A historical approach: using historic aerial imagery to unravel the relationship between

stream power, vegetation and stream morphology changes on Last Chance Creek (CA)

Eliza H. Malakoff

Senior Integrative Exercise

March 13, 2019

Submitted in partial fulfillment of the requirements for a Bachelor of Arts degree from

Carleton College, Northfield, Minnesota

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ABSTRACT

Meadow streams in the Sierra Nevada mountains are ecological and economic keystone habitats that maintain biodiversity and benefit water quality for the millions of people who receive drinking water from Sierra Nevada watersheds. Overexploitation of these watersheds from grazing, mining, logging and road-building over the past 200 years has, however, led to incision and habitat loss. This study utilizes historical aerial imagery of a montane meadow creek in the Plumas National Forest (CA) and ESRI spatial software to compare the predictive power – and relationship between - two major drivers of stream bank stability: (1) stream-adjacent vegetation type and (2) the potential for sediment transport, also known as stream power. I selected eighteen 400-meter reaches located in areas of high and low stream power to measure changes in stream morphology and vegetational composition along between the years 1954, 1977 and 2016. I found no evidence that stream power is correlated with stream morphology change or vegetational composition along Last Chance Creek, but strong evidence of longitudinal trends in vegetation and stream morphology. These trends were not consistent across the two time periods. I interpret the results as evidence that changing groundwater accessibility along the stream trace may be a driver of stream and vegetation changes within reaches. The lack of consistency between the two time periods may be the result of changes in groundwater accessibility due to seasonal changes, drought conditions, or the influence large restoration projects along the stream. The results of this study highlight the importance of being able to “zoom out” using historical aerial imagery, in order to identify the spatial and temporal patterns within which the stream exists, and that cannot be observed from single-visit field assessments of stream health.

INTRODUCTION

Riparian zones are both ecological and economic keystone habitats, with benefits

disproportionate to land area. In California, riparian zones maintain greater biodiversity

than any other California community type (Schoenherr, 1992) and allow major coastal

cities like San Francisco to exist. Much of the riparian habitat in the state is located in the

Sierra Nevada mountains. As far east as the Nevada border, Sierra Nevada stream

corridors foster wet, cool meadows that serve as oases in the largely dry landscape

(Kattelmann and Embury, 1991). They contain a unique array of vegetation, birds (Siegel

et al., 2014), and nearly 83% of California amphibians (Evelyn and Sweet, 2012)

including several species endemic to the Sierra Nevadas (Graber, 1996). The dense

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vegetation that thrives on the elevated water-table surrounding streams also serves as a

natural filter that stores excess nitrogen (Norton et al. 2011), traps harmful bacteria

(Knox et al., 2008) and prevents sedimentation and severe flooding downstream (Plumas

National Forest, 2010). These ecosystem services are valuable because Sierra Nevada

streams deliver over 65% of the water used by humans in California, including irrigation

for the Central Valley and drinking water for 23 million people (Klausmeyer and

Fitzgerald, 2012).

These habitats, however, have been degraded over the past 200 years. Ubiquitous

cattle grazing, beginning in the early 1800s and peaking in the early 1900s, nearly

eliminated riparian vegetation in many areas, resulting in bare streambanks prone to bank

failure and incision (Sawyer, 2016). Mining, logging and the infrastructure created for

mining communities along streams also altered sediment movement along stream

corridors, increasing sediment transport in some locations and limiting it in others.

Growing demand for water from expanding population centers to the west has literally

drained some riparian areas. Throughout most of California, only an estimated 2-10% of

pre-European settlement riparian habitat remains (Sawyer, 2016). As demand for

freshwater continues to grow, riparian habitats are now more vulnerable to non-native

species invasion, fragmentation and high severity fires than at any point in history

(Sawyer, 2016). Anthropologic climate change will likely exacerbate these threats as an

increasingly sporadic precipitation regime alters the timing of peak flows necessary for

the recruitment of riparian vegetation, increases the likelihood of severe fire, drought and

extreme flooding, and reduces the reliability of alternative water sources such as the

Colorado River (Viers et al. 2013; McLaughlin, Ackerly and Klos et al., 2017)

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The science of riparian zone restoration is over half a century old (Leopold and

Maddock, 1953) and, in response to legislation like the Wild and Scenic Rivers Act of

1968 and the Clean Water Act of 1972, a suite of restoration methods has been developed

and improved since the 1980s. These include promoting willow recruitment along

streambanks, filling gullies and headcuts, and rerouting incised streams through

artificially created ponds in a method known as “pond-and-plug” (Plumas National

Forest, 2010). Although the documentation of restoration projects is often uneven, these

interventions appear to be effective at bolstering local nutrient cycling, increasing

riparian zone biodiversity and reducing sedimentation in the high Sierras (Ramstead,

Allen and Springer, 2012).

Funding is, however, scarce for such projects, necessitating a cost-effective and

quick method for prioritizing and designing restorations. Such a process should take into

account many factors including the estimated economic and ecological benefits of

restoring a certain location, the quality of local relationships with a diverse set of land-

users, the projected timeline and cost of a restoration project, and the plausibility of long-

term monitoring and assessment, but also a detailed understanding of the ecohydrological

and geomorphological drivers of local degradation. In-situ monitoring and on-the-ground

field assessments such as Proper Functioning Condition surveys, which are widely used

by public land agencies (Gonzalez et al., 2013) can provide insight into the degree and

nature of degradation at a location but are time consuming and do not provide

information about the long-term eco-hydrological and geomorphological processes that

determine the success or failure of major restoration projects.

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Fortunately, a multitude of historical aerial images available as far back as the

1920s, together with dramatic improvements in spatial statistical software, make it

possible to test hypotheses and manage water resources within the context of historical

trends. In absence of historical field data, comparing aerial photos over time, in

conjunction with other forms of data, may be a way of discerning which system drivers

hold the most power of prediction for long-term bank stability. With most aerial photos

available for free or at low cost online, a historical approach to understanding present day

stream systems may be a promising method that can be replicated cost-effectively for

many streams in the Sierra Nevadas.

In this study, I examine three sets of aerial images spanning 62 years of Last

Chance Creek, a degraded montane stream in the Plumas National Forest near the

Nevada-California border, in order to compare the predictive power – and relationship

between - two major drivers of stream bank stability: (1) stream-adjacent vegetation type

and (2) the potential for sediment transport, also known as stream power. Additionally, I

evaluate the practical implications of using a historic-image based method of determining

the drivers of stream instability.

Stream Power, Sediment, and Montane Riparian Zones

Stream power is an estimate of the potential of a river to transport sediment along

a stream reach in units of Watts / meter. Bagnold (1966) computed stream power by

estimating the force created by water accelerating downhill through a river corridor,

expressed as:

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Ω = ρ𝔤QS

where Ω is total stream power, ρ is the density of water, 𝔤 is gravitational acceleration, Q

is stream discharge (in cubic meters per second) and S is the slope of the stream at the

sample location. The banks and bed of two stream reaches with similar total stream

power experience different sheer stress if one reach is wider than the other, since the

velocity of the water will be lower in the wider channel (Leopond and Maddock, 1966).

Thus, total stream power is typically averaged across the entire width of the stream by

dividing Ω by stream width. This property is denoted by 𝜔 and will be called “specific

stream power” in this paper, but is also named “unit stream power” or “mean stream

power”.

Conventional fluvial geomorphology predicts a systematic relationship between

stream power and the morphological behavior of a stream channel. The Channel Incision

Model, also known as the Channel Evolution Model, describes a cycle of five stages of

stream morphology, each initiated by a minimal stream power threshold (Fig. 1).

Bledsoe, Watson and Biedenhorn (2002) describe the model as a way of predicting how a

given reach of stream will change systematically over time if left undisturbed. This model

forms the backbone of many stream assessment systems today, which rely on accurate

categorization of a stream reach into one of the five stages. According to the model, stage

I and stage II reaches are characterized as incised channels without widespread bank

failure where sediment transport is greater than sediment supply; incision occurs in

locations of high local stream power. Stage III reaches are transitional, and involve

channel widening due to bank failure. This process leads to an increase of sediment,

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which is deposited in areas of lower stream power along the stream; these areas are

known as stage IV reaches. Stage IV reaches experience low bank failure rates and the

formation of natural levees along the channel. Stage V reaches occur where sediment

supply and sediment transport are in equilibrium, and are characterized by gently sloping

banks that promote riparian vegetation and a well-connected floodplain. In an ideal

undisturbed stream channel in net equilibrium, the cycle of stages from incision to

equilibrium propagate upstream as a knickpoint migrates, such that stage III reaches

would be expected upstream of stage V reaches. Multiple waves of incision may

propagate up a stream channel simultaneously, so that the sequence of stages is repeated

multiple times along the length stream.

More recently, to avoid the assumption of unidirectionality or of a natural self-

regulation towards a “climax” state, researchers with the U.S. Department of

Agriculture’s Forest Service and other land management agencies have adopted a

language of “transitional states” rather than formal classifications (Stillwater Sciences,

2012). I will refer primarily to the stages of incision described by the Channel Incision

Model because they are easier categories to use with aerial geomorphic data. It is useful

to acknowledge, however, that alternative-states language recognizes the reality that both

stream degradation and restoration occur according to threshold laws rather than in a

linear fashion if constrained to a human time frame. In alternative-states language, Level

1 reaches refer to stream sections that may attain full meadow and riparian functionality

without significant structural intervention, and Level 3 reaches refer to deeply incised

stream channels that are not naturally irreversible within a human time frame. Level 2

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reaches are roughly analogous to stage III of the traditional Channel Incision Model and

represent a transitional phase between Level 1 and Level 3 reaches.

A vital piece of both the Channel Incision Model and alternate-state “level”

categories is the inclusion of a threshold-driven transitional phase. Evidence that a stream

power threshold does exist comes primarily from long-term field studies at multiple

scales and predicts that the required threshold of specific stream power to initiate incision

occurs in the range of 30 W/m2 to 40 W/m2 (Bizzi and Lerner, 2015). Total stream power

can also be used to identify a threshold of incision (Larson, Fremier and Greco, 2006;

Bizzi and Lerner, 2015). However, not all studies succeed in identifying a threshold of

stream power that reliably predicts the geomorphological state observed in the field.

Longitudinal changes in stream power may more accurately predict locations of

catastrophic flood effects and erosion (Reinsfeld et al., 2002; Bizzi and Lerner, 2015), or

the effect of stream power thresholds may be

obscured by other stream channel characteristics, such as local sedimentation and

substrate type (Stacey and Rutherford, 2007).

The relationship between stream power and bank stability is of particular interest

in Sierra Nevada meadows and riparian zones, which have historically been supported by

the high water tables and a relatively stable state of sediment transport equilibrium.

Furthermore, although channel slope in meadow streams is not as high as the channel

slopes found in most areas where stream power has been studied, even slight differences

in shear stress can determine the success of restoration structures like the plugs used to

create artificial floodplains in the “Pond and Plug” method (Plumas National Forest,

2010). This suggests stream power may be useful in forecasting geomorphological

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responses in low-grade meadow streams. Investigating this relationship between stream

power and stream morphology through a historical lens will provide insight into the

degree to which stream power should be taken into account during the placement and

design of restorations in the future.

Riparian Meadow Vegetation, Stream Power and Stream Bank Stability

The presence of riparian zone vegetation alters the relationship of running water with

sediment at the local, landscape and regional scales, in many cases shaping the most

iconic elements of a landscape. Riparian meadow vegetation provides support to stream

banks both above and below ground. The roots of rushes, sedges and herbaceous

vegetation such as willow and alder increase the cohesiveness of saturated soils. Whereas

the banks of streams in dry meadows fail frequently and in small blocks, banks colonized

by sedges and rushes, which thrive in wetter meadows, have up to five times greater

shear strength than dry meadow banks and form large blocks that take years to be

removed (Micheli and Kirchner, 2001b). When these larger wet vegetation blocks do fail,

they are less easily removed from the streambed, armoring the new raw bank edge while

revegetation and stabilization of the bank occurs (Micheli and Kirchner, 2001b). Above

ground, large herbaceous species like willow increase the roughness of stream channels

and floodplains, minimizing the speed and effective power of water to compromise bank

blocks (Bendix, 1999). For all the reasons above, banks that lack wet meadow vegetation

are order of magnitude more vulnerable to erosion (Micheli and Kirchner, 2002a).

The relationship between vegetation and bank stability is, however, not

unidirectional. Rather, the recruitment or removal of vegetation on streambanks can

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initiate positive feedback loops; recruitment of vegetation on vulnerable banks may result

in continued colonization and stability, whereas the removal of vegetation may initiate

incision and propagate wet vegetation loss and further bank instability. Meadows along

Sierra Nevada creeks have high evapotranspiration rates (Loheide et al., 2009), indicating

they are reliant on high water tables in the well-drained soils where they typically occur.

The degradation of many meadows is widely thought to have been initiated 150-200

years ago, when cattle overgrazing became ubiquitous in the area. Modern analog studies

show that cattle selectively browse on certain riparian plant types and disproportionately

congregate near water (Mceldowney et al., 2019) where delicate plants are trampled and

unable to recolonize. Bare banks vulnerable to erosion resulted in widespread bank

failure and, without riparian vegetation downstream to capture sediment, sediment

transport quickly outpaced supply, producing rapid incision of many stream channels. As

incision propagated downstream, increasing bank heights by 1-2 m., the local water table

lowered and became inaccessible to many riparian species (Micheli and Kirchner,

2001b). Incision and vegetation loss continues today (Micheli and Kirchner, 2001b)

despite the removal of cattle grazing pressure.

If vegetation and incision are related, then what role might stream power have in

mitigating or initiating vegetation-morphological feedback loops? Explanations of

meadow degradation in the Sierra Nevadas tend to assume that incision is initiated by a

specific local disturbance that is independent of stream power trend, such as overgrazing

or the introduction of a sediment source such as a nearby dirt road. Studies in other

regions suggest, however, that even large disturbances in vegetation may have less

significance in determining stream morphology than stream power (Kasprak et al., 2013),

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and that the influence of vegetation over reach morphology may be especially limited in

larger streams (Eaton and Giles, 2008). On the other hand, stream power may be directly

related to vegetation distribution, because the acceleration of water determines which

areas of bank will be saturated or pulverized during flooding. Light flooding recruits

hydric species and excludes xeric species, whereas heavy flooding prevents all vegetation

recruitment (Harris, 1987). Regular floods, for example, prevent sagebrush (Artemisia

tridentata), which has little tolerance for wet conditions, from encroaching along the

banks of chapperal-dominated streams (Bendix, 1999). Instead, flood-tolerant alder

(Alnus rhombifolia) and cottonwood (Populus fremontii) inhabited areas of highest

stream power (Bendix, 1999).

The explicit relationship between stream power and vegetation is rarely studied,

but slight changes in stream power along Sierra streams may dictate where and how

stabilizing riparian vegetation is established along the stream bank, thus providing a

secondary mechanism for stream morphology control through stream power change. Or,

stream power may initiate incision independent of vegetation, with vegetation response

occurring afterwards, thus giving stream power greater predictive power in determining

stream morphology than vegetation.

Hypotheses

This study is designed to explore the relationship between stream power and vegetation

and determine whether one of those two variables better predicts the magnitude of stream

morphology change over time. Using aerial imagery over three time periods since 1954, I

collected data about vegetational composition, stream morphology change, and predictive

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stream power along 18 400 meter long reaches. Nine reaches constituting my control

group were located in areas along the stream where stream power changes rapidly over

the reach and nine reaches were located in areas where stream power remained close to

zero. I tested four alternative hypothesis:

1. Stream power predictive: Stream reaches in the control group experience less

morphological change than reaches outside of the control group, and vegetational

composition is not correlated with the magnitude of morphological change.

2. Vegetation predictive: Stream reaches in the control group do not experience less

or more morphological change than reaches outside of the control group, but

vegetation composition predicts the magnitude of morphological change. The

prominence of sagebrush is associated with greater morphological change than of

hydric vegetation.

3. Both stream power and vegetation predictive: Stream reaches in the control group

experience less morphological change than reaches outside of the control group,

and vegetation correlates with the magnitude of morphological change. Sagebrush

covers a higher percentage of area surrounding streams in the treatment group,

and hydric vegetation covers a higher percentage of area surrounding streams in

the control group.

4. Neither stream power nor vegetation predictive: Stream reaches in the control

group do not experience less or more morphological change than reaches outside

of the control group, and vegetational composition is not correlated with the

magnitude of morphological change.

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STUDY AREA

Last Chance Creek is entirely located in the Beckwourth Ranger District of the

Plumas National Forest in Californiaand runs 62 km westward from an escarpment near

the Nevada border through the eastern Sierra Nevada mountains (Figure 2). Last Chance

Creek is a tributary to Indian Creek in the watershed of the Middle Fork of the Feather

River, one of the first nationally designated Wild and Scenic Rivers. The Feather River

provides water to local communities and rangeland before delivering water to the

Oroville Reservoir and, eventually, the Sacramento River. Located in a rain shadow,

Beckwourth District is one of the driest areas of the Sierra Nevada mountains, receiving

only 43- 60 cm of rain a year, mostly in the form of snow (Readle, 2014).

Last Chance Creek is a spring fed stream that originates on the western base of

Mountain View Peak. Much of the creek is located in an alluvial valley interpreted as an

ancient lake bed infilled with sediment (Readle, 2014). The area has been glaciated four

times, most recently around 20,000 years ago (Readle, 2014). Steep, jagged mountain

sides and moraines are common in the area. The sediments in the valley are derived from

surrounding bedrock, mainly tertiary igneous rock including volcanic flows, pyroclastic

deposits and granitic sand (Loheide et al., 2009). The soil is fine, cohesive and well-

drained (Readle, 2014) loam to loamy-sand (Plumas Corporation, 1992).

Today, the stream is bordered by nearly 21 km of contiguous flat, open meadow

and, like many Sierra Nevada meadow streams, has a very low slope gradient (Figure).

At least nine meadow tributaries funnel water along the bedrock-meadow interface into

Last Chance Creek. The creek is primarily surrounded by xeric vegetation consisting of

sagebrush and Sierra juniper, and riparian areas vegetated with willow and alder (Plumas

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Corporation, 1992). The eastern portion of the stream is more heavily wooded, with

Yellow Pine, Jeffrey Pine, aspen, and Douglas fir commonly found around – and

sometimes encroaching into – the creek valley. Evidence of degradation along the

meadows of Last Chance Creek include incised, terraced channels as well as remaining

hydric vegetation located far back from the stream edge along abandoned stream

channels (figure?). Loheide et al. (2009) interpreted this type of vegetation distribution in

Tuolumne meadows as evidence for meadow degradation, with remaining hydric

vegetation located along still-elevated portions of the water table at the edges of the

diminished meadow system.

The water and biodiversity at Last Chance Creek has likely attracted human

habitation for thousands of years. Indigenous peoples in the Feather River watershed

likely utilized resources from many streams and rivers and may have actively contributed

to the fire regime of the area (Anderson et al., 1996). Beginning in the early 1800s, wet

meadows were attractive places for white settlers to graze cows, but the California gold

rush, in which the Feather River played a large role, likely increased the amount of

human traffic through the area. Overgrazing, mining, and timber exploitation were

ubiquitous throughout the Sierra Nevadas through the early 1900s. Beaver populations,

which have served as keystone species in Sierra Nevada systems for thousands of years

(Plumas Corporation, 1992), also declined. Several roads and a railroad were constructed

within the Last Chance Creek Watershed (Plumas Corporation, 1992) in the 19th century.

At the time of a proposal for a new dam to be placed on Last Chance Creek in 1960, the

middle fork of the Feather River watershed contained 6,260 kilometers squared of

irrigated land, according to documentation from the time period (Brown and Banks,

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1960), much of which likely comprised range-land. The destructive effect of cattle on

wet-meadow systems and the ecological alterations that subsequently occur are well

documented in central Sierra Nevada streams, including in the Plumas National Forest

(Plumas Corporation, 1992). Livestock grazing and timber production both continue

inside the watershed today, but at a far smaller scale.

In the 1980s, growing concern over degrading water quality conditions in the

Feather River prompted the formation of the Feather River Coordinated Resource

Management (FR-CRM) group to manage, push for and support restoration projects

along the Feather River and tributaries (Wilcox, 2009). Early restorations included

supporting vegetation recruitment and building bank-stabilizing structures, including rock

grades. Beginning in the early 2000s, the FR-CRM in conjunction with the U.S. Forest

Service began a series of ambitious restorations along Last Chance Creek. According to

Readle (2014), between 2000 and 2014, four restorations occurred along Last Chance

Creek affecting up to 2000 m. of stream, and at least 10 tributaries received restorative

treatments ranging from vegetation transplantation, rock armoring, construction of fences

to exclude cattle, sod and rock emplacement, and transformation of headcuts into step-

pool systems. At least six of the tributaries, and all of the restorations on the main

channel of Last Chance Creek, utilized the pond and plug restoration method, in which a

stream is rechannelized into an abandoned or created stream channel constructed with

earthen plugs to mimic the high sinuosity of meadow streams. In 2014, a small (0.004

km2) meadow at the head of Last Chance Creek also received restorative treatment

(Plumas Corporation, 2011).

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Since the early 2000s, Last Chance Creek has received some attention in the

research community both as a study area for assessing the benefits of large-scale stream

restoration (Loheide and Gorelick, 2006; Ohara et al., 2013), and for the potential the

creek may have for groundwater storage (Readle, 2014).

METHODS

Experimental Design and Reach Selection

I selected eighteen 400 meter long reaches of Last Chance Creek for this study (Fig. 1).

Half of the study reaches occur where stream power increases over a short distance, and

half of the study reaches are located along areas of consistent low stream power. All

reaches are located in the upper 46 km of the stream, upstream of the point where the

stream is becomes surrounded by dense forest. I compared three primary variables across

study sites: (1) total and specific stream power, (2) change in reach area and shape

between 1954, 1977 and 2016, and (3) vegetation type by area within 25 m. and 100 m.

of the stream in 1954, 1977 and 2016. A summary of my methods is outlined in Figure 2.

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ArcGIS Workflow

Stream PowerComputation

Stream Morphology

Change

Download DEMs

Slope calculation

Delineate streams on DEM

“Burn” verified dataset into DEM

Fill elevation sinks on DEM

Create points each 10 m.along the stream

Calculate flow accumulation at each point

Export data table to Excel

Discharge calculation

Calculate slope

Download gage datafrom Indian Creek

Compute flood returnintervals

Relate Q2 to discharge area

Calculate Q2 for dischargearea at each stream point

Calculate stream powerfor each point

Use HydroTools to createa running average for streampower

Convert image to grayscaleand re-categorize veg

Re-categorize veg

VegetationComposition

“Fishnet” buffers into15 X 25 m. grid

Build 100 meter buffers around stream reaches

Georectify aerial imagery

Trace out streams

Use RMS error for stream buffer

Compute overlap

Compute ratio of “sameness” to “difference”

Establish misidentificationerror

AnoB + BnoA

AoverB

AnoB B noA

AoverB

Figure 3. A graphical summary of the methods used to compute stream power, obtain information about vegetational composition, and determine stream morphology change over time.

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Stream Power Computation

Slope

I used Gartner’s (2016) workflow for ArcGIS software, the Hydrology toolset, and Excel

to calculate stream power along Last Chance Creek. The method involves using three-

dimensional topographic maps known as digital elevation models (DEMs) to delineate

stream systems within a watershed and calculate the slope along a selected reach. A

flowchart summary of the methods are displayed in FIGURE X. This study utilized

open-source DEMs of the Last Chance Creek watershed from ASTER Global, a product

of the National Aeronautics and Space Administration (NASA) and Management and

Engineering Technologies International (METI), Inc. The DEMs were created in 2011

with a resolution of 1/3 Arc seconds, or approximately 10 m. To increase the spatial

accuracy of the delineated streams, streams from the California Statewide Stream-Based

Hydrography (CalHydro) Dataset were “burned” into the existing DEMs, a process which

involves artificially lowering portions of the DEM to “force” the modelled drainage

systems into true stream channel locations. Burning is commonly used for stream systems

in flat areas such as meadows and prairies (Gartner, 2016) where topographic differences

may be small. On the burned DEMs, the stream channel was artificially lowered 20 m.

along the channels in the CalHydro dataset, decreasing the horizontal distance between

the real and delineated stream channels by up to 150 m. At the end of the process, all

delineated drainages were within 50 m. of the real channel (figure?). I also experimented

burning the verified CalHydro dataset at 10 m. and 50 m., but found the results to be less

accurate than at 20 m.

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The slope along Last Chance Creek was calculated at 10 m. intervals. Because of

DEM elevation data can contain up to 3 m. of error, it is standard practice to remove

anomalous slope data. Any 10 m. segments showing positive slopes were interpreted as

artifacts of an incorrectly delineated stream and were excluded from analysis. Running

averages, rather than actual values, are also commonly used to increase the accuracy of

the slope data. I used a running average of 3 km to “smooth” the slope data, which is

similar in scale to the running averages used by Jain et al. (2005) and Gartner (2016).

HydroTools, an Excel Add-in produced by Carl Renshaw at Dartmouth College,

facilitates graphing stream power with distance, and includes a tool for reach-smoothing.

Discharge

I used a 2-year return interval flow volume to calculate stream power along Last

Chance Creek, in keeping with other GIS analyses of stream power and widespread

consensus that 1.5-2 year flows are the most influential on streamscapes (Wolman and

Miller, 1960; Reinfelds et al. 2002, Jain et al. 2005). It is worth noting that increasing

flow in the stream power equation likely does not change the relative relationship

between stream power and geomorphic response along a single stream (Magilligan 1992,

Gurnell et al. 200?). I determined the return frequency of the peak annual flows, in order

to calculate the flow of a flood with a two-year return interval (Q2), using data from two

gauges maintained by the U.S. Geological Survey. Both gauges are located on Indian

Creek, which Last Chance Creek empties into. The first gage is located near Taylorsville

CA and has recorded flow data from 1912-2017. The second is located near Crescent

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Mills CA and has flow data from 1906-1997. I calculated Q2 at each location, removing

outliers in each dataset to obtain an R2 value of 0.97 or higher for each equation. By

relating Q2 at each location with the discharge area of the gage station, I determined an

overall relationship between Q2 and discharge area for the watershed of:

Q2 = (0.0573 * DA) + 39.372

where Q2 is the flow of flood event with a frequency of two years, and DA is the

discharge area of the stream at that point. This equation was used to compute flow from

DEM-extracted discharge area data along Last Chance Creek.

Specific Stream Power and width measurements

Most measurements of specific stream power over a large area assume a regional

equation relating width and discharge area (Gartner, 2016). However, there is not a

published equation for the Sierra Nevada region. Furthermore, the width of Last Chance

Creek does not vary in a predictable way with discharge area. To calculate specific

stream power, I measured the stream width of each reach in the treatment group at five

points along the reach and averaged.

Analysis of Morphological Change Over Time

Grayscale aerial photographs of the Last Chance Creek area dating back to 1954,

and high-resolution color imagery from the National Agriculture Imagery Program

(NAIP) dating back to 2005, are available at no cost through the USGS EarthExplorer

tool. I initially viewed imagery from the years 1954, 1977, 2005, 2007, 2011 and 2016,

but found few obvious differences in stream morphology from 2005 onwards. Therefore,

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the analysis only included aerial imagery from 1954 (October 5), 1977 (June 1) and 2016

(August 5). The NAIP data from 2016 has a resolution of 1 m. and is available for

download already georectified. The 1954 and 1977 aerial images do not include

georeferencing data and, due to the curvature of the lens through which the images were

taken, are slightly distorted. After georectification, the 1954 and 1977 images had

apparent resolutions in the range of 1.5-2 m. To ensure a greater degree of accuracy in

georeferencing and to avoid major warping effects, each clipped piece of image

(approximately 1000 X 1000 m.) surrounding each study site was georectified separately,

using large trees and permanent infrastructure such as roads as anchor points. Each image

was georeferenced with no fewer than five anchor points, and the Root Mean Squared

error for each image was recorded.

I traced the geometry of the stream from each image using the “create polygon”

tool available in ESRI ArcMap. I traced at a scale of 1:600 for the 2016 imagery and at a

scale between 1:1500 and 1:2000 for the 1954 and 1977 imagery. The smaller scale used

for 1954 and 1977 imagery was necessary due to the lower quality of the image. The

minimal mapping unit (MMU) for the images was ~5 m2. I used the root mean square

(RMS) error of each manual georectification to estimate the spatial accuracy of each

traced polygon. I did this by including an interior and exterior buffer equivalent in width

to the RMS error of the image from which the polygon was traced for each stream reach

traced from 1954 and 1977 imagery. To assign a numerical value to the amount of

morphological change in a reach between two years, I calculated “change in stream

morphology” as the ratio between the area of the reach that did not overlap between the

two years and the area of the reach that did overlap (Figure). In essence, this is a

24

numerical indicator of the amount of change versus stability experienced by a reach

between two years.

Lastly, to gain greater insight into the meter-scale sediment dynamics along each

reach over which stream power changes, each reach in the treatment group was

subdivided into five equal sections. The change in area across years, as well as the width

and average, maximum and minimum stream power and specific stream power within

each section were collected.

Vegetation Composition Along Stream Bank

I characterized the vegetation in a 100 m. wide buffer around each stream

polygon. Using the “Fishnet” tool in ESRI ArcMap, I filled each 100 m. wide buffer zone

with a grid of 25 X 25 m. squares. The buffers contained a different number of squares,

depending on the curvature of the stream polygon (curvier polygons had buffers that

contained fewer squares). The same grid was used to characterize vegetation in all three

aerial images. Each square was categorized according to the dominant vegetation type

contained within it, including trees, hydric, willow (or alder), shrubby, or unvegetated

(Fig. 4). “Dominant” was considered to mean the vegetation type that took up the greatest

area within the square, with the exception of some plots that were clearly sage-brush

dominated but partially obscured by tree canopy. If a square contained no more than 25%

of any vegetation type, then it was categorized either as rocky or non-applicable.

Examples of non-applicable squares include squares in which a paved road or building

took up greater than 75% of the area of the square.

25

Figure 4. Top: charts showing the misidentification error before and after recategorization of vegetation types for two error tests. Below: Samples of visual categorizations of vegetation types in grayscale and color.

26

To test the consistency with which squares were assigned categories, I

recategorized the vegetation surrounding three stream polygons according to the 2016

imagery and compared the results between the two categorizations. The three images

were selected to represent the full range of vegetation environments sampled. I also tested

the effect low resolution and grayscale on vegetation identification consistency by

converting each of the three resampling images to grayscale and reducing the resolution

to 1.5 m. I also increased the contrast 40% and included a slight blur effect so that the

modified 2016 images best resembled actual imagery from 1954 and 1977. The results

from these error tests are shown in Figure Xa. The most commonly misidentified

vegetation type in both tests were rock, shrub and hydric. Rocks and shrubs were most

commonly confused as sagebrush, and hydric and willow were commonly misidentified

as one another. To decrease the misidentification error in the final analysis, I enveloped

rock and shrub types into sagebrush, and combined the willow and hydric categories.

Figure Xb shows the improved identification errors for each vegetation type after

recategorization.

RESULTS

Trends between vegetation composition within 25 m. wide buffer and within a

100 m. wide buffer were highly similar across all reaches. The values referred to in this

results section are for vegetation within 1 100 m. wide buffer.

27

Influence of stream power on stream morphology change and vegetation

Reaches of Last Chance Creek located along changing gradients of stream power

did not experience a different amount of morphological change than those reaches located

in areas with no change in stream power. Morphology change within each reach also was

not correlated with increases or decreases in total stream power or specific stream power.

Excluding the control reaches, the amount of hydric vegetation had no correlation to

specific stream power in any year. The amount of sagebrush was positively correlated to

specific stream power only in 1954 (p < .001) and the amount of tree was correlated

positively with specific and total stream power in both 1954 (p < .001) and 2016 (p <

.001). The correlation between treed area and stream power is an artifact of the tendency

for pine trees to colonize steep slopes.

Vegetation and stream morphology change

Reaches in the treatment group did not have more tree or sagebrush than the

control group in any year. In 2016, reaches in the treatment group had marginally more

hydric vegetation than control reaches, with a difference in mean hydric vegetation of 9%

(p=0.667, df = 15.984). The percent sagebrush and percent hydric vegetation also were

not correlated for every time period, but the percent change in sagebrush and percent

change in hydric vegetation were inversely related from both 1954 to 1977 (p < .001 )

and from 1977 to 2016 (p < .05). From 1954 to 1977, the percent change in sagebrush

was also positively correlated with stream morphology change (p < .05) and percent

change in hydric vegetation was negatively correlated (p < .05). From 1977 to 2016, less

28

Figure 5. Scatterplots showing changes in streambank morphology, sagebrush and hydric vegetation with distance downstream. Circles represent vegetation measured within a 100 m. buffer, triangles represent vegetation measured with a 25 m. buffer. Asterisks refer to significance: *p < .10, **p <.05, ***p<.01.

29

vegetation change and less morphological change occurred, and the two variables were

not correlated.

Longitudinal trends in vegetation and morphology change

The percent change in hydric and sagebrush vegetation was, however,

consistently correlated with distance from the headwaters of the stream, as summarized in

Figure 5. From 1954 to 1977, the percent change in sagebrush and cahgne in morphology

increased with distance downstream (p < . 01) and the amount of hydric vegetation

decreased (p < .01). From 1977 to 2016, the change in sagebrush decreased with distance

downstream, whereas hydric vegetation increased. In reality, this reflected a local

maximum of sagebrush increase at around 20 km, and a local minimum change in hydric

vegetation at 40 km.

DISCUSSION

This study was designed to assess the predictive power of vegetation and stream

power on stream morphology change over time, with four possible scenarios: (1) stream

power is predictive of morphology change, but vegetation is not, (2) stream power is not

predictive of morphology change but vegetation is, (3) stream power and morphology

change are both predictive of morphology change, indicating a positive feedback loop

between all three variables or (4) neither stream power nor vegetation is predictive of

morphology change. This study finds no evidence that stream power is correlated with

stream morphology change along Last Chance Creek; vegetation composition may be

more closely associated with stream morphology change. However, this relationship was

30

not consistent across the two time periods. Additionally, instead of being independently

related at specific sites, changes in both stream morphology and vegetation display strong

longitudinal patterns between time periods. I propose that longitudinal trends in

vegetation are related to changing groundwater accessibility along the stream trace as

distance from the headwater increases. Seasonal and decadal changes in available

moisture, as well as large restorations that alter local hydrology may shape or disrupt

these longitudinal patterns. The results of this study highlight the importance of being

able to “zoom out” using historical aerial imagery, in order to identify the spatial and

temporal context within which the stream exists.

Below, I first discuss some possible reasons and implications of the lack of

detected influence of stream power on reaches along Last Chance Creek, before

exploring mechanisms behind the observed longitudinal patterns within and between time

periods. Finally, I examine the utility of using historical aerial images to learn about the

behavior of specific streams, including a tradeoff between scale and three-dimensional

observations, and remaining challenges associated with extracting data from historical

imagery.

The missing influence of stream power along Last Chance Creek

The lack of apparent influence of stream power on vegetation regime or stream

morphology change in Last Chance Creek runs counter to decades of research relating the

power of water, sediment movement and ecohydrological processes. Yet, other studies

have also failed to find a relationship between stream power and ecomorphological reach

characteristics, perpetuating the need to understand the effect of using stream power as a

31

parameter over half a century since the parameter was proposed. The discrepancy could

be attributed to the dynamism of unique stream systems, but in many cases may also be

an artifact of the error and resolution inherent in different ways of computing stream

power, or the scales at which the effect of stream power is studied.

The most likely explanations for the lack of correlation between stream power and

morphology or vegetation in this study include the low slope of Last Chance Creek, the

possible error inherent in the DEM, or the presence of variables that confound the

realized effects of stream power. Likely, all three contributed to the results. For instance,

Stacey and Rutherford (2007) suggest that the low resolution of the GIS DEM, in

addition to differences among streams in substrate strength, caused the relationship

between stream power and erosive characteristics in a survey of over 1000 Virginia

stream sites to be obscured. It is worth noting, too, that studies such as those by Bizzi and

Lerner (2015) and Magilligan et al. (2015) that successfully predicted morphology along

streams using digitally-computed stream power occurred in environments with steeper

topography and larger rivers. If the low resolution of the DEM is the reason why a

nuanced relationship between stream power and stream morphology is obscured, then this

would pose a major limitation for utilizing a remote sensing approach to stream

management in montane meadows. However, without ground-truthed field data and the

measurement of additional variables such as substrate strength and local shear bank

stress, I can not conjecture as to which factor may play the greatest role in producing in

the lack of correlation between stream power and stream morphology change.

32

A bigger scale: downstream trends across two time periods

Many studies have demonstrated the local influence of vegetation on bank

stability in montane meadow streams, but few have examined this relationship in the

context of both longitudinal and temporal trends. This study suggests that the location of

a reach along Last Chance Creek has strong implications for the eco-hydrological regime

at that location

Both Lord et al. (2011) and Bergmann (2004) described mechanisms by which

vegetation can be related longitudinally to stream paths in montane meadow streams.

Bergmann (2004) detailed the effect of a migrating knickpoint on vegetation upstream

and downstream of it, noting that significantly more mesic vegetation grew upstream of

the knickpoint and more xeric vegetation grew below the knickpoint, where water table

levels dropped below the root depth of native mesic plants. Lord et al. (2011) examined

longitudinal patterns of vegetation and water table depth in the Great Basin, determining

that headwater reaches of streams are typically areas of groundwater recharge whereas

down-valley areas are typically areas of groundwater loss. Bergmann (2004) observed

longitudinal patterns along a 100 m. scale reach, and Lord et al. observed longitudinal

patterns along reaches up to a few thousand m. long. This study finds an observed

relationship between vegetation response and distance from headwaters at a much larger

scale and suggests that both mechanisms likely contribute to vegetation patterning along

the creek.

My finding that sagebrush and hydric vegetation were negatively correlated

between both 1954-1977 and 1977-2016 supports the widespread consensus that these

33

vegetation types occupy segregated and exclusive environments (Bergmann, 2004; Lord

et al., 2011). This result also concurs with prior research at Last Chance Creek (Loheide

and Gorelick, 2007; Readle et al. 2014) demonstrating that sagebrush occupies most areas

where water table depths are greater than 0.5 to 1.5 m. that the threshold separating these

environments at Last Chance Creek are ground to water-table depths ranging from -0.5 to

-1.5 m. depending on the time of year. They also note that sagebrush and mesic

vegetation are segregated laterally along the streambank, where water tables have

declined the most. This can be observed along many of the sample reaches in this study

across all three time periods (Figure). Additionally, the observation that increases in

sagebrush and decreases in hydric vegetation are associated with greater stream

morphology change is also consistent with other explorations of incision in montane

meadow streams (Zong et al. 1996; Micheli and Kirchner, 2002b; Essaid and Hill, 2014)

and the model incision theory in the Sierra Nevada, which predicts Type III bank failure

and widening after initial incision leading to sagebrush dominance and the loss of hydric

vegetation.

Sagebrush and hydric vegetation are, however, only correlated with change in

stream morphology from 1954-1977, and not from 1977-2016. The longitudinal pattern

present from 1954-1977 may align with Lord et al. (2011)’s proposal that most meadow

systems have headwaters capable of recharging groundwater aquifers, and that this

capability declines with distance downstream. Between 1954 and 1977, sagebrush

increased more at the downstream parts of the stream than at the head of the stream (p

value), while the opposite was true of hydric vegetation (p value) (Figure refer).

However, contrary to the expected longitudinal distribution of the plants in 1954, down-

34

valley reaches of the creek actually had as much as 30% more hydric vegetation than

headwater reaches, whereas the headwater reaches comprised up to 80% sagebrush.

I offer two proposals for this discrepancy. First, the noted changes between 1954

and 1977 may be an artifact of the two times of year during which the photos were taken.

Imagery of the headwaters in September of 1954 shows a narrow, confined stream with

uneven sinuosity, whereas imagery from 1977, taken in June, shows multiple mesic

meadows delineated as dark patches contributing to willow-lined headwaters of Last

Chance Creek (Figure). Indeed, Loheide and Gorelick (2007) noted a definitive

relationship between groundwater availability and season, with peak availability

occurring between May and June. This theory, however, does not completely explain

why hydric vegetation was so much more common in the down-valley in 1954, nor the

results of increased sagebrush in the down-valley at the same time as groundwater

recharge occurred farther upstream.

It is possible that hydric vegetation occurred primarily within incised terrace

stream channels, and on the edges of degraded meadows, thus maintaining a presence

despite ongoing bank failure and widening. Readle (2014) noted this arrangement of

mesic vegetation along the edges of degraded meadow edges was a common occurrence

around Last Chance Creek and that long-lived woody species may remain in place for

some time after initial incision begins lowering of the water table. It is also worth noting

that, while change in vegetation held predictive power over morphological change in this

study, overall vegetation regime did not appear to adequately predict changes in

morphology at either the 25 m. buffer or 100 m. buffer scale, contrary to intuitive

understanding of the relationship between bank stability and vegetation type. There are

35

multiple explanations possible for this phenomenon, but it may relate to in part the reality

that the entirety of Last Chance Creek had already been incised for more than a century

prior to 1954, such that vegetation may be responding more to morphological changes

than controlling them.

The period from 1954-1977, however, is not predictive of trends observed from

1977-2016, perhaps denoting a shift in stream-wide ecohydrological regime due to

restoration efforts along the stream. I note three specific changes to support this theory.

First, although sagebrush and hydric vegetation remain negatively correlated (p-value),

neither is predictive of stream morphological change. Second, a clear trend between

change in sagebrush and change in hydric vegetation with downstream direction is

present, but unlike in the period from 1954-1977, the amount of change in sagebrush

increases along the first 20 km before trending downwards with distance downstream,

mirrored by trends in hydrological change (figure). Lastly, the magnitude of both

vegetation change and morphological change are decreased in this period than the

preceding one.

I suggest that two driving mechanisms may be responsible for these fundamental

changes. First, the effects of the record-breaking drought that occurred in the Sierra

Nevadas are visible in aerial imagery from 2016. This drought may skew the data in two

ways. The continued low flow may mask stream channel changes from the period

preceding the drought because the stream is channelized, leaving bare banks open to

recolonization by vegetation, and artificially deflating the total stream channel

morphology changes that occurred prior to the drought. Another explanation involves the

large number of restorations on Last Chance Creek and its tributaries that occurred

36

beginning in the 1980s, including pond and plug restorations that have taken place since

the early 2000s. Although only one reach lies within a restored area, restorations have the

potential to alter sediment transport and stream power along the length of the stream. In

theory, pond and plug restorations stabilize streambanks, decrease sediment load,

decrease shear force of flooding by diverting water to overland flow, and sponge water

into groundwater aquifers (Plumas National Forest, 2010). In practice, few studies

assessing the effects of these restorations downstream have occurred; in a review of 48

assessment studies, only a handful even mentioned downstream impacts of the restoration

rather than local variables like plant community composition and bank geometry

(Ramstead et al., 2012). Preliminary data from modelled and realized pond and plug

restorations on creeks throughout the Plumas and Tahoe national forests may in fact have

the desired effect of increasing summer low-flow and decreasing the magnitude of

flooding in the winter and spring (Plumas National Forest, 2010). This would likely

promote near-bank hydric vegetation and bank stability in wet conditions, but under

drought conditions the restorations may simply be a confounding variable that decouples

stream morphology and vegetation by altering stream behavior before vegetation lag can

catch up. However, it is important to note that the “downstream effects” of restorations

are still not well understand and have not been measured in these studies, which typically

did not examine areas more than than 100 m. downstream of the restoration site.

A historical approach: using historical aerial imagery to understand streams today

The results of this study show the potential for historic aerial imagery to provide

insight into longitudinal and temporal trends that would otherwise be obscured in studies

37

involving single snapshots in time. The sophisticated spatial statistical software and

robust record of aerial photographs available today offer a remarkable opportunity to

make resource management decisions within the context of spatial and temporal patterns

that are outside of the scope of single-stop, qualitative field assessments. However,

utilizing a method of remote sensing to understand stream system requires tradeoff; the

ability to zoom out comes at the cost of resolution, nuance, and dimensionality. Here, I

discuss some of the limitations of this method, and advocate that best management

practices for stream restoration should not abandon qualitative field assessment

approaches but should include historical and remote sensing approaches as part of the

decision-making process.

The prevalence of uncertainty throughout the spatial analysis process, in many

cases without a standardized way of quantifying it, is the primary complication in using

historic aerial images for analysis. For instance, historic aerial images cannot be ground-

truthed like remote-sensing methods that are ongoing. The difficulty of handling

uncertainty in these types of studies is compounded by the generally low resolution and

quality of historic aerial data. Low resolution and quality make the already-difficult task

of extracting three-dimensional understanding of stream processes from a two-

dimensional image even more difficult. For instance, identifying characteristics of stream

incision and deposition, such as terracing and head-cut formation are nearly impossible in

grayscale, blurred aerial photos. The difficulty of delineating narrow streams in low-

resolution images also bounds the utility of a remote-sensing approach to streams and

rivers large enough that they a) are visible in the image and b) take up enough area that

small errors in delineation and measurement are tolerated. For instance, a large river such

38

as the Sacramento may have a mean annual erosion rate of as little as 3.7 m. per year

(Larson et al., 2006); as a result, a single year, or even several years, of erosion may not

be measurable in a short intervening time period, particularly if the image is blurred,

shadowed, or otherwise distorted. RMS error can be a useful indicator of possible spatial

uncertainty, but is only as accurate as the anchor points selected which, when working

with low-resolution or warped aerial images, are also easily misplaced.

In addition, as is apparent by my analysis of plant mis-identification in this study,

plant identification is time-consuming and difficult to constrain accurately. Even in a

system such as montane meadows, in which the diversity of macro-vegetation is strongly

limited and visibly distinct, the potential for misidentification remains large. Aerial

categorizations of vegetation is also usually biased towards larger, more obviously

identifiable vegetation types, even though smaller or less conspicuous plants may play an

equally important role. For instance, grasses that play a large role in bank stabilization

study (Micheli and Kirchner 2002a) have likely been undervalued in this because they are

difficult to identify in gray-scale historical images. Futhermore, although methods such

as the Normalized Differentiation Vegetation Index (NDVI) or pattern identification for

black and white images holds promise for a more expedient way of identifying

vegetation, both methods suffer from similar unconstrained uncertainties and error.

Lastly, remote sensing simply cannot gather certain variable information vital to

understanding eco-hydrological systems. One major factor that cannot be obtained via

remote sensing is information about substrate size, type, and quality. Substrate is known

to be a powerful predictor of bank stability (Stacey and Rutherford, 2007). For instance,

in dry meadow systems such as the one studied in this paper, silt and clay contribute

39

more to incision processes than coarser particles (Anderson, Bledsoe and Hession et al.,

2004). These changes in particle size along the stream gradient may help account for

some of the longitudinal variation observed along Last Chance Creek, but a method based

solely on aerial imagery does not allow for verification of this speculation.

CONCLUSION

This study found no evidence of stream power influence over vegetation or stream

morphology on Last Chance Creek, but strong evidence that stream - longitudinal

processes have the potential to be an important factor in vegetation regime and stream

morphology change. These longitudinal trends may be related to groundwater

accessibility along the stream trace, perhaps mediated by substrate changes along the

creek. However, inconsistencies in the relationship between vegetation and stream

morphology change, as well as in longitudinal trends, between 1954 and 1977 and 1977

and 2016 show that the longitudinal trends observed may be temporally unstable. It was

beyond the scope of this study to make field measurements or examine historic climate

and precipitation data that may elucidate the underlying causes of these inconsistencies

between time periods, but I suggest a few possibilities including the effects of large-scale

restoration projects on stream flow and interannual seasonal change between the photos

that may obscure annual or decadal changes. I conclude that while historic and spatial

approaches to assessing stream processes can be challenging due to low resolution, a lack

of dimensional information and large inherent statistical error, the ability to “zoom out”

both temporally and spatially can highlight patterns in stream change that would be

40

obscured in stream assessment approaches that only involve field observations limited in

spatial and temporal scale.

ACKNOWLEDGEMENTS

I would like to thank my advisor, Mary Savina, for her warmth, wisdom, and everlasting

sense of wonder. Wei Hsin of the Carleton ArcGIS lab proved essential in producing this

study. I also thank Jen Natali for the initial concept for this project and Andy Hoyt for

statistical consultation, as well as the Carleton geology department.

REFERENCES CITED

Anderson, M.K., and Moratto, M.J., 1996, Native American Land-Use Practices and Ecological Impacts, in Sierra Nevada Ecosystems Project: Final report to Congress, Davis, CA, Univeristy of California, Centers for Water and Wildland Resources, p. 187–206, https://pubs.usgs.gov/dds/dds-43/VOL_II/VII_C09.PDF (accessed March 2019).

Bagnold, R.A., 1966, An approach to the sediment transport problem from general physics: Physiographic and Hydraulic Studies of Rivers, Geologic Survey professional paper 422-1.

Bendix, J., 1999, Stream Power Influence on Southern Californian Riparian Vegetation Author: Journal of Vegetation Science, v. 10, p. 243–252.

Bergmann, K., 2004, Stream incision and sagebrush expansion on the Kern Plateau, California: A Hydrologic Perspective: Carleton College (accessed March 2019).

Bizzi, S., and Lerner, D.N., 2015, The use of stream power as an indicator of channel sensitivity to erosion and deposition processes: river research and rpplications, v. 31, p. 16–27, doi:10.1002/rra.2717.

Bledsoe, B.P., Watson, C.C., and Biedenharn, D.S., 2002, Quantification of incised channel evolution and equilibrium: Journal of the American Water Resources Association, v. 38, p. 861–870.

41

Dickard, M. et al.., 2013, Riparian area management: proper functioning condition assessment for lotic areas: National Forest Service.

Eaton, B.C., and Giles, T.R., 2009, Assessing the effect of vegetation-related bank strength on channel morphology and stability in gravel-bed streams using numerical models: Earth Surface Processes and Landforms, v. 34, p. 712–724, doi:10.1002/esp.1768.

Essaid, H.I., and Hill, B.R., 2014, Watershed-scale modeling of streamflow change in incised montane meadows: Water Resources Research, p. 2657–2678, doi:10.1002/2013WR014420.

Evelyn, C.J., and Sweet, S.S., 2012, Conservation status of amphibians and reptiles on USDA National Forests , Pacific Southwest Region: Institute for Computational Earth Systems Sciences, Universtiy of California, Santa Barbara, prepared for USDA National Forest, Region 5.

Finlayson, D.P., and Montgomery, D.R., 2003, Modeling large-scale fluvial erosion in geographic information systems: Geomorphology, v. 53, p. 147–164, doi:10.1016/S0169-555X(02)00351-3.

Gartner, J., 2016, Stream power: origins, geomorphic applications, and GIS procedures stream power: origins, geomorphic applications, and GIS procedures: Water Publications, v. 1, https://extension.umass.edu/riversmart/.

Graber, D.M., 1996, Status of terrestrial vertebrates, in Sierra Nevada Ecosystems Project: Final report to Congress, Davis, CA, Univeristy of California, Centers for Water and Wildland Resources p. 709-754 (accessed February 2019).

Harris, R.R., 1987, Occurrence of vegetation on geomorphic surfaces in the active floodplain of a California alluvial stream: The American Midland Naturalist, v. 118, p. 393–405.

Jain, V., Preston, N., Fryirs, K., and Brierley, G., 2006, Comparative assessment of three approaches for deriving stream power plots along long profiles in the upper Hunter River catchment, New South Wales, Australia: Geomorphology, v. 74, p. 297–317.

Kasprak, A. et al., 2013, Differentiating the relative importance of land cover change and geomorphic processes on fine sediment sequestration in a logged watershed: Geomorphology, v. 185, p. 67–77, doi:10.1016/J.GEOMORPH.2012.12.005.

Kattelmann, R., and Embury, M., 1996, Riparian Areas and Wetlands, in Sierra Nevada Ecosystems Project: Final report to Congress, Davis, CA, Univeristy of California, Centers for Water and Wildland Resources, v. 3, https://pubs.usgs.gov/dds/dds-43/VOL_II/VII_C09.PDF (accessed March 2019).

42

Klausmeyer, Kirk and Fitzgerald, K., 2012, Where does California’s water come from? Land conservation and the watersheds that supply California’s drinking water: The Nature Conservancy of California (unpublished report).

Knox, A.K., Dahlgren, R.A., Tate, K.W., and Atwill, E.R., 2008, Efficacy of natural wetlands to retain nutrient, sediment and microbial pollutants: Journal of Environment Quality, v. 37, p. 1837, doi:10.2134/jeq2007.0067.

Larsen, E.W., Premier, A.K., and Greco, S.E., 2006, Cumulative effective stream power and bank erosion on the Sacramento River, California, USA: Journal of the American Water Resources Association, v. 42, p. 1077–1097, doi:10.1111/j.1752-1688.2006.tb04515.x.

Leopold, L.B., and Maddock, T.J., 1953, The Hydraulic Geomtry of Stream Channels and Some Physiographic Implications: Geological Survey Professional Paper 252, p. 57, doi:10.3133/PP252.

Loheide, S.P., et al. 2009, A framework for understanding the hydroecology of impacted wet meadows in the Sierra Nevada and Cascade Ranges, California, USA: Hydrology, p. 229–246, doi:10.1007/s10040-008-0380-4.

Lord, M.L., Jewett, D.G., Miller, J.R., Germanoski, D., and Chambers, J.C., 2011, Geomorphology, hydrology, and ecology of Great Basin meadow complexes: implications for management and restoration: USDA Forest Service, https://www.fs.fed.us/rm/pubs/rmrs_gtr258/rmrs_gtr258_044_067.pdf (accessed March 2019).

Lynn Zong, K., Swanson, S., and Myers, T., 1996, Drought year changes in streambank profiles on incised streams in the Sierra Nevada Mountains: Geomorphology, v. 15, p. 47–56, doi:10.1016/0169-555X(95)00125-O.

Magilligan, F.J., Buraas, E.M., and Renshaw, C.E., 2015, The efficacy of stream power and flow duration on geomorphic responses to catastrophic flooding: Geomorphology, p. 175–188, doi:10.1016/J.GEOMORPH.2014.08.016.

Mceldowney, A.R.R.et al., 2002, Sediment movement and filtration in a riparian meadow following cattle use: Journal of Range Management, v. 55, p. 367–373.

McLaughlin, B.C. et al., 2017, Hydrologic refugia, plants, and climate change: Global Change Biology, v. 23, p. 2941–2961, doi:10.1111/gcb.13629.

Micheli, E.R., and Kirchner, J.W., 2002, Effects of wet meadow riparian vegetation on streambank erosion. 2. Measurements of vegetated bank strength and consequences for failure mechanics: Earth Surface Processes and Landforms, v. 27, p. 687–697, doi:10.1002/esp.340.

43

Micheli, E.R., and Kirchner, J.W., 2002, Effects of wet meadow riparian vegetation on streambank erosion. 1. Remote sensing measurements of streambank migration and erodibility: Earth Surface Processes and Landforms, v. 27, p. 627–639, doi:10.1002/esp.338.

Neeson, T.M. et al., 2008, Factors affecting accuracy of stream channel slope estimates derived from geographical information systems: North American Journal of Fisheries Management, v. 28, p. 722–732, doi:10.1577/M05-127.1.

Norton, J.B. et al., 2011, Soil Carbon and Nitrogen Storage in Upper Montane Riparian Meadows: Ecosystems, v. 14, p. 1217–1231, doi:10.1007/s10021-011-9477-z.

Ohara, N. et al., 2014, Modelling atmospheric and hydrologic processes for assessment of meadow restoration impact on flow and sediment in a sparsely gauged California watershed: Hydrological Processes, v. 28, p. 3053–3066, doi:10.1002/hyp.9821.

Plumas Corporation, 2012, Feather River coordinated resource management 2011 annual report to signatory agencies:, www.feather-river-crm.org. (accessed December 2018).

Plumas Corporation, Inc. 1992, East Branch North Fork Feather River, Spanish Creek and Last Chance Creek non-point source water pollution: prepared for the USDA Plumas National Forest, https://www.plumascorporation.org/uploads/4/0/5/5/40554561/spanlast.pdf (accessed March 2019).

Ramstead, K.M., Allen, J.A., and Springer, A.E., 2012, Have wet meadow restoration projects in the Southwestern U.S. been effective in restoring geomorphology, hydrology, soils, and plant species composition? Environmental Evidence, v. 1, p. 11, doi:10.1186/2047-2382-1-11.

Readle, J.R., 2014, Groundwater storage potential in degraded meadows along Last Chance Creek, Plumas County, CA: California State University, 44 p.

Reinfelds, I., Cohen, T., Batten, P., and Brierley, G., 2004, Assessment of downstream trends in channel gradient, total and specific stream power: A GIS approach: Geomorphology, p. 403–416, doi:10.1016/j.geomorph.2003.10.003.

Sawyer, S.C., 2016, Non-meadow riparian Zones: Natural Range of Variation (NRV) of non-meadow riparian zones in the bioregional assessment area. Unpublished report. USDA Forest Service, Pacific Southwest Region, Vallejo, CA.

Schoenherr, A. A, 1992, A natural history of California: University of California Press, Berkeley and Los Angeles.

44

Siegel, R.B. et al., 2014, Vulnerability of birds to climate change in California’s Sierra Nevada: Avian Conservation and Ecology, v. 9, p. 7, doi:10.5751/ACE-00658-090107.

Specialists, R., 2010, The pond-and-plug treatment for stream and meadow restoration: resource effects and design considerations: a briefing paper for Plumas National Forest resource specialists and managers: USDA National Forest.

Stacey, M., and Rutherfurd, I., 2007, Testing specific stream power thresholds of channel stability with GIS:, in proceedings of the Australian stream management conference, 5th, Thurgoona: New South Wales, Charles Stuart University, p. 384-389.

Stillwater Sciences, Inc., 2012, A guide for restoring functionality to mountain meadows of the Sierra Nevada: prepared for American Rivers, Nevada City, CA.

Stromberg, J.C., Lite, S.J., and Dixon, M.D., 2010, Effects of stream stream flow patterns on riparian vegetation of a semiarid river: implications for a changing climate: River Research and Applications, v. 26, p. 712–729, doi:10.1002/rra.

Viers, J.H. et al., 2013, Montane meadows in the Sierra Nevada: changing hydroclimatic conditions and concepts for vulnerability assessment: General Technical Report, p. 63, doi:10.1080/10106040108542217.

Wilcox, J.G., 2009, Water management implications of restoring meso-scale watershed features: prepared for USDA Plumas National Forest. http://www.plumascorporation.org/uploads/4/0/5/5/40554561/wm_implications.pdf (accessed December 2018).

Zonge, L., and Swanson, S., 1996, Changes on streambanks in the Sierra Nevada mountains: perspectives from a dry and a wet year: Restoration Ecology, v. 4, p. 192–199, doi:10.1111/j.1526-100X.1996.tb00119.x.