Impact of Coastal Fog on Gage Height in an Old Growth

IMPACT OF COASTAL FOG ON GAGE HEIGHT

IN AN OLD GROWTH REDWOOD FOREST - PRAIRIE CREEK,

REDWOOD STATE AND NATIONAL PARKS

Koa Lavery

A Thesis Presented to

The Faculty of Humboldt State University

In Partial Fulfillment of the Requirements for the Degree

Master of Science

In Natural Resources: Forestry, Watershed and Wildland Studies

Committee Membership

Dr. Kristine Brenneman, Committee Chair

Dr. Alison O’Dowd, Committee Member

Dr. Raymond Burke, Committee Member

Dr. Alison O’Dowd, Graduate Coordinator

December 2015

ABSTRACT

IMPACT OF COASTAL FOG ON GAGE HEIGHT IN PRAIRE CREEK IN AN OLD GROWTH REDWOOD FOREST

Koa Lavery

Streams have a diurnal fluctuation in quantity of flow that corresponds with rates of evapotranspiration. As much as 45% of water consumed by California coastal redwoods (Sequoia sempervirens) appears to come from fog. The relation between fog and gage height was investigated for the first sixteen precipitation-free days of July, 2013 in the old growth portion of Prairie Creek Watershed in coastal northwestern California

(Redwood National and State Parks). Satellite imagery was used to determine presence of fog and a pressure transducer was used to determine gage height in Prairie Creek near the confluence with Boyes Creek. This study tested the hypothesis that as fog increased, the diurnal fluctuation and typical summer downward trend of gage height (as a measure of stream flow) would be disrupted. A positive relation between fog and gage height was found. The relation between average daily fog coverage of watershed and diurnal fluctuation in gage height had an R squared of 0.3261. Fog coverage and time of day maximum gage height occurred had an even higher correlation (R squared 0.5295). Time of maximum gage height varied between 7am and 1pm. Gage height peaked later in the day when more of the watershed was covered in fog. A lag in gage height response to fog coverage was a confounding factor. Stream measurements that represented changes ii

over greater time spans (one day) showed a higher correlation with fog coverage than did metrics on shorter time scales (30 minutes) (R squared 0.5295 vs 0.06430 respectively).

Although the obvious short term variation in gage height is a few millimeters, a few

millimeters is 2% to 3% of total flow and Prairie Creek is a relatively small stream. The

implications of a continual 2%-3% change over multiple years or on large streams and

rivers could send a massive ripple through riparian ecosystems. Ultimately, this research

supports a long held belief that fog has an impact on streams.

iii

ACKNOWLEDGEMENTS

Thank you to two people who died while this thesis was in progress, my mom and

Suzie Seeman. They both had an amazing appetite for learning and I am still working to digest all they shared with me. I fully appreciate my committee sticking with me over the years and Bud Burke for keeping up with the project so he was able to officially jump in at the end; it has been a saga. Thanks to Dr. B. for improving my grammar and teaching me about water bears. Thanks to Alison for recognizing what I ment to say and figuring out how to say it as well as helping me meet deadlines. Moral support from my dad and

Aunt Lynette who spent a summer sitting next to me and the computer trouble shooting was essential. Thanks to Joe Seney for giving me the idea to investigate fog. Since this thesis took over seven years to complete the list is too long to include everyone who helped me wade through the infinity of thoughts to nail a few down on paper; some of the people who were crucial to completing this project are Anjii Hansen, Katie Wilson, Wes

Smith, Mat Lavery (diagnosing major computer issues for months), Judy Wartella, Vicki

Ozaki, Alicia Torregorsa, and Mel Nordquist.

TABLE OF CONTENTS

ABSTRACT ...... ii

ACKNOWLEDGEMENTS ...... iv

LIST OF FIGURES ...... vi

INTRODUCTION ...... 1

Redwood utilization of fog ...... 3

Relation between Photosynthetically Available Radiation (PAR), Evapotranspiration and Streamflow Dynamics ...... 5

Streamflow Diurnal Fluctuation ...... 5

Redwood use of fog ...... 6

Fog Impact on Streamflow ...... 8

Fog Detection ...... 10

Materials and methods ...... 13

Study Area ...... 13

Stream Gage Measurements ...... 16

Satellite Imagery to Detect Fog ...... 17

RESULTS ...... 22

Streamflow Measurements ...... 22

Comparison of Fog and Gage Height ...... 26

DISCUSSION ...... 37

Recommendations ...... 41

REFERENCES ...... 44

Personal Communication Citations...... 51

LIST OF FIGURES

Figure 1. Prairie Creek watershed in Redwood National and State Parks (Humboldt County, CA). Green triangle shows Prairie Creek above Boyes Creek (PAB) gaging station. Graphic created in ArcGIS 10.1 ...... 14

Figure 2. GOES-15 satellite image taken on July 1, 2013 at 6:15pm. Turquoise polygon is Prairie Creek watershed. White pixels are fog moving onto coast from ocean. Each pixel is 1 km2...... 19

Figure 3. Shows Geos-15 image taken on July 1, 2013 at 6:15pm. Image on the left shows pixels before classifying as fog, and not fog. Image on right shows pixels that have been reclassified as fog on left side of image (in green) and pixels reclassified as not fog on right side of image (in salmon). Turquoise line is boundary of Prairie Creek watershed...... 20

Figure 4. Comparison of gage height (dotted line) measured on Prairie Creek above Boyes Creek (PAB), and precipitation (solid line) measured in Orick at station MORIC1. The graph reveals a period with no precipitation between July 1 and 16, 2013. This period was used to investigate fog impact on gage height. All data points after July 16, 2013 were removed due to a decrease in sensor resolution by a factor of ten from 0.03 centimeters (thousandths of a foot) to 0.3 centimeters (hundredths of a foot), and spikes (e.g. July 17 and August 8) in data that are associated with sensor removal from stream...... 23

Figure 5 Rating curve for Prairie Creek above Boyes (PAB). Eight discharge measurements were made by Redwood National Park personnel between June 12th and August 19th to construct this rating curve. At least two points (i.e. 25% of the points) did not follow the trend line. R squared = 0.9265...... 25

Figure 6. Comparison of raw and detrended gage heights for Prairie Creek above Boyes Creek (PAB) shows the best fit line (fit to raw data) for July 1 through July 17, 2013. The equation for the best fit line was used to remove the downward trend in flow to enable comparison of flows on different days without the impact of decreasing groundwater flow. Dashed red line shows raw gage heights. Solid blue line shows detrended gage heights...... 26

Figure 7. A comparison of the percentage of fog covering Prairie Creek watershed detected in each satellite image (blue line) and daily average fog detected in each satellite image (red dashed line) between July 1 and 16, 2013 are similar. July 4, 5, 13, and 14 had no fog as noted by a star...... 27

Figure 8. Plot showing gage heights measured every 10 minutes (blue line) and daily average of percentage of watershed covered in fog (orange dotted line) between July 1 and July 17, 2013. A fog event on July 11 preceded a reversal of decreasing gage heights (i.e. gage heights began to rise after large fog event)...... 28

Figure 9. No clear relationship was found between detrended gage height (m) and percentage of watershed covered in fog (R squared = 0.0643)...... 29

Figure 10. Gage heights averaged for each day (dotted red line) do not show the diurnal fluctuation exhibited in gage heights taken every ten minutes (blue line)...... 30

Figure 11. Gage heights (red dotted line) tends to go up after fog covering watershed (blue solid line) exceeds 35%. The lag in gage heights increasing after fog events varies...... 31

Figure 12. The correlation between detrended daily average gage height and daily average percentage of watershed covered in fog and daily average gage height appears to be negligible (R2=0.0196)...... 31

Figure 13. Percentage of Prairie Creek watershed covered in fog (from GOES-15 satellite) plotted with daily fluctuation in gage height recorded on Prairie Creek above Boyes Creek gaging station. Daily fluctuation in gage height (dotted line) is generally inverse of the percentage of watershed covered in fog ...... 33

Figure 14. Daily diurnal fluctuation in gage height correlates with fog about 30% of the time. When less than 40% of Prairie Creek watershed is covered in fog, diurnal fluctuation varies dramatically. When greater than 40% of the watershed is covered in fog daily diurnal fluctuation is below 0.0014 detrended meters...... 34

Figure 15. Fog and gage height generally correspond with one another when fog is averaged over each day and gage height is measured as time of maximum height for each day during the period between July 1 and 16, 2013...... 35

Figure 16. Time of daily maximum gage height plotted against daily average percentage of watershed covered in fog yields a positive relationship and an R squared of 0.530 between July 1 and 16, 2013...... 36

vii

INTRODUCTION

Coastal fog in California is a meteorological phenomenon to which ecosystems

have adapted. Many plant species evolved to utilize and are dependent on coastal

summer fog (Dawson 1998; Williams 2008; Carbone et al., 2012). These fog-dependent species range from pine trees such as Torrey pine (Pinus torreyanna ssp. Insularis) and

Bishop pine (Pinus muricata D. Don) to chaparral shrubs such as manzanita

(Arctostaphylos sp.) to lush, massive conifers like California coastal redwoods (Sequoia

sempervirons D. Don Endl.).

Coastal fog is most common in summer due to the location of the Pacific High

Pressure System (Pacific High), which is generally present off the North Coast of

California during June, July, and August (Bakun, 1990). The Pacific High drives three

factors that contribute to coastal fog formation: 1) an inversion layer (functions as a lid

that keeps fog on the ground); 2) ocean upwelling (creates a strip of cold ocean water that

cools the air as it flows onshore, thus preventing low pressure storms from reaching land

thereby creating summers with little or no precipitation); and 3) onshore wind (blows air

over cool upwelled waters and onto land) (Leipper, 1994; Koracin et al., 2001; Lewis et

al,. 2003). These three factors driven by the Pacific High combined with adiabatic

cooling from turbulent spirals result in onshore summer fog as air is cooled to dew point

and prevented from mixing with dry air aloft (Thompson and Burk, 2005; R. Stepp pers.

Comm, 2012; C. Haynes and S. Madrone pers. comm., 2012).

Movement and duration of fog can impact many aspects of an ecosystem such as

propagation of disease, nutrient cycling, and pollution transport. Agriculture is massively

impacted by leaf wetness; if leaf wetness exceeds a threshold, disease can propagate

(Melching et al., 1989; Castello et al., 1995). Crops negatively affected by leaf wetness

include corn, soy, and wine grapes (Huber and Gillespie 1992, Gubler 1999). Fog may

increase ecosystem vulnerability to disease such as sudden oak death (Phytophthora

ramoram) because colonization success of many pathogens is controlled by leaf wetness

(Duniway 1983, Davidson et al. 2002, Hansen et al. 2008). Fog affects terrestrial

nitrogen abundance by transporting nitrogen from ocean to land (Ewing et al. 2009). Fog

has also been shown to transport pollutants. Yang-Ling et al. (2009) found that fog in

Taiwan has a lower pH (4.9) than rain (pH 5.5) due to pollutant transport. Other studies

have demonstrated that fog also transports mercury from ocean to land (Ritchie et al.

2006, Weiss-Penzias et al. 2012).

Climate change is likely to affect fog regimes (Bakun, 1990). Examples of the

uncertainty climate change will have on fog regimes are evidenced by the difference

between predictions and inferred trends. Bakun (1990) speculated that climate change

may result in more coastal fog as winds strengthen and enhance upwelling and drive

coastal fog in the summer. However, Johnstone and Dawson (2010) reported that coastal

fog between Monterey and Arcata, California, decreased 33% between 1951 and 2008 as compared to the period from 1901 to 1925. They imply climate change is the cause and

go on to suggest the trend is likely to continue and will tax redwood ecosystems.

Although researchers still differ in their opinions regarding the impact climate

change will have on fog, they all agree fog regimes are likely to change in the future. An

analysis of Pacific Coast Streams on the west coast of United States found baseflow in

most streams has decreased and suggested changing climate is the cause (Sawaske and

Freyberg 2014). Fog regimes may change enough to impact the time of year redwood-

dominated streams go dry, which would in turn would impact aquatic organisms such as

benthic macroinvertebrates and fish (McElravy et al. 1989, Strange 1989). Consequently,

knowing how fog is linked to the rest of the redwood ecosystem may help predict salmon

runs and salmon passage which could assist with management decisions (Ozaki et al.

1994, Madej et al. 2011).

Redwood utilization of fog

One ecosystem that is particularly vulnerable to changes in fog regimes is the

redwood ecosystem of the California Pacific Coast. Redwoods meet their water

requirements by using both groundwater and fog (Dawson 1998, Limm et al. 2009).

Fischer and Williams (2008) demonstrated that fog plays an important role in reducing evapotranspiration and supplying moisture for vegetation during the summer in

Mediterranean climates. Using isotopic analysis Limm et al. (2009) found the degree to which plants in redwood ecosystems are able to directly absorb moisture from fog is primarily determined by the duration of fog (i.e. the longer the fog event, the more fog plants sequester).

Redwood use of fog was also detected by measuring direction and speed of sapflow in the cambium of redwoods. Since the cambium caries water (and the phloem caries sugars), it seems plausible that redwoods are absorbing groundwater when sap in the cambium is flowing up (from roots to leaves), conversely redwoods are absorbing fog when sap is flowing down (from leaves to roots). Sap ordinarily flows up from roots to leaves, consequently sap flowing from leaves to roots is considered reverse sap flow

(Burgess et al. 2001). Burgess and Dawson (2004) documented reverse sap flow

(meaning water was flowing from leaves towards roots) in redwoods during fog events.

In Mediterranean climates during the summer and fall, streams are fed by the same groundwater that redwoods use (Gasith and Resh 1999). Fog may provide an alternative source of water to trees thereby reducing redwoods’ demand of groundwater and increasing groundwater flow to streams. Plants can utilize fog via direct absorption and via drip. Up to 45% of a redwood’s water consumption has been inferred to be from root uptake of fog drip and another 6-8% from foliage absorption of fog (Dawson 1998,

Burgess and Dawson 2004). The 45% calculation was done in an area approximately

40km (25 miles) inland, near Sonoma, California (Dawson 1998). The area sampled near

Sonoma probably has less fog than more coastal and northern redwood forests because coastal fog tends to dissipate at it moves inland. Limm (2009) found that 80% of redwood forest species can directly absorb fog moisture through foliage. Consequently, coastal redwood forests may derive a higher percentage of water from fog than has been measured in inland forests.

Relation between Photosynthetically Available Radiation (PAR), Evapotranspiration and

Streamflow Dynamics

Redwoods have poor stomatal control, consequently redwood evapotranspiration is more weather dependent than other plants (Dawson 1998). Due to poor stomatal control, evapotranspiration rate of redwoods is highly related to both seasonal and diurnal fluctuations in photosynthetically active radiation (PAR) (Burgess and Dawson 2004).

Fog reduces PAR, which in turn reduces vapor pressure deficit which reduces plant water needs.

Streamflow Diurnal Fluctuation

Summertime stream hydrographs in forested watersheds have peaks and troughs that are roughly opposite the peaks and troughs of solar radiation (Lundquist and Cayan

2002). Lundquist and Cayan (2002) have shown that diurnal fluctuation in streamflow is common as recorded in data from United States Geological Survey (USGS) stream gauges throughout the United States. In coastal northwestern California, preliminary analysis, for this study, of a tributary to Prairie Creek (Little Lost Man Creek) suggests diurnal fluctuation in streamflow is measurable during most low flows. Analysis of gage height from Little Lost Man Creek showed, in August 2011 diurnal fluctuation resulted in a gage variation of 5% to 20% (USGS GAUGE 11482468 REDWOOD CA ORICK CA).

Because fog blocks PAR (which should reduce evapotranspiration) and provides water to trees, the magnitude of diurnal fluctuation in streamflow seems likely to be impacted by fog presence. The typical diurnal fluctuation seen in streamflow is a sine wave (Lundquist and Cayan 2002). The crest of the wave (highest flow) occurs around

sunrise and the trough (lowest flow) occurs shortly after noon. Diurnal fluctuation in

streamflow seems to be predominantly controlled by riparian vegetation (Bren 1997).

Redwood use of fog

Redwoods preferentially grow in riparian forest in the Pacific Northwest and are

fog obligates. Redwoods predominantly grow on alluvial fans that are adjacent to

streams (Franklin and Dyrness 1973, Naiman et al. 1998). Redwoods only naturally occur along the California coast where fog occurs during at least 35% of summer season

(Marotz and Lahey 1975, Dawson 1998, Johnstone and Dawson 2010).

Redwood morphology is thought to be related to fog (Went, 1955). Utilization of fog may enable redwoods to exceed heights that would not be possible if height were strictly confined by capillary action (Koch et al. 2004). Fog provides an alternative source of moisture and relieves the redwood from “pumping” water from the roots to the canopy in the summer when soil is less moist (Simonin et al. 2009). Redwoods have some of the largest leaf area indices in the world (Westman and Whittakare 1975) and this is thought to be an adaptation to capture fog and to enable redwoods to survive low rainfall Mediterranean summers (Burgess and Dawson 2004, Oldham et al. 2010).

In Mediterranean climates, streamflow is lowest in the summer months, which is when fog is most abundant. Dawson (1998) estimated that redwoods around 45 meters

(150 feet) tall use 600 ±145 liters of water/day. Isotopic analysis of water in redwood trees suggests that redwoods in Sonoma County California get up to 45% of their water from fog (Dawson 1998). Because fog predominantly occurs in summer, redwoods may a get a substantial amount of their summer water needs from fog. Consequently,

7 groundwater that redwoods would have used is free to flow into streams as fog is used instead.

Total fog water used by redwoods in an entire forest in comparison to streamflow suggests fog has the potential to make an impact in flow. If one medium-size redwood

(45 meters,150 feet) uses 600 liters (21 cubic feet, 160 gallons) of water per day, then an old growth forest of redwood trees (approximately half being over 50 meters) could potentially lower the flow of a small stream during summer (Sillett and VanPelt 2007,

Dawson 1998). If Prairie Creek watershed (10,300 hectares or 25,400 acres) has 40 trees per hectare (100 trees per acre) and each tree uses 600 liters per day for two months, approximately 90 billion liters (3 billion cubic feet) will be used by redwoods (Veirs

1982, Lorimer et al. 2009). If fog does not serve as an alternative to ground water, redwoods will prevent this water from flowing to Prairie Creek (Gonzalez et al. 2010).

Because fog predominantly occurs in summer and approximately 50% of redwood water has been documented to come from fog, assuming 50% of summer time redwood water consumption comes from fog is a conservative estimate. If 50% of the water a redwood uses in summer is from fog, fog accounts for approximately 45 billion liters (1.5 billion cubic feet) of redwood water consumption. If Prairie Creek has a flow of 70 liters per second (2.5 cubic feet per second) for two months, more than 360 million litters (12 million cubic feet) flow past the gaging station. Two months of fog water contributed to

Prairie Creek watershed is equivalent to approximately 12,500%

(45,000,000,000/360,000,000*100) of summer Prairie Creek flow. Conversely, redwoods may contribute more water to the watershed than they use by functioning as

condensation nuclei, which causes fog to precipitate and flow into Prairie Creek (Fischer

and Still 2004, Sawaske and Freyberg 2014a).

Fog Impact on Streamflow

Studies in redwood forests have suggested fog influences streamflow by reducing

evapotranspiration, but more research is needed (Madej and Torregrosa 2011, Klien

2012, L. Reid and E. Keppeler pers. comm. 2012). Klein (2012) anecdotally noted that stream discharge in the Mattole Valley (Humboldt County, CA) seemed to increase during fog events. Onging studies in the Caspar Creek watershed in Fort Bragg

California (Mendocino County) incorporate fog screens in an effort to account for the impact of fog on streamflow (L. Reid and E. Keppeler pers. comm. 2012). Harr (1982) found that a portion of a watershed in Andrew’s Experimental Forest (Portland, Oregon) dominated by Douglas fir (Psuedosuga menzezii) received 44% more precipitation (as measured by trough collectors) than a prairie in the same watershed and concluded the increased precipitation was due to fog drip. Ingwerson (1985) followed up on Harr’s

work and noticed stream flow increased during summer months as vegetation returned.

Ingwerson (1985) inferred that the ratio of fog drip to evapotranspiration changed in

summer causing higher streamflows in summer months once trees were a few years old

and captured more fog. In 1986 Ingraham and Matthews used isotopic ratios to analyze

ground water. Although their findings were inconclusive they stated fog drip may

provide a “substantial” contribution to groundwater.

In contrast to the observations previously described, streamflow is known to be measurably diminished by evapotranspiration of riparian vegetation during summer flows. Kepeler and Ziemer (1990) found that removal of 67% of timber in the Casper

Creek watershed resulted in higher discharge over the dry season. Regarding flow on shorter time scales, diurnal fluctuation in streamflow was also found to be heavily influenced by evapotranspiration of riparian vegetation during the dry season (Bren

1997). Despite the above studies, there remains much to learn about the relation between riparian vegetation and streamflow. Regardless of whether fog increases or decreases in the future, knowing if a change in fog regime translates into a change in streamflow via changes in evapotranspiration will improve understanding of redwood ecosystems.

At the onset of the research presented here, no one had published directly addressing the impact of fog on streamflow (Sawaske and Freyberg 2014). In 2014

Sawaske and Freyberg measured fog drip and found a relation between fog and streamflow in the Santa Cruz Mountains. In this study, satellite imagery was used to detect fog and investigate the linkage between summer time fog and gage height in a coastal California redwood ecosystem. The objective of this research was to determine if the impact of summer fog was detectable in gage height data in a redwood-dominated

Prairie Creek watershed. The intent of this research was to help natural resource managers anticipate the impacts that potential changing fog regimes will have on redwood forests and associated stream ecosystems.

Fog Detection

Satellite imagery has been used to determine the duration of fog events with mixed levels of success. Combs et al. (2010) worked to develop a fog climatology for the

Eureka, California area. Geostationary Earth Orbiting Satellites (GEOS-15 otherwise known as GOES West) scan the west coast of the United states approximately every 30 minutes. Jedlovec and Laws (2003) developed a method to identify fog by using temperature and reflectance (fog is warmer than clouds). Subtracting the 3.6 micron channel (shortwave infrared, represents reflectivity) from the 10.7 micron channel

(longwave infrared, represents temperature) causes warm clouds (i.e. fog) to stand out in infrared imagery. In an effort to capture the variations in fog presence throughout the day, Combs et al. (2010) analyzed images taken at midnight, 6am, noon and 8pm.

Ultimately Combs et al.’s (2010) interpretation of absence or presence of fog misinterpreted the burn off of summer fog by 4 hours. The authors attributed the error in interpretation to a lack of resolution. GEOS-West is a geosynchronous satellite that provides 1 km2 true color scans during the day and 4 km2 infrared scans in the night.

Combs et al. (2010) ended up with a resolution of 12 km2 because they averaged the values of nine pixels in infrared (to reduce image file size). Jedlovec and Laws (2003) mention the difficulty and importance of detecting the presence of fog using satellite imagery.

Johnstone and Dawson (2010) used airport data (from Monterey and Arcata) to infer presence or absence of fog and then correlated the presence of fog with a

11 temperature differential between coastal and inland areas. Temperature differentials then were used to extrapolate fog record back to the 1900’s.

Fog has also been detected directly by using passive and active means. Records of fog harvest from plants date back 2000 years (Glas 1764). The first recorded academic collection of fog was conducted in South Africa (Marloth 1903). Many devices for fog collection have since been invented. Some collect fog from one direction while others are multidirectional. The general approach entails using plastic mesh, monofilament or metal wire as condensation substrate with a funnel below (Loewe 1960, Kerfoot 1968,

Schrumenauer and Cereceda 1994, Fischer et al. 2008). A few active fog collectors have also been constructed including one by Demoz et al. (1996) when the design involved a fan moving air through a tunnel with a series of strings that fog condenses on causing the fog to drip into a collection chamber.

The most technologically advanced method of detecting fog is via isotopic signatures. A fog signature is determined by mechanically collecting fog and using a mass spectrometer to determine the abundance of oxygen 18. Rainwater from the same local is also analyzed via a mass spectrometer to determine oxygen 18 content. The wood of a tree can then be analyzed for oxygen 18 abundance and a mixing model used to determine percentage each water source provided to the tree (Ingraham and Matthews

1988, Ingraham and Matthews 1995, and Dawson 1998, Fischer 2007). Williams (2006) took isotopic analysis of fog a step further by correlating isotopic fog signatures with tree ring growth.

In the literature, fog is often referred to as occult precipitation because the

moisture contribution to ecosystems is difficult to measure compared to precipitation and the impact is generally unknown (Dawson 1998). At the onset of the research presented here, no one had formally tested the impact of fog on streamflow (Sawaske and Freyberg

2014). This study investigates the linkage between summer time fog and gage height in

the Prairie Creek watershed, a coastal California redwood ecosystem.

MATERIALS AND METHODS

Prairie Creek watershed was chosen as the study area because the watershed

typically becomes completely inundated with summer fog, is dominated by old growth

redwoods and has existing stream gaging stations. To determine the time period to

investigate, gage height and precipitation for the 2013 fog season (May through August) were plotted next to one another. The period of interest (July1 through July 16) was chosen for analysis because there were no precipitation events during this time and the pressure transducer to measure stage height was functional.

Study Area

Prairie Creek is 22.5 km long and drains 103 km2 (25,425 acres). The watershed

is located within Redwood National and State Parks near Orick, California (Figure 1)

(Strange 1989). Mean annual precipitation is 172 cm (68 inches) and mean annual air

temperature is 11 degrees Celsius (52 degrees Fahrenheit) (PRISM Climate Group 2013).

Four species of salmonids can be found in Prairie Creek; Chinook salmon (Oncorhynchus

tshawytshca), coho salmon (O. kisutch), steelhead (O. mykiss), and coastal cutthroat trout

(O. clarki clarki) (Wright 2011).

Figure 1. Prairie Creek watershed in Redwood National and State Parks (Humboldt County, CA). Green triangle shows Prairie Creek above Boyes Creek (PAB) gaging station. Graphic created in ArcGIS 10.1

The Prairie Creek watershed is divided into east and west portions by geology and into north and south portions by vegetation. The entire watershed is underlain by the

Mesozoic Franciscan Complex. More specifically, the watershed is comprised of two geologic units, the Lacks Creek formation on the eastern side of Prairie Creek and the

Prairie Creek formation on western side of Prairie Creek (Harden et al. 1982, Sacklin et al. 1988). Prairie Creek flows north to south separating the two units. The Lacks Creek formation is the bedrock of the Bald Hills, which reach heights of 900 meters (3,000 feet). The hills to the west are called the Gold Bluffs and reach heights of 250 meters

(800 feet).

The riparian area in the upper watershed is dominated by redwoods whereas the riparian area in the lower watershed is dominated by red alder (Alnus ruba) (Lisle et al.

2007). The plants commonly found in the Prairie Creek watershed include redwoods (S. sempervirons), Douglas-fir (Pseudotsuga menziesii), tanoak (Notholithocarpus densiflorus), western hemlock (Tsuga heterophyllla), Sitka spruce (Picea sitchensis), red alder (Alnus rubus), sword fern (Polystichum munitum), evergreen huckleberry

(Vaccinium ovatum), redwood sorrel (Oxalis oregana), rhododendron (Rhododendron macrophyllum), and salmonberry (Rubus spectiabilis) (Mahony and Stuart 2007).

Land use throughout the Prairie Creek watershed has ranged from berry harvesting and fishing to logging and hay farming (Fritschle 2009, J. Wheeler pers. comm. 2013). The Yurok Tribe utilized the Prairie Creek watershed for thousands of years prior to colonization. Yurok people harvested food and weaving materials from the

watershed (Lewis 1973). Prairie Creek is now jointly managed by California State Parks

and the National Park System (California State Parks 2011)

Stream Gage Measurements

Stream gage data were obtained from the National Park Service for Prairie Creek

above Boyes Creek (PAB) gaging station. Flow measurements were taken in 10-minute

intervals (using a Druck PS9800 pressure transducer and Cambell Scientic CR10X data

loggers).

A comparison of an AA pygmy meter and Swoffer (Swoffer Instruments

Incoporated, Seattle, Washington) meter was conducted by Koa Lavery. The Swoffer was calibrated according to manufacturer’s instructions by walking a known length while the sensor was in a swimming pool. Eight discharge measurements were taken during the period of interest to develop a low flow rating curve. Swoffer was used instead of the

AA pygmy meter because the propeller was observed to spin better in low flow.

A rating curve for the gaging station named PAB was made from eight flow measurements once a week during July and August of 2013. A line was fit to the flow measurements. The equation of the line was used to convert gage height measurements to flow.

A downward trend in gage height that occurred toward the end of summer 2013 was removed to allow for comparison of flow between days at beginning and end of study period. The downward trend was removed in excel by making a best fit line of flow plotted against time. Each date and time were plugged into the equation for the best

fit line and the resulting value was subtracted from the flow thereby leveling/detrending flow data. Detrending gage heights resulted in some negative gage heights. All gage heights were all increased by 0.00255m to ensure that none of the gage heights would be negative. The daily minimum and maximum zeroed detrended gage heights were subtracted to determine the amount of gage fluctuation for each day.

Satellite Imagery to Detect Fog

Satellite imagery was used to determine the absence or presence of fog as well as

duration and spatial extent in Prairie Creek watershed. Satellite imagery captured by

(Geostationary Operation Environmental Satellite) GOES-15 (otherwise known as GOES

West), on routine scans for Pacific United States was obtained from the National Oceanic

and Atmospheric Administration Comprehensive Large Array-Data Stewardship System

(NOAA CLASS) website, covering an extent from latitude -125 to -123 and longitude 43 to 38 north. GOES-15 scans occur in approximately 30 minute intervals and have a resolution of 1km2 during the day. Imagery after dark (infrared) was not used because the

resolution was lower (4km2). Consequently night time fog presence was inferred based on fog presence between evening and morining. When Cindy Combs and co-authors analyzed satellite imagery for fog absence and presence they used the infrared channel and concluded that this analysis had an error in determining presence or absence because the resolution was too low (Combs et al., 2010). Because fog tends to increase as temperature drops, fog is usually present throughout the night if there is fog in the early evening and following morning, therefore the inference of nighttime fog presence based

18 on presence or absence of fog at sunrise and sunset can be made (Johnstone and Dawson

2010, O’Brien et al. 2013).

Data were converted from Man computer Interactive Data Access System

(McIDAS) Area files to Geostationary Earth Orbit Tagged Image Files (GeoTIFF) using the NOAA Weather and Climate Toolkit 3.6.7 (NWCT) export function. Initially a single image was converted from McIDAS Area file to GeoTIFF as 8bit with 1600 grid and “cell size” set to “Auto”; these parameters resulted in a cell size of 0.03875 pixels.

The image was imported to ArcGIS 10.1 (Environmental Systems Research Institute,

Redland California). Zoomed in at a ratio of 1:66,406, numerous pixels were measured and the length of the pixels were found to be between 3.25 and 3.24 km. Because the original image had pixels of 1 km2, 0.03875 pixels was divided by 3.25 generating a quotient of 0.011933904 as pixel size for resolution of 1km2. When NWCT was used to convert McIDAS files to GeoTIFFs at 8bits and a cell size of 0.01 and “Grid = Auto”,

NWCT crashed, consequently the process was rerun using 8bits, cell size of 0.0125, and

“Grid=Auto”, the files exported and opened in ArcGIS. All files were then converted to

GeoTIFFs using the batch function in NWCT at 8bits, cell size of 0.0125 and

“Grid=Auto”.

GeoTIFFs were imported into ArcGIS, batch statistics were run and pyramids were built. ArcGIS was used to project (using “warp” algorithm) the imagery from the

WGS84 coordinate system to WGS84 UTM Zone 10. The values of pixels in the images were reclassified as zeros (no fog) and ones (fog). The values of fog and no fog were determined by investigating values along the edge of obvious fog with the identify tool.

The value of 3,000 captured the majority of fog. All values equal to or above 3,000 were

reclassified as “1” and all values below 3,000 were reclassified as “0” using the batch

version of “reclassify" (Figure 2, and Figure 3). The reclassified images were converted from raster to vector using the batch version of “raster to vector” tool. The polygonized fog shape files were “cut” with a shapefile of Prairie Creek watershed using the batch version of “cut” so data would only be generated for area inside watershed. Statistics were recalculated in batch mode to ensure that areas were accurate.

Figure 2. GOES-15 satellite image taken on July 1, 2013 at 6:15pm. Turquoise polygon is Prairie Creek watershed. White pixels are fog moving onto coast from ocean. Each pixel is 1 km2.

All band 1 files were copied to a new directory to isolate images taken in the

visible spectrum. Images taken after 6pm and before 8am were removed using the

following technique. A batch text file of directory with fog shapefiles was made using

Windows 8 command prompt. The text file was opened in Excel (Microsoft Office

Professional Plus 2010 Version 14.0.7145.5000 (32-bit)) and all files with a date stamp

20 between 2 Greenwich Mean Time (GMT) and greater than or equal to 16 GMT were eliminated from the list. A bat file was used to take all files in the list and copy them to a new directory.

All band 1 files were copied to a new directory to isolate images taken in visible spectrum. Images taken after 6pm and before 8am were removed using the following technique. A batch text file of directory with fog shapefiles was made using Windows 8 command prompt. The text file was opened in Excel (Microsoft Office Professional Plus

2010 Version 14.0.7145.5000 (32-bit)) and all files with a date stamp between 2

Greenwich Mean Time (GMT) and greater than or equal to 16 GMT were eliminated

from the list. A bat file was used to take all files in the list and copy them to a new

directory.

All polygons of fog were merged into a single shape file and each polygon was

attributed with its prior file name, which included time and date of image capture. All

fog polygons from the same image were added together to determine total fog in each

image. The area of fog inside Prairie Creek watershed for each image was divided by

total area of Prairie Creek watershed and multiplied by 100 to determine percentage of

watershed covered by fog. All times and dates were converted from GMT to Pacific

Standard (PDT) in Excel by subtracting seven divided by 24 from time dates.

The time of maximum gage height was calculated in Excel using “max if”

function. The daily average percentage of Prairie Creek Watershed covered by fog was

also calculated in Excel using “average” function. Both daily average percentage of

watershed covered in fog and daily time of maximum gage height were plotted against

time for July 1 through July 16, 2013. Time of daily maximum gage height (y-axis) was

also plotted against daily average percent of watershed covered in fog (x-axis), best fit line was drawn and R-squared was calculated. The Spearman rank correlation was calculated using daily average percentage of watershed covered in fog and daily time of maximum gage height.

RESULTS

Streamflow Measurements

Gage heights during days of rain needed to be removed from analysis, because the influence of precipitation was the dominant factor influencing streamflow, precipitation was plotted with gage height. When precipitation data, in Orick, California

(approximately 12km south of PAB) retrieved from station MORIC1, were plotted against gage height, for the 2013 fog season an increase in gage height was observed following precipitation events (Figure 4). Precipitation and gage height during the study period generally correlate (Figure 4). The Spearman rank correlation between gage height (dependent variable) and precipitation (independent variable) for May through

August 2013 was 0.97, which is considered significant. Due to the impact of precipitation on gage height the study period was chosen during times precipitation influence was minimal. During the 2013 fog season, eight precipitation events occurred between May 1 and June 30 and resumed in August. Therefore, the study period was narrowed to July 1 through August 31, 2013 because no precipitation events occurred during this period. All gage heights that exhibited an influence from precipitation were removed in order to eliminate the impact of precipitation on gage height, thus improving the ability to detect the impact of fog on gage height.

Gage heights were graphed and inspected for anomalies to see if there was an obvious fog signature. Spikes in flow on July 17 and August 8 initially appeared to be caused by fog. After conferring with the Vicki Ozaki from Redwood National Park, it was concluded the pressure transducer was removed from stream for servicing i.e. stranded (Figure 4). The stranding was inferred due to readings much higher than surrounding points, loss of diurnal fluctuation and a decrease in sensor resolution by a

factor of ten from 0.03 centimeters (thousandths of a foot) to 0.3 centimeters (hundredths of a foot). Consequently all data points after July 16, 2013 were removed. Ultimately

the study period started on July 1, 2013 because of prior precipitation and ended July 16

2013 due to corrupted gage height data after July 16 (Figure 4).

To convert gage heights to streamflow, a rating curve was constructed using eight

streamflow measurements collected between June 12 and August 19, 2013. The PAB

gaging station rating curve for July and August of 2013 was fit with the equation y =

0.3618x + 1.7032 which gave an R squared of 0.93 (Figure 5). Discharge measurements

were made using a Swoffer model 3000 current velocity and stream discharge indicator

with a three-inch propeller, which may not have had high enough resolution for summer

low flows. According to V. Ozaki (pers. comm. 2014), the three-inch propeller on the

Swoffer model 3000 may have greater than 10% error in low flow (less than 0.2 cubic

meters per second or approximately seven cubic feet per second). According to USGS

(1968) when water depth is below 0.46 meters (1.5 feet), the flow measurement should be

taken at 60% of water depth. Gage heights ranged from 18 cm to 11cm (0.6 to 0.36 feet)

during the study period. The gage height is generally indicative of the depth at thalweg;

consequently the rest of the stream is shallower than the reading on the gage and

therefore the meter cannot accurately measure discharge in many parts of the stream.

The shallowness of Prairie Creek during summer flows makes converting gage to flow

volume questionable. Summer streamflows were at a height such that the bottom of

propeller of the Swoffer would be 3.8 cm to -0.4 cm (1.5 to -0.16 inches) from the bottom

of the stream at the deeper areas. The top of the propeller was barely submerged in the

shallower areas and would have to be buried for the center of the propeller to be at the

correct depth. Gage height is usually converted to cubic meters per second, but due to

questionable streamflow measurements, analysis was predominantly done using gage

height instead of streamflow.

0.140

0.130

0.120

0.110

0.100 y = 0.0102x + 0.0482 R² = 0.9265 0.090

0.080 Discharge (cms) Discharge 0.070

0.060

0.050

0.040 0.162 0.165 0.168 0.168 0.174 0.174 0.189 0.192 Gage Height (m)

Figure 5. Rating curve for Prairie Creek above Boyes (PAB). Eight discharge measurements were made by Redwood National Park personnel between June 12th and August 19th to construct this rating curve. At least two points (i.e. 25% of the points) did not follow the trend line. R squared = 0.9265.

To enable comparison of gage heights overtime without the influence of

decreasing flow, gage heights were leveled. Raw PAB gage heights show diurnal

fluctuation July 1 through July 16, 2013 (Figure 6). Gage heights ranged from a high of

0.186 meters on July 1 to a low of 0.171 meters on July 16, 2013. Before gage heights

were detrended, an overall decrease in gage height over the study period was observed.

After raw gage heights were detrended, a general increase in gage height starting July 11

was apparent (Figure 6).

0.188 0.005 Raw Gage Height (m) 0.186 0.004

0.184 Detrended Gage Height (m) 0.003 0.182 Linear (Raw Gage Height (m)) 0.002 0.18 0.001 0.178 0 0.176 -0.001 0.174 Raw Gage Height(m) -0.002

0.172 Detrended Gage Height(m) 0.17 y = -0.0007x + 30.993 -0.003 R² = 0.8615 0.168 -0.004 7/1/2013 7/2/2013 7/3/2013 7/4/2013 7/5/2013 7/6/2013 7/7/2013 7/8/2013 7/9/2013 Date 7/10/2013 7/11/2013 7/12/2013 7/13/2013 7/14/2013 7/15/2013 7/16/2013 7/17/2013

Comparison of Fog and Gage Height

A comparison of fog data points taken every 30 minutes to daily average fog was

made to determine which would be more representative of fog events (Figure 7). Plotting

the two next to each revealed similarities. When fog was measured as a percentage of the

watershed covered in each GOES image, values during the study period varied from 0%

to 96%. Daily average percent of watershed covered by fog ranged from 0% to 87%.

Each data point is for an image taken in which fog was detected. Consequently, each day

has a different number of data points depending on how foggy the day was. July 4, 5, 13

and 14, 2013 had no fog. All other days had at least some fog. The days with no fog are

not included in graphs because imagery without fog resulted in no data. Daily average

fog was determined to be a representative metric for fog.

100 %Watershed FogPerImage 90 DailyAvg%Fog 80 Times with no fog/no data 70

20 Percent Watershed Covered inFog 10

0 7/1/13 7/1/13 7/2/13 7/6/13 7/7/13 7/8/13 7/8/13 7/9/13 7/9/13 Date 7/10/13 7/11/13 7/12/13 7/12/13 7/15/13 7/15/13 7/16/13

Daily average fog and detrended gage heights were compared to determine if fog

was impacting streamflow. Plotting daily average fog with detrended gage height data

taken every ten minutes shows changes in downward trend were preceded by fog events.

The overall downward pattern in detrended gage height flattened out after a fog event

covering over 50% of watershed on July 3. An increase in detrended gage height

occurred after consecutive fog events on July 12 and 13 (Figure 8). Although a continual

diurnal fluctuation appears in gage height data, the rate and timing of fluctuation vary.

The bottom of the trough tends to flatten out during or after fog events that covered

approximately 40% or more of the watershed (Figure 8).

Daily Avg % Watershed 100 Covered in Fog 0.007 90 Detrended Gage Height (m) 0.006 80 70 0.005 60 0.004 50 0.003 40

30 0.002 20 0.001 Detrended Gage Height(m) 10 PercentWatershed of Covered Fog in 0 0 7/1/2013 7/2/2013 7/3/2013 7/4/2013 7/5/2013 7/6/2013 7/7/2013 7/8/2013 7/9/2013 7/10/2013 7/11/2013 7/12/2013 7/13/2013 7/14/2013 7/15/2013 7/16/2013 7/17/2013 Date

The correlation between daily average fog and gage height was tested by plotting

the two against each other (Figure 9). Without accounting for a lag in gage height

changing due to fog, there appears to be no correlation between percentage of the

watershed covered in fog and detrended gage heights that correspond to times GOES

images were taken during daylight. A best fit line yields an R squared of 0.0643 (Figure

9).

0.005

0.004 y = 1E-05x + 0.0002 R² = 0.0643 0.003

0.002

0.001

-0.001 Detrended Gage Height(m) -0.002

-0.003 0 10 20 30 40 50 60 70 80 90 100 Percent Watershed Covered in Fog

Figure 9. No clear relationship was found between detrended gage height (m) and percentage of watershed covered in fog (R squared = 0.0643).

Daily average gage height was used instead of gage height recorded at ten-minute intervals to determine if the diurnal fluctuation may have been producing too much noise for a correlation to be seen between fog and gage height. Looking at gage heights measured every ten minutes and daily average gage height makes a “flattening” effect averaging the diurnal fluctuation evident (Figure 10).

0.007 Detrended zeroed Gage height 0.006 Detrended Zeroed Daily Avg Gage Height (m) 0.005

0.004

0.003

0.002

Detrended ZeroedGage Height (m) 0.001

0 7/1/2013 7/2/2013 7/3/2013 7/4/2013 7/5/2013 7/6/2013 7/7/2013 7/8/2013 7/9/2013 Date 7/10/2013 7/11/2013 7/12/2013 7/13/2013 7/14/2013 7/15/2013 7/16/2013 7/17/2013

Figure 10. Gage heights averaged for each day (dotted red line) do not show the diurnal fluctuation exhibited in gage heights taken every ten minutes (blue line).

The fog metric and the gage height were both averaged for each day to coarsen

the data in hopes of seeing a more general relationship (Figure 11). Initially, fog and

gage height were out of phase. As was also shown in Figure 8, Figure 11 shows gage

height begins to rise after fog events exceeding 35% coverage of the watershed. Even

when averaged over a day, when fog and gage height are paired with each other the

correlation between fog and gage height is negligible (Figure 12).

100 0.0055 Daily Avg % Watershed 90 Covered in Fog 0.005 80 Detrended Daily Avg Gage 0.0045 Height 70 0.004 60 0.0035 50 0.003

Fog 40 30 0.0025 20 0.002 10 0.0015 0 0.001 Detrended ZeroedGage Height (m) Percentage of WatershedCovered in 7/1/2013 7/2/2013 7/3/2013 7/4/2013 7/5/2013 7/6/2013 7/7/2013 7/8/2013 7/9/2013 Date 7/10/2013 7/11/2013 7/12/2013 7/13/2013 7/14/2013 7/15/2013 7/16/2013

Figure 11. Gage heights (red dotted line) tends to go up after fog covering watershed (blue solid line) exceeds 35%. The lag in gage heights increasing after fog events varies.

0.006

0.005 y = 6E-06x + 0.003 R² = 0.0196 0.004

0.003

0.002

0.001

Detrended Daily Average Gage Height (m) 0 0 20 40 60 80 100

Daily Average Percentage of Watershed Covered in Fog

Figure 12. The correlation between detrended daily average gage height and daily average percentage of watershed covered in fog and daily average gage height appears to be negligible (R2=0.0196).

Daily fluctuation in gage height was used as another metric to determine if fog

was influencing the amount of water in the stream. Like the daily average gage height,

daily fluctuation provides a single metric per day. Unlike the daily average gage height, daily fluctuation in gage height accounts for the degree of minimums and maximums.

The daily fluctuations in gage height during daylight hours were compared with the daily average percentage of Prairie Creek watershed covered in fog. The daily fluctuation roughly goes up when fog is not present and goes down when fog is present. When the two variables are plotted over time an inverse relation is evident (Figure 13) and the R squared is 0.3261 (Figure 14). Although the level of gage fluctuation can be low when fog is not present, if 40% or more of the watershed is covered in fog, the level of daytime fluctuation is low.

0.0035 %Watershed FogPerImage 100

Daily Flux 90 0.0030 80

0.0025 70

60 0.0020 50 0.0015 40

0.0010 30

Detrended andZeroed Gage Height(m) 0.0005 10

0.0000 0 Percentage of Watershed Covered in Fog 7/2/2013 7/5/2013 7/8/2013 6/29/2013 7/11/2013 7/14/2013 7/17/2013 Date

0.0035

0.0030

0.0025

0.0020 y = -2E-05x + 0.0022 R² = 0.3261

(detrended m) 0.0015

0.0010 Daily Diurnal Fluctuation Gage inHeight 0.0005

0.0000 0 10 20 30 40 50 60 70 80 90 100 Percentage of Watershed Covered in Fog

Comparing daily average percentage of watershed covered in fog to time of maximum daily gage height shows fog and gage height generally corresponded with one another (Figure 15). On July 1, 6, 11 and 16, 2013 fog coverage increased followed by an increase in time of maximum daily gage height on the following day. On July 3 both values increased. The earliest maximum daily gage height occurred at 7:10 am and the latest maximum daily gage height occurred at noon. Average daily percentage of the watershed covered in fog ranged from 0% to 87%.

DailyAvg%Fog 100 12:30 PM Time of Max Gage 90 Height 11:30 AM 80

70 10:30 AM 60

50 9:30 AM

40 8:30 AM 30

20 7:30 AM Time of Daily Maximum Gage Height Daily Average % of Watershed Covered in Fog 10

0 6:30 AM 7/1/2013 7/2/2013 7/3/2013 7/4/2013 7/5/2013 7/6/2013 7/7/2013 7/8/2013 7/9/2013 7/10/2013 7/11/2013 7/12/2013 7/13/2013 7/14/2013 7/15/2013 7/16/2013 Date

Plotting variables from Figure 15 against each other yields an R squared 0.5295

(Figure 16). Most of the points far from best fit line are near a gage height of zero.

Although there are only four points above 30% of the watershed covered in fog, all of

these points lay close to best fit line. Spearman rank correlation for daily average

percentage of watershed covered in fog and time of daily maximum gage height was

0.568.

1:00 PM

11:48 AM

10:36 AM y = 0.0017x + 0.3358 R² = 0.5295

9:24 AM

8:12 AM

Time of Daily Maximum Gage Height Time ofGage Daily Maximum 7:00 AM 0 10 20 30 40 50 60 70 80 90 100 Daily Average % of Watershed Covered in Fog

Figure 16. Time of daily maximum gage height plotted against daily average percentage of watershed covered in fog yields a positive relationship and an R squared of 0.530 between July 1 and 16, 2013.

DISCUSSION

Metrics averaged on a daily time scale suggest a relation between fog and gage

height. Previous research showing plant use of fog and the impact riparian vegetation on

streamflows is supported by the findings presented here (Bren 1997, Dawson 1998,

Burgess et al. 2001, Lim et al 2009, Klein 2012, Sawaske and Freyberg 2014). When the

average percentages of the Prairie Creek watershed covered by fog for each day were

compared to the time of daily maximum gage height, the relation between fog and gage

height looked highly probable (Figure 15 and Figure 16). When data were compared on a finer time scale the relationship was not evident due to time lags and noise of diurnal fluctuation (Figure 9). The biggest impediment to determining the degree of influence fog has on gage height is calculating time lag. The typical summertime downward trend in gage height flattens or reverses multiple times after fog events. A variable lag in gage height response to fog masks the relationship and makes pairing fog with gage height difficult.

The signature of fog may manifest via multiple signals in gage height. Diurnal fluctuation in streamflow is generally thought to be driven by evapotranspiration; consequently low gage heights occur in the afternoon and high gage heights occur in morning (Bren 1997) (Figure 6). The slope of gage height transitioning between peaks and valleys varies. A steep increase in gage height may indicate nighttime fog causing vegetation to use fog as a water source thereby freeing up groundwater to flow into the

stream (Lim et al. 2009, Sawaske and Freyberg 2014a). Rounded and flat troughs

indicate slower and reduced drops in gage height and may be a sign that fog buffered the

impact of evapotranspiration on streamflow by blocking the sun (Sawaske and Freyberg

2014).

The most obvious fog signature was revealed in gage height data that showed a

disruption in the ordinary summer time downward trend over the study period due to a

lack of precipitation (Figure 6). Once gage heights were detrended an upward movement

in gage height can be seen starting around July 11 potentially due to fog events just prior

to flattening and increasing gage heights (Figure 7).

Different time intervals of satellite images (approximately 30 minutes) and gage

height measurements (10 minutes) initially made seeing a relation difficult. A time lag

between fog events and a change in gage height looks likely (Figure 8 and Figure 11)

(Lundquist and Cayan 2002). The lag appears to vary depending on amount, timing and duration of fog. For example, on July 3, 2013 87% of the watershed was covered in fog, followed by two days of no fog, after which an increase in gage height occurred. When there were two consecutive days of fog on July 11 and 12 (81% and 72% respectively) followed by two days of no fog, gage height also increased. Even though a pattern seems apparent, plotting gage height against percentage of the watershed covered in fog yields an R squared of only 0.0643 (Figure 9). Comparing fog and gage height measurements from the exact same time does not account for time lag and daily fog pattern.

Using daily fluctuation in gage height instead of shorter time intervals or daily averages partially accounts for time lag and made the impact of fog more apparent (R

squared = 0.5295). The increase in R squared when using daily fluctuation in gage height

instead of 10 minute gage heights or average gage height is dramatic when compared

with previous R squared values 0.0196 and 0.0643 (Figure 9 and Figure 12 respectively).

Looking at the time gage height peaked each day is a means of revealing daily

changes in gage height due to fog. Without fog, gage height peaks shortly after sunrise.

When fog is present, gage height peaks later in the day (Sawaske and Freyberg 2014)

(Figure 15). When metrics are converted to a single daily event (maximum gage height), the relation between fog and gage height increases dramatically (Figure 16). Fog is most abundant and provides the most water to vegetation in the night (Limm et al. 2009,

Johnstone and Dawson 2010). Fog generally follows a diurnal pattern of forming in the evening, peaking in early morning and burning off midday (Johnstone and Dawson 2010,

O’Brien et al. 2013). Therefore, averaging the percentage of the watershed covered by fog for each day is more representative of what happened over the course of a day than looking at measurements taken on shorter time intervals. When fog blocks the sun and provides an alternative source of water, plants and gage height do not respond instantly

(Bren 1997). Using the time of maximum gage height targets the time a reversal in trend occurs and serves as a natural indicator of myriad factors influencing gage height and thereby partially accommodates for the lag in response of gage heights to fog.

Fog has been known to reduce overall water needs of redwood trees and other vegetation while providing an alternative source of water. This study demonstrates tree

use of fog creates a ripple effect that propagates through the ecosystem impacting stream

gage height (Dawson 1998, Burgess and Dawson 2004, Johnstone and Dawson 2010).

Other research that has explored the relationship between fog and streamflow found that increases in summertime streamflow coincide with fog events (Sawaske and Freyberg

2014). Judging by how closely fog and gage height correlate and the simplicity of the causal mechanism, evapotranspiration, further research seems very likely to confirm and quantify the relationship between fog and streamflow.

RECOMMENDATIONS

There are several factors that could increase the accuracy and reliability of researching the relationship between fog and streamflow. First, knowing how much contact the vegetation had with the fog and the overall intensity of fog would allow for better quantification of amount of water likely contributed to the watershed by fog.

Additionally, the height of the boundary layer and bottom of fog data could be used to make a three dimensional fog layer. Fog height could be inferred by detection of a layer of abrupt temperature drop known as the boundary layer. The boundary layer usually works as a lid determining the top of fog. Satellite data from Sunnomi VIIRs, or a skew

T plot from Buffkit could be used to find the height of the boundary layer (Lewis et al.

1973, O’Brien et al. 2013). Ceilometer data could be used to determine the bottom on the fog. Moreover, deployment of weather stations at a series of elevations in a transect perpendicular to the coast line to collect barometric pressure, photosynthetically active radiation (PAR), passive or active fog collectors and wetness sensors could also serve as an effective means of measuring fog duration, timing, frequency and intensity (liquid water content) (Fischer and Still 2007, Burgess and Dawson 2004).

The relationship between fog and gage height could be further refined by collecting more detailed low streamflow data. More precise low streamflow measurements taken with a device such as a Marsh-McBirney meter that is designed to measure low streamflow would likely improve the quality of the data and allow for gage

42 heights to be more accurately converted to volume of streamflow. Knowing watershed- wide subsurface flow rates would assist in determining lag in response to fog.

Lastly, a signal analysis approach could shed more light on the relation between fog and streamflow. Using Fourier transforms (to remove diurnal signal) and other statistical tools (capable of detecting changes in slope) to disaggregate multiple signals in streamflow would increase the likelihood of isolating a fog signature in flows (Smith et al. 1998). A Fourier transform could remove the undulations that occur on 24 hour interval due to the impact of the sun. Once the ordinary impact of the sun was removed the impact of fog would be more visible.

The ideal investigation of the relation between fog and streamflow would use a plethora of instrumentation to quantify the relation between fog and streamflow over multiple fog seasons by measuring the relations between PAR, leaf wetness, fog collection (drip and absorption), sap flow, isotopic signature of water (groundwater, precipitation, fog drip, fog absorption, and stream water) in Prairie Creek watershed vegetation, ground water flow rates (including depth and volume), streamflow (gage height and volume per unit of time).

While more research would be helpful, the results presented here suggest that fog plays a role in the timing of maximum height as well as the amount of fluctuation in gage height. The cumulative impact of changes in gage height may change the timing of tributaries running dry. In extremely foggy years fish may be able to pass through more of a stream for a longer period of time and vice versa in a low fog year. The potential impact on fish migration patterns could cascade through the stream food web as the

43 presence or absence of fish would impact the pressures on food sources (McEravy et al.

1989).

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PERSONAL COMMUNICATION CITATIONS

C. Haynes and S. Madrone, 2012, Lecturers Humboldt State University

E. Keppeler, 2012, Hydrologist United States Forest Service

T. Marquette, 2015, Retired Steam Technician Redwood National Park Service

V. Ozaki, 2013 & 2014, Geologist at Redwood National Park Service

R. Stepp, 2012, Professor Emeritus Humboldt State University

J. Wheeler, 2013, Park Ranger and Interpreter Redwood National Park Service