National Park Service

U.S. Department of the Interior

Natural Resource Stewardship and Science

Lake Temperature Monitoring in Southwest

Alaska Parks

A Synthesis of Year-Round, Multi-Depth Data from 2006 

through 2018

Natural Resource Report NPS/SWAN/NRR—2020/2191
The production of this document cost $22,474, including costs associated with data collection, processing, analysis, and 

subsequent authoring, editing, and publication.

ON THE COVER

Readying the temperature array at Lake Clark for downloading

Photo credit: NPS/KRISTA BARTZ
Lake Temperature Monitoring in Southwest 

Alaska Parks

A Synthesis of Year-Round, Multi-Depth Data from 2006 

through 2018

Natural Resource Report NPS/SWAN/NRR—2020/2191

Krista K. Bartz and Paul W. C. Gabriel

National Park Service 

Southwest Alaska Inventory & Monitoring Network 

240 West 5th Avenue 

Anchorage, AK 99501 

November 2020

U.S. Department of the Interior 

National Park Service

Natural Resource Stewardship and Science 

Fort Collins, Colorado
ii

The National Park Service, Natural Resource Stewardship and Science office in Fort Collins, 

Colorado, publishes a range of reports that address natural resource topics. These reports are of 

interest and applicability to a broad audience in the National Park Service and others in natural 

resource management, including scientists, conservation and environmental constituencies, and the 

public. 

The Natural Resource Report Series is used to disseminate comprehensive information and analysis 

about natural resources and related topics concerning lands managed by the National Park Service. 

The series supports the advancement of science, informed decision-making, and the achievement of 

the National Park Service mission. The series also provides a forum for presenting more lengthy 

results that may not be accepted by publications with page limitations. 

All manuscripts in the series receive the appropriate level of peer review to ensure that the 

information is scientifically credible, technically accurate, appropriately written for the intended 

audience, and designed and published in a professional manner. 

This report received formal peer review by subject-matter experts who were not directly involved in 

the collection, analysis, or reporting of the data. Data in the report were collected and analyzed using 

methods based on established, peer-reviewed protocols and were analyzed and interpreted within the 

guidelines of the protocols. 

Views, statements, findings, conclusions, recommendations, and data in this report do not necessarily 

reflect views and policies of the National Park Service, U.S. Department of the Interior. Mention of 

trade names or commercial products does not constitute endorsement or recommendation for use by 

the U.S. Government. 

This report is available in digital format from the Southwest Alaska Network website and the Natural 

Resource Publications Management website. If you have difficulty accessing information in this 

publication, particularly if using assistive technology, please email irma@nps.gov. 

Please cite this publication as:

Bartz, K. K. and P. C. W. Gabriel. 2020. Lake temperature monitoring in southwest Alaska parks: A 

synthesis of year-round, multi-depth data from 2006 through 2018. Natural Resource Report 

NPS/SWAN/NRR—2020/2191. National Park Service, Fort Collins, Colorado. 

https://doi.org/10.36967/nrr-2279700.

NPS 953/173831, November 2020
iii

Contents

Page

Figures.................................................................................................................................................... v

Tables ................................................................................................................................................... vii

Appendices ............................................................................................................................................ xi

Executive Summary ............................................................................................................................xiii

Acknowledgments ................................................................................................................................ xv

List of Terms ...................................................................................................................................... xvii

Acronyms and Abbreviations............................................................................................................. xvii

Introduction ............................................................................................................................................ 1

Methods .................................................................................................................................................. 3

Study Sites ...................................................................................................................................... 4

Field Methods ................................................................................................................................. 7

Data Management........................................................................................................................... 8

Status Summary.............................................................................................................................. 9

Trend Analysis................................................................................................................................ 9

Stratification Patterns ................................................................................................................... 10

Results .................................................................................................................................................. 11

Status Summary ............................................................................................................................ 11

Lake Clark ............................................................................................................................... 11

Kijik Lake ................................................................................................................................ 13

Naknek Lake ............................................................................................................................ 14

Lake Brooks............................................................................................................................. 17

Trend Analysis — Means ............................................................................................................. 18

Lake Clark and Kijik Lake ...................................................................................................... 20

Naknek Lake and Lake Brooks ............................................................................................... 20

Trend Analysis — Standard Deviations ....................................................................................... 20

Lake Clark and Kijik Lake ...................................................................................................... 22
iv

Contents (continued)

Page

Naknek Lake and Lake Brooks ...............................................................................................22

Stratification Patterns...................................................................................................................22

Lake Clark ...............................................................................................................................25

Kijik Lake................................................................................................................................28

Naknek Lake............................................................................................................................28

Lake Brooks.............................................................................................................................28

Discussion............................................................................................................................................29

Status Summary............................................................................................................................29

Trend Analysis — Means.............................................................................................................30

Trend Analysis — Standard Deviations.......................................................................................30

Stratification Patterns...................................................................................................................31

Considerations and Next Steps.....................................................................................................31

Conclusions..........................................................................................................................................37

Literature Cited ....................................................................................................................................39
v

Figures

Page

Figure 1. Map of the five park units included in the Southwest Alaska Network. ...............................1

Figure 2. Map of temperature monitoring sites (red points) in Katmai (KATM) and Lake 

Clark (LACL) national parks and preserves. .........................................................................................4

Figure 3. Monthly climate normals for the 30-year time period from 1981 to 2010, 

measured at the King Salmon and Port Alsworth airports near the SWAN temperature 

array sites (NOAA 2020). ......................................................................................................................6

Figure 4. Generic temperature array design during summer and winter months..................................8

Figure 5. Heatmaps depicting maximum and minimum daily mean temperatures at the 5 

m depth, organized by array and water year. .......................................................................................12

Figure 6. Boxplots of maximum and minimum daily mean temperatures at the 5 m depth 

in Lake Clark (LCLAR01), organized by month for water years on record before 2017 

(2006–2016).........................................................................................................................................13

Figure 7. Boxplots of maximum and minimum daily mean temperatures at the 5 m depth 

in Kijik Lake (KIJIL01), organized by month for water years on record before 2017 

(2010–2016).........................................................................................................................................14

Figure 8. Boxplots of maximum and minimum daily mean temperatures at the 5 m depth 

in the North basin of Naknek Lake (NAKNL01), organized by month for water years on 

record before 2017 (2008–2016)..........................................................................................................15

Figure 9. Boxplots of maximum and minimum daily mean temperatures at the 5 m depth 

in the West basin of Naknek Lake (NAKNL02), organized by month for water years on 

record before 2017 (2008–2016)..........................................................................................................16

Figure 10. Boxplots of maximum and minimum daily mean temperatures at the 5 m 

depth in the Iliuk basin of Naknek Lake (NAKNL03), organized by month for water 

years on record before 2017 (2011–2016). ..........................................................................................17

Figure 11. Boxplots of maximum and minimum daily mean temperatures at the 5 m 

depth in Lake Brooks (LBROO01), organized by month for water years on record before 

2017 (2010–2016)................................................................................................................................18

Figure 12. Daily mean water temperature by site, depth, and year for the Lake Clark 

(LCLAR01), Kijik Lake (KIJIL01), and Lake Brooks (LBROO01) arrays. .......................................23

Figure 13. Daily mean water temperature by site, depth, and year for arrays located in the 

North (NAKNL01), West (NAKNL02), and Iliuk (NAKNL03) basins of Naknek Lake. ..................24
vi

Figures (continued)

Page

Figure 14. Duration of continuous summer stratification by water year at six temperature 

array sites. ............................................................................................................................................26

Figure 15. Duration of continuous winter stratification by water year at six temperature 

array sites. ............................................................................................................................................27

Figure 16. Map of instrument line locations over time for two arrays in Lake Clark 

National Park and Preserve (LACL)....................................................................................................32

Figure 17. Map of instrument line locations over time for two arrays in Katmai National 

Park and Preserve (KATM). ................................................................................................................33

Figure 18. Map of instrument line locations over time for two arrays in Katmai National 

Park and Preserve (KATM). ................................................................................................................34

Figure 19. Daily mean depth recorded by a water level logger co-located with the “5 m” 

water temperature logger on the Lake Clark array. .............................................................................35

Figure B-1. Isotherms for six temperature arrays. ..............................................................................51
vii

Tables

Page

Table 1. Temperature array site information.........................................................................................3

Table 2. General characteristics of the four lakes containing SWAN temperature arrays....................5

Table 3. Grades and approvals assigned to temperature array data in AQUARIUS.............................9

Table 4. Results of seasonal Kendall tests for changes over time in mean water 

temperature by depth at six array sites.................................................................................................19

Table 5. Results of seasonal Kendall tests for changes over time in water temperature 

standard deviation (SD) by depth at six array sites..............................................................................21

Table A-1. List of data deleted in AQUARIUS, itemized by park, site, depth (in meters), 

time period (from/to date and time), count of hours deleted, and a brief account of why 

the data were deemed erroneous, graded as unusable, and subsequently deleted................................45

Table A-2. List of data downgraded in AQUARIUS, itemized by park, site, depth (in 

meters), time period (from/to date and time), and a brief account of why the data were 

deemed less than good and subsequently downgraded........................................................................48

Table C-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Lake Clark, 2006–2018 water years (WY). ........................................................................53

Table C-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Lake Clark, 2006–2018 water years (WY). ........................................................................54

Table C-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, 

summarized by month for Lake Clark, 2006–2018 water years (WY)................................................55

Table C-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, 

summarized by water year for Lake Clark, 2006–2018.......................................................................56

Table D-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Kijik Lake, 2010–2018 water years (WY)..........................................................................57

Table D-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Kijik Lake, 2010–2018 water years (WY)..........................................................................58

Table D-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, 

summarized by month for Kijik Lake, 2010–2018 water years (WY). ...............................................59

Table D-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, 

summarized by water year for Kijik Lake, 2010–2018. ......................................................................60

Table E-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Naknek Lake’s North basin, 2008–2018 water years (WY). ..............................................61
viii

Tables (continued)

Page

Table E-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Naknek Lake’s North basin, 2008–2018 water years (WY). ..............................................62

Table E-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, 

summarized by month for Naknek Lake’s North basin, 2008–2018 water years (WY)......................63

Table E-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, 

summarized by water year for Naknek Lake’s North basin, 2008–2018.............................................64

Table F-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Naknek Lake’s West basin, 2008–2018 water years (WY). ...............................................65

Table F-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Naknek Lake’s West basin, 2008–2018 water years (WY). ...............................................66

Table F-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, 

summarized by month for Naknek Lake’s West basin, 2008–2018 water years (WY).......................67

Table F-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, 

summarized by water year for Naknek Lake’s West basin, 2008–2018..............................................68

Table G-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Naknek Lake’s Iliuk basin, 2011–2018 water years (WY).................................................69

Table G-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Naknek Lake’s Iliuk basin, 2011–2018 water years (WY).................................................70

Table G-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, 

summarized by month for Naknek Lake’s Iliuk basin, 2011–2018 water years (WY). ......................71

Table G-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, 

summarized by water year for Naknek Lake’s Iliuk basin, 2011–2018. .............................................72

Table H-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Lake Brooks, 2010–2018 water years (WY).......................................................................73

Table H-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by 

month for Lake Brooks, 2010–2018 water years (WY).......................................................................74

Table H-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, 

summarized by month for Lake Brooks, 2010–2018 water years (WY).............................................75

Table H-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, 

summarized by water year for Lake Brooks, 2010–2018. ...................................................................76
ix

Tables (continued)

Page

Table I-1. Results of seasonal Kendall tests for changes over time in water temperature 

means and standard deviations at the array located in the West basin of Naknek Lake 

(NAKNL02).........................................................................................................................................77

xi

Appendices

Page

Appendix A..........................................................................................................................................45

Appendix B..........................................................................................................................................49

Appendix C..........................................................................................................................................53

Appendix D..........................................................................................................................................57

Appendix E ..........................................................................................................................................61

Appendix F...........................................................................................................................................65

Appendix G..........................................................................................................................................69

Appendix H..........................................................................................................................................73

Appendix I ...........................................................................................................................................77

xiii

Executive Summary

Water temperature mediates numerous ecosystem processes in lakes, including stratification, 

decomposition, evaporation, production, and upwelling. The Southwest Alaska Network (SWAN) 

monitors water temperature hourly year-round at six mid-lake sites in Katmai and Lake Clark 

national parks and preserves. This monitoring relies on the use of programmable data loggers 

attached at various depths to moored vertical lines called temperature arrays. Here, we use the data 

generated via temperature arrays from 2006 through 2018 to assess the status and trend of lake 

temperature and the pattern of stratification in four lakes of interest to park managers. Our findings 

indicate that:

● status metrics for the most recent water year on record (2017) fell within the range of previous 

years’ temperatures at most sites;

● monthly mean surface water temperatures have warmed during the period of record at all sites, 

with some sites exhibiting strong warming trends in deeper waters; and

● stratification patterns have vacillated during the period of record at all sites, with shifts driven 

by inconsistencies in winter water temperatures.

Although the temperature array dataset is relatively limited in both space (6 sites) and time (7–12 

years per site), it encompasses more than 4 million records in a region where continuous water 

temperature data are generally lacking, and where temperature-sensitive Pacific salmon species are 

culturally, economically, and ecologically important. All lakes equipped with temperature arrays 

support spawning, rearing, and migration of Pacific salmon. Moreover, protecting the habitat 

necessary to support salmon runs is part of the enabling legislation for these parks. Therefore, we 

examine how these results might affect salmon at various life stages, in the context of a changing 

climate. For example:

● daily mean temperatures at the 5 m depth exceeded the State water quality threshold (15 °C) at 

some point during the periods of record at all arrays, as did weekly mean temperatures for most 

arrays;

● daily mean temperatures at depths below 20 m did not exceed the 15 °C threshold at any array;

● collectively, these findings indicate that deeper waters may still offer refuge to older rearing 

juveniles and migrating adults, even when temperatures higher in the water column are 

suboptimal.

We finish by comparing these results to other studies of temperature trends and stratification patterns, 

and by offering ideas for future analyses of this dataset.

xv

Acknowledgments

The monitoring described in this report would not have been possible without the assistance, 

guidance, and logistical support of the following individuals: L. Bennett, J. Shearer, C. Moore, M. 

Shephard, D. Young, T. Hamon, E. Booher, J. Nelson, K. Junghans, M. Harrison, R. Richotte, and D. 

Welty, as well as many volunteers who helped more intermittently than those listed above. T. 

Shepherd and R. Frith facilitated data management. Additionally, reviews by D. Holt, R. Weissinger, 

R. Damstra, A. Miller, and C. Miller helped advance this report from a rough draft to a final product.

xvii

List of Terms

Dimictic: a lake stratification pattern characterized by two periods of mixing per year, typically in 

spring and fall.

Meromictic: a lake stratification pattern characterized by a lack of mixing between layers.

Mixing: top-to-bottom circulation or turnover of lake water, in the absence of stratification.

Monomictic: a lake stratification pattern characterized by one period of mixing per year, typically in 

winter.

Monotonic trend: a trend that always goes in one direction (e.g., increasing or decreasing).

Non-parametric: the characteristic of a statistical test that makes no assumptions regarding the

underlying probability distributions of the variables being assessed.

Polymictic: a lake stratification pattern characterized by more than two periods of mixing per year. 

Some polymictic lakes are too shallow to develop multiple layers; others are deep enough to develop 

multiple layers, but the layers are interrupted occasionally by strong winds.

Seasonal Kendall test: a non-parametric test that analyzes seasonal data for monotonic trends.

Sen slope: a non-parametric alternative to the parametric slope of a regression line, estimated by 

calculating the median of all pairwise slopes in a given dataset.

Water year: the 12-month period from October 1 of a given year through September 30 of the 

following year. A water year is named for the calendar year in which it ends (e.g., water year 2017 

spans October 1, 2016 through September 30, 2017).

Acronyms and Abbreviations

ALAG Alagnak National Wild River

ANIA Aniakchak National Monument and Preserve

C Celsius

cm centimeter(s)

I&M Inventory and Monitoring

KATM Katmai National Park and Preserve

KEFJ Kenai Fjords National Park

km kilometer(s)

LACL Lake Clark National Park and Preserve

m meter(s)

NPS National Park Service

SWAN Southwest Alaska Network

yr year

1

Introduction

In 1998, the National Park Service (NPS) initiated a natural resource Inventory and Monitoring 

(I&M) Program. The purpose of the program was to develop a baseline inventory of significant 

natural resources in national parks, and to monitor key ecological indicators — known as Vital Signs 

— over time. Now the program includes more than 270 park units organized into 32 regional 

networks.

The Southwest Alaska Network (SWAN) is one of four networks established within Alaska under the 

Inventory and Monitoring Program. It consists of five park units: Lake Clark National Park and 

Preserve (LACL), Katmai National Park and Preserve (KATM), Kenai Fjords National Park (KEFJ), 

Alagnak National Wild River (ALAG), and Aniakchak National Monument and Preserve (ANIA). 

Collectively, these park units comprise 3.8 million ha, extending across 650 km of the Alaska and 

Kenai Peninsulas (Figure 1).

Figure 1. Map of the five park units included in the Southwest Alaska Network.
2

Freshwater resources in SWAN park units are abundant, featuring thousands of kilometers of rivers 

and two of the largest lakes in the National Park system: Naknek Lake (58,825 ha) in KATM and 

Lake Clark (31,116 ha) in LACL. The Naknek Lake and Lake Clark watersheds are so extensive that 

they cover 41% and 32% of the land area within their respective park units. In establishing these park 

units, Congress recognized the cultural, ecological, recreational, and economic importance of 

freshwater resources, with reference to protecting and maintaining lakes in their natural state in the 

enabling legislation (ANILCA 1980).

Lakes integrate water, energy, nutrients, sediments, and pollutants from the surrounding land and air 

(Schindler 2009). Therefore, lake water quality can be a useful indicator of broad scale stressors, 

such as climate change (Williamson et al. 2009). The SWAN monitors several lake water quality 

parameters, including temperature, pH, conductivity, and dissolved oxygen. Of these parameters, 

temperature is particularly important, in that values of the other parameters are temperaturedependent (Wilde 2006), as are many physical, chemical, and biological processes in lake 

ecosystems (Regier et al. 1990, Wrona et al. 2005). Given that lake temperature is a dominant driver 

which, in turn, is linked to air temperature at broad scales (O’Reilly et al. 2015), and given that air 

temperature has risen in Alaska and is projected to continue rising throughout this century (Markon 

et al. 2018), understanding lake temperature change is crucial for managing freshwater resources in 

Alaskan parklands. It is also crucial for retaining thriving populations of sockeye salmon 

(Oncorhynchus nerka), a temperature-sensitive, lake-dependent keystone species in southwest Alaska 

(Woll et al. 2014).

Many climate-related studies of lakes have focused on surface water temperature recorded during 

summer. However, studies that have examined water temperature throughout the water column yearround have observed a variety of interrelated changes. These include earlier onset and later 

breakdown of thermal stratification in the fall and spring (Niedrist et al. 2018), as well as shortened 

duration of ice cover in the winter (Magnuson et al. 2000) — all of which may cause shifts in the 

overall pattern of stratification (Woolway and Merchant 2019).

The SWAN uses several approaches to monitor lake temperature, ranging from year-round 

measurements at targeted locations to once-a-year measurements at randomly selected sites. These 

measurements rely on various types of equipment to address specific monitoring objectives listed in 

the SWAN freshwater monitoring protocol (Shearer et al. 2015a). One of these objectives is the 

subject of this report — specifically: to assess the status and trend of lake temperature, as well as the 

pattern of lake stratification within SWAN parklands. To accomplish this objective, the SWAN uses 

moored installations known as temperature arrays. The arrays record water temperatures in four lakes 

with relatively easy access, heavy use, and (consequently) focused concern for park managers. 

Within these lakes, status will be assessed at single depth near the lake surface, using summary 

statistics for the most recent full water year on record, relative to previous years. Trends will be 

assessed for all available depths, using non-parametric tests that account for seasonal cycles in water 

temperature. Finally, stratification will be assessed by defining the type of mixing observed at each 

array over time (i.e., monomictic, dimictic, polymictic).
3

Methods

The SWAN monitors lake temperature hourly year-round using programmable data loggers attached 

at various depths to moored installations called temperature arrays. The SWAN operates six arrays 

distributed among four lakes previously identified as high priority for monitoring (Table 1, Figure 2; 

Shearer et al. 2015a). Four of the arrays are located in KATM: three in different basins of Naknek 

Lake (North, West, and Iliuk) and one in Lake Brooks. The remaining two arrays are located in 

LACL: one in Lake Clark and another in Kijik Lake.

Table 1. Temperature array site information. Latitude and longitude represent approximate locations of 

instrument lines. Actual locations change slightly over time as the lines are retrieved, downloaded, and 

redeployed.

Park Lake Basin Site ID

Year

Installed

Latitude

(DD) A

Longitude

(DD) A

Depths

Monitored (m) B

Total 

Loggers C

LACL Clark Middle LCLAR01 2006 60.26433 −154.23468 0, 5, 10. 20, etc., 100 12

LACL Kijik D KIJIL01 2010 60.29763 −154.33072 0, 5, 10. 20, etc., 90 11

KATM Naknek North NAKNL01 2008 58.63639 −155.63863 0, 5, 10. 20, etc., 70 9

KATM Naknek West NAKNL02 2008 58.61551 −156.03696 0, 5, 10, 15, 20, 25 6

KATM Naknek Iliuk NAKNL03 2011 58.52188 −155.60320 0, 5, 10. 20, etc., 100 12

KATM Brooks D LBROO01 2010 58.53035 −155.85318 0, 5, 10. 20, etc., 50 7

A DD = decimal degrees, using WGS84 as the datum.

B Etc. = 10-meter increments between 20 m and the deepest depth listed.

C The total, across sites, is 57 loggers (or individual time series).

D Kijik Lake and Lake Brooks have only one basin each.
4

Figure 2. Map of temperature monitoring sites (red points) in Katmai (KATM) and Lake Clark (LACL) 

national parks and preserves. Parklands are depicted in green; lakes are depicted in blue. Site 

abbreviations NAKL01, NAKNL02, and NAKNL03 represent Naknek Lake North, West, and Iliuk basins, 

respectively; LBROO01, LCLAR01, and KIJIL01 represent Lake Brooks, Lake Clark, and Kijik Lake, 

respectively.

Study Sites

While these lakes vary in size and depth, from 5 to 588 km2 and from 82 to 266 m, respectively

(Table 2), all are oligotrophic, with low biological activity, low nutrient loads, and low to moderate 

acid buffering capacity. Previous studies have described their stratification patterns as discontinuous 

cold polymictic (Naknek Lake and Lake Brooks; LaPerriere 1997), weakly stratified during summer 

and inversely stratified during winter (Lake Clark; Wilkens 2002), and stratified with isothermal 

waters below 30 m (Kijik Lake; Brabets and Ourso 2006).
5

Table 2. General characteristics of the four lakes containing SWAN temperature arrays.

Park Lake

Elevation 

(m)

Max Depth 

(m)

Area

(km2)

Volume 

(km3)

Glacier Cover 

(%) A

Spawning Salmon Abundance 

(fish) B

LACL Clark 77.4 265.8 327.1 27.3 5.9 362,400

LACL Kijik 114.0 108.2 4.5 C 0.0 B

KATM Naknek 10.4 175.3 588.2 25.2 2.7 1,883,800

KATM Brooks 18.9 82.3 75.6 3.4 0.0 64,600

A The percentage of glacier cover in each lake’s watershed, based on data from the RGI Consortium (2017).

B The mean annual abundance of spawning sockeye salmon, for years when various data sources overlapped 

(2001–2005 and 2007– 2011; Young 2014 and D. Young, unpublished data for Lake Clark; ADFG 2020a and b 

for Naknek Lake; and ADFG, unpublished data for Lake Brooks). Data were not available for Kijik Lake during 

this time period.

C A volume estimate is not available for this lake.

The lakes also vary in elevation from 10 to 114 m above sea level (Table 2), but all occur within the 

same overarching hydrologic unit (19040002; Seaber et al. 1987) and climate region (Bristol Bay; 

Bieniek et al. 2012). Climate in this region is largely influenced by high latitude, complex 

topography, marine proximity, and the interaction of these features with global atmospheric 

circulation (Simpson et al. 2002). Long term climate data are available for two nearby monitoring 

stations: King Salmon and Port Alsworth (Figure 1). Records for the most recent 30-year period 

(1981–2010) were similar at each station, although King Salmon had a narrower range of monthly 

mean air temperatures (−8.8 to 13.1 °C) than Port Alsworth (−9.2 to 14.4 °C), and was cooler and 

wetter, in terms of annual mean temperature (1.7 vs. 2.2 °C) and total precipitation (41.5 vs. 33.6 cm) 

(Figure 3; NOAA 2020). At both stations, September was the wettest month of the year (Figure 3). 

During winter months, Lake Clark and Kijik Lake had higher fractions of precipitation falling as 

snow versus rain than Naknek Lake and Lake Brooks (73.0% and 74.1% vs. 66.7% and 64.6%, 

respectively, for December through February of 1980–2009), according to estimates by McAfee et al.

2014.
6

Figure 3. Monthly climate normals for the 30-year time period from 1981 to 2010, measured at the King 

Salmon and Port Alsworth airports near the SWAN temperature array sites (NOAA 2020).
7

Field Methods

Each array was built from three lines (Figure 4): an instrument line (vertical rope with the data 

loggers attached), an anchor line (identical to the instrument line, minus the data loggers), and a 

bridle line (horizontal rope linking the two other lines). Data loggers were attached year-round to the 

instrument line, typically at depths approximating 5 m, 10 m, 20 m, 30 m, etc., down to 100 m unless 

the lake bottom intervened (Table 1). However, data loggers on the shallowest array (Naknek Lake 

— West basin) were attached at 5-m intervals, from 5 m to 25 m, to increase resolution. For the icefree portion of the year (approximately June through September), a data logger was also attached at 

the surface between 0 m and 1 m depth (hereafter called the 0 m logger). Note that the instrument 

and anchor lines were moored to the lake bottom, so the actual logger depths fluctuated somewhat 

with lake level. All data loggers were Onset HOBO® Pro v2 U22-001 devices (accuracy: ±0.21 °C 

from 0 to 50 °C) and were downloaded once or twice per year. For additional details, see Shearer et 

al. 2015b.
8

Figure 4. Generic temperature array design during summer and winter months.

Data Management

Data downloaded from temperature arrays were stored in a proprietary, centralized database called 

AQUARIUS Time-SeriesTM (Aquatic Informatics, Inc.). Each array-by-depth combination was stored 

as a separate time series — 57 time series in total. Once imported into AQUARIUS, data were 

assigned grades and approval ratings (Table 3). Erroneous data, such as air temperatures logged 

during array maintenance, were graded as unusable and omitted from further analysis (Appendix A). 

All other downgraded data were incorporated into the analysis. At the time of analysis, graded and 

approved data were available through August, 2018 for all 57 time series. These data are accessible 

via the public portal of AQUARIUS at https://irma.nps.gov/aqwebportal/.
9

Table 3. Grades and approvals assigned to temperature array data in AQUARIUS.

Grade Approval Explanation

Good Approved No issues noted

Fair Approved Minor issues noted (e.g., time stamps not on the hour)

Poor Approved Major issues noted (e.g., temperature sensor drifted over time)

Undefined Approved Gap in data noted (e.g., maintenance prevented data logging)

Unusable Rejected Erroneous data noted (e.g., air temperature recorded)

Prior to analysis, hourly data were examined to determine whether data gaps occurred randomly or 

with a recurring pattern that could affect analysis results. We found that, while some large data gaps 

lasted several months and occurred randomly, the majority lasted four hours or less and tended to 

occur in the late afternoon, during the warmest hours of the day when the arrays were typically 

serviced (Appendix A). To address these data gaps, we used a rule-of-thumb called the “3/5 rule,” 

which is commonly applied to missing climate data. According to the rule, any month missing more 

than three consecutive daily values, or more than five daily values in total, is omitted from the 

calculation of monthly means (Anderson and Gough 2018). Here, we applied the rule twice: first to 

filter out days with missing hours (when calculating daily metrics), and then to filter out months with 

missing days (when calculating monthly metrics). Days and months that met the 3/5 rule criteria 

were included without interpolation of missing values.

Status Summary

Various metrics were calculated to provide an annual snapshot of temperature in lakes with arrays. 

We focused primarily on metrics listed in the SWAN freshwater monitoring protocol (Shearer et al. 

2015a and b), such as the warmest and coldest daily mean temperatures for each month, the standard 

deviation of those means for each month, and the warmest day and week of each year. To this list, we 

added the total number of daily means per year above 15 °C, the State of Alaska’s temperature 

threshold for waters supporting fish migration and rearing (ADEC 2018). These metrics were 

selected to highlight monthly and yearly — rather than hourly — patterns (Shearer et al. 2015a). 

Collectively, they summarize the current status of water temperature as well as the status earlier in 

the time series, because the calculations cover each year on record. The metrics were computed for 

every depth of each array using base commands in R software (R Core Team 2017). Data that did not 

meet the 3/5 rule requirements (described above) were omitted.

Trend Analysis

Seasonal Kendall analysis was used to examine whether lake temperature has changed since the 

beginning of monitoring at each array. This is a non-parametric, rank-based test that accounts for 

seasonal cycles in water quality variables like temperature, first by comparing values within userdefined seasons (e.g., months, quarters, etc.) and then by aggregating the results for each season into 

an overall test for trend (Helsel and Hirsch 2002). For example, if months are selected as seasons, the 

test analyzes trends in each month separately, then combines the 12 monthly results into a single 

monotonic trend (i.e., a trend in only one direction, either increasing or decreasing). The test returns a 
10

final p-value and test statistic, Kendall’s tau (τ). The value of τ ranges from −1 to 1, signifying the 

direction of the trend. A negative τ represents cooling and a positive τ represents warming, if the 

associated p-value is at or below the threshold for statistical significance (p = 0.05, for this analysis). 

For each value of τ, a non-parametric Sen slope may be calculated, estimating the median annual 

change for a given time series (Hirsch et al. 1982).

Seasonal Kendall tests were applied to both the mean and standard deviation of water temperature to 

examine changes over time in central tendency and variation in the dataset. Changes in variation 

were included because lake temperature is expected to become increasingly variable (Niedrist et al. 

2018), as extreme weather events become increasingly frequent (Schär et al. 2004, Coumou and 

Rahmstorf 2012). Monthly means and standard deviations were calculated in R from raw hourly data 

for each time series. Months that did not meet the requirements of the 3/5 rule (described above) 

were omitted from the calculation of monthly means and standard deviations. The 0 m data were also 

excluded due to the limited ability to detect trends in datasets with small samples sizes (i.e., <20 

observations; Schertz et al. 1991). All seasonal Kendall tests and Sen slopes were computed in R 

using Version 2.3.1 of the ‘EnvStats’ package (Millard 2013).

Stratification Patterns

Stratification is the process whereby lakes develop discrete layers of water with contrasting

temperatures and, therefore, densities. Various stratification patterns occur among lakes, depending 

on their chemistry, morphometry, location, and surrounding climate (Wetzel 2001). Here, these 

patterns are named via the classification system devised by Lewis (1983) and simplified by Boehrer 

and Schultze (2008). See the List of Terms above for details.

We assessed stratification patterns at each array by first defining stratified waters as having a daily 

mean temperature difference of >|1| °C between the shallowest (5 m) and deepest loggers (Woolway 

and Merchant 2019). We then quantified the duration of stratification by water year (October 1–

September 30) and season (summer, winter) at each array. Summer stratification included dates when 

the temperature difference between layers (i.e., top minus bottom) was >1 °C, typically June–

October. Winter (or inverse) stratification included dates when the temperature difference between 

these layers was < −1 °C, typically December–April. Duration was measured as the longest 

continuous period of stratification, with one exception. Specifically, if a period of stratification was 

interrupted midway by a one- to three-day period of mixing, then it was considered continuous for 

the purpose of this report. Finally, the first and last years in each array’s time series were excluded 

from calculations of summer stratification, because we did not capture the beginning or ending of 

stratification in these years.
11

Results

Water temperature has been monitored continuously via temperature array for varying time periods, 

ranging from 7 years in the Iliuk basin of Naknek Lake to 12 years in Lake Clark. The resulting 

dataset includes 6 sites, with 6–12 depths per site, totaling 57 time series containing more than 4 

million data points (Table 3). For an overview of key features and data gaps in the time series at each 

site, see Appendix B.

Status Summary

For this report, our summary of current status describes the 2017 water year relative to previous years 

on record. Water year 2017 was selected because it was the most recent complete year on record at 

the time of analysis. Here, for the sake of brevity, we highlighted the 5 m depth, as in past annual 

reports (e.g., Shearer and Moore 2010, Moore and Shearer 2011). Comparable summaries for other 

depths are available upon request.

Lake Clark

The warmest day (i.e., highest daily mean temperature) in 2017 at the 5 m depth (14.89 °C) occurred 

on August 3 and was within the range recorded in previous years, both for temperature (11.50–15.91 

°C) and for date (July 18–September 19) (Figures 5 and 6). The warmest week (i.e., highest weekly 

mean temperature) in 2017 at the 5 m depth (14.33 °C, beginning July 29) was also within the range 

recorded in previous years (11.25–14.72 °C, beginning July 14–September 13). Both metrics (i.e., 

warmest day and week) were at the high and early end of the range. The coolest day (i.e., lowest 

daily mean temperature) in 2017 at the 5 m depth (0.55 °C) occurred on January 19, and was similar 

to the median value for previous coolest days in January (0.51 °C; Figures 5 and 6). Daily mean 

temperature at the 5 m depth did not exceed 15 °C in 2017, similar to the range recorded in previous 

years (0–1 day >15 °C). For details, see Appendix C.
12

Figure 5. Heatmaps depicting maximum and minimum daily mean temperatures at the 5 m depth, 

organized by array and water year. Site abbreviations are as follows: LCLAR01 = Lake Clark; KIJIL01 = 

Kijik Lake; NAKNL01, NAKNL02, and NAKNL03 = Naknek Lake North, West, and Iliuk basins, 

respectively; and LBROO01 = Lake Brooks.
13

Figure 6. Boxplots of maximum and minimum daily mean temperatures at the 5 m depth in Lake Clark 

(LCLAR01), organized by month for water years on record before 2017 (2006–2016). Maximum and 

minimum daily means for 2017 (red and blue dots, respectively) are superimposed for comparison. In 

each box, the midline is the median, the lower and upper box edges are the 25th and 75th percentiles, 

and the whiskers extend up to 1.5 times the interquartile range from the edge to the furthest datum within 

that distance.

Kijik Lake

The warmest day in 2017 at the 5 m depth (15.58 °C) occurred on August 5 and exceeded the range 

from previous years (12.04–15.10 °C), which occurred within a narrow period (July 31–August 23) 

(Figures 5 and 7). The warmest week in 2017 (15.34 °C, beginning August 4) also exceeded the 

range from previous years (11.88–14.92 °C, beginning July 30–August 18). The coolest day in 2017 

at the 5 m depth (1.79 °C) occurred on December 8. Overall, the warmest and coolest days at the 5 m 

depth in 2017 were warmer than the median values during summer months and cooler than the 

median values during winter months, for previous years on record (2010–2016) (Figure 7). Lastly, 

daily mean temperature at the 5 m depth exceeded 15 °C for 9 consecutive days in 2017. This 

number of exceedances surpassed the range recorded in previous years (0–2 days >15 °C). See 

Appendix D for additional information.
14

Figure 7. Boxplots of maximum and minimum daily mean temperatures at the 5 m depth in Kijik Lake 

(KIJIL01), organized by month for water years on record before 2017 (2010–2016). Maximum and 

minimum daily means for 2017 (red and blue dots, respectively) are superimposed for comparison. In 

each box, the midline is the median, the lower and upper box edges are the 25th and 75th percentiles, 

and the whiskers extend up to 1.5 times the interquartile range from the edge to the furthest datum within 

that distance.

Naknek Lake

North Basin (NAKNL01)

The warmest day in 2017 at the 5 m depth (14.83 °C) occurred on July 30 and was within the range 

recorded in previous years, both for temperature (11.77–15.83 °C) and for date (July 15–September

20) (Figures 5 and 8). The warmest week in 2017 (14.45 °C, beginning July 26) was also within the 

range recorded in previous years (11.47–15.48 °C, beginning July 14–September 14). By 

comparison, the coolest day in 2017 (1.77 °C) occurred on February 20. Overall, the warmest and 

coolest days at the 5 m depth in 2017 were warmer than the median values for previous years on 

record (2008–2016) (Figure 8). Finally, daily mean temperature at the 5 m depth remained below the

15 °C threshold in 2017, matching the low end of the range recorded in previous years (0–20 days 

>15 °C). For details, see Appendix E.
15

Figure 8. Boxplots of maximum and minimum daily mean temperatures at the 5 m depth in the North 

basin of Naknek Lake (NAKNL01), organized by month for water years on record before 2017 (2008–

2016). Maximum and minimum daily means for 2017 (red and blue dots, respectively) are superimposed 

for comparison. In each box, the midline is the median, the lower and upper box edges are the 25th and 

75th percentiles, and the whiskers extend up to 1.5 times the interquartile range from the edge to the 

furthest datum within that distance.

West Basin (NAKNL02)

The warmest day in 2017 at the 5 m depth (16.23 °C) occurred on July 31 and was within the range 

recorded in previous years, both for temperature (12.51–16.30 °C) and for date (July 15–September

19) (Figures 5 and 9). The warmest week in 2017 (15.93 °C, beginning July 29) was warmer than 

previous weeks on record (11.98–15.88 °C, beginning July 14–August 31). The coolest day in 2017 

(0.17 °C) occurred on January 18. Overall, the warmest and coolest days at the 5 m depth in 2017 

tended to be warmer than the median values for previous years on record (2008–2016), except during 

winter months, when 2017 temperatures were at or below the median (Figure 9). Daily mean 

temperature at the 5 m depth exceeded 15 °C for 24 days in 2017. This number of exceedances was 

within the range recorded in previous years (0–37 days >15 °C). See Appendix F for details.
16

Figure 9. Boxplots of maximum and minimum daily mean temperatures at the 5 m depth in the West 

basin of Naknek Lake (NAKNL02), organized by month for water years on record before 2017 (2008–

2016). Maximum and minimum daily means for 2017 (red and blue dots, respectively) are superimposed 

for comparison. In each box, the midline is the median, the lower and upper box edges are the 25th and 

75th percentiles, and the whiskers extend up to 1.5 times the interquartile range from the edge to the 

furthest datum within that distance.

Iliuk Basin (NAKNL03)

The warmest day in 2017 at the 5 m depth (13.15 °C) occurred on July 28, which was earlier than in 

previous years (July 30–August 18), but within the range of recorded high temperatures (9.87–13.33 

°C) (Figures 5 and 10). The warmest week in 2017 (12.58 °C, beginning July 28) was also within the 

range recorded in previous years (9.63–15.48 °C, beginning July 27–August 14). The coolest day in 

2017 (0.52 °C) occurred on January 22. Similar to the West basin site, the warmest and coolest days 

at the 5 m depth in 2017 were warmer than the median values for previous years on record (2011–

2016), except during winter months, when 2017 temperatures were typically at or below the median 

(Figure 10). Daily mean temperature at the 5 m depth did not exceed 15 °C in 2017, which was 

comparable to previous years on record (0–1 day >15 °C). For details, see Appendix G.
17

Figure 10. Boxplots of maximum and minimum daily mean temperatures at the 5 m depth in the Iliuk 

basin of Naknek Lake (NAKNL03), organized by month for water years on record before 2017 (2011–

2016). Maximum and minimum daily means for 2017 (red and blue dots, respectively) are superimposed 

for comparison. In each box, the midline is the median, the lower and upper box edges are the 25th and 

75th percentiles, and the whiskers extend up to 1.5 times the interquartile range from the edge to the 

furthest datum within that distance.

Lake Brooks

The warmest day in 2017 at the 5 m depth (15.33 °C) occurred on August 3 and was within the range 

recorded in previous years, both for temperature (11.49–15.91 °C) and for date (July 31–September

3) (Figures 5 and 11). The warmest week in 2017 (14.85 °C, beginning July 29) was also within the 

range recorded in previous years (11.24–15.40 °C, beginning July 25–August 31). The coolest day in 

2017 (1.61 °C) occurred on January 12. Overall, the warmest and coolest days at the 5 m depth in 

2017 tended to be warmer than the median value for previous years on record (2010–2016), when 

summarized by month (Figure 11), particularly for October. Lastly, daily mean temperature at the 5 

m depth exceeded 15 °C for 2 days in 2017. This number of exceedances was within the range 

recorded in previous years (0–32 days >15 °C). See Appendix H for additional information.
18

Figure 11. Boxplots of maximum and minimum daily mean temperatures at the 5 m depth in Lake Brooks 

(LBROO01), organized by month for water years on record before 2017 (2010–2016). Maximum and 

minimum daily means for 2017 (red and blue dots, respectively) are superimposed for comparison. In 

each box, the midline is the median, the lower and upper box edges are the 25th and 75th percentiles, 

and the whiskers extend up to 1.5 times the interquartile range from the edge to the furthest datum within 

that distance.

Trend Analysis — Means 

Seasonal Kendall results indicated that monthly mean water temperature at the 5 m depth has 

warmed during the period of record at all six arrays (Table 4). The rate of warming at this depth 

varied among arrays from 0.05 to 0.24 °C per year. For some arrays, strong warming trends extended 

to deeper waters. Conversely, cooling trends were apparent at depths below 5 m at one array (Kijik). 

Overall, the strength of trends in KATM exceeded that in LACL. Lake-specific results are 

highlighted below.
19 

Table 4. Results of seasonal Kendall tests for changes over time in mean water temperature by depth at six array sites. Periods of record included 

in the tests differ by site as shown. The test statistic (τ) indicates the direction of the trend. Positive τ values with p-values <0.05 (dark red) are 

interpreted as significant warming trends. Negative τ values with p-values <0.05 (dark blue) or 0.05–0.10 (light blue) are interpreted as significant 

or substantial cooling trends, respectively. For each value of τ, the Sen slope estimates the median annual change in temperature (Δ/yr, in °C). 

Cells with dashes represent no data at a given depth (gray). Values between −0.001 and 0.001 are rounded to 0.000.

Depth

(m)

LCLAR01 A

(9/2006–8/2018)

KIJIL01 A

(8/2010–8/2018)

NAKNL01 A

(9/2008–8/2018)

NAKNL02 A, B

(9/2008–8/2018)

NAKNL03 A

(8/2011–8/2018)

LBROO01 A

(9/2010–8/2018)

τ p Δ/yr τ p Δ/yr τ p Δ/yr τ p Δ/yr τ p Δ/yr τ p Δ/yr

5 0.184 0.004 0.047 0.203 0.013 0.063 0.426 0.000 0.154 0.320 0.000 0.123 0.460 0.000 0.244 0.315 0.000 0.139

10 0.173 0.006 0.037 −0.016 1.000 0.000 0.396 0.000 0.127 0.336 0.000 0.113 0.437 0.000 0.240 0.351 0.000 0.131

20 0.136 0.045 0.024 −0.291 0.001 −0.054 0.314 0.000 0.096 0.271 0.000 0.088 0.444 0.000 0.220 0.286 0.001 0.093

30 0.035 0.576 0.005 −0.113 0.228 −0.013 0.270 0.000 0.067 – – – 0.476 0.000 0.210 0.119 0.164 0.035

40 0.011 0.873 0.004 −0.294 0.004 −0.061 0.096 0.188 0.030 – – – 0.484 0.000 0.155 0.000 1.000 0.000

50 0.044 0.569 0.005 −0.024 0.940 0.000 0.163 0.025 0.044 – – – 0.421 0.000 0.143 −0.024 0.803 −0.009

60 −0.034 0.603 −0.005 0.020 0.707 0.003 0.089 0.225 0.019 – – – 0.500 0.000 0.142 – – –

70 0.005 0.968 0.001 −0.010 1.000 0.004 0.056 0.454 0.011 – – – 0.500 0.000 0.139 – – –

80 −0.057 0.395 −0.008 −0.179 0.055 −0.010 – – – – – – 0.492 0.000 0.122 – – –

90 −0.081 0.230 −0.009 0.083 0.259 0.017 – – – – – – 0.508 0.000 0.115 – – –

100 −0.041 0.532 −0.003 – – – – – – – – – 0.476 0.000 0.109 – – –

A LCLAR01 = Lake Clark; KIJIL01 = Kijik Lake; NAKNL01, NAKNL02, and NAKNL03 = Naknek Lake North, West, and Iliuk basins; LBROO01 = Lake Brooks.

B Results from other depths at the NAKNL02 array (i.e., 15 m and 25 m) indicate significant warming trends (see Appendix I for details). 
20

Lake Clark and Kijik Lake

Water at the three shallowest depths (5, 10, and 20 m) exhibited significant warming trends during 

the period of record at the Lake Clark array, with increases ranging from 0.02 to 0.05 °C per year. 

Other depths had no significant trends. At Kijik Lake, the 5 m depth exhibited a significant warming 

trend during the period of record but, unlike other arrays, warming was limited to this depth stratum. 

Also unlike other arrays, significant cooling trends were apparent deeper in the water column, at the 

20 m and 40 m depths. Rates of change varied from 0.06 °C per year at the 5 m depth to −0.06 °C per 

year at the 40 m depth.

Naknek Lake and Lake Brooks

Significant warming trends in Naknek Lake occurred not only near the surface, but also throughout 

the water column at two of the three arrays (NAKNL02 and NAKNL03). At the third array 

(NAKNL01), warming trends were limited to the upper strata (5 m to 30 m) and the 50 m depth. 

Rates of warming varied among these arrays from 0.04 to 0.24 °C per year (at NAKNL01 and 

NAKNL03, respectively). Finally, waters from 5 m to 20 m depth exhibited significant warming 

during the period of record at the Lake Brooks array, with increases of as much as 0.14 °C per year. 

Mean water temperatures below 20 m at Lake Brooks were stable over time.

Trend Analysis — Standard Deviations

Trends in the standard deviation (SD) of mean temperature were less uniform than trends in mean 

temperature, in that no particular depth exhibited consistent increases or decreases across all arrays. 

However, where significant trends in SD occurred, they generally indicated increased variation over 

time (Table 5). The magnitude of increase was small, ranging from 0.01 to 0.03 °C per year. As with 

the analysis of means, the analysis of SDs revealed stronger trends in KATM than in LACL. Lakespecific results are presented below.
21 

Table 5. Results of seasonal Kendall tests for changes over time in water temperature standard deviation (SD) by depth at six array sites. Periods 

of record included in the tests differ by site as shown. The test statistic (τ) indicates the direction of the trend. Positive τ values with p-values <0.05 

(dark red) or 0.05–0.10 (light red) are interpreted as significant or substantial warming trends, respectively. Negative τ values with p-values <0.05 

(dark blue) are interpreted as significant cooling trends. For each value of τ, the Sen slope estimates the median annual change in SD (Δ/yr, in 

°C). Cells with dashes represent no data (gray). Values between −0.001 and 0.001 are rounded to 0.000.

Depth

(m)

LCLAR01 A

(9/2006–8/2018)

KIJIL01 A

(8/2010–8/2018)

NAKNL01 A

(9/2008–8/2018)

NAKNL02 A, B

(9/2008–8/2018)

NAKNL03 A

(8/2011–8/2018)

LBROO01 A

(9/2010–8/2018)

τ p Δ/yr τ p Δ/yr τ p Δ/yr τ p Δ/yr τ p Δ/yr τ p Δ/yr

5 −0.138 0.031 −0.010 0.190 0.020 0.020 0.052 0.486 0.005 0.180 0.012 0.020 0.071 0.461 0.009 0.149 0.080 0.018

10 −0.060 0.349 −0.004 0.178 0.042 0.008 0.085 0.245 0.008 0.252 0.001 0.028 0.056 0.573 0.007 0.185 0.029 0.016

20 −0.106 0.121 −0.004 0.050 0.598 0.003 0.147 0.072 0.010 0.261 0.000 0.023 0.167 0.075 0.015 0.185 0.029 0.015

30 −0.055 0.401 −0.003 0.119 0.228 0.002 0.200 0.006 0.012 – – – 0.270 0.004 0.018 0.143 0.093 0.010

40 −0.056 0.401 −0.001 −0.005 1.000 0.000 0.181 0.012 0.010 – – – 0.071 0.461 0.007 0.113 0.186 0.009

50 −0.001 1.000 0.000 −0.065 0.598 −0.001 0.211 0.004 0.011 – – – 0.000 1.000 0.000 0.077 0.372 0.003

60 0.010 0.873 0.001 −0.077 0.452 −0.001 0.207 0.004 0.012 – – – −0.016 0.897 −0.001 – – –

70 −0.007 0.936 0.000 −0.142 0.132 −0.001 0.148 0.041 0.008 – – – −0.008 0.965 0.000 – – –

80 −0.003 0.983 0.000 −0.029 0.821 0.000 – – – – – – −0.040 0.696 −0.002 – – –

90 0.056 0.424 0.001 0.104 0.228 0.000 – – – – – – −0.008 0.965 −0.001 – – –

100 −0.002 0.984 0.000 – – – – – – – – – 0.016 0.897 0.002 – – –

A LCLAR01 = Lake Clark; KIJIL01 = Kijik Lake; NAKNL01, NAKNL02, and NAKNL03 = Naknek Lake North, West, and Iliuk basins; LBROO01 = Lake Brooks.

B Results from other depths at the NAKNL02 array (i.e., 15 m and 25 m) indicate significant warming trends (see Appendix I for details). 
22

Lake Clark and Kijik Lake

Lake Clark exhibited a significant decrease in SD at the 5 m depth during the period of record, with a 

rate of change of −0.01 °C per year. Conversely, Kijik Lake exhibited significant increases in SD at 

the 5 m and 10 m depths, with changes of 0.02 °C and 0.01 °C per year, respectively.

Naknek Lake and Lake Brooks

Trends in SD at Naknek Lake varied by basin. For example, in the West basin (NAKNL02), SD 

increased during the period of record throughout the water column. Elsewhere in the lake, SD 

increased only at the 30 m depth (i.e., NAKNL03) and below (i.e., NAKNL01). At Lake Brooks, 

waters at the 10 m and 20 m depths also exhibited significant increasing trends in SD. The rate of 

change in these lakes varied from 0.01 to 0.03 °C per year.

Stratification Patterns

Differences in temperature between the top and bottom depths suggested a dimictic stratification 

pattern at most arrays early and late in the time series, interspersed with monomictic or polymictic 

patterns in isolated years (Figures 12 and 13). Changes between patterns were driven by variability in 

the duration of winter stratification, which was unexpectedly asynchronous across arrays in some 

years (e.g., water year 2013). In general, the mean start dates of continuous stratification varied less 

than the mean end dates, when compared across arrays, for both winter and summer stratification. 

Also, winter stratification tended to be short or nonexistent in water years 2014–2016.
23

Figure 12. Daily mean water temperature by site, depth, and year for the Lake Clark (LCLAR01), Kijik Lake (KIJIL01), and Lake Brooks 

(LBROO01) arrays. Black and gray lines at the bottom of each plot represent days when the water column was stratified during winter and 

summer, respectively.

15

10

5

0

15

10

5

0

15

10

5

0

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Calendar Year

LCLAR01

KIJIL01

LBROO01

50 m

60 m

70 m

80 m

90 m

100 m

0 m

5 m

10 m

20 m

30 m

40 m

Temperature (°C)
24

Figure 13. Daily mean water temperature by site, depth, and year for arrays located in the North (NAKNL01), West (NAKNL02), and Iliuk 

(NAKNL03) basins of Naknek Lake. Black and gray lines at the bottom of each plot represent days when the water column was stratified during 

winter and summer, respectively.

15

10

5

0

15

10

5

0

15

10

5

0

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Calendar Year

NAKNL01

NAKNL02

NAKNL03

50 m

60 m

70 m

80 m

90 m

100 m

0 m

5 m

10 m

20 m

30 m

40 m

Temperature (°C)
25

Lake Clark

The mean duration of summer stratification for water years 2007–2017 was 122 days (SD = 13 days; 

range = 102–139 days). Mean start and end dates were June 14 and October 13, respectively (Figure 

14). The mean duration of winter stratification for water years 2007–2018 was 76 days (SD = 50 

days; range = 0–145 days), with a mean start date of January 10 and a mean end date of April 3 

(Figure 15). Lake Clark did not stratify in winter during water year 2016 at the location of the 

temperature array.
26

Figure 14. Duration of continuous summer stratification by water year at six temperature array sites. 

Asterisks at Kijik Lake (KIJIL01) in 2016 represent truncated data in that year. Abbreviations for other 

sites are as follows: LCLAR01 = Lake Clark; NAKNL01, NAKNL02, and NAKNL03 = Naknek Lake North, 

West, and Iliuk basins, respectively; and LBROO01 = Lake Brooks.
27

Figure 15. Duration of continuous winter stratification by water year at six temperature array sites. Blank 

years indicate no winter stratification; asterisks at Kijik Lake (KIJIL01) represent truncated data. 

Abbreviations for other sites are as follows: LCLAR01 = Lake Clark; NAKNL01, NAKNL02, and NAKNL03 

= Naknek Lake North, West, and Iliuk basins, respectively; and LBROO01 = Lake Brooks.
28

Kijik Lake

Summer stratification lasted, on average, 168 days (SD = 11 days; range = 151–180 days) during 

water years 2011–2015, and 2017, both the longest duration and smallest variation of all the arrays. 

Average start and end dates of summer stratification were May 23 and November 6, respectively 

(Figure 14). Winter stratification lasted an average of 90 days (SD = 64 days; range = 1–149 days) 

during the water years 2011–2015 and 2017–2018, with average start and end dates of December 10 

and March 10 (Figure 15). Note that water year 2016 was excluded due to array movement (see 

Appendix B for details).

Naknek Lake

North Basin (NAKNL01)

Summer stratification varied in duration from 104 to 148 days (mean = 126, SD = 15) during water 

years 2009–2017. Mean start and end dates were June 9 and October 13, respectively (Figure 14). In 

comparison, winter stratification varied in duration from 0 to 121 days (mean = 57, SD = 59) during 

water years 2009–2018, with mean start and end dates of January 15 and March 22 (Figure 15). Like 

Lake Clark, the North basin of Naknek Lake did not stratify in winter during water year 2016 at the 

location of the temperature array.

West Basin (NAKNL02)

Summer stratification lasted, on average, 99 days (SD = 22 days; range = 60–136 days) during water 

years 2009–2017. Average start and end dates of summer stratification were May 31 and September 

7, respectively (Figure 14). Winter stratification lasted an average of 31 days (SD = 25 days; range = 

3–81 days) during the water years 2009–2018, with average start and end dates of December 29 and 

January 29 (Figure 15). Of the six sites with temperature arrays, this site displayed a consistent 

polymictic pattern, less discernable in Figures 14 and 15 (which display the longest continuous 

period of stratification) than in Figure 13 (which displays any period when the top and bottom-most 

depths differed by > 1 °C).

Iliuk Basin (NAKNL03)

The mean duration of summer stratification for water years 2012–2017 was 116 days (SD = 22 days; 

range = 87–142 days). Mean start and end dates were June 10 and October 4, respectively (Figure 

14). The mean duration of winter stratification for water years 2012–2018 was 37 days (SD = 49 

days; range = 0–115 days), with a mean start date of January 16 and a mean end date of February 28 

(Figure 15). The Iliuk basin of Naknek Lake did not stratify in winter during water year 2016 at the 

location of the temperature array.

Lake Brooks

Summer stratification lasted, on average, 121 days (SD = 17 days; range = 93–145 days) during 

water years 2011–2017, with average start and end dates of June 9 and October 8, respectively 

(Figure 14). Like other arrays, the duration of winter stratification varied widely. It lasted an average 

of 43 days (SD = 49 days; range = 0–138 days) during the water years 2011–2018, with average start 

and end dates of January 7 and March 5 (Figure 15). Water years 2015 and 2016 failed to stratify 

during winter at this array.
29

Discussion

Water temperature regulates key physical processes in lakes (Schindler and Rogers 2009). It also 

regulates most biological processes in lake-dwelling organisms, nearly all of which have optimal

temperature ranges for survival, growth, and reproduction. Here, we examined the status and trend of 

water temperature and the pattern of stratification at six sites in four lakes with high monitoring 

priority. Our findings indicate that: (1) temperature status metrics summarizing the most recent, 

complete water year on record generally fell within the range of previously recorded temperatures at 

the 5 m depth, (2) monthly mean water temperatures have warmed during the period of record at all 

sites at the 5 m depth, and (3) stratification patterns have vacillated during the period of record, 

driven by inconsistencies in winter water temperature.

Status Summary

Our finding that temperature metrics for the current year typically fell within the range of previous 

years depends, in part, on the definition of the terms “current” and “previous.” This study defined 

current as the most recent complete year on record at the time of analysis (2017). Although generally 

warmer than normal, the mean annual air temperature in 2017 (−1.50 °C) was below the recordbreaking temperatures of 2016 (−0.06 °C) and 2019 (0.17 °C) for the state of Alaska (NOAA NCEI 

2020). Had either of these years been selected as current rather than 2017, the results of our status 

summary likely would have differed. Also, although metrics for 2017 were within the previously 

recorded range (2006–2016), they might not be within the historical range. In other words, the 

recorded range might denote a shifted baseline (Pauly 1995), relative to water temperatures 

experienced before monitoring began.

All lakes equipped with arrays support migration, spawning, and rearing of multiple Pacific salmon 

species, with sockeye salmon (O. nerka) being most abundant. Temperature preferences of salmon 

depend not only on species and population, but also on life stage. For example, subyearling sockeye 

tend to prefer warmer water temperatures than older juveniles and adults (Sauter et al. 2001). 

Subyearlings also tend to occupy shallow waters at lake edges, where peak temperatures are often

warmer than those recorded at mid-lake arrays. For instance, daily mean temperatures at the Lake 

Brooks array in 2017 reached 15.33 °C and 15.74 °C at the 5 m and 0 m depths, respectively, but at a 

monitoring site on the lake shore nearby, the peak was 16.72 °C (K. Bartz, unpublished data). While 

warm lake edge temperatures might be optimal for young juveniles, they are suboptimal for adults, 

which also rely on lake edges for migration and spawning. Fortunately, adults are capable swimmers 

that can seek cooler, deeper waters and/or postpone spawning, but doing so has a metabolic cost for 

fish that stopped eating when they left saltwater (Fenkes et al. 2016).

At all six arrays, daily mean temperatures at 5 m depth exceeded the State’s water quality threshold 

of 15 °C at some point during the period of record. Weekly mean temperatures at 5 m depth also 

exceeded this threshold at four of the six arrays (i.e., Kijik, Brooks, and the North and West basins of 

Naknek Lake; Appendices D, E, F, and H). This is noteworthy because the ability of fish to endure 

thermal stress decreases as the duration of the exceedance increases (Sullivan et al. 2000). Endurance 

also depends on the existence of cold water refugia at other depths. Our data indicate that daily mean
30

temperatures at three of the arrays exceeded the 15 °C threshold at deeper depths during the period of 

record. Conversely, none of the arrays had exceedances at or below 30 m depth. Although such deep 

pelagic waters are inaccessible to younger juvenile sockeye salmon, older juveniles make daily 

vertical migrations to this depth and beyond, occupying deeper waters during the day than they do at 

night (Levy 1987). Scheuerell and Schindler (2003) generally observed juvenile sockeye below 60 m 

during the day in three southwest Alaska lakes, and hypothesized that these migrations are intended 

to minimize the ratio of predation risk to foraging gain, rather than select temperatures to maximize 

their growth rate, as proposed by Brett (1971).

Trend Analysis — Means

Mean water temperature at the 5 m depth increased during the periods of record at all arrays. At some 

arrays, particularly those in KATM, warming trends extended down the water column. Of the 57 

time series examined, 28 exhibited significant warming trends, while only 2 exhibited significant 

cooling. Although the periods of record were relatively short (i.e., 7 to 12 years), the consistency in

direction of the trends was noteworthy. Also noteworthy were the rates of change (e.g., increases of 

0.02 to 0.24 °C per year, across all depths). At the low end of this range, the rates are small, when 

considered on an annual timestep. However, if the rates persist, they may amount to ecologically 

significant changes over time, especially given findings of exceedances of the 15 °C threshold during 

the periods of record at all arrays.

These rates — if converted to a decadal time scale — would fit or exceed the range reported in a 

global synthesis of summer surface water trends for 246 lakes (i.e., significant increases of 0.13 to 

1.35 °C per decade; O’Reilly et al. 2015). According to this synthesis, other lakes in southwest 

Alaska also exhibited accelerated rates of change (1.03 and 0.85 °C per decade at Iliamna Lake and 

Lake Aleknagik, where the time series spanned 14 and 21 years, respectively).That we observed rates 

of change exceeding those noted by O’Reilly et al. (particularly in the Iliuk basin of Naknek Lake) is 

likely a function of the abbreviated time series at that site (7 years), and the start and end years of that 

time series (2011–2018), as opposed to unique characteristics of the lake basin.

The cooling trends observed in deeper waters of Kijik Lake might reflect the short length of the time 

series at that site (2010–2018), as these results fluctuated from nonsignificant to significant with the 

addition of the final year of data, during the course of the analysis. Alternatively, the results could 

reflect processes identified by Niedrist et al. (2018) to explain similar cooling below the epilimnion 

of a mountain lake in Austria. Specifically, they hypothesized that cooling at deeper depths could 

result from a lengthening of summer stratification, which could shorten the duration of mixing, 

limiting heat transfer from the surface to the deeper water layers. We did not examine trends in the 

duration of stratification due to the brevity of the time series, but this could be assessed in the future 

as the period of record lengthens.

Trend Analysis — Standard Deviations

The frequency of extreme air temperatures is increasing globally, and the increase is related to 

climate change (Coumou and Rahmstorf 2012, Bathiany et al. 2018). Therefore, anticipating the 

effects of climate change requires incorporating trends in variation, as well as trends in means, when 

examining air temperature (Bathiany et al. 2018). Given that air temperature is a major driver of 
31

water temperature, the same is likely true of variation and means within aquatic systems (Steel et al. 

2012). Of the 57 water temperature time series examined in our study, most had no significant trend 

in variation. However, in time series where a significant trend was observed, all but one exhibited 

increases.

The biological consequences of increasing means are well studied, for both air and water 

temperature. The consequences of increasing variation have become a topic of interest more recently, 

with the majority of studies focusing on effects in terrestrial systems (Thompson et al. 2013). These 

studies have identified a range of potential effects of increasing variation, either independent of or in 

conjunction with increasing means. The effects include altered thermal tolerances, development, and 

survival of individuals, modified growth rates of populations, and shifted distributions and 

performance of species (Harley et al. 2009, Bozinovic et al. 2011, Steel et al. 2012, Vasseur et al. 

2014). One particularly relevant study exposed salmon eggs to experimental thermal regimes with 

differing means and variances, and found that variation affected both emergence timing and 

development at emergence, independent of warming (Steel et al. 2012).

Stratification Patterns

The stratification pattern assigned to a lake depends mainly on the presence of ice cover and the 

frequency of stratification each year (Lewis 1983, Woolway and Merchant 2019). The pattern, in 

turn, determines the timing and intensity of various processes in lake ecosystems, including nutrient 

upwelling, biogeochemical cycling, and oxygenation (North et al. 2014, Yankova et al. 2017). 

Hence, changes in pattern could have cascading effects on multiple ecosystem processes.

Changes in stratification pattern due to warming climate have already been documented at several 

lakes in Europe (Ficker et al. 2017, Kainz et al. 2017). These changes are likely to become more 

widespread by the end of this century, according to a recent study of 635 lakes worldwide (Woolway 

and Merchant 2019). The study used outputs from a representative suite of climate models as inputs 

to a one-dimensional lake model, in order to project changes in surface water temperature, lake ice 

duration, and stratification pattern. Many lakes were projected to shift their stratification pattern, (i.e., 

from dimictic to monomictic, or from monomictic to meromictic), mixing less frequently in response 

to climate change. Furthermore, lakes that were normally ice-covered in winter and classified as 

dimictic — but that were also currently experiencing occasional ice-free winters — were more likely 

to shift patterns, from dimictic to monomictic, by the year 2100. Lakes in our study, which are 

primarily dimictic, might share this trajectory, given the short or absent winter stratification in some 

recent years (e.g., 2015, 2016) and the short duration of ice cover in those years (P. Kirchner, 

unpublished data).

Considerations and Next Steps

One potentially confounding factor in our protocol is the fact that the arrays are moored at the bottom 

of the lake, not the surface. Therefore, while any given 5 m logger is theoretically 5 m below the 

water surface, in practice it is located at a fixed depth from the bottom, and the water level fluctuates 

above it as dictated by the annual hydrograph. Stated another way, the 5 m logger is a known

distance above the bottom, but it is not always 5 m below the surface.
32

Another potentially confounding (and related) factor is variation in the deployment location of the 

instrument line over time. If all locations for a given array have identical bathymetry (i.e., equal 

maximum depths), then this is not an issue. However, if some locations are shallower than others, 

then an unaccounted for source of variation exists in the dataset. Clearly, it would be problematic if, 

for example, deployment locations were becoming shallower over time. Unfortunately, systematic 

metadata on deployment depths do not exist prior to 2018. Instead, latitude and longitude were 

recorded for the full time series, under the assumption that consistency in X-Y coordinates would 

ensure roughly comparable Z coordinates (depths). Bathymetry maps for these lakes are not very 

accurate, so estimating depths from coordinates is tenuous. However, if the maps are taken as truth, 

then they suggest that deployment locations at three arrays have crossed 5-m bathymetry contour 

lines over time (LCLAR01, NAKNL01, and LBROO01; Figures 16–18).

Figure 16. Map of instrument line locations over time for two arrays in Lake Clark National Park and 

Preserve (LACL). Park boundaries are shown in the upper inset, along with boundaries of two nearby 

parks: Katmai National Park and Preserve (KATM) and Kenai Fjords National Park (KEFJ). In the lower 

insets, contour lines represent 5 m depth increments. The maximum distance between instrument line 

locations is 128 m and 734 m at Kijik Lake (KIJIL01) and Lake Clark (LCLAR01), respectively.
33

Figure 17. Map of instrument line locations over time for two arrays in Katmai National Park and Preserve 

(KATM). Park boundaries are shown in the upper inset, along with boundaries of two nearby parks: Lake 

Clark National Park and Preserve (LACL) and Kenai Fjords National Park (KEFJ). In the lower insets, 

contour lines represent 5 m depth increments. The maximum distance between instrument line locations 

is 249 m and 150 m at the North (NAKNL01) and West (NAKNL02) basins of Naknek Lake, respectively.
34

Figure 18. Map of instrument line locations over time for two arrays in Katmai National Park and Preserve 

(KATM). Park boundaries are shown in the upper inset, along with boundaries of two nearby parks: Lake 

Clark National Park and Preserve (LACL) and Kenai Fjords National Park (KEFJ). In the lower insets, 

contour lines represent 5 m depth increments. The maximum distance between instrument line locations 

is 259 m and 291 m at the Iliuk basin of Naknek Lake (NAKNL03) and Lake Brooks (LBROO01), 

respectively.

One way to explore these confounding factors is to co-locate a water level logger with a water 

temperature logger on each array (e.g., the 5 m logger). Although all arrays are now equipped with 

level loggers year-round at the 5 m depth, this was not the norm in the past. The longest available 

array-specific level logger record pertains to the 5 m depth at the Lake Clark array and dates back to 

2007 (Figure 19). It contains numerous data gaps and high day-to-day variation. More troubling is 

the year-to-year variation in water level, presumably caused by differing deployment locations. If the 

majority of the discrepancy from 5 m occurred later in the time series and was biased shallow, then 

the trend analysis results would show greater warming than what actually occurred. Instead, the 

overall bias later in the time series was deep, albeit increasing, so the trend analysis results could 

represent a conservative estimate of warming at this site. Also, the warming trend found at 5 m 

persisted when the seasonal Kendall analysis was redone using data averaged across depths from 5 m 

to 20 m (τ = 0.15, p = 0.03, slope = 0.03 °C per year), suggesting that the trend was robust 

throughout the epilimnion at this array.
35

Figure 19. Daily mean depth recorded by a water level logger co-located with the “5 m” water 

temperature logger on the Lake Clark array. Ideally, these data should be centered around the 5 m depth 

(dotted line), although some seasonal variation is expected. These data were not used to adjust the 

inputs to the status summary or trend analysis.

Surface water temperatures are closely tied to air temperatures, which are warming in Alaska at twice 

the rate of the national average (Markon et al. 2018). In the Bristol Bay region, where these arrays 

are located, climate change is already apparent in the seasonal air temperature records. For example, 

the Bristol Bay region’s average air temperature in winter (December–February), spring (March–

May), and summer (June–August) increased by 4.2 °C, 2.2 °C and 0.8 °C, respectively, from 1949 to 

2012, all of which were statistically significant increases at the 95% level (Bieniek et al. 2014). 

These warming trends are projected to continue (Chapin III et al. 2014), with winter air temperatures 

expected to continue warming faster than other seasons (Lader et al. 2017).

Indeed, a recent study by Jones and Arp (2014) used empirical lake temperature data from western 

Alaska to hindcast water temperatures at 0 m depth back to 1985 for 22 lakes. Results were then used 

to forecast water temperatures at 0 m depth to the year 2100, via a global climate model (GFDLCM3) under two trajectories of future greenhouse gas concentrations (RCP 6.0 and RCP 8.5). The 

study included empirical data from arrays at Lake Brooks and Naknek Lake (LBROO01 and 

NAKNL01). It projected increases in maximum daily mean temperatures of 2–3 °C in Lake Brooks 
36

(from 12.0 °C to 14.2–15.1 °C) and 4–5 °C in Naknek Lake (from 12.1 °C to 15.9–17.5 °C) by 2100, 

but our arrays are already recording temperatures exceeding the upper ends of those ranges.

Future field work should focus on ensuring that arrays are re-deployed in the same location where 

they were retrieved, or at a location with a comparable total depth. Future analyses could explore 

several areas of interest. For example, the time series at the Lake Clark array could be extended to 

include comparable data collected from 1999 to 2000 at a similar location (Wilkens 2002). A metaanalysis using data collected by arrays in other locations would also be worthwhile (e.g., Kodiak and 

Togiak National Wildlife Refuges and parks within the Great Lakes Network). Clearly, level logger 

data could be incorporated as a covariate when examining trends in lake temperature. Other 

covariates, such as air temperature, wind speed, and ice cover, could also be incorporated to 

determine the sensitivity of lake temperature at various depths to changes in these variables.

Preliminary analyses suggest that years with shorter ice cover have colder lake temperature at depth, 

and that days with higher wind speed have larger drops in lake temperature at the surface (P. Gabriel, 

unpublished data). Addition of these covariates to future analyses would strengthen our 

understanding of water temperature changes in these lakes.
37

Conclusions

The temperature array data synthesized here represent the longest running continuous time series 

collected by the SWAN freshwater monitoring program. It is a rich dataset, comprised of 6 sites with 

6–12 depths per site, totaling 57 separate time series. It is also applicable for addressing monitoring 

objectives, stated as questions in our protocol, such as: what are the status and trend of lake 

temperature and stratification patterns in priority lake systems?

For status, we found that temperature metrics summarizing the most recent, complete water year on 

record (2017) generally fell within the range of previous years’ temperatures at the 5 m depth. Water 

year 2017 was selected because it was the most recent complete year on record at the time of 

analysis, and the 5 m depth was highlighted for the sake of brevity. We expect that the selected year 

and highlighted depth play large roles in determining current status results. Hence, the results should 

be considered a snapshot of lake temperature in time and space.

For trends, we found that monthly mean water temperatures at the 5 m depth have warmed during the 

period of record at all array sites. Monthly standard deviations exhibited no comparable pattern at the 

5 m depth. However, where significant trends in standard deviation occurred at other depths, they 

generally indicated increased variation over time.

For stratification, we found that differences in temperature between the top and bottom depths 

suggested a dimictic stratification pattern at most arrays early and late in the time series, interspersed 

with monomictic or polymictic patterns in isolated years. The mean start dates of continuous 

stratification tended to vary less than the mean end dates, when compared across arrays, for both 

winter and summer stratification. Also, winter stratification tended to be short or nonexistent in water 

years 2014–2016, likely due to the brief duration or absence of ice cover in those years.

39

Literature Cited

Alaska Climate Research Center (ACRC). 2020. Products: annual statewide summaries. Available at: 

http://akclimate.org/products (accessed 3 June 2020).

Alaska Department of Environmental Conservation (ADEC). 2018. Water Quality Standards 18 

AAC 70.

Alaska Department of Fish and Game (ADFG). 2020a. Bristol Bay salmon escapement. Available at: 

https://www.adfg.alaska.gov/index.cfm?adfg=commercialbyareabristolbay.escapement (accessed 

5 March 2020).

Alaska Department of Fish and Game (ADFG). 2020b. Fish count data search. Available at: 

https://www.adfg.alaska.gov/sf/FishCounts/ (accessed 5 March 2020).

Alaska National Interest Lands Claim Act (ANILCA). 1980. Public Law 96-487 – December 2, 

1980. 94 Stat. 2371. 96th Congress. Washington, D.C.

Anderson, C. I., and W. A. Gough. 2018. Accounting for missing data in monthly temperature series:

testing rule-of-thumb omission of months with missing values. International Journal of 

Climatology 38:4990–5002.

Bathiany, S., Dakos, V., M. Scheffer, and T. M. Lenton. 2018. Climate models predict increasing 

temperature variability in poor countries. Science Advances 4, eaar5809, 

doi:10.1126/sciadv.aar5809.

Bieniek, P. A., J. E. Walsh, R. L. Thoman, U. S. Bhatt. 2014. Using climate divisions to analyze 

variations and trends in Alaska temperature and precipitation. Journal of Climate 27:2800–2818.

Bieniek, P. A., U. S. Bhatt, R. L. Thoman, H. Angeloff, J. Partain, J. Papineau, F. Fritsch, E. 

Holloway, J. E. Walsh, C. Daly, M. Shulski, G. Hufford, D. F. Hill, S. Calos, and R. Gens. 2012. 

Climate divisions for Alaska based on objective methods. Journal of Applied Meteorology and 

Climatology 51, doi:10.1175/JAMC-D-11-0168.1.

Boehrer, B., and M. Schultze. 2008. Stratification of lakes. Reviews of Geophysics 46, RG2005, 

doi:10.1029/2006RG000210.

Booher, E. C. J., J. Nelson, and K. K. Bartz. 2016. Freshwater monitoring in Southwest Alaska 

Network parks: 2014 field season summary. Natural Resource Data Series NPS/SWAN/NRDS—

2016/1005. National Park Service, Fort Collins, Colorado.

Booher, E. C. J., K. M. Junghans, and K. K. Bartz. 2018. Freshwater monitoring in Southwest Alaska 

Network parks: 2016 field season summary. Natural Resource Data Series NPS/SWAN/NRDS—

2018/1188. National Park Service, Fort Collins, Colorado.
40

Bozinovic, F., D. A. Bastías, F. Boher, S. Clavijo-Baquet, S. A. Estay, and M. J. Angilletta. 2011. 

The mean and variance of environmental temperature interact to determine physiological 

tolerance and fitness. Physiological and Biochemical Zoology. 84:543–552.

Brabets, T. P., and R. T. Ourso. 2006. Water quality, physical habitat, and biology of the Kijik River 

Basin, Lake Clark National Park and Preserve, Alaska, 2004–2005. U.S. Geological Survey 

Scientific Investigations Report 2006-5123.

Brett, J. R. 1971. Energetic responses of salmon to temperature. A study of some thermal relations in 

physiology and freshwater ecology of sockeye salmon (Oncorhynchus nerka). American 

Zoologist 11:99–113.

Chapin, F. S., III, S. F. Trainor, P. Cochran, H. Huntington, C. Markon, M. McCammon, A. D. 

McGuire, and M. Serreze, 2014: Ch. 22: Alaska. Climate Change Impacts in the United States: 

The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. 

Yohe, Eds., U.S. Global Change Research Program, 514–536. doi:10.7930/J00Z7150.

Coumou, D., and S. A. Rahmstorf. 2012. A decade of weather extremes. Nature Climate Change 2:

491–496.

Fenkes, M., H. A. Shiels, J. L. Fitzpatrick, and R. L. Nudds. 2016. The potential impacts of 

migratory difficulty, including warmer waters and altered flow conditions, on the reproductive 

success of salmonid fishes. Comparative Biochemistry and Physiology. Part A, Molecular & 

Integrative Physiology 193:11–21. https://doi.org/10.1016/j.cbpa.2015.11.012.

Ficker, H., M. Luger, and H. Gassner. 2017. From dimictic to monomictic: empirical evidence of 

thermal regime transitions in three deep alpine lakes in Austria induced by climate change. 

Freshwater Biology 62:1335–1345.

Harley, C. D. G., and R. T. Paine. 2009. Contingencies and compounded rare perturbations dictate 

sudden distributional shifts during periods of gradual climate change. Proceedings of the National 

Academy of Sciences of the United States of America 106: 11172–11176.

Helsel, D. R., and R. M. Hirsch. 2002. Statistical methods in water resources techniques of water 

resources investigations. Book 4, chapter A3. U.S. Geological Survey.

Hirsch, R. M., J. R. Slack, and R. A. Smith. 1982. Techniques of trend analysis for monthly water 

quality data. Water Resources Research 18:107–121.

Jones, B. M., and C. D. Arp. 2014. Thermal response of western Alaska lakes to past, present, and 

future changes in climate. Western Alaska Landscape Conservation Cooperative Final Report.

Kainz, M. J., R. Ptacnik, S. Rasconi, and H. H. Hager. 2017. Irregular changes in lake surface water 

temperature and ice cover in subalpine Lake Lunz, Austria. Inland Waters 7:27–33.
41

Lader, R., J. E. Walsh, U. S. Bhatt, P. A. Bieniek. 2017. Projections of twenty-first-century climate 

extremes for Alaska via dynamical downscaling and quantile mapping. Journal of Applied 

Meteorology and Climatology 56:2393–2409.

LaPerriere, J. D. 1997. Limnology of two lake systems of Katmai National Park and Preserve, 

Alaska: physical and chemical profiles, major ions, and trace elements. Hydrobiologia 354:88–

99.

Levy, D. A. 1987. Review of the ecological significance of diel vertical migrations by juvenile 

sockeye salmon (Oncorhynchus nerka). Pages 44–52 in H. D. Smith, L. Margolis, and C. C. 

Wood, editors. Sockeye salmon (Oncorhynchus nerka) population biology and future 

management. Canadian Special Publication of Fisheries and Aquatic Sciences 96.

Lewis, W. M., Jr. 1983. A revised classification of lakes based on mixing. Canadian Journal of 

Fisheries and Aquatic Sciences 40:1779–1787.

Magnuson, J. J., D. M. Robertson, B. J. Benson, R. H.Wynne, D. M. Livingstone, T. Arai, R. A. 

Assel, R. G. Barry, V. Card, E. Kuusisto, N. G. Granin, T. D. Prowse, K. M. Stewart, and V. S. 

Vuglinski. 2000. Historical trends in lake and river ice cover in the Northern Hemisphere. 

Science 289:1743–1746.

Markon, C., S. Gray, M. Berman, L. Eerkes-Medrano, T. Hennessy, H. Huntington, J. Littell, M. 

McCammon, R. Thoman, and S. Trainor. 2018. Alaska. In Impacts, Risks, and Adaptation in the 

United States: Fourth National Climate Assessment, Volume II [Reidmiller, D.R., C.W. Avery, 

D.R. Easterling, K.E. Kunkel, K.L.M. Lewis, T.K. Maycock, and B.C. Stewart (eds.)]. U.S. 

Global Change Research Program, Washington, DC, USA, pp. 1185–1241. doi:

10.7930/NCA4.2018.CH26

McAfee, S. A., J. Walsh, and T. S. Rupp. 2014. Statistically downscaled projections of snow/rain 

partitioning for Alaska. Hydrological Processes 28:3930–3946.

Millard, S. P. 2013. EnvStats: an R package for environmental statistics. New York: Springer-Verlag.

Moore, C. and J. Shearer. 2011. Water quality and surface hydrology of freshwater flow systems in 

southwest Alaska: 2010 annual summary report. Natural Resource Technical Report 

NPS/SWAN/NRTR—2011/428. National Park Service, Fort Collins, Colorado.

National Oceanographic and Atmospheric Association (NOAA). 2020. NOAA Regional Climate 

Centers website. Available at: https://xmacis.rcc-acis.org/ (accessed 24 April 2020).

National Oceanographic and Atmospheric Association National Centers for Environmental 

Information (NOAA NCEI). 2020. Climate at a glance: statewide time series. Available at:

https://www.ncdc.noaa.gov/cag/ (accessed 4 June 2020).
42

Niedrist, G. H., R. Psenner, and R. Sommaruga. 2018. Climate warming increases vertical and 

seasonal water temperature differences and inter-annual variability in a mountain lake. Climatic 

Change 151:473–490

North, R. P., R. L. North, D. M. Livingstone, and O. Köster. 2014. Long-term changes in hypoxia 

and soluble reactive phosphorus in the hypolimnion of a large temperate lake: consequences of a 

climate regime shift. Global Change Biology 20:811–823.

O’Reilly, C. M., S. Sharma, D. K. Gray, S. E. Hampton, et al. 2015. Rapid and highly variable 

warming of lake surface waters around the globe. Geophysical Research Letters 42, 

doi:10.1002/2015GL066235.

Pauly, D. 1995. Anecdotes and the shifting base-line syndrome of fisheries. Trends in Ecology and 

Evolution 10:430.

R Core Team. 2017. R: a language and environment for statistical computing. R Foundation for 

Statistical Computing, Vienna, Austria. Online at http://www.R-project.org/.

Randolph Glacier Inventory (RGI) Consortium. 2017. Randolph Glacier Inventory – A dataset of 

global glacier outlines: Version 6.0. Technical Report, Global Land Ice Measurements from 

Space, Boulder, Colorado. https://doi.org/10.7265/N5-RGI-60.

Regier, H. A., J. A. Holmes, and D. Pauly. 1990. Influence of temperature changes on aquatic 

ecosystems: an interpretation of empirical data. Transactions of the American Fisheries Society 

119:374–389.

Sauter, S. T., J. McMillan, and J. Dunham. 2001. Issue Paper 1: Salmonid behavior and water 

temperature. Prepared as part of U.S. EPA Region 10 Temperature Water Quality Criteria 

Guidance Development Project. EPA-910-D-01-001.

Schär, C., P. L. Vidale, D. Lüthi, C. Frei, C. Häberli, M. A. Liniger, and C. Appenzeller. 2004. The 

role of increasing temperature variability in European summer heatwaves. Nature 427:332–336. 

https://doi.org/10.1038/nature02300.

Schertz, T. L, R. B. Alexander, and D. J. Ohe. 1991. The computer program ESTimate-TREND

(ESTREND), a system for the detection of trends in water quality data. U.S. Geological Survey 

Water Resources Investigations 91-4040. Online at https://pubs.usgs.gov/wri/wri91-4040/.

Scheuerell, M. D., and D. E. Schindler. 2003. Diel vertical migration by juvenile sockeye salmon: 

Empirical evidence for the antipredation window. Ecology 84:1713–1720.

Schindler, D. E., and L. A. Rogers. 2009. Responses of Pacific salmon populations to climate 

variability in freshwater ecosystems. Pages 1127–1142 in C. Krueger and C. E. Zimmerman, 

editors. Pacific Salmon: ecology and management of western Alaska’s populations. American 

Fisheries Society, Symposium 70, Bethesda, Maryland.
43

Schindler, D. W. 2009. Lakes as sentinels and integrators for the effects of climate change on 

watersheds, airsheds, and landscapes. Limnology and Oceanography 54:2349–2358.

Seaber, P. R., F. P. Kapinos, and G. L. Knapp. 1987. Hydrologic unit maps. U.S. Geological Survey 

Water Supply Paper 2294. U.S. Geological Survey, Denver, Colorado.

Shearer, J., and C. Moore. 2010. Water quality and surface hydrology of freshwater flow systems in 

southwest Alaska: 2009 Annual Summary Report. Natural Resource Technical Report 

NPS/SWAN/NRTR—2010/304. National Park Service, Fort Collins, Colorado.

Shearer, J., C. Moore, and K. K. Bartz. 2015a. Monitoring freshwater systems in the Southwest 

Alaska Network: Protocol narrative. Natural Resource Report NPS/SWAN/NRR—2015/925. 

National Park Service, Fort Collins, Colorado.

Shearer, J., C. Moore, K. K. Bartz, E. C. J. Booher, and J. Nelson. 2015b. Monitoring freshwater 

systems in the Southwest Alaska Network: Standard operating procedures. Natural Resource 

Report NPS/SWAN/NRR—2015/925.1. National Park Service, Fort Collins, Colorado.

Simpson, J. J., G. L. Hufford, M. D. Fleming, J. S. Berg, and J. Ashton. 2002. Long-term climate 

patterns in Alaskan surface temperature and precipitation and their biological consequences. 

IEEE Transactions on Geoscience and Remote Sensing 40:1164–1184.

Steel, E. A., A. Tillotson, D. A. Larsen, A. H. Fullerton, K. P. Denton, and B. R. Beckman. 2012. 

Beyond the mean: The role of variability in predicting ecological effects of stream temperature 

on salmon. Ecosphere 3:104. http://dx.doi.org/10.1890/ES12-00255.1.

Sullivan, K., D. J. Martin, R. D. Cardwell, J. E. Toll, and S. Duke. 2000. An analysis of the effects of 

temperature on salmonids of the Pacific Northwest with implications for selecting temperature 

criteria. Sustainable Ecosystems Institute, Portland, Oregon.

Thompson, R. M., J. Beardall, J. Beringer, M. Grace, and P. Sardina. 2013. Means and extremes: 

Building variability into community-level climate change experiments. Ecology Letters 16:799–

806.

Vasseur, D. A., J. P. DeLong, B. Gilbert, H. S. Greig, C. D. G. Harley, K. S. McCann, V. Savage, T. 

D. Tunney, M. I. O’Connor. 2014. Increased temperature variation poses a greater risk to species 

than climate warming. Proceedings of the Royal Society B 281: 20132612. 

http://dx.doi.org/10.1098/rspb.2013.2612

Weissinger, R., and D. Sharrow. 2018. Status and trends of water quality at Zion National Park: 

Water years 2006–2016. Natural Resource Report NPS/NCPN/NRR—2018/1628.National Park 

Service, Fort Collins, Colorado.

Wetzel, R. G. 2001. Limnology: Lake and River Ecosystems. Third Edition. Academic Press, San 

Diego, CA.
44

Wilde, F. D. 2006. Temperature. U.S. Geological Survey Techniques of Water-Resources 

Investigations, Book 9, Chapter 6.1, https://doi.org/10.3133/twri09A6.1.

Wilkens, A. X. 2002. The limnology of Lake Clark, Alaska. M.S. Thesis, University of Alaska, 

Fairbanks.

Williamson, C. E., J. E. Saros, and D. W. Schindler. 2009. Sentinels of change. Science 323:887–

889.

Woll, C., D. Albert, and D. Whited. 2014. A preliminary classification and mapping of salmon 

ecological systems in the Nushagak and Kvichak watersheds, Alaska. The Nature Conservancy, 

Anchorage, Alaska.

Woolway, R. I., and C. J. Merchant. 2019. Worldwide alteration of lake mixing regimes in response 

to climate change. Nature Geoscience 12: 271–276.

Wrona, F. J., T. D. Prowse, and J. D. Reist. 2005. Freshwater ecosystems and fisheries. Pages 353–

452 in Arctic Climate Impact Assessment. Cambridge University Press, New York, NY.

Yankova, Y., S. Neuenschwander, O. Köster, and T. Posch. 2017. Abrupt stop of deep water turnover 

with lake warming: drastic consequences for algal primary producers. Scientific Reports. 

7:13770.

Young, D. B. 2014. Lake Clark sockeye salmon escapement and population monitoring, 2008–2011.

Lake Clark National Park and Preserve, Port Alsworth, Alaska.
45

Appendix A

Table A-1. List of data deleted in AQUARIUS, itemized by park, site, depth (in meters), time period 

(from/to date and time), count of hours deleted, and a brief account of why the data were deemed 

erroneous, graded as unusable, and subsequently deleted. Site abbreviations are as follows: LCLAR01 = 

Lake Clark; KIJIL01 = Kijik Lake; NAKNL01, NAKNL02, and NAKNL03 = Naknek Lake North, West, and 

Iliuk basins, respectively; and LBROO01 = Lake Brooks.

Park Site ID Depth(s) From To Count Deletion Rationale

LACL LCLAR01 All 06/19/2007 19:00 06/19/2007 21:00 3 Air temperature

LACL LCLAR01 All 06/20/2007 09:00 06/20/2007 10:00 2 Air temperature

LACL LCLAR01 All 10/09/2007 15:00 10/09/2007 20:00 6 Air temperature

LACL LCLAR01 All 10/15/2007 15:00 10/15/2007 17:00 3 Air temperature

LACL LCLAR01 All 05/28/2008 15:00 05/28/2008 19:00 5 Air temperature

LACL LCLAR01 All 07/23/2008 11:00 07/23/2008 13:00 3 Air temperature

LACL LCLAR01 All 09/26/2008 10:00 09/26/2008 10:00 1 Air temperature

LACL LCLAR01 All 06/10/2009 15:00 06/11/2009 11:00 15 Air temperature

LACL LCLAR01 All 09/15/2009 10:00 09/15/2009 10:00 1 Air temperature

LACL LCLAR01 All 06/04/2010 11:00 06/04/2010 13:00 3 Air temperature

LACL LCLAR01 All 09/17/2010 16:00 09/17/2010 17:00 2 Air temperature

LACL LCLAR01 0 08/18/2011 00:00 08/18/2011 12:00 13 Air temperature

LACL LCLAR01 All 09/23/2011 16:00 09/23/2011 16:00 1 Air temperature

LACL LCLAR01 All 06/15/2012 16:00 06/15/2012 16:00 1 Air temperature

LACL LCLAR01 All 10/09/2012 15:00 10/09/2012 15:00 1 Air temperature

LACL LCLAR01 100 01/03/2013 23:00 01/04/2013 02:00 4 Battery spike

LACL LCLAR01 60 05/09/2013 14:00 05/09/2013 16:00 3 Battery spike

LACL LCLAR01 20 05/18/2013 14:00 05/18/2013 16:00 3 Battery spike

LACL LCLAR01 All 06/13/2013 15:00 06/13/2013 16:00 2 Air temperature

LACL LCLAR01 10 06/15/2013 17:00 06/15/2013 20:00 4 Battery spike

LACL LCLAR01 20 09/23/2013 00:00 09/23/2013 02:00 3 Battery spike

LACL LCLAR01 All 10/21/2013 15:00 10/21/2013 15:00 1 Air temperature

LACL LCLAR01 All 06/04/2014 11:00 06/04/2014 11:00 1 Air temperature

LACL LCLAR01 All 09/15/2014 13:00 09/15/2014 14:00 2 Air temperature

LACL LCLAR01 All 05/29/2015 18:00 05/29/2015 18:00 1 Air temperature

LACL LCLAR01 All 07/14/2015 11:00 07/14/2015 11:00 1 Air temperature

LACL LCLAR01 All 09/15/2015 15:00 09/15/2015 16:00 1 Air temperature

LACL LCLAR01 All 06/03/2016 12:00 06/03/2016 12:00 1 Air temperature

LACL LCLAR01 All 07/14/2016 15:00 07/14/2016 15:00 1 Air temperature

LACL LCLAR01 All 09/13/2016 14:00 09/13/2016 16:00 3 Air temperature

LACL LCLAR01 All 06/13/2017 14:00 06/13/2017 14:00 1 Air temperature
46

Park Site ID Depth(s) From To Count Deletion Rationale

LACL LCLAR01 All 09/25/2017 13:00 09/25/2017 14:00 2 Air temperature

LACL LCLAR01 All 05/16/2018 14:00 05/16/2018 15:00 2 Air temperature

LACL LCLAR01 All 09/19/2018 15:00 09/19/2018 15:00 1 Air temperature

LACL KIJIL01 All 10/04/2011 14:00 10/04/2011 14:00 1 Air temperature

LACL KIJIL01 All 06/26/2012 15:00 06/26/2012 15:00 1 Air temperature

LACL KIJIL01 30 09/10/2012 22:00 09/11/2012 00:00 3 Battery spike

LACL KIJIL01 30 11/06/2012 20:00 11/06/2012 23:00 4 Battery spike

LACL KIJIL01 70 01/11/2013 11:00 01/11/2013 13:00 3 Battery spike

LACL KIJIL01 10 03/06/2013 17:00 03/06/2013 20:00 4 Battery spike

LACL KIJIL01 70 03/22/2013 20:00 03/22/2013 23:00 4 Battery spike

LACL KIJIL01 5 06/19/2013 09:00 06/19/2013 12:00 4 Battery spike

LACL KIJIL01 All 06/25/2013 19:00 06/25/2013 19:00 1 Air temperature

LACL KIJIL01 All 06/04/2015 13:00 06/04/2015 14:00 2 Air temperature

LACL KIJIL01 All 01/27/2016 04:00 06/02/2016 13:00 3058 Array movement

LACL KIJIL01 All but 40 06/02/2016 15:00 06/02/2016 15:00 1 Air temperature

LACL KIJIL01 All 06/21/2017 14:00 06/21/2017 14:00 1 Air temperature

LACL KIJIL01 All 06/21/2018 17:00 06/21/2018 17:00 1 Air temperature

KATM NAKNL01 All 09/23/2009 15:00 09/23/2009 18:00 4 Air temperature

KATM NAKNL01 All but 5 08/09/2010 11:00 08/09/2010 11:00 1 Air temperature

KATM NAKNL01 All 07/07/2011 15:00 07/08/2011 11:00 21 Air temperature

KATM NAKNL01 All 10/07/2011 12:00 10/07/2011 12:00 1 Air temperature

KATM NAKNL01 All 06/11/2012 14:00 06/11/2012 14:00 1 Air temperature

KATM NAKNL01 All 10/08/2012 13:00 10/08/2012 13:00 1 Air temperature

KATM NAKNL01 All 06/13/2014 15:00 06/13/2014 15:00 1 Air temperature

KATM NAKNL01 0, 5, 10 09/23/2014 11:00 09/23/2014 11:00 1 Air temperature

KATM NAKNL01 All 06/10/2015 16:00 06/10/2015 16:00 1 Air temperature

KATM NAKNL01 All 10/14/2015 13:00 10/14/2015 13:00 1 Air temperature

KATM NAKNL01 All 06/08/2016 12:00 06/08/2016 12:00 1 Air temperature

KATM NAKNL01 All 09/08/2016 10:00 09/08/2016 12:00 3 Air temperature

KATM NAKNL01 20 01/11/2017 11:00 02/23/2017 20:00 3553 Logger malfunction

KATM NAKNL01 All 06/08/2017 10:00 06/08/2017 11:00 2 Air temperature

KATM NAKNL01 All 09/08/2017 12:00 09/08/2017 12:00 1 Air temperature

KATM NAKNL01 0 06/06/2018 15:00 06/06/2018 15:00 1 Air temperature

KATM NAKNL01 0 06/19/2018 10:00 06/19/2018 10:00 1 Battery spike

KATM NAKNL02 All 09/28/2009 14:00 09/28/2009 15:00 2 Air temperature

KATM NAKNL02 All 08/09/2010 18:00 08/09/2010 18:00 1 Air temperature

KATM NAKNL02 20, 25 08/25/2011 11:00 08/25/2011 11:00 1 Air temperature

KATM NAKNL02 All 06/13/2012 12:00 06/13/2012 12:00 1 Air temperature
47

Park Site ID Depth(s) From To Count Deletion Rationale

KATM NAKNL02 20 11/10/2012 09:00 11/10/2012 11:00 3 Battery spike

KATM NAKNL02 All 08/07/2013 15:00 08/07/2013 15:00 1 Air temperature

KATM NAKNL02 All 06/13/2014 17:00 06/13/2014 17:00 1 Air temperature

KATM NAKNL02 All 10/14/2015 11:00 10/14/2015 11:00 1 Air temperature

KATM NAKNL02 All 06/09/2016 10:00 06/10/2016 09:00 24 Air temperature

KATM NAKNL02 All 09/08/2016 14:00 09/08/2016 14:00 1 Air temperature

KATM NAKNL02 All 09/08/2017 10:00 09/08/2017 11:00 2 Air temperature

KATM NAKNL02 All 06/04/2018 13:00 06/04/2018 17:00 5 Air temperature

KATM NAKNL02 All 06/05/2018 12:00 06/06/2018 12:00 25 Air temperature

KATM NAKNL02 All 09/12/2018 13:00 09/12/2018 13:00 1 Air temperature

KATM NAKNL03 All 06/11/2012 16:00 06/11/2012 16:00 1 Air temperature

KATM NAKNL03 All 08/19/2014 22:00 08/20/2014 16:00 19 Air temperature

KATM NAKNL03 All 06/10/2015 12:00 06/10/2015 12:00 1 Air temperature

KATM NAKNL03 All 06/08/2016 14:00 06/08/2016 15:00 2 Air temperature

KATM NAKNL03 All 09/07/2016 15:00 09/07/2016 16:00 2 Air temperature

KATM NAKNL03 All 06/08/2017 15:00 06/08/2017 15:00 2 Air temperature

KATM NAKNL03 All 09/12/2018 10:00 09/12/2018 10:00 1 Air temperature

KATM LBROO01 All 07/19/2011 15:00 07/19/2011 16:00 2 Air temperature

KATM LBROO01 All 05/20/2012 10:00 05/20/2012 10:00 1 Air temperature

KATM LBROO01 50 07/21/2013 17:00 07/21/2013 19:00 3 Battery spike

KATM LBROO01 10 08/20/2013 14:00 08/20/2013 16:00 3 Battery spike

KATM LBROO01 All 09/24/2013 10:00 09/24/2013 10:00 1 Air temperature

KATM LBROO01 All 06/24/2014 12:00 06/24/2014 12:00 1 Air temperature

KATM LBROO01 All 09/23/2014 14:00 09/23/2014 14:00 1 Air temperature

KATM LBROO01 All 06/11/2015 10:00 06/11/2015 10:00 1 Air temperature

KATM LBROO01 All 10/06/2015 16:00 10/06/2015 16:00 1 Air temperature

KATM LBROO01 All 06/07/2016 17:00 06/07/2016 18:00 2 Air temperature

KATM LBROO01 All 09/13/2016 14:00 09/13/2016 16:00 2 Air temperature

KATM LBROO01 All 06/13/2017 14:00 06/13/2017 14:00 1 Air temperature

KATM LBROO01 5 09/07/2017 12:00 09/07/2017 12:00 1 Air temperature

KATM LBROO01 All 06/07/2018 10:00 06/07/2018 11:00 2 Air temperature
48

Table A-2. List of data downgraded in AQUARIUS, itemized by park, site, depth (in meters), time period 

(from/to date and time), and a brief account of why the data were deemed less than good and 

subsequently downgraded. Site abbreviations are as follows: LCLAR01 = Lake Clark; KIJIL01 = Kijik 

Lake; NAKNL01 = Naknek Lake, North basin; NAKNL03 = Naknek Lake, Iliuk basin; and LBROO01 = 

Lake Brooks.

Park Site ID Depth(s) From To Downgrade Rationale

LACL LCLAR01 All 06/19/2007 20:00 09/26/2008 11:00 Time stamp off the hour

LACL LCLAR01 All but 50 06/15/2009 06:00 09/15/2009 11:00 Time stamp off the hour

LACL LCLAR01 30 09/20/2009 07:00 06/04/2010 12:00 Time stamp off the hour

LACL LCLAR01 10 06/13/2010 10:00 09/17/2010 16:00 Time stamp off the hour

LACL LCLAR01 30 06/04/2014 13:00 05/29/2015 17:00 Time stamp off the hour

LACL KIJIL01 All 10/04/2011 14:00 07/02/2014 13:00 Time stamp off the hour

KATM NAKNL01 60 06/13/2014 16:00 09/23/2014 10:00 Time stamp off the hour

KATM NAKNL03 30 06/10/2015 13:00 09/12/2018 09:00 Temperature sensor drift

KATM LBROO01 All but 0 07/19/2011 16:00 09/24/2013 10:00 Time stamp off the hour

KATM LBROO01 10 06/24/2014 12:00 09/23/2014 14:00 Time stamp off the hour
49

Appendix B

Below is an overview of key features and data gaps in the time series at each temperature array site. 

Data gaps are also summarized visually in Figure B-1.

Lake Clark

Water temperature in Lake Clark has been monitored continuously via array since August, 2006 for 

depths from 5 m to 100 m, and since June, 2012 for the 0 m depth. Prolonged data gaps occurred 

early in the time series at 20, 50, 80, and 90 m depths, as well as later in the time series at 20 m

depth. In July, 2015, the array was removed, rebuilt, and redeployed during the course of four days. 

Of the six arrays, this occupies the deepest lake and was the first deployed.

Kijik Lake

Water temperature in Kijik Lake has been monitored continuously via array since August, 2010 for 

depths from 5 m to 90 m, and since June, 2012 for the 0 m depth. The time series contained no 

extended data gaps until January, 2016 when, presumably, wind-driven movement followed by lake 

ice entrainment caused the array to drift substantially (Booher et al. 2018). The array was repositioned in June, 2016 but the 40 m logger failed to launch after downloading, so the data gap at 

that depth continued through June, 2017, when the logger was again serviced. Of the six arrays, this 

occupies the smallest lake.

Naknek Lake

North Basin (NAKNL01)

Water temperature in the North basin of Naknek Lake has been monitored continuously since 

August, 2008 at depths from 5 m to 70 m, and since June, 2012 at the 0 m depth. The instrument line

was removed, rebuilt, and redeployed within 24 hours in July, 2011, and in 2013 the 0 m logger was 

not deployed. Aside from that and one data gap in 2017, when the 20 m logger malfunctioned, no 

prolonged gaps have occurred.

West Basin (NAKNL02)

Water temperature in the West basin of Naknek Lake has been monitored continuously since August, 

2008 at depths from 5 m to 25 m, and since June, 2012 at the 0 m depth. No protracted data gaps 

have occurred during this time frame. The longest gap (48 hours) happened in June, 2018 when the 

instrument line was removed, rebuilt, and redeployed. Of the six arrays, this is the shallowest.

Iliuk Basin (NAKNL03)

Water temperature in the Iliuk basin of Naknek Lake has been monitored continuously since August, 

2011 at depths from 5 m to 100 m, and since June, 2012 at the 0 m depth. A splice failure in July, 

2013 left the instrument line deployed solo in the water column, without the usual means of retrieval 

(Booher et al. 2016). As a result, two summers of 0 m data were lost (2013 and 2014). Eventually, 

the instrument line was recovered and rebuilt, causing a week-long data gap in August, 2014 across 

all depths. Of the six arrays, this has the shortest time series.
50

Lake Brooks

Water temperature in Lake Brooks has been monitored continuously since August, 2010 at depths 

from 5 m to 50 m, and since May, 2012 at the 0 m depth. No prolonged data gaps have occurred 

during this time span. The longest gaps (3 hours at 10 and 50 m depths) resulted from battery voltage 

spikes in 2013.
51

Figure B-1. Isotherms for six temperature arrays. Gray areas represent depths/times with no data. Site 

abbreviations are as follows: LCLAR01 = Lake Clark; KIJIL01 = Kijik Lake; NAKNL01, NAKNL02, and 

NAKNL03 = Naknek Lake North, West, and Iliuk basins, respectively; and LBROO01 = Lake Brooks.

53

Appendix C

Table C-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Lake Clark, 2006–2018 water years (WY). 

Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2006 WY 2007 WY 2008 WY 2009 WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 7.52 8.11 7.44 8.01 7.94 7.78 6.80 6.91 8.22 8.28 8.01 8.34

November – 5.74 5.57 4.92 5.85 5.56 5.46 5.07 5.42 5.66 5.88 5.65 5.90

December – 3.76 4.44 3.64 4.24 4.30 3.66 3.24 4.14 4.70 4.37 4.46 4.50

January – 0.66 2.74 1.14 3.20 1.43 0.87 2.31 2.99 4.06 3.48 2.85 3.75

February – 1.91 1.11 1.18 1.26 1.62 1.03 1.82 2.71 2.81 2.89 1.09 0.91

March – 2.41 1.53 1.08 1.62 1.56 1.32 1.31 1.93 2.78 2.38 1.22 0.93

April – 3.39 2.27 2.01 2.34 2.33 2.41 1.44 2.57 2.91 3.23 3.09 2.22

May – 4.28 3.69 3.56 3.46 3.85 3.34 2.39 3.63 3.75 5.10 4.06 3.47

June – 9.90 8.46 8.51 8.50 8.23 7.85 4.41 9.58 10.21 10.42 10.82 6.75

July – 14.96 11.01 14.11 10.49 11.80 10.27 10.15 13.76 * 14.39 14.75 11.11

August * 15.91 13.04 12.09 11.02 11.98 13.01 12.60 15.03 14.38 14.28 14.89 11.31

September 11.14 13.53 10.17 10.93 11.50 10.90 11.03 11.58 11.67 12.01 12.67 11.41 *
54

Table C-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Lake Clark, 2006–2018 water years (WY). 

Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2006 WY 2007 WY 2008 WY 2009 WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 5.79 5.54 4.91 5.81 5.66 5.26 5.07 5.51 5.69 5.87 5.58 5.95

November – 3.82 4.47 3.18 4.28 4.35 3.55 3.09 4.22 4.75 4.42 4.57 4.50

December – 0.52 2.54 0.55 2.42 0.77 1.05 0.95 2.23 4.06 3.20 2.55 3.62

January – 0.02 0.08 0.29 0.34 0.94 0.19 0.67 2.27 2.38 2.56 0.55 0.60

February – 0.72 0.11 0.64 0.35 1.37 0.48 0.80 0.81 1.71 2.58 0.78 0.30

March – 1.40 0.59 0.76 1.12 1.32 0.86 0.36 1.15 1.94 2.16 1.00 0.78

April – 1.95 1.22 0.79 1.63 1.42 1.35 0.33 1.96 2.50 2.61 1.25 0.91

May – 2.98 1.78 2.08 2.32 2.39 2.31 1.48 2.61 2.94 3.17 2.71 2.39

June – 4.03 3.64 3.58 3.51 3.86 3.39 2.42 3.69 3.74 4.82 4.52 3.50

July – 8.89 7.16 5.92 8.65 7.22 5.49 4.95 8.33 * 10.79 7.51 6.37

August * 10.30 10.52 8.10 8.20 9.99 9.78 6.69 9.19 11.47 10.33 9.59 8.98

September 7.67 7.92 6.35 7.93 7.62 7.49 4.47 6.83 8.08 8.56 7.63 8.09 *
55

Table C-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, summarized by month for Lake Clark, 2006–2018 water 

years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2006 WY 2007 WY 2008 WY 2009 WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 0.52 0.78 0.86 0.54 0.73 0.68 0.52 0.40 0.73 0.58 0.83 0.63

November – 0.61 0.35 0.44 0.51 0.37 0.57 0.44 0.37 0.24 0.50 0.32 0.41

December – 1.11 0.53 0.81 0.48 1.35 0.66 0.66 0.71 0.20 0.34 0.52 0.22

January – 0.22 0.87 0.26 1.03 0.13 0.24 0.40 0.17 0.48 0.28 0.78 0.92

February – 0.41 0.32 0.16 0.27 0.08 0.15 0.32 0.64 0.32 0.09 0.10 0.15

March – 0.26 0.25 0.11 0.16 0.07 0.10 0.27 0.20 0.25 0.12 0.06 0.04

April – 0.51 0.32 0.35 0.22 0.28 0.35 0.33 0.20 0.12 0.17 0.62 0.45

May – 0.36 0.58 0.48 0.37 0.49 0.32 0.26 0.29 0.27 0.44 0.33 0.32

June – 2.07 1.55 1.58 1.45 1.38 1.43 0.54 1.84 2.26 1.48 1.80 0.98

July – 1.29 1.05 2.27 0.57 1.14 1.30 1.59 1.28 * 1.07 1.99 1.18

August * 1.46 0.92 1.29 0.74 0.56 0.90 1.78 1.82 0.92 1.01 1.25 0.68

September 1.17 1.62 0.96 0.87 0.98 0.97 1.22 1.05 1.11 1.15 1.58 0.85 *
56

Table C-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, summarized by water year for Lake Clark, 2006–2018. 

Asterisks indicate water years with one or more missing months of data.

Water Year

Warmest Day

Date

Warmest Day

24-hr mean (°C)

Warmest Week

Dates

Warmest Week

7-day mean (°C)

Days >15 °C

(n)

2006* 09/06 11.14 08/24–08/30 10.78 0

2007 08/13 15.91 08/11–08/17 14.72 1

2008 08/25 13.40 08/21–08/27 13.05 0

2009 07/18 14.11 07/14–07/20 13.35 0

2010 09/19 11.50 09/13–09/19 11.25 0

2011 08/16 11.98 08/12–08/18 11.41 0

2012 08/17 13.01 08/13–08/19 12.54 0

2013 08/02 12.60 08/25–08/31 11.93 0

2014 08/03 15.03 08/01–08/07 14.55 1

2015* 08/16 14.38 08/11–08/17 14.03 0

2016 07/30 14.39 07/26–08/01 13.78 0

2017 08/03 14.89 07/29–08/04 14.33 0

2018* 08/11 11.31 08/11–08/17 10.65 0
57

Appendix D

Table D-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Kijik Lake, 2010–2018 water years (WY). 

Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 9.23 9.01 7.29 8.17 9.83 8.58 9.35 9.74

November – 5.63 5.71 4.41 5.45 5.20 5.98 5.70 5.76

December – 3.35 1.91 2.22 2.67 3.86 3.81 2.82 3.24

January – 2.79 1.99 2.32 3.14 3.02 3.04 2.11 2.68

February – 2.79 2.19 2.45 3.25 2.66 * 2.23 2.39

March – 2.77 2.26 2.68 3.33 3.13 * 2.34 2.48

April – 3.40 3.23 3.93 3.90 3.57 * 3.75 2.99

May – 4.87 5.27 6.01 8.56 6.51 * 6.14 4.44

June – 9.72 9.22 11.29 11.12 13.14 * 11.94 9.27

July – 12.19 11.84 13.05 13.87 14.62 13.81 14.83 13.06

August 12.28 12.17 12.04 14.27 14.27 15.10 14.07 15.58 13.69

September 11.78 11.40 11.55 13.17 13.08 12.67 12.76 13.01 *
58

Table D-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Kijik Lake, 2010–2018 water years (WY). 

Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 5.75 5.82 4.52 5.53 5.35 6.17 5.76 5.80

November – 3.09 1.72 1.94 2.64 3.67 3.77 3.44 3.04

December – 2.33 1.74 2.09 2.36 2.56 2.24 1.79 2.66

January – 2.43 1.89 2.13 2.60 2.33 2.45 1.92 2.14

February – 2.59 1.97 2.33 2.89 2.39 * 2.11 2.15

March – 2.67 2.15 2.45 3.20 2.63 * 2.23 2.39

April – 2.74 2.28 2.69 3.33 3.15 * 2.35 2.44

May – 3.43 3.35 3.96 3.95 3.63 * 3.80 3.01

June – 5.29 5.48 5.93 7.87 6.82 * 6.40 4.60

July – 9.87 9.49 11.00 11.05 12.86 11.18 11.67 9.37

August 11.32 11.02 10.74 13.15 13.21 12.86 12.24 13.25 11.03

September 9.30 9.21 7.39 8.35 10.00 8.80 9.59 9.92 *
59

Table D-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, summarized by month for Kijik Lake, 2010–2018 water 

years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 1.07 1.01 0.90 0.79 1.27 0.64 1.19 1.28

November – 0.67 1.29 0.83 0.80 0.39 0.77 0.65 0.80

December – 0.26 0.05 0.04 0.08 0.38 0.48 0.24 0.19

January – 0.10 0.03 0.05 0.16 0.22 0.13 0.06 0.17

February – 0.07 0.06 0.04 0.09 0.08 * 0.04 0.07

March – 0.03 0.03 0.06 0.05 0.14 * 0.03 0.03

April – 0.20 0.30 0.40 0.18 0.11 * 0.47 0.21

May – 0.38 0.56 0.54 1.56 0.81 * 0.79 0.42

June – 1.38 1.20 1.65 0.89 1.91 * 1.66 1.53

July – 0.72 0.64 0.79 0.91 0.44 0.83 1.12 1.14

August 0.33 0.36 0.42 0.36 0.28 0.58 0.59 0.69 1.01

September 0.66 0.61 1.15 1.41 0.91 1.24 1.11 0.80 *
60

Table D-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, summarized by water year for Kijik Lake, 2010–2018. 

Asterisks indicate water years with one or more missing months of data.

Water Year

Warmest Day

Date

Warmest Day

24-hr mean (°C)

Warmest Week

Dates

Warmest Week

7-day mean (°C)

Days >15 °C

(n)

2010 * 08/06 12.28 08/03–08/09 12.14 0

2011 07/31 12.19 07/31–08/06 12.07 0

2012 08/23 12.04 08/18–08/24 11.88 0

2013 08/07 14.27 08/03–08/09 13.95 0

2014 08/08 14.27 08/06–08/12 13.86 0

2015 08/11 15.10 08/06–08/12 14.92 2

2016 * 08/03 14.07 07/30–08/05 13.79 0

2017 08/05 15.58 08/04–08/10 15.34 9

2018 * 08/09 13.69 08/06–08/12 13.57 0
61

Appendix E

Table E-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Naknek Lake’s North basin, 2008–2018 water 

years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2008 WY 2009 WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 9.64 10.05 9.63 9.21 8.19 8.33 10.35 9.94 11.13 10.20

November – 6.18 7.63 6.69 7.10 6.20 6.86 6.77 7.13 7.46 7.41

December – 4.06 4.98 4.93 3.96 3.33 5.18 5.75 5.40 5.69 5.41

January – 1.17 3.22 1.03 1.38 2.44 3.20 4.70 3.87 3.65 4.10

February – 1.02 1.51 1.69 1.47 1.46 2.74 3.14 3.09 2.17 1.65

March – 1.34 1.93 2.37 1.58 0.86 1.94 2.63 2.70 2.13 2.15

April – 1.97 2.51 3.28 2.76 1.35 2.39 2.72 3.65 3.64 2.88

May – 3.82 3.89 4.95 3.89 2.91 6.18 4.91 7.24 5.89 4.65

June – 9.35 9.46 7.78 7.95 7.56 9.90 12.30 12.07 11.29 9.04

July – 15.83 11.07 11.01 10.38 14.55 15.07 12.32 15.30 14.83 11.95

August * 12.24 11.31 11.98 11.89 13.28 15.39 14.09 15.58 14.47 13.44

September 12.02 11.92 11.77 11.05 11.55 12.59 13.28 13.31 14.65 12.88 *
62

Table E-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Naknek Lake’s North basin, 2008–2018 water 

years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2008 WY 2009 WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 6.22 7.73 6.77 7.16 6.19 6.83 6.82 6.64 7.01 7.32

November – 4.05 4.95 4.99 3.89 3.41 5.26 5.79 5.45 5.74 5.44

December – 0.87 3.22 0.50 1.25 0.98 3.17 4.71 3.90 3.61 4.16

January – 0.69 0.59 0.76 1.17 1.05 2.72 3.16 3.10 2.15 1.06

February – 0.66 0.75 0.98 1.23 0.47 1.29 2.19 2.72 1.77 1.06

March – 1.00 1.51 1.74 1.48 0.34 1.27 1.19 2.35 1.78 1.29

April – 1.35 1.78 2.38 1.60 0.44 1.50 2.16 2.68 2.17 2.09

May – 2.07 2.56 3.29 2.19 1.36 2.42 2.72 3.72 3.52 2.91

June – 3.90 3.94 4.77 3.88 2.99 4.78 5.05 3.29 6.27 4.80

July – 6.76 5.84 7.95 5.96 7.48 9.73 6.90 11.84 9.69 9.30

August * 8.19 7.22 9.39 10.15 6.99 10.71 12.66 11.33 11.49 8.32

September 7.31 10.11 9.76 7.43 7.93 7.58 8.38 10.11 11.67 9.93 *
63

Table E-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, summarized by month for Naknek Lake’s North basin, 

2008–2018 water years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes 

indicate no data.

Month WY 2008 WY 2009 WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 1.06 0.64 0.97 0.72 0.60 0.44 1.08 0.95 1.30 0.77

November – 0.60 0.88 0.48 1.03 0.74 0.49 0.26 0.55 0.51 0.61

December – 0.76 0.59 1.60 0.89 0.76 0.70 0.31 0.47 0.57 0.38

January – 1.13 1.04 0.08 0.06 0.39 0.16 0.45 0.25 0.46 1.06

February – 0.10 0.24 0.23 0.08 0.35 0.53 0.24 0.11 0.13 0.16

March – 0.10 0.09 0.19 0.02 0.17 0.19 0.35 0.08 0.09 0.27

April – 0.18 0.27 0.29 0.39 0.26 0.27 0.17 0.30 0.45 0.24

May – 0.55 0.39 0.49 0.42 0.47 1.05 0.58 0.96 0.61 0.47

June – 1.79 1.86 0.96 1.17 1.24 1.42 2.11 1.83 1.54 1.32

July – 2.83 1.38 0.89 1.20 2.24 1.44 1.64 1.19 1.51 0.78

August * 1.18 0.82 0.50 0.50 1.50 1.24 0.39 1.16 0.70 1.20

September 0.95 0.53 0.60 0.98 0.95 1.43 1.12 1.09 1.01 0.69 *
64

Table E-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, summarized by water year for Naknek Lake’s North basin, 

2008–2018. Asterisks indicate water years with one or more missing months of data.

Water Year

Warmest Day

Date

Warmest Day

24-hr mean (°C)

Warmest Week

Dates

Warmest Week

7-day mean (°C)

Days >15 °C

(n)

2008 * 08/29 13.23 08/27–09/03 12.63 0

2009 07/15 15.83 07/14–07/20 15.36 6

2010 09/20 11.77 09/14–09/20 11.59 0

2011 08/17 11.98 08/12–08/18 11.59 0

2012 08/13 11.89 08/28–09/04 11.47 0

2013 07/29 14.55 07/26–08/01 13.80 0

2014 08/03 15.39 07/31–08/06 15.03 4

2015 08/17 14.09 08/16–08/22 14.00 0

2016 08/10 15.58 08/06–08/12 15.48 20

2017 07/30 14.83 07/26–08/01 14.45 0

2018 * 08/11 13.44 08/05–08/11 12.99 0
65

Appendix F

Table F-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Naknek Lake’s West basin, 2008–2018 water 

years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicated no data.

Month WY 2008 WY 2009 WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 9.80 9.74 9.77 9.55 8.71 8.24 10.82 9.11 11.51 10.08

November – 4.64 7.02 6.22 6.39 3.22 6.79 5.56 7.05 6.95 6.89

December – 1.39 1.82 1.40 1.76 1.20 1.90 4.23 3.56 1.86 2.10

January – 0.71 1.55 0.86 0.76 1.37 1.88 2.83 1.95 1.34 1.63

February – 1.06 1.50 1.82 1.38 1.06 1.88 1.58 1.63 1.69 0.96

March – 1.43 1.97 2.65 1.45 0.92 1.12 1.68 2.01 2.93 1.68

April – 2.28 2.75 3.39 2.61 2.34 3.32 3.68 5.41 4.66 2.79

May – 6.75 6.60 6.46 4.39 7.05 10.92 8.66 10.71 8.15 7.51

June – 12.12 12.36 9.92 10.22 15.22 11.90 14.21 13.74 12.95 *

July – 16.12 12.18 12.00 11.62 15.90 14.82 14.80 15.67 16.23 14.08

August * 13.43 12.38 12.51 12.80 15.54 15.98 14.94 15.74 16.10 14.45

September 12.59 12.38 12.53 11.54 11.94 12.82 14.00 12.67 16.30 13.10 *
66

Table F-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Naknek Lake’s West basin, 2008–2018 water 

years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2008 WY 2009 WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 4.11 7.06 6.04 6.83 3.09 6.83 5.49 6.97 7.05 6.85

November – 0.55 0.84 0.53 0.17 0.05 1.59 4.18 1.74 1.62 0.83

December – 0.36 0.54 0.22 0.19 0.28 0.30 2.42 0.80 0.27 1.11

January – 0.21 0.12 0.24 0.20 0.62 0.84 0.22 0.83 0.17 0.02

February – 0.64 0.49 0.86 0.18 0.27 0.01 0.85 1.00 0.44 0.19

March – 1.00 1.27 1.87 1.00 0.39 0.63 0.23 0.69 1.58 0.91

April – 1.45 1.77 2.56 1.15 0.98 1.03 1.78 2.09 2.87 1.71

May – 2.30 1.50 3.32 2.43 2.32 3.50 3.57 5.21 3.47 2.88

June – 6.08 6.50 6.86 4.65 6.46 7.53 8.35 8.62 8.65 *

July – 11.89 10.39 10.28 8.95 10.02 11.06 11.48 13.50 11.46 11.66

August * 11.69 10.29 10.71 11.14 12.67 13.78 12.33 14.62 13.23 10.80

September 9.68 9.73 9.89 9.62 8.71 8.32 11.05 8.60 11.62 10.17 *
67

Table F-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, summarized by month for Naknek Lake’s West basin, 

2008–2018 water years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes 

indicate no data.

Month WY 2008 WY 2009 WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 1.74 0.68 1.08 0.86 1.51 0.41 1.42 0.53 1.46 1.09

November – 1.23 1.97 1.75 2.28 0.88 1.62 0.31 1.69 1.95 1.93

December – 0.34 0.41 0.35 0.49 0.24 0.44 0.58 0.77 0.44 0.31

January – 0.14 0.52 0.20 0.18 0.22 0.32 0.80 0.29 0.33 0.59

February – 0.13 0.37 0.28 0.34 0.24 0.70 0.19 0.15 0.32 0.24

March – 0.12 0.21 0.25 0.11 0.14 0.15 0.43 0.31 0.36 0.26

April – 0.23 0.29 0.24 0.48 0.44 0.72 0.52 1.02 0.46 0.35

May – 1.32 1.26 0.93 0.51 1.17 2.46 1.19 1.31 1.24 1.37

June – 1.82 1.79 0.84 1.74 2.33 1.17 1.90 1.48 1.28 *

July – 1.40 0.54 0.55 0.89 1.75 0.93 0.85 0.65 1.25 0.79

August * 0.54 0.59 0.44 0.47 0.73 0.62 0.58 0.30 1.02 1.21

September 0.87 0.74 0.66 0.58 0.78 1.32 0.84 1.25 1.47 0.74 *
68

Table F-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, summarized by water year for Naknek Lake’s West basin, 

2008–2018. Asterisks indicate water years with one or more missing months of data.

Water Year

Warmest Day

Date

Warmest Day

24-hr mean (°C)

Warmest Week

Dates

Warmest Week

7-day mean (°C)

Days >15 °C

(n)

2008 * 08/30 13.32 08/30–09/05 12.62 0

2009 07/15 16.12 07/14–07/20 15.88 11

2010 09/19 12.53 08/24–08/30 12.18 0

2011 08/18 12.51 08/12–08/18 11.98 0

2012 08/14 12.80 08/12–08/18 12.37 0

2013 07/31 15.90 07/27–08/02 15.26 6

2014 08/06 15.98 08/04–08/10 15.66 16

2015 08/08 14.94 08/05–08/11 14.39 0

2016 09/04 16.30 08/31–09/06 15.86 37

2017 07/31 16.23 07/29–08/04 15.93 24

2018 * 08/05 14.45 07/31–08/06 14.12 0
69

Appendix G

Table G-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Naknek Lake’s Iliuk basin, 2011–2018 water 

years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 7.26 6.99 6.70 8.36 7.95 8.89 8.26

November – 6.06 5.64 6.23 6.00 6.46 6.69 6.65

December – 3.82 3.95 4.79 5.32 5.27 5.28 5.27

January – 1.38 2.33 3.21 4.45 4.03 3.78 4.21

February – 1.80 1.62 2.95 3.39 3.35 1.30 2.81

March – 1.56 1.04 2.14 2.75 2.99 1.88 2.57

April – 2.85 4.23 2.46 2.74 3.80 3.44 2.94

May – 3.83 2.52 4.07 5.50 6.31 5.04 4.28

June – 8.31 6.47 8.70 11.99 10.30 9.19 9.93

July – 9.95 15.40 13.60 11.71 12.84 13.15 11.10

August 9.87 10.61 11.90 * 12.97 13.18 12.96 10.29

September 9.25 10.26 10.38 10.66 11.56 12.72 11.11 *
70

Table G-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Naknek Lake’s Iliuk basin, 2011–2018 water 

years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 6.11 5.69 6.28 5.98 6.56 6.71 6.65

November – 3.83 3.95 4.81 5.32 5.34 5.38 5.28

December – 1.39 0.86 3.27 4.48 4.05 3.78 4.23

January – 1.09 1.30 2.88 3.01 3.36 0.52 0.21

February – 1.08 0.70 0.41 2.27 3.00 0.60 0.22

March – 1.25 0.25 1.42 1.79 2.70 1.31 1.83

April – 1.41 0.41 1.75 2.28 3.04 1.95 2.22

May – 2.74 1.27 2.52 2.77 6.84 3.15 2.96

June – 3.98 2.58 4.14 5.66 6.27 5.24 4.70

July – 6.20 5.49 8.81 8.27 9.41 7.71 7.48

August 8.62 8.30 8.50 * 10.85 11.61 10.82 8.53

September 7.35 7.06 6.66 8.55 7.45 9.36 8.37 *
71

Table G-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, summarized by month for Naknek Lake’s Iliuk basin, 

2011–2018 water years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes 

indicate no data.

Month WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 0.40 0.41 0.12 0.65 0.45 0.69 0.50

November – 0.70 0.45 0.46 0.18 0.39 0.40 0.43

December – 0.81 0.94 0.52 0.27 0.38 0.42 0.30

January – 0.09 0.32 0.11 0.38 0.22 1.09 1.31

February – 0.17 0.31 0.92 0.27 0.11 0.23 0.77

March – 0.07 0.21 0.20 0.27 0.07 0.17 0.18

April – 0.41 0.21 0.23 0.13 0.24 0.43 0.23

May – 0.29 0.37 0.49 0.64 0.72 0.50 0.38

June – 1.40 0.91 1.27 1.92 1.38 1.33 1.29

July – 0.90 2.65 1.29 1.05 1.15 1.40 1.12

August 0.43 0.61 0.90 * 0.67 0.53 0.61 0.55

September 0.64 0.76 1.13 0.65 1.06 1.10 0.59 *
72

Table G-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, summarized by water year for Naknek Lake’s Iliuk basin, 

2011–2018. Asterisks indicate water years with one or more missing months of data.

Water Year

Warmest Day

Date

Warmest Day

24-hr mean (°C)

Warmest Week

Dates

Warmest Week

7-day mean (°C)

Days >15 °C

(n)

2011 * 08/15 9.87 08/13–08/19 9.63 0

2012 08/18 10.61 08/14–08/20 10.04 0

2013 07/30 15.40 07/27–08/02 12.82 1

2014 * 07/27 13.60 07/31–08/06 13.33 0

2015 08/08 12.97 08/05–08/11 12.69 0

2016 08/08 13.18 08/03–08/09 13.04 0

2017 07/28 13.15 07/28–08/03 12.58 0

2018 * 07/08 11.10 07/27–08/02 10.17 0
73

Appendix H

Table H-1. Highest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Lake Brooks, 2010–2018 water years (WY). 

Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 9.56 9.27 8.14 9.11 10.99 9.49 11.84 10.27

November – 6.88 7.15 6.02 7.34 6.63 7.04 7.44 7.44

December – 4.31 3.04 2.10 4.57 5.45 5.06 4.61 4.94

January – 1.39 1.31 1.26 2.41 4.18 3.22 2.48 3.57

February – 1.74 1.62 1.31 2.17 2.17 2.36 1.84 1.41

March – 2.52 1.68 1.72 1.26 2.02 2.29 2.21 1.83

April – 3.13 2.56 3.10 2.61 2.63 3.81 3.11 3.18

May – 5.46 3.86 5.24 6.88 6.02 7.85 6.06 5.59

June – 8.18 8.14 11.22 10.02 13.88 12.63 11.05 12.15

July – 11.24 11.58 15.91 14.04 13.52 15.51 14.76 13.07

August * 11.49 11.77 14.15 14.93 15.08 15.26 15.33 13.88

September 12.20 11.33 11.50 13.14 13.24 13.03 15.71 12.34 *
74

Table H-2. Lowest daily mean water temperatures (°C) at the 5 m depth, summarized by month for Lake Brooks, 2010–2018 water years (WY). 

Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 6.96 7.26 6.19 7.35 6.74 7.20 7.45 7.45

November – 4.35 2.66 2.17 4.75 5.47 5.09 4.78 4.99

December – 1.06 1.04 0.76 2.34 4.08 3.23 2.36 3.43

January – 1.22 1.14 1.01 1.94 2.08 2.34 1.61 0.33

February – 1.15 1.21 1.15 0.81 1.59 2.08 1.63 0.52

March – 1.78 1.61 1.32 0.78 1.10 1.87 1.83 1.38

April – 2.54 1.67 1.77 1.28 1.77 2.34 2.22 1.39

May – 3.12 2.33 2.93 2.66 2.79 3.90 3.11 3.05

June – 5.24 3.97 5.15 5.98 6.54 6.04 5.75 5.55

July – 7.98 6.09 10.12 9.99 7.48 11.93 9.21 9.64

August * 8.59 9.07 10.91 12.68 13.06 14.21 11.86 9.12

September 9.41 8.75 7.95 9.23 10.79 9.62 12.12 9.75 *
75

Table H-3. Standard deviation of daily mean water temperatures (°C) at the 5 m depth, summarized by month for Lake Brooks, 2010–2018 water 

years (WY). Asterisks indicate that data in a given month did not meet criteria of the 3/5 rule at the time of analysis, and dashes indicate no data.

Month WY 2010 WY 2011 WY 2012 WY 2013 WY 2014 WY 2015 WY 2016 WY 2017 WY 2018

October – 0.82 0.64 0.68 0.47 1.21 0.76 1.49 0.83

November – 0.71 1.42 1.11 0.81 0.31 0.72 0.76 0.80

December – 1.20 0.65 0.34 0.83 0.44 0.60 0.59 0.45

January – 0.04 0.04 0.08 0.13 0.63 0.28 0.27 1.11

February – 0.21 0.14 0.04 0.54 0.16 0.09 0.06 0.26

March – 0.22 0.02 0.10 0.16 0.27 0.10 0.12 0.15

April – 0.19 0.31 0.41 0.38 0.25 0.45 0.26 0.44

May – 0.60 0.46 0.59 1.28 0.80 1.04 0.77 0.71

June – 1.00 1.34 1.84 1.18 2.27 1.75 1.73 1.48

July – 0.87 1.32 1.9. 1.46 1.53 1.24 1.56 0.89

August * 0.57 0.66 0.82 0.68 0.48 0.29 0.86 1.07

September 0.86 0.84 1.02 1.18 0.70 1.08 1.20 0.56 *
76

Table H-4. Warmest day, warmest week, and days exceeding 15 °C at the 5 m depth, summarized by water year for Lake Brooks, 2010–2018. 

Asterisks indicate water years with one or more missing months of data.

Water

Year

Warmest Day

Date

Warmest Day

24-hr mean (°C)

Warmest Week

Dates

Warmest Week

7-day mean (°C)

Days >15 °C

(n)

2010 * 09/17 12.20 09/14–09/20 12.06 0

2011 08/17 11.49 08/29–09/06 11.24 0

2012 08/30 11.77 08/27–09/02 11.53 0

2013 07/31 15.91 07/25–07/31 15.00 4

2014 08/06 14.93 08/02–08/08 14.55 0

2015 08/03 15.08 08/15–08/21 14.23 1

2016 09/03 15.71 08/31–09/06 15.40 32

2017 08/03 15.33 07/29–08/04 14.85 2

2018 * 08/10 13.88 08/05–08/11 13.51 0
77 

Appendix I

Table I-1. Results of seasonal Kendall tests for changes over time in water temperature means and 

standard deviations at the array located in the West basin of Naknek Lake (NAKNL02). The test statistic 

(τ) indicates the direction of the trend. Positive τ values with p-values <0.05 are interpreted as significant 

warming trends. For each value of τ, the Sen slope estimates the median annual change (Δ/yr, in °C). 

Values between −0.001 and 0.001 are rounded to 0.000.

Depth

(m) 

NAKNL02 – Means

(9/2008–8/2018)

NAKNL02 – Standard Deviations

(9/2008–8/2018)

τ p Δ/yr τ p Δ/yr

5 0.320 0.000 0.123 0.180 0.012 0.020

10 0.336 0.000 0.113 0.252 0.001 0.028

15 0.306 0.000 0.112 0.260 0.000 0.023

20 0.271 0.000 0.088 0.261 0.000 0.023

25 0.289 0.000 0.074 0.317 0.000 0.027

The Department of the Interior protects and manages the nation’s natural resources and cultural heritage; provides scientific 

and other information about those resources; and honors its special responsibilities to American Indians, Alaska Natives, and 

affiliated Island Communities.

NPS 953/173831, November 2020
National Park Service

U.S. Department of the Interior

Natural Resource Stewardship and Science

1201 Oakridge Drive, Suite 150

Fort Collins, CO 80525

EXPERIENCE YOUR AMERICA TM