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Title
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Identification of Degraded Lotic Freshwater that Affects Salmon Habitat, Shellfish Beds and Recreation in South Puget Sound Using Water Quality Data and Land Use Aanalysis
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Date
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2012
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Creator
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Loft, Don S
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Subject
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Environmental Studies
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IDENTIFICATION OF DEGRADED LOTIC FRESHWATER THAT AFFECTS
SALMON HABITAT, SHELLFISH BEDS AND RECREATION IN SOUTH PUGET
SOUND USING WATER QUALITY DATA AND LAND USE ANALYSIS
by
Don Loft
A Thesis-Essay of Distinction
Submitted in Partial Fulfillment
of the requirements for the degree
Master of Environmental Studies
The Evergreen State College
July 2012
© 2012 by Don Loft. All rights reserved.
This Thesis for the Master of Environmental Studies Degree
by
Don Loft
has been approved for
The Evergreen State College
by
__________________________________
Judith Bayard Cushing, Ph.D.
_______________________
Date
ABSTRACT
Identification of Degraded Lotic Freshwater that Affects Salmon Habitat, Shellfish Beds
and Recreation in South Puget Sound Using Water Quality Data and Land Use Analysis
Don Loft
Ecosystems are becoming degraded through human activity and neglect, resulting
in the decline of productive habitats, increasing levels of toxins and a threat of species
extinction. The quality of water in aquatic habitats is essential, because water is the basis
for all life. To improve the health of an ecosystem, we must maintain the quality of water
that sustains it. The purpose of this thesis project was to use water quality standards to
identify habitats with degraded water quality for key species of salmonids, shellfish beds,
as well as human recreation.
This research will answer the following questions: 1) Which water quality
parameters define healthy and sustainable salmon habitats in lotic freshwater systems?
2) By using water quality standards as criteria, which freshwater lotic systems are
degraded in two South Puget Sound watersheds? 3) What are the degrading effects of
different types of land use and impervious surfaces having on water discharging from
drainage systems into creeks, streams and wetlands?
This study focused on two watersheds in Western Washington’s South Puget
Sound, WRIA 11 (Nisqually) and WRIA 13 (Deschutes), and utilized water quality data
analysis and geographic information system (GIS) to map the analysis results. The next
level of analysis used the GIS hydrology tools to define the flow basins and identify land
use features within those flow basins. This was performed on those sites considered to
have highly degraded water quality. The selected flow basins were used to identify land
use that could potentially affect water quality and degrade habitat.
The analysis compared existing water quality data against the Washington State
Department of Ecology water quality standards to develop a color coded tier ranking
system based on how often the data failed to meet the criteria standards. The ranking
system resulted in an enhanced method for the identification of degraded and healthy
lotic freshwater systems. The methods used in this thesis project produced a valuable
tool, graphically representing potential target areas for salmon recovery and habitat
restoration and enhancement.
Table of Contents
Page
List of Figures ……………………………………………………………………….. vii
List of Tables ……………………………………………………………………....... viii
Acknowledgments ……………………………………………………………….......
x
Chapter One:
Water Quality Standards, Criteria and Methods of Analysis for Two
Watersheds in South Puget Sound …………………………………………………
Introduction ………………………………………………………………………….
Area of Study ………………………………………………………...........................
Nisqually River Basin (WRIA 11) ……………………………………............
Fish Species and Habitat (WRIA 11) ……………………………………........
Deschutes River Basin (WRIA 13) …………………………………………...
Fish Species and Habitat (WRIA 13) ……………………………………........
WDFW Salmonid Stock Inventory ……………………………………….......
Water Quality Standards ……………………………………………………….......
Dissolved Oxygen …………………………………………………………….
Bacteria Levels ………………………………………………………………..
Turbidity …………………………………………………………………........
pH ………………………………………………………………………..........
Temperature ………………………………………………………………......
Data Analysis …………………………………………………………………….......
Source of Data …………………………………………………………...........
Data Organization …………………………………………………………….
Methods …………………………………………………………………………........
Tabular Data Analysis …………………………………………………….......
Aggregation of Ranking Results ………………………………………….......
GIS Spatial Data and Join Tables …………………………………………….
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Chapter Two:
Identification of Degraded Habitat in Lotic Freshwater Systems ………………..
Results and Conclusions …………………………………………………………….
Results for WRIA 11 ………………………………………………………….
DO Results ……………………………………………………………
FC 50 cfu Results ……………………………………………………..
FC 100 cfu Results ……………………………………………………
Turbidity Results ……………………………………………………...
Temperature Results ………………………………………………….
Results for WRIA 13 ………………………………………………………….
DO Results ……………………………………………………………
FC 50 cfu Results ……………………………………………………..
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Table of Contents (Continued)
Page
Chapter Two (Continued)
FC 100 cfu Results …………………………………………………… 29
Turbidity Results ……………………………………………………... 31
pH Results ……………………………………………………………. 31
Temperature Results …………………………………………………. 32
Final Site Selection for Degraded Water Quality ……………………………. 37
WRIA 11 Lotic Systems ……………………………………………... 38
WRIA 11 Lentic Systems ……………………………………………. 40
WRIA 11 Lotic System Tide Gates ………………………………….. 40
WRIA 11 FC in Lotic Systems ………………………………………. 42
WRIA 13 Lotic Systems ……………………………………………... 43
WRIA 13 Lentic Systems ……………………………………………. 44
WRIA 13 FC in Lotic Systems ………………………………………. 45
Conclusions WRIA 11 ……………………………………………………….. 46
Conclusions WRIA 13 ……………………………………………………….. 46
Chapter Three:
Anthropogenic Land Use and Impervious Surfaces Adjacent to Salmon Habitat
and Shellfish Beds …………………………………………………………………...
Introduction ………………………………………………………………………….
Natural Systems vs. Developed Systems …………………………………………...
Land Use and Estuarine Habitats ………………………………………………….
Threats to Puget Sound Shellfish …………………………………………………...
Impact of Development and Fecal Coliform ……………………………………….
Reports and Surveys ………………………………………………………………...
Classifications ………………………………………………………………………..
Criteria ……………………………………………………………………………….
Study Areas …………………………………………………………………………..
Description of Growing Area ……………………………………………………….
Nisqually Reach ………………………………………………………………
Henderson Inlet ……………………………………………………………….
Reports by Washington State Department of Health ……………………………..
Nisqually Reach ………………………………………………………………
Henderson Inlet ……………………………………………………………….
Report by Washington State Department of Ecology ……………………………..
Land Use ……………………………………………………………………………..
Land Use Types ………………………………………………………………
WRIA 11 Parcels ……………………………………………………………..
WRIA 13 Parcels ……………………………………………………………..
Impervious Surfaces ………………………………………………………………...
The Problem …………………………………………………………………..
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Table of Contents (Continued)
Page
Chapter Three (Continued)
Analysis of the Lower Henderson Inlet Sub-Basin Septic Systems as Source of
Fecal Coliform Loading on Shellfish Beds ……………………………………........ 67
Point and Non-Point Source Pollution ……………………………………….. 68
Spatial Analysis ………………………………………………………………. 69
Conclusions …………………………………………………………………... 71
Chapter Four:
Defining Flow Basin Influence on Degraded Lotic Systems ……………………...
Introduction ………………………………………………………………………….
Watershed Function and Stressors …………………………………………………
Spatial Analysis of Ohop Creek in WRIA 11 ……………………………………...
Flow Basins …………………………………………………………………...
Joining Spatial and Tabular Data ……………………………………………..
Flow Basin Analysis ………………………………………………………….
Analysis of Flow Basin Land Use ……………………………………………
Analysis of Land Use and Water Quality …………………………………….
Conclusions …………………………………………………………………...
Final Thoughts ……………………………………………………………………….
References ……………………………………………………………………………
Appendix …………………………………………………………………………......
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List of Figures
Page
Figure 1.1: Washington State Map and Location of Project Watersheds ……….........
3
Figure 2.1: Tide Gate in Westport, Washington ……………………………………...
41
Figure 3.1: The Lower Henderson Inlet Sub-Basin and Shellfish Bed Area …………
68
Figure 3.2: The 35% FC 50 Monitoring Sites in the Lower Henderson Inlet …........... 69
Figure 3.3: The FC 50 Target Creeks in the Lower Henderson Inlet ………………...
70
Figure 3.4: Target Parcels with Septic Systems in the Lower Henderson Inlet ……… 71
Figure 4.1: The Ohop Flow Basin Feature ……………………………………………
77
Figure 4.2: The Ohop Flow Basin with Land Use Features ………………………….
77
Figure 4.3: Segment A of the Ohop Land Use Analysis ……………………………..
80
Figure 4.4: Segment B of the Ohop Land Use Analysis ……………………………...
80
Figure 4.5: Segment C of the Ohop Land Use Analysis ……………………………...
81
Figure 4.6: Segment D of the Ohop Land Use Analysis ……………………………..
81
Figure 4.7: The Ohop Flow Basin Four Monitoring Sites. …………………………...
83
vii
List of Tables
Page
Chapter 1
Table 1.1: WDFW’s Salmonid Stock Inventory Stock Status WRIA 11 & 13 ………
5
Table 1.2: WA surface water quality standards DO, FC, Turbidity and pH …….........
8
Table 1.3: Washington State surface water quality standards for Temperature ….......
8
Table 1.4: Criteria, Functions and Formulas for DO, FC, pH and Turbidity ………......... 11
Table 1.5: Measure of data spread for (Min, Max, Quartiles, & Median) ………........ 12
Table 1.6: Criteria, Functions & Formulas Seasonal Use Temperature Analysis …….. 14
Chapter 2
Table 2.1: Results for degraded DO levels (geo mean < 8mg/L) WRIA 11 …………. 20
Table 2.2: Results for degraded FC levels (geo mean > 50 cfu) WRIA 11 ………….. 21
Table 2.3: Results for degraded FC levels (geo mean > 100 cfu) WRIA 11 ………… 22
Table 2.4: Results for degraded turbidity levels (geo mean > 7.8 NTU) WRIA 11 …. 23
Table 2.5: All Year Temperature Results for Char Rearing WRIA 11 ………………. 24
Table 2.6: F/W/Sp Temperature Results for Salmon & Trout WRIA 11 ……………. 25
Table 2.7: Summer Temperature Results for the Core Salmonid WRIA 11 ………… 26
Table 2.8: Fall Temperature Results for the Char Spawning WRIA 11 ……………... 27
Table 2.9: Results for degraded DO levels (geo mean < 8mg/L) WRIA 13 …………. 28
Table 2.10: Results for degraded FC levels (geo mean > 50 cfu) WRIA 13 ………… 29
Table 2.11: Results for degraded FC levels (geo mean > 100 cfu) WRIA 13 ……….. 30
Table 2.12: Results for degraded turbidity levels (geo mean > 6.8 NTU) WRIA 13 ... 31
Table 2.13: Results for degraded pH levels (geo mean < 6.5 or > 8.5) WRIA 13 …… 32
Table 2.14: All Year Temperature Results for Char Rearing WRIA 13 ……………... 34
Table 2.15: F/W/Sp Temperature Results for Salmon & Trout WRIA 13 …………... 35
Table 2.16: Summer Temperature Results for the Core Salmonid WRIA 13 ……….. 36
Table 2.17: Fall Temperature Results for the Char Spawning WRIA 13 ……………. 37
Table 2.18: Aggregated Lotic Results Across Multiple Parameters WRIA 11 …........ 39
Table 2.19: Aggregated Lentic Results Across Multiple Parameters WRIA 11 ……... 40
Table 2.20: Aggregated Tide Gate Results Across Multiple Parameters WRIA 11 …. 42
Table 2.21: Aggregated Fecal Coliform Results Across Two Parameters WRIA 11 ... 43
Table 2.22: Aggregated Lotic Results Across Multiple Parameters WRIA 13 ……… 44
Table 2.23: Aggregated Lentic Results Across Multiple Parameters WRIA 13 ……... 45
Table 2.24: Aggregated Fecal Coliform Results Across Two Parameters WRIA 13 ... 47
Chapter 3
Table 3.1; DOH Sites Not Meeting Water Quality Standards For Nisqually Reach … 57
Table 3.2: DOH Sites Not Meeting Water Quality Standards For Henderson Inlet …. 57
Table 3.3: Sites Classified As Conditionally Approved Plus Two Approved Sites …. 58
Table 3.4: Creeks Not Meeting TMDL Standards …………………………………… 59
Table 3.5: Totals for Land Use Type in WRIA 11 ………………………………….. 62
Table 3.6: Totals for Land Use Type by County in WRIA 11 ……………………...
62
viii
List of Tables (Continued)
Page
Chapter 3 (Continued)
Table 3.7: Totals for Land Use Type in WRIA 13 ………………………………….. 63
Table 3.8: Totals for Land Use Types by County in WRIA 13 ……………………..
64
Chapter 4
Table 4.1: Ohop Creek Site Selections ………………………………………………. 75
Table 4.2: Ohop Flow Basin Calculated Land –Use …………………………………. 78
Table 4.3: Aggregated Water Quality Results for the Ohop Monitoring Sites ………. 83
Table 4.4: Site 4 Water Quality Result Ranking, Segment D Land Use Totals ……... 84
Table 4.5: Site 3 Water Quality Result Ranking, Segment C Land Use Totals ……... 85
Table 4.6: Site 2 Water Quality Result Ranking, Segment B Land Use Totals ……... 87
Table 4.7: Site 1 Water Quality Result Ranking, Segment A Land Use Totals ……... 87
Appendix
Table A1: Lotic Site Locations WRIA 11 …………………………………………… 92
Table A2: Lentic Site Locations WRIA 11 …………………………………………... 93
Table A3: Lotic System Tide Gate Locations WRIA 11 …………………………….. 93
Table A4: Lotic Site Locations WRIA 13 …………………………………………… 94
Table A5: Lentic Site Locations WRIA 13 …………………………………………... 95
ix
Acknowledgements
The first person I would like to acknowledge is Randy Lehr, Ph.D. for first
guiding me on the path of identifying degraded freshwater systems through the analysis
of water quality data. My work on the Chehalis River Basin Water Quality Project with
Mr. Lehr provided the rudimentary foundation for my thesis project. My second partner
in the Chehalis River Basin Water Quality Project was Joel Green, Ph.D. His expertise in
fish biology helped me to refine my focus on the fundamental water quality parameters
that support seasonal use by a variety salmonid species in aquatic habitats. Green played
an important role in proofing the data for outliers and statistical analysis. He also
performed the major role in the State-of-the-River report we co-authored.
The next acknowledgements go to people who supported my original water
quality project for the Chehalis Basin. My thanks and admiration goes to the following
people: Mike Kelly (Grays Harbor College) for his friendship and facilitating my contract
to study the water quality in the Chehalis Basin. Janel Spaulding (Chehalis Basin
Partnership) for her support and networking which supported my project. David Rountry
(Department of Ecology) for his guidance on water quality issues and standards. Harry
Pickernell (Water Resources Specialist The Chehalis Tribes) for the water quality data
supplied for my project and his expertise as field research and lab methods.
I would now like to acknowledge those people who were instrumental in the
creation and support of my thesis project. When I entered the MES program, I had a
fairly good idea where I wanted to take it. Through the support of many professors and
fellow students in my undergrad and graduate programs helped me clarify my thesis
objectives. Special thanks to: Dylan Fischer, Ph.D., Rob Knapp, Ph.D., Rob Cole, Ph.D.,
Martha Henderson, Ph.D., Ralph Murphy, Ph.D., Timothy Quinn, Ph.D., Alison Styring,
Ph.D., Anna Wederspahn, MES, Tim Benedict, MES, Noel Ferguson, MES, Chris
Holcomb, MES, Nahal Ghoghaie, MES, Scott Morgan (Office of Sustainability), Gavin
Glore (Mason Conservation District), and Jerilyn Walley, MES for support and proof
reading my thesis.
For help in GIS spatial analysis for hydrology, I would like to give my thanks to
Greg Stewart, Ph.D. for all his help and guidance. Thanks to Gerardo Chin-Leo, Ph.D. for
his help in crafting and organizing my thesis from prospectus to thesis project. Special
thanks goes to Gail Wootan (Assistant Director to the MES Program) for her gentle and
caring support to get my thesis to the goal line.
My utmost gratitude goes to my esteemed thesis reader Judith Cushing, Ph.D.
whose help and guidance got me to the finish line. Her input was invaluable, her support
unwavering. When I got off track, she nudged me back on path. Judith’s support and
suggestions on methods and refinements has made the crafting of my thesis a pleasure to
accomplish. I could not have picked a better person to be my thesis reader. I am a better
graduate student for having known her.
x
Chapter One:
Water Quality Standards, Criteria and Methods of Analysis for Two Watersheds in
South Puget Sound
Introduction:
The key element to assessing the health of salmon habitat is the collection of
reliable data on that habitat. If there are no existing data for a habitat sustainability study,
then field work is needed to obtain samples to analyze. When data are available on the
study area’s lotic freshwater salmon habitat, it is often obtained from many of the
stakeholders in the area, and that can range from state and federal environmental agencies
as well and local tribes and conservation districts. For this study data were acquired from
The Washington State Department of Ecology, which maintains a large database
collected from stakeholders from all regions in the state. The data used in this study were
downloaded from their Environmental Information Management System (EIM) for two
watersheds in the South Puget Sound area. Watersheds in Washington State are referred
to as Water Resource Inventory Areas (WRIA) which defines the flow direction of runoff
water from rainfall and snow melt. The salmon habitats in lotic freshwater systems in this
study are WRIA 11 (Nisqually) and WRIA 13 (Deschutes).
Water quality data are used to determine if any salmon habitats or recreational
streams have become degraded and for potential impact on shellfish. The water quality
parameters used in this study are dissolved oxygen, fecal coliform, turbidity, pH and
temperature. These data will undergo analysis that compares the data against the water
quality standards outlined in the Clean Water Act and by The Washington State
Department of Ecology. The criteria for high water quality standards will be used to
compare samples taken at specific monitoring sites and then ranked by percentage of time
the samples do not meet the criteria. A second level of criteria for low water quality
standards will be used for some of the parameters, as this is needed for some species
habitat requirements. This ranking will be graphically represented in a geographic
1
information system (GIS) format. Monitoring sites showing the highest percentage of
times not meeting the criteria will be given further analysis of the flow basins to those
sites and the land use within those flow basins.
This thesis project produced enhanced methods for identifying degraded lotic
streams, creeks and drainage of freshwater systems. This was accomplished through the
use of water quality data analysis and applying those findings to GIS maps. GIS
hydrology tools were used to identify land use features within flow basins that could
potentially affect the water quality results found at specific monitoring sites. This
information could be used to target potential habitat restoration and mitigation projects.
The defined flow basins for these sites could also assist in assembling strategies for a
more in-depth research within troubled habitats.
Area of Study:
This study took place in the Pacific Northwest and focused on two watersheds
that discharge runoff water into Puget Sound. The Puget Sound, in the western part of
Washington State, is a large estuarine system carved out by the advancement and
receding of the last glacial age over 13,000 years ago. The approximate mile thick glacial
ice stretched down from Canada to what is now near the State Capitol of Washington,
with the Olympic Mountains to the west and the Cascades to the east. The retreating
glacier scoured the landscape leaving behind many lakes, rivers and streams that now
shed runoff water, melting ice and snow from the mountains and hills surrounding Puget
Sound on the east, west and south. The north end of the Puget Sound opens to oceanic
saltwater of the Pacific through the Strait of Juan de Fuca (Ecy, 2011).
The two watersheds in this study are located at the south end of Puget Sound; the
Nisqually (WRIA 11) and the Deschutes (WRIA 13). The Washington State map shown
in Figure 1.1 defines the location of WRIA 11 and WRIA 13 in relation to South Puget
Sound and respectively to each other. These watersheds were chosen because of the
different land uses respective to each.
2
Figure 1.1: Washington State map showing location of watersheds in this study.
Locations: The Nisqually River Basin (WRIA11) and the Deschutes River Basin (WRIA 13).
Nisqually River Basin (WRIA 11): The Nisqually River headwaters begin on
the south face of Mt. Rainer, traveling southwest, then west through the upper basin, and
northwest through the mid to lower basin. From headwater to the Nisqually Refuge
Estuary, the Nisqually River flows approximately 78 miles and drains roughly 760 square
miles. The elevation from headwater to estuary ranges from over 14,000 feet to sea level,
although most of this watershed lies below 1,000 feet (PCPWU, 2011).
Fish Species and Habitat (WRIA 11): Anadromous fish species that migrate
and inhabit the Nisqually basin are chum, coho, chinook and pink salmon, steelhead,
rainbow and cutthroat trout. Also a non-anadromous species of sockeye salmon called
kokanee was introduced to Alder Lake. The anadromous sockeye have been seen
occasionally in the basin and are believed to be strays or kokanee that have left the
reservoir. Although there is little evidence of the presence of bull trout, they are believed
to live within the basin. Listed as threatened under the Endangered Species Act are the
Nisqually fall chinook and Nisqually bull trout. A candidate for listing is the Nisqually
coho salmon. Numerous warm water species have been introduced to low-land lakes;
including bluegill, bullheads, pumpkinseed, yellow perch and largemouth bass. The
3
primary salmon habitat is found in the subbasins Mashel, Muck/Murry, and the
Tanwax/Kreger/Ohop. Salmonids also utilize the main channel of the Nisqually River.
Access to other subbasins is limited because of natural and manmade barriers
(WPN, 2002).
Deschutes River Basin (WRIA 13): The headwaters of the Deschutes River
begin in Cougar Mountain at an elevation of 3,870 feet above sea-level, and flows
through steep terrain in Lewis County to the rolling topography of the mid-waters in
Thurston County and to the relatively flat grassy prairies and urban areas. The Deschutes
River discharges about 60% of WRIA 13, approximately 270 square miles. The
remainder of WRIA 13 discharges directly into Puget Sound. The Deschutes River
discharges into Capitol Lake impoundment in downtown Olympia before entering Budd
Inlet of South Puget Sound (WA Ecology, 2005). Percival Creek also discharges into
Capitol Lake and not the main stem of the Deschutes River. WRIA 13 also contains two
large flow basins that do not discharge directly into the main channel of the Deschutes
River, Capitol Lake or Budd Inlet. To the west of Budd Inlet is Eld Inlet, and to the east,
Henderson Inlet. Eld Inlet receives runoff water from McLane, and Swift Creeks at the
southernmost tip, and Green Cove Creek which drains runoff from Cooper Point.
Henderson Inlet receives runoff discharge from two main channels, the Woodland and
Woodard Creeks, which both originate in dense urban areas of Olympia and Lacey.
Dobbs, Meyer, Myer and Goose Creeks are several smaller creeks that discharge from
less dense urban and more rural areas into Henderson Inlet.
Fish Species and Habitat (WRIA 13): With assistance from Native Tribes and
tribal organizations, the Washington State Department of Fish and Wildlife (WDFW)
publishes the State Salmon and Steelhead Stock Inventory (SASSI) report on the
condition of local and introduced fish species. The information used here is from the
Deschutes River Watershed Initial Assessment (Draft May 1995). The SASSI states that
the Deschutes River watershed supports three salmon species, the chum, chinook, and
coho in addition to the winter steelhead. Also utilizing WRIA 13 are the Henderson Inlet
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fall chum, McLane Creek steelhead and a variety of other fish including Dolly Varden,
sea-run cutthroat trout, pygmy whitefish and Olympic mudminnow (Ecy-2, 2011).
WDFW Salmonid Stock Inventory: WDFW’s Salmonid Stock Inventory (SaSI)
issues reports on the health of salmonids species throughout Washington State. The SaSI
is a tool developed by Native Tribes and WDFW to inventory and monitor Washington’s
salmonids species, because of their cultural and commercial importance to the state’s
ecosystems and its people. The inventory data is compiled on all wild salmonid species
and is assessed to determine if the stock is healthy, depressed, critical, extinct, or
unknown. This results in a recovery action plan to prioritize restoration. Anthropogenic
activities have placed heavy pressures on salmonid stocks through urban development,
industry, forestry, agriculture, overfishing and hydropower dams to name a few. Many of
Washington’s species have become imperiled over time. As of 2002, out of the 598
species that have been identified, 180 are rated as healthy, 132 as depressed, 26 as
critical, 9 as extinct and 251 were given a status of unknown. Table 1.1 shows the SaSI
inventory status for species in WRIAs 11 and 13 (WDFW, 2011).
Table 1.1: WDFW’s Salmonid Stock Inventory (SaSI) Stock Status WRIA 11 and 13.
Source: Washington Department of Fish and Wildlife (WDFW) Salmonid Stock Inventory (SaSI)
Stock Status for species (WDFW, 2011).
Species
Chinook
Chum
Coho
Pink
Steelhead
Stock Name
Nisqually Chinook
Nisqually Winter Chum
Nisqually Coho
Nisqually Pink
Nisqually Winter Steelhead
WRIA
11
11
11
11
11
Chum
Chum
Coho
Steelhead
Eld Inlet Fall Chum
Henderson Inlet Fall Chum
Deschutes Coho
Deschutes Winter Steelhead
13
13
13
13
1992 Status
Healthy
Healthy
Healthy
Healthy
Healthy
2002 Status
Depressed
Healthy
Healthy
Unknown
Depressed
Healthy
Unknown
Healthy
Healthy
Healthy
Unknown
Critical
Not Rated
5
Water Quality Standards:
The dependent variable is water quality in streams, creeks and drainage systems
that support salmonid rearing and spawning habitats as well as a healthy water quality
fecal coliform standard for shellfish and recreational purposes. The measure of water
quality in lotic systems was determined by freshwater parameters for sustainable
salmonid spawning and rearing habitats, as outlined by the Department of Ecology’s
Water Quality Standards for Surface Water of the State of Washington, Chapter 173201A WAC (WA Ecology, 2006). Water quality parameters used in this Thesis (with
corresponding tables referenced in the above mentioned publication) are: dissolved
oxygen (Table 200 (1)(d)), bacteria levels (Table 200 (2)(b)), turbidity (Table 200 (1)(e)),
temperature (Table 200 (1)(c)) and pH (Table 200 (1)(g)). The criteria used for analysis
are those defined in Washington State water quality standards 173-201A WAC, and are
outlined below:
Dissolved Oxygen: Dissolved oxygen levels can be influenced by water
temperature and the large die-off and decay of organic plant material and algae. Stagnant
water or stream water flow that lacks a rough substrate for mixing can also be the cause
of low dissolved oxygen levels. The water quality standards for dissolved oxygen levels
are: 8 mg/L (Low Standard) or above in streams and creeks are necessary to support
salmon spawning and rearing, and 9.5 mg/L (High Standard) or above are needed to
support char and trout (WA Ecology, 2006).
Bacteria Levels: Elevated bacterial levels in stream water can cause problems
such as eutrophication from the growth and decay of plants and algae which depletes
dissolved oxygen. Also, higher bacteria levels can increase stream turbidity as well as
bacterial diseases. Water quality data collected for fecal coliform (FC) was from
Ecology’s EIM database. The standard of measurement is colony forming units (cfu). The
standards used for analysis were the high standard of 50 cfu/100 ml for exceptional water
quality to protect stream water discharge over shellfish beds, and the lower standard of
100 cfu/100 ml to protect recreational primary contact (Green, et al., 2009). Long-term
testing at a sampling site that receives a geometric mean over 100 cfu/100 ml with at least
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10% of the sample data above 100 cfu/100 ml gets placed on the 303(d) listing. This is
used for a Total Maximum Daily Load (TMDL) study to find and correct point source(s)
of bacteria in fresh surface water (WA Ecology, 2006).
Turbidity: High water quality standards for streams and creeks measure for
5 Nephelometric Turbidity Units (NTU) above background turbidity for individual rivers,
streams and creeks. When water gets to elevated levels of suspended solids, it can have
several adverse effects on fish in spawning and rearing habitats. Turbid water dissuades
fish from taking advantage of habitats because of irritation caused to their gills. Also,
suspended solids settle out over stream beds, covering suitable substrate for laying eggs.
Turbidity decreases the amount of light that is received by benthic plants and algae, thus
decreasing productivity that supports invertebrate habitat and food supply (WA Ecology,
2006).
pH: The high and low standards for pH are between 6.5 and 8.5. Water quality
standards for pH are a factor in the health of lotic systems, but will not be the primary
focus of this research. In future work pH may be utilized if needed for a more in depth
analysis.
The Washington State standards for surface water quality parameters for
dissolved oxygen, fecal coliform, turbidity and pH, have been summarized in Table 1.2
below. For this study the numerical values of these standards were used as the criteria for
analysis. These standards have two levels of protection. The high standards are adopted
for high or extraordinary fresh surface water quality to protect char and salmon habitats
for spawning, rearing, migration and to protect shellfish from stream water discharge
over shellfish beds. The low standards are adopted to protect dissolved oxygen levels for
salmon spawning, bacteria levels for recreational primary contact protection and turbidity
levels to protect salmon migration and rearing. The pH range is the same for both high
and low standards.
7
Table 1.2: Washington State surface water quality standards for dissolved oxygen, fecal
coliform, turbidity and pH. Source: WA Ecology, 2006 and Green, Loft and Lehr, 2009.
High
Low
Parameter
Description
Description
Standards
Standards
Lowest 1-Day
Lowest 1-Day
Dissolved
minimum to protect
9.5 mg/L
8 mg/L
minimum to protect
Oxygen
Char Spawning and
Salmon Spawning
Rearing
Max. Geometric
Mean for
Max. Geometric
Fecal
50
extraordinary water
100
Mean to protect for
Coliform
cfu/100 ml
quality of streams
cfu/100 ml
recreation primary
(Bacteria)
flowing to shellfish
contact
beds
5 NTU
To protect salmon
10 NTU
To protect salmon
Turbidity
above
spawning, rearing
above
migration and
background
and migration
background
rearing
To protect salmon
To protect salmon
pH
6.5 to 8.5
spawning, rearing,
6.5 to 8.5
spawning, rearing,
and migration
and migration
Temperature: This water quality parameter was analyzed differently from the
analysis method employed for the other parameters. Because species utilize habitat at
different times of the year and for different purposes, the temperature datasets were
broken into seasonal use categories (see Table 1.3). The timeframe used for suitable
habitat temperatures will be discussed in further detail in the Data Analysis section of this
chapter (WA Ecology, 2006).
Table 1.3: Washington State surface water quality standards for temperature.
Source: WA Ecology, 2006 and Green, Loft and Lehr, 2009.
Category
Temperature
○
○
Fall Char Spawning
9 C (48.2 F)
Char Rearing
12 C (54.6 F)
Salmon & Trout Spawning
Time Period
Sept. 16th – Dec. 22nd
○
○
○
○
Sept. 16th – June 14th
○
○
June 15th – Sept. 15th
(F/W/Sp)
13 C (55.4 F)
Core Summer Salmonid Habitat
16 C (60.8 F)
All Year
8
Data Analysis:
The objective of this data analysis was to identify those lotic fresh water habitats
that exhibit poor standards of water quality, or had a tendency toward poor water quality.
The criteria were set for pass or fail for the high and low water quality standards on the
sample parameter result numbers (Summarized in Tables 1.2 and 1.3 in the previous
section). The sample parameters of dissolved oxygen (DO), turbidity (TURB) and fecal
coliform (FC) each have two criteria for analysis. Each of these parameters was measured
for the high and low water quality, as outlined by the Washington State standards for
surface water quality. The pH parameter has one measure for both high and low water
quality standards. Temperature (TEMP) data were arranged by seasonal needs for
salmon, char and trout. Four temperature criteria were used for specific seasonal habitat
utilization during spawning, rearing and migration.
Source of Data: Washington Department of Ecology’s Environmental
Information Management System (EIM) Database Search was used to acquire fresh
surface water quality data on the above parameters for the two watersheds in this study,
the Nisqually (WRIA 11) and the Deschutes (WRIA 13). The parameter data download
produced three file folders, one each for study, location and results (Ecy-3, 2011).
Data Organization: The folder containing the study data was left intact as it
gives information for study ID, study name, purpose, start and end dates, grant loan
numbers and Ecology’s lead contact person. The location folder was used to extract data
for two location tables, one tabular to provide location information data and one for
spatial coordinates to use in creating features for the watershed maps in GIS. The analysis
portion of this project was performed using the data contained within the results folder.
The Washington Department of Ecology (WADOE) requires quality assurance levels to
be included with every data upload to the EIM database. Every study must do this to
enter result parameters for samples taken at a study location. This is referred to as the
Study Quality Assurance (QA) Assessment Level (Ecy-3, 2011).
9
The Study QA Assessment is defined by five levels for reliability for the data.
Level 1 is the lowest level of reliability and Level 5 is the highest. The levels are assigned
as follows:
Level 1 - Data neither Verified nor Assessed for Usability
Level 2 - Data Verified
Level 3 - Data Verified and Assessed for Usability
Level 4 - Data Verified and Assessed for Usability in a Formal Study Report
Level 5 - Data Verified and Assessed for Usability in a Peer-Reviewed Study Report
These levels of reliability are located within the EIM dataset field titled “Study QA
Assessment Level” (Ecy-3, 2011). For analysis to lend credibility to this study, only the
data for formal and peer-reviewed reports were used. Data for QA Levels 4 and 5 were
extracted from the EIM download. Data for QA Levels 1 through 3 were discarded.
The next step in analysis was to group the data sets by WRIA and water quality
result parameter. Both WRIA 11 and WRIA 13 were assigned their corresponding
parameter datasets for DO, TURB, FC, pH, and TEMP. Each parameter was then further
sorted alphabetically by its Location ID and Study ID. These two identifiers were made
from unique combinations of letters and numbers. Then the parameters were sorted from
start to end date of the study. With the exception to the TEMP datasets, the last step was
alphanumeric sorting for the Sample ID from smallest to largest. The final sort for TEMP
was from the time of day the sample was taken (when provided), because the time of day
can have an effect on water temperature in some locations.
The analysis objective for this project was to define the water quality at specific
sampling sites where ongoing and long-term monthly sample collections were taken
within WRIA 11 and WRIA 13. Organizing the data as described above provided block
sets of data for each sample site location. These block datasets arranged the data for
useful extraction of site and study information, start and end dates, and to analyze the
result parameter numbers site by site.
Methods:
Tabular Data Analysis:
Information for the site, study and result parameter number analysis was
performed in Microsoft (MS) Excel. The Washington State standards for surface water
quality (High and Low) were used for the criteria in the analysis formulas. Refer back to
10
Tables 1.2 and 1.3 in the Water Quality Standards section. Each site and parameter provided
information on site location, study information and the result parameter numbers taken for
each site. Formulas were created for each site location dataset to acquire the total number of
records, the number of records not meeting the criteria and the percentage of those records
that did not meet the criteria. Also assembled from these records were the study site’s
geometric mean, minimum (smallest) and maximum (largest) from the result parameter
numbers. Other information assembled from each dataset was the QA assurance level, start
date, end date and unique identifiers for the site location and the study. A formula bar was
created for each parameter dataset to organize and assess the information described above.
The algorithms were exported and aggregated for each parameter in each WRIA for this
study. This created join tables for the GIS portion of this analysis, discussed later in this
section. Discussion on the land use and impervious surface GIS procedures for this study are
in following chapters.
The criteria functions and formulas for DO, FC, pH and TURB are summarized in
Table 1.4 below. To acquire the total number of records for each site’s dataset the ‘count’
function was used. To get the number of records within that dataset that did not meet the
criteria the ‘count if’ function was used. To get the percentage of records that did not meet
the criteria the ‘count if’ function result was divided by the ‘count’ function result.
Table 1.4: Criteria, Functions and Formulas for DO, FC, pH and TURB.
Source: Washington State water quality standards 173-201A WAC (WA Ecology, 2006) and
Green, Loft and Lehr, (2009). Formula functions in Microsoft Excel.
PARAMETER FORMULAS
E
F
G
pH
TURB (WRIA 11)
TURB (WRIA 13)
[Range]
[Range]
[Range]
[Range]
[Range] < 6.5 pH or
2 Criteria 1 (High)*
COUNTIF [Range] < 9.5 mg/L [Range] > 50 CFU [Range] > 8.5 pH
[Range] > 7.8 NTU** [Range] > 6.8 NTU**
3 Criteria 2 (Low)*
COUNTIF [Range] < 8 mg/L [Range] >100 CFU
Same
[Range] > 12.8 NTU** [Range] > 11.8 NTU**
4 Percent Rank 1 (High) DIVIDE
C2 / C1
D2 / D1
E2 / E1
F2 / F1
G2 / G1
5 Percent Rank 2 (Low) DIVIDE
C3 / C1
D3 / D1
Same
F3 / F1
G3 / G1
* Actual Field Name consists of Math Function + Criteria + Measurement Units.
** Criteria number includes the calculated background turbidity for each WRIA.
A
Field Name
1 Records
B
Function
COUNT
C
DO
D
FC
[Range]
It should be noted that the high and low water quality standard criteria were not
applied across all parameters for this study. The exceptions are temperature, pH and
turbidity. The temperature data were set for seasonal use by different species of salmon,
11
char and trout spawning and rearing habitation. The pH standard for fresh surface water
quality is in a range from 6.5 pH to 8.5 pH. The standard for pH is the same for high or
low water quality. The turbidity high water quality standard is 5 NTU above background,
and the low water quality standard is 10 NTU above background (WA Ecology, 2006).
For this study only the high water quality standard was used for the turbidity analysis.
The low water quality standard was used only to identify those sites of extreme turbidity.
Background turbidity is somewhat unique to each lotic system. Different background
levels can be influenced by soil types and streambed substrates. The typical measure for
background levels is to sample the turbidity of a stream above a disturbance site to gain
normal stream function turbidity levels. Examples of a disturbance include timber
practices, road construction, culvert removal or replacemant and stream modification.
To establish background turdibity for this study a different metric needed to be
applied. Instead of monitoring turbidity at a specific site of disturbance, the object of this
study is to monitor turbidity effect from different land use types throughout the whole
watershed. Data downloaded from EIM (Ecy-3, 2011) did not provide adequate results
for headwaters for either Deschutes or Nisqually Rivers. The best option was to define a
normal turbidity level from aggregated datasets for each watershed. To accomplish this,
the results data were arranged in ascending order and calculated for the minimum (Min),
1st quartile (Q1), median, 3rd quartile (Q3), and maximum (Max). This measure of the
spread is shown in Table 1.5 below.
Table 1.5: Measure of data spread for WRIAs 11 & 13 (Min, Max, Quartiles, & Median)
Source: Datasets were acquired from EIM (Ecy-3, 2011), Quartiles functions in Microsoft Excel.
Min
0.1
Q1
2.8
WRIA 11
Median
5.2
Q3
9.425
Max
1598
Min
0.2
Q1
1.8
WRIA 13
Median
2.6
Q3
6
Max
2500
To establish the normal NTU background levels for each of these two systems,
the first quartile (Q1) was used as the standard background. The criteria analysis NTU
number for high water quality standards is 5 NTU above background (Refer to Table 1.2
in the section Water Quality Standards). To create the turbidity criteria number for each
12
watershed, the following defined formula was used: Q1 plus 5 NTU equals Criteria NTU.
Q1 for WRIA 11 is 2.8 NTU and Q1 for WRIA 13 is 1.8 NTU. The formulas are:
WRIA 11: Background of 2.8 NTU (Q1) + 5 NTU = 7.8 NTU.
WRIA 13: Background of 1.8 NTU (Q1) + 5 NTU = 6.8 NTU.
Therefore the turbidity criteria are 7.8 NTU for WRIA 11 and 6.8 NTU for WRIA 13.
These adjusted water quality standards were applied as criteria to their respective
watershed turbidity datasets for analysis.
The final water quality parameter for analysis was to segregate stream (lotic) and
lake (lentic) temperature data, with stream water temperature being the primary study
objective and lake water temperature secondary for analysis. Lentic temperatures were
included in the analysis when the lotic sample site temperatures had a high frequency of
not meeting the analysis criteria. Unlike the other water quality parameters, the
temperature criteria were not based on the high and low water quality standards, but
rather on seasonal use by salmonid species. For a summary of seasonal use, refer to
Table 1.3 in the section on Water Quality Standards. Data were sorted into four
categories by seasonal use and the temperature criteria established for each of those
seasonal use categories. As with the other water quality parameters, algorithms were
created for each site and category to organize the information from blocks of site and the
seasonal specific datasets. Three information (INFO) fields were added to the seasonal
temperature algorithms before export to GIS join tables. This information was necessary to
add context for each of the seasonal tables when the information was accessed in GIS. The
new fields were: Category, Temp Criteria and Sample Period. The Category field holds
information on species, the Temp Criteria field holds the temperature analysis criteria in
degrees centigrade and the Sample Period field holds information on the month and day
range of samples taken.
The summarized criteria formulas for TEMP were categorized by seasonal use
and are shown in Table 1.6, below. As with the previous four parameters, to acquire the
total number of records for each site’s dataset the ‘count’ function was used. To get the
number of records that did not meet the criteria the ‘count if’ function was used, and for the
percentage of records that did not meet the criteria the ‘count if’ result was divided by the
‘count’ result.
13
Table 1.6: Info, Criteria, Functions and Formulas for Seasonal Use Temperature Analysis.
Source: WA water quality standards 173-201A WAC (WA Ecology, 2006) and Green, Loft and
Lehr, 2009. Formulas in MS Excel.
INFO
A
Field Name
Category
Temp_Criteria
Sample_Period
1 Records
2 Criteria
3 Percent Rank
B
Function
Text
Text
Text
COUNT
COUNTIF
DIVIDE
C
Temp 1
Char Rearing
12 deg. C
All_Year
[Range]
[Range] > 12 deg. C
C2 / C1
TEMPERATURE FORMULAS
D
E
Temp 2
Temp 3
Fall/Winter/Spring Core Summer
Salmon & Trout
Salmonid Habitat
13 deg. C
16 deg. C
Sept. 16th-June 14th June 15th-Sept. 15th
[Range]
[Range]
[Range] > 13 deg. C [Range] > 16 deg. C
D2 / D1
E2 / E1
F
Temp 4
Fall
Char Spawning
9 deg. C
Sept. 16th-Dec. 22nd
[Range]
[Range] > 9 deg. C
F2 / F1
Before data could be used in the GIS portion of this analysis, a minimum number
of records (per study sample site) was needed so that the ranking of results output would
not be skewed. The Percent Rank field in each join table was used for tier ranking the
GIS display symbology. Tier ranking is explained in more detail in the next section. For
the GIS image output, the tier ranking was divided into five tiers to display the
percentage range of records not meeting the analysis criteria. For this study, sample sites
containing fewer than 5 records were discarded from the analysis.
The parameter formula and functions described above in Table 1.4 for DO, FC,
pH, and Turbidity (TURB), and Table 1.6 for Temperature (TEMP) yielded a list which
ranked each parameter by the percentage of times that study sample sites did not meet the
analysis criteria for water quality standards. Each site was given a percentile rank ranging
from zero to 100%. These percentiles were divided into a five tier ranking to define the
quality of water at a given site. The ranking was in percentile degrees from excellent to
degraded water quality. Based on the percentage of times a study sample site did not meet
the analysis criteria, the tier ranking is as follows:
Excellent (0 to 5%),
Good (>5 to 15%),
Fair (>15 to 25%),
Poor (>25 to 35%)
Degraded (>35 to 100%).
This tier ranking method was previously used in a report to analyze the water quality in
the Chehalis River Basin in Western Washington (Green et al., 2009). The procedure in
14
this study takes the analysis process several steps further than the aforementioned report.
The objective of this study was to identify the more degraded sample sites, and define
possible contributing factors from land use and impervious surfaces. This chapter deals
with the identification of degraded sites and Chapter 2 will analyze the land use and
impervious surface contributing factors. To further refine this analysis of degraded sites
two more steps were added.
After the percentile ranking of sites and dividing the list of percentile results into
five tiers, the range for the degraded water quality was selected by keeping only those
sites that rank from greater than 35% to 100% of the time not meeting the analysis
criteria. The next step used the geometric mean as a qualifier for the most degraded water
quality at sample sites. Of the sites ranking greater than 35% to 100% that did not meet
the analysis criteria, not all sites were severely degraded. For example, a site that had a
rank of 42.86% for fecal coliform (FC), with an analysis criteria of 50 CFU, could have a
minimum number of 8 CFU and a maximum number of 260 CFU, yet still have a
geometric mean of 45.37 CFU which is lower than the analysis criteria. This would be a
parameter dataset with prevailing result numbers low enough to average less than the
criteria of 50 CFU. Furthermore, a site that has the same rank of 42.86% with a minimum
number of 6 CFU and a maximum of 270 CFU can have a geometric mean of 57.35 CFU
which is higher than the analysis criteria. This would be from the dataset with numbers
that trended higher, thus giving a geometric mean result higher than the analysis criteria
of 50 CFU.
To simplify the above analysis process for finding the most degraded study
sample sites, I started with sites that had a tier rank of 35% and greater. Of those study
sample sites, only the records that had geometric means that also meet the parameter
analysis criteria (50 NTU) were selected for the final parameter ranking analysis tables.
For the FC example, all records in that group with a geometric mean of 50 CFU and
above were selected. Although some of the other parameter ranking records did not meet
the geometric mean standard, they were still included for various reasons. Explanations
will be included in the table description. The resulting tables provide lists of sites that
15
will undergo further analysis for land use and impervious surfaces. These tables are
shown in the section on Results and Conclusions in Chapter Two.
Aggregation of Ranking Results:
The methods described above created sample site ranking tables for the individual
parameters (DO, FC, pH, TURB and TEMP) in each of the watersheds within this study.
The individual tables do not allow for criteria failures across multiple parameters. The
final step in identifying the most degraded sites for water quality was to aggregate the
tables for all the parameters and rank each sample site across all parameters not meeting
the criteria at least 35% of the time. This also establishes a possible correlation between
parameters. For example, a lotic site that shows a high frequency of dissolved oxygen and
temperatures failing to meet the analysis criteria might indicate that high temperatures
could account for the low dissolved oxygen results. Thus, the aggregated tables could
provide a more refined depiction of the possible causes of degraded water quality at a
given site.
The ranking results aggregation tables were produced from the 35% parameter
ranking and geometric mean standard, exceptions included, from all the sample sites in
WRIA 11 and WRIA 13. These sample sites were in a variety of habitats including
streams, creeks and rivers (lotic), and lakes, ponds and reservoirs (lentic), and included
sample site locations at tide gates in WRIA 11. Because the primary purpose of this study
was to identify degraded lotic sites, the sample sites were stratified into three groups;
lotic, lentic, and tide gates. The lentic group was set apart as an independent variable that
could have a possible influence on water quality. Tide gates are typically located at or
near the mouth of a creek or stream where tidal influence is present. That places tide
gates at the end of a lotic system as it merges with an estuary. In this study the tide gates
were treated as an independent variable to lotic system water quality.
The above mentioned water quality data analysis process revealed some
interesting observations about water quality for both lentic systems and tide gates, as well
as the intended analysis for lotic systems. These findings will be discussed in detail in the
Results and Conclusions section in Chapter Two. The tables created for the lotic system
16
ranking analysis and the aggregation of ranking result tables are joined to GIS features to
graphically represent the data.
GIS Spatial Data and Join Tables:
Spatial data is information that has a reference to a geographic coordinate. This
information is used to create features on a map like points, lines and polygons that
represent features in the real world. These features can contain information about the
feature like ID, location, shape, length and area. These are stored in a geographic
information system (GIS) in a tabular form. These attribute tables in GIS can be
expanded by providing more information to the system. A whole database of information
can be created by adding fields within the GIS software to populate the attribute tables
with more feature data.
Another way to populate GIS attribute tables is by the use of joins or relates. The
GIS join tables are external tabular data appended to the feature attribute table by a
common field. The appended data becomes a permanent part of the feature attribute table
as long as the join is not removed. Relate tables work similarly to join tables in that they
need a common field to provide information to the feature attribute table. The difference
is that relate tables are a lookup source of information and are not appended to the feature
attribute table.
For this study, the data came from the Washington Department of Ecology’s EIM
database (Ecy-3, 2011). From the data extracted for WRIA11 and WRIA 13, two tables
were created externally from GIS, in Microsoft Excel. One table was used for location
information and to generate GIS spatial features for the water quality monitoring sites.
The other table was made up of the sample results and was used for the tabular data
analysis. When both steps were completed, the analysis results table was joined to the
GIS location feature data so spatial analysis on the sampling sties could be performed.
The external tables had two primary functions for this study. First, to show the location of
sample study sites analyzed and second, to provide a colored tier ranking for the percentage
of time the data set did not meet the criteria for each of the parameters. The tables for the
aggregation of ranking results were used to create a set of GIS features to graphically
represent those sites that were the most degraded across multiple parameters. Some of the
17
features created for the most degraded sites were used as end points for defining flow basins
and to calculate area of influence from land use. The spatial analysis for flow basins and land
use is explained in Chapter Four.
18
Chapter Two:
Identification of Degraded Habitat in Lotic Freshwater Systems
Results and Conclusions:
The following tables, in the section on Results for WRIA 11, show the results of
analysis performed on water quality data for Dissolved Oxygen (DO), Fecal Coliform
(FC), Turbidity (TURB) and Temperature (TEMP) in the Nisqually Basin (WRIA 11).
For the pH parameter in WRIA 11, none of the study sample sites had a percentile rank
above 35%. The following tables, in the section on Results for WRIA 13, show the
results of analysis for Dissolved Oxygen (DO), Fecal Coliform (FC), pH, Turbidity
(TURB) and Temperature (TEMP) in the Deschutes Basin (WRIA 13). The TEMP data
were subject to a different criteria method than the other parameters. The analysis criteria
for DO, FC, pH and TURB were based on the high and low water quality standards (See
Table 1.2) and the criteria for TEMP were based on seasonal temperature requirements
for salmonids (See Table 1.3). The results in the tables below were derived from a series
of criteria designed to cull out sample sites showing degraded water quality. The first step
defined the percentage of times the study site samples failed to meet the criteria
established for each parameter. Those results were organized in descending order by
percent. The second step took all records that had a percentile criteria failure equal to or
greater than 35% of the samples with the geometric mean of that dataset also not meeting
each parameter’s criteria. Example 1: Criteria = FC > 50 cfu. Records > 50 cfu = 50%
and Geometric Mean = 66.99. Both are greater than the criteria. This is referred to as
the 35% plus geometric mean standard. A few records equal to or above 35% and where
the geometric mean did meet the parameter analysis criteria were kept in this list for
various reasons. Example 2: Criteria = FC > 50 cfu. Records > 50 cfu = 50% and
Geometric Mean = 27.31. Geometric Mean not greater than the criteria. These records
were considered sites of interest and will be explained for each parameter where they
occur. Usually analysis records like the second example were due to high intermittent
failures to meet the criteria.
19
Results for WRIA 11:
The following tables were used in the Nisqually Basin analysis study. The tables
in this section show results from the series parameter analysis criteria to establish those
sites that show the highest potential for degraded aquatic habitat.
DO Results: The analysis criterion for this data set was < 8 mg/L. Of 59 total
sites where DO samples were taken, the results shown in Table 2.1 are the 25 sites that
met the 35% plus geometric mean standard described above. For this parameter, the
percentile range was from 40% to 100% of the time not meeting the analysis criteria. The
minimum geometric mean is 1.28 mg/L for the Site ID – TG13L (Record #4), and the
maximum is 7.96 mg/L for Site ID – MEDO.O (Record # 15).
Table 2.1: Results for degraded dissolved oxygen levels (geo mean < 8mg/L) WRIA 11.
Results for DO: Twenty-five sites met the 35% plus geometric mean standard.
#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Site Info
Site_ID
HARPI11
WHIPI11
MED0.1
TG13L
TG14L
TG8L
TG9L
ST#TH11
TG10L
TG15L
TG12L
MC5.8
TG9W
MC4.3
MED0.0
TG4L
MC4.7
MC5.4
TG5L
MC3.1
MC3.7
MC4.3T
OHOPCR(RM6.3)
CLETH11
TG2L
DO - Analysis Results - WRIA 11
QA Records < 8mg/L < 9.5mg/L % 8mg/L
% 9.5mg/L Min
Max
Geo Mean
L-5
6
6
6
100.00%
100.00%
2.14
5.37
3.60
L-5
6
6
6
100.00%
100.00%
2.99
4.58
3.84
L-4
9
9
9
100.00%
100.00%
0.68
6.76
3.00
L-4
10
10
10
100.00%
100.00%
0.39
2.66
1.82
L-4
8
8
8
100.00%
100.00%
2.10
6.20
3.99
L-4
5
5
5
100.00%
100.00%
4.92
7.04
5.99
L-4
9
9
9
100.00%
100.00%
1.15
7.01
3.90
L-5
9
8
9
88.89%
100.00%
2.46
8.90
4.33
L-4
9
8
9
88.89%
100.00%
3.30
8.25
5.48
L-4
9
8
9
88.89%
100.00%
3.07
9.02
5.07
L-4
7
6
7
85.71%
100.00%
2.22
8.71
5.56
L-4
11
9
11
81.82%
100.00%
5.80
8.35
7.26
L-4
7
5
5
71.43%
71.43%
2.90
13.05
6.15
L-4
10
7
10
70.00%
100.00%
5.91
9.27
7.34
L-4
6
4
5
66.67%
83.33%
6.64
10.78
7.96
L-4
9
6
6
66.67%
66.67%
4.74
13.10
7.82
L-4
10
6
10
60.00%
100.00%
6.46
8.96
7.67
L-4
10
6
9
60.00%
90.00%
6.05
9.57
7.71
L-4
10
6
9
60.00%
90.00%
4.13
77.00
7.75
L-4
9
5
8
55.56%
88.89%
5.93
10.39
7.77
L-4
11
6
11
54.55%
100.00%
5.60
8.46
7.46
L-4
11
5
10
45.45%
90.91%
5.98
9.50
7.92
L-4
52
23
29
44.23%
55.77%
2.50
13.10
7.91
L-5
5
2
5
40.00%
100.00%
2.70
9.02
6.71
L-4
10
4
5
40.00%
50.00%
1.46
13.90
7.31
20
FC 50 cfu Results: The analysis criterion for this dataset was > 50 cfu/100 ml.
Out of 52 total sites where FC samples were taken, the results shown in Table 2.2 are the
16 sites that met the 35% plus geometric mean standard. For this parameter, the
percentile range was from 37.5% to 80% of the times not meeting the analysis criteria.
The minimum geometric mean is 50.73 cfu/100 ml for Site ID – MC3.1 (Record # 11),
and the maximum is 97.26 cfu/100 ml for Site ID – SC12CS4 (Record # 1).
Table 2.2: Results for degraded fecal coliform levels (geo mean > 50 cfu) WRIA 11.
Results for FC 50 cfu: Sixteen sites met the 35% plus geometric mean standard.
Site Info
# Site_ID
1 SC12 CS4
2 MC 3.1
3 LIITLEMASHELRV
4 OHOPCR(RM0.1)
5 YELMCR(RM0.1)
6 RSET
7 OHOP2.0
8 OHOP3.3
9 OHOP6.0
10 MED0.0
11 MC3.1
12 OHOP2.2D
13 YELMCR(RM0.1)
14 N FLOW
15 OHOP6.2T
16 TG9W
FC 50 cfu - Analysis Results - WRIA 11
QA Records > 50cfu > 100cfu % > 50cfu % > 100cfu Min
Max
L-4
5
4
3 80.00%
60.00%
2
L-4
8
6
1 75.00%
12.50%
35
L-4
23
14
10 60.87%
43.48%
5
L-4
23
14
10 60.87%
43.48%
5
L-4
12
7
5 58.33%
41.67%
10
L-4
12
7
2 58.33%
16.67%
23
L-4
7
4
3 57.14%
42.86%
11
L-4
7
4
3 57.14%
42.86%
8
L-4
7
4
3 57.14%
42.86%
9
L-4
7
4
2 57.14%
28.57%
14
L-4
9
5
2 55.56%
22.22%
17
L-4
6
3
3 50.00%
50.00%
9
L-4
24
11
5 45.83%
20.83%
5
L-4
7
3
3 42.86%
42.86%
5
L-4
7
3
3 42.86%
42.86%
6
L-4
8
3
3 37.50%
37.50%
5
930
120
2650
700
385
130
330
1400
610
410
225
610
4000
5450
270
1700
Geo Mean
97.26
62.22
80.16
67.24
60.40
53.69
67.31
69.20
76.92
60.00
50.73
66.99
54.69
92.44
57.35
64.63
FC 100 cfu Results: The analysis criterion for this data setwas > 100 cfu/100 ml.
Of 52 total sites where FC samples were taken, the results shown in Table 2.3 are the
14 sites that met the 35% plus geometric mean standard. For this parameter, the
percentile range was from 46.43% to 94.75% of the times not meeting the analysis
criteria. The minimum geometric mean is 104.75 cfu/100 ml for Site ID – OHOP
CR(RM0.1) (Record # 12), and the maximum is 1176.17 cfu/100 ml for Site ID – S PIPE
(Record # 1). For Record # 1 the maximum parameter result number is very high at
1,090,000 cfu/100 ml (highlighted in blue with red numbers). It is possible that this is an
outlier in the data. With this one record eliminated from analysis, the Site ID – S Pipe
would still rank very high in this study. Without the outlier, the S PIPE site would rank
21
94.44% for > 100 cfu/100 ml and the maximum result is 62,000cfu/100 ml. The adjusted
geometric mean for this site would be 804.71cfu, which is still the highest for this set of
records.
Table 2.3: Results for degraded fecal coliform levels (geo mean > 100 cfu) WRIA 11.
Results for FC 100 cfu: Fourteen sites met the 35% plus geometric mean standard.
Site Info
# Site_ID
1 S PIPE
2 COMBINED 1
3 OHOPCR(RM0.1)
4 9 GLACIS RD NE
5 WASH
6 H3 STORM
7 1 D'MILLUHR DR
8 12A SCENIC DR
9 12B SCENIC DR
10 OHOPCR(RM3.3)
11 OHOPCR(RM2.0)
12 OHOPCR(RM0.1)
13 MUCKCR(RM6.2)
14 MCALLISTER3.1
FC 100 cfu - Analysis Results - WRIA 11
QA Records > 50cfu > 100cfu % > 50cfu % > 100cfu Min
Max
Geo Mean
L-4
19
18
18 94.74%
94.74%
15 1090000 1176.17
L-4
6
5
5 83.33%
83.33%
43
1000
484.07
L-4
13
12
9 92.31%
69.23%
40
6510
223.02
L-4
9
7
6 77.78%
66.67%
10
8700
208.82
L-4
5
5
3 100.00%
60.00%
64
245
111.29
L-4
5
4
3 80.00%
60.00%
15
15500
362.39
L-4
5
3
3 60.00%
60.00%
5
5300
126.63
L-4
5
3
3 60.00%
60.00%
23
19000
244.96
L-4
5
3
3 60.00%
60.00%
5
2500
117.53
L-4
22
19
13 86.36%
59.09%
5
2915
196.33
L-4
21
17
12 80.95%
57.14%
10
2400
189.08
L-4
49
32
27 65.31%
55.10%
5
1535
104.75
L-4
11
7
6 63.64%
54.55%
10
4775
116.39
L-4
28
23
13 82.14%
46.43%
15
2500
133.07
Turbidity Results: The analysis criterion for this data set was > 7.8 NTU (5 NTU
above background). Of 34 total sites where TURB samples were taken, the results shown
in Table 2.4 show the 5 sites that met the 35% plus geometric mean standard. There are
also 6 sites that were 35% or greater that didn’t meet the geometric mean standard, but
were of significant interest because of the maximum NTU numbers (highlighted in blue).
For this parameter, the percentile range was from 36.36% to 56.25% of the times not
meeting the analysis criteria. The minimum geometric mean is 4.72 NTU for Site ID –
NISQUALLY (3.7) (Record # 10), and the maximum is 9.18 NTU for Site ID – 11A070
(Record # 6). The maximum result NTU numbers could be the result of mass wasting or
erosion during storm events. Precipitation data would need to be added to the analysis to
make that determination.
22
Table 2.4: Results For Degraded Turbidity Levels (Geo Mean > 7.8 NTU) WRIA 11.
Results for Turbidity: Five sites met the 35% plus geometric mean standard and 6 sites were
added because of the high maximum NTU numbers that met the 35% or greater, but did not meet
the geometric mean standard (blue highlight).
#
1
2
3
4
5
6
7
8
9
10
11
Site Info
Site_ID
NISQUALLY(39.7)
11A090
LIITLEMASHELRV
OHOPCR(RM0.1)
OHOPCR(RM2.0)
11A070E
11A070D
11A080D
LYNCH CREEK
NISQUALLY(3.7)
OHOPCR(RM3.3)
Turbidity - Analysis Results - WRIA 11
QA Records > 7.8_NTU > 12.8_NTU % > 7.8 NTU % > 12.8 NTU Min
L4
16
9
5
56.25%
31.25%
L5
18
10
4
55.56%
22.22%
L4
35
18
12
51.43%
34.29%
L4
68
34
16
50.00%
23.53%
L4
22
11
4
50.00%
18.18%
L5
120
59
38
49.17%
31.67%
L5
132
64
46
48.48%
34.85%
L5
30
12
9
40.00%
30.00%
L4
36
14
7
38.89%
19.44%
L4
16
6
2
37.50%
12.50%
L4
22
8
5
36.36%
22.73%
Max
2
1.5
0.7
1.3
3.2
1.5
1.1
1
1.2
1.1
2.8
60
410
165.2
248
22.1
720
850
205
85.2
50
28.5
Geo Mean
7.43
8.22
7.48
9.15
7.88
9.18
8.37
7.07
6.24
4.72
7.53
Temperature Results: The temperature analysis was broken down into four
seasonal and inhabitation temperature requirements for a variety of species. The four
categories were segmented from the total temperature records for each monitoring site by
date range and temperature requirements for suitable species habitation. The first
category (Temp 1) has a monitoring range for an entire annual cycle for char rearing and
foraging. The temperature criteria for this category is 12○C (54.6○F) and the results of
analysis are shown in Table 2.5 below. The second category (Temp 2) has a date range
from September 16th to June 14th for fall, winter and spring (F/W/Sp) salmon and trout
○
○
spawning. The temperature criteria for this category is 13 C (55.4 F) and the results of
analysis are shown in Table 2.6 below. The third category (Temp 3) has a date range
from June 15th – September 15th for core summer salmonid habitat. The temperature
○
○
criteria for this category is 16 C (60.8 F) and the results of analysis are shown in Table
2.7 below. The fourth category (Temp 4) has a date range from September 16th –
December 22nd for fall char spawning. The temperature criteria for this category is 9○C
(48.2○F) and the results of analysis are shown in Table 2.8 below. All of these
temperature tables show the results based on the 35% plus geometric mean standard.
Information of date ranges and temperature criteria are from the Department of Ecology’s
23
Water Quality Standards for Surface Water of the State of Washington, Chapter 173201A WAC (WA Ecology, 2006).
Analysis for the first category (Temp 1) all year char rearing took continuous
annual water quality temperature data with an analysis criteria of 12○C (54.6○F) for
WRIA 11. This resulted in 21 records that met the 35% plus geometric mean standard.
The percentage of time that the data did not meet the analysis criteria ranged from
46.67% to 100% with a geometric mean range from 12.16 ○C to 19.94○C. The results are
listed in Table 2.5 below.
Table 2.5: All Year Temperature Results for the Char Rearing Category in WRIA 11.
Results for Temp 1: Twenty-one records met the 35% plus geometric mean standard.
Site_ID
CLETH11
SILVERLAKE
TANWAXLAKE
RAPJOHNLAKE
OHOPLAKE
RAPPI11
OHOPI11
OHOPCR(RM6.3)
MID-LAKEDRAIN
RSWT
TG13L
TANPI11
OHOPLAKESTA2
MINERALLAKE
WHIPI11
CLEARLAKE
OHOPLAKESTA3
MC4.4TLBU
OHOPLAKESTA1
TG9W
MUCKCR(RM6.2)
Site Info
QA Category
L5 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
L5 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
L5 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
L4 Char_Rearing
Period
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
All_Year
Temp 1 - Analysis Results - WRIA 11
Records > 12 deg C % > 12 deg C Min Max Geo Mean
47
47
100.00% 14.6 23.1
19.41
23
23
100.00% 13.5 25.8
19.17
21
21
100.00% 12.7 26.1
19.94
21
20
95.24%
12 25.8
17.73
19
18
94.74% 11.4 24.5
18.77
10
9
90.00%
12 20.2
15.85
15
12
80.00% 10.1 23.1
15.70
51
38
74.51%
6.2 27.1
15.19
35
25
71.43%
4.4 16.2
12.16
7
5
71.43% 6.98 19.2
13.17
10
7
70.00% 11.3 17.04
13.14
16
11
68.75%
8.9 23.6
14.84
134
92
68.66%
5.5 25.2
13.47
25
17
68.00%
8.3 23.8
15.29
42
27
64.29%
8.4 22.5
14.40
24
15
62.50%
6.9 24.6
14.69
55
34
61.82%
5 25.9
12.90
5
3
60.00% 9.16 16.7
12.83
73
43
58.90%
5.8 26.1
12.46
6
3
50.00% 8.24 20.01
12.16
15
7
46.67%
4.5 22.2
12.19
Analysis for the second category (Temp 2) for salmon and trout spawning took
ongoing water quality temperature data that had a date range from September 16th to June
14th. This covered the fall, winter and spring (F/W/Sp) salmon and trout spawning
○
○
habitation period with an analysis criteria of 13 C (55.4 F) for WRIA 11. This resulted
in 5 records that met the 35% plus geometric mean standard. The percentage of time that
24
the data did not meet the analysis criteria ranged from 52.17% to 100% with a geometric
mean range from 13.03 ○C to 18.40○C. The results are listed in Table 2.6 below.
Table 2.6: F/W/Sp Temperature Results for Salmon & Trout Category WRIA 11.
Results for Temp 2: Five records met the 35% plus geometric mean standard.
Site Info
QA Category
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
Site_ID
CLETH11
OHOPI11
RAPPI11
TANPI11
WHIPI11
Temp 2 - Analysis Results - WRIA 11
Period Records > 13 deg C % > 13 deg C Min Max Geo Mean
F/W/Sp
32
32
100.00% 14.6
21
18.40
F/W/Sp
8
6
75.00% 10.1 16.2
13.93
F/W/Sp
6
4
66.67%
12 19.7
15.14
F/W/Sp
9
5
55.56%
8.9 16.5
13.19
F/W/Sp
23
12
52.17%
8.4 20.1
13.03
The analysis for the third category (Temp 3) for core salmonid habitat, took
ongoing water quality temperature data that had a date range from June 15th to September
15th. This covered the core summer salmonid habitation period with an analysis criteria of
○
○
16 C (60.8 F) for WRIA 11. This resulted in 16 records that met the 35% plus geometric
mean standard. Four sites in this analysis were included that did not meet the geometric
mean standard. Site ID – 11A070D had a high percentage failure of 84.85% not meeting
the criteria (Blue Highlight). Three other sites (Site IDs: MINERALLAKE,
CLEARLAKE and HARPI11) had a percentage criteria failure range from 40% to 56%
and maximum temperature records from 23.8○C to 24.6○C (blue highlight). The
percentage of time that the data in this table did not meet the analysis criteria ranged from
40% to 100% with a geometric mean range from 13.57 ○C to 22.01○C. The results are
listed in Table 2.7 below.
25
Table 2.7: Summer Temperature Results for the Core Salmonid Category in WRIA 11.
Results for Temp 3: Sixteen records met the 35% plus geometric mean standard. One record
had a high criteria failure rate and three records failed the criteria analysis with high
maximum temperatures (blue highlight).
Site_ID
CLETH11
OHOPCR(RM6.3)
OHOPLAKESTA3
OHOPCR(RM6.0)
11A070D
TANWAXLAKE
MASHELRV(RM6.0)
MUCKCR(RM6.2)
OHOPLAKE
SILVERLAKE
OHOPLAKESTA1
OHOPI11
RAPJOHNLAKE
OHOPLAKESTA2
OHOPCR(RM0.1)
WHIPI11
TANPI11
MINERALLAKE
CLEARLAKE
HARPI11
Site Info
QA Category
L5 Core Salmonid
L4 Core Salmonid
L4 Core Salmonid
L4 Core Salmonid
L5 Core Salmonid
L4 Core Salmonid
L4 Core Salmonid
L4 Core Salmonid
L4 Core Salmonid
L4 Core Salmonid
L4 Core Salmonid
L5 Core Salmonid
L4 Core Salmonid
L4 Core Salmonid
L4 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L4 Core Salmonid
L4 Core Salmonid
L5 Core Salmonid
Temp 3 - Analysis Results - WRIA 11
Period Records > 16 deg C % > 16 deg C Min Max Geo Mean
Summer
15
15
100.00% 20.1 23.1
21.75
Summer
21
21
100.00% 16.5 27.1
22.01
Summer
21
19
90.48% 13.6 25.9
19.31
Summer
7
6
85.71% 14.8 24.4
18.91
Summer
33
28
84.85%
9.3
17
13.57
Summer
21
17
80.95% 12.7 26.1
19.94
Summer
5
4
80.00% 14.3 20.5
17.77
Summer
5
4
80.00%
15 22.2
18.45
Summer
19
15
78.95% 11.4 24.5
18.77
Summer
23
18
78.26% 13.5 25.8
19.17
Summer
28
21
75.00% 11.3 26.1
17.93
Summer
7
5
71.43%
12 23.1
17.99
Summer
21
14
66.67%
12 25.8
17.73
Summer
56
37
66.07%
11 25.2
17.21
Summer
17
10
58.82% 12.6 21.4
16.23
Summer
19
11
57.89%
9.9 22.5
16.25
Summer
7
4
57.14%
11 23.6
17.28
Summer
25
14
56.00%
8.3 23.8
15.29
Summer
24
12
50.00%
6.9 24.6
14.69
Summer
40
16
40.00%
6.4 24.06
12.34
The analysis for the fourth category (Temp 4) for char spawning, took ongoing
water quality temperature data that had a date range from September 16th to December
22nd. This covered the fall char spawning habitation period with an analysis criteria of
9○C (48.2○F) for WRIA 11. This resulted in 13 records that met the 35% plus geometric
mean standard. Two sites in this analysis were included that did not meet the geometric
mean standard. Site IDs OHOPCR(RM9.9) and OHOPCR(RM0.1) had a 44.44% to
46.15% percentage failure not meeting the criteria and high maximum temperatures from
13.3○C to 15○C (blue highlight). The percentage of time that the data in this table did not
meet the analysis criteria ranged from 40% to 100% with a geometric mean range from
7.64 ○C to 19.14○C. The results are listed in Table 2.8 below.
26
Table 2.8: Fall Temperature Results for the Char Spawning Category in WRIA 11.
Results for Temp 4: Thirteen records met the 35% plus geometric mean standard. Two records
had high maximum temperatures (blue highlight).
Site_ID
CLETH11
MC4.3
MC5.4
MC5.8
OHOPLAKESTA2
OHOPCR(RM6.3)
HARTSLAKE
11A070E
MC4.7
OHOPCR(RM9.9)
OHOPCR(RM0.1)
OHOPLAKESTA3
MC3.7
OHOPLAKESTA1
MC4.5
Site Info
QA Category
L5 Char Spawning
L4 Char Spawning
L4 Char Spawning
L4 Char Spawning
L4 Char Spawning
L4 Char Spawning
L4 Char Spawning
L5 Char Spawning
L4 Char Spawning
L4 Char Spawning
L4 Char Spawning
L4 Char Spawning
L4 Char Spawning
L4 Char Spawning
L4 Char Spawning
Period
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Temp 4 - Analysis Results - WRIA 11
Records > 9 deg C % > 9 deg C Min Max Geo Mean
16
16
100.00%
19 19.5
19.14
6
6
100.00% 9.01 9.73
9.36
6
6
100.00% 9.02
9.8
9.40
5
5
100.00% 9.06 9.77
9.34
41
29
70.73%
6.2
18
12.37
12
8
66.67%
6.7
18
12.04
8
5
62.50%
6.9 20.8
11.93
39
22
56.41%
3.6 16.4
9.18
6
3
50.00% 8.95 9.75
9.34
13
6
46.15%
4.2 13.3
8.69
18
8
44.44%
2.8
15
7.64
16
7
43.75%
6 18.3
10.46
7
3
42.86% 8.99 9.75
9.31
21
9
42.86%
6.2
18
10.52
5
2
40.00% 8.94 9.72
9.24
Results for WRIA 13:
The following tables show the results of analysis performed on water quality data
for Dissolved Oxygen (DO), Fecal Coliform (FC), pH, Turbidity (TURB) and
Temperature (TEMP) in the Deschutes Basin (WRIA 13). The results in the following
tables were derived from a series of criteria designed to cull out sample sites showing
degraded water quality. The study sites in the following tables are a list of those meeting
the 35% plus geometric mean standard. These tables were used in the Deschutes Basin
analysis study.
DO Results: The analysis criterion for this data set was < 8 mg/L. Of 49 total
sites where DO samples were taken (See Table 2.9), there were 13 sites that met the 35%
plus geometric mean standard. Included in this table are two sites, Site ID – PATTH11
(Record #14) and Site ID – WARTH11 (Record #15), that did not meet the geometric
mean standard, but were above the 35 percentile. One other site of interest included was
WD5.1 (Record # 16), which did meet the geometric mean standard, but was below the
35 percentile standard. These three sites are of interest because they failed to meet the
dissolved oxygen analysis criteria 90% or more of the time for the high water quality
27
standard of 9.5mg/L. For this parameter, the percentile range was from 25% to 100% of
the times not meeting the analysis criteria. The minimum geometric mean is 4.99 mg/L
for the Site ID – SPDITCH2 (Record #2), and the maximum is 8.24 mg/L for Site ID –
WARTH11 (Record # 15).
Table 2.9: Results for Degraded Dissolved Oxygen Levels (Geo Mean < 8mg/L) WRIA 13.
Results for DO: Fourteen sites met the 35% plus geometric mean standard. Three sites had low
DO records and failed the criteria 90% to 100% for high water quality standards of 9.5mg/L
(Blue Highlight).
#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Site Info
Site_ID
BLATH11
SPDITCH2
WD6.8
WD6.9
CHATH11
OFFTH11
WD6.2
LAWTH11
WL3.4
PATTH21
SPDITCH1
HICTH11
WL1.9T
PATTH11
WARTH11
WD5.1
QA Records < 8mgL
L5
6
L5
6
L4
5
L4
12
L5
7
L5
6
L4
12
L5
6
L4
13
L5
5
L5
5
L5
7
L4
11
L5
10
L5
15
L4
12
DO - Analysis Results - WRIA 13
< 9.5mgL % 8mgL
% 9.5mgL
Min Max Geo Mean
6
6
100.00%
100.00% 6.22 7.99
6.95
6
6
100.00%
100.00% 3.19 7.44
4.99
5
5
100.00%
100.00% 5.52 7.55
6.76
11
12
91.67%
100.00% 3.51 8.20
5.48
6
7
85.71%
100.00% 4.20 8.23
6.22
5
5
83.33%
83.33% 6.96 9.75
7.69
10
12
83.33%
100.00% 2.30 8.26
5.02
4
6
66.67%
100.00% 5.73 8.82
7.42
8
11
61.54%
84.62% 5.41 9.90
7.37
3
4
60.00%
80.00% 6.00 9.74
7.66
3
5
60.00%
100.00% 2.74 9.30
5.73
4
7
57.14%
100.00% 6.38 8.39
7.47
5
9
45.45%
81.82% 1.85 10.90
6.84
4
9
40.00%
90.00% 6.45 9.74
8.08
6
14
40.00%
93.33% 7.60 9.74
8.24
3
12
25.00%
100.00% 2.76 9.36
7.42
FC 50 cfu Results: The analysis criterion for this dataset was > 50 cfu/100 ml.
Of 83 total sites where the FC samples were taken, the results shown in Table 2.10 are
the 14 sites that met the 35% plus geometric mean standard. Included in this table are two
sites, Site ID – SWO Stormwater (Record #15) and Site ID – WL3.1T (Record #16), that
did not meet the geometric mean standard, but were above the 35th percentile. These two
sites were of interest because of their high cfu/100 ml count. For this parameter, the
percentile range was from 36.36% to 80% of the times not meeting the analysis criteria.
The minimum geometric mean is 34.79 cfu/100 ml for Site ID – SWO Stormwater
(Record #15), and the maximum is 99.41 cfu/100 ml for Site ID – WL0.2 (Record # 3).
28
Table 2.10: Results for Degraded Fecal Coliform Levels (geo mean > 50 cfu) WRIA 13.
Results for FC 50 cfu: Fourteen sites that met the 35% plus geometric mean standard. Two sites
had high maximum cfu/100ml numbers (blue highlight).
#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Site Info
Site_ID
WOODLANDCR7
WD0.0
WL0.2
MY0.1
WOODLANDCR6
WL2.6
WL1.95T
WD6.9
WL3.45
CC0.0
WL2.9
WL2.25T
WL3.1
CC0.2
SWO STORMWATER
WL3.1T
QA Records > 50cfu
L4
5
L4
9
L4
12
L4
8
L4
6
L4
10
L4
7
L4
9
L4
11
L4
11
L4
11
L4
10
L4
11
L4
7
L4
5
L4
11
FC >50 CFU - Analysis Results - WRIA 13
> 100cfu
% > 50cfu % > 100cfu Min Max Geo Mean
4
2
80.00%
40.00%
5
163
51.81
7
4
77.78%
44.44%
7
445
78.46
9
7
75.00%
58.33%
6
740
99.41
6
4
75.00%
50.00%
1 1200
90.89
4
1
66.67%
16.67%
22
150
63.98
6
4
60.00%
40.00%
10
650
79.12
4
3
57.14%
42.86%
9
380
72.93
5
2
55.56%
22.22%
17
245
59.32
6
4
54.55%
36.36%
6 4000
74.05
6
2
54.55%
18.18%
11
285
54.03
6
2
54.55%
18.18%
8
735
59.92
5
5
50.00%
50.00%
3 2900
70.05
5
2
45.45%
18.18%
19
880
56.43
3
1
42.86%
14.29%
28
260
54.09
2
2
40.00%
40.00%
5 1700
34.79
4
2
36.36%
18.18%
1 63000
40.94
FC 100 cfu Results: The analysis criterion for this data set was > 100 cfu/100 ml.
Out of 83 total sites where FC samples were taken, the results shown in Table 2.11 are
the 43 sites that met the 35% plus geometric mean standard. For this parameter, the
percentile range was from 45.45% to 100% of the times not meeting the analysis criteria.
The minimum geometric mean is 106.09 cfu/100 ml for Site ID – SPDITCH2 (Record #
43), and the maximum is 1184.21 cfu/100 ml for Site ID – MANHOLESOUTH (Record
# 12).
29
Table 2.11: Results for Degraded Fecal Coliform Levels (geo mean > 100 cfu) WRIA 13.
Results FC 100 cfu: Forty-three sites that met the 35% plus geometric mean standard.
#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
Site Info
Site_ID
13-IND-BOUL-TC
13-IND-CENT
WL2.6SW
WOODLANDCR1
WOODLANDCR2
13-IND-QUIN
14MCLANEMC1
14MCLANEMC2
DB1.0
WOODLANDCR3
W DRAIN
MANHOLESOUTH
MARTIN WAY
WOODLANDCR4
14MCLANEUN1
WL1.0
WOODLANDCR5
14MCLANEMC2.5
WD2.9
WD3.4
WD5.1
WL1.1T
WL1.2T
FLEMING CR
SL0.8
CC0.6
CC0.4
CREEKB
DB0.1
14MCLANEDV1
WD6.8
DBTRIB0.73
14MCLANESW1
DOBBS CREEK
DB1.0
WL3.8T
DB0.1
DB0.731
DOBBSCRK
SL0.1
TANGLEWILDE
WL1.6
SPDITCH2
QA Records > 50cfu
L4
11
L4
10
L4
6
L4
4
L4
6
L4
11
L4
11
L4
11
L5
9
L4
8
L5
5
L4
5
L4
5
L4
8
L4
10
L4
10
L4
7
L4
11
L4
9
L4
9
L4
9
L4
9
L4
9
L4
7
L4
7
L4
6
L4
6
L4
6
L4
11
L4
11
L4
5
L5
10
L4
10
L4
5
L4
8
L4
7
L5
10
L5
10
L5
10
L4
10
L4
9
L4
10
L5
11
FC >100CFU - Analysis Results - WRIA 13
> 100cfu
% > 50cfu % > 100cfu Min Max Geo Mean
11
11
100.00%
100.00%
115 2200
373.47
10
10
100.00%
100.00%
100 1680
370.04
6
6
100.00%
100.00%
280 3100
597.40
4
4
100.00%
100.00%
452 3450
797.89
6
6
100.00%
100.00%
125 1900
390.25
11
10
100.00%
90.91%
90 1570
436.33
11
10
100.00%
90.91%
90
455
232.50
11
10
100.00%
90.91%
60
455
208.48
9
8
100.00%
88.89%
78 5400
512.20
8
7
100.00%
87.50%
60 5200
491.49
5
4
100.00%
80.00%
57 7570
303.89
5
4
100.00%
80.00%
90 8200
1184.21
5
4
100.00%
80.00%
55 4300
377.58
8
6
100.00%
75.00%
62 1325
284.01
10
7
100.00%
70.00%
73
298
130.66
10
6
100.00%
60.00%
58
300
139.01
7
3
100.00%
42.86%
58 6350
224.62
10
8
90.91%
72.73%
30
260
142.85
8
6
88.89%
66.67%
27
480
127.44
8
6
88.89%
66.67%
12
610
129.10
8
6
88.89%
66.67%
22
515
149.06
8
6
88.89%
66.67%
33 5200
252.52
8
6
88.89%
66.67%
38
930
207.43
6
5
85.71%
71.43%
5
980
145.19
6
4
85.71%
57.14%
20
670
110.35
5
5
83.33%
83.33%
6 1800
224.89
5
4
83.33%
66.67%
17
530
136.01
5
3
83.33%
50.00%
25 2900
207.02
9
8
81.82%
72.73%
18 6000
246.09
9
7
81.82%
63.64%
30 1500
145.57
4
4
80.00%
80.00%
20
550
162.65
8
7
80.00%
70.00%
7
700
141.09
8
5
80.00%
50.00%
10 7000
110.55
4
2
80.00%
40.00%
25
680
124.36
6
5
75.00%
62.50%
2 3300
142.95
5
5
71.43%
71.43%
5 7800
317.16
7
7
70.00%
70.00%
19 2900
131.14
7
6
70.00%
60.00%
31 5300
143.51
7
6
70.00%
60.00%
35 4900
157.62
7
5
70.00%
50.00%
4
660
106.69
6
6
66.67%
66.67%
5 6400
219.52
6
6
60.00%
60.00%
10 1100
108.97
6
5
54.55%
45.45%
2 5800
106.09
30
Turbidity Results: The analysis criterion for this data set was > 6.8 NTU (5 NTU
above background). Of 14 total sites where TURB samples were taken, the results shown
in Table 2.12 show the 5 sites that met the 35% plus geometric mean standard. For this
parameter, the percentile range was from 75% to 100% of the times not meeting the
analysis criteria. The minimum geometric mean is 11.39 NTU for Site ID – CREEKA
(Record # 5), and the maximum is 58.08 NTU for Site ID – W DRAIN (Record # 2).
Also noteworthy in Table 2.12 is that four of the sites (Records 1 through 4) had all
minimum NTU results above the analysis criteria of > 6.8 NTU (shown in dark blue).
The first two sites (Records #1 & #2) also show a very high maximum NTU count
(shown in light blue). The Site ID – CREEKB (Record # 1) has a minimum of 29.75
NTU and a maximum of 222 NTU. Water quality samples for the CREEKB site were
taken over an eleven month period for 1/27/2000 to 11/15/2001. The Department of
Ecology’s EIM (Ecy-3, 2011) provided no further data for turbidity at this location after
11/15/2001. To understand the cause for high NTU counts at this site, it would be
necessary to continue water quality sampling and field survey of the flow basin influence
at this site to establish the source of disturbance or cause of erosion. The other maximum
result NTU numbers could also result from mass wasting or erosion during storm events.
Precipitation data would need to be added to the analysis to make that determination.
Table 2.12: Results for Degraded Turbidity Levels (geo mean > 6.8 NTU) WRIA 13.
Five sites met the 35% plus geometric mean standard. Four site with very high minimum NTU
numbers (dark blue highlight), and two of those sites with very high maximum NTU numbers
(light blue highlight).
#
1
2
3
4
5
Site Info
Site_ID
CREEKB
W DRAIN
FLEMING CR
SWO STORMWATER
CREEKA
Turbidity - Analysis Results - WRIA 13
QA Records > 6.8_NTU > 11.8_NTU % > 6.8 NTU % > 11.8 NTU Min Max Geo Mean
L4
7
7
7
100.00%
100.00% 29.75
222
58.00
L5
7
7
6
100.00%
85.71%
8
200
58.08
L4
7
7
6
100.00%
85.71%
11
68
26.77
L4
6
6
4
100.00%
66.67%
10
79
21.27
L4
8
6
4
75.00%
50.00%
5
18
11.39
pH Results: The analysis criterion for this data set was < 6.5 or > 8.5. Of 48 total
sites where pH samples were taken, the results shown in Table 2.13 are the 6 sites that
met the 35% plus geometric mean standard. For this parameter, the percentile range was
from 50% to 100% of the times not meeting the analysis criteria. The minimum
31
geometric mean is 5.85 pH for Site ID – GO0.4 (Record # 1), and the maximum is 6.4 pH
for Site ID – CC0.4 (Record # 5). The minimum pH is below the analysis criteria at all
six sites and the maximum also has a low pH for Site ID – GO0.4 (Record # 1). The
geometric means for all six sites are also below the analysis criteria. This shows a
tendency for a more acidic aquatic habitat. Note: A lower (or more acidic) pH level has
an effect on aquatic ecosystems. Acidification can indirectly affect biota by altering the
availability of food, and is likely to reduce the number of microorganisms that are
important to the decomposition of organic matter, thus reducing availability of nutrients
to other consumers. Failure of eggs to develop or increased mortality has been
demonstrated in field and lab tests. The pH in freshwater systems can widely vary from
acidic to alkaline, but extremes where pH is much less than 5 or greater than 9 are found
to be harmful to organisms (Allan, 1995).
Table 2.13: Results for Degraded pH Levels (geo mean < 6.5 or > 8.5) WRIA 13.
Results for pH: 6 sites met the 35% plus geometric mean standard.
#
1
2
3
4
5
6
Site Info
Site_ID
GO0.4
CC0.6
CC0.2
SL0.8
CC0.4
SPDITCH2
pH - Analysis Results - WRIA 13
QA Records < 6.5 or > 8.5 % < 6.5 or > 8.5 Min
Max
Geo Mean
L4
5
5
100.00%
5.6
6.2
5.85
L4
6
5
83.33%
5.88
7.68
6.29
L4
8
6
75.00%
5.92
6.86
6.39
L4
7
5
71.43%
5.9
7.8
6.39
L4
7
4
57.14%
5.95
7.07
6.40
L5
6
3
50.00%
4.92
7.06
6.29
Temperature Results: As with the previous section on temperature results, the
analysis for temperature was broken down into four seasonal and inhabitation
temperature requirements for a variety of species. The four categories were segmented
from the total temperature records for each monitoring site by date range and temperature
requirements for suitable species habitation. The first category (Temp 1) has a monitoring
range for an entire annual cycle for char rearing and foraging. The temperature criterion
for this category is 12○C (54.6○F) and the results of analysis are shown in Table 2.14. The
second category (Temp 2) has a date range from September 16th to June 14th for fall,
winter and spring (F/W/Sp) salmon and trout spawning. The temperature criterion for this
32
○
○
category is 13 C (55.4 F) and the results of analysis are shown in Table 2.15. The third
category (Temp 3) has a date range from June 15th – September 15th for core summer
○
○
salmonid habitat. The temperature criterion for this category is 16 C (60.8 F) and the
results of analysis are shown in Table 2.16. The fourth category (Temp 4) has a date
range from September 16th – December 22nd for fall char spawning. The temperature
criterion for this category is 9○C (48.2○F) and the results of analysis are shown in Table
2.17. All the following temperature tables show the results based on the 35% plus
geometric mean standard. Information of date ranges and temperature criteria are from
the Department of Ecology’s Water Quality Standards for Surface Water of the State of
Washington, Chapter 173-201A WAC (WA Ecology, 2006).
The analysis for the first category (Temp 1) all year char rearing took continuous
annual water quality temperature data with an analysis criterion of 12○C (54.6○F) for
WRIA 13. This resulted in 11 records that met the 35% plus geometric mean standard.
Eight sites in this analysis were included that did not meet the geometric mean standard.
One site (Site ID – SRC) failed to meet the analysis criterion 66.67% of the time. The
other seven sites (Site IDs: WARTH11, G07001162238, W RM 3.7, W RM 4.2,
13A060D, W RM 3.8 and 13A060E) were included because of the high maximum
temperatures greater than 6○C above analysis criterion. The percentage of time that the
data did not meet the analysis criterion ranged from 35.29% to 100% with a geometric
mean range from 9.63 ○C to 19.16○C. The results are listed in Table 2.14 below.
33
Table 2.14: All Year Temperature Results for the Char Rearing Category in WRIA 13.
Results for Temp 1: Eleven records met the 35% plus geometric mean standard. One record had a
criteria failure of 66.67%, and seven records had high maximum temperatures (blue highlight).
Site_ID
CHATH11
MCITH11
BLATH11
LAWTH11
OFFTH11
PATTH21
LONTH11
MUNTH11
LOWER
PATTH11
HICTH11
SCR
WARTH11
G07001162238
W RM 3.7
W RM 4.2
13A060D
W RM 3.8
13A060E
Site Info
QA Category
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L4 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
L5 Char_Rearing
Temp 1 - Analysis Results - WRIA 13
Period Records > 12 deg C % > 12 deg C Min Max Geo Mean
All_Year
34
34
100.00% 12.8 23.8
18.86
All_Year
58
58
100.00% 14.2 21.14
18.56
All_Year
53
53
100.00%
13 22.3
17.92
All_Year
66
64
96.97% 11.8 21.7
18.19
All_Year
61
59
96.72%
11 25.1
18.11
All_Year
27
26
96.30%
12 22.2
19.16
All_Year
59
54
91.53%
9.6 23.8
17.86
All_Year
28
23
82.14% 10.1 22.8
15.57
All_Year
11
9
81.82% 10.9
15
12.85
All_Year
84
60
71.43% 9.98 23.6
15.34
All_Year
88
60
68.18% 8.86 23.7
14.88
All_Year
6
4
66.67%
5.2 14.5
9.91
All_Year
229
113
49.34%
5.9 24.4
11.91
All_Year
26
12
46.15% 3.97 22.56
10.32
All_Year
36
16
44.44%
4.3 20.4
10.85
All_Year
43
19
44.19%
4.5 22.1
10.60
All_Year
120
45
37.50%
2.2 19.8
9.73
All_Year
41
15
36.59%
4.4 21.1
10.67
All_Year
119
42
35.29%
2.7 18.9
9.63
Analysis for the second category (Temp 2) for salmon and trout spawning, took
ongoing water quality temperature data with a date range from September 16th to June
14th. This covered the fall, winter and spring (F/W/Sp) salmon and trout spawning
○
○
inhabitation period with an analysis criterion of 13 C (55.4 F) for WRIA 13. This
resulted in 10 records that met the 35% plus geometric mean standard. One site (Sire ID –
WARTH11) was included because of the high maximum temperatures 9○C above
analysis criterion. The percentage of time that the data did not meet the analysis criterion
ranged from 43.55% to 100% with a geometric mean range from 11.33 ○C to 17.77○C.
The results are listed in Table 2.15 below.
34
Table 2.15: F/W/Sp Temperature Results for Salmon & Trout Category WRIA 13.
Results for Temp 2: Ten records met the 35% plus geometric mean standard. One record was
at 9○C above criteria (blue highlight).
Site_ID
MCITH11
BLATH11
PATTH21
OFFTH11
LONTH11
CHATH11
LAWTH11
PATTH11
HICTH11
MUNTH11
WARTH11
Site Info
QA Category
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
L5 Salmon/Trout
Period
F/W/Sp
F/W/Sp
F/W/Sp
F/W/Sp
F/W/Sp
F/W/Sp
F/W/Sp
F/W/Sp
F/W/Sp
F/W/Sp
F/W/Sp
Temp 2 - Analysis Results - WRIA 13
Records > 13 deg C % > 13 deg C Min Max Geo Mean
38
38
100.00% 14.2 20.9
17.77
35
34
97.14%
13 19.9
16.97
11
10
90.91%
12
21
17.60
29
26
89.66%
11 22.2
16.91
35
27
77.14%
9.6 20.8
15.95
13
10
76.92% 12.8 23.8
17.11
29
21
72.41% 11.8
20
15.34
42
27
64.29% 10.09 22.19
14.33
43
23
53.49% 8.86 20.2
13.09
6
3
50.00% 10.1 18.2
13.88
124
54
43.55%
5.9
22
11.33
The analysis for the third category (Temp 3) for core salmonid habitat, took
ongoing water quality temperature data with a date range from June 15th to September
15th. This covered the core summer salmonid habitation period with an analysis criterion
○
○
of 16 C (60.8 F) for WRIA 13. This resulted in 14 records that met the 35% plus
geometric mean standard. Two sites in this analysis were included that did not meet the
geometric mean standard. Site ID – 13A060D (blue highlight) was included because its
46.67% failure to meet analysis criterion and geometric mean was less than 1 degree
below the standard. Site ID – WARTH11 (blue highlight) on Ward Lake failed to meet
analysis criterion 43.81% of the time. (This was noteworthy because Ward Lake failed to
meet each analysis criteria in all four temperature categories from 43.55% to 57.89% of
the time.) The percentage of time that data in this table did not meet the analysis criteria
ranged from 43.81% to 100% with a geometric mean range from 12.63 ○C to 21.47○C.
The results are listed in Table 2.16 below.
35
Table 2.16: Summer Temperature Results for the Core Salmonid Category in WRIA 13.
Results for Temp 3: Fourteen records met the 35% plus geometric mean standard. Two sites in
this analysis (blue highlight) were included that did not meet the geometric mean standard. One
of those records was less than 1 degree from geometric mean standard, and the other was a lake
that had high failure to meet analysis criteria year-round.
Site_ID
BLATH11
CHATH11
LAWTH11
MCITH11
W RM 4.2
G07001162238
LONTH11
PATTH21
W RM 3.8
OFFTH11
W RM 3.7
HICTH11
PATTH11
MUNTH11
13A060D
WARTH11
Site Info
QA Category
L5 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L4 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
L5 Core Salmonid
Temp 3 - Analysis Results - WRIA 13
Period Records > 16 deg C % > 16 deg C Min Max Geo Mean
Summer
18
18
100.00% 16.8 22.3
19.94
Summer
21
21
100.00% 17.3 23.8
20.03
Summer
37
37
100.00% 18.36 21.7
20.79
Summer
20
20
100.00% 18.2 21.14
20.17
Summer
5
5
100.00% 16.8 22.1
18.78
Summer
5
5
100.00% 20.23 22.56
21.47
Summer
24
23
95.83% 15.1 23.8
21.05
Summer
16
15
93.75% 15.8 22.2
20.32
Summer
4
3
75.00% 15.3 21.1
18.94
Summer
32
23
71.88% 13.4 25.1
19.26
Summer
3
2
66.67%
16 20.4
18.37
Summer
45
28
62.22% 9.63 23.7
16.83
Summer
42
25
59.52% 9.98 23.6
16.41
Summer
22
12
54.55% 10.56 22.8
16.06
Summer
30
14
46.67% 11.1 19.8
15.39
Summer
105
46
43.81% 6.21 24.4
12.63
The analysis for the fourth category (Temp 4) for char spawning, took ongoing
water quality temperature data with a date range from September 16th to December 22nd.
This covered the fall char spawning inhabitation period with an analysis criterion of 9○C
(48.2○F) for WRIA 13. This resulted in 17 sites that met the 35% plus geometric mean
standard. Nine of those sites did not have enough records for an adequate analysis. Each
site contained only two records with only one result each meeting the criterion. The
following sites should be recommended for further ongoing temperature monitoring,
before a proper analysis could be performed: sites with Site IDs DB0.1, SL0.1, WL0.2,
WL1.0, WL2.25T, WL2.6, WL2.9, WL3.1 and WL3.4. Two sites were included that did
not meet the geometric mean standard; sites 13A060D and 13A060E had a 42.5% to
48.65% percentage failure rate of not meeting the criterion, and high maximum
temperatures from 14○C to 14.8○C (blue highlight). Two other sites were included that
did not meet the geometric mean standard; sites W RM 3.7 and W RM 4.2 had a 37.5%
failure rate, not meeting the criterion, and high maximum temperatures range from
36
16.1○C to 17.9○C (blue highlight). One monitoring location (Site # 13A150D) failed
53.33% of the time. The percentage of time that the data in this table did not meet the
analysis criteria ranged from 37.5% to 100% with a geometric mean range from 8.24 ○C
to 18.71○C. The results are listed in Table 2.17, below.
Table 2.17: Fall Temperature Results for the Char Spawning Category in WRIA 13.
Results for Temp 4: Seventeen records met the 35% plus geometric mean standard. Nine of those
sites did not have enough records for an adequate analysis (blue highlight). Four sites had high
maximum temperatures (blue highlight). One site failed 53.33% of the time (blue highlight).
Site Info
Site_ID
QA Category
BLATH11
L5 Char Spawning
LONTH11
L5 Char Spawning
OFFTH11
L5 Char Spawning
PATTH11
L5 Char Spawning
G07001162238 L4 Char Spawning
W RM 3.1
L5 Char Spawning
WD0.0
L4 Char Spawning
WARTH11
L5 Char Spawning
13A150D
L5 Char Spawning
DB0.1
L4 Char Spawning
SL0.1
L4 Char Spawning
WL0.2
L4 Char Spawning
WL1.0
L4 Char Spawning
WL2.25T
L4 Char Spawning
WL2.6
L4 Char Spawning
WL2.9
L4 Char Spawning
WL3.1
L4 Char Spawning
WL3.4
L4 Char Spawning
13A060D
L5 Char Spawning
13A060E
L5 Char Spawning
W RM 3.7
L5 Char Spawning
W RM 4.2
L5 Char Spawning
Period
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Fall
Temp 4 - Analysis Results - WRIA 13
Records > 9 deg C % > 9 deg C Min Max Geo Mean
9
9
100.00% 18.1 19.2
18.71
7
7
100.00% 16.7 18.9
18.06
8
8
100.00% 15.9 19.6
18.53
15
15
100.00% 10.09 22.19
13.73
7
6
85.71% 5.85 17.93
11.48
10
7
70.00%
7.5 11.4
9.46
3
2
66.67% 8.86 15.8
10.95
57
33
57.89% 6.44 21.85
11.86
15
8
53.33%
2.7 13.1
7.82
2
1
50.00% 8.57 11.28
9.83
2
1
50.00%
7.9 12.25
9.84
2
1
50.00% 7.64 12.69
9.85
2
1
50.00% 7.88 11.55
9.54
2
1
50.00% 7.42 11.47
9.23
2
1
50.00% 7.86 11.23
9.40
2
1
50.00%
7.9 11.1
9.36
2
1
50.00% 7.82 10.99
9.27
2
1
50.00%
8.1 11.22
9.53
37
18
48.65%
2.2
14
8.54
40
17
42.50%
2.7 14.8
8.33
8
3
37.50%
4.3 16.1
8.55
8
3
37.50%
4.5 17.9
8.24
Final Site Selection for Degraded Water Quality:
The final step in identifying the most degraded sites for water quality was to
aggregate the analysis result tables for all the parameters and rank each sample site across
all those qualifying parameters (parameters meeting the 35% plus geometric mean
standard). The sample sites were ranked in descending order from the most degraded
over-all water quality. The effects on water quality are also influenced by the type of
system, whether it is lotic (flowing) like streams and rivers, or lentic (still or slow37
movement) like found in wetlands, lakes and ponds. The data sets for WRIA 11 also
included another type influence, tide gates on water quality. This is a system that
fluctuates between lotic and lentic (as tide gates open and close) during tidal cycles and
therefore was set apart from those other two categories. The data analysis results had also
revealed a high number of stream channel monitoring sites failing to meet the analysis
criteria for fecal coliform (FC). Those sites became yet another category for the lotic
system analysis. One thing to note about the aggregation of analysis data on the tables for
fecal coliform (FC) is that the qualifier for entry in these tables was to meet the 35% plus
geometric mean standard for 50 cfu/100 ml. The data that met the analysis criterion of
100 cfu/100 ml was also included in the table for the qualifying sites, even if it was below
the 35% plus geometric mean standard, in order to refine the over-all degraded water
quality value for FC at the selected sites. These categories are represented in the
aggregation tables for both Nisqually (WRIA 11) and the Deschutes (WRIA 13). The
categories are as follows:
Lotic Systems
Lentic Systems
Lotic System Tide Gates (WRIA 11 Only)
Lotic Systems FC
WRIA 11 Lotic Systems: The aggregated analysis results across multiple
parameters for the Nisqually streams, creeks and rivers produced 49 monitoring sites with
an over-all percentage range from 34.2% to 90.91% degraded water quality. The number
of qualifying parameters in Table 2.18 (Below) ranged from 1 to 5 entries. For the
Nisqually Basin, there were no significant analysis results for lotic system pH. The lotic
site location table for these 49 monitoring sites is in Appendix Table A1.
38
Table 2.18: Aggregated Lotic Results Across Multiple Parameters WRIA 11.
Forty-nine monitoring sites with an Over-All percentage range from 34.2% to 90.91%.
DO
FC 50
FC 100
Turbidity
Temp 1
Temp 2
Temp 3
Temp 4
Site_ID
% < 8mgL % > 50cfu % > 100cfu % > 7.8 NTU % > 12 deg C % > 13 deg C % > 16 deg C % > 9 deg C % Over All
MC5.8
81.82%
100.00%
90.91%
MC4.3
70.00%
100.00%
85.00%
MED0.1
100.00%
43.75%
100.00%
81.25%
MID-LAKEDRAIN
71.43%
83.33%
77.38%
OHOPCR(RM6.3) G93
50.00%
100.00%
75.00%
RSWT
71.43%
75.00%
75.00%
73.81%
11A070D
48.48%
84.85%
66.67%
OHOPCR(RM6.3) G95
44.23%
74.51%
43.33%
100.00%
66.67%
65.75%
NISQUALLY(39.7)
56.25%
75.00%
65.63%
OHOPCR(RM2.0) G93
80.95%
57.14%
50.00%
62.70%
OHOPCR(RM3.3)
86.36%
59.09%
36.36%
75.00%
50.00%
61.36%
MUCKCR(RM6.2)
63.64%
54.55%
46.67%
80.00%
61.21%
OHOPCR(RM0.1) TAX
92.31%
69.23%
41.18%
58.82%
44.44%
61.20%
MC4.4TLBU
60.00%
60.00%
MASHELRV(RM6.0) TAX
38.89%
80.00%
59.44%
OHOPCR(RM6.0) TAX
52.94%
75.00%
50.00%
59.31%
RSET
58.33%
16.67%
100.00%
58.33%
OHOPCR(RM0.1) G93
65.31%
55.10%
50.00%
56.80%
MC3.1
55.56% 55.56%
22.22%
50.00%
100.00%
56.67%
OHOPCR(RM6.0) G93
37.93%
85.71%
44.44%
56.03%
MC5.4
60.00% 44.44%
11.11%
100.00%
53.89%
11A090D
55.56%
50.00%
52.78%
LIITLEMASHELRV
60.87%
43.48%
51.43%
51.93%
MC4.7
60.00% 44.44%
50.00%
51.48%
MED0.0
66.67% 57.14%
28.57%
50.79%
OHOPCR(RM2.0) G93
50.00%
50.00%
50.00%
MUCK01
50.00%
50.00%
MUCKCR(RM0.1)
50.00%
50.00%
MASHELRV(RM6.0) G9
50.00%
50.00%
YELMCR(RM0.1) TAX
58.33%
41.67%
50.00%
50.00%
MC3.7
54.55%
42.86%
48.70%
MASHELRIVER
47.37%
50.00%
48.68%
OHOPCR(RM9.9) G95
50.98%
46.15%
48.57%
11A080D
40.00%
55.56%
47.78%
11A070E
49.17%
35.83%
56.41%
47.14%
NISQUALLY(3.7) TAX
37.50%
57.89%
42.86%
50.00%
47.06%
LAC05
50.00%
50.00%
40.00%
46.67%
SOUTHCK03
50.00%
50.00%
40.00%
46.67%
MUCK22
45.83%
45.83%
MASHELRV(RM3.2)
41.67%
25.00%
50.00%
60.00%
44.17%
TANWAXCR(RM0.3) TAX
38.46%
30.77%
52.63%
50.00%
42.97%
MUCK04
35.00%
50.00%
42.50%
MC4.5
44.44%
40.00%
42.22%
MUCK24
40.00%
40.00%
MUCK18
41.67%
37.50%
39.58%
MUCK23
38.46%
38.46%
LYNCH CREEK
50.00%
13.64%
38.89%
50.00%
38.13%
TANWAX(RM10.2)
40.00%
20.00%
42.86%
34.29%
MC4.3T
45.45% 42.86%
14.29%
34.20%
39
WRIA 11 Lentic Systems: The aggregated analysis results across multiple
parameters for Nisqually lakes, ponds and reservoirs produced 18 monitoring sites with
an over-all percentage range from 40% to 90.48% degraded water quality. The
environmental effects on lentic system water quality differ from those on streams and
creeks due to the still or slow-movement of water. Ambient heat and direct sunlight,
especially in the summer, raise water temperature in impoundments because of their
exposed surface area lacking adequate shade. The number of qualifying parameters in
Table 2.19 below ranged from 1 to 5 entries. Note that the majority of failures to meet the
analysis criteria are in the four temperature columns. For the Nisqually Basin, there were
no significant analysis results for lentic system pH. The lotic site location table for these
18 monitoring sites is in Appendix Table A2.
Table 2.19: Aggregated Lentic Results Across Multiple Parameters WRIA 11.
Eighteen monitoring sites with an Over-All percentage range from 40% to 90.48%.
Site_ID
TANWAXLAKE
SILVERLAKE
CLETH11
OHOPLAKE
RAPJOHNLAKE
OHOPI11
RAPPI11
WHIPI11
OHOPLAKESTA3
OHOPLAKESTA2
ST#TH11
HARPI11
MINERALLAKE
TANPI11
OHOPLAKESTA1
CLEARLAKE
HARTSLAKE
ST_TH11
DO
FC 50
FC 100
Turbidity
Temp 1
Temp 2
Temp 3
Temp 4
% < 8mgL % > 50cfu % > 100cfu % > 7.8 NTU % > 12 deg C % > 13 deg C % > 16 deg C % > 9 deg C % Over All
100.00%
80.95%
90.48%
100.00%
78.26%
89.13%
40.00%
100.00%
100.00%
100.00%
100.00%
88.00%
94.74%
78.95%
86.84%
95.24%
66.67%
80.95%
80.00%
75.00%
71.43%
75.48%
90.00%
66.67%
50.00%
68.89%
100.00%
64.29%
52.17%
57.89%
68.59%
61.82%
90.48%
43.75%
65.35%
68.66%
46.15%
66.07%
70.73%
62.90%
88.89%
36.27%
62.58%
100.00%
47.62%
40.00%
62.54%
68.00%
56.00%
62.00%
68.75%
55.56%
57.14%
60.48%
58.90%
75.00%
42.86%
58.92%
62.50%
50.00%
56.25%
45.71%
50.00%
62.50%
52.74%
40.00%
40.00%
WRIA 11 Lotic System Tide Gates: The EIM dataset for Nisqually (WRIA 11)
included water quality data for monitoring sites at tide gates. Although analyzing the tide
gates was not the original intent of this thesis, the data analysis revealed that tide gates
seem to have a significant effect on water quality. When WRIA 11 data were aggregated
across multiple parameters and divided into lotic and lentic, tide gates did not fit
40
completely in the lotic category. This is because tide gates open and close on tidal cycles,
thus creating temporary barriers to stream flow and salmonid migration. Figure 2.1 shows
a typical tide gate in Western Washington. The lotic system tide gate location
descriptions are shown in Appendix Table A3.
Figure 2.1: Tide Gate in Westport, Washington.
Tide Gates Open and Close During Tidal Cycles Causing Intermittent Barriers to Stream Flow
and Fish Passage.
The multiple parameter aggregation in Table 2.20 below showed a failure to meet the
analysis criteria that ranged across 1 to 7 parameters, where the tables for lotic showed a
criteria failure across 1 to 5 parameters out of 8 possible parameter categories. Of the 16
tide gate monitoring sites, 13 failed to meet the criteria for DO ranging from 40% to
100%. In the FC50 category, 7 sites failed in a range from 37.5% to 50%. In the FC100
category, 7 sites failed in a range from 14.29% to 37.5%. (Reminder: The results for
FC100 were included in all cases where FC50 failed the 35% plus geometric mean
standard, whether FC100 met that standard or not.) The turbidity category did not show
failure to meet the 35% plus geometric mean standard at tide gates. The number of sites
41
and failure rate for tide gates varied widely by seasonal use, and are given below for all 4
temperature categories:
Temp 1 (All Year Char Rearing) 7 of 16 sites failed from 40% to 70%
Temp 2 (F/W/Sp Salmon & Trout Spawning) 1 of 16 sites failed 40%
Temp 3 (Core Summer Salmonid Habitat) 10 of 16 sites failed from 50% to 100%
o Note: 2 sites failed 50% and 8 sites failed 100% of the time.
Temp 4 (Fall Char Spawning) 9 of 16 sites failed from 50% to 100%
Table 2.20: Aggregated Tide Gate Results Across Multiple Parameters WRIA 11.
Eighteen monitoring sites with an Over-All percentage range from 28.57% to 100%.
Site_ID
TG14L
TG8L
TG10L
TGBL
TG4L
TG3L
TG13L
TG12L
TG2L
TG9L
TG15L
TG9W
TG5L
TG11W
TG1L
TG11L
DO
FC 50
FC 100
Turbidity
Temp 1
Temp 2
Temp 3
Temp 4
% < 8mgL % > 50cfu % > 100cfu % > 7.8 NTU % > 12 deg C % > 13 deg C % > 16 deg C % > 9 deg C % Over All
100.00%
100.00% 100.00%
100.00%
100.00%
88.89%
100.00%
94.44%
41.18%
100.00%
100.00%
80.39%
66.67%
100.00%
66.67%
77.78%
50.00%
100.00%
75.00%
100.00% 44.44%
22.22%
70.00%
100.00%
100.00%
72.78%
85.71%
44.44%
50.00%
100.00%
70.04%
40.00%
100.00%
50.00%
63.33%
100.00% 37.50%
12.50%
100.00%
62.50%
88.89%
40.00%
50.00%
50.00%
57.22%
71.43% 37.50%
37.50%
50.00%
40.00%
100.00%
50.00%
55.20%
60.00% 37.50%
37.50%
40.00%
100.00%
55.00%
50.00%
33.33%
37.50%
40.28%
42.86% 50.00%
16.67%
36.51%
42.86%
14.29%
28.57%
WRIA 11 Lotic Systems FC: This aggregated analysis was for fecal coliform as
it affects shellfish beds (50 cfu/100 ml) and for recreational primary contact
(100 cfu/100 ml). The qualifier for being listed in this table (Table 2.21 below) was lotic
monitoring sites (not including tide gates) where fecal coliform > 50 cfu/100 ml (FC 50)
met the 35% plus geometric mean standard. The analysis criterion of > 100 cfu/100 ml
(FC 100) was included with the qualifying monitoring sites even though the 35% plus
geometric mean standard was not met for the FC 100 category. This method allowed for
identification of those sites that had degraded water quality for both shellfish beds and
recreational primary contact, or just for shellfish beds.
The aggregated analysis results for the two fecal coliform categories in Nisqually
streams, creeks and rivers produced the 28 monitoring sites listed in Table 2.21 below
42
(location description included). The FC 50 category had a percentage range from 36% to
100% that met the 35% plus geometric mean standard, and the FC 100 category had a
range from 12.5% to 94.74%. The over-all percentage range for fecal coliform
aggregated results analysis ranged from 26% to 94.74% for degraded water quality.
Table 2.21: Aggregated Fecal Coliform Results Across Two Parameters WRIA 11.
Twenty-eight monitoring sites with an Over-All percentage range from 26% to 94.74%.
FC 50
FC 100
Site_ID
% > 50cfu % > 100cfu % Over All
Location
S PIPE
94.74%
94.74%
94.74% S PIPE (PIPE DISCHARGE - BOAT LAUNCH)
COMBINED 1
83.33%
83.33%
83.33% COMBINED (S. PIPE & N. FLOW)
WASH
100.00%
60.00%
80.00% WASH CREEK NEAR MOUTH
9 GLACIS RD NE
77.78%
66.67%
72.22% 9 GLACIS RD NE (ROADSIDE DITCH)
H3 STORM
80.00%
60.00%
70.00% H3 STORM VAULT
SC12 CS4
80.00%
60.00%
70.00% SC12 CS4
MCALLISTER3.1
82.14%
46.43%
64.29% MCALLISTER CREEK (RM3.1) BELOW I - 5
1 D'MILLUHR DR
60.00%
60.00%
60.00% 1 D'MILLUHR DR (DRAINAGE DITCH)
12A SCENIC DR
60.00%
60.00%
60.00% 12A SCENIC DR (ROADSIDE DITCH)
12B SCENIC DR
60.00%
60.00%
60.00% 12B SCENIC DR (ROADSIDE DITCH)
OHOPCR(RM0.1) G95
60.87%
43.48%
52.17% OHOP CREEK NEAR MOUTH
OHOP2.0
57.14%
42.86%
50.00% OHOP CREAK @ HIGHWAY 7
OHOP3.3
57.14%
42.86%
50.00% OHOP CREEK @ OHOP VALLEY ROAD
OHOP6.0
57.14%
42.86%
50.00% NEAR OROVILLE ROAD, JUST DS OF THE LAKE
OHOP2.2D
50.00%
50.00%
50.00% DITCH AT PETERSON ROAD
MC3.2
50.00%
50.00% RM3.2
MC 3.1
75.00%
12.50%
43.75% MCALLISTER CREEK BELOW I-5
N FLOW
42.86%
42.86%
42.86% N FLOW (RAVINE DISCHARGE - BOAT LAUNCH)
OHOP6.2T
42.86%
42.86%
42.86% LYNCH CREEK NEAR MOUTH
SC10 CS2
40.00%
40.00%
40.00% YELM CREEK NEAR MOUTH
SC11 CS3
40.00%
40.00%
40.00% Control Structure #3 south end wetland cell #2
SC9 CS1
40.00%
40.00%
40.00% Control Structure #1 northeast corner wetland cell #1
RSUS
40.00%
40.00% RED SALMON UPSTREAM OF WASK CREEK
MED0.05
37.50%
37.50% MEDICINE CREEK RM 0.05
YELMCR(RM0.1) G95
45.83%
20.83%
33.33% YELM CREEK NEAR MOUTH
OHOPCR(RM9.9) G93
39.13%
26.09%
32.61% OHOP CREEK BELOW TWENTY-FIVE MILE CREEK
MC4.35
50.00%
12.50%
31.25% MCALLISTER JUST D/S OF LOG OBSTRUCTION
TANWAXCR(RM0.3) G95
36.00%
16.00%
26.00% TANWAX CREEK @ HARTS LAKE ROAD
WRIA 13 Lotic: The aggregated analysis results across multiple parameters for
the Deschutes streams, creeks and rivers produced 37 monitoring sites with an over-all
percentage range from 34.85% to 93.33% degraded water quality. The number of
qualifying parameters in Table 2.22 below ranged from 1 to 5 entries. The lotic site
location table for these 37 monitoring sites is in Appendix Table A4.
43
Table 2.22: Aggregated Lotic Results Across Multiple Parameters WRIA 13.
Thirty-seven monitoring sites with an Over-All percentage range from 34.85% to 93.33%.
DO
FC 50
FC 100
pH
Turbidity
Temp 1
Temp 2
Temp 3
Temp 4
Site_ID
% 8mgL % > 50cfu % > 100cfu % < 6.5 or > 8.5 % > 7.8 NTU % > 12 deg C % > 13 deg C % > 16 deg C % > 9 deg C % Over All
W DRAIN
100.00%
80.00%
100.00%
93.33%
WD6.8
100.00% 80.00%
80.00%
86.67%
FLEMING CR
85.71%
71.43%
100.00%
85.71%
CC0.6
83.33%
83.33%
83.33%
83.33%
LOWER
81.82%
81.82%
CREEKB
83.33%
50.00%
100.00%
77.78%
G07001162238
46.15%
100.00%
85.71%
77.29%
CREEKA
75.00%
75.00%
SL0.8
85.71%
57.14%
71.43%
71.43%
WL1.0
100.00%
60.00%
50.00%
70.00%
W RM 3.1
70.00%
70.00%
CC0.4
83.33%
66.67%
57.14%
69.05%
DB0.1 (DSAR)
70.00%
72.73%
50.00%
64.24%
WD0.0
77.78%
44.44%
66.67%
62.96%
SPDITCH2
100.00% 54.55%
45.45%
50.00%
62.50%
WL0.2
75.00%
58.33%
50.00%
61.11%
GO0.4
50.00%
33.33%
100.00%
61.11%
W RM 4.2
44.19%
100.00%
37.50%
60.56%
WD5.1
25.00% 88.89%
66.67%
60.19%
SWO STORMWATER
40.00%
40.00%
100.00%
60.00%
SPDITCH1
60.00%
60.00%
SL0.1
70.00%
50.00%
50.00%
56.67%
WD6.9
91.67% 55.56%
22.22%
56.48%
W RM 3.8
36.59%
75.00%
55.79%
13A150D
53.33%
53.33%
WL3.4
61.54% 54.55%
36.36%
50.00%
50.61%
WD6.2
83.33% 44.44%
22.22%
50.00%
WL2.25T
50.00%
50.00%
50.00%
50.00%
WL2.6
60.00%
40.00%
50.00%
50.00%
WL1.9T
45.45%
45.45%
SCR
44.26%
22.95%
66.67%
44.63%
13A060D
37.50%
46.67%
48.65%
44.27%
CC0.2
42.86%
14.29%
75.00%
44.05%
W RM 3.7
37.50%
29.17%
44.44%
66.67%
37.50%
43.06%
WL2.9
54.55%
18.18%
50.00%
40.91%
13A060E
35.29%
42.50%
38.90%
WL3.1
36.36%
18.18%
50.00%
34.85%
WRIA 13 Lentic: Aggregated analysis results across multiple parameters for the
Deschutes lakes, ponds and reservoirs produced 11 monitoring sites with an over-all
percentage range from 46.92% to 100% degraded water quality. The environmental
effects on lentic system water quality differ from that of streams and creeks, due to the
still or slow-movement of water. Ambient heat and direct sunlight, especially in the
summer, raise water temperature in impoundments, because of open non-shaded areas.
Note that the majority of failures to meet the analysis criteria were in the dissolved
oxygen (DO) and three to the four temperature categories. The number of site failures
and failure rates for DO and all 4 temperature categories are as follows:
44
Dissolved Oxygen (DO) 8 of 11 sites failed from 40% to 100%
Temp 1 (All Year Char Rearing) 11 of 11 sites failed from 49.34% to 100%
Temp 2 (F/W/Sp Spawning) 11 of 11 sites failed 43.55% to 100%
Temp 3 (Summer Salmonid Habitat) 11 of 11 sites failed from 43.81% to 100%
Temp 4 (Fall Char Spawning) 5 of 11 sites failed from 57.89% to 100%
o Note: 1 site failed 57.89% and 4 sites failed 100% of the time
The number of qualifying parameters in Table 2.23 below ranged from 3 to 5
entries. The lotic site location table for these 11 monitoring sites is found in Appendix
Table A5.
Table 2.23: Aggregated Lentic Results Across Multiple Parameters WRIA 13.
Eleven monitoring sites with an Over-All percentage range from 46.92% to 100%.
Site_ID
MCITH11
BLATH11
LONTH11
CHATH11
OFFTH11
PATTH21
LAWTH11
PATTH11
MUNTH11
HICTH11
WARTH11
DO
FC 50
FC 100
pH
Turbidity
Temp 1
Temp 2
Temp 3
Temp 4
% 8mgL % > 50cfu % > 100cfu % < 6.5 or > 8.5 % > 7.8 NTU % > 12 deg C % > 13 deg C % > 16 deg C % > 9 deg C % Over All
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
97.14%
100.00%
100.00%
99.43%
91.53%
77.14%
95.83%
100.00%
91.13%
85.71%
100.00%
76.92%
100.00%
90.66%
83.33%
96.72%
89.66%
71.88%
100.00%
88.32%
60.00%
96.30%
90.91%
93.75%
85.24%
66.67%
96.97%
72.41%
100.00%
84.01%
40.00%
71.43%
64.29%
59.52%
100.00%
67.05%
82.14%
50.00%
54.55%
62.23%
57.14%
68.18%
53.49%
62.22%
60.26%
40.00%
49.34%
43.55%
43.81%
57.89%
46.92%
WRIA 13 Lotic Systems FC: The same standard was applied for this analysis as
for the fecal coliform aggregated table for WRIA 11 (See Table 2.21). The aggregated
analysis was for fecal coliform as it affects shellfish beds (50 cfu/100 ml) and
recreational primary contact (100 cfu/100 ml). The qualifier for being listed in Table 2.24
below was lotic monitoring sites where fecal coliform > 50 cfu/100 ml (FC 50) met the
35% plus geometric mean standard. The analysis criterion of > 100 cfu/100 ml (FC 100)
was included with the qualifying monitoring sites even though the 35% plus geometric
mean standard was not met for the FC 100 category. This method allowed identification
of sites that had degraded water quality for both shellfish beds and recreational primary
contact, or just for shellfish beds.
45
The aggregated analysis results for the two fecal coliform categories in Deschutes
streams, creeks and rivers produced the 39 monitoring sites listed in Table 2.24 below.
The FC 50 category had a percentage range from 36.36% to 100% that met the 35% plus
geometric mean standard. The FC 100 category had a range from 16.67% to 100%. The
over-all percentage for the fecal coliform aggregated results analysis ranged from 27.27%
to 100% for degraded water quality.
Conclusions WRIA 11:
The lotic and fecal coliform aggregation tables were analyzed for the Nisqually
River Basin (WRIA 11) and revealed a cluster of multiple parameter failures for the
Ohop Creek monitoring sites. The lotic aggregation table had 10 of 49 monitoring sites in
the Ohop Creek system with failure across multiple parameters, with an over-all failure
range from 48.57% to 70%. The fecal coliform aggregation table had 4 of 28 monitoring
sites across multiple parameters with an over-all failure range from 32.25% to 52.17%.
The 14 Ohop Creek sites with indications of degraded water quality across multiple
parameters were selected for further spatial analysis in GIS (Chapter 4). This study area
was selected for land use influences on water quality.
Conclusions WRIA 13:
Fecal coliform analysis for WRIA 13 revealed a cluster of monitoring sites in the
Henderson Inlet sub-basin. Of 39 sites in this analysis, 17 monitoring site streams have
their discharge point in the area of the Henderson Inlet shellfish beds. The monitoring
sites are on the following streams: Woodland Creek, Woodard Creek, Dobbs Creek and
Quail Creek. Table 2.24 includes the location description for these creeks. There is
enough concentrated failure to meet FC standards that the Henderson Inlet flow basin
was chosen for spatial analysis in GIS (Chapter 3).
46
Table 2.24: Aggregated Fecal Coliform Results Across Two Parameters WRIA 13.
Thirty-nine monitoring sites with an Over-All percentage range from 27.27% to 100%. Of the 39
lotic monitoring sites in the Henderson Inlet Sub-Basin, 4 streams, with 17 monitoring site in this
table, have their discharge points to the Henderson Inlet shellfish beds.
Site_ID
13-IND-BOUL-TC
13-IND-CENT
WL2.6SW
WOODLANDCR1
WOODLANDCR2
13-IND-QUIN
14MCLANEMC1
14MCLANEMC2
DB1.0 (BEDI)
WOODLANDCR3
MANHOLESOUTH
MARTIN WAY
WOODLANDCR4
14MCLANEUN1
14MCLANEMC2.5
WD2.9
WD3.4
WL1.1T
WL1.2T
DB0.1 (BEDI)
DBTRIB0.73
14MCLANEDV1
WL3.8T
WOODLANDCR5
DB1.0 (DSAR)
TANGLEWILDE
14MCLANESW1
DB0.731
DOBBSCRK
MY0.1
DOBBS CREEK
WOODLANDCR7
WL1.6
WL1.95T
WL3.45
WOODLANDCR6
CC0.0
WL3.1
WL3.1T
FC 50
FC 100
% > 50cfu % > 100cfu % Over All
Location
100.00% 100.00% 100.00% INDIAN CREEK AT BOULEVARD RD SE
100.00% 100.00% 100.00% INDIAN CREEK AT CENTRAL ST SE
100.00% 100.00% 100.00% STORMWATER @ 21ST COURT NE
100.00% 100.00% 100.00% WOODLANDCR1 (@ HAWKS PRAIRIE RD)
100.00% 100.00% 100.00% WOODLANDCR2 (NEAR HOLLYWOOD DRIVE)
100.00%
90.91%
95.45% INDIAN CREEK NEAR MOUTH
100.00%
90.91%
95.45% MCLANE MTH AT DS MOST DELPHI RD CROSSING
100.00%
90.91%
95.45% MCLANE AT UPSTREAM DELPHI RD. CROSSING
100.00%
88.89%
94.44% DOBBS CREEK U/S OF CAMP GROUND
100.00%
87.50%
93.75% WOODLANDCR3 (@ PLEASANT GLADE ROAD)
100.00%
80.00%
90.00% MANHOLE NEAR CARPENTER ROAD
100.00%
80.00%
90.00% ROADWAY SURFACE RUNOFF @ MARTIN WAY
100.00%
75.00%
87.50% WOODLANDCR4 (@ DRAHAM ROAD)
100.00%
70.00%
85.00% UN-NAMED WEST TRIB MOUTH AT DELPHI RD
90.91%
72.73%
81.82% MCLANE AT 2901 DELPHI DRIVEWAY BRIDGE
88.89%
66.67%
77.78% WOODARD CREEK OFF LIBBY ROAD
88.89%
66.67%
77.78% WOODARD CREEK @ 36TH AVE
88.89%
66.67%
77.78% QUAIL CREEK @ MOUTH
88.89%
66.67%
77.78% JORGENSON CREEK
81.82%
70.00%
75.91% DOBBS CREEK AT JOHNSON POINT ROAD
80.00%
70.00%
75.00% CULVERT - downstream sampling location D2
81.82%
63.64%
72.73% UN-NAMED EAST TRIB AT 2542 DELPHI RD
71.43%
71.43%
71.43% TANGLEWILDE STORMWATER TO WOODLAND CREEK
100.00%
42.86%
71.43% WOODLANDCR5 (@ MARTIN WAY)
75.00%
62.50%
68.75% DOBBS CREEK U/S OF CAMP GROUND
66.67%
66.67%
66.67% BURIED STORMWATER PIPE @ MARTIN WAY
80.00%
50.00%
65.00% SWIFT CREEK MOUTH AT DELPHI RD
70.00%
60.00%
65.00% D2 - First large bridge crossing mainstem Dobbs
70.00%
60.00%
65.00% D3 - Bridge over mainstem on Elm Road
75.00%
50.00%
62.50% MYER CREEK AT MOUTH
80.00%
40.00%
60.00% DOBBS CREEK @ JOHNSON PT ROAD
80.00%
40.00%
60.00% WOODLANDCR7 (@ UNION MILLS ROAD SE)
60.00%
60.00%
60.00% WOODLAND CREEK AT PLEASANT GLADE
57.14%
42.86%
50.00% PALM CREEK
54.55%
36.36%
45.45% WOODLAND CREEK AT TROUT FARM
66.67%
16.67%
41.67% WOODLANDCR6 (@ PACIFIC AVENUE)
54.55%
18.18%
36.36% COLLEGE CREEK @ MOUTH
45.45%
18.18%
31.82% WOODLAND CREEK D/S OF I-5
36.36%
18.18%
27.27% WOODLAND CREEK @ I-5
47
Chapter Three:
Anthropogenic Land Use and Impervious Surfaces Adjacent to Salmon Habitat and
Shellfish Beds
Introduction:
Human development has altered landscapes and ecosystems in a variety of ways.
These impacts can, and do, alter ecosystem habitats that have been evolving for
thousands of years. Species have been able to adapt to environmental stressors over long
periods of time, but many species struggle to adapt to the rapid growth of the human
impact and hydrological alteration of their ecosystem habitats.
Different types of Land use can affect aquatic systems in many ways, by nutrient
loading that can cause eutrophication, toxic algal blooms, the depletion of dissolved
oxygen and contaminate loading which can affect plant and aquatic species diversity and
abundance. Nutrient loading can come in the form of fertilizers used on lawns and
gardens by urban areas residents, or the runoff from agricultural farms. Residential
wastewater treatment failure of septic systems or leaky sewage treatment lines not only
adds to nutrient loading, but can also increase pathogenic health risks in recreational
waterways. Residential areas, whether urban or rural, generate a lot of waste. Landfills
contain the castoffs of products, broken or unwanted, many which have been
manufactured with harmful toxic chemicals. Plastic packaging and other plastic products
containing phthalates find their way into dump sites, roadway ditches, empty lots, the
gullets of seabirds and even all the way to the large plastic floating island the size of
Texas in the middle of the Pacific Ocean.
Contaminants come in many forms from highly toxic to cloudy water due to
suspended solids. These can affect aquatic species in many ways by making water too
turbid for invertebrate habitation, changing the pH balance, subjecting biota to lethal or
sub-lethal doses of pesticides and herbicides, and mutation and reproductive issues from
endocrine disrupting chemicals and pharmaceuticals.
48
Natural Systems vs. Developed Systems:
The pristine environment provides near ideal solutions for storm water runoff and
pollution control. As rainwater infiltrates into the ground, it is treated by plants and
organisms and is filtered, cleaned, taken up and utilized to sustain life, and returned to the
atmosphere through evapotranspiration. When the infiltrated ground is saturated, excess
rainwater runs off into naturally occurring channels, from creeks to streams to rivers, and
is ultimately returned to the ocean, where it evaporates and begins the hydrologic cycle
all over again in the form of clouds and rain. Each stage of the hydrologic cycle provides
benefits to plants that give us air to breath and even some plants used for food crops.
Mammal and aquatic species also gain benefit from this natural occurring system for
sustenance and suitable habitats. This entire ecosystem service is provided to us all free
of charge, yet we spend a lot of time and money to change and control it.
It has been known for some time now that human development has altered
landscapes and ecosystems in a variety of ways. A Google Scholar ™ search on the
words “Anthropogenic Impact” quickly yields about 142,000 articles on the subject.
Adding to that search the words “…Water Quality,” produces over 84,000 articles. These
published sources demonstrate our awareness of the effect we have on habitat and
species, from the activities we engage in on land and water. These impacts can and do
alter ecosystem habitats that have been long established and evolved for thousands of
years, long before humans set foot on these shores. Species struggle to adapt with the
rapid growth of human impact and hydrological alteration of those ecosystems. We have
become aware of the problems brought on by our presence, yet the question remains,
“How do we fix those problems?” This is fundamentally not an easy question to answer!
Many scientists, biologists, academia, student and volunteer groups seek to understand
ecosystem function, in the hopes of solving at least some of the distress imposed upon
species and habitat. People will continue to build and develop urban areas, as well as
create new urban areas in pristine ecosystems, so the idea is not to inhibit this growth and
development, but rather to find solutions that reduce our impervious footprint, toxic
contamination and nutrient loading and to help us grow and develop in smarter ways to
reduce the effect of our existence.
49
What ever is deposited on the land and infiltrates into the ground will eventually
make its way into drainage ditches, creeks, streams, rivers and ultimately to the estuaries
and oceans. What we do on land can and does effect marine ecosystems. One example is
how bacteria and pathogens flowing downstream to discharge points in the estuaries that
host shellfish beds. Referring to Table 1.2 from the Washington State surface water
quality standards in chapter one, the one parameter in this water analysis for bacteria
levels does not pertain to salmonids and their habitat as much as it does for the protection
of shellfish beds and recreational use. In this thesis fecal coliform data, which is a byproduct of human and other mammal excretion, were used for analysis. The point sources
of fecal coliform contamination are from failure in septic and wastewater systems,
domestic pets and wildlife, farm animals and agriculture. This can result in the discharge
of pathogens from land use to shellfish beds.
Land Use and Estuarine Habitats:
The focus of the following literature review is the anthropogenic impact on
shellfish beds in the Puget Sound, although studies outside this area were also used to
help develop an understanding of how humans affect estuarine habitats. Stressors to a
variety of shellfish species have impacts that depend on the proximity to anthropogenic
changes to the ecosystem, and the needs of specific species to propagate and remain
productive. According to Dr. Megan N. Dethier (2006), the “human effects on habitat
attributes” are defined as: “direct loss of habitat; alteration of substrate type; pollution or
other alterations in nearshore characteristics; alteration of runoff from land and beach
porewater; changes in nearshore plankton, introduced species; increased susceptibility to
predators and parasites;” and “nearshore aquaculture.” The relationship between human
nearshore development and bacteriological quality of water samples taken from their
respective watersheds was also studied. It was found that there is a significant
demographic and land use association with an abundance of fecal coliform in a populated
watershed. Turbidity, which could be caused by anthropogenic activities, was also found
to be related with bacterial abundance. The higher levels of bacteria found in these
estuarine systems can have an increased health risk to humans (Mallen, et al., 2000).
Shellfish play an important role in filtration of nutrients and toxins that run off nearshore
50
lands, which makes them a valuable indicator species for monitoring land use and
evaluating the quality of water that runs off that land and into our estuaries and oceans.
Ultimately the quality and quantity of nutrients, microorganisms, and toxins that are
taken up by shellfish, and then recreationally and commercially harvested, are returned
for human consumption.
Threats to Puget Sound Shellfish:
Shellfish survival and productivity can come under threat in a number of ways.
The most obvious may be from over-harvest by recreational or commercial methods.
Less apparent are those due to alterations of primary ecosystem functions from human
activity and development. Because each shellfish species has distinctive habitat
requirements, for example, sediment type for growth and recruitment, alterations to these
habitats can have a negative impact on shellfish population and productivity. Different
species have preference to different substrate types. Any process, natural or
anthropogenic, that alters substrate habitats by amount of sediment loading, organic
material, and grain size can have a negative effect on shellfish populations. Changes in
river and stream runoff from the land can alter the sediment loads on shellfish beds
(Dethier, 2006). For example, these changes can be sourced by upstream erosion,
landslides or disturbances caused by development, agriculture or logging.
Key parameters according to Dr. Megan N. Dethier (2006) are temperature,
turbidity, salinity, dissolved oxygen, [toxins], pollutants, and food type, all of which can
be affected by land use and shoreline modification. These parameters need to remain at
optimal levels for shellfish to remain productive. When physical conditions of a habitat
deteriorate by anoxic water or excessive sediment, this will place pressures on
productivity. Geoduck juveniles have some mobility for avoiding poor conditions, but the
adults are immobile and subject to higher mortality. Geoduck habitats have been
destroyed by marine construction, and aquaculture projects have competed for their space
(Goodwin & Pease, 1989). Littleneck clams in the larval stage can be affected not only
by salinity and temperature, but high turbidity can reduce their survival rate. High
siltation in their habitats can smother their populations. Dredging nearby or upland
development adds silt to the water columns that will settle upon habitat substrates. The
51
littleneck clams have also been known to be very susceptible to high concentrations of
copper from paint used on boat bottoms (Dethier, 2006).
Impact of Development and Fecal Coliform:
Over a four year period, Michael Mallin (2000) and his research team produced a
study to analyze the distribution and abundance of enteric pathogens in estuaries near
population demographics and development. Their report suggests that nearshore
development poses an increased health risk to humans and has significant consequences
to the environment. The research was performed in the Chesapeake Bay area, but the
fundamentals could apply anywhere there is nearshore development.
Mallin et al., (2000) found a significant correlation between the abundance of
bacteria and turbidity, and fecal coliform bacteria associated with suspended solids in the
water column. This suggests that suspended sediments are a transport mechanism for
fecal bacteria in aquatic systems. Fecal bacteria are known to survive longer in
association with particles of sediment, and disturbance of these sediments can re-release
the fecal bacteria into the water (Mallen, et al., 2000). This occurs with disturbances such
as storm event erosion or clearing land for development.
Input of bacteria into coastal water can have either point or nonpoint sources.
Most point sources can come from failing septic systems and occasionally from leaky
sewer mains or wastewater treatment failures. Nonpoint inputs can vary depending on
land use and demographics. Research by Michael Mallin (2000) revealed a significant
relationship between bacterial loading in the estuaries and watershed population size. He
states that a likely important source of bacterial loading is population. More humans
mean more domesticated animals, which leads to more fecal bacteria deposited on the
land. Also, population size was significantly correlated with the area of estuarine closures
to shellfish beds. The consequence of this is the economic loss to the local community
(Mallen, et al., 2000; Maiolo and Tschtter, 1981).
As it turns out, this study reveals that the percent of impervious surfaces in a
watershed is the most important anthropogenic aspect that contributes to fecal coliform
loads. The impervious surfaces taken into account here are parking lots, driveways, roads,
52
sidewalks and roof tops. Impervious surfaces tend to concentrate and rapidly pass on
storm water that picks up and concentrates pollutants as it rushes to streams and rivers on
its way to the awaiting shellfish in the estuary. A linear regression analysis was
performed and indicated that the area of impervious surfaces alone explained 95% of
variability in average fecal coliform abundance in estuaries, suggesting that urban coastal
areas can reduce their environmental impact by land use practices that reduce impervious
surface area. Methods for doing this include incorporating bioswales, raingardens, and
constructed wetlands into development (Mallen, et al., 2000).
Reports and Surveys:
In order to get a graphic understanding of the relationship between land use and
nearshore habitats specific to shellfish, spatial and tabular data were acquired from the
Washington State Department of Health (DOH) and tabular data from the Washington
State Department of Ecology (DOE). The spatial data from DOH represents commercial
growing areas for shellfish in Western Washington including Puget Sound, and marine
water sampling stations in nearshore habitats. Tabular data from DOE represents water
quality sample stations and results from surface water samples taken at those sites.
Classifications:
Shoreline surveys for the Department of Health had evaluated the drainage system
discharges, agricultural activities, on-site sewage systems as well as wastewater treatment
plants, which could have potential adverse effect on the classification of commercial
shellfish growing areas. The actual and possible pollution sources were defined in these
reports according to the following categories (taken verbatim from the Shoreline Surveys
of Berbells (2003 & 2007) and Zabel-Linclon (2005)):
Direct Impact – A “Direct Impact” is a pollution source that is defined by the
National Shellfish Sanitation Program (NSSP) as any waste discharge that has an
immediate adverse effect on the growing area.
Indirect Impact – An “Indirect Impact” is a pollution source that is defined by the
NSSP as any waste discharge that reaches the growing area in a roundabout way.
53
Potential Source – A “Potential Source” is a pollution source that may influence
the water quality in the area. Inadequate setbacks, neglect or abuse of sewage
disposal systems, overgrazed pastures and a large number of wildlife are
examples that could cause a site to be identified as a potential source of
contamination.
No Impact – “No Impact” means the potential source is managed via proper onsite practices or treatment methods so that there is no negative impact on water
quality.
Segments of the shellfish growing areas are presently “classified as APPROVED,
RESTRICTED, UNCLASSIFIED, OR PROHIBITED” which is based on the
identification of pollution source and water quality (Zabel-Linclon, 2005)
The classification of “Conditionally Approved” is in part on wastewater treatment
plant disruptions, but primarily based on rainfall events. A rainfall event that is equal to
or greater than one inch during a 24-hours period will have an imposed restriction to
commercial shellfish harvest for a period of five days. This is true for entire growing
areas classified as conditionally approved (Berbells, 2003). The reasoning behind the five
day wait to lift the restriction on a conditionally approved site after a rainfall event was
revealed during an informational interview with Lawrence Sullivan from the DOH. A
heavy rainfall event will wash contaminates into storm drains, ditches, streams and river
channels. Bacteria are DOH’s contamination of concern, for public health reasons.
Surface water runs off the land and into estuaries where the shellfish are waiting to filter
whatever comes their way. The five day wait period is broken down as, three days for the
bacteria to clean out of the bay to return to a good water quality. Two days after that for
the shellfish to flush out and start taking in water that meets standards.
Criteria:
Shellfish growing areas are classified based on meeting criteria levels for bacteria.
The water samples are taken over a period of time, once a month until approximately 30
samples have been taken. Then these samples are tested for fecal coliform and the results
(organisms/100ml) are recorded. The results are statistically tested for geometric mean
and 90th percentile. The approved standard for shellfish growing waters is stated as: the
54
geometric mean is “not greater than 14 organisms/100ml,” and a “90th percentile not
greater than 43 organisms/100ml (Berbells, 2003 & 2007; Zabel-Linclon, 2005).
Study Areas:
Two areas were selected in South Puget Sound to highlight how human activities
and development have affected commercial shellfish growing areas. The selected areas
were picked for various reasons. One is a wildlife preserve (Nisqually Wildlife Refuge)
that is influenced by activities in its upper watershed; another one is subject to
wastewater treatment, and an area that has been listed with the Department of Ecology as
a 303d for not meeting water quality standards (Henderson Inlet). The reports from DOH
cover the Sanitary Survey and Shoreline Survey for each of these areas.
Description of Growing Area: (As described in reports)
Nisqually Reach: The Nisqually Reach shellfish area is located in
southern Puget Sound in Thurston County. The area extends from Johnson Point
at the northwest to Dupont at the southeast. The Nisqually River, which is the
largest river in the southern Puget Sound, discharges into the southeast end of the
area. The total area of the Nisqually Reach shellfish area, including all
“Approved”, “Restricted”, “Prohibited”, and “Unclassified” areas, is
approximately 4380 acres. Within this area there are approximately 2800 acres
classified as “Approved”. A marina closure zone, classified as “Prohibited”,
occupies an area of approximately 80 acres located at Baird Cove.
Approximately 190 acres between Mill Bight and Puget Marina are
“Unclassified”. Fifty acres of shellfish beds located immediately north of the
mouth of McAllister Creek are classified as “Restricted”, and approximately 1500
acres of the Nisqually River delta located east and west of the river mouth are
classified as “Prohibited” (Melvin, 2006).
The Nisqually Reach shellfish area is located in southern Puget Sound
approximately nine miles east of Olympia.” . . . “Oysters, clams, and geoducks
are harvested commercially from the area. Most of the intertidal oyster and clam
harvest takes place on shellfish beds located at the western edge of the Nisqually
River delta. Subtidal commercial geoduck beds are located along the entire
length of the area. The majority of the area is classified as “Approved” for
commercial shellfish harvest (Melvin, 2006).
55
Henderson Inlet: Henderson Inlet is located at the southern end of the
Puget Sound. The inlet and its watersheds are within the boundaries of Thurston
County. Henderson Inlet is a narrow, shallow embayment approximately six
miles in length with an average width of one half mile. The inlet is open at the
north with Dana Passage to the west, Case Inlet to the north, and Nisqually Reach
to the east (Melvin, 2007).
Development along the marine shoreline and the adjacent uplands is rural
residential. All of the homes along the shoreline and in the adjacent uplands use
on-site systems for the treatment and disposal of sewage. The city of Lacey and a
portion of the city of Olympia are located in watersheds that drain into Henderson
Inlet. Urban development associated with the cities of Lacey and Olympia is
connected to a sewage treatment system. The outfall for this system is located in
the southern end of Budd Inlet, approximately 9.5 miles from the mouth of
Henderson Inlet (Melvin, 2007).
Henderson Inlet is an active commercial shellfish harvest area. Oysters,
hard-shell clams, and geoducks are harvested from the inlet. There are no
recreational shellfish beaches in Henderson Inlet (Melvin, 2007).
The Henderson Inlet commercial shellfish area is located in southern
Puget Sound. The northern half of the inlet is classified as approved for
commercial shellfish harvest and the southern half is classified as conditionally
approved and prohibited. Shellfish harvest in the conditionally approved area is
based on rainfall with a five day closure resulting from 24 hour rainfall totals of
one-half inch or more. Oysters and hard shell clams are harvested commercially
from the area (Melvin, 2007).
Reports by Washington State Department of Health:
Nisqually Reach: The Nisqually Reach shellfish beds are directly impacted by
discharges from the Nisqually River and McAllister Creek. There are 32 marine water
sampling stations in the Nisqually Reach shellfish bed areas. Four of those sites did not
meet water quality standards. Those are sites numbers 224, 234, 235 and 236 (Table 3.1
Below). The sites listed exceeded both the geometric mean and 90th percentile. Sites 224
and 234 are located at the mouth of McAllister Creek, and sites 235 and 236 are located
at the mouth of the Nisqually River. The shellfish beds in the delta from these two
aquatic systems have been classified as Prohibited. No sites within the Nisqually Reach
have been classified as Conditionally Approved.
56
Table 3.1: DOH Sites Not Meeting Water Quality Standards for Nisqually Reach.
Sites in the Prohibited area (Melvin, 2006).
Site #
Geometric Mean
Est. 90th Percentile
224
14.6
70
234
19.1
69
235
16.7
70
236
15.2
73
Henderson Inlet: Henderson has been placed on the 303(d) list as defined by the
Clean Water Act for exceeding the Total Daily Maximum Load for fecal coliform
bacteria. Five sites did not meet the geometric mean, while six sites did not meet the
standard for the 90th percentile (Table 3.2 Below). To be de-listed, a site must meet both
geometric mean and 90th percentile. The site locations are in the southern end of
Henderson Inlet where they are influenced by several discharge points. Table 3.3 shows
the sites classified as Conditionally Approved.
Table 3.2: DOH Sites Not Meeting Water Quality Standards for Henderson Inlet.
Sites in the Prohibited area (Melvin, 2007).
Site #
Geometric Mean
Est. 90th Percentile
185
26.7
185
186
17.8
107
187
12.4
69
188
14.3
89
189
14.0
80
212
16.1
63
57
Table 3.3: Sites Classified As Conditionally Approved Plus Two Approved Sites.
Henderson Inlet Sites classified as Conditionally Approved (CA). The results show a
spike in fecal coliform (FC) in November of 2005. The table only lists spikes in FC at CA
sites. Note: the two sites normally classified as Approved were also affected by this storm
event (Melvin, 2007).
Site #
Date
CFU/100ML
Conditional
Approved
199
Sep-03
46
Yes
197
Jan-04
70
Yes
198
Jan-04
33
Yes
200
Jan-04
79
Yes
200
Feb-04
33
Yes
200
Jun-04
46
Yes
198
Aug-04
33
Yes
194
Oct-04
240
Yes
200
Oct-04
540
Yes
190
Nov-05
240
Yes
191
Nov-05
240
Yes
192
Nov-05
110
Yes
193
Nov-05
350
Yes
194
Nov-05
350
Yes
195
Nov-05
350
Yes
197
Nov-05
350
Yes
198
Nov-05
350
Yes
199
Nov-05
540
Yes
200
Nov-05
240
Yes
201
Nov-05
110
Yes
The 24 hour rainfall of 1.68 inches on Nov 1st 2005 caused high levels of bacterial
loading in marine waters at several sample stations. Table 3.3 above shows sites that are
classified as Conditionally Approved with the addition of two sites that are normally
classified as Approved. The results show a spike in fecal coliform loading in November
58
2005 storm event. The table only lists spikes in FC at CA sites. Note: two sites normally
classified as Approved were affected by this storm event (Melvin, 2007).
Report by Washington State Department of Ecology:
The federal Clean Water Act requires that the Washington State Department of
Ecology (DOE) conduct a Total Maximum Daily Load (TMDL) study of bodies of water
on the 303(d) list. The 303(d) list is for those bodies of water that do not meet water
quality standards. Four creeks in this study are; Meyers Creek, Sleepy Creek, Dobbs
Creek and Goose Creek. All four of these creeks discharge directly into Henderson Inlet.
TMDL samples were taken during critical storm events when bacterial loading is higher.
Sites that consistently exceed the standard of 100 cfu/100ml are placed on the 303(d)
listing until they meet the target standard. The four sites mentioned (Table 3.4 Below)
failed to meet either the geometric mean or the 90th percentile. Table 3.4 also defines the
percent of fecal coliform reduction needed to meet the target value of 100 cfu/100ml
(Sargeant, et al., 2006).
Table 3.4: Creeks Not Meeting TMDL Standards.
Creeks in Henderson Inlet flow basin not meeting TMDL standards for geometric mean,
90th percentile and the needed percentile reduction of fecal coliform to meet water quality
standards (Sargeant, et al., 2006).
Geometric
Site Name
Mean
Est. 90th Percentile
FC Reduction Needed
Meyer Creek
109
741
87%
Sleepy Creek
90
835
88%
Dobbs Creek
299
2420
96%
Goose Creek
54
773
87%
59
Land Use:
The need to define land use increased with population size. Thousands of years
ago indigenous people lived with nature and within its natural cycles. Some peoples were
nomadic as they searched for the things they needed to survive and to be productive.
Some peoples found lands that supplied their every need and they tended to stay more
localized. Whatever the productive strategies of humans were, it was successful and
populations grew. As populations developed in more localized areas, mainly for the
availability of resources, the need to define how the land was used and by who increased.
With increased populations, it became necessary to draw judicial boundaries and
enforce resource management practices. You could do certain activities on your own
property, but your activities were regulated by how they affected your neighbors. With
the need to provide food, water, energy and shelter for a growing population,
environmental issues were generally considered local. With the worldwide changes to
farmland, forests and waterways now upon us, this leaves us facing many challenges to
manage trade-offs in sharing the planet’s resources (Foley, et al., 2005).
Our ancient ancestors lived at a time when ecosystems were mostly pristine and
their impact on the environment was minimal. With the large populations of today, our
impact on the environment is being felt, and many of the disposable products of modern
society are coming back to haunt us. Even hundreds of years ago we could throw
something away and this wonderful planet we lived on would just absorb it. But now we
are discovering that there is no place called “Away.” Our ecosystems have become so
impacted by our activities and our byproducts that those things we thought we could
throw away are now revisiting us through our food, air and water.
Never before in our long human history has it been so important that we change
our thinking on how we use the land we have been given. October 31st 2011 was the day
set by the United Nation’s Demographers when it was stated that the world human
population would hit 7 Billion (Biello, 2011). Our large population has surpassed our
carrying capacity and we face an increased need to share global resources. We have
overseen an incredible loss of biodiversity and increase of pollution to air and water
(Foley, et al., 2005). Our land use problem is two-fold. We need to identify and fix the
60
errors of past land use practices, and also change the way we use our land in order to
minimize our ecological footprint upon it.
Land Use Types: Anthropogenic land use has an effect on the quality of runoff
water from different types of land uses such as urban, commercial, agriculture, industrial,
forestry and recreational. With increasing population and the need for urban and
industrial development, the burden of our environmental footprint increases. River
systems and the watersheds that supply them with runoff water have had their hydrology
altered by increased development. More urban development means more impervious
surfaces. A study by Park et al. (2007) used LANDSAT data to show that land use is a
significant factor in the contribution of pollutant loading. Impervious surfaces and current
stormwater management systems contribute to rapid storm surge into streams and rivers
that normally reach capacity during heavy storm events. Along with impervious surfaces,
the system of creeks and streams that have been channelized into long straight ditches
contribute to storm surge. The purpose of these ditches is to quickly remove runoff water
from developed locations, to prevent water from ponding on property and agricultural
fields. The problem with channelization is the destruction of habitat, undercutting roads
and utilities, increase in non-point pollution, contributions to flooding downstream, and
the devaluation of property in those flood prone areas (US EPA, Date Unknown).
WRIA 11 Parcels: The boundary for the Nisqually River Basin cuts through
three counties: Thurston, Pierce and Lewis. Parcel data were collected for all three of
those counties and the parcels specific to WRIA 11 were extracted from those data using
GIS procedures. A categorical data method was used on the Nisqually River Basin parcel
data set to create different land use types for calculating area and percent of the overall
land use totals. The categorical information for land use types was taken from the land
use code field in each parcel data set.
All the land use type parcels contained within that boundary cover an area of
483,420.90 acres (100% of all land use types). The dominate land use type in the
Nisqually watershed was determined to be Forest at 264,653.17 acres, or 54.75% of the
total land use. That was followed by land that was Underdeveloped, Undeveloped or
61
Open Spaces at 102,412.10 acres, or 21.18% of total. Smaller land use types were for
Residential (58,530.15 acres, or 12.11%) and Agriculture (26,708.06 acres, or 5.52%).
All the land use totals for WRIA 11 can be seen in Table 3.5. The breakdown of totals per
county is in Table 3.6.
Table 3.5: Totals for Land Use Type in WRIA 11.
WRIA 11 Parcels
Land Use Type
All Parcels
Forest
Under & Undeveloped & Open Spaces
Residential
Agriculture
Other (Gov., Schools, Exemptions, etc)
Timber
Manufacturing
Recreation & Cultural
Parks
Industry
Service
Retail
Land Use Totals
Total Acreage
% of Total
483420.90
100.00%
264653.17
54.75%
102412.10
21.18%
58530.15
12.11%
26708.06
5.52%
12635.96
2.61%
7158.29
1.48%
4061.95
0.84%
3100.28
0.64%
2612.74
0.54%
915.64
0.19%
378.14
0.08%
254.42
0.05%
Table 3.6: Totals for Land Use Type by County in WRIA 11.
WRIA 11 Parcels
Land Use Type
All Parcels
Forest
Under & Undeveloped & Open Spaces
Residential
Agriculture
Other (Gov., Schools, Exemptions, etc)
Timber
Manufacturing
Recreation & Cultural
Parks
Industry
Service
Retail
Thurston County
Acreage
% Land Use
108490.24
100.00%
29042.39
26.77%
40577.08
37.40%
20296.44
18.71%
10686.00
9.85%
3319.03
3.06%
584.93
0.54%
49.82
0.05%
2680.77
2.47%
591.14
0.54%
458.48
0.42%
92.70
0.09%
111.46
0.10%
Pierce County
Acreage
% Land Use
208707.29
100.00%
73268.33
35.11%
61799.71
29.61%
36331.45
17.41%
15625.06
7.49%
8443.58
4.05%
6205.36
2.97%
3928.83
1.88%
243.40
0.12%
1983.12
0.95%
457.16
0.22%
278.75
0.13%
142.54
0.07%
Lewis County
Acreage
% Land Use
166223.37
100.00%
162342.45
97.67%
35.31
0.02%
1902.26
1.14%
397.00
0.24%
873.35
0.53%
368.00
0.22%
83.30
0.05%
176.11
0.11%
38.48
0.02%
0.00
0.00%
6.69
0.00%
0.42
0.00%
62
WRIA 13 Parcels: The boundary for the Deschutes River Basin cuts through two
counties, Thurston and Lewis. Parcel data were collected for both counties and the
parcels specific to WRIA 13 were produced and calculated in the same manner as in
WRIA 11.
All the land use type parcels contained within the Deschutes River Basin
(WRIA 13) cover an area of 225,395.53acres (100% of all land use types). The dominate
land use type in the Deschutes watershed was determined to be Forest at 76,356.76 acres,
or 33.88% of the total land use. That was followed by Residential at 47,509.72 acres, or
21.08% of the total land use. The next land use type category was Other (Gov., Schools,
Exemptions, etc.). Another way to look at this category is that of public lands or public
use parcels. This land use type covers 42,837.00 acres, or 19.01% of the total land use
total. Underdeveloped, Undeveloped or Open Spaces was the next largest land use at
31,813.41 acres, or 14.11% of the total. Smaller areas of land were used for and
Agriculture (9,277.41 acres, or 4.12%), Recreation & Cultural (5,713.41 acres, or
2.53%), Industry (4,679.23, or 2.08%) and Manufacturing (2,980.15 acres, or 1%). The
remaining land use categories were less than 1% each. All the land use totals for WRIA
13 can be seen in Table 3.7. To see the breakdown of totals per county, see Table 3.8
below.
Table 3.7: Totals for Land Use Type in WRIA 13.
WRIA 13 Parcels
Land Use Type
All Parcels
Forest
Residential
Other (Gov., Schools, Exemptions, etc.)
Under & Undeveloped & Open Spaces
Agriculture
Recreation & Cultural
Industry
Manufacturing
Parks
Retail
Service
Timber
Land Use Totals
Total Acreage
% of Total
225395.53
100.00%
76356.76
33.88%
47509.72
21.08%
42837.00
19.01%
31813.41
14.11%
9277.41
4.12%
5713.56
2.53%
4679.23
2.08%
2980.15
1.32%
1209.14
0.54%
1075.50
0.48%
1009.58
0.45%
933.57
0.41%
63
Table 3.8: Totals for Land Use Types by County in WRIA 13.
WRIA 13 Parcels
Land Use Type
All Parcels
Forest
Residential
Other (Gov., Schools, Exemptions,
etc.)
Under & Undeveloped & Open
Spaces
Agriculture
Recreation & Cultural
Industry
Manufacturing
Parks
Retail
Service
Timber
Thurston County
Acreage
% Land Use
198545.78
100.00%
49507.01
24.93%
47509.72
23.93%
Lewis County
Acreage
% Land Use
26849.75
100.00%
26849.75
100.00%
0.00
0.00%
42837.00
21.58%
0.00
0.00%
31813.41
9277.41
5713.56
4679.23
2980.15
1209.14
1075.50
1009.58
933.57
16.02%
4.67%
2.88%
2.36%
1.50%
0.61%
0.54%
0.51%
0.47%
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
Impervious Surfaces:
Anthropogenic growth and development have generated a continuing expansion
of impervious surfaces. Many of these come in the form of roads, parking lots, rooftops,
sidewalks and driveways. Impervious surfaces can diminish water quality in two primary
ways. First, contaminates are deposited on surfaces, like roads, parking lots and
driveways, and are then washed into drainage systems that discharge directly into streams
and creeks during rain events. Second, runoff water from rain events and especially
heavy storm events discharge quickly into streams and creeks that cause channel and
bank erosion. This erosion causes high turbidity and can erode or even cover prime
spawning habitat substrate. Because impervious surfaces inhibit storm water from
percolation into the soil, a storm surge discharge is generated. This abnormally high
discharge of water enters the lotic systems earlier than it would otherwise, thus creating
higher water levels and higher flow velocity. This also generates higher flood risk to
susceptible communities. Impervious surfaces remove the natural buffers that would slow
down runoff water and help to recharge groundwater.
64
We pave over the permeable surfaces, erect huge box stores, build our homes, lay
down roads so we can get from one place to the other, and for our convenience cluster
stores together in large impermeable Mega-Malls, so we can shop from store to store
without having to move our car. These parking lots and malls with huge box stores create
a very large impervious footprint that thwarts the natural hydrological cycle.
The Problem: The built environment comes with external cost for which we have
not accounted. The increasing numbers of buildings, parking lots and roads have created
impervious surfaces that block the natural infiltration of rainwater into the ground. The
urbanization of a watershed has profound effects that degrade downstream habitats, as
well as increase flood frequency and decrease the base flow of a river (Asleson, et al.,
2009). U.S. urban land use is projected to increase from 3.1% in the year 2000 to 8.1% in
2050, an area larger than the state of Montana. The growth in urban land necessitates the
need for more planning and regional management of resources and ecosystem services
for this growing population (Nowak & Walton, 2005). Currently, to deal with the
rainwater not infiltrated into groundwater, we build an infrastructure of ditches and
modify stream channels to more quickly and effectively shed excess surface water away
from buildings and parking lots. As long as we are living on higher ground, it may seem
like a good solution to rapidly remove unwanted water downstream to the river. But,
what of the logic that challenges practical ecological reasoning; the need we have to build
in the river’s flood plain.
We build in flood plains because it is flat, easy and cheaper to build there (seldom
mindful the reason it is called a flood plain). Rivers flood from time to time as part of
their natural cycle. This flooding event replenishes nutrients to habitats for free. Also,
that rich soil is a desirable influence for living and working there. So, we build up the
flood plain with more impervious surfaces, the last place for rainwater to get absorbed
into the ground before reaching the river and ultimately the ocean. The efficient drainage
system created to shed storm water, now adds extra water to the river’s flood stage during
storm events. To protect our need to build in the flood plain, we invest more money to
build dikes and levees to protect our flood plain investment, and dams to hold back runoff
water. Those solutions come at of big cost to economy and ecosystems.
65
Another unexpected consequence of impervious surfaces is the contaminant
deposits upon them that get washed directly into streams and rivers, thus impacting
habitats and the species that occupy aquatic habitats. Our growing population and the
increase of urbanization and industrialization are considered the primary factors
responsible for the increase of pollution of our aquatic systems. These water bodies
receive residential and industrial waste from surface runoff. We now have to build
conventional methods for treating this waste, which are generally costly and not
ecofriendly (Dhote & Dixit, 2009). In contrast, there are systems being developed that
mimic a more natural self-purification, water impoundment and infiltration system. A
study by Scholz & Kazemi Yazdi (2009) showed that runoff water and pollution levels
can be successfully reduced by utilization of storm water detention areas.
With increasing population and the need for urban and industrial development, the
burden of our environmental footprint increases. River systems and the watersheds that
supply them with runoff water have been hydrology altered by this increased
development. More urban development means more impervious surfaces. A study by
Park et al., (2007) used LANDSAT data to show that land use as a significant factor in
the contribution of pollutant loading. Impervious surfaces and current storm water
management systems contribute to rapid storm surge into streams and rivers that
normally reach capacity during heavy storm events. Along with impervious surfaces that
contribute to storm surge are the system of creeks and streams that have been channelized
into long straight ditches. The purpose of these ditches is to quickly remove runoff water
from developed locations, to prevent water from ponding on property and agricultural
fields. The problem with channelization is that it destroys habitat, undercuts roads and
utilities, increases non-point pollution, contributes to flooding downstream, and the
devaluation of property in those flood prone areas (US EPA, Date Unknown).
To sum it up, urban areas are growing, and with this growth we generate a larger
ecological footprint. The question we should be asking ourselves in our quest for
development is, “Do we want the land we build on to inhibit the natural hydrologic cycle,
or do we want it to work with the natural cycles to reduce the negative impact of our
presence upon it?”
66
The effect of urban growth with its impervious surfaces is a big problem to tackle,
and some communities show a growing desire to work with natural system to solve the
problems created by growth. Mallin (2000) suggests low-impact development practices
can solve many of the problems produced by the urban impervious footprint.
Analysis of the Lower Henderson Inlet Sub-Basin Septic Systems as Source of Fecal
Coliform Loading on Shellfish Beds:
The WRIA 13 water quality parameter tabular data analysis, discussed in
Chapter 2, revealed 17 monitoring sites on streams having their discharge points into the
Henderson Inlet shellfish beds. Henderson Inlet sub-basin is divided into an upper and
lower flow basin. The upper basin sheds water from a variety of residential and
commercial land use parcels. This area is drained by two main creek systems, Woodland
Creek and Woodard Creek. Woodland Creek flows north through the residential and
commercial districts of the City of Lacey, and discharges at the southern end of
Henderson Inlet estuary. Woodard Creek flows north and drains parts of east Olympia
and western parts of Lacey, before it discharges into the mid portion of Henderson Inlet
estuary on the west side.
Lower Henderson Inlet basin sheds water from residential, agricultural and forest
land adjacent to the inlet on it west and east banks. Figure 3.1 shows the boundary of the
lower basin in the shaded area on the map and the shellfish beds area. Many of the
residential and agricultural parcels in this area have septic systems for wastewater
treatment. Also, seven of the monitoring sites in this basin failed to meet the analysis
criterion for FC 50 at the 35% plus geometric mean standard. Lower Henderson Inlet
basin was chosen for GIS spatial analysis because of the density of parcels within it with
septic systems. This analysis was set up to identify potential point sources of fecal
coliform bacterial contamination. Upper Henderson Inlet flow basin also contained
parcels on septic systems, but are more spread out. The same methods for spatial analysis
can be used in both upper and lower flow basins. The density of septic systems and the
close proximity to shellfish beds is why the lower flow basin was chosen for this study.
67
Figure 3.1: The Lower Henderson Inlet Sub-Basin and Shellfish Bed Area.
The Lower Henderson Inlet Sub-Basin (Shaded) and the General Location of Shellfish Beds
(Orange Arrows).
Point and Non-Point Source Pollution: The source of pollution can be placed in
two categories, point source and non-point source. Point source is when a specific
location can be identified as the discharge of a pollutant. Some examples of this would be
effluence for industry or wastewater treatment. Non-point is when the source of a
pollutant cannot be determined by a specific source. Some examples of this would be
open area where domesticated or wild animals defecate, especially near streams, creeks
and drainage systems. The point source exception to this for domesticated animals would
be where cattle have direct access to lotic systems. When livestock are known to use the
fields next to flowing water and have access to it for drinking water, the chances of fecal
matter getting mixed into the stream are highly suspect.
The Lower Henderson Inlet basin has some agricultural farms with livestock and
this may be a source for fecal coliform in the local creeks, but the more likely scenario is
failing septic systems. This was the reason that identifying potential failed septic systems
68
as the point source was chosen for spatial analysis using GIS. The section on Spatial
Analysis below shows the results of locating septic systems as a possible point source of
the fecal coliform concentrations in local creeks.
Spatial Analysis: All the site locations in WRIA 13 were created using the site
location coordinates provided with the water quality data collected from the Department
of Ecology’s EIM system (Ecy-3, 2011). The geographic coordinate locations were then
turned into point features that identified locations of each monitoring sites. The tables
that were created from the tabular analysis of water quality results were joined to those
monitoring site features. An attribute query was performed to identify those sites where
FC 50 failed to meet the 35% plus geometric mean standard. The result of the attribute
query is shown in Figure 3.2. The seven site features are shown as yellow dots in the
map.
Figure 3.2: The 35% FC 50 Monitoring Sites in the Lower Henderson Inlet.
The FC 50 monitoring sites with results of 35% plus geometric mean standard are shown as
yellow dots.
69
The next step was to identify and create new line features for the creek systems
where the target monitoring sites were located. The new features represent Dobbs Creek,
Sleepy Creek and Swayne Creek. Swayne Creek was not identified in the EIM (Ecy-3,
2011) data, but the Creek name was acquired from a Department of Ecology report on
Henderson Inlet TMDL (Sargeant, et al., 2006). Figure 3.3 shows the new creek features
with red lines.
Sleepy
Swayne
Dobbs
Figure 3.3: The FC 50 Target Creeks in the Lower Henderson Inlet.
The FC 50 Target Creeks Identified are Dobbs Creek, Sleepy Creek and Swayne Creek.
The next step identified the target of single residential parcels with septic systems. Single
resident parcels are the dominant land use type in the lower Henderson Inlet which is why they
were the first choice in looking for failed septic systems. If this spatial analysis investigation were
followed through to conclusion, and no failing septic systems were found among the single
dwellings, then the analysis parameters would be expanded to greater distances from creek
channels and to also include multiple dwelling residential and agricultural land use.
The “locate by distance” (spatial analysis tool) of 1000 feet was selected for parcel
boundary proximity from the target creeks. The distance was arbitrarily picked to gain
70
information on septic systems closest to the target creeks. Note: this is just a place to start the
search for failing septic systems. This method narrows the search pattern on the number of
locations to begin checking septic systems. If no failing septic systems are found in the first
search, then the “locate by distance” can query for those parcels 2000 feet and so on. The target
septic systems are the parcels in Figure 3.4 (below) highlighted in red boundaries.
Figure 3.4: Target Parcels with Septic Systems in the Lower Henderson Inlet.
Parcel Boundaries are within a distance of 1000 feet of the Target Creeks.
Conclusions: This spatial analysis method successfully identified those single
resident parcels with septic systems that are in close proximity to the target creeks. Each
of the parcel data records contains information on the parcel owner, contact and location.
From this information a list of target septic systems can be generated for the Department
of Health or the Department of Ecology to investigate possible failure of septic systems
that discharge wastewater into the local creeks. This method was based on the fecal
coliform standards of 50 cfu/100ml (WA Ecology, 2006) as analysis criterion to protect
against the contamination of shellfish beds. The spatial analysis method used here can
also be applied for recreational primary contact by changing the fecal coliform standard
71
to 100 cfu/100ml (WA Ecology, 2006) as the analysis criterion. Review of these water
quality standards can be found in Table 1.2 in Chapter One.
The easiest phase of the identification of parcels with target septic systems was
performed with the GIS software. The majority of the work in this study was the
management of the water quality tabular data. Those data records needed to be proofed
for quality assurance, organized by monitoring sites, algorithms created for each
parameter based on water quality criteria, a tier ranking system defined, ranking results
tables created, aggregation tables created across multiple parameters and ranking tables
prepared as join table for the GIS location features.
The GIS preparation for the Henderson Inlet study needed the acquisition and
creation of spatial layers in part for display purposes and spatial analysis. The layers for
map image display started with an orthophoto (geographically referenced aerial photo) of
Thurston County. On this image were two other layers, a boundary of the lower
Henderson Inlet flow basin and a hydrology layer for the Henderson Inlet basin creeks.
To create a land use layer for the lower Henderson Inlet, Thurston County parcel data
were used (refer to the section on Land Use / WRIA 13 Parcels). The Geoprocessing Clip
tool in ArcGIS® was used to create parcel features for the lower Henderson Inlet flow
basin area. The water quality data downloaded from EIM (Ecy-3. 2011) provided the
coordinates for the location of each monitoring site. The coordinates were converted to
point features in GIS and used to join the ranking tables for analysis. The last layer
needed was the target creeks that were generated from the first phase of the analysis
process. These were turned into a feature for the next phase of identifying target parcels
with septic systems.
This method for identification of target parcels works well with the above
mentioned analysis structure. This method can be easily replicated for other flow basins
where sewer system types are part of the parcel data. Thurston County did include that
type of information, whereas Pierce and Lewis Counties did not. Other sources of data
would need to be obtained if this type of analysis were performed for Pierce or Lewis
Counties.
72
Chapter Four:
Defining Flow Basin Influence on Degraded Lotic Systems
Introduction:
Identifying degraded water quality at specific sampling sites in streams and rivers
is only one part of the picture of degraded habitats. To understand what is influencing the
water quality results at these sample sites, one must also understand the potential
contributing factors to those results. Water flows downhill, therefore the drainage area
above these water quality sampling sites would be the potential influence on the results
acquired from those sample sites. This area is defined as the flow basin to each of those
sample sites. Within those flow basins are different aquatic systems like streams and
creeks, wetlands, as well as forest and many different other land use types.
Flow basins are digitally defined by using ArcGIS® hydrology tools to analyze
digital elevation models (DEM) for flow direction and accumulation. This method can be
used to calculate area for any spatial data flow point; in the case of this thesis those points
represent the water quality sampling sites of interest. The points of interest are sites not
meeting water quality standard for the highest percentage of time sampled. Once the flow
basin is generated, it is used to calculate the different land use types within its boundary.
The different land use types are extracted from parcel layers provided by the counties
represented by watersheds in this study. The parcel information comes from Thurston,
Peirce and Lewis Counties in western Washington State.
Before defining the land use influences, it is important to describe the types of
impacts from anthropogenic activities from those different land uses.
Watershed Function and Stressors:
Watersheds support diverse components of an ecosystem, while providing a
system that purifies water by filtering out pollution, sediments, and toxins. Increased
population, economic activities, and development have put stresses on watersheds by
added pollution and depletion of resources. This anthropogenic activity may well cause a
decline in living standards and destabilize the integrity of the ecosystem. Ranking among
73
the most urgent issues facing us today, by 200 leading scientists from more than 50
countries, sustainable watershed management should receive some of the highest
priorities (Levy et al., 2006).
It should also be noted that flowing aquatic systems like streams, rivers, and even
drainage ditches have not only that which can be seen within their channels, but also
include the unseen system and the not so obvious. A watershed does what it says; it sheds
water and ultimately flows down slope to its lowest point. Watersheds are integrated
systems of water channels, wetlands, runoff, and ground water. Watersheds are also
complex ecosystems that have established equilibrium of plants and animals over
thousands of years. These complex ecosystems cycle nutrients and basic elements
throughout the watershed with the help of down slope movement, runoff from rainfall,
and animals spreading nutrients throughout local and upstream habitats, to just name a
few. Water seen in a channel actually extends beyond its banks as groundwater. If the
water table is high, the ground water flows toward the open stream channel, and if the
water table is low, the water in the channel will saturate the surrounding soils and
replenish the groundwater. Lipophilic organic toxins in stream water can bond with soil.
In low water table conditions, these contaminants can become imbedded in the soils
around the stream banks as water saturates the surrounding soils. In high water table
conditions, and especially during storm events, lipophilic toxins can enter the aquatic
system through soil erosion. Any contaminants deposited on the ground follow along the
same complex system as do the nutrients and elements. This is why it is so important to
have a functional riparian zone along the edge of flowing systems to filter and uptake
contaminants. A riparian zone is nature’s plant and microorganism remediation system.
This is how nature takes care of itself, but what about anthropogenic disturbances to
nature’s filtration system?
Urban development has impacted greatly the health and equilibrium of
watersheds. Urban watersheds should be perceived as integrated physical systems. Their
function is dependent on the interaction of chemicals, ecological elements, and the
hydrology of the system. Urbanization can have a negative impact in a cumulative
manner as drainage systems and smaller streams contribute higher peak flows as well as
heavy sediment and pollutant loads to the higher order waterways downstream. As
74
waterways travel further downstream through heavily urbanized areas, the stream
conditions worsen. The accumulated impairment is passed down to and affects public
economic and social costs. These impairments correspond to a loss of ecological services.
The communities downstream are often forced to offset these loses (Platt, 2006). Where
natural systems are obstructed by urban development, the best approach is the
establishment of green zones to replace nature’s riparian zones. Planting trees and shrubs,
constructed wetlands and other bioretention areas can be incorporated into these green
zones help mitigate toxic urban runoff.
Spatial Analysis of Ohop Creek in WRIA 11:
The tabular analysis of water quality data (Chapter Two) for the Nisqually River
Basin (WRIA 11) revealed a cluster of multiple parameter failures for the Ohop Creek
monitoring sites. The aggregation table showed 10 of 49 monitoring sites in the Ohop
Creek system had failures across multiple parameters. The Ohop Creek sites, with several
indications of degraded water quality, were selected in this study for further spatial
analysis in GIS.
As many of the 10 monitoring sites were clustered in groups and would have been
cumbersome to generate flow basins to each of the 10 sites to perform an analysis of the
Ohop Creek system. To make the analysis manageable, four of the sites were selected.
The protocol for selection was that all sites had to have failure across multiple
parameters, have an over-all failure rate of 60% or greater and must be spaced at least
one river mile (R.M.) from each other. The four sites selected are listed in Table 4.1
below with a description of each site.
Table 4.1: Ohop Creek Site Selections.
Site ID, over-all percent failure rate, river mile (R.M.) and description of the location.
Site ID
Site 1
Site 2
Site 3
Site 4
Over-All %
61.2 %
OHOPCR(RM0.1)_TAX
62.7%
OHOPCR(RM2.0)_G93
61.36%
OHOPCR(RM3.3
65.75%
OHOPCR(RM6.3)_G95
R.M.
0.1
2.0
3.3
6.3
Location Description
Ohop Creek Near Mouth
Ohop Creek @ Highway 7
Ohop Creek @ Ohop Valley Rd.
250 Ft. Below Ohop Lk. Outlet
75
To prepare the water quality tabular data for spatial analysis, the GIS features
were created for the Ohop Creek flow basin. Because the Ohop Creek sub-basin is within
WRIA 11 and did not have its own defined flow basin feature in GIS, therefore one had
to be created. The other challenge was creating flow basins for each of the four
monitoring sites on Ohop Creek. First the features for four monitoring sites were created
as a separate layer. These four feature points became the flow points in the next step. This
step was accomplished by use of the GIS hydrology tools on a 30 meter DEM to define
flow basins to those specific flow points. With the Ohop flow basin created, it then was
used to clip out the hydro layers for streams and lakes, and to clip out the Ohop parcel
layer from the Pierce County parcel data. The parcel data were grouped into land use
types and their boundaries recalculated. The recalculation of parcel area was necessary
because the Ohop flow basin boundary line cut through some parcels on its edges.
Flow Basins: The Ohop flow basin was generated as a single feature with four
polygons each representing an area above each of the flow points (see Figure 4.1). The
purpose of this was to define all the drainage areas that terminated at those specific
monitoring sites, and delineates the specific areas of influence on water quality sample
results taken at each monitoring site. The entire Ohop flow basin feature was used to clip
out all the land use parcels contained within it (See Figure 4.2). Table 4.2 gives the
number of parcels within the Ohop flow basin and the calculated land use area in acres
and the percent of the total each land use type represents.
Finally the Ohop flow basin feature was divided into four separate polygon
features that represent the flow basins to each of the monitoring sites (flow points).These
polygons represented the area of flow between monitoring sites or from headwaters to
monitoring sites. Each polygon was used as segments to clip land use features for each of
those segments, and to calculate the area of land use types within each segment. More
detail on the land use analysis of each flow basin segment in the following sections.
76
Figure 4.1: The Ohop Flow Basin Feature.
Flow basin single feature showing 4 polygons, one to each flow point.
Figure 4.2: The Ohop Flow Basin with Land Use Features.
Flow basin with land use features clipped from the Pierce County parcel data.
77
Table 4.2: Ohop Flow Basin Calculated Land Use.
Land use type, number of parcels, calculated acreage and the percent land use.
Land Use
FOREST
UNDEVELOPED
RESIDENTIAL
AGRICULTURE
Num. of Parcels
138
547
1204
57
Acres
18411.58
3501.21
2154.43
1035.57
Percent
68.69%
13.06%
8.04%
3.86%
PARKS
MISC.
TIMBER
GOVERNMENT
RECREATION
FLOODWAY
INDUSTRIAL
SCHOOLS
TRANSPORT.
UTILITIES
WATER
SERVICES
RETAIL
FIRE STATION
29
163
22
6
5
10
3
12
23
5
4
9
8
1
555.20
482.25
171.02
165.74
96.15
71.58
71.40
33.46
25.05
11.58
7.64
5.77
5.15
0.05
2.07%
1.80%
0.64%
0.62%
0.36%
0.27%
0.27%
0.12%
0.09%
0.04%
0.03%
0.02%
0.02%
0.00%
Joining Spatial and Tabular Data: The objective of the Ohop Creek study was
to look for possible relationships between land use and the quality of water that flows
through the Ohop Basin. Two types of data were needed to assess the effects of land use
on water quality, the spatial data in GIS and the tabular water quality data from the
analysis in Chapter Two. The spatial data were geographic features such as hydrology
layers, the flow basin layer and the parcel layer. In order to join the tabular data to this
map, point features had to be created to represent the locations of the monitoring sites.
The spatial site location layer was then joined with the water quality tabular data. The
water quality tabular data was the aggregated ranking table (results across multiple
parameters) that was then joined to the point features by site ID.
Flow Basin Analysis: When all the spatial and tabular data were set up for this
section, the next step was to perform the analysis. The four water quality monitoring sites
78
on Ohop Creek divided the Ohop flow basin into four segments. Each segment
represented an area of land use types, number of parcels for each type, acreage of each
parcel type and the percentage each land use type represented in the total of its own
segment. This method showed the variation of land use between each monitoring site.
Once the variation of land use was related to the variation of water quality ranking
results, the relationship between the two could be assessed.
Analysis of Flow Basin Land Use: The Ohop Flow Basin was divided into four
flow basin segments based on monitoring site locations and all down gradient flows to
each monitoring site. Each segment was created as an individual polygon feature that
represented land area that drained to each specific monitoring site. These flow basin
polygons were divided between upper to lower site and headwaters to the next lowest
site, so that there was no overlap of land use area. The segmented flow basins are defined
as follows:
Segment A: Between Site 1 (Lower) and Site 2 (Upper)
Segment B: Between Site 2 (Lower) and Site 3 (Upper)
Segment C: Between Site 3 (Lower) and Eastern Tributary Headwaters (Upper)
Segment D: Between Site 4 (Lower) and Northern Tributary Headwaters (Upper)
The four flow basin segment polygons were used to clip parcel data from the
Pierce County parcel data. The parcel data segments were used to identify the different
land use types within each of the segments. Before the analysis of land use type area,
each parcel segment was recalculated so that it accurately represented the total amount of
acreage per parcel. As mentioned previously, the necessity for recalculation was due to
the flow basin boundaries that had clipped off boundary edges on of some parcels.
The following figures show the map location of each segment, total acreage of
each segment, and the breakdown of each land use type by number of parcels, total
acreage of each land use type and the percent rank of each land use type of the total
acreage per segment. For detailed information see Figure 4.3 for Segment A, Figure 4.4
for Segment B, Figure 4.5 for Segment C and Figure 4.6 for Segment D.
79
Figure 4.3: Segment A of the Ohop Land Use Analysis.
Land use details: number of parcels, acres and percent of total.
Figure 4.4: Segment B of the Ohop Land Use Analysis.
Land use details: number of parcels, acres and percent of total.
80
Figure 4.5: Segment C of the Ohop Land Use Analysis.
Land use details: number of parcels, acres and percent of total.
Figure 4.6: Segment D of the Ohop Land Use Analysis.
Land use details: number of parcels, acres and percent of total.
81
Analysis of Land Use and Water Quality: With the land use analysis and the
water quality data analysis (Chapter Two) completed, the next step was comparing land
use to water quality results. Much of analyzing that relationship was subjective, because
it lent itself to assumptions based on known causes of degraded water quality. For
example: disturbances to soil can cause elevated turbidity levels, fertilizer for gardens
and agriculture can elevate bacterial and nutrient levels, agribusiness feedlots can
contaminate streams with fecal coliform, higher stream temperatures and eutrophication
can deplete dissolved oxygen, lack of riparian zones can elevate stream temperature,
acidic or alkaline chemical contamination can alter stream pH, and impervious surfaces
can alter stream hydrology. These are only a few examples, and GIS is just a
representation of the real world. To know what causes degraded water quality, it would
take a real-world focused study of an affected area. The benefit of doing the analysis first
in GIS is that it helps identify those areas to focus on for the on-the-ground studies.
The land use totals were presented in the above Figures 4.3 through 4.6. To
compare the land use by segment, the next step was to identify the percent rank failure to
meet water quality standards at the flow points for each of those segments. This is
provided by the GIS join tables that contained those results. What remained was to look
at the water quality parameter failure ranking and the higher percentages of land use that
could be influencing the results of water quality sampling. Those comparisons are in the
following section on conclusions.
Notice the relationship of each of the monitoring sites (see Figure 4.7 below) to
the land use segments (Refer to Figure 4.3 through Figure 4.6 above). Everything that
flows to Segment A arrives from all the upper segments (B, C & D). Everything that
flows to Segment B arrives from Segment C and Segment D. Segment D enters Segment
C’s flow basin at two-thirds of the way down its flow basin after the forested parcels of
Segment C. Segment D is the only one not influenced by other land use flow basins.
82
Figure 4.7: The Ohop Flow Basin Four Monitoring Sites.
Ohop flow basin’s monitoring sites in relation to land use.
Table 4.3: Aggregated Water Quality Results for the Ohop Monitoring Sites.
83
Conclusions: By looking at the relationship of water quality to land use by each
land use segment, we see that water quality sample results are affected by what happens
up stream, including the other upstream land use segments. To analyze this relationship I
emphasize that inferring a causal effect on water quality due to surrounding land use is
subjective. The methods outlined in this study are not the end point for resolving water
quality issues, but rather a beginning. The objective of this study is to identify problem
areas.
The Ohop flow basin is a holistic system and to understand how water quality
effects changes throughout this basin, it was logical to track those changes from
headwaters to the mouth of Ohop Creek. Starting with the water quality ranking at Site 4,
the primary issues were temperature and dissolved oxygen (see Table 4.4). Site 4 is
located near the outlet of the large water body of Ohop Lake. Large bodies of water
normally tend to absorb heat from the sun and have higher general temperatures than the
surrounding creeks and streams. There is a strong correlation between water temperature
and dissolved oxygen. As water temperature increases, the dissolved oxygen in the water
is released into the atmosphere. The primary land use in Segment D is forest and
undeveloped land. In respect to land use and water quality it would seem that the land use
types are not the main factor for the water quality results, but rather the large body of
water from Ohop Lake.
Table 4.4: Site 4 Water Quality Result Ranking, Segment D Land Use Totals.
Site 4
Segment D
84
Downstream from Site 4 is Site 3 and Segment C where water quality seems to be
more influenced by land use. For this land use segment, water temperature result ranking
had decreased from the results upstream. Fecal coliform had emerged as an issue at Site 3
(See Table 4.5).
Table 4.5: Site 3 Water Quality Result Ranking, Segment C Land Use Totals.
Site 3
Segment C
First taking the relationship of stream temperature to land use, the notable
changes are in the summer and fall monitoring period. The summer change went from
100% of the time not meeting water quality standards in Segment D, to 75% of the time.
Fall went from 66% to 50% of the time. There are a couple of land use features that could
affect the drop in result ranking by the time the stream flow reached Site 3. The majority
of parcels in Segment C consist of forested land. This amounts to 76.03% of the total land
use (9730.25 acres) in Segment C, most of which is located in the headwaters of the
upper basin. Forested stretches of stream channels tend to stay cool in temperature
because of shading. This majority of forested land is also above the point where Site 4
discharges water from Ohop Lake into Segment C. The mixing of cooler forested stream
water with warmer water from Ohop Lake could account for the reduction in failure rates
to meet analysis criteria. Another factor that could influence the results at Site 3 is the
riparian zones along its banks throughout the Ohop Creek’s pathway in the developed
areas of Segment C. A visual survey could be made of this by looking at an orthophoto of
this area.
85
The next notable change in Segment C is the emergence of issues with fecal
coliform in the stream water. The result ranking for FC 50 (Protection of Shellfish Beds)
is 86% and FC 100 (Primary Recreational Contact) is 59% of the times samples failed the
water quality standards. Wildlife could be a contributing factor, but more likely for these
high numbers the problem lies with land use development. From the developed areas the
possible contributors to fecal coliform in the stream water would be residential and
agriculture. The next highest land use to forest parcels is residential with 427 parcels
covering 1066.88 acres, and fourth in line is agriculture at 32 parcels covering 571.13
acres or 4.46% of the total land use in Segment C. The limiting factor for further analysis
on the fecal coliform relationship is the Pierce County’s parcel data, which did not
include information on sewer system types. The fecal coliform problem could be caused
by leaky or failing septic systems, livestock and domestic animals or a combination
thereof. To get a better answer to the question of the fecal coliform source is to acquire
information on Pierce County septic systems and livestock populations as well as how
many of the livestock have direct access to Ohop Creek. Because the Ohop Creek flow
basin is completely within Pierce County, the same need for further research information
will also be needed for all the other land use segments.
The next downstream relationship is Site 2 and Segment B. The most notable
change here is that the water temperature result ranking has improved. There were no
ranking failure rates above 35%, which could be an outcome of the cooler stream water
from the forested headwaters and riparian zones in Segment C. Fecal coliform still
remains an issue at around the same result ranking as with Site 3. For this segment,
turbidity has emerged as an issue (see Table 4.6). From a visual survey of this area of an
orthophoto, two things become apparent: the reduction of riparian zones and Ohop Creek
being adjacent to large open agriculture field and small residential farms. The second
largest percent of land use in this segment is residential with 25 parcels at 250.72 acres or
21.92% of total land use. The fourth largest percentage is agriculture with eight parcels
consisting of 66.21 acres at 5.79% of total land use. The large open access to Ohop Creek
could be a possible cause of the higher levels of turbidity. This could be a result from
bank erosion of livestock access to the creek. Further site surveys would be needed
before a conclusion could be made in that respect.
86
Table 4.6: Site 2 Water Quality Result Ranking, Segment B Land Use Totals.
Site 2
Segment B
This brings the water quality land use relationship study to the termination point
at Site 1 of the Ohop Creek flow basin. The direct land use influences here are from
Segment A and are contributed to by all the other upstream land use segments. At Site 1
there is a noticeable increase in the analysis criteria rates for fecal coliform. There was an
11% increase for FC 50 to a result ranking of 92% failure of water quality standards.
There was also an increase in FC 100 to a 69% result ranking (see Table 4.7). This would
suggest that Segment A has additional point/non-point source contributions of fecal
coliform to Ohop Creek. In this land use segment residential and agriculture are roughly a
little more than 5% each of the total land use. As with the other land use segments, more
information is needed on septic systems and livestock populations.
Table 4.7: Site 1 Water Quality Result Ranking, Segment A Land Use Totals. Fecal
coliform issues increased at Site 1 from ranking at Site 2 & Site 3 Water temperature
result ranking issues reemerged at Site 1.
Site 1
Segment A
87
The final notable change in Segment A is that stream water temperature issues
have returned. This could be a result from the lack of riparian vegetation on both sides of
Ohop Creek. A portion of the creek runs through open fields and then is only shaded by
trees to the southeast of the channel. This allows the creek to get a good exposure of
afternoon sunlight. Riparian plantings on both sides of Ohop Creek may help to resolve
the temperature issues.
Final Thoughts:
Using water quality data analysis and developing the land use types for analyzing
the relationship between the two has been an arduous process, but well worth the effort.
This type of study can become a useful tool for the decision making process of groups
and agencies to identify those areas best suited for planning the use of restoration dollars.
The maps produced in this type of study give good geographical and tabular information
for potential project sites. The GIS information produced can be used as a platform to
receive further information as well as the creation of new spatial data as field projects
move forward.
There are two other things to acknowledge in this study about the identification of
degraded water quality in Henderson Inlet and Ohop Creek. Those two sites had already
been decided on as part of this thesis study when it was discovered those two locations
had been assigned actual restoration projects. The Department of Health and Department
of Ecology launched a Total Maximum Daily Load (TMDL) study of Henderson Inlet.
The Nisqually Indian Tribe, USFWS, South Puget Sound Salmon Enhancement Group,
National Fish and Wildlife Foundation, Stream Team and the Nisqually Land Trust did
enhancement work on the Ohop Creek channel and added plantings of riparian
vegetation. The discovery of these two major projects, midway through this thesis study,
was taken as a validation of the important work in this thesis to identify areas best suited
for environmental restoration and enhancement projects.
88
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Ecy-2. 2011. Deschutes River Watershed Initial Assessment, Draft May 1995.
Washington Department of Ecology Publication (Draft).
Web Site: http://www.ecy.wa.gov/pubs/95158.pdf
Ecy-3. 2011. Environmental Information Management System (EIM). Washington
Department of Ecology Database Search. Web Site: http://www.ecy.wa.gov/eim/
Pierce County Public Works & Utilities, Surface Water Management (PCPWU) 2011.
Appendix C: Nisqually River Basin Risk Assessment. Flood Risk Assessment,
Nisqually River Basin Plan. Partial Publication, Date Unknown. Pierce County
Webpage:
http://www.co.pierce.wa.us/xml/services/home/environ/water/ps/basinplans/nisqu
ally/New082508/AppendixC-090308.pdf
US EPA (Date Unknown) Stream Channelization, Region 7 Fact Sheet Number 1. United
States Environmental Protection Agency.
http://www.epa.gov/reigon7/wetlands/pdf/ChannelizedFS04-Final.pdf
WA Ecology 2006. Chapter wac173-201A WAC, Water Quality Standards for Surface
Waters of the State of Washington. (Note: Repeat) Last update 5/6/11. Published
on the web at:
https://fortress.wa.gov/ecy/publications/publications/wac173201a.pdf
Wahsington Department Fish and Wildlife (WDFW) 2011. Fisheries Management,
Salmonid Stock Inventory (SaSI) and State Salmon and Steelhead Stock Inventory
(SASSI) reports. Web Site: http://wdfw.wa.gov/conservation/fisheries/sasi/
91
Appendix
Table A1: Lotic Site Locations WRIA 11
Site_ID
% Over All
MC5.8
90.91%
MC4.3
85.00%
MED0.1
81.25%
MID-LAKEDRAIN
77.38%
OHOPCR(RM6.3) G93
75.00%
RSWT
73.81%
11A070D
66.67%
OHOPCR(RM6.3) G95
65.75%
NISQUALLY(39.7)
65.63%
OHOPCR(RM2.0) G93
62.70%
OHOPCR(RM3.3)
61.36%
MUCKCR(RM6.2)
61.21%
OHOPCR(RM0.1) TAX
61.20%
MC4.4TLBU
60.00%
MASHELRV(RM6.0) TAX
59.44%
OHOPCR(RM6.0) TAX
59.31%
RSET
58.33%
OHOPCR(RM0.1) G93
56.80%
MC3.1
56.67%
OHOPCR(RM6.0) G93
56.03%
MC5.4
53.89%
11A090D
52.78%
LIITLEMASHELRV
51.93%
MC4.7
51.48%
MED0.0
50.79%
OHOPCR(RM2.0) G93
50.00%
MUCK01
50.00%
MUCKCR(RM0.1)
50.00%
MASHELRV(RM6.0) G9
50.00%
YELMCR(RM0.1) TAX
50.00%
MC3.7
48.70%
MASHELRIVER
48.68%
OHOPCR(RM9.9) G95
48.57%
11A080D
47.78%
11A070E
47.14%
NISQUALLY(3.7) TAX
47.06%
LAC05
46.67%
SOUTHCK03
46.67%
MUCK22
45.83%
MASHELRV(RM3.2)
44.17%
TANWAXCR(RM0.3) TAX
42.97%
MUCK04
42.50%
MC4.5
42.22%
MUCK24
40.00%
MUCK18
39.58%
MUCK23
38.46%
LYNCH CREEK
38.13%
TANWAX(RM10.2)
34.29%
MC4.3T
34.20%
Location
MCALLISTER BELOW WETLAND
MCALLISTER CREEK AT BLUE BRIDGE
MEDICINE CREEK UPSTREAM FROM MOUTH
MID-LAKE DRAINAGE (W OF 391ST )
OHOP CREEK BELOW OHOP LAKE
WEST TRIB. OF RED SALMON CREEK
NISQUALLY RIVER AT NISQUALLY
OHOP CREEK 250 FEET BELOW OHOP LAKE OUTLET
NISQUALLY RIVER ABOVE MASHEL RV
OHOP CREEK @ HIGHWAY 7
OHOP CREEK @ OHOP VALLEY ROAD
MUCK CREEK AT ROY (WARREN AVE)
OHOP CREEK NEAR MOUTH
TRIB OPPOSITE MEDICINE CREEK, L/B U/S
MASHEL RIVER @ ALDER CUT-OFF RD
OHOP CREEK BELOW OHOP LAKE
EAST TRIB. OF RED SALMON
OHOP CREEK NEAR MOUTH
MCALLISTER CREEK BELOW I-5
OHOP CREEK BELOW LYNCH CREEK
U/S OF LITTLE MCALLISTER
Nisqually R abv Powell Cr
LITTLE MASHEL RIVER @ HIGHWAY 161
MCALLISTER CREEK AT STEILACOOM ROAD
MEDICINE CREEK AT MOUTH
OHOP CREEK @ HIGHWAY 7
MUCK CR AT MOUTH
MUCK CREEK NEAR MOUTH
MASHEL RIVER @ ALDER CUT-OFF RD
YELM CREEK NEAR MOUTH
MCALLISTER CREEK ABOVE MARTIN WAY
MASHEL RIVER @ MOUTH
OHOP CREEK BELOW TWENTY-FIVE MILE CREEK
Nisqually R @ McKenna
NISQUALLY RIVER AT NISQUALLY
NISQUALLY RIVER @ HANDICAP ACCESS
LACAMAS CR AT 8TH AVE S.
SOUTH CR NEAR 294TH ST E. PIERCE CO.
MUCK CR AT 8TH AVE E.
MASHEL RIVER @ HIGHWAY 7
TANWAX CREEK @ HARTS LAKE ROAD
MUCK CR AT WARREN STREET, NEAR ROY
MCALLISTER BELOW RESIDENTIAL AREA
MUCK CR AT 70TH AVE E.
MUCK CR AT 8TH AVE S.
MUCK CR AT WEILER RD, PIERCE CO.
LYNCH CREEK @ OHOP VALLEY EXTENSION RD
TANWAX CREEK @ 352ND AVE
TRIB TO MCALLISTER D/S OF BLUE BRIDGE
92
Table A2: Lentic Site Locations WRIA 11
Site_ID
TANWAXLAKE
SILVERLAKE
CLETH11
OHOPLAKE
RAPJOHNLAKE
OHOPI11
RAPPI11
WHIPI11
OHOPLAKESTA3
OHOPLAKESTA2
ST#TH11
HARPI11
MINERALLAKE
TANPI11
OHOPLAKESTA1
CLEARLAKE
HARTSLAKE
ST_TH11
% Over All
90.48%
89.13%
88.00%
86.84%
80.95%
75.48%
68.89%
68.59%
65.35%
62.90%
62.58%
62.54%
62.00%
60.48%
58.92%
56.25%
52.74%
40.00%
Location
TANWAXLK (MIDDLE) Lake/Pond/Reservoir
SILVERLK (MIDDLE) Lake/Pond/Reservoir
CLEAR Lake/Pond/Reservoir
OHOPLK (MIDDLE) Lake/Pond/Reservoir
RAPJOHNLK (MIDDLE) Lake/Pond/Reservoir
OHOP Lake/Pond/Reservoir
RAPJOHN Lake/Pond/Reservoir
WHITMAN Lake/Pond/Reservoir
OHOP LAKE STATION 3 (NORTH END) Lake/Pond/Reservoir
OHOP LAKE STATION 2 (MIDDLE) Lake/Pond/Reservoir
ST. CLAIR Lake/Pond/Reservoir
HARTS Lake/Pond/Reservoir
MINERALLK (MIDDLE) Lake/Pond/Reservoir
TANWAX Lake/Pond/Reservoir
OHOP LAKE STATION1 (SOUTH END)
CLEARLK (MIDDLE) Lake/Pond/Reservoir
HARTSLK (MIDDLE) Lake/Pond/Reservoir
ST. CLAIR Lake/Pond/Reservoir
Table A3: Lotic System Tide Gate Locations WRIA 11
Site_ID
TG14L
TG8L
TG10L
TGBL
TG4L
TG3L
TG13L
TG12L
TG2L
TG9L
TG15L
TG9W
TG5L
TG11W
TG1L
TG11L
% Over All
100.00%
100.00%
94.44%
80.39%
77.78%
75.00%
72.78%
70.04%
63.33%
62.50%
57.22%
55.20%
55.00%
40.28%
36.51%
28.57%
Location
TIDE GATE 14 BY LAND
TIDE GATE 8 BY LAND
TIDE GATE 10 BY LAND
TIDE GATE B BY LAND
TIDE GATE 4 BY LAND
TIDE GATE 3 BY LAND
TIDE GATE 13 BY LAND
TIDE GATE 12 BY LAND
TIDE GATE 2 BY LAND
TIDE GATE 9 BY LAND
TIDE GATE 15 BY LAND
TIDE GATE 9 BY WATER
TIDE GATE 5 BY LAND
LITTLE MCALLSITER AT MOUTH
TIDE GATE 1 BY LAND
STORMWATER D/S OF LITTLE MCALLISTER
93
Table A4: Lotic Site Locations WRIA 13. Numerals in Red indicate monitoring sites
where only one water quality parameter failed to meet analysis criteria.
Site_ID
W DRAIN
WD6.8
FLEMING CR
CC0.6
LOWER
CREEKB
G07001162238
CREEKA
SL0.8
WL1.0
W RM 3.1
CC0.4
DB0.1 (DSAR)
WD0.0
SPDITCH2
WL0.2
GO0.4
W RM 4.2
WD5.1
SWO
STORMWATER
SPDITCH1
SL0.1
WD6.9
W RM 3.8
13A150D
WL3.4
WD6.2
WL2.25T
WL2.6
WL1.9T
SCR
13A060D
CC0.2
W RM 3.7
WL2.9
13A060E
WL3.1
Count Over All
Location
3 93.33% WOODLAND CREEK (STORM DRAIN)
3 86.67% WOODARD CREEK @ PACIFIC AVE
FLEMINGCR (CREEKC) (@ JOHNSON POINT
3 85.71% RD)
3 83.33% COLLEGE CREEK @ BIKE PATH
1 81.82% INDIAN CREEK
3 77.78% CREEKB (NW OF FLEMING CREEK)
3 77.29% BLACK R. @ Belmore Rd.
1 75.00% CREEKA (EAST OF SWAYNE DR NE)
3 71.43% SLEEP CREEK @ MOUTH
3 70.00% WOODLAND CREEK AT HOLLYWOOD DRIVE
1 70.00% WOODLAND CREEK (RM 3.1)
3 69.05% COLLEGE CREEK @ CENTURY COURT
3 64.24% DOBBS CREEK AT JOHNSON POINT ROAD
3 62.96% WOODARD CREEK @ WOODARD BAY
4 62.50% SMITH PRAIRIE DITCH 2
3 61.11% WOODLAND CREEK @ HAWKS PRARIE ROAD
3 61.11% BLACK R. @ Belmore Rd.
3 60.56% WOODLAND CREEK (RM 4.2)
3 60.19% WOODARD CREEK OFF LINDELL ROAD
SWO STORMWATER (END OF SWAYNE DRIVE
3 60.00% NE)
1 60.00% SMITH PRAIRIE DITCH 1
3 56.67% SLEEP CREEK @ MOUTH
3 56.48% WOODARD CREEK @ BIKE PATH
2 55.79% WOODLAND CREEK (RM 3.8)
1 53.33% Deschutes R nr Rainier
4 50.61% WOODARD CREEK @ 36TH AVE
3 50.00% WOODARD CREEK @ ENSIGN RD
3 50.00% EAGLE CREEK
3 50.00% WOODLAND CREEK @ 21ST COURT NE
1 45.45% FOX CREEK
3 44.63% SWIFT CREEK
3 44.27% DESCHUTES RIVER AT E ST BRIDGE
3 44.05% COLLEGE CREEK BEHIND TOP FOODS
5 43.06% WOODLAND CREEK (RM 3.7)
3 40.91% WOODLAND CREEK @ DRAHAM RD
2 38.90% DESCHUTES RIVER AT E ST BRIDGE
3 34.85% WOODLAND CREEK D/S OF I-5
94
Table A5: Lentic Site Locations WRIA 13
Site_ID
Count Over All
Location
MCITH11
3 100.00% MCINTOSH Lake/Pond/Reservoir
BLATH11
5 99.43% Black Lake/Pond/Reservoir
LONTH11
4 91.13% LONG Lake/Pond/Reservoir
CHATH11
4 90.66% CHAMBERS Lake/Pond/Reservoir
OFFTH11
5 88.32% OFFFUT Lake/Pond/Reservoir
PATTH21
4 85.24% PATTISON (SOUTH ARM) Lake/Pond/Reservoir
LAWTH11
4 84.01% LAWRENCE Lake/Pond/Reservoir
PATTH11
5 67.05% PATTISON (NORTH ARM) Lake/Pond/Reservoir
MUNTH11
3 62.23% MUNN Lake/Pond/Reservoir
HICTH11
4 60.26% HICKS Lake/Pond/Reservoir
WARTH11
5 46.92% WARD Lake/Pond/Reservoir
95
Loft_DMES2012.pdf