basin)

DEGREE OF WARMING

(°C)

SCA LOST (KM^2)

SCA LOST

(% of basin)

SWE LOST

(KM^3)

DECEMBER 1518 1513.90 43.8 1 10% 147.33 0.023

2 20% 306.69 0.049

3 34% 520.92 0.088

4 50% 763.72 0.134

JANUARY 1518 1531.94 44.3 1 10% 147.33 0.026

2 19% 297.67 0.055

3 33% 502.88 0.096

4 48% 740.41 0.144

FEBRUARY 1623 1459.03 42.2 1 10% 148.08 0.025

2 22% 318.72 0.055

3 38% 558.50 0.098

4 52% 765.22 0.136

MARCH 1784 1231.27 35.6 1 17% 209.72 0.014

2 35% 426.96 0.031

3 51% 623.15 0.057

4 69% 855.42 0.101

APRIL 2165 735.15 21.3 1 26% 194.69 0.008

2 54% 399.15 0.019

3 75% 553.24 0.029

4 88% 648.71 0.036

59

ELEVATION (METERS) PERCENT OF BASIN

Below 1100 569.03 34.28%

1101 to 1300 102.98 6.20%

1301 to 1500 115.01 6.93%

1501 to 1700 107.49 6.48%

1701 to 1900 108.24 6.52%

1901 to 2100 120.27 7.25%

2101 to 2300 130.79 7.88%

2301 to 2500 130.79 7.88%

2501 to 2700 115.76 6.97%

2701 to 2900 88.70 5.34%

2901 to 3100 48.11 2.90%

3101 to 3300 20.30 1.22%

3301 to 3500 2.26 0.14%

Figure 4.9: Hypsometric curve of the Stanislaus River Basin showing distribution

of area with elevation. Roughly 56% of the basin’s area lies between 1300 and

2900 meters.

Z-min (m) =7 Z-max (m) = 3388

Drainage Area ( ) = 3455.51

Average Elevation (m) = 1346.48

Elev

atio

n (

met

ers)

Rel

ief

(met

ers)

60

Figure 4.10: “At-risk” snow covered area within the Stanislaus River Basin under

different warming scenarios.

61

4.3 Tuolumne River Basin

The rain to snow transition zone within the Tuolumne River Basin, from

December to February, occurs near 1400 m in elevation. Approximately 2,459.5 or

46% of the basin’s total area is snow covered during these months. In February and

March, warmer temperatures and less precipitation result in the rain to snow transition

occurring near 1532 m and 1728 m respectively. Roughly 84% of the basin’s SCA exists

at elevations between 1700 and 3100 m. By April, warmer precipitation and less

precipitation in the form of snow result in the rain to snow transition occurring near 2158

m in elevations. Only 29% of the basin’s total area is snow dominated in April.

Under a 0.5°C warming scenario, 164.9 or 7% of the basin’s December to

February SCA would be at-risk of becoming rainfall dominated. Furthermore,

approximately 0.03 of SWE would be potentially lost. The greatest loss of SCA and

SWE, under a 0.5°C warming, would occur at the lower elevations between 1500 and

1700 m. Elevations above 2100 m would remain unaffected by a 0.5°C warming.

In March, a 0.5°C warming would result in 121.0 or 6% of the basin’s March

SCA becoming rainfall dominated (Table 4.2). Elevations between 1700 and 2100 m,

historically 40% snow covered in March, would lose 103.7 of previously SCA

becoming 21% snow covered. Most of the snow loss in March would occur between

1900 and 2100 m. Similarly in April, 0.5°C warming would result in 121.8 or 8% of

the basin’s April SCA becoming rainfall dominated. Most of the snow loss in April

would occur at the higher elevations between 2100 and 2500 m.

62

A further 1°C warming would result in 332.2 or 13% of the basin’s

December to February SCA transition to a rainfall dominated regime (Table 4.2). The

greatest loss of SCA and SWE would occur at the lower elevations between 1500 and

1900 m (Figure 4.11a and Figure 4.13a). Average volume of SWE loss from December to

February would equal 0.05 . Roughly 83% of the loss in SWE would occur at

elevations between 1500 and 1900 m (Figure 4.13a). Snow dominated regions between

2100 and 2300 m, previously unaffected by a 0.5°C warming, would lose 6.50 of

SCA (Figure 4.11a). Higher elevations above 2300 m would remain unaffected by a 1°C

warming.

In March, 233.8 or 12% of the basin’s SCA would be at-risk of becoming

rainfall dominated. Elevations between 1700 and 1900 m, historically 10% snow

dominated in March, would lose 26.3 of SCA becoming completely rainfall

dominated (Figure 4.11a). Similarly in April, 221.0 or 14% of the basin’s April

SCA would be at-risk of becoming rainfall dominated (Table 4.2). All previously snow

covered regions between 2100 and 2300 m would become rainfall dominated (Figure

4.12a). The greatest amounts of snow loss in April would occur at elevations between

2300 to 2500 m (Figure 4.11a). Elevations above 2700 m would remain unaffected by a

1°C increase in temperature (Figure 4.11a).

In December, January, and February, an increase in average temperatures from 1°

to 2°C would result in all previously snow covered regions between 1500 and 1700 m

becoming rainfall dominated (Figure 4.12b). Elevations between 1700 and 1900 m,

63

historically 96% snow covered from December to February, contains 10% of the basin’s

SCA. Following a 2°C temperature increase, 241.8 of SCA would be lost; only 3%

of the region would still remain snow covered (Figure 4.11b). Average volume of SWE

loss from December to February would equal 0.10 . Most of the loss in SCA and

SWE would occur between 1700 and 1900 m (Figure 4.11b and Figure 4.13b). Higher

elevations above 2300 m would remain cold and unaffected by a 2°C warming between

December and February (Figure 4.11b).

In March following a 2°C temperature increase, all previously snow dominated

regions between 1700 and 2100 m would become rainfall dominated (Figure 4.12b). In

addition, elevations between 2100 and 2500 m would lose 251.8 or 46% of region’s

previously SCA (Figure 4.11b). Average volume of SWE loss in March would equal

0.03 ; most of this loss would occur between 1900 and 2500 m (Figure 4.13b).

Higher elevations above 2500 m would remain unaffected by a 2°C warming in March.

In April, a 2°C warming would result in approximately 441.2 or 29% of the

basin’s previously SCA becoming rainfall dominated. All previously snow covered

regions between 2100 and 2500 m would become rainfall dominated (Figure 4.12b). The

greatest loss of SCA and SWE would occur between 2300 and 2500 m (Figure 4.11b and

Figure 4.13b). The higher elevations between 2700 and 2900 m, historically completely

snow dominated in April, would lose 15.0 of SCA (Figure 4.11b). A 2°C warming

in April would contribute to 0.01 of SWE loss; roughly 80% of this loss would

occur between 2300 and 2500 m (Figure 4.13b).

64

Following a 3°C warming scenario, 853.9 or 35% of the basin’s December to

February SCA would be at-risk of becoming rainfall dominated. Average volume of

SWE loss would equal . Most of the loss in SCA and SWE, from December to

February, would occur between 1700 and 2100 m (Figure 4.11c and Figure 4.13c).

Elevations between 1900 and 2100 m, historically 98% snow covered from December to

February, would remain only 3% snow dominated (Figure 4.12c).

In March following a 3°C warming, all snow covered regions between 1700 and

2300 m would become rainfall dominated (Figure 4.12c). Higher elevations between

2300 and 2500 m would lose 164.6 or 57% of the regions historically SCA (Figure

4.11c). A 3°C warming in March would contribute to 0.03 of SWE loss with most of

this loss occurring between 2100 and 2500 m (Figure 4.13c).

In April following 3°C warming, 712.6 or 46% of the basin’s April SCA

would be at-risk of becoming rainfall dominated. The higher elevations between 2500

and 2900 m, historically completely snow covered in April, would lose 300.7 of

SCA becoming only 5% snow covered (Figure 4.11c). Approximate volume of SWE loss

in April, following a 3°C warming, would equal 0.02 . The greatest volume of SWE

loss would occur within the 2700 to 2900 m elevation band (Figure 4.13c).

Under an extreme 4°C increase in winter temperatures, all previously snow

covered regions between 1900 and 2100 m would become rainfall dominated (Figure

4.12d). Elevations between 2100 and 2300 m, historically completely snow covered from

December to March, would lose 234.0 of SCA becoming 9% snow covered (Figure

65

4.11d). Elevations above 2500 m, previously unaffected by a 3°C increase in

temperatures, show increased vulnerability to snow loss under a 4°C warming scenario

(Figure 4.11d). Average volume of SWE loss from December to February would equal

0.18 . The greatest volume of SWE loss during these months would occur between

1700 and 2300 m (Figure 4.13d). Snow dominated regions above 2700 m would remain

unaffected by a 4°C warming between December and February (Figure 4.11d)

In March, a 4°C warming would result in 872.0 or 43% of the basin’s

historical March SCA becoming rainfall dominated. Most of the loss in SCA (83%)

would occur between 1900 and 2500 m (Figure 4.11d). The basin’s higher elevations

between 2500 and 2900 m, historically completely snow covered in March, would lose

121.8 of SCA, becoming 82% snow covered (Figure 4.11d). Approximate volume of

SWE loss in March would equal 0.05 with most of this loss (81%) occurring

between 2100 and 2700 m (Figure 4.13d).

In April, 1080.2 or 70% of the basin’s April SCA would be at-risk of

becoming rainfall dominated following a 4°C warming. Elevations between 2700 and

3100 m contain 43% of the basin’s April SCA and are historically completely snow

covered. Following a 4°C warming, 462.3 of SCA within this band would be lost

(Figure 4.11d); only 31% of the region would still remain snow covered. Most of the

snow loss in April (81%) would occur at elevations between 2300 and 2900 m (Figure

4.11d). The cumulative volume of SWE loss in April would equal 0.05 with most of

this loss occurring between 2700 and 3100 m (Figure 4.13d).

66

Figure 4.11: Estimated loss of SCA within the Tuolumne River Basin, at the different

elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0

50

100

150

200

250

300

350

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

a)

050

100150200250300350

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

b)

050

100150200250300350

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

c)

0

50

100

150

200

250

300

350

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

1300 TO 15001500 TO 17001700 TO 19001900 TO 21002100 TO 23002300 TO 25002500 TO 27002700 TO 29002700 TO 2900

EL

EV

AT

ION

20

0 (

ME

TE

RS

) d)

67

Figure 4.12: Estimated fractional loss of SCA within the Tuolumne River Basin, at the

different elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.

FR

AC

TIO

NA

L S

CA

MONTH

a)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.

FR

AC

TIO

NA

L S

CA

MONTH

b)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.FR

AC

TIO

NA

L S

CA

MONTH

c)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.FR

AC

TIO

NA

L S

CA

MONTH

1300 TO 15001500 TO 17001700 TO 19001900 TO 21002100 TO 23002300 TO 25002500 TO 27002700 TO 29002900 TO 31003100 TO 3300 E

LE

VA

TIO

N 2

00

(M

ET

ER

S) d)

68

Figure 4.13: Estimated volume of SWE loss within the Tuolumne River Basin, at the

different elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

a)

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

b)

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

c)

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

1500 TO 1700

1700 TO 1900

1900 TO 2100

2100 TO 2300

2300 TO 2500

2500 TO 2700

2700 TO 2900

2900 TO 3100

3100 TO 3300

EL

EV

AT

ION

20

0 (

ME

TE

RS

) d)

69

MONTH

SNOWLINE ELEVATION

(meters)

SCA

( )

SCA

(% of basin)

DEGREE OF WARMING

(°C)

SCA LOST

( )

SCA LOST (% of basin)

SWE LOST

( )

DECEMBER 1398 2446.7 45.5 1 14% 353.3 0.06

2 26% 637.4 0.12

3 37% 899.8 0.17

4 45% 1102.7 0.21

JANUARY 1359 2549.0 47.4 1 14% 362.3 0.05

2 25% 646.4 0.10

3 36% 920.8 0.15

4 44% 1113.2 0.19

FEBRUARY 1532 2382.8 44.3 1 12% 285.6 0.04

2 22% 525.4 0.07

3 31% 745.7 0.11

4 40% 950.9 0.14

MARCH 1728 2004.0 37.3 1 12% 233.8 0.01

2 23% 466.8 0.02

3 33% 671.3 0.03

4 44% 872.0 0.05

APRIL 2158 1537.2 28.6 1 14% 221.0 0.01

2 29% 441.2 0.01

3 46% 712.6 0.02

4 70% 1080.2 0.05

Table 4.2: Warming Scenarios considered likely by the IPCC applied to the

Tuolumne River Basin for the months of December to April; area of at risk-snow,

SCA, and volume of SWE loss were then calculated.

70

ELEVATION (METERS) PERCENT OF BASIN

Below 1100 2116.00 39.37%

1101 to 1300 276.62 5.15%

1301 to 1500 317.21 5.90%

1501 to 1700 348.78 6.49%

1701 to 1900 259.33 4.82%

1901 to 2100 266.10 4.95%

2101 to 2300 257.83 4.80%

2301 to 2500 288.65 5.37%

2501 to 2700 314.96 5.86%

2701 to 2900 345.78 6.43%

2901 to 3100 323.98 6.03%

3101 to 3300 159.36 2.96%

3301 to 3500 75.17 1.40%

3501 to 3700 21.80 0.41%

3701 to 3900 3.76 0.07%

Rel

ief

(met

ers)

Elev

atio

n (

met

ers)

Z-min (m) =11 Z-max (m) = 3789

Drainage Area ( ) = 5357.33

Average Elevation (m) = 1501.77

Figure 4.14: Hypsometric curve of the Tuolumne River Basin showing

distribution of area with elevation. Approximately 56% of the basin’s area lies

above 1300 meters.

71

Figure 4.15: “At-risk” snow covered area within the Tuolumne River Basin under

different warming scenarios.

72

4.4 Merced River Basin

The rain to snow transition zone within the Merced River Basin, during the

months of December and January occurs near 1500 m. In February, slightly warmer

temperatures and less precipitation result in the rain to snow transition shifting higher to

1624 m. From December to March, approximately 32% of the basin’s total area is snow

covered. Furthermore, roughly 82% of the basin’s SCA exists at the higher elevations

above 2100 m. In April, warmer temperatures and less precipitation in the form of

snowfall result in the rain to snow transition occurring near 2283 m in elevations.

Roughly 20% of the basin’s total area is snow covered in April.

Under a 0.5°C warming scenario, 56.4 or 5% of the basin’s December to

February SCA would be at-risk of becoming rainfall dominated. Furthermore,

approximately 0.08 of SWE would be lost. The greatest loss in SCA and SWE

would occur between 1900 and 2100 m. During the early winter, from December to

February, elevations above 2100 m would remain cold and unaffected by a 0.5°C

increase in average winter temperatures.

In March and April however, warmer temperatures and less precipitation result in

the rain to snow transition occurring near 1900 and 2300 m respectively. A 0.5°C

warming in March would result in 90.9 or 8% of the basin’s previously SCA

becoming more rainfall dominated. Elevations between 1900 and 2300 m, roughly 61%

snow covered in March, would become only 37% snow dominated. In April, a 0.5°C

warming would result in 106.7 or 15% of the basin’s April SCA becoming rainfall

73

dominated. The basin’s higher elevations between 2300 and 2700 m would lose

104.5 of SCA. Historically this elevation band is 57% snow dominated. Following a

0.5°C increase in average temperatures, only 35% of the region would still remain snow

dominated.

A further 1°C warming would result in 119.5 or 9% of the basin’s December

to February SCA transition to a rainfall dominated precipitation regime (Table 4.3).

Lower elevations between 1500 and 1900 m would lose roughly 21.3 or 72% of the

regions previously SCA (Figure 4.16a). The greatest loss of SCA and SWE, following a

1°C warming, would occur between 1900 and 2100 m (Figure 4.16a and Figure 4.18a).

Average volume of SWE loss from December to February would equal of 0.02 .

Most of this loss (67%) would occur at the mid-elevations between 1900 and 2100 m

(Figure 4.18a). Higher elevations above 2300 m would remain unaffected by a 1°C

warming earlier in the winter, between December and February (Figure 4.16a).

A 1°C warming in March would result in 212.0 or 18% of the basin’s

previously SCA becoming rainfall dominated (Table 4.3). The greatest loss of SCA

(78%) would occur between 2100 and 2300 m (Figure 4.16a). The basin’s higher

elevations between 2300 and 2500 m that are historically completely snow covered in

March would lose 26.3 of previously SCA (Figure 4.16a). Average volume of SWE

loss in March, following a 1°C warming, would equal 0.01 .

In April, following a 1°C warming, 203.0 or 28% of the basin’s SCA would

be at-risk of becoming rainfall dominated (Table 4.3). All previously snow covered

74

regions between 2300 and 2500 m would become rain dominated (Figure 4.17a). Most of

the at-risk SCA in April under a 1°C warming (62%) lies between 2500 and 2700 m

(Figure 4.16a). Volume of SWE loss in April would equal 0.01 most of this loss

would occur between 2300 and 2700 m (Figure 4.18a).

As temperatures increase from 1° to 2°C, almost all previously snow covered

regions between 1500 and 2100 m would become rainfall dominated (Figure 4.17b). The

2100 to 2300 m elevation band, historically 98% snow covered from December to March,

contains roughly 226.0 or 18% of the basin’s SCA. Following a 2°C increase in

average temperatures, only 26% of the region would still remain snow dominated.

Average volume of SWE loss from December to February would equal 0.05 . Most of

this SWE loss (from December to February) would occur between 2100 and 2300 m

(Figure 4.18b).

In March following a 2°C warming, approximately 454.0 of previously SCA

would be at-risk of becoming rainfall dominated. All snow covered regions between

1900 and 2300 m would become rainfall dominated (Figure 4.16b). The greatest loss of

SCA would occur between 2100 and 2500 m (Figure 4.16b). The cumulative volume of

SWE loss in March would equal 0.02 ; roughly 55% of this loss would occur at the

mid-elevations, between 2300 and 2500 m (Figure 4.18b).

In April, following a 2°C increase in average temperatures, 377.3 or 53% of

the basin’s previously SCA would become rainfall dominated. Elevations between 2500

and 2900 m, historically 97% snow covered, would lose 302.9 of SCA becoming

75

only 22% snow dominated (Figure 4.16b). Volume of SWE loss in April, under a 2°C

warming, would equal 0.02 ; roughly 68% of this loss would occur between 2500

and 2700 m (Figure 4.18b).

With a 3°C increase in winter temperatures, 549.0 or 42% of the basin’s

December to February SCA would become rainfall dominated. The 2100 to 2300 m

elevation band would lose roughly 213.5 , or 95% of the region’s historical SCA

(Figure 4.16c). Approximately volume of SWE loss would equal 0.10 with the

greatest loss occurring within the mid-elevations, between 2100 and 2300 m (Figure

4.18c).

In March, a 3°C increase in average winter temperatures would result in the at-

risk SCA increasing from 454.0 to 640.4 . Most of the loss in SCA (94%) would

occur between 2100 and 2700 m (Figure 4.16c). The higher elevations between 2300 and

2700 m would lose 395.4 of SCA remaining only 10% snow dominated (Figure

4.16c).

A 3°C warming in April would result in 536.7 or 75% of the basin’s

previously SCA becoming rainfall dominated. Roughly 72% of the snow loss in April

would occur between 2500 and 2900 m (Figure 4.16c). All previously snow dominated

regions between 2500 and 2900 m would become rainfall dominated (Figure 4.17c). The

cumulative volume of SWE loss in April would equal 0.03 ; approximately 42% of

this loss would occur between 2500 and 2700 m (Figure 4.18c).

76

Under an extreme 4°C warming, 224.0 or 98% of basin’s previously SCA

that lies between 2100 and 2300 m would become rainfall dominated (Figure 4.16d).

Elevations between 2300 and 2500 m, historically completely snow covered from

December to March, would lose 247.8 of SCA becoming only 6% snow dominated

(Figure 4.16d). Average volume of SWE loss from December to February would equal

0.14 . Most of the loss in SWE (66%) would occur within the mid-elevations,

between 2100 and 2500 m (Figure 4.18d). Snow dominated regions above 2900 m would

remain cold and unaffected by a 4°C warming between December and February (Figure

4.16d).

In March under a 4°C increase in average temperatures, 807.3 or 70% of the

basin’s previously SCA would become rainfall dominated. Most of the snow loss in

March would occur between 2100 and 2700 m (Figure 4.16d). Elevations between 2500

and 2700 m, almost completely snow dominated in March, would lose 117.8 of

SCA, becoming only 46% snow covered (Figure 4.16d). Approximate volume of SWE

loss in March would equal 0.06 most of this loss (82%) would occur between 2300

and 2900 m (Figure 4.18d).

In April under a 4°C warming scenario, 645.7 or 90% of the basin’s

previously SCA would become rainfall dominated. The basin’s higher elevations,

between 2300 and 2900 m that have historically been completely snow dominated would

transition to a more rain dominated precipitation regime (Figure 4.17d). Most of the snow

loss in April (81%) would occur at mid-elevations between 2500 and 3100 m (Figure

77

4.16d). The cumulative volume of SWE loss in April would equal 0.04 ; most this

loss would also occur within the basin’s mid-elevations, between 2500 and 2700 m

(Figure 4.18d).

78

Figure 4.16: Estimated loss of SCA within the Merced River Basin, at the different

elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

a)

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR APR

SC

A L

oss

(k

m²)

MONTH

b)

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

c)

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

1500 TO 1700

1700 TO 1900

1900 TO 2100

2100 TO 2300

2300 TO 2500

2500 TO 2700

2700 TO 2900

2900 TO 3100

3100 TO 3300

EL

EV

AT

ION

20

0 (

ME

TE

RS

)

d)

79

Figure 4.17: Estimated fractional loss of SCA within the Merced River Basin, at the

different elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.FR

AC

TIO

NA

L S

CA

MONTH

a)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.FR

AC

TIO

NA

L

SC

A

MONTH

b)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.FR

AC

TIO

NA

L

SC

A

MONTH

c)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.FR

AC

TIO

NA

L

SC

A

MONTH

1500 TO 17001700 TO 19001900 TO 21002100 TO 23002300 TO 25002500 TO 27002700 TO 29002900 TO 3100

d)

EL

EV

AT

ION

20

0 (

ME

TE

RS

)

80

Figure 4.18: Estimated volume of SWE loss within the Merced River Basin, at the

different elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

a)

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

b)

00.010.020.030.040.050.06

DEC. JAN. FEB MAR APR.SW

E L

oss

(k

m³)

MONTH

c)

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

1500 TO 17001700 TO 19001900 TO 21002100 TO 23002300 TO 25002500 TO 27002700 TO 29002900 TO 31003100 TO 3300 E

LE

VA

TIO

N

20

0 (

ME

TE

RS

) d)

81

MONTH

SNOWLINE ELEVATION (meters)

SCA

( )

SCA

(% of basin)

DEGREE OF WARMING

(°C)

SCA LOST

(% of basin)

SCA LOST

)

SWE LOST

( )

DECEMBER 1503 1271.1 35.0 1 10% 125.5 0.02

2 28% 350.3 0.06

3 47% 593.1 0.11

4 61% 780.2 0.15

JANUARY 1477 1285.4 35.4 1 9% 113.5 0.02

2 24% 302.2 0.05

3 42% 542.0 0.10

4 55% 709.6 0.14

FEBRUARY 1624 1304.2 35.9 1 9% 119.5 0.02

2 21% 268.3 0.05

3 40% 517.2 0.10

4 55% 711.9 0.13

MARCH 1871 1157.6 31.9 1 18% 213.5 0.01

2 39% 455.5 0.02

3 55% 642.0 0.04

4 70% 808.9 0.06

APRIL 2283 716.4 19.7 1 28% 203.0 0.01

2 53% 379.7 0.02

3 75% 539.0 0.03

4 90% 648.0 0.04

Table 4.3: Warming Scenarios considered likely by the IPCC applied to the Merced

River Basin for the months of December to April; area of at risk-snow, SCA, and

volume of SWE loss were then calculated.

82

ELEVATION (METERS) PERCENT OF BASIN

Below 1100 1731.14 47.63%

1101 to 1300 185.67 5.11%

1301 to 1500 147.33 4.05%

1501 to 1700 144.32 3.97%

1701 to 1900 126.28 3.47%

1901 to 2100 141.32 3.89%

2101 to 2300 231.52 6.37%

2301 to 2500 269.86 7.43%

2501 to 2700 220.24 6.06%

2701 to 2900 182.66 5.03%

2901 to 3100 136.06 3.74%

3101 to 3300 69.91 1.92%

3301 to 3500 33.07 0.91%

3501 to 3700 12.78 0.35%

3701 to 3900 2.26 0.06%

Elev

atio

n (

met

ers)

Rel

ief

(met

ers)

Z-min (m) =20 Z-max (m) = 3768

Drainage Area ( ) = 3633.66

Average Elevation (m) = 1399.95

Figure 4.19: Hypsometric curve of the Merced River Basin showing distribution

of area with elevation. Approximately 47% of the basin’s area exists above

1300 meters.

83

Figure 4.20: “At-risk” snow covered area within the Merced River Basin under

different warming scenarios.

84

4.5 Upper San Joaquin River Basin

From December to February, the rain to snow transition in the Upper San Joaquin

River Basin occurs near 1500 m. Warmer temperatures and less precipitation in March

and April result in the rain to snow transition rising to 1879 m, and 2336 m respectively.

Roughly 57% of the basin’s total area is snow covered during the winter months.

Furthermore, most of the basin’s SCA (97%) exists at the higher elevations above 1900

m.

Under a 0.5°C warming scenario, 180.6 or 6% of the basin’s December to

February SCA would be at-risk of becoming rainfall dominated (Table 4.4). The

cumulative volume of SWE loss would equal 0.03 . The largest amount of loss in

SCA and SWE would occur within the mid-elevations between 1900 to 2100 m. During

the early winter, from December to February, elevations above 2300 m would remain

cold and unaffected by a 0.5°C increase in average winter temperatures.

In March however, a 0.5°C warming would result in or 8% of the

basin’s previously SCA becoming rainfall dominated (Table 4.4). Elevations between

1900 and 2100 m would lose of SCA becoming only 5% snow dominated.

Most of the snow loss in March would occur at elevations between 1900 and 2500 m.

In April under a 0.5°C warming scenario, 174.4 or 9% of the basin’s

previously SCA would become rainfall dominated. Elevations between 2300 and 2500 m

would lose 56.4 of SCA becoming only 3% snow dominated. The 2500 to 2700 m

elevation band, historically 75% snow dominated, would lose 102.3 of SCA

85

becoming only 47% snow dominated. The basin’s higher elevations above 2900 m would

remain cold and unaffected by a 0.5°C increase in average winter temperatures.

A further 1°C warming would result in 335.7 or 11% of the basin’s

December to February SCA transition to a rainfall dominated regime (Table 4.4). The

basin’s lower elevations, between 1500 and 1900 m, historically 6% snow dominated,

would lose 21.8 of SCA becoming only 2% snow dominated (Figure 4.21a). Most of

the snow loss from December to February (92%) would occur between 1900 and 2300 m

(Figure 4.22a). At-risk SCA within the 1500 to 1900 m elevation band would increase

from 165.4 to 311.7 (Figure 4.21a). The basin’s higher elevations above 2300

m would remain cold and unaffected by a 1°C warming earlier in the winter, between

December and February (Figure 4.21a).

In March, a 1°C increase in average temperatures would result in 366.8 , or

14% of the basin’s previously SCA becoming rainfall dominated. Elevations between

2100 and 2300 m, historically 66% snow dominated, would lose 204.4 of SCA

becoming only 18% snow dominated. Most of the snow loss in March would occur

between 1900 and 2500 m (Figure 4.21a). The cumulative volume of SWE loss in March

would equal 0.02 with 92% of this loss occurring within the mid-elevations,

between 2100 and 2500 m (Figure 4.23a). Elevations above 2500 m would remain cold

and experience insignificant snow loss in March (Figure 4.21a).

Under a 1°C warming in April, at-risk SCA within the 2500 to 2700 m elevation

band would increase from 102.2 to 190.2 (Figure 4.21a); only 22% of the

86

region would still remain snow covered. The cumulative volume of SWE loss in April

would equal 0.01 with most of this loss occurring at the mid-elevations between

2500 and 2900 m (Figure 4.23a).

As winter temperatures further increase by 2°C, elevations between 1500 and

1700 m would become completely rainfall dominated (Figure 4.22b). Furthermore, at-

risk SCA at the 1700 to 2100 m elevation band would increase from 180.1 to

213.7 (Figure 4.21b). From December to February, elevations between 2100 and

2500 m, historically completely snow covered, would lose 381.4 of previously SCA;

only 51% of the region would still remain snow covered (Figure 4.21b). Average volume

of SWE loss in December, January, or February would equal 0.01 (Table 4.4).

In March, 704.3 or 27% of the basin’s previously SCA would be at-risk of

becoming rainfall dominated following a 2°C warming. Elevations between 1900 and

2300 m would lose all previously SCA (Figure 4.22b). The higher elevations between

2300 and 2700 m contain roughly 27% of the basin’s SCA and are historically

completely snow covered. Following a 2°C warming in March, 360.8 of previously

SCA would be lost (Figure 4.21b); only 49% of the region would still remain snow

covered. Volume of SWE loss in March would equal 0.04 with most of this loss

occurring within the mid-elevations, between 2100 and 2700 m (Figure 4.23b).

In April, 626.9 or 34% of the basin’s SCA would become rainfall dominated

following a 2°C warming. All previously snow covered regions between 2500 and 2700

m would become rainfall dominated (Figure 4.22b). The basin’s higher elevations

87

between 2700 and 2900 m would lose of SCA becoming 37% snow covered

(Figure 4.21b). The cumulative volume of SWE loss in April would equal 0.03 with

most of this loss occurring within the mid-elevations, between 2700 and 2900 m (Figure

4.23b).

Following a 3°C warming scenario, all previously snow covered regions between

1700 and 1900 m would become rainfall dominated (Figure 4.22c). From December to

February, elevations between 1900 and 2300 m would lose 608.1 of SCA becoming

only 6% snow covered (Figure 4.21c). The cumulative volume of SWE loss would equal

0.18 . The greatest amount of loss in SCA and SWE would occur within the mid-

elevations, between 1900 and 2500 m (Figure 4.21c and Figure 4.23c).

In March, 1044.1 or 40% of the basin’s previously SCA would become

rainfall dominated following a 3°C warming (Table 4.4). Elevations between 2300 and

2700 m would lose all previously SCA (Figure 4.22c). The basin’s higher elevations

between 2700 and 2900 m contain 15% of the regions SCA and are historically

completely snow covered. Following a 3°C warming in March, 106.0 of previously

SCA within this band would be lost (Figure 4.21c); roughly 73% of the region would

remain snow covered. Cumulative volume of SWE loss in March would equal 0.07 ;

most of this loss would occur between 2300 and 2700 m (Figure 4.23c).

In April following a 3°C warming, at-risk SCA at the 2700 to 2900 m elevation

band would increase from 250.3 to 388.6 (Figure 4.21c). Elevations between

2900 and 3100 m, historically completely snow covered in April, would lose 177.4

88

of SCA becoming 47% snow dominated (Figure 4.21c). The cumulative volume of SWE

loss in April would equal 0.04 (Table 4.4). Most of the loss in SWE (73%) would

occur within the basin’s mid-elevations, between 2500 and 2900 m (Figure 4.23c). Snow

dominated regions existing above 3100 m would remain unaffected by a 3°C warming in

April (Figure 4.21c).

Under an extreme 4°C warming, all previously snow covered regions between

1900 and 2100 m would become rainfall dominated (Figure 4.22d). From December to

February, elevations between 2100 and 2500 m would lose 736.9 of SCA, becoming

only 7% snow covered (Figure 4.21d). Average volume of SWE loss from December to

February would equal 0.25 . Most of the loss in SWE (57%) would occur within the

basin’s mid-elevations, between 2100 and 2500 m (Figure 4.23d). Snow dominated

regions above 2900 m would remain cold and unaffected by a 4°C warming earlier in the

winter, between December and February (Figure 4.21d).

In March, 1044.1 or 40% of the basin’s previously SCA would become

rainfall dominated following a 4°C increase in average temperatures. Most of this loss

(52%) would occur within the mid-elevations, between 2300 and 2700 m (Figure 4.21d).

Elevations between 2500 and 2700 m would lose 345.8 of SCA becoming only 3%

snow covered (Figure 4.21d). The approximate volume of SWE loss in March would

equal 0.11 . Roughly 82% of this loss in SWE would occur within the mid-elevations,

between 2300 and 2900 m (Figure 4.23d).

89

In April, a 4°C increase in average temperatures would result in 1200.4 or

64% of the basin’s previously SCA becoming rainfall dominated. All previously snow

dominated regions between 2700 and 2900 m would become rainfall dominated (Figure

4.22d). Elevations between 2900 and 3300 m would lose 460.0 of previously SCA

becoming only 27% snow covered (Figure 4.21d). A 4°C warming in April would result

in 0.06 of SWE loss with most of this loss occurring between 2700 and 3100 m

(Figure 4.23d).

90

Figure 4.21: Estimated loss of SCA within the Upper San Joaquin River Basin, at the

different elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

a)

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

b)

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

c)

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

1500 TO 1700

1700 TO 1900

1900 TO 2100

2100 TO 2300

2300 TO 2500

2500 TO 2700

2700 TO 2900

2900 TO 3100

3100 TO 3300

d)

EL

EV

AT

ION

20

0 (

ME

TE

RS

)

91

Figure 4.22: Estimated factional loss of SCA within the Upper San Joaquin River Basin,

at the different elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming

scenario.

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.

FR

AC

TIO

NA

L S

CA

MONTH

a)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.

FR

AC

TIO

NA

L S

CA

MONTH

b)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.

FR

AC

TIO

NA

L S

CA

MONTH

c)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.

FR

AC

TIO

NA

L S

CA

MONTH

1500 TO 17001700 TO 19001900 TO 21002100 TO 23002300 TO 25002500 TO 27002700 TO 29002900 TO 31003100 TO 3300

EL

EV

AT

ION

20

0 (

ME

TE

RS

) d)

92

Figure 4.23: Estimated volume of SWE loss within the Upper San Joaquin River Basin, at

the different elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming

scenario.

0

0.02

0.04

0.06

0.08

0.1

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

a)

0

0.02

0.04

0.06

0.08

0.1

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

b)

0

0.02

0.04

0.06

0.08

0.1

DEC. JAN FEB MAR APR

SW

E L

oss

(k

m³)

MONTH

c)

0

0.02

0.04

0.06

0.08

0.1

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

1500 TO 17001700 TO 19001900 TO 21002100 TO 23002300 TO 25002500 TO 27002700 TO 29002900 TO 31003100 TO 3300

EL

EV

AT

ION

20

0 (

ME

TE

RS

) d)

93

MONTH

SNOW LINE ELEVATION

(meters)

SCA

( )

SCA

(% of basin)

DEGREE OF WARMING

(°C)

SCA

LOST (% of basin)

SCA

LOST

( )

SWE LOST

( )

DECEMBER 1530 2939.9 62.9 1 12% 352.5 0.06

2 22% 647.2 0.11

3 34% 1012.5 0.18

4 41% 1194.9 0.26

JANUARY 1530 2931.6 62.7 1 11% 329.2 0.07

2 19% 571.3 0.12

3 31% 897.5 0.19

4 42% 1244.8 0.27

FEBRUARY 1530 2963.2 63.4 1 11% 325.5 0.06

2 21% 609.6 0.11

3 32% 937.4 0.17

4 43% 1271.9 0.22

MARCH 1879 2591.8 55.5 1 14% 366.8 0.02

2 27% 704.3 0.04

3 40% 1044.1 0.07

4 52% 1346.3 0.11

APRIL 2336 1870.2 40.0 1 17% 313.4 0.01

2 34% 626.9 0.02

3 49% 924.6 0.04

4 64% 1200.4 0.06

Table 4.4: Warming Scenarios considered likely by the IPCC applied to the Upper

San Joaquin River Basin for the months of December to April; area of at risk-snow,

SCA, and volume of SWE loss were then calculated.

94

ELEVATION (METERS) PERCENT OF BASIN

Below 1100 778.75 16.66%

1101 to 1300 181.91 3.89%

1301 to 1500 180.41 3.86%

1501 to 1700 250.31 5.36%

1701 to 1900 245.80 5.26%

1901 to 2100 352.54 7.54%

2101 to 2300 424.70 9.09%

2301 to 2500 366.07 7.83%

2501 to 2700 357.80 7.66%

2701 to 2900 399.15 8.54%

2901 to 3100 333.75 7.14%

3101 to 3300 297.67 6.37%

3301 to 3500 287.15 6.14%

3501 to 3700 169.13 3.62%

3701 to 3900 43.60 0.93%

3901 to 4100 4.51 0.10%

Rel

ief

(met

ers)

Elev

atio

n (

met

ers)

Z-min (m) =172 Z-max (m) = 4037

Drainage Area ( ) = 4673.25

Average Elevation (m) = 2163.97

Figure 4.24: Hypsometric curve of the Upper San Joaquin River Basin showing

distribution of area with elevation. Approximately 80% of the basin’s area

exists above 1300 m.

95

Figure 4.25: “At-risk” snow covered area within the Upper San Joaquin River

Basin under different warming scenarios.

96

4.6 Kings River Basin

Within the Kings River Basin, the rain to snow transition zone from December to

February occurs at elevations between 1500 and 1600 m. In March and April, slightly

warmer temperatures and increased snowmelt result in the rain to snow transition

occurring at 1768 m, and 2167m respectively. Average SCA from December to March

encompasses 3008.3 or 69% of the basin’s total area. Compared to other basins in

the San Joaquin Watershed, roughly 94% of the basin’s total SCA exists at the higher

elevations, between 1900 and 3700 m.

Under a 0.5°C warming scenario, or 3% of the basin’s December to

February SCA would be at-risk of becoming rainfall dominated (Table 4.5). Average

volume of SWE loss would equal 0.02 . The greatest loss of SCA and SWE would

occur at elevations between 1700 and 2100 m. During the early winter, from December to

February, elevations above 2100 m would remain cold and experience insignificant loss

in SCA or SWE loss.

In March however, a 0.5°C warming would result in , or 4% of the

basin’s previously SCA becoming rainfall dominated (Table 4.5). Elevations between

1700 and 2100 m, historically 28% snow dominated, would lose 71.4 of SCA

becoming only 12% snow dominated. Most of the snow loss in March would occur

between 1700 and 2300 m.

In April, a 0.5°C warming would result in the loss of 127.8 of previously

SCA. Elevations between 2100 and 2500 m, historically 38% snow dominated in April,

97

would lose 239.8 of previously SCA; only 20% of the region would still remain

snow covered. The cumulative volume of SWE loss in April would equal 0.01 with

most of this loss occurring within the mid-elevations, between 2300 and 2500 m.

A further 1°C warming would result in 215.2 , or 7% of the basin’s

December to February SCA transitioning to a rainfall dominated precipitation regime

(Table 4.5). All previously snow covered regions between 1500 and 1700 m would

become rainfall dominated (Figure 4.27a). Elevations between 1700 and 1900 m,

historically 47% snow covered, would lose roughly 70.7 of SCA becoming only

14% snow covered (Figure 4.26a). The greatest loss of SCA and SWE would occur at

elevations between 1900 and 2100 m (Figure 4.26a and Figure 4.28a). Elevations

between 2100 and 2300 m would lose of previously SCA (Figure 4.26a).

Average volume of SWE loss in December, January, and February would equal

0.04 . The basin’s higher elevations above 2300 m would remain cold and relatively

unaffected by a 1°C increase in average winter temperatures between December and

February (Figure 4.26a).

In March, following a 1°C warming, 1.4 or 9% of the basin’s SCA would be

at-risk of becoming rainfall dominated. Roughly 91% of this loss would occur between

1900 and 2300 m (Figure 4.26a). Similarly in April, 299.2 or 12% of the basin’s

previously SCA would become rainfall dominated. All previously snow covered regions

between 2100 and 2300 m would be lost (Figure 4.27a). The basin’s higher elevations

between 2300 and 2700 m would lose 282.6 of previously SCA becoming 45%

98

snow dominated (Figure 4.26a). Elevations above 2700 m would remain cold and

unaffected by a 1°C warming in April (Figure 4.26a).

As winter temperatures further increase by 2°C, elevations between 1700 and

1900 m would become 98% rainfall dominated (Figure 4.27b). At-risk SCA at the 1900

to 2100 m elevation band would increase from 99.2 to 174.6 (Figure 4.26b).

From December to February, elevations between 2100 and 2300 m are historically

completely snow dominated. Following a 2°C increase in temperature, 149.6 of

SCA within this elevation band would be lost; only 50% of the region would still remain

snow covered (Figure 4.26b). Average volume of SWE loss during the months of

December, January, or February would equal 0.08 . Most of the loss in SCA and

SWE would occur between 1900 and 2300 m (Figure 4.27b and Figure 4.28b). Elevations

above 2500 m would remain cold and unaffected by a 2°C warming between December

and February (Figure 4.26d).

In March, following a 2°C warming, all previously snow covered regions between

1700 and 2100 m would become rainfall dominated (Figure 4.27b). The basin’s higher

elevations, between 2100 and 2500 m would lose an average of 172.4 or 5% of the

region’s previously SCA (Figure 4.26b). The cumulative volume of SWE loss in March

would equal 0.01 ; most of this loss (85%) would occur between 2100 and 2500 m

(Figure 4.28b).

A 2°C warming in April would result in approximately 699.8 , or 28% of the

basin’s previously SCA becoming rainfall dominated. All snow covered regions between

99

2100 and 2500 m would become rainfall dominated (Figure 4.27b). The greatest loss in

SCA however would occur at elevations between 2500 and 2700 m (Figure 4.26b). The

basin’s higher elevations between 2700 and 2900 m, historically completely snow

covered in April, would lose roughly 98.5 of SCA becoming 77% snow dominated

(Figure 4.26b). A 2°C warming in April would result in 0.03 of SWE loss (Table

4.5).

Following a 3°C warming, all previously snow covered regions between 1700 and

1900 m would become rainfall dominated (Figure 4.27c). From December to February,

elevations between 2100 and 2300 m would lose an average of 256.6 or 8% the

region’s previously SCA (Figure 4.26c). Volume of SWE loss within the 2100 to 2300 m

elevation band would equal 0.07 (Figure 4.28c). At-risk SCA within the 2300 and

2500 m elevation band would increase from to (Figure 4.26b and

4.26c). Average volume of SWE loss in December, January, and February would equal

0.12 (Table 4.5). The greatest loss of SCA and SWE would occur within the basin’s

mid-elevations, between 1900 and 2300 m (Figure 4.26c and Figure 4.28c).

In March, following a 3°C warming, 798.3 or 27% of the basin’s previously

SCA would become rainfall dominated. Furthermore, all snow covered regions between

2100 and 2300 m would become rainfall dominated (Figure 4.27c). Elevations between

2300 and 2700 m, historically completely snow covered, would lose 370.6 of SCA

becoming 49% snow covered (Figure 4.26c). The cumulative volume of SWE loss in

100

March would equal 0.03 with the greatest loss occurring within the basin’s mid-

elevations, between 2300 and 2500 m (Figure 4.28c).

In April, a 3°C warming would result in elevations between 2500 and 2700 m

becoming completely rainfall dominated (Figure 4.27c). At-risk SCA at the 2700 to 2900

m elevation band would increase from 98.5 to 389.4 (Figure 4.26c). As a result,

only 9% of the region between 2700 and 2900 m would still remain snow covered.

Volume of SWE loss in April would equal 0.05 (Table 4.5). The greatest loss of

SCA and SWE, following a 3°C warming, would occur between 2500 and 2900 m

(Figure 4.26c and Figure 4.28c). Higher elevations above 3100 m would remain cold and

unaffected by a 3°C warming in April (Figure 4.26c).

Under an extreme 4°C warming, all previously snow covered regions existing

between 1900 and 2300 m would become rainfall dominated (Figure 4.27d). In

December, 1124.5 , or 35% of the basin’s December SCA would be at-risk of

becoming rainfall dominated. Approximately 69% of this loss would occur between 1900

and 2500 m (Figure 4.26d). The cumulative volume of SWE loss in December would

equal 0.20 . Elevations between 2500 and 2900 m, historically completely snow

covered, contains 26% of the basin’s December SCA. Following a 4°C warming,

219.5 of SCA within this elevation band would be lost (Figure 4.26d); roughly 74%

of the region would remain snow covered.

In January and February, a 4°C warming would result in an average loss of

978.7 , or 30% of the basin’s previously SCA. Average volume of SWE loss in

101

January and February would equal 0.17 (Figure 4.28d). The greatest loss of SCA

and SWE in January and February would occur between 1900 and 2500 m (Figure 4.26d

and Figure 4.28d). Snow covered regions above 2900 m would remain unaffected

between December and February (Figure 4.26d).

A 4°C warming in March would result in 40% of the basin’s previously SCA

becoming rainfall dominated. Furthermore, elevations between 2300 and 2500 m would

lose all their SCA and become completely rainfall dominated (Figure 4.27d). The basin’s

higher snow covered regions between 2500 and 2900 m would lose 460.8 of SCA

remaining only 45% snow covered (Figure 4.26d). Approximate volume of SWE loss in

March would equal 0.06 . Most of this SWE loss would occur at the basin’s mid-

elevations, between 2300 and 2700 m (Figure 4.28d).

In April, 1449.3 or 59% of the basin’s previously SCA would be at-risk of

becoming rainfall dominated. All previously snow covered regions existing between

2700 and 2900 m would become rainfall dominated (Figure 4.27d). At-risk SCA within

the 2900 to 3100 m elevation band would increase from 62.4 to 333.7 (Figure

4.26c and Figure 4.26d). Most of the snow loss in April (80%) would occur within the

basin’s mid-elevations, between 2500 and 3100 m (Figure 4.26d). Volume of SWE loss

in April, following a 4°C warming, would equal 0.07 . Most of this SWE loss would

also occur within the mid-elevations, between 2300 and 3100 m (Figure 4.28d). Snow

dominated regions existing above 3300 m would remain cold and unaffected by a 4°C

warming in April (Figure 4.26d).

102

Figure 4.26: Estimated loss of SCA within the Kings River Basin, at the different

elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

a)

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

b)

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

c)

0.0

100.0

200.0

300.0

400.0

500.0

DEC. JAN. FEB. MAR. APR.

SC

A L

oss

(k

m²)

MONTH

1500 TO 1700

1700 TO 1900

1900 TO 2100

2100 TO 2300

2300 TO 2500

2500 TO 2700

2700 TO 2900

2900 TO 3100

3100 TO 3300 ELEV

ATI

ON

20

0 (

MET

ERS)

d)

103

Figure 4.27: Estimated fractional loss of SCA within the Kings River Basin, at the

different elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.FR

AC

TIO

NA

L S

CA

MONTH

a)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.FR

AC

TIO

NA

L S

CA

MONTH

b)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.

FR

AC

TIO

NA

L S

CA

MONTH

c)

0%

20%

40%

60%

80%

100%

DEC. JAN. FEB. MAR. APR.FR

AC

TIO

NA

L

SC

A

MONTH

1500 TO 17001700 TO 19001900 TO 21002100 TO 23002300 TO 25002500 TO 27002700 TO 29002900 TO 31003100 TO 3300 EL

EVA

TIO

N 2

00

(M

ETER

S) d)

104

Figure 4.28: Estimated volume of SWE loss within the Kings River Basin, at the different

elevation bands, following a 1.0°C, 2.0°C, 3.0°C, and 4.0°C warming scenario.

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

a)

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

b)

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

c)

0

0.01

0.02

0.03

0.04

0.05

0.06

DEC. JAN. FEB. MAR. APR.

SW

E L

oss

(k

m³)

MONTH

1500 TO 17001700 TO 19001900 TO 21002100 TO 23002300 TO 25002500 TO 27002700 TO 29002900 TO 31003100 TO 3300

d)

EL

EV

AT

ION

20

0 (

ME

TE

RS

)

105

MONTH

SNOW LINE ELEVATION

(meters)

SCA

)

SCA

(% of basin)

DEGREE

OF WARMING

(°C)

SCA LOST

(% of basin)

SCA

LOST

( )

SWE LOST

( )

DECEMBER 1531 3201.4 73% 1 7% 219.5 0.04

2 15% 480.3 0.08

3 23% 732.9 0.12

4 35% 1124.5 0.20

JANUARY 1568 3209.7 73% 1 7% 215.7 0.03

2 15% 466.8 0.08

3 21% 689.3 0.12

4 31% 1008.7 0.18

FEBRUARY 1599 3171.4 73% 1 7% 210.5 0.03

2 14% 436.0 0.07

3 21% 669.7 0.11

4 30% 948.6 0.16

MARCH 1768 2981.9 68% 1 9% 271.4 0.00

2 17% 514.2 0.01

3 27% 798.3 0.03

4 40% 1205.7 0.06

APRIL 2167 2476.8 56% 1 12% 299.2 0.02

2 28% 699.8 0.03

3 44% 1093.7 0.05

4 59% 1449.3 0.07

Table 4.5: Warming Scenarios considered likely by the IPCC applied to the Kings

River Basin for the months of December to April; area of at risk-snow, SCA, and

volume of SWE loss were then calculated.

106

ELEVATION (METERS) PERCENT OF BASIN

Below 1100 611.12 13.97%

1101 to 1300 135.30 3.09%

1301 to 1500 148.08 3.39%

1501 to 1700 156.35 3.58%

1701 to 1900 214.23 4.90%

1901 to 2100 247.31 5.65%

2101 to 2300 304.43 6.96%

2301 to 2500 318.72 7.29%

2501 to 2700 402.91 9.21%

2701 to 2900 429.97 9.83%

2901 to 3100 375.84 8.59%

3101 to 3300 339.76 7.77%

3301 to 3500 340.52 7.79%

3501 to 3700 246.55 5.64%

3701 to 3900 88.70 2.03%

3901 to 4100 13.53 0.31%

Z-min (m) =291 Z-max (m) = 4113

Drainage Area ( ) = 4372.57

Average Elevation (m) = 2346.40

Elev

atio

n (

met

ers)

Rel

ief

(met

ers)

Figure 4.29: Hypsometric curve of the Kings River Basin showing distribution

of area with elevation. Approximately 83% of the basin’s area lies above 1300

m.

107

Figure 4.30: “At-risk” snow covered area within the Kings River Basin under

different warming scenarios.

108

Chapter 5: Discussion

This work applied a binary snow classification approach to identify the most “at-

risk” areas of snow loss within the nested basins of California’s Sacramento and San

Joaquin Watersheds. Results from the analysis confirm all five basins of the San Joaquin

Watershed and the Feather River Basin, in the Sacramento Watershed, are highly

sensitive to snow loss with warming winter temperatures. Furthermore, if warming trends

considered by the IPCC to be highly likely continue, large previously snow dominated

regions existing between 1500 and 2100 m in the San Joaquin watershed would become

almost completely rainfall dominated. In the Feather River Basin, implications are even

more alarming with the disappearance of SCA across multiple elevation bands, and the

complete loss of snow dominated regions by March.

5.1 Elevation Dependency of Temperature

The decline of winter snow cover due to warming within all five basins of the San

Joaquin Watershed, and the Feather River Basin is closely correlated with elevation and

temperature. Snow dominated regions existing near the 3°C isotherm are more

susceptible to warming trends relative to higher elevation snow dominated regions with

colder temperatures. The strong dependency of mean temperatures on elevation, which

influences snow accumulation and melt, is in agreement with previous studies (Cayan et

al., 2008; Knowles et al., 2006; Maurer et al., 2007; Vicuna and Dracup 2007).

Impacts of increased warming on snow loss vary by basin with the key parameter

being basin elevation relative to the freeze line, or the rain to snow transition zone.

109

Basins with significant area at the higher elevations well above freezing, such as the

Kings and Upper San Joaquin River Basin (Figure 4.24 and Figure 4.29), show less

pronounced loss of SCA relative to the more moderate to mid-elevation basins like the

Feather River Basin (Figure 4.4). In the Feather River Basin, greatest snowfall reductions

across multiple elevation bands occur where mean winter temperatures are close to

freezing. The slightest warming beyond 0°C would have a substantial impact on snow

loss across multiple elevation bands. These findings confirm previous studies that have

shown snow dominated regions where temperatures are close to freezing, are generally

more susceptible to temperature fluctuations relative to higher elevations with sub-

freezing climatologies (Knowles et al., 2006; Kapnick and Hall, 2012).

5.2 Trends in Loss of Inter-monthly SWE

Within all five nested basins of the San Joaquin Watershed, largest inter-month

volumes of SWE loss are shown to occur during the precipitation rich months of

December, January, and February, and at elevations between 1700 and 2900 m (Figure

4.8, Figure 4.13, Figure 4.18, Figure 4.23, and Figure 4.28). The hypsometric curves

show that relative to the Northern Sierra Nevada basins, the Central and Southern Sierra

Nevada basins are more evenly distributed with significantly more area above 2000 m

(Figure 4.4, Figure 4.9, Figure 4.14, Figure 4.19, Figure 4.24, and Figure 4.29).

Consequently, most of the SWE accumulation (and loss) tends to occur within these more

moderate to mid-elevations in the Southern Sierra Nevada Mountains. Furthermore,

because these moderate to mid-elevations accumulate a significant amount of snowpack

110

and SWE, their sensitivity to snow loss with increased warming is a significant cause for

concern.

Similarly, within the Feather River Basin of the Sacramento Watershed, largest

volumes of SWE loss, due to warming, are shown to occur during the month of February

(Figure 4.3). Peak SWE loss in February, and to a lesser extent March, indicates a

warmer, riper snowpack during the mid to later portions of the winter snow season, when

temperatures are no longer well below freezing. February also coincides with one of the

major snow producing months in the Sierra Nevada Mountains, and in Feather River

Basin. Approximately 70% of the basin’s total area is snow covered in February (Table

4.0).

Almost all the at-risk volume of SWE within the Feather River Basin exists at

elevations between 1300 and 2100 m (Figure 4.3). This can be explained using the

hypsometric curve of the basin (Figure 4.4). Roughly 77% of the Feather River Basin’s

total area exists between 1300 and 2100 m. Furthermore, a strong maximum distribution

of area with elevation exists between 1500 and 1900 m (Figure 4.4). As a result, most of

the basin’s snowpack accumulation (and loss) tend to occur within these moderate

elevations. As shown in Figure 4.2c and Figure 4.3c, roughly 85% of the basin’s

February snowpack would disappear under a 3°C warming in February; most of this

snow exists between 1300 and 2100 m. These results confirm previous studies that have

shown, following a 2° to 3°C warming, majority of SWE losses in the Northern Sierra

Nevada Mountains would occur at elevations between 1300 and 2100 m (Mote et al.,

111

2005; Knowles et al., 2006; Maurer et al., 2007). Furthermore, these findings highlight

the precarious state of the Northern Sierra Nevada snowpack under a warmer climate.

5.3 Warming in High Precipitation Months

Month-by-month trend analysis shows effects of future warming on snow

accumulation would critically depend on warming in precipitation-rich months, yielding

the largest impacts when increased warming coincides with the greatest snowfall

amounts, and suitably warm monthly mean temperatures (Knowles et al., 2006). Winter

snowpack within these vulnerable elevations provides a sizable form of natural water

storage that reservoir managers have grown to rely on to sustain downstream demands.

As these mountainous climates continue to experience warming, shifts in SCA, or

fraction of precipitation occurring as rain instead of snow represent a significant water

management concern.

The loss of previously snow-covered area, as snowlines recede to higher

elevations, would lead to an increase in direct runoff from winter storms. This is

primarily due to more precipitation occurring as rainfall instead of snowfall, and an

increase in the contributing area of the watershed to direct runoff. Lower elevation

mountain basins, such as the Feather River Basin, would have to contend with a larger

snowpack/runoff response to increased air temperatures relative to higher elevation

basins like the Kings and Upper San Joaquin River Basin (Anderson et al., 2008).

112

5.4 Future Challenges and Reservoir Adaptation Strategies

One of the greatest challenges posed to water managers in California involves

adapting to changes in peak flow timing and snowmelt derived runoff. Snowlines rising

to higher elevations would result in a larger contributing area to direct runoff from winter

storms, and therefore an increase in flood risks. In addition, a smaller winter snowpack

translates to less natural water storage for use later during the year. The combination of

greater flood risk and reduced seasonal snowpack storage threatens to exacerbate existing

tension between flood control and water supply storage (Knowles et al., 2006).

An important step towards climate change adaptation for agencies managing

California’s water resources involves revising archaic reservoir operating procedures.

Since most of California’s dams were built during the mid-1900s, hydrological records

used to create flood operation rule curves are based on climate trends during the first half

of the 20th

century (Willis et al., 2011; Georgakakos et al., 2012). However, warming

experienced in recent decades has shifted the amount of precipitation falling as rain

versus snow, (Knowles et al., 2006) and altered the timing of snowmelt derived

streamflow (Stewart et al., 2005; Regonda et al., 2005; Hidalgo et al., 2008; Fritze et al.,

2011). Moreover, hydrological simulations show continued warming would result in

greater shifts in streamflow timing resulting from increased snow loss at the more

moderate to mid-elevations that currently store the largest amounts of snow (Knowles et

al., 2004; Maurer et al., 2007). The loss of this immense amount of naturally stored

water, and its earlier arrival at the downstream reservoirs, poses a challenge to reservoir

113

managers (Anderson et al., 2008; Maurer et al., 2007; Vicuna and Dracup, 2007). Since

reservoirs are operated under flood protection during the winter and early spring, early

winter runoff is permitted to pass through the reservoirs unabated (Figure 5.1). An earlier

and/or shorter snowmelt spring runoff would make it more challenging for reservoir

managers to refill flood control space in the spring, and bring water levels to storage

capacity for the start of the summer season when demand peaks, and California receives

little precipitation (Figure 5.1). As a result, adapting to climate induced shifts in peak

streamflow and snow loss is crucial to insure adequate water supply for the summer and

fall when demand is greatest.

Developing greater flexibility into flood-control rule curves, that defines the

maximum allowable reservoir pool elevation, would assist reservoir managers in adapting

to a warming climate. Flexibility can be built into rule curves by using parameters that

describe how the basin’s antecedent hydrological conditions would alter maximum flood

pool drawn-down and refill rates (Willis et al., 2011; Georgakakos et al., 2012). During

dry years for example, managers would be able to adjust draw-down rates to store more

water while still maintaining adequate flood control storage space. Conversely during wet

years, reservoir pool elevations could be drawn down and maintained at lower elevations

to increase flood control storage.

5.5 Case Study with Existing Reservoirs

Examples of large reservoirs designed for flood control during the winter, and

water supply storage during the summer and fall are the Oroville Reservoir in the Feather

114

River Basin, the New Melones Reservoir in the Stanislaus River Basin, and the Don

Pedro Reservoir in the Tuolumne River Basin. Originally built in 1923, Don Pedro

Reservoir has an approximate storage capacity of 2.5 . Operation protocol requires

that from October 7 through April 27 of the following year, the reservoir maintain

approximately (340, 000 acre-feet) of flood control storage space. Flood control

storage requirements increase from zero on September 8 to the maximum reservation of

on October 7. This reserved space is maintained through April 27 after which,

unless snowmelt parameters indicate the need for additional storage, it can gradually be

brought back to zero by June 3. Flood control space at the Don Pedro Reservoir occurs

between 0.24 km and 0.25 km (801.9 to 830.0 feet) (Don Pedro Pre-licensing Document

2011).

Our analysis examined historical reservoir pool elevations in relation to the

cumulative volume of SWE loss within the Feather, Stanislaus, Tuolumne, and Kings

River Basin, under varying climate warming scenarios (Figure 5.1). Under a 0.5° to 3°C

warming scenario, from December to February, the Don Pedro reservoir in the Tuolumne

River Basin could potentially adjust the volume of winter storage to compensate for the

loss of SWE that would occur within the basin. However under a 4°C warming scenario,

the Don Pedro reservoir would be incapable of buffering the cumulative volume of SWE

loss while still maintaining adequate flood control space (Figure 5.2).

In contrast to the Don Pedro reservoir, the Oroville Reservoir in the Stanislaus

River Basin has a gross storage capacity of 4.4 . Primarily built as part of the State

115

Water Project (SWP), the reservoir serves to provide water for irrigation and industrial

uses as well as for flood management, power generation, and water quality enhancement

to the Sacramento-San Joaquin Delta. From September to June, the facility is operated

under flood control requirements. Under these requirements, Lake Oroville is operated to

maintain up to 0.9 (750,000 acre feet) of flood control storage space. However,

unlike the Don Pedro Reservoir, in the Tuolumne River Basin, the Oroville facility has

greater flexibility built into its flood control operations (Figure 5.3). Depending on

antecedent basin conditions and hydrological forecasts, available storage space can be

brought down or increased respectively. This flexibility, that incorporates real time basin

conditions, could enhance water management objectives in a changing climate signaled

by diminishing snowpack, and shifts in snowmelt derived streamflow. Some efforts exist

to make flood operations more responsive to watershed conditions under a changing

climate (Georgakakos et al., 2005; Lee et al., 2006, Dettinger et al., 2011). These results

further affirm the urgency for creating greater flexibility into flood control rule curves.

One method in which this can be accomplished is by incorporating hydro-climatic

forecasts, and snowmelt parameters, with reservoir optimization standards.

5.6 Error Analysis and Study Assumptions

This study provides a preliminary evaluation of how future climate warming

scenarios could affect snow accumulation and reservoir storage in California. Although

various snow accumulation and melt parameters were incorporated in the analysis, certain

invariable assumptions require that the results are approached with a degree of

116

skepticism. For example, the 800 m PRISM dataset used is of a higher spatial resolution

compared to previous studies but still remains coarse and could be over or under

estimating snow cover. In addition, the analysis does not account for changes in

precipitation and land cover, or future changes in atmospheric circulation patterns. The

selected rain versus snow temperature threshold of 3°C is a feasible value that provided

the most realistic representation of snow cover across all basins. However, in reality, the

rain versus snow temperature threshold for individual winter storms is not a constant

value but varies depending on the atmospheric circulation patterns. Snow can precipitate

at temperatures above 0°C when a cold atmospheric layer lies above a warm surface

layer. Conversely, rain can still fall at temperatures below 0°C such as when a warm

precipitating layer lies above a stable cold layer at the surface (Nolin and Daly, 2006).

Nevertheless, our analysis uses mean monthly temperatures where these transitory

differences that exist from one storm to another should average out. Furthermore, a 3°C

rain versus snow temperature threshold accounts for the variability in the rain to snow

transition that would exist in a dry versus a wet year, within the thirty year record (from

1971-2000). However, this work could be built upon and further enhanced by using a

more refined, and storm specific rain versus snow temperature threshold.

Results from the gridded 1 km SWE output generated through the SNODAS

model are reasonably close to measurements derived from SNOTEL sites; however,

uncertainties still exist in the output data. In addition, the snow classification approach

presented in this study is based on a 30-year historical average of temperature and

precipitation data, and a range of winter atmospheric conditions. Using a more recent and

117

extensive historical record of temperature and precipitation, as well as incorporating

physical snow characteristics could enhance the study’s results. Furthermore, using a

historical daily record of reservoir storage elevations is a reasonably close way of

determining where flood control pool elevations have been maintained in the past.

However, variability in storage elevations exists from year to year depending on

antecedent basin conditions, hydrologic forecasts, and downstream water demands and/or

outflow requirements.

Finally, using historical mean monthly temperature data and a climatologically

data driven approach to predict future impacts of warming on snow loss (and water

availability) may fail to convey the full range of potential future outcomes. This study

could therefore be further enhanced by including a normal probability analysis for each

month’s Tmean distribution over the thirty year period. The uncertainty within our

predicted estimates would then be the loss in SCA and SWE that would occur when using

a new monthly Tmean value that is plus or minus one (or two) standard deviations away

from the actual monthly mean. Incorporating a probability analysis into this study could

be instrumental in conveying a broader range of potential future outcomes that water

resources planners and reservoir managers could then work within.

118

Figure 5.1: Reservoir operating procedures at the Don Pedro Reservoir during the 2006

water year. The blue line shows reservoir pool elevations drawn down at the end of

October to generate flood control space for winter storms. Once the threat posed by large

winter storms has passed (typically in April but depending on seasonal forecasts),

reservoirs change functionality to water supply storage. Spring snowmelt is collected in

an attempt to fill-up reservoirs to storage capacity to satisfy down-stream demands in the

summer and fall.

720

730

740

750

760

770

780

790

800

810

820

830

840

0

2000

4000

6000

8000

10000

12000

5-Sep-05 25-Oct-05 14-Dec-05 2-Feb-06 24-Mar-06 13-May-06 2-Jul-06 21-Aug-06 10-Oct-06 29-Nov-06

Ele

va

tio

n (

feet

)

Dis

ch

arg

e (c

fs)

MONTH

DON PEDRO RESERVOIR. WATER YEAR 2006: WET

DISCHARGE RES. ELEVATION (feet)

FLOOD CONTROL MAX. CAPACITY

119

Figure 5.2: Historical reservoir pool elevations and potential adjustments at the Don

Pedro within the Tuolumne River Basin. The reservoir has a total storage capacity of 2

million acre-feet (2.5 km³) as well as 340 thousand acre-feet (0.4 km³) of flood control

storage. Maximum storage capacity occurs at 830 feet above sea level. During the winter

months, maximum flood control storage space exists between 802 and 830 feet. Blue

lines indicate historical reservoir pool elevations.

760

770

780

790

800

810

820

830

840

EL

EV

AT

ION

(fe

et)

MONTH

DON PEDRO RESERVOIR WITHIN THE

TUOLUMNE RIVER BASIN Monthly Storage Elevation & Adjustments

MAX. STORAGE

FLOOD CONTROLELEVATION

RESERVOIRELEVATION (1995 TO2010)RESERVOIRELEVATION (1985 to2010)0.5 Degree Warming

1 Degree Warming

2 Degree Warming

3 Degree Warming

4 Degree Warming

FLOOD CONTROL

120

Figure 5.3: Historical reservoir pool elevations and potential adjustments at the Oroville

Reservoir within the Feather River Basin. The reservoir has a total storage capacity of 3.5

million acre-feet (4.3 km³) as well as 750 thousand acre-feet (0.9 km³) of flood control

storage. Maximum storage capacity occurs at 900 feet above sea level. During the winter

months, maximum flood control storage space exists between 843 and 900 feet.

Minimum food control storage exists between 873 and 900 feet respectively. Blue lines

indicate historical reservoir pool elevations.

760

780

800

820

840

860

880

900

920

EL

EV

AT

ION

(fe

et)

MONTH

OROVILLE RESERVOIR WITHIN THE FEATHER

RIVER BASIN Monthly Storge Elevations & Adjustments

MAX. STORAGE

MIN. FLOOD CONTROLELEVATION

MAX. FLOODCONTROL ELEVATION

MONTHLY RES. LEVELS(1995 TO 2010)

MONTHLY RES. LEVELS(1985 TO 2010)

0.5 Degree Warming

1 Degree Warming

2 Degrees Warming

3 Degrees Warming

4 Degrees Warming

MAX . FLOOD CONTROL

MIN. FLOOD CONTROL

121

Figure 5.3: Historical reservoir pool elevations and potential adjustments at the New

Melones Reservoir within the Stanislaus River Basin. The reservoir has a total storage

capacity of 2.4 million acre-feet (2.9 km³) as well as 450 thousand acre-feet (0.5 km³) of

flood control storage. Maximum storage capacity occurs at 1087 feet above sea level.

During the winter months, flood control storage space exists between 1050 and 1087 feet

above sea level.

940

960

980

1000

1020

1040

1060

1080

1100

EL

EV

AT

ION

(fe

et)

MONTH

NEW MELONES RESERVOIR WITHIN THE

STANISLAUS RIVER BASIN Monthly Storage Elevations & Adjustments

MAX. STORAGE

MAX. FLOODCONTROL ELEVATION

MONTHLY RES. LEVELS(1995 TO 2010)

MONTHLY RES. LEVELS(1985 TO 2010)

0.5 Degrees Warming

1 Degree Warming

2 Degrees Warming

3 Degrees Warming

4 Degrees Warming

FLOOD CONTROL

122

Figure 5.4: Historical reservoir pool elevations and potential adjustments at the Pine Flat

reservoir within the Kings River Basin. The reservoir has a total storage capacity of 1.0

million acre-feet (1.2 km³) as well as 475 thousand acre-feet (0.6 km³) of flood control

storage. Maximum storage capacity occurs at 951 feet above sea. A proposal by the

United States Army Corps of Engineers (Corps) involves raising Pine Flat reservoir by 20

feet to increase reservoir storage capacity.

760

780

800

820

840

860

880

900

920

940

960

980

1000

ELEV

ATI

ON

(fe

et)

MONTH

PINE FLAT RESERVOIR WITHIN THE

KINGS RIVER BASIN Monthly Storge Elevations & Adjustments

NEW HT.

MAX. STORAGE

NEW FLOOD CONTROLELEVATION.

MAX. FLOOD CONTROLELEVATION

MONTHLY RES. LEVELS(1995 TO 2010)

MONTHLY RES. LEVELS(1985 TO 2010)

0.5 Degree Warming

1 Degree Warming

2 Degrees Warming

3 Degrees Warming

4 Degrees Warming

FLOOD CONTROL

SPACE

123

Chapter 6: Conclusion

In Western North America, surface water supplies depend on a highly seasonal

and variable pattern of winter snowfall accumulation, and subsequent runoff that is

sensitive to climate variability and change. Concurrent with a warming planet, recent

studies have documented increased shifts in the hydrologic systems that cannot be solely

attributed to natural variability (Barnett et al., 2008; Fritze et al., 2011). The latest report

by the Intergovernmental Panel on Climate Change (IPCC 2007) further reaffirms that

climate change is occurring globally, and that human activity is the primary cause.

Within California, shifts in the hydrologic systems are evident in a greater

proportion of precipitation occurring as rain instead of snow (Knowles et al., 2006), a

declining trend in winter SWE (Mote et al., 2005; Kapnick and Hall, 2012), and an

advancement in timing and volume of snowmelt derived streamflow (Stewart et al., 2005;

Maurer et al., 2007; Dettinger et al., 2011; Fritze et al., 2011). Given that temperatures

over western North America have been steadily increasing on the order of 1°C per

century (Mote et al., 2005; Hamlet et al., 2007), and that warming is expected to

accelerate due to human activity (IPCC 2007; Solomon et al., 2007), understanding

snowpack vulnerability at the basin scale is crucial for water managers.

Findings from this study are relevant to water managers tasked with managing

California’s water resource infrastructure. Faced with an expanding population and

increased strains on water resource availability, sustaining future water demands hinges

124

on developing effective adaptation strategies for a warmer climate. Results from this

study show all five basins within the San Joaquin Watershed, as well as the Feather River

Basin in the Sacramento Watershed are highly sensitive to snow loss with warming

winter temperatures. Furthermore, if warming trends considered by the IPCC to be highly

likely continue, large, historically snow dominated regions would become completely

rainfall dominated.

In addition to the loss of previously snow covered area, higher snowlines translate

to a larger surface area contributing to direct runoff from winter storms and thus, an

increase in flood risks. Shifts in precipitation trends from a mostly snow dominated to a

mostly rain dominated regime translate to higher winter flows, earlier peak flows, and

lower summer base flows. Shifts in streamflow timing threaten to disrupt reservoir

operation guidelines that maintain vacated flood control space in the winter, with the

anticipation that spring snowmelt runoff can be captured, and used to re-fill reservoir

flood control space for the summer and fall, when demand is greatest. Results from this

study underscore a fundamental change in future water supply availability. Moreover,

they stress the urgency for developing an integrated management approach that utilizes

scientific advances in hydro-climatic forecasting, with reservoir optimization.

Efforts to make reservoir operations more adaptive to climate warming and basin

conditions would require integrating competing objectives into an optimization approach.

For example, tension between flood control objectives and water supply storage could be

averted by employing more dynamic flood operation rule curves that account for

125

antecedent basin conditions, as well as short and long-term forecasts (Willis et al., 2011).

Using better storm forecasting technologies, allowing for earlier flood releases, or

increasing storage capacity based on real time basin conditions could improve

California’s future water management efficiency.

At recent workshops hosted by the Western States Water Council (WSWC), water

managers representing federal, state, and local agencies have been stressing the need for

more instrumentation, and better monitoring of snow and water conditions at the basin

scale (Olsen et al., 2009). The results presented in this thesis could be put to immediate

use in determining which specific regions within the Sierra Nevada Mountains are highly

susceptible to SWE loss, and therefore should be more closely monitored. Understanding

basin and, at a finer scale, elevation specific vulnerability to SWE loss due to warming

would be instrumental in assessing possible impacts, and guiding mitigation strategies.

An additional recommendation emphasized by water managers was the need to

build greater flexibility into reservoir operations to cope with a warming climate.

Following these workshops, the United State Army Corp of Engineers (USACE) pledged

to re-examine their reservoir operation requirements, and investigate the extent of

changes in Corps rule curves that would be needed to mitigate SWE losses (Olsen et al.,

2009). While the results presented in this study cannot be used independently to guide

engineering storage adjustments, they provide some preliminary insight that future work

can build upon.

126

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