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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