Flathead Lake Basin LSPC Modeling Approach and Quality Assurance Project Plan

Technical Advisory Group Review Draft DO NOT CITE OR QUOTE

February 9, 2012

Prepared for:

U.S. Environmental Protection Agency Montana Operations Office Project Manager: Jason Gildea

Prepared by:

Tetra Tech, Inc.

This quality assurance project plan (QAPP) has been prepared according to guidance provided in EPA Requirements for Quality Assurance Project Plans (EPA QA/R-5, EPA/240/B-01/003, U.S. Environmental Protection Agency, Office of Environmental Information, Washington, DC, March 2001) and EPA Guidance for Quality Assurance Project Plans for Modeling (EPA QA/G- 5M, EPA/240/R-02/007, U.S. Environmental Protection Agency, Office of Environmental Information, Washington, DC, December 2002) to ensure that environmental and related data collected, compiled, and/or generated for this project are complete, accurate, and of the type, quantity, and quality required for their intended use. Tetra Tech will conduct the work in conformance with the quality assurance program described in the quality management plan for Tetra Tech’s Fairfax Center and with the procedures detailed in this QAPP.

Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page ii Revision History

Document Name Revision Release Date Revisions Flathead Basin Modeling and 1.0 May 19, 2011 Submitted to EPA and DEQ for Nutrient TMDL Development – initial, internal review Modeling Approach and Quality Assurance Project Plan – Preliminary Review Draft Flathead Lake Basin LSPC 2.0 February 9, 2012 Addressed comments provided by Modeling Approach and Quality EPA and DEQ including a Assurance Project Plan – significant re-write of Section 4.5 Technical Advisory Group Review Draft

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page iii Approvals

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page iv Contents Contents ...... iv 1.0 Introduction ...... 1 1.1 Description of the Watershed ...... 3 1.2 Modeling Purpose and Need ...... 5 1.3 Project/Task Organization ...... 6 2.0 Modeling Goals and Objectives ...... 9 3.0 Model Selection ...... 11 3.1 Prior Studies ...... 11 3.2 Model Needs ...... 12 3.3 LSPC Watershed Model ...... 13 4.0 LSPC Model Setup ...... 15 4.1 Simulation Period ...... 17 4.2 Supporting Documentation ...... 17 4.3 Watershed Segmentation ...... 19 4.4 Waterbody Representation ...... 23 4.4.1 Streams ...... 23 4.4.2 Lakes ...... 24 4.5 Weather Data ...... 28 4.6 Land Use/Land Cover Representation ...... 29 4.6.1 Time-Variable Land Use ...... 31 4.6.2 Roads ...... 35 4.6.3 Timber Harvest ...... 36 4.6.4 Forest Fires ...... 38 4.6.5 Septic Systems ...... 39 4.6.6 Urban Stormwater ...... 42 4.6.7 Bank Erosion and Mass Wasting ...... 43 4.6.8 Agriculture ...... 45 4.6.9 Golf Courses ...... 47 4.7 Point Sources ...... 50 4.7.1 Wastewater Treatment Plants and Other Non-stormwater NPDES Dischargers ...... 50 4.7.2 Non-MPDES Dischargers ...... 51 5.0 Model Calibration and Uncertainty Analysis ...... 52 5.1 LSPC Model Calibration ...... 52 5.1.1 Flow Calibration...... 53 5.1.2 Water Chemistry Calibration Sites ...... 55 5.1.3 Acceptance Criteria for Model Calibration ...... 58 5.1.4 Model Validation ...... 60 5.2 Uncertainty Analysis ...... 61 5.3 Reconciliation with User Requirements ...... 63 6.0 Nondirect Measurements (Secondary Data Acquisition Requirements) ...... 64 6.1 Quality Control for Nondirect Measurements ...... 64 6.2 Data Management and Hardware/Software Configuration ...... 64 7.0 Model Uses and Scenarios ...... 65 7.1 Existing Condition Scenario ...... 65 7.2 Natural Occurring Scenario ...... 66 7.3 Water Quality Standards Compliance ...... 66 7.4 BMP Scenarios ...... 66 8.0 Assessment and Response Actions and Reports to Management ...... 67 8.1 Assessment and Response Actions ...... 67

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page v 8.1.1 Model Development Quality Assessment ...... 68 8.1.2 Software Development Quality Assessment ...... 68 8.1.3 Surveillance of Project Activities ...... 69 8.2 Reports to Management ...... 69 9.0 Model Documentation ...... 70 10.0 Schedule ...... 71 11.0 References ...... 73 Appendix A ...... 77 Meteorological Data for Flathead Lake Basin ...... 77 LSPC Modeling ...... 77 Appendix B ...... 78 Precipitation Data ...... 78 Appendix C ...... 79 Temperature Data ...... 79

Appendices

Appendix A: Meteorology Data Appendix B: Precipitation Data Appendix C: Temperature Data

Figures

Figure 1. Location of the Flathead Lake basin...... 4 Figure 2. Organizational structure of the Flathead Lake Basin modeling project...... 8 Figure 3. Location of the Phase I modeling watersheds...... 16 Figure 4. Phase 1 modeling subwatersheds for the Middle Fork Flathead River Planning Area...... 20 Figure 5. Phase 1 modeling subwatersheds for the North Fork Flathead River Planning Area...... 21 Figure 6. Phase 1 modeling subwatersheds for the Swan River Planning Area...... 22 Figure 7. Stream channel representation in the LSPC model...... 23 Figure 8. Relationship between surface area and volume for selected lakes within the Flathead Lake Basin...... 27 Figure 9. Conceptual schematic of the two-step virtual lake approach for representing lake impact...... 28 Figure 10. LSPC organizational structure and model attributes for land uses and subwatersheds...... 29 Figure 11. Land Cover/Land Use in the Flathead Lake Basin (from 2006 NLCD)...... 30 Figure 12. Schematic of hydrologic simulations associated with land units in LSPC...... 31 Figure 13. Conceptual representation of time-variable land use in LSPC...... 32 Figure 14. Western Montana ECA Recovery Curves (Personal communication, Craig Kendall, Flathead National Forest, December 6, 2011)...... 37 Figure 15. Exposed eroding terrace, North Fork Flathead River (Sugden 2011)...... 44 Figure 16. Flathead Lake Basin Golf Courses...... 48 Figure 17. Conceptual overview of the watershed model calibration process...... 52 Figure 18. Flow calibration stations for the Phase I modeling...... 54

Tables

Table 1. Comparison of U.S. EPA recommended guidelines (as described in U.S. EPA QA/G-5M) and proposed project plan document outline ...... 2 Table 2. Land management in the Flathead Lake basin ...... 5

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page vi Table 3. Nutrient-listed waters within the Flathead Lake Basin from the 2010 303(d) List...... 6 Table 4. Sediment-Listed waters with the Flathead Lake Basin from the 2010 303(d) List...... 9 Table 5. Draft Numeric Nutrient Standards for Wadeable Streams in the Flathead Basin ...... 10 Table 6. Inventory of precipitation data for modeling (10/1/1979 – 9/30/2007) ...... 18 Table 7. Available data for lakes to be explicitly modeled in LSPC ...... 25 Table 8. Summary of available land use/activity data for describing the current and historic conditions in the Flathead Lake Basin ...... 33 Table 9. Roads summary ...... 35 Table 10. Preliminary Pollutant concentrations (mg/L) used to simulate roads in the Flathead Lake LSPC model ...... 35 Table 11. Summary of available forest fires data ...... 38 Table 12. Burn severity of selected fires in the Flathead National Forest...... 39 Table 13. Burn severity of selected fires in the Flathead National Forest ...... 39 Table 14. DEQ septic system nitrate loading matrix...... 41 Table 15. DEQ septic system phosphorus loading matrix ...... 41 Table 16. Compilation of National, Regional, and Local Stormwater Discharge Data ...... 43 Table 17. Golf Courses in the Flathead Lake Basin...... 47 Table 18. MPDES permitted facilities in the Flathead Lake Basin ...... 50 Table 19. Calibration sites for flow ...... 53 Table 20. Phase I Calibration sites for total phosphorus ...... 56 Table 21. Phase I Calibration sites for total nitrogen ...... 57 Table 22. Phase I Calibration sites for total suspended solids and suspended sediment concentration ...... 58 Table 23. Performance Targets for HSPF Hydrologic Simulation (Magnitude of Annual and Seasonal Relative Mean Error (RE); Daily and Monthly R2) ...... 60 Table 24. Performance Targets for HSPF Water Quality Simulation (Magnitude of Annual and Seasonal Relative Average Error (RE) on Daily Values) ...... 60 Table 25. Tentative schedule ...... 71

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page vii Abbreviations and Acronyms

ASAE American Society of Agricultural Engineers BMP Best Management Practices CAFO Concentrated Animal Feeding Operation CAMA Computer-Assisted Mass Appraisal CBOD carbonaceous biological oxygen demand CFD cumulative frequency distribution CSKT Confederated Salish and Kootenai Tribes CVS concurrent version control system DEM digital elevation model DEQ Department of Environmental Quality (Montana) DMR discharge monitoring report DNNC Draft Numeric Nutrient Criteria DNRC Department of Natural Resources and Conservation (Montana) DO dissolved oxygen DOH Department of Health DQO data quality objective DRG digital raster graphics ECA Equivalent Clearcut Area FLBS Flathead Lake Biological Station FNF Flathead National Forest FORTRAN FORmula TRANslation FWP Fish, Wildlife, and Parks (Montana) FWS U.S. Fish and Wildlife Service GIS Geographic Information System HSPF Hydrologic Simulation Program FORTRAN HU hydrologic unit ICIS Integrated Compliance Information System ITSD Information Technology Services Division LANDSAT Land Remote-Sensing Satellite LIDAR Light Detection And Ranging LSPC Loading Simulation Program C++ MANAGE Method for Assessment, Nutrient-Loading, and Geographic Evaluation of Nonpoint Pollution MCA Montana Code Annotated MGD Millions of gallons per day MPDES Montana Pollutant Discharge Elimination System MS4 Municipal Separate Storm Sewer System NAIP National Agriculture Imagery Program NCDC National Climatic Data Center NDWRCDP National Decentralized Water Resources Capacity Development Project NEP Natural Erosion Potential NHD National Hydrography Dataset NHN National Hydrology Network (British Columbia) NLCD National Land Cover Dataset NPDES National Pollutant Discharge Elimination System NRIS Natural Resource Information System NURP Nationwide Urban Runoff Program NWIS National Water Information System

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page viii ODEQ Oregon Department of Environmental Quality PIBO PACFISH/INFISH Biological Opinion QA quality assurance QAPP Quality Assurance Project Plan QC quality control SNOTEL snow telemetry SU Stand Unit SUSTAIN System for Urban Stormwater Treatment and Analysis Integration Model SWMM Storm Water Management Model SWTS Subsurface Wastewater Treatment Systems TAG Technical Advisory Group TIGER Topologically Integrated Geographic Encoding and Referencing TKN total Kjeldahl nitrogen TMDL total maximum daily load TOL Task Order Leader TN total nitrogen TP total phosphorus TSS total suspended solids USDA U.S. Department of Agriculture U.S. EPA U.S. Environmental Protection Agency USFS U.S. Forest Service (U.S. Department of Agriculture) USGS U.S. Geological Survey (U.S. Department of the Interior) WEPP Water Erosion Prediction Project Model WWTP wastewater treatment plant

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Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 1

1.0 Introduction In 2001, the Montana Department of Environmental Quality (DEQ) completed Total Maximum Daily Loads (TMDLs) for nutrients in Flathead Lake. In the TMDL report, DEQ stated that nutrient loads (i.e., nitrogen and phosphorus) to Flathead Lake need to be reduced by 25 percent to meet water quality standards. However, the TMDL also recognized that there were many uncertainties regarding nutrient loading, target development, and sources in the Flathead Lake watershed. Also, the report did not address the other impaired waterbodies in the Flathead Lake watershed. An adaptive management strategy was proposed to address these issues, which called for additional monitoring, planning, and development of a watershed model (i.e., Phase II of the Flathead Lake TMDL effort). The purpose of this report is to describe the approach for developing and using a watershed model to help develop TMDLs for Flathead Lake and impaired waterbodies in the Flathead Lake watershed. U.S.EPA has contracted with Tetra Tech, Inc. to support the development of the watershed model.

In addition, this report serves as the Quality Assurance Project Plan (QAPP) for the modeling process, as recommended by U.S. EPA. The QAPP provides a general description of the data quality objectives (DQOs) and quality control (QC) procedures to ensure that the final product satisfies user requirements. U.S. EPA (2002) recommends that a QAPP document contains 22 elements (as defined in Table 1). However, this report is both a project plan and a QAPP, and as such, presents the required quality elements in the order that makes the most sense for project planning. Table 1 provides a cross reference between the U.S. EPA quality elements and the sections within this document where they are discussed.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 2 Table 1. Comparison of U.S. EPA recommended guidelines (as described in U.S. EPA QA/G-5M) and proposed project plan document outline U.S. EPA QA Element Flathead Lake Project Plan A1 Title and Approval Sheet Section i – Table of Contents/Title Page/ A2 Table of Contents Approval Sheet A3 Distribution List A4 Project/Task Organization Section 1.3 A5 Problem Definition/Background Section 1.1, Section 2.0 A6 Project/Task Description Sections 4 and 5 A7 Quality Objectives and Criteria Section 5.1 A8 Special Training/Certification Not Applicable A9 Documents and Records Sections 2 and 4; discussed with individual elements. B1 Sampling Process Design Not Applicable B2 Sampling Methods Not Applicable B3 Sample Handling and Custody Not Applicable B4 Analytical Methods Not Applicable B5 Quality Control Not Applicable B6 Instrument/Equipment Testing, Inspection, and Not Applicable Maintenance B7 Instrument/Equipment Calibration and Frequency Not applicable B8 Inspection/Acceptance Requirements for Supplies Not Applicable and Consumables B9 Non-direct Measurements Section 6.0 B10 Data Management Section 6.0 C1 Assessment and Response Actions Section 8.1 C2 Reports to Management Section 8.2 D1 Data Review, Verification, and Validation Section 5.1 D2 Verification and Validation Methods Section 5.1 D3 Reconciliation with User Requirements Section 5.3

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1.1 Description of the Watershed Flathead Lake is a large, glacial lake located in northwest Montana near the cities of Bigfork, Kalispell, and Polson. It is the largest natural freshwater lake in the western United States, with a maximum depth of 370.7 feet and a surface area of 191 square miles (Flathead Lake Biological Station [FLBS] 2001). The Flathead Lake watershed spans two countries (United States and Canada), the Flathead Indian Reservation, six counties (Flathead, Lake, Lewis and Clark, Lincoln, Missoula, and Powell), and has a total area of 7,093 square miles. Major cities in the watershed include Kalispell (pop. 21,640), Whitefish (pop. 8,400), Columbia Falls (pop. 5,361), Polson (pop. 5,231), and Bigfork (4,270) (Montana Department of Commerce, 2012). Between 1990 and 2009 the population of Flathead County increased by more than 50 percent, from 59,218 to 89,624 (US Census 2009).

Numerous large and small rivers drain the watershed. The National Hydrography Dataset (NHD) reports 14,055 miles of streams in the watershed, and 3,372 lakes (192,114 total acres) (U.S. Geological Survey [USGS] 2008). Major tributaries include the: North, Middle, and South Forks of the Flathead River, Swan River, Stillwater River, Whitefish River, and Ashley Creek (Figure 1). The North, Middle, and South Forks join near the City of Hungry Horse to form the main-stem of the Flathead River. The Stillwater River, Whitefish River, and Ashley Creek discharge into the Flathead River in the vicinity of Kalispell. The Swan River discharges directly into Flathead Lake at Bigfork. Kerr Dam, located at the southern end of the lake, is used to maintain the lake’s elevation between 2,883 and 2,893 feet above sea level (Personal Communications, PPL Montana, April 8, 2008).

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 4

Figure 1. Location of the Flathead Lake basin.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 5 Land in the Flathead Lake basin is managed by a number of private, city, county, state, and federal entities. The U.S. Forest Service (USFS) owns the majority of the watershed (52.6 percent) followed by Glacier National Park (13.8 percent) and private ownership (11.5 percent) (Table 2; Figure 1).

Table 2. Land management in the Flathead Lake basin Management Percent Acres U.S. Forest Service 53.4% 2,418,254 Glacier National Park 14.0% 635,380 Private Lands 11.2% 507,034 British Columbia 8.6% 389,492 Water 4.1% 187,615 Montana State Lands 4.3% 193,884 Private Industrial Forest 2.1% 93,663 CSKT 1.3% 59,550 Other <1% 40,635 Total 100% 4,525,507 CSKT = Confederated Salish and Kootenai Tribes

1.2 Modeling Purpose and Need The water quality modeling effort described in this report is intended to help address nutrient sources, targets, and watershed nutrient loading to Flathead Lake. In addition, ten waterbodies in the Flathead Lake watershed were listed on Montana’s 2010 303(d) list because of water quality impairments due to nutrients or nutrient response variables: . Challenge Creek – Headwaters to mouth (Granite Creek) . Ashley Creek – Ashley Lake to Smith Lake . Ashley Creek – Bridge crossing on Kalispell airport road to the mouth (Flathead River) . Spring Creek – Headwaters to mouth (Ashley Creek) . Fish Creek – Headwaters to mouth (Ashley Lake) . Lake Mary Ronan (threatened status on 303(d) list) . Stillwater River – Logan Creek to mouth . Sheppard Creek – Headwaters to mouth (Griffin Creek-Logan Creek-Talley Lake) . Whitefish River – Whitefish Lake to the mouth, confluence with the Stillwater River Listed causes of impairment for these waterbodies included chlorophyll-a, dissolved oxygen, nitrate, nitrate/nitrite, total Kjehldahl nitrogen (TKN), total nitrogen, and total phosphorus (Table 3). TMDLs need to be developed for these waterbodies, and models can help facilitate TMDL development for these streams and lakes in addition to fulfilling the needs of the Flathead Lake Phase II effort.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 6 Table 3. Nutrient-listed waters within the Flathead Lake Basin from the 2010 303(d) List TMDL Waterbody Name, Location ID305B Pollutant Planning Area Description CHALLENGE CREEK, Flathead MT76I002_040 headwaters to mouth (Granite Phosphorus (Total) Headwaters Creek) Oxygen, Dissolved MT76O002_010 ASHLEY CREEK, Ashley Lake to Smith Lake Phosphorus (Total) Total Kjeldahl Nitrogen Nitrate/Nitrite (Nitrite + Nitrate as N) ASHLEY CREEK, bridge MT76O002_030 crossing on Kalispell airport road Oxygen, Dissolved

to the Flathead River Phosphorus (Total) Total Kjeldahl Nitrogen Nitrate/Nitrite (Nitrite + Nitrate as N) Flathead - SPRING CREEK, headwaters to MT76O002_040 Oxygen, Dissolved Stillwater mouth (Ashley Creek) Phosphorus (Total)

Total Kjeldahl Nitrogen FISH CREEK, headwaters to MT76O002_050 Phosphorus (Total) mouth (Ashley Lake) STILLWATER RIVER, Logan Phosphorus (Total) MT76P001_010 Creek to mouth Nitrates SHEPPARD CREEK, Nitrate/Nitrite (Nitrite + Nitrate MT76P001_050 headwaters to mouth (Griffin as N) Creek-Logan Creek-Talley Lake) Phosphorus (Total) WHITEFISH RIVER, Whitefish MT76P003_010 Lake to the mouth, confluence Nitrogen (Total) with the Stillwater River Chlorophyll a (Threatened Flathead Lake MT76O004_020 LAKE MARY RONAN Status)

1.3 Project/Task Organization The organizational aspects of this modeling project provide the framework for conducting the necessary tasks. The organizational structure and function can also facilitate task performance and adherence to QC procedures and quality assurance (QA) requirements. Key task roles are filled by the persons who are leading the various technical phases of the project and the persons who are ultimately responsible for approving and accepting final products and deliverables.

The project organization chart, provided in Figure 2, illustrates the relationships and lines of communication among all participants and data users. The responsibilities of these persons are described below.

Jason Gildea, the U.S. EPA Project Coordinator, will provide oversight for this contract. He will review and approve the QAPP and ensure that all contractual issues are addressed as work is performed. Mr. Gildea will also provide overall project/program oversight for this study and will work with the Tetra

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 7 Tech Task Order Leader (TOL) to ensure that project objectives are attained. Mr. Gildea will also have the following responsibilities: . Providing oversight for model selection, data selection, model calibration, model validation, and adherence to project objectives . Maintaining the official approved QAPP . Coordinating with the TAG, DEQ, and others to ensure technical quality and contract adherence

The U.S. EPA Region QA Officer, Linda Himmelbauer, will be responsible for reviewing and approving this QAPP. Her responsibilities will also include conducting external performance and system audits and participating in Agency QA reviews of the study.

Kevin Kratt will serve as the Tetra Tech TOL. Mr. Kratt will supervise the overall project, including study design and model application. He will provide general oversight and guidance to the Modeling Leader and the rest of the Tetra Tech project team. The Modeling Leader, John Riverson, will assist the TOL in fulfilling his responsibilities. Specific responsibilities of the Tetra Tech TOL include the following: . Coordinating project assignments, establishing priorities, and scheduling . Ensuring completion of high-quality products within established budgets and time schedules . Acting as primary point of contact for the U.S. EPA Project Coordinator . Providing guidance, technical advice, and performance evaluations to those assigned to the project . Implementing corrective actions and providing professional advice to staff . Preparing and reviewing preparation of project deliverables developed to support the project . Providing support to EPA in interacting with the project team, technical reviewers, the TAG, and others to ensure that technical quality requirements of the study design objectives are met

The Tetra Tech QA Officer is John O’Donnell, whose primary responsibilities include the following: . Providing support to the Tetra Tech TOL in preparing and distributing the QAPP . Reviewing and internally approving the QAPP . Monitoring QC activities to determine conformance

Tetra Tech modeling staff will be responsible for developing model input data sets, applying the model, comparing model results to observed data, calibrating the model, and writing documentation. They will implement the QA/QC program, complete assigned work on schedule and with strict adherence to the established procedures, and complete required documentation. Other technical staff will perform literature searches; assist in secondary data gathering, compilation, and review; and help complete other deliverables to support the development of the draft and final TMDL report by U.S. EPA.

The Modeling QC Officer, Dr. Jonathan Butcher, will provide additional oversight. Dr. Jonathan Butcher, an Associate Director with Tetra Tech, is familiar with the model and its application for TMDL development and will provide final review of the model setup and output. The Modeling QC Officer will be responsible for performing evaluations to ensure that QC is maintained throughout the data collection and analysis process. QC evaluations will include reviewing site-specific model equations and codes (when necessary), double-checking work as it is completed, and providing written documentation of these reviews to ensure that the standards set forth in the QAPP and in other planning documents are met or

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 8 exceeded. Other QA/QC staff, including technical reviewers and technical editors selected as needed, will provide peer review oversight of the content of the work products and ensure that they comply with U.S. EPA’s specifications.

Figure 2. Organizational structure of the Flathead Lake Basin modeling project.

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2.0 Modeling Goals and Objectives Sufficient monitoring data will never be available to fully identify all of the potential nutrient sources and to quantify the relative importance of each of the sources within a basin the scale of the Flathead. Because of this, development of a basin-scale nutrient loading modeling system is proposed. The purpose of this exercise is to develop a tool to support the following goals:

. Complete the Phase II Allocation Strategy for the Flathead Basin as specified in the 2001 Nutrient Management Plan and Total Maximum Daily Loads for Flathead Lake (DEQ 2001). . Complete all necessary nutrient-related TMDLs for all impaired waters in the Flathead Lake Basin. . Develop an overall, basin-scale plan for managing nutrients in the Flathead Lake Basin. A secondary goal is to support development of all necessary sediment-related TMDLs for all impaired waters in the Flathead Basin (Table 4).

Table 4. Sediment-Listed waters with the Flathead Lake Basin from the 2010 303(d) List

TMDL Planning Area Waterbody ID Waterbody Name, Location Description

Flathead - Stillwater MT76P004_010 WHITEFISH LAKE SHEPPARD CREEK, headwaters to mouth (Griffin Flathead - Stillwater MT76P001_050 Creek-Logan Creek-Talley Lake) Flathead - Stillwater MT76P001_030 LOGAN CREEK, headwaters to mouth (Tally Lake)

Flathead - Stillwater MT76P001_010 STILLWATER RIVER, Logan Creek to mouth

Flathead - Stillwater MT76O002_050 FISH CREEK, headwaters to mouth (Ashley Lake)

Flathead - Stillwater MT76O002_010 ASHLEY CREEK, Ashley Lake to Smith Lake

Flathead Lake MT76O003_010 FLATHEAD LAKE

To support each of these goals, the following modeling objectives must be met:

. Determine the historic annual loads of nitrogen, phosphorus, and sediment to Flathead Lake and within each of the impaired subwatersheds. . Determine the relative magnitude of the sources of nitrogen, phosphorus, and sediment to Flathead Lake and within each of the impaired subwatersheds. Key sources to quantify include: roads, timber harvest, fires, wastewater treatment plants, septic systems, agriculture, forest lands, and urban runoff (including residential/commercial development, golf courses, etc.) . Determine the significance of the various pathways (e.g., baseflow, interflow, or surface runoff) of nitrogen and phosphorus within the Basin. . Determine the load reductions that are needed within each impaired subwatershed to meet the applicable TMDL targets. Montana’s draft numeric nutrient criteria (DNNC) for wadeable streams are presented in Table 5 and are specified as total phosphorus (TP), total nitrogen (TN), and benthic chlorophyll-a values that apply from July 1st through September 30th of each year.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 10 . Assess the potential benefits of a variety of agricultural, silvicultural, and urban best management practices to reducing annual nitrogen and phosphorus loads to Flathead Lake. . Assess the potential benefits of a variety of agricultural, silvicultural, and urban best management practices to allow the DNNC in the impaired subwatersheds to be met and to support maintenance/attainment of water quality targets for Flathead and Whitefish Lakes.

Table 5. Draft Numeric Nutrient Standards for Wadeable Streams in the Flathead Basin Benthic TP TN Level III Ecoregion Chlorophyll-a (µg/L) (µg/L) 2 (mg Chla/m ) Northern Rockies 25 300 120 Canadian Rockies 25 300 120 Source: DEQ (2011). Note: Draft recommended threshold values only apply from July 1st through September 30th of each year.

Additionally, the modeling system could eventually be used to support land use planning decisions, voluntary nutrient reduction strategies, nutrient trading, and evaluate potential impacts from new or altered sources (e.g., mining in British Columbia, timber harvest). However, these potential uses of the modeling system are not directly addressed in this project plan/QAPP.

The ―Flathead Lake Model‖ that is currently under development by the FLBS simulates the response of the lake to internal and external changes within the lake, but does not estimate external nutrient loading from within the basin to the lake. The proposed basin-scale LSPC modeling system could therefore be coupled with the ―Flathead Lake Model‖ to evaluate both cause (e.g., where is the nutrient load coming from?) and effect (e.g., to what extent and how does that load change the biological characteristics of the lake?). However, this potential use of the modeling system is not directly addressed in this project plan/QAPP.

While these models can never replace ―real monitoring data‖, they could become invaluable tools to assist in both managing existing nutrient loading problems as well as in preventing future problems. Output from the models can be used in combination with monitoring data and other relevant information to support future decision making.

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3.0 Model Selection This section of the QAPP describes the model selection process, including summarizing the needs of a watershed model for the Flathead Lake Basin.

3.1 Prior Studies DEQ performed a literature search for the Flathead Lake Basin and found 1,977 relevant studies completed by DEQ, U.S. EPA, USGS, Montana Bureau of Mines and Geology, CSKT, Montana PPL, U.S. Bureau of Reclamation, and other private and public agencies (DEQ 2008). From that literature search and communications with watershed stakeholders, it appears that a number of different modeling efforts have previously been conducted in the watershed and it is understood that the FLBS is now working on an updated lake response model for Flathead Lake.1 However, none adequately modeled flow and water quality at the entire Flathead Lake Basin scale. The following additional studies were also identified during the literature review: . Brick C. 1986. A model of groundwater response to reservoir management and the implications for kokanee salmon spawning, Flathead Lake, Montana. . Chase, K.J., 2011. Development of a precipitation-runoff model to simulate unregulated streamflow in the South Fork Flathead River Basin, Montana: U.S. Geological Survey Scientific Investigations Report 2011–5095, 39 p. . Dolph J, and King A. 1992. Sensitivity of the Regional Water Balance in the Columbia River Basin to Climate Variability: Application of a Spatially Distributed Water Balance Model. Watershed management: Balancing Sustainability and Environmental Change. . Fagre DB, Comanor PL, White JD, Hauer FR, and Running SW. 1997. Watershed responses to climate change at Glacier National Park. Journal of the American Water Resources Association 33 (4): 755-765. . Ferreira RF, Adams DB, and Davis RE. 1992. Development of Thermal Models for Hungry Horse Reservoir and Lake Koocanusa, Northwestern Montana and British Columbia. Helena, MT: U.S. Geological Survey. . Hall CAS, Jourdonnais JH, and Stanford JA. 1989. Assessing the impacts of stream regulation in the Flathead River basin, Montana, U. S. A. I. Simulation modeling of system water balance. Regulated Rivers: Research & Management 3 (1): 61-77. . Kubitschek J. 1994. Hungry Horse Selective Withdrawal Hydraulic Model Study. Denver, CO: Bureau of Reclamation, Hydraulics Branch. . Marotz BL, Althen C, and Gustafson DL. 1994. Hungry Horse Mitigation: Aquatic Modeling of the Selective Withdrawal System - Hungry Horse Dam, Montana. Kalispell, MT: Montana Dept. of Fish, Wildlife and Parks. . Montana Air Quality Bureau and Bison Engineering. 1983. Flathead River Basin environmental impact study air quality modeling analysis prepared for Air Quality Bureau, Montana Department of Health and Environmental Sciences by Bison Engineering/Research. . Nemani RR. 1987. Mapping forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation. Ph.D. Mapping forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation, University of Montana.

1 Dr. Bonnie Ellis, FLBS, personal communication, January 18, 2012.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 12 . PBS&J. 2009. Stream Temperature Assessment for Ashley Creek and the Whitefish River. Prepared for the Flathead Conservation District and Montana Department of Environmental Quality. Project No. 100003320. . Park GG, and Schmidt PS. 1973. Numerical Modeling of Thermal Stratification in A Reservoir With Large Discharge-To-Volume Ratio1. Journal of the American Water Resources Association 9 (5): 932-941. . Poole GC. 2000. Analysis and dynamic simulation of morphologic controls on surface- and ground-water flux in a large alluvial flood plain. Ph.D. Analysis and dynamic simulation of morphologic controls on surface- and ground-water flux in a large alluvial flood plain, University of Montana . Poole GC, Stanford JA, Running SW, Frissell CA, Woessner WW, and Ellis BK. 2004. A patch hierarchy approach to modeling surface and subsurface hydrology in complex flood-plain environments. Earth Surface Processes and Landforms 29 (10): 1259-1274. These studies will be reviewed to assess whether any of their findings can be used to inform the development of the watershed model. They may help with the parameterization of some aspects of the model or with the general understanding of hydrologic and water quality issues in the basin.

TAG Notes:  DEQ is currently updating the literature search.  Are there other surface water or ground water modeling studies that we have missed?

3.2 Model Needs This section presents the needs that U.S. EPA and Tetra Tech identified for modeling efforts in the Flathead Lake Basin. The needs were identified based on a consideration of the characteristics of the watershed combined with the project goals and objectives presented in Section 2.0. The watershed model should: . Simulate a watershed with o Mixed land uses including agriculture, forest, grassland, and urban uses o A variety of nonpoint sources (e.g., agriculture/irrigation, timber harvest) and point sources (e.g., wastewater treatment plants and industrial discharges) o Various pollutant transport mechanisms (e.g., groundwater contributions, sheet flow) . Simulate a very large watershed . Provide adequate time-step estimation of flow and not over-simplify storm events to provide accurate representation of rainfall events/snowmelt and resulting peak runoff . Simulate an acceptable snowmelt routine . Be customizable to account for varying watershed characteristics and user desires . Be able to account for numerous lakes of various sizes, many of which stratify . Be able to simulate the pollutants of concern (e.g., nitrogen, phosphorus, dissolved oxygen, and chlorophyll-a). . Be cost effective

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 13 Based upon these needs, DEQ, U.S. EPA, and Tetra Tech chose the Loading Simulation Program C++ (LSPC) model to simulate watershed processes in the Flathead Lake watershed. Section 3.3 provides a general description of this model.

3.3 LSPC Watershed Model LSPC is a watershed modeling system that includes Hydrologic Simulation Program–FORTRAN (HSPF) algorithms for simulating watershed hydrology, erosion, and water quality processes, as well as in-stream transport processes. LSPC integrates a geographic information system (GIS), comprehensive data storage and management capabilities, the original HSPF algorithms, and a data analysis/post-processing system into a convenient PC-based Windows environment. The algorithms of LSPC are identical to a subset of those in the HSPF model. LSPC is maintained by the U.S. EPA Office of Research and Development in Athens, Georgia, and is a component of U.S. EPA’s National TMDL Toolbox (http://www.epa.gov/athens/wwqtsc/index.html).

A brief overview of the HSPF model is provided below; a detailed discussion of HSPF-simulated processes and model parameters is available in the HSPF user's manual (Bicknell et al. 1996). HSPF is a comprehensive watershed and receiving water quality modeling framework that was originally developed in the mid-1970s. During the past several years it has been used to develop hundreds of U.S. EPA- approved TMDLs, and it is generally considered the most advanced hydrologic and watershed loading model publically available. The hydrologic portion of HSPF/LSPC is based on the Stanford Watershed Model (Crawford and Linsley 1966), which was one of the pioneering watershed models. The HSPF framework is developed in a modular fashion with many different components that can be assembled in different ways, depending on the objectives of the individual project. The model includes these major modules: . PERLND for simulating watershed processes on pervious land areas . IMPLND for simulating processes on impervious land areas . SEDMNT for simulating production and removal of sediment . RCHRES for simulating processes in streams and vertically mixed lakes . SEDTRN for simulating transport, deposition, and scour of sediment in streams

All of these modules include many submodules that calculate the various hydrologic, sediment, and water quality processes in the watershed. Many options are available for both simplified and complex process formulations. Spatially, the watershed is divided into a series of subbasins or subwatersheds representing the drainage areas that contribute to each of the stream reaches. These subwatersheds are then further subdivided into segments representing different land uses. For the developed areas, the land use segments are further divided into pervious and impervious fractions. The stream network links the surface runoff and subsurface flow contributions from each of the land segments and subwatersheds and routes them through the water bodies using storage-routing techniques. The stream-routing component considers direct precipitation and evaporation from the water surfaces, as well as flow contributions from the watershed, tributaries, and upstream stream reaches. Flow withdrawals and diversions can also be accommodated.

The stream network is constructed to represent all the major tributary streams, as well as different portions of stream reaches where significant changes in water quality occur. Like the watershed components, several options are available for simulating water quality in the receiving waters. The simpler options consider transport through the waterways and represent all transformations and removal processes using simple first-order decay approaches. Decay may be used to represent the net loss due to processes like settling and adsorption.

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Advantages of choosing LSPC for this application include: . LSPC simulates o All of the necessary constituents o Both rural and urban land uses o Both stream and lake processes o Both surface and subsurface impacts to flow and water quality . The time-variable nature of the modeling enables a straightforward evaluation of the cause-effect relationship between source contributions and waterbody response and direct comparison to relevant water quality criteria. . The proposed modeling tools are free and publicly available. This is advantageous for distributing the model to interested stakeholders and amongst government agencies. . LSPC provides storage of all modeling and point source permit data in a Microsoft Access database and text file formats to provide for efficient manipulation of data. . LSPC presents no inherent limitations regarding the size and number of watersheds and streams that can be modeled. . LSPC provides post-processing and analytical tools designed specifically to support TMDL development and reporting requirements. . A comprehensive modeling framework using the proposed LSPC approach facilitates development of TMDLs not only for this project, but also for potential future projects to address other impairments throughout the basin.

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4.0 LSPC Model Setup LSPC model setup, calibration, and validation will occur in two phases. Phase I of the modeling effort will consist of model setup, calibration, and validation for the headwaters regions of the Flathead Lake Basin. This area is shown in Figure 3 and includes: . North Fork Flathead River watershed from the headwaters to USGS gage 123555002 . Middle Fork Flathead River watershed from the headwaters to USGS gage 123585003 . Swan River watershed from the headwaters to the confluence with Swan Lake. It is proposed that the South Fork Flathead River Planning Area be modeled as a point source at USGS gage 12362500 (South Fork Flathead River near Columbia Falls, MT) below Hungry Horse Reservoir. The rationale for this proposal is based on the following: . The available data adequately characterize hydrology and nutrient/sediment dynamics for downstream modeling purposes4. . It is believed that potential downstream hydrologic effects of historic, current, and future dam operations practices can be adequately assessed by considering the Hungry Horse Reservoir as a point source. . Approximately 63% percent of the South Fork Flathead River watershed above the Hungry Horse Dam is designated wilderness. . There are no 303(d) listed waterbodies in this portion of the watershed that need to be specifically addressed through the TMDL process.

During Phase II, the upper watershed and lake models will be linked, and the remaining watershed modeling will be completed (i.e., the lower Flathead Lake Basin).

The purpose of the phased approach is to group the Flathead Lake watershed into regions having similar watershed characteristics and water quality issues and to sequence the modeling so that lessons learned during Phase I can be applied to Phase II. The headwaters regions of the basin are similar in that snow accumulation and melt drive hydrologic processes, and water quality is primarily impacted by timber harvest, forest fires, and unpaved roads. In contrast, the lower portion of the watershed has more connectivity to groundwater, and water quality is impacted by agriculture, irrigation, stormwater runoff, point sources, and septic systems.

The remainder of this project plan/QAPP discusses the model setup, calibration, and validation for the Phase I modeling. It is envisioned that the project plan/QAPP will be amended at a later date to include the Phase II modeling.

2 USGS Gage 123555000: North Fork Flathead River near Columbia Falls, Montana 3 USGS Gage 123585000: Middle Fork Flathead River near West Glacier, Montana 4 EPA plans to prepare a whitepaper summarizing and evaluating the available sediment and nutrient water quality data upstream and downstream of Hungry Horse Dam.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 16

Figure 3. Location of the Phase I modeling watersheds.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 17 4.1 Simulation Period The model simulation will span the period October 1, 1979 through September 30, 2008. The time period reflects the availability of data needed to calibrate the model and the need to have a variety of meteorological conditions. Table 6 indicates that the proportion of missing precipitation data for the stations of interest is generally good for this time period, and better than would be found for older periods (see Appendix A for details). In addition, the majority of the available flow and water quality data for calibration is available from this period.

TAG Notes: Table 6 and Appendix A will be updated to include data through 9/30/2008 prior to finalization of the QAPP.

4.2 Supporting Documentation A series of brief technical reports have been prepared by the U.S. Environmental Protection Agency (U.S. EPA) in support of this effort to set up a water quality simulation model for the Flathead Lake Basin. The series includes separate reports covering a broad range of topics including: . Groundwater (U.S. EPA 2011a) . Urban Stormwater Sources (U.S. EPA 2010a) . Point Source Discharges (U.S. EPA 2011b) . Agriculture/Irrigation (Wendt 2011) . Timber Harvest (U.S. EPA 2011c) . Forest Fires (U.S. EPA 2010b) . Roads (U.S. EPA 2010c) . Septic Systems (U.S. EPA 2009) . Lakes and Reservoirs (U.S EPA 2011d) . Existing and historic water quality in nutrient impaired waters (U.S. EPA 2011e)

When combined, these technical reports are intended to define a preliminary conceptual understanding of the current water quality conditions relative to nutrients, sources of nutrients, and the ways in which water and nutrients are transported within the Basin. These reports are available on-line at http://montanatmdlflathead.pbworks.com/w/page/46631925/Flathead%20Lake%20Watershed%20Nutrien t%20TMDLs and are incorporated herein by reference.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 18 Table 6. Inventory of precipitation data for modeling (10/1/1979 – 9/30/2007)

Percent Avg. Precipitation (in/yr) Agency Station ID Station Name Missing Measured Gap filled 240755 BIGFORK 13 S 4 20.9 22.4 242104 CRESTON 0.7 20 20.1 242629 EAST GLACIER 4.4 26.2 28.4 243139 FORTINE 1 N 10.9 13.5 19.8 244328 HUNGRY HORSE DAM 1.2 33.2 33.7 244558 KALISPELL GLACIER AP 0 16.9 16.9

244560 KALISPELL 9 NNE 66.2 4.2 17.6

245043 LINDBERGH LAKE 1.3 23.7 23.7

246218 OLNEY 6.8 20.6 21.9 NCDC NCDC (Daily) 246580 PLEASANT VALLEY 5 SE 77.2 3.2 26.9 246635 POLSON 7.7 14.5 15.1 246640 POLSON KERR DAM 0.7 15.3 15.3 247286 ST IGNATIUS 2 16.3 17.7 247448 SEELEY LAKE R S 1.6 19.9 20.3 248087 SWAN LAKE 14 24.4 29.8 248809 WEST GLACIER 1 28.3 28.6 248902 WHITEFISH 13.2 17.9 19.8 13A15S BADGER PASS 2.1 55.6 55.8 13B25S BISSON CREEK 44.1 18.7 27.8 13A24S EMERY CREEK 5.3 38.2 40.6 13A19S FLATTOP MTN. 0 71.7 71.7

14A11S GRAVE CREEK 0.5 49.1 49.3

14A14S HAND CREEK 0.5 29.7 29.7 13B22S KRAFT CREEK 79.5 7.9 26.2

(Daily) 13B24S MOSS PEAK 21.7 49.5 50.4 SNOTEL 13A25S NOISY BASIN 0 71.6 71.6 13B07S NORTH FORK JOCKO 34.4 46.2 58.8 13A26S PIKE CREEK 5.3 46.9 49.6 14A12S STAHL PEAK 0 64.5 64.5 12B17S WOOD CREEK 0 29.5 29.5

MT2812 ESSEX 16.2 29.9 36.9

MT6615 POLEBRIDGE 38.9 12.8 20.7

MT7204 ROUND BUTTE 1 NNW 14.7 11.2 13.2 NCDC NCDC

(Hourly) MT7978 SUMMIT 12.6 32.6 38.9

MT8101 SWIFT DAM 13.6 20.6 24.5

NCDC NCDC

airway

surface (Hourly) 24146 KALISPELL 0 16.9 16.9

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 19 LSPC calibration is improved by having a ―spin-up‖ period prior to the beginning of calibration; initial hydrology and pollutant process states are difficult to estimate, and having a spin-up period allows internal model processes to reach equilibrium. As a result, the actual simulation will begin October 1, 1978, allowing one year of spin-up. Meteorological data are required for spin-up, but model output for October 1, 1978 to September 30, 1979 is disregarded. The model will be run and results summarized on a water year basis instead a calendar year basis because (1) it will better reflect annual snowfall/snowmelt cycles that begin in the fall and end in the spring, and (2) it is easier to estimate initial moisture conditions without a snowpack on the ground.

The parameters of concern in the Flathead Lake Basin are flow, nitrogen, phosphorus, chlorophyll-a, and sediment. To address these parameters the following will be simulated in LSPC: flow, sediment (sand, silt, and clay), temperature, dissolved oxygen (DO), carbonaceous biological oxygen demand (CBOD), nitrate, nitrite, ammonia, organic nitrogen, orthophosphorus, organic phosphorus, and phytoplankton (chlorophyll-a). Sediment and temperature will be modeled because several forms of nutrients can be attached to sediment and because temperature influences a variety of in-stream processes affecting nutrients, DO, and chlorophyll-a. CBOD is simulated because of its impact on DO.

4.3 Watershed Segmentation LSPC will be configured to simulate the Flathead Lake basin as a series of hydrologically connected subwatersheds. The spatial subdivision of the watersheds allows for a more refined representation of pollutant sources, and a more realistic description of hydrologic factors. Subwatersheds have already been created by using Montana Department of Natural Resource and Conservation’s 6th Code Hydrologic Units5 (DNRC 2006). The 6th code HUs were further delineated, where needed, to capture spatial variation in sources, hydrology, and jurisdictional boundaries. 303(d) listed streams were also delineated into distinct subwatersheds, as well as all major lakes. Watersheds were delineated by using the USGS 30-meter Digital Elevation Models (DEMs), 1:24,000 scale USGS digital topographic quadrangles (Digital Raster Graphics – DRGs), and/or the high-resolution NHD streams layer. Delineations of some subwatersheds were finalized manually to account for lakes, correction of errors in the NHD, and to be consistent with the requirements of LSPC inputs. Output from LSPC is for the most downstream point of each subwatershed (sometimes referred to as the ―pour point‖). Subwatersheds were therefore delineated to obtain modeling output at key flow or water quality stations and at political boundaries (e.g., United States/Canada border), as defined by DEQ.

The Montana 6th code HUs do not cover the Canadian portion of the watershed. Therefore, watersheds were manually delineated for the Canadian portion using publically available 90-meter Digital Elevation Data and the British Columbia National Hydrology Network (NHN) streams coverage.

The modeling subwatersheds are shown in Figure 4 through Figure 6.

5 Available online at http://nris.mt.gov/gis/default.asp

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Figure 4. Phase 1 modeling subwatersheds for the Middle Fork Flathead River Planning Area.

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Figure 5. Phase 1 modeling subwatersheds for the North Fork Flathead River Planning Area.

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Figure 6. Phase 1 modeling subwatersheds for the Swan River Planning Area.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 23 4.4 Waterbody Representation LSPC allows the user to specify the type of waterbody as a stream reach or a reservoir reach. Regardless of whether a waterbody is a stream or a reservoir/lake, LSPC represents them in essentially the same fashion – as a completely mixed waterbody with unidirectional flow. There are some minor differences in the way that pollutant processes are calculated, but the hydrologic processes are defined by the same parameters and FTABLE (i.e., a function table containing depth-volume-discharge relationships for each reach). The major difference is that the FTABLE can be configured to represent the properties of a lake – permanent storage of water, a large surface area and storage volume, and outflow moderated by a dam and/or control structure.

4.4.1 Streams Each stream subwatershed in LSPC will be represented with a single stream assumed to be a completely mixed, one-dimensional segment with a trapezoidal cross-section (Figure 7).

Figure 7. Stream channel representation in the LSPC model.

Input parameters for the reaches include initial depth, length, depth, width, slope, Manning’s roughness coefficient, and coefficients to describe the shape of the stream channel. The methodology for determining these parameters is described below:

. IDEPTH (Reach Initial Water Depth) – Assumed to be half the bankfull depth. . LENGTH (Reach Length) – Determined from the NHD high resolution stream reach network6 or British Columbia’s NHN stream reach network7. . DEPTH (Reach Bankfull Depth) – Reach bankfull depth values will be obtained from measured data collected by multiple agencies throughout the watershed and/or extrapolated where measured data are not available. For example, PACFISH/INFISH Biological Opinion (PIBO) Effectiveness Monitoring data from monitoring sites within the Flathead Basin, combined with available data from the Montana Department of Environmental Quality and others, will be used to establish regression relationships between channel morphology and drainage area.

6 Available online at http://nhd.usgs.gov/. 7 Available online at http://www.geobase.ca/geobase/ en/data/nhn/index.html.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 24 . WIDTH (Reach Bankfull Width) – Reach bankfull width values will be obtained from measured data collected by multiple agencies throughout the watershed. Where no data are available, values will be estimated using the relationships described above for DEPTH.

. SLOPE (Reach Slope) – Reach slopes will be calculated based on elevation data from the USGS 30-meter National Elevation Dataset (USGS 2002) and Canadian 90-meter elevation data. . MANN (Manning’s Roughness Coefficient for the Stream Channel) – An estimated coefficient of 0.02 will be applied as an initial value to each stream reach based on typical literature values (Schwab et al. 1993). This may be modified during calibration. . R1 (Reach ratio of Bottom Width to Bankfull Width) – Reach cross-sectional area (XA) can also be related to watershed area by equations similar to those given above. With the trapezoidal assumption, R1 can then be expressed as a function of XA as R1 = 2 XA/(D W) – 1. . R2 (Reach Side Slope of Floodplain) – R2 is a product of the local topography and will be calculated based on DEM processing. . W1 (Reach Floodplain Width Factor) – W1 is a product of the local topography and will be calculated based on DEM processing.

4.4.2 Lakes For modeling purposes, only lakes, ponds, and reservoirs that are directly connected to the perennial stream network and meet the following criteria will be explicitly modeled:

. Impaired for nutrients on Montana’s 303(d) list . Located immediately upstream or downstream of stream segments that are impaired for nutrients on Montana’s 303(d) list . Impacted by upstream anthropogenic activities . Impact natural streamflow through dam/reservoir regulation . Surface area of 100 acres or greater

Of the approximately 5,300 waterbodies that are in the high-resolution National Hydrography Dataset for the Flathead Lake Basin, 51 lakes and reservoirs meet the criteria presented above (Table 7). The available data for the lakes are discussed in Flathead Basin TMDLs Technical Memorandum — Lakes and Reservoirs (U.S. EPA 2010b) and Table 7 summarizes some of the available information for these lakes.

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Table 7. Available data for lakes to be explicitly modeled in LSPC

b g

e

c

d

a

f

Waterbody Name

e

Lake area Lake Maximumdepth Bathymetry Hydrology Trophic State Stratification Nutrient Reservoir / Dam HUC 17010206 North Fork Planning Area 25 Ye Bowman Lake 1,722 FWS -- Oligotrophic L -- 3 s Cyclone Lake 140 ------No L -- 39 Ye Kintla Lake 1,713 FWS -- Oligotrophic -- -- 0 s Logging Lake 1,114 -- FWS ------Ye Lower Quartz Lake 166 -- FWS -- -- L -- s Quartz Lake 872 -- FWS ------Trout Lake 214 ------Upper Kintla Lake 464 -- FWP ------HUC 17010207 Middle Fork Planning Area Lake Ellen Wilson 210 ------Harrison Lake 404 -- FWS -- Yes L -- Hidden Lake 270 ------43 FWP, Lake McDonald 6,869 -- Yes U D -- 0 FWS HUC 17010208 Flathead Lake Planning Area 25 FWP, Ye Ashley Lake 2,850 O Oligomesotrophic U L D Dam 5 FWS* s FWP, Ye Echo Lake 716 66 -- Oligomesotrophic -- -- FWS* s Hell Roaring h 5 ------Dam Reservoir Jessup Mill Pond 22 ------Dam h 14 FWP, Lake Blaine 382 -- Mesooligotrophic ------1 FWS* FWP, Ye Lake Mary Ronan 1,516 47 -- Mesoeutrophic L -- FWS* s Lake Monroe 48 ------Lone Lake 133 ------Lower Foy Lake 14 ------Middle Foy Lake 41 ------Turtle Lake 50 ------Dam h Smith Lake 453 ------U D -- HUC 17010209 South Fork Planning Area Big Salmon Lake 972 ------George Lake 114 -- FWP ------Hungry Horse 23,57 Ye -- -- U D -- U L D Dam Reservoir 7 s Lion Lake 39 88 -- -- Oligotrophic -- -- Dam h

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Page 26

b g

e

c

d

a

f

Waterbody Name

e

Lake area Lake Maximumdepth Bathymetry Hydrology Trophic State Stratification Nutrient Reservoir / Dam Sunburst Lake 148 -- FWP ------HUC 17010210 Stillwater River Planning Area Ye Bull Lake 107 ------L -- s Dog Lake 102 ------Fish Lake 32 ------Duck Lake 60 ------No L -- Lower Stillwater DEQ, Ye 250 53 -- Mesotrophic L D -- Lake FWS* s Skyles Lake 38 13 FWP -- Oligomesotrophic No -- -- Spencer Lake 30 -- FWP -- Mesooligotrophic ------49 FWP, Ye Tally Lake 1,211 D Oligotrophic U L -- 5 FWS* s Upper Stillwater 592 75 FWS* -- Oligotrophic -- L -- Lake Upper Whitefish Ye 80 -- FWP ------Lake s 22 DEQ, Ye Whitefish Lake 3,315 -- Oligotrophic U L D -- 3 FWS* s HUC 17010211 Swan River Planning Area Crystal Lake 187 ------Elk Lake 118 ------High Park Lake 220 ------15 FWP Ye Holland Lake 413 Oligotrophic L -- 6 s Glacier Lake 104 ------Gray Wolf Lake 339 ------12 FWP Ye Lindberg Lake 816 -- Oligotrophic L -- 1 s Lost Lake 110 ------Mud Lake 144 ------13 DEQ, Ye i Swan Lake 3,271 D Oligotrophic U L D Dam 3 FWS* s Turquoise Lake 186 ------a. Lake areas were extracted from the high resolution NHD and are reported in acres. b. Maximum depths are reported from Ellis and Craft (2008). c. Bathymetry data that were provided by various entities. An asterisk (*) refers to a map provided by FWS that was originally created by Montana Fish and Game. d. Data were downloaded from NWIS or provided by DNRC (Ashley Creek). Data are available at the lake/reservoir outlet (O) or on the major upstream (U) or downstream (D) stream reach. e. Trophic states are reported from Ellis and Craft (2008). Stratification data are from unpublished Volunteer Monitoring Program data or DEQ data. f .Nutrient data were obtained from multiple sources; only nutrient data collected since the year 2000 were evaluated. Sample stations are located on the lake (L), and on waterbodies immediately upstream (U) and downstream (D) of the lake. g. Dams and regulating reservoir data were obtained from multiple source; “Dam” denotes the presence of a dam on a waterbody. h. Data on these dams/reservoirs are limited and may include the following: construction date, construction material, length, height, volume, and year of construction.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 27 i. A check dam is located on the Swan River downstream of Swan Lake. The check dam is part of the hydroelectric power plant facility and diverts water down the power canal.

The lakes listed in Table 7 will be modeled as lake reaches within their respective subwatersheds. For lakes with available bathymetry data, lake volumes and LSPC FTABLES will be generated using the approach described in Chapra (2002). For lakes without bathymetry data, lake volumes and LSPC FTABLES will be generated using the approach described in Hollister and Milstead (2010). Nitrogen- and phosphorus-load reduction during transport through the lake will be simulated via first order decay rates (e.g., Vollenweider, Bachman). Decay rates are typically selected based upon residence times, which are not available for most of the lakes. Therefore, a generic set of assumptions that relate volume and consumptive use to lake surface area will be generated. For example, Figure 8 shows the relationship between surface area and lake volume for 102 lakes in the Basin. With such a large population of lakes (i.e., more than 600), the errors should tend to average out if valid assumptions are generated. Residence time may also be a parameter that is varied during calibration.

1.E+07

1.E+06

1.E+05

1.E+04

ft) -

1.E+03

Volume(ac 1.E+02

y = 4.0144x1.4299 1.E+01 R² = 0.9337

1.E+00 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 Surface Area (ac)

Figure 8. Relationship between surface area and volume for selected lakes within the Flathead Lake Basin.

Modeling the rest of the lakes within the Basin as lake reaches is problematic because of the extremely limited data on bathymetry, lake volume, or discharge rate. Additionally, many of the lakes can be characterized as follows: . Lakes that are isolated and disconnected from the stream network . Lakes that are connected to the stream network but are distant from the main reach within a subwatershed . Oxbow lakes, sloughs, and other small waterbodies along large rivers

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 28 . Waterbodies that do not appear on aerial imagery

These lakes will not be explicitly modeled, but their impacts will be accounted for by simulating them as generic unit-volume lakes (one for each hydrometeorological area), assuming the watershed area to lake volume ratio and the land use mix for each lake in a generic set is fairly constant. Implementing these virtual lakes is a two-step process. First, the areas upstream of lakes in all subwatersheds having the same weather station and set of model parameters will be routed into a composite virtual lake with average bathymetry and outlet representation to attenuate outflow. Second, the total virtual lake outflow from this will be proportionally divided into multiple outlets according to the drainage area from the subwatershed from which the inflow originated, and routed back to the corresponding subwatershed from which the flow originated. Figure 9 is a conceptual schematic of the two-step process for representing lake impact without explicitly simulating every single lake. In Figure 9, the flow pathways labeled 1… N represent flows going into the virtual lake (step 1), and 1’…N’ represent flows leaving the virtual lake, which are routed back to the respective subwatersheds of origin (step 2).

Figure 9. Conceptual schematic of the two-step virtual lake approach for representing lake impact.

4.5 Weather Data The LSPC model is driven by precipitation and other climatologic data (e.g., air temperature, cloud cover, wind speed). Of these, the most critical inputs are precipitation, air temperature, solar radiation, and potential evapotranspiration. Appropriate representation of these variables is therefore required to develop

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 29 a valid model. Ideally, these data should be represented on an hourly time-step to allow the model to better predict hydrologic response.

A variety of meteorology data are available for the Flathead Lake watershed; a summary of the available data is provided in Appendix A, along with a summary of the methods used for processing and preparing the data for use in LSPC.

4.6 Land Use/Land Cover Representation As described in Section 4.2, LSPC divides a large watershed into smaller subwatersheds that are hydrologically connected by reach segments. Each subwatershed is then further divided into (one to many) individual land use categories, defined by the land uses existing in the subwatershed. Figure 10 presents the organization structure and model attributes of land uses and subwatersheds in LSPC.

Figure 10. LSPC organizational structure and model attributes for land uses and subwatersheds.

As a starting point, land use/land cover information will be obtained from the 2006 National Land Cover Data (NLCD) (Figure 11). The NLCD is a 30-meter land use classification for the entire United States that was obtained from LANDSAT imagery (Homer et al. 2007). The Flathead Lake Basin falls into NLCD zones 10 and 19. As described below, more specificity may be added to the NLCD to facilitate accurate consideration of the land uses having the greatest impact on sediment and/or nutrient loading (e.g., forest roads, forest harvest, agriculture, etc.).

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Figure 11. Land Cover/Land Use in the Flathead Lake Basin (from 2006 NLCD).

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All land use categories in rural and urban areas will be subdivided into pervious land units (which typically include forested, cropland, pasture, etc.) and/or impervious land units (paved surfaces) depending on their physical nature. Hydrologic and water quality algorithms will then be used to quantitatively describe processes for each of these lands. Figure 12 provides a schematic of the hydrologic simulation associated with land units in LSPC.

Figure 12. Schematic of hydrologic simulations associated with land units in LSPC.

Specific nonpoint sources (e.g., an eroding hillslope) will not be explicitly included in the model. Rather, they will be considered in how the land uses are characterized in the model setup. Because LSPC represents precipitation driven nonpoint sources as land use loads, it is necessary to separately evaluate activities (e.g., fertilization of agriculture lands, forest harvest, etc.) conducted in a watershed that may contribute substantially to nutrient or sediment loading. Characterizing the activities will allow for the development of site-specific land use parameters (e.g., nutrient or sediment export concentrations or rates) for use in the model. The following sections describe how land use activities will be evaluated to parameterize the Flathead Lake Basin Model.

4.6.1 Time-Variable Land Use A time-variable land use option exists in LSPC that further increases the flexibility for representing intermittent events like forest harvest and fires, which have an initial impact that diminishes over time as vegetation regrows. This option may also facilitate simulation of changing land use over time such as the increase in residential/commercial development that has occurred in the Flathead Lake Basin over the last 30 plus years. By taking ―snapshots‖ of variable land use over time, it is possible to create a series of time-variable land use area distributions to feed into the watershed model to represent the impacts of the varying land uses. Because LSPC assumes a linear interpolation between successive land use tables, the land use snapshot layers will be sequenced at different time intervals to better approximate non-linear rates of recovery, as conceptualized in Figure 13.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 32 While the 2006 NLCD 30-meter land use classifications will serve as the base land use layer for characterization of the ―current‖ condition (i.e., 2007/2008), U.S. EPA and their contractors devoted considerable additional effort to define the current condition for the land use features/activities that may have the greatest influence on hydrology and nutrient and sediment loading. These land use/features/activities include urban stormwater, septic systems, agriculture, forest harvest, forest fire, and forest roads. Table 8 lists the U.S. EPA source documents for each of these and also provides a summary of the available data for describing the current condition. Table 8 also provides a summary of the available data describing historic land use activities and the proposed approach. For most land use activities, three to four ―snapshots‖ (i.e., late 1970s early 1980s if data are available, early 1990s, early 2000s, and 2007/2008) will be used to facilitate dynamic land use simulations throughout the modeling period.

Figure 13. Conceptual representation of time-variable land use in LSPC.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 33 Table 8. Summary of available land use/activity data for describing the current and historic conditions in the Flathead Lake Basin Land Current Condition Historic Condition Use Available Data and Data Source Document Description of Available Data Data Gaps Activity Approach Gaps Urban U.S. EPA. 2010. Summary of Urban Limited stormwater discharge quality data, locations of permitted Use 1992 and 2001 National Land use/land Stormwater Stormwater Sources in the Flathead stormwater facilities/outfalls, and municipal boundaries as of Land Cover Data sets to cover data for Lake Basin– Public Review Draft. 2007/2008. provide historic snapshots. the late 1970s Prepared by Tetra Tech for the U.S. or early Environmental Protection Agency, 1980s. Region 8, Montana Operations Office. Helena, MT. March 1, 2010. Septic U.S. EPA. 2009. Flathead Basin The number and locations of septic systems has been estimated using This technical memo was TBD. Two options are Systems TMDLs Technical Memo – Septic the May 2008 CAMA cadastral database. never completed. available: 1) Use 1990 and Systems – Internal Review Draft. 2000 “septic density” data Prepared by the U.S. Environmental from NRIS, and/or; 2) use Protection Agency, Region 8, Montana county databases to Operations Office. Helena, MT. March determine if historical 2, 2009. snapshots can be estimated. Flathead County Wastewater Study This study has not yet been completed. It is understood that it will Unknown at this time. (ongoing) include an updated septic system database.

Agriculture Wendt, Alan R. 2011. The Flathead Agricultural land was delineated based on NAIP 2009 aerial imagery Detailed data is only available The historic extent of Land use/land Valley Agricultural Impacts Report. (July 2009). Crop type was determined by examining the NAIP imagery for the valley area north of agricultural lands can be cover data for Prepared by Alan R. Wendt for the and imagery from the 2009 Flathead Basin LIDAR & Imagery Project Flathead Lake. estimated based on the 1992 the late Montana Department of Environmental (September 2009), personal knowledge, interviews with local farmers, and 2001 NLCD. 1970’s or Quality and Flathead River local agricultural service providers, and windshield surveys. Types and early 1980’s. Commission. April 2011. rates of fertilizer applications and irrigation were also characterized. Forest U.S. EPA. 2011. Summary of Timber A GIS coverage has been developed containing well over 35,000 No data currently available for Each polygon representing Harvest Harvest in the Flathead Lake Basin – polygons representing individual Stand Units (SUs) in the Flathead private industrial forests (e.g., harvested SU’s has a unique Public Review Draft. Prepared by Tetra Lake Basin where harvest has occurred between the early 1900’s and Plum Creek) and CSKT. The date. These data can be Tech for the U.S. Environmental 2008. Each SU varies by type (e.g., clear-cut, commercial thinning, British Columbia data set is used to represent historical Protection Agency, Region 8, Montana selective cut, etc.), age, area, and location. The period of record and incomplete. conditions back to Operations Office. Helena, MT. robustness of the data set varies by the land management agency approximately 1983. September 28, 2011. responsible for tracking harvest.

Forest U.S. EPA. 2010. Summary of Road GIS roads data were compiled from: U.S. Census Bureau TIGER; ITSD Data is not available from the TBD Historic road Roads Network in the Flathead Lake Basin – Transportation Framework; Flathead CSKT and private industry data may be Public Review Draft. Prepared by Tetra National Forest; Glacier National Park; Flathead, Lake, and Missoula (e.g., Plum Creek or F.H. difficult and Tech for the U.S. Environmental Counties; DNRC; and two data sets from British Columbia, Canada. A Stoltze). Also, several roads time intensive Protection Agency, Region 8, Montana “Master Roads Coverage” representing conditions in 2007/2008 has coverages did not have recent to obtain. Operations Office. Helena, MT. March been prepared including 10,202 miles of roads with 6,126 miles of road information (for example, 1, 2010. roads having surface pavement attribute data. the publication date of the FNF coverage is 2005). Forest Fire U.S. EPA. 2010. Flathead Basin TMDL GIS data were compiled from Glacier National Park, the Flathead Data on burn severity is only Each polygon representing a Data on burn Technical Report – Forest Fires – National Forest (FNF), Montana Department of Natural Resources and available for a subset of the burned area has a unique severity is Stakeholder Review Draft. Prepared by Conservation (DNRC) and British Columbia’s Ministry of Forests and FNF fires. date. These data can be only available Tetra Tech for the U.S. Environmental Range. The data set includes polygons representing fire perimeters for used to represent historical for a subset Protection Agency, Region 8, Montana 1,797 fires occurring between 1919 and 2008. The period of record conditions back to the early of the FNF Operations Office. Helena, MT. March varies by agency. Burn severity data is available for a subset of the FNF 1980’s fires. 1, 2010. data set only.

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Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 35 4.6.2 Roads A literature review of road impacts to hydrology and water quality, followed by a summary and evaluation of the available spatial data describing roads in the Flathead Lake Basin is presented in Flathead Basin TMDL Technical Memo – Roads (U.S. EPA, 2010c). Road data from multiple sources were combined to create a ―master‖ GIS shapefile and database. As shown in Table 9, there are approximately 10,000 miles of roads in the database, divided into three categories, each with a unique suite of road surface materials (e.g., asphalt, native material, etc.).

Table 9. Roads summary Category Description Length (miles) Percentage Roads Primary Asphalt 0.75 0.01% Roads Primary Paved 1,420 13.92% Roads Secondary Crushed aggregate & gravel 765 7.50% Roads Secondary Bituminous surface treatment 0.02 0.0002% Roads Unpaved Dirt 28 0.27% Roads Unpaved Native material 3,402 33.35% Roads Unpaved Natural 362 3.55% Roads Unpaved Unknown 4,076 39.95% Total 10,202 100.00%

All roads will be simulated as a separate land use category with primary and secondary roads categorized as impervious surfaces and unpaved roads categorized as pervious surfaces. Nutrients and TSS concentrations for each type of road will initially be set to be the values listed in Table 10 based upon the following: . Roads Primary: Concentrations will be based on data obtained from data in the literature (Caltrans 2003). . Roads Secondary: No direct data are currently available for secondary roads. Concentrations are assumed to be the same as primary roads. . Roads Unpaved: Initial Concentrations are based on data from the US Forest Service in the Lake Tahoe watershed. Values shown are the median of 20 samples at the road. These initial values may be modified as described below.

Table 10. Preliminary Pollutant concentrations (mg/L) used to simulate roads in the Flathead Lake LSPC model Total Total Dissolved Total Dissolved Modeled Land Use Suspended Nitrogen Nitrogen Phosphorus Phosphorus Solids Roads Primary 793 3.27 0.60 1.65 0.08 Roads Secondary 793 3.27 0.60 1.65 0.08 Roads Unpaved 846 1.95 0.01 1.27 0.40

Separate from the LSPC platform, the Water Erosion Prediction Project (WEPP) model will be used on a representative subset of roads in the Flathead Lake Basin (stratified by road type, road surface material, slope, degree of BMP implementation, etc.) to develop a range of road sediment loading rates that will be compared to the preliminary values in Table 10 and to the LSPC output. If necessary, the LSPC loading rates from roads will be modified during the calibration process using the WEPP estimates and any available local data (Sugden and Woods 2007).

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 36 LSPC nutrient loading rates from roads will be based on locally-derived estimates, if available. If local data are not available, nutrient loading rates will be based on the WEPP sediment loading estimates and potency factors calculated from Table 10. For example, Table 10 indicates that the relationship between TP and TSS concentrations for unpaved roads is 1.27 mg/L to 846 mg/L (0.0015). This potency factor would be applied to the WEPP sediment loading rates to obtain a TP loading rate from unpaved roads.

Additionally, a GIS analysis will be conducted to determine the number of road-stream intersections and the proximity of road segments to perennial waterbodies. Road-stream intersections will be based on intersections with the available road GIS layers and the 1:24K NHD stream layer.

To account for the fact that pollutant delivery potential is greater on roads that are closer to streams, the roads will be subdivided into three categories (within 100 meters of a stream with no critical area, within 100 meters and a critical area, and outside of a 100 meter buffer). A buffer distance of 100 meters has been selected based upon the findings of McGurk and Fong (1995) and is based on studies involving in- stream invertebrate diversity. Critical areas will be based on criteria reported by Costick (1996) to determine Natural Erosion Potential (NEP). The NEP includes the following criteria: slope greater than 40 percent, relative area of bare soil greater than 40 percent, and a K-factor greater than 0.28. Sediment loads generated outside of a 100 meter buffer and/or critical areas will be assumed to be zero (McGurk and Fong 1995).

TAG NOTE: 1. Local/regional road runoff pollutant concentration data is needed, especially for nitrogen and phosphorus.

2. Historic spatial road data (i.e., representing the period between 1978 and the present) are needed to use the time-varied land use option in LSPC.

4.6.3 Timber Harvest A literature review of the potential effects of timber harvest on hydrology and water quality, followed by a summary and evaluation of the available spatial data in the Flathead Lake Basin is presented in a Summary of Timber Harvest in the Flathead Lake Basin (U.S. EPA 2011d). A GIS coverage has been developed containing well over 35,000 polygons representing individual Stand Units (SUs) in the Flathead Lake Basin where harvest has occurred between the early 1900’s and 2008. Each SU varies by type (e.g., clear-cut, commercial thinning, selective cut, etc.), age, area, and location; resulting in a unique hydrologic and pollutant fate/transport response.

The Flathead LSPC model will be parameterized for Timber harvest based on the Equivalent Clearcut Area (ECA). The procedure is documented in ―Forest Hydrology, Hydrologic Effects of Vegetation Manipulation, Part II (USDA Forest Service 1976). The procedure uses the equivalent clearcut area (ECA) concept to estimate existing water yield, and how it may be affected by management activities. Data inputs include elevation, aspect, average annual precipitation, and roads. The model also estimates the degree of hydrologic recovery that occurs following forest disturbance. The rates of recovery are based on vegetation habitat types (USDA Forest Service 1976) and growth and yield models for tree species as they regrow based on long term permanent plot data for western Montana (Figure 14).

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1.20

1.00

0.80

Fast (A) 0.60 Moderate (B) Slow (C) 0.40

0.20 Fraction of Remaining ECA Remaining of Fraction

0.00 0 20 40 60 80 100 120 140 160 Years

Figure 14. Western Montana ECA Recovery Curves (Personal communication, Craig Kendall, Flathead National Forest, December 6, 2011).

It is important to note that the recovery curves presented in Figure 14 were developed for a model whose objective was to characterize the time required for a forest to return to a pre-harvest condition. Furthermore, the focus of that model was hydrology and timber yield. When characterizing erosion impact, fast-growing vegetation that helps to stabilize the soils would be expected to appear in the clear- cut area within a much shorter timeframe (i.e. 2-5 years) following timber harvest. This early vegetation will significantly reduce the erosion potential of the harvested area. To account for this in the model, a composite curve with two trajectories will be derived to characterize vegetation regrowth. First, the clear- cut area will change in the model to grass or shrubland during the first 2 to 5 years. Second, the forest recovery curve will be superimposed to project the regrowth trajectory from grassland to full-canopy forest.

Since each harvest unit is unique (in terms of the type and degree of harvest), ECA’s will be calculated for each polygon in the GIS database. The ECA is essentially the percentage of the actual harvested area that behaves like a clearcut. For example, if the ECA for a 100 acre polygon representing a harvest unit is 10 acres, then the land use would be re-classified in the LSPC model to 10 acres clearcut and 90 acres Forest.

A GIS analysis of the watershed will then be performed to compartmentalize the watershed into areas with similar physiographic characteristics (e.g., areas with shallow slopes and stable soils, areas with steep slopes and unstable soils, etc.). Groups will be created representing a range of soil erosion and water yield potential (low, moderate, high). The Water Erosion Prediction Project (WEPP) Model will be run in

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 38 a representative subset of each of the low, moderate, and high groups to generate sediment loading rates and hydrologic response that will be used to parameterize, or assist in calibrating, the LSPC model.

As with forest roads, LSPC nutrient loading rates from harvested lands will be based on locally-derived estimates, if available. If local data are not available, nutrient loading rates will be based on the WEPP sediment loading estimates and potency factors calculated from Table 10.

Finally, the groups will be further subdivided into three categories (within 100 meters of a stream with no critical area, within 100 meters and a critical area, and outside of a 100 meter buffer) as described in Section 4.6.2 to account for the fact that sediment delivery potential increases with proximity to streams.

TAG NOTE:  Forest harvest data are currently unavailable for private industrial forests and CSKT.  Locally/regionally derived nutrient loading rates from harvested lands are needed.

4.6.4 Forest Fires Burned forest land is similar to harvested forest both in the impacts to hydrology and chemistry and in that it is not accurately represented in available land cover datasets. Since forest fires can impact stream flow and water quality, a review of pertinent literature regarding the impact of fires upon hydrology and water quality and an inventory of available fires data were conducted. These data and evaluations are presented in Flathead Basin TMDL Technical Memo – Forest Fires (U.S. EPA 2010b). A summary of the data is presented in Table 11. Similar to forest harvest, A GIS coverage has been developed containing polygons representing the perimeters of burned areas in the Flathead Lake Basin.

Table 11. Summary of available forest fires data Flathead Glacier British b Analysis Summary National b b DNRC a National Park Columbia Forest Period of Record (years) 24 41 84 20 Years with Recorded 20 19 24 20 Fires Number of Fires during 70 153 44 1,530 Period of Record Total Area Burned (acres) 390,689 710,022 238,391 3,047 Average Area Burned per 5,581 4,461 5,418 152 year (acres) Median Area Burned per 69 270 688 75 year (acres) a. Flathead National Forest data includes forest fires within the National Forest and surrounding areas but excludes forest fires in Glacier National Park. b. Dataset only includes fires that occurred within the Flathead Lake Basin.

With one exception, LSPC will be parameterized using the methods described above for forest harvest. ECA’s (using the recovery curves presented above in Section 4.6.3) will be calculated for each burned area polygon in the GIS database. The burned areas will then be grouped into areas with similar physiographic characteristics (e.g., areas with shallow slopes and stable soils, areas with steep slopes and unstable soils, etc.) and the WEPP Model will be run in a representative subset of the each of the groups to generate sediment loading rates and hydrologic response that will be used to parameterize, or assist in

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 39 calibrating, the LSPC model. Finally, as with forest harvest and roads, the groups will be further subdivided into three categories (within 100 meters of a stream with no critical area, within 100 meters and a critical area, and outside of a 100 meter buffer) as described in Section 4.6.2 to account for the fact that pollutant delivery potential increases with proximity to streams.

The exception to this approach relates to vegetative and hydrologic recovery. It has been the experience of the Flathead National Forest’s hydrologist that there is often a lag-time between the initial burn and the onset of vegetative recovery, especially in areas with high soil-burn severity (Personal communications, Craig Kendall, Flathead National Forest, December 6, 2011).

Dean Sirucek (Hydrologist, Flathead National Forest) compiled burn severity data for selected fires in the Flathead National Forest to obtain a general understanding of burn severity in the Flathead Lake watershed. As shown in Table 12, on average, 37percent of the burned areas were classified as having a low burn severity, 47percent moderate severity, and 15percent high severity. A 5-year lag-time will be added to the recovery curves used in the ECA analysis for severely burned areas.

TAG NOTE: Table 12. Burn severity Forestof selected fire data fires are in currently the Flathead unavailable National for CSKTForest. lands.

Table 13. Burn severity of selected fires in the Flathead National Forest Low Low Moderate Moderate High High Total Burn Burn Burn Burn Burn Burn Fire Severity Severity Severity Severity Severity Severity Fire Name Acres (acres) % (acres) % (acres) % Helen Ck & Lewis Ck 2,989 1,532 51 936 31 521 17 2000 Corporal Ck, Railey 50,762 14,930 29 23,888 47 11,944 24 Mtn., Calbick 2007 Moose 2001 66,802 40,910 61 25,036 37 856 1 Skyland (Flathead 3,152 310 10 1,970 63 872 28 Portion) 2007 Brush Ck 2007 24,573 6,263 25 13,309 54 5,001 20 Sundog 2006 1,500 250 17 700 47 550 37 Holland Peak 2006 1,840 800 43 1,000 54 40 2 Kelly Point Fires 2005 3,353 664 20 2,255 67 434 13 Blackfoot Lake Complex 30,014 22,905 76 6,688 22 421 1 2003 Wedge Canyon 2003 35,360 2,670 8 22,180 63 10,510 30 Crazy Horse 2003 11,027 6,391 58 4,503 41 133 1 Robert & Trapper 2003 45,069 22,284 49 17,812 40 4,973 11 TOTALS 276,441 119,909 120,277 3,6255 AVERAGES 37 47 15 STANDARD 22.3 13.6 12.4 DEVIATION

4.6.5 Septic Systems Flathead County is currently in the process of completing a wastewater study in the upper Flathead Valley through a grant from the Montana Department of Natural Resources and Conservation (DNRC).

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 40 According to Flathead County (personal communication, Joe Russel, December 28, 2011), study results will include, among other things, a database with updated, accurate septic system location information.

At this time, it is envisioned that the Flathead County data (septic system number and location) will be used to generate a GIS-coverage to inform the LSPC model.

For areas outside the Flathead County study area, the locations of septic systems will be estimated using an approach developed by DEQ that relies on cadastral data and WWTP service areas. A GIS-coverage will be generated by plotting points in the centroid of every land parcel that is identified as ―dwelling‖ or ―mobile‖ in the cadastral database. DEQ research (unpublished) has shown that these properties are the most likely to have septic systems (versus vacant land or industrial structures). This coverage will then be edited by removing all points that are located within the service areas of WWTPs based on the assumption that no residential property within the service area of a WWTP uses a septic system.

The DEQ draft estimation methodology makes the following assumptions: . Only residential properties with a permanent or mobile home have a septic system . Residential properties within a public WWTP service area do not use septic systems . Commercial and industrial properties outside of urban areas served by WWTP do not have septic systems (i.e. their effects are negligible compared to the total effects of residential properties) . Multi-family, shared, community, and such systems are identified as individual systems rather than as clustered systems

Nitrogen and phosphorus loads from septic systems will be estimated outside the LSPC platform using an approach under development by DEQ (Personal Communication, Eric Regensburger, DEQ. December 27, 2011). The estimated loads will then be input into the LSPC model as point sources within each modeling sub-watershed. As described in the following, a separate approach has been developed for nitrogen and phosphorus.

TAG NOTE: On 2/7/2012, the Whitefish Lake Institute released a draft document summarizing the results of a study of septic leachate to littoral areas of Whitefish Lake. Upon finalization, the data and information contained in the final report will be included in the modeling analysis as appropriate.

Nitrogen DEQ’s method for estimating nitrogen loading (in the form of NO3) from septic systems uses a matrix (Table 14) and is based on the four primary factors impacting the amount of denitrification: soil type beneath the drainfield; soil type in the riparian area; distance to surface water; and depth to ground water below the drainfield. In the matrix (Table 14 ) each drainfield is assigned a percent denitrification factor for each of the four criteria. The percentages assigned for each column are then added to provide the total percent nitrate removal for that septic system. The nitrate loading rate (30.5 lbs/year for a conventional system) to the surface water is then reduced accordingly. Any system with a percent reduction of 100% or more is assumed to contribute no nitrate to the surface water. This method assumes steady-state conditions exist in that it does not account for the time needed for the nitrogen load to migrate towards the receiving surface water. That lag time is dependent on the distance to the receiving water and the travel rate through both the vadose and saturated zones.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 41 Table 14. DEQ septic system nitrate loading matrix. Percent Nitrogen Load Soil Type @ Soil Type within 100’ Distance to surface Reductiona Drainfieldb of surface waterb water (ft) 0 A A <100 10 B 100 > 500 20 C B 500 > 5000 30 D C 5000 > 20,000 50 D >20,000 Notes: a. The total nitrogen reduction is the sum of the individual reductions for each column of the table. For example, the nitrogen load reduction associated with a drainfield in a type C soil that drains to a surface water with type B soil, and is 200 feet from the nearest surface water would be 50% (i.e., 20% + 20% + 10% = 50% or 30.5 lbs/year * 0.5 = 15.25 lbs/year). b. Soil drainage class: A = excessively drained or somewhat excessively drained B = well drained or moderately well drained C = somewhat poorly drained D = poorly drained or very poorly drained

Phosphorus DEQ’s method for estimating phosphorus loading to surface waters from septic systems uses a matrix similar to nitrogen (Table 15). The DEQ matrix combines three factors that have been shown to impact the amount of phosphorus attenuation: soil type beneath the drainfield; calcium carbonate percent in the soil beneath the drainfield; and distance to surface water. In the matrix (Table 15) each drainfield is assigned a percent phosphorus reduction for only one of the first three columns (the soil and calcium carbonate type), and then an additional percent phosphorus reduction for the fourth column (distance to surface water). The percentages assigned for each column are then added to provide the total percent phosphorus removal for that septic system. The phosphorus loading rate (6.44 lbs/year for a conventional or level 2 system) to the surface water is then reduced accordingly. Any system with a percent reduction of 100% or more is assumed to contribute no phosphorus to the surface water. This method assumes steady-state conditions exist in that it does not account for the time needed for the phosphorus load to migrate towards the receiving surface water. That lag time is dependent on the distance to the receiving water and the travel rate through both the vadose and saturated zones.

Table 15. DEQ septic system phosphorus loading matrix Soil Type @ Percent Soil Type @ (2) Soil Type @ (2) Drainfield (2) Distance to Phosphorus Drainfield Drainfield (CaCO3 >1% surface water (ft) Load Reduction (CaCO3 <= 1%) (CaCO3 >=15%) and <15%) 0 A A A <100 10 B 20 B C 30 B D 100 > 500 40 C 60 C D 500 > 5,000 90 D 100 >5,000

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 42 4.6.6 Urban Stormwater Stormwater can be defined as ―water runoff that occurs when precipitation from rain or snowmelt flows over the ground,‖ (U.S. EPA 2003b). Stormwater runoff is natural in the environment, but can be exacerbated by impervious surfaces (e.g., parking lots, roads, roofs, etc.) that reduce infiltration and create excessive overland runoff. When stormwater runoff flows into a surface waterbody, the excess flow and pollutant loads can adversely impact beneficial uses (National Research Council, 2008).

The regulated and unregulated stormwater facilities in the Flathead Lake Basin are described in a Summary of Urban Stormwater Sources in the Flathead Lake Basin (U.S. EPA 2010a). This summary document reports that there are two small MS4s, six industrial facilities, and approximately 205 construction sites with stormwater permits in the Flathead Lake Basin. In addition, there are numerous unregulated stormwater sources throughout the basin including commercial areas, construction sites less than one acre that are not subject to local ordinances, and municipal and residential areas that fall outside the definition of a regulated small MS4 under the NPDES Phase II program. These areas have impervious surfaces that have the potential to contribute similar pollutants via stormwater runoff as those areas covered under the NPDES Stormwater Program.

LSPC does not explicitly model stormwater infrastructure (i.e., pipes, conveyances, etc.) and modeling stormwater using another model (e.g., SWMM, SUSTAIN, etc.) is currently beyond the scope of this project. However, in each delineated subwatershed, the LSPC model uses a separate set of parameters for hydrology and contaminant runoff for each category of land use and soil type. To the extent practicable, model subwatersheds will be delineated to isolate the larger stormwater areas within the Flathead Lake Basin (e.g., Kalispell, Whitefish, Bigfork, etc.) to facilitate a more site-specific examination of stormwater.

A compilation of national, regional, and local stormwater discharge TSS, TN, and TP data is provided in Table 16. Between 1978 and 1983, the EPA conducted the Nationwide Urban Runoff Program (NURP) that examined stormwater quality from separate storm sewers in different land uses. This program studied 81 outfalls in 28 communities throughout the U.S. and included the monitoring of approximately 2,300 storm events (Burton and Pitt 2001). These data, however, are now dated and do not necessarily represent the quality of stormwater runoff in the Flathead Lake Basin, but have been presented to provide a point of comparison to data collected by the seven small MS4’s (i.e., Billings, Bozeman, Helena, Great Falls, Kalispell, Butte and Missoula) in Montana. The ―Montana Composite‖ data presented in Table 16 represents a composite of two stormwater grab samples collected annually since 2007 from two sample sites at each of the small MS4’s. The Kalispell data represent the results from 20 samples collected from two sites within the boundaries of Kallispells MS4 permit area. Both the Montana Composite and Kalispell values fall within, or very near, the range of the NURP values.

Urban land uses will initially be parameterized in the LSPC model based on the stormwater discharge data from the City of Kalispell (Table 16). These values may be modified during the calibration process and may be modified to account for pollutant reductions in areas where stormwater BMP are known to have been implemented.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 43 Table 16. Compilation of National, Regional, and Local Stormwater Discharge Data8

a Nationwide Urban Runoff Program Values Montana Kalispell Parameter Open/ c Residential Mixed Commercial Composite (n) (n = 20) Non-urban TSS (mg/L) 101.00 67.00 69.00 70.00 139.00 (123) 55.00 Total Nitrogen 2.64b 1.85b 1.75b 1.51b 2.20 (120) 2.56 (mg/L) Total Phosphorus 0.38 0.26 0.20 0.12 0.40 (124) 0.18 (mg/L) a. From U.S. EPA. Results of the Nationwide Urban Runoff Program. Water Planning Division, PB 84-185552, Washington, D.C. December 1983 (as summarized in Burton and Pitt, 2001). b. Estimated as the sum of TKN + (NO2+NO3) c. Composite of stormwater grab samples collected at two sites each from the Cities of Billings, Bozeman, Helena, Great Falls, Kalispell, Butte and Missoula. Samples are collected by these municipalities as part of their MPDES permit requirements. Data downloaded from ICIS. Period of record is 2007 – 2011.

4.6.7 Bank Erosion and Mass Wasting Bank and bluff erosion is potentially a significant source of sediment (and attached nutrient) loading in the Flathead Lake Basin. Areas of eroding glacial outwash/till terraces are common along all three forks of the Flathead River, Swift Creek, and many of the tributaries to the Middle Fork Flathead River draining the southeastern flank of Glacier National Park. These features have not been systematically mapped, nor have they been studied extensively within the Basin. However, a study of these features in the Swift Creek Watershed conducted by Land & Water Consulting, Inc. (2005) provides insight on the mechanisms of bank/bluff failure, trends over time, and sediment loading rates.

8 The Whitefish Lake Institute and 48 North Engineering recently submitted a draft report to DEQ containing recently collected water quality data from the City of Big Fork’s stormwater management system. These data will be evaluated when they become available.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 44

Figure 15. Exposed eroding terrace, North Fork Flathead River (Photo: Brian D. Sugden 2011).

The lower Flathead River is still responding to construction and operation of the Kerr and Hungry Horse Dams, which have significantly altered the hydrography of the lower river and back water up for almost 24 miles from Flathead Lake. Dam operation, among other things such as increased recreational boating, bank armoring, and land use practices, is thought to have exacerbated bank erosion and bank slumping along the lower river, causing landowner concerns over property loss. Areas of active erosion along this stretch of the river have been mapped as part of a channel migration zone analysis (Boyd et.al. 2010).

Bank erosion has also been studied in a number of the Flathead Lake Basin tributaries. For example, tributary bank erosion was the topic of a DEQ study in the Flathead-Stillwater TMDL Planning Area, where an assessment of all actively/visually eroding and slowly eroding/undercut/vegetated streambanks was conducted along 20 reaches within Ashley Creek, East and West Fork Swift Creek, Haskill Creek, Logan Creek, Sheppard Creek, and portions of the Stillwater River (Watershed Consulting 2009). Study results showed an estimated average background sediment loading rate from bank erosion of 16.1 tons/year per 1000 feet of stream. Reaches with anthropogenic influence were estimated to yield an average of 39.1 tons/year per 1,000 feet of stream.

Obtaining an accurate sediment and nutrient (especially phosphorus) simulation will require consideration of bank and bluff erosion, and quantifying loads from these features is necessary for TMDL source assessment purposes. However, LSPC is not a detailed hydraulic/channel erosion model and it is not possible to fully and explicitly represent bank and bluff erosion in LSPC. These features, therefore, will be evaluated outside of the LSPC platform. The results of the evaluations will be used to inform the LSPC model.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 45 The method for best informing the LSPC model relative to these features has not yet been determined. Tetra Tech will coordinate with DEQ, U.S. EPA, and the Technical Advisory Group to develop and implement an approach for representing bank and bluff erosion.

4.6.8 Agriculture Based on the 2006 NLCD, approximately three percent (132,000 acres) of the Flathead Lake Basin is comprised of agricultural lands (Figure 11). The most intensive agriculture in the Basin occurs in an area extending from the mouth of the Flathead River north to approximately Columbia Falls and Whitefish. A more detailed analysis of agriculture was completed in this area (Figure 16) and is summarized in The Flathead Valley Agricultural Impacts Report (Wendt 2011). The Wendt report provides details regarding: . The types of crops (e.g., hay, cereal grains, oilseeds, pulse crops, seed potatoes, and summer fallow or other agricultural practices) and where they are located. . The types and numbers of livestock and locations of CAFOs. . The locations of irrigated lands and types of irrigation. . The types and magnitudes of fertilizers applied to agricultural lands. . An assessment of trends in agriculture in the Flathead Valley.

The Wendt report is incorporated herein by reference and will be used as a starting point for parameterizing agricultural land uses in the Flathead Lake LSPC model.

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Figure 16. Agriculture Study Area.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 47 4.6.9 Golf Courses Of the land uses in the urban landscape, turf is the most intensively managed (King et. al. 2001). In many cases, chemical additions on golf courses are similar to, and often greater than, those used in intensive agriculture (Winter et. al. 2002). There are 10 golf courses in the Flathead Lake Basin (Table 17 and Figure 17). Fertilizer application rates were obtained from ___ and are summarized in Table 18. Golf courses within the Flathead Lake Basin will be simulated as a separate land use category. Nutrient concentrations in the LSPC model will initially be set to the values listed in Table 18.

Table 17. Golf Courses in the Flathead Lake Basin Ownership Name Location Acres Public Glacier View Golf Club West Glacier 85 Private Iron Horse Golf Club Whitefish 127 Municipal Buffalo Hill Golf Club Kalispell 163 Public Village Greens Golf Course Kalispell 120 Public Whitefish Lake Golf Club Whitefish 199 Public Meadow Lake Golf Course Columbia Falls 109 Public Eagle Bend Golf Club Bigfork 199 Public Big Mountain Golf Club Flathead 165 Municipal Polson Country Club Polson 183 Public Mountain Crossroads Golf Course Kalispell 21 Total 1,371

TAG NOTE:

The type, magnitude, and frequency of fertilizer applications on golf courses is needed.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 48

Figure 17. Flathead Lake Basin Golf Courses.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 49 Table 18. Golf Course Fertilizer Application (Data were not available this time).

Month Narrative Description TN (lb/day) TP (lb/day) Oct - April No fertilizer is applied (?) -- -- May ? ?

June ? ? July ? ? August ? ? September ? ?

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 50 4.7 Point Sources In LSPC, point sources will be simulated by inputting the time series of available flow and water quality into the model reach to which a point source discharges. There are three general types of point sources that will be modeled: NPDES-permitted municipal and industrial facilities, non-permitted facilities, and NPDES-permitted stormwater discharging facilities. Data for the former two types of facilities are presented in Summary of Permitted Point Sources in the Flathead Lake Basin (U.S. EPA 2011b). A discussion of regulated stormwater dischargers in the Flathead Lake Basin is presented in Summary of Urban Stormwater Sources in the Flathead Lake Basin (U.S.EPA 2011b). All three general types of point sources are briefly summarized in the following subsections.

4.7.1 Wastewater Treatment Plants and Other Non-stormwater NPDES Dischargers There are 20 facilities in the Flathead Lake Basin that have an MPDES permit to discharge wastewater to surface water or groundwater. The facilities consist of publicly owned water and wastewater treatment plants (WWTPs), industrial sites, fish hatcheries, and smaller privately owned treatment systems. Table 19 summarizes the facilities and their receiving waterbodies.

Table 19. MPDES permitted facilities in the Flathead Lake Basin Design Flow Facility (MGD) Receiving waterbody Bigfork WWTP 0.50 Flathead Lake Burlington-Northern Whitefish Facility 0.096 Whitefish River Columbia Falls Aluminum Company Not Reported Flathead River Columbia Falls WWTP 0.550 Flathead River Creston National Fish Hatchery a Mill Creek Don Abbey Household 0.5 Flathead Lake Ehrman Nine Lease Subdivision Not Applicable Groundwater Flathead Lake Fish Hatchery Not Reported Flathead Lake Glacier National Park WWTP 0.25 McDonald Creek Hungry Horse Dam WWTP 0.009 South Fork Flathead River International RV LLC N/A Groundwater Kalispell WWTP 5.4 Ashley Creek Kootenai Lodge/Lake County Water & Sewer Not Applicable District Groundwater Meadow Gold Dairy 0.0329 Smith Spring Creek Plum Creek Manufacturing Facility Not Applicable Groundwater Polson WWTP 0.650 Flathead River Stampede Packing Company Not Applicable Groundwater Whitefish WTP Not Reported Unnamed Reservoir Whitefish WWTP 1.8 Whitefish River Yellow Bay WWTP 0.033 Flathead Lake Italicized receiving waterbodies are on Montana’s 2008 303(d) list. a. The hatchery may receive up to 19,000 gallons per minute of water from Jessup Mill Pond

Seven of the permitted facilities discharge directly to 303(d) listed waterbodies (italicized in Table 19), and all of the facilities discharge to waterbodies that ultimately flow to Flathead Lake.

Facility size (and design flow) varies from small package plants (e.g., Hungry Horse WWTP, with a design flow of 0.009 MGD) to large publically owned treatment plants (e.g., Kalispell WWTP, with a

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 51 design flow of 5.4 MGD). Permit limits vary for each facility and eight facilities have nitrogen and/or phosphorus permit limits.

Flow and water quality data were obtained from discharge monitoring reports (DMRs) and facility records to characterize the effluent. Based on the available data, the facilities that discharged the largest estimated loads of total phosphorus were Whitefish WWTP (1,814 lbs/yr in 2008), Kalispell WWTP (1,039 lbs/yr in 2007), Columbia Falls WWTP (404 lbs/yr in 2008), and Bigfork WWTF (150 lbs/yr in 2005). Total nitrogen loads were estimated at 49.1 tons (in 2008), 1.08 tons (in 2007), 0.89 tons (in 2006), 6.34 tons (in 2007), respectively.

The existing DMR data for each facility will be used to create a time series to input into the LSPC model. Gaps in the DMR data will be filled by extrapolating the existing data to account for seasonal and annual trends.

4.7.2 Non-MPDES Dischargers There are a number of smaller, non-MPDES permitted facilities in the basin that dispose of wastewater via non-discharging ponds and/or spray irrigation. These facilities are not required to have a MPDES permit because they do not directly discharge to surface or ground water. However, they have the potential to collect and/or move wastewater over long distances from a variety of different households or businesses. An example of this type of facility is the Lakeside WWTP.

With the exception of the Lakeside WWTP (described in the Summary of Permitted Point Sources in the Flathead Lake Basin (U.S. EPA 2011b), there are little to no data available for most of these non-MPDES dischargers. In modeling the Flathead Lake Basin, it is important to understand how these systems operate so that flow and pollutant loadings can be routed correctly. Additional information on any existing non- permitted facilities would improve model performance and TMDL development. Depending on the available information, it may be appropriate to model these using the approach described in Section 4.6.5 for septic systems.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 52 5.0 Model Calibration and Uncertainty Analysis This section describes the methodology that will be used to calibrate the LSPC model, corroborate its performance, and evaluate uncertainty in model predictions.

5.1 LSPC Model Calibration Calibration is defined as ―the process of adjusting model parameters within physically defensible ranges until the resulting predictions give the best possible fit to the observed data‖ (U.S. EPA 2003a). For LSPC, calibration is required for both hydrology (flow) and water chemistry and is an iterative procedure of parameter evaluation and refinement as a result of comparing simulated and observed values at specified locations in a watershed. Calibration is required for parameters that cannot be deterministically and uniquely evaluated from topographic, climatic, physical, and chemical characteristics of the watershed and compounds of interest. Because these characteristics vary throughout a watershed, calibration generally occurs at more than one site. Also, calibration generally covers several years to capture a variety of climactic conditions. The calibration procedure results in parameter values that produce the best overall agreement between simulated and observed values throughout the calibration period. Section 5.1.1 describes the proposed flow calibration sites for the Flathead Lake watershed and Section 5.1.2 describes the proposed water quality calibration sites.

Typically, the performance of a calibrated model is evaluated through a model corroboration or ―validation‖ test. Model validation is defined as ―subsequent testing of a pre-calibrated model to additional field data, usually under different external conditions, to further examine the model’s ability to predict future conditions‖ (U.S. EPA 1997). Its purpose is to ensure that the calibrated model properly assesses all the variables and conditions that can affect model results, and demonstrate the ability to predict field observations for periods separate from the calibration effort (Donigian 2003). Model validation will occur at the same sites as the calibration, but a different set of years will be used to validate the model. The specific time periods for calibration and validation have not yet been identified.

In places where snowfall and snowmelt play a significant role in watershed hydrology, snow calibration is a critical first step. Because the snowpack acts like a giant reservoir of water over the entire landscape, it is important to ensure that the ―storage‖ and ―release‖ are properly timed. Snow telemetry data (expressed as snowpack water-equivalent depths) are used to verify this. Land and stream hydrology are next, followed by water quality, as outlined in Figure 18.

Figure 18. Conceptual overview of the watershed model calibration process.

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5.1.1 Flow Calibration Flow calibration sites were chosen based on the following criteria: . Available period of record between 1980 and present. . At least three years of continuous average daily flow records. . Average daily flows during the period of record have a minimum of 50 percent completeness.

Based on these criteria, there are seven flow gages (six USGS gages and 1 Flathead National Forest gages) with adequate data for calibrating Phase I of the LSPC model. The stations are located on both small streams (i.e., Big Creek, Teepee Creek, and Tuchuck Creek) and large streams (i.e., North and Middle Forks of the Flathead River and the Swan River). Figure 19 shows the location of the flow calibration stations and relevant characteristics are presented in Table 20.

Table 20. Calibration sites for flow No. of Station ID Station Name Begin End Completeness samples Big Creek at Lookout FL7012 4/11/1986 9/30/1995 2,422 70.0% Bridge, MT Middle Fork Flathead River 12358500 *10/1/1980 12/28/2009 10,677 >99.9% near West Glacier, MT North Fork Flathead River 12355500 *10/1/1980 12/1/2009 10,653 100% near Columbia Falls, MT North Fork Lost Creek near 12369650 8/31/1982 6/28/1989 2,015 80.8% Swan Lake, MT Swan River near Condon, 12369200 *10/1/1980 9/30/1992 4,383 100% MT Teepee Creek near Polson, 12370900 10/1/1982 9/30/1987 1,876 100% MT 12355150 Tuchuck Creek 10/1/1985 9/30/1988 944 86.2% * Flow data collected prior to 10/1/1980 are not displayed.

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Figure 19. Flow calibration stations for the Phase I modeling.

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5.1.2 Water Chemistry Calibration Sites Water chemistry calibration sites were chosen based on the following criteria: . Available period of record between 1980 and present. . Data for parameters of interest. . Samples collected during representative flow conditions . Strategic location (e.g., significant contributor to loads, representative of certain land use/topography)

The total phosphorus, total nitrogen, and total suspended solids and suspended sediment concentration calibration sites for the Phase 1 model area are presented in Table 21 to

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 56 Table 23.

Table 21. Phase I Calibration sites for total phosphorus No. of Agency Site ID Site Name Latitude Longitude Begin End samples Middle Fork Flathead River Planning Area M F Flathead River near USGS 123585000 48.4952 -114.0101 5/21/2009 12/10/2007 30 West Glacier MT Coal Creek near West USGS 482518113420101 48.4216 -113.7012 3/16/2004 8/31/2007 86 Glacier, MT Pinchot Creek near USGS 482520113420201 48.4222 -113.7015 3/22/2004 8/31/2007 83 West Glacier, MT McDonald Creek bl USGS 483133113594801 McDonald Lake, Glacier 48.5258 -113.9967 2/8/2005 8/3/2007 42 Nat'l Prk Challenge Cr 13 MI USFS FL6005 48.2311 -113.3208 5/29/1986 9/21/1987 24 ENE Essex MT North Fork Flathead River Planning Area Flathead River at USGS 12355000 Flathead British 49.0016 -114.4751 1/24/1980 8/25/2009 122 Columbia N F Flathead River nr USGS 12355500 48.4955 -114.1276 5/21/1999 8/21/2003 20 Columbia Falls MT Big Cr @ Lookout USFS FL7012 48.5975 -114.2233 4/21/1986 9/15/1987 28 Bridge Swan River Planning Area Swan River at C10SWANR05 5/24/2007 9/23/2008 15 Porcupine Bridge DEQ 47.8850 -113.8547 Swan River - Porcupine FBC06008 7/14/1992 11/11/1993 17 Bridge DEQ = Montana Department of Environmental Quality; USFS = U.S. Forest Service; USGS = U.S. Geological Survey

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 57 Table 22. Phase I Calibration sites for total nitrogen No. of Agency Site ID Site Name Latitude Longitude Begin End samples Middle Fork Flathead River Planning Area M F Flathead River near USGS 123585000 48.4952 -114.0101 5/21/1999 6/11/2003 7 West Glacier MT Coal Creek near West USGS 482518113420101 48.4216 -113.7012 3/16/2004 8/31/2007 91 Glacier, MT Pinchot Creek near USGS 482520113420201 48.4222 -113.7015 3/22/2004 8/31/2007 82 West Glacier, MT McDonald Creek bl USGS 483133113594801 McDonald Lake, Glacier 48.5258 -113.9967 2/8/2005 8/3/2007 43 Nat'l Prk North Fork Flathead River Planning Area Flathead River at USGS 12355000 Flathead British 49.0016 -114.4751 1/24/1980 6/17/2002 29 Columbia N F Flathead River nr USGS 12355500 48.4955 -114.1276 4/12/2000 6/17/2002 5 Columbia Falls MT Swan River Planning Area Swan River at DEQ C10SWANR05 47.8850 -113.8547 5/24/2007 9/23/2008 15 Porcupine Bridge DEQ = Montana Department of Environmental Quality; USFS = U.S. Forest Service; USGS = U.S. Geological Survey

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 58 Table 23. Phase I Calibration sites for total suspended solids and suspended sediment concentration No. of Agency Site ID Site Name Latitude Longitude Begin End samples Middle Fork Flathead River Planning Area M F Flathead River near USGS 123585000 5/21/1999 10/17/2007 29 West Glacier MT 48.4952 -114.0101 Coal Creek near West USGS 482518113420101 48.4216 -113.7012 86 Glacier, MT 12/10/2003 7/6/2007 Pinchot Creek near USGS 482520113420201 84 West Glacier, MT 48.4222 -113.7015 12/20/2003 7/6/2007 McDonald Creek bl USGS McDonald Lake, Glacier 2/8/2005 7/8/2007 37 483133113594801 Nat'l Prk 48.5258 -113.9967 Challenge Cr 13 MI USFS FL6005 147 ENE Essex MT 48.2311 -113.3208 6/29/1983 10/16/1995 North Fork Flathead River Planning Area Flathead River at USGS 12355000 Flathead British 49.0016 -114.4751 1/24/1980 8/25/2009 163 Columbia N F Flathead River nr USGS 12355500 20 Columbia Falls MT 48.4955 -114.1276 5/21/1999 8/21/2003 Big Cr @ Lookout USFS FL7012 48.5975 -114.2233 4/11/1986 10/30/1995 860 Bridge Swan River Planning Area Swan River at DEQ C10SWANR05 47.885 -113.855 5/24/2007 9/23/2008 15 Porcupine Bridge DEQ 5018JI01 Jim Creek 47.627 -113.811 4/14/1989 5/19/1989 13 DEQ = Montana Department of Environmental Quality; USFS = U.S. Forest Service; USGS = U.S. Geological Survey Note: DEQ evaluated samples for total suspended solids and USFS and USGS evaluated samples for suspended sediment concentration.

5.1.3 Acceptance Criteria for Model Calibration The principal study questions for this project are to determine the sources of nutrient loads within the watershed, the reductions needed to achieve water quality standards, and the likely response to various BMPs. The general objective for model calibration is, therefore, to create a reliable modeling tool that can be used to evaluate such responses. To create such a tool, it will be necessary to first calibrate and then validate the model.

While model developers will strive to achieve the highest quality of fit possible during calibration and validation, they must keep in mind the decision purposes of the models. As such, some degree of uncertainty in model predictions is acceptable; however, bias—a systematic deviation between model predictions and observations—should be avoided to the extent practicable.

Hydrodynamic and water quality models are often evaluated through visual comparisons, in which the simulated results are plotted against the observed data for the same location and time and are visually evaluated to determine if the model is able to mimic the trend and overall magnitude of the observed conditions. If the model predictions follow the general trend and reproduce the overall magnitude of the observed data, the model is said to represent the dynamics of the system well. The merit of this method is that it is straightforward, taking full advantage of the strength of human intelligence in pattern

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 59 identification. This method works particularly well when data are limited in quantity and contain significant uncertainty. The limitation of this method is that it relies on the subjective judgment of modelers and lacks quantitative measures to differentiate among sets of calibration result.

An alternative approach aimed at overcoming the limitations of the visual comparison method is to quantify the goodness of fit using a series of statistical measures. Ideally, if there are a large number of data and most of the data are accurate, a quantitative approach can be used to evaluate the model’s performance. However, in reality, the amount of water quality data is generally limited, and the available data often contains errors and uncertainties. Therefore, the validity of the quantitative statistical method is often compromised by uncertainties in the observed data. In addition, there is no widely acceptable range of error statistics defined for water quality model calibration. Due to data limitations, it is generally not expected that a water quality model can reproduce the exact timing of water quality in a dynamic system. For example, if a water quality model has mimicked the time variable feature in the river very well but with a 1-day shift in time, poor error statistics can result. In this case, the error statistic does not make any practical sense unless it is interpreted with the visual comparison of trend and magnitude.

In this study, a dual approach will be adopted to guide the calibration of LSPC. The dual approach will be implemented in a two-stage manner. For the first stage, the model calibration will be guided through the visual comparison approach, which would allow the calibration effort to be led toward reproducing the trend and overall dynamics of the river. After the model has been calibrated to the trend and overall dynamics, the second stage involves fine tuning the parameters and then calculating various error statistics to find a most appropriate calibration within the range of calibration parameters determined in stage one.

To conduct the dual calibration process, a set of basic statistical methods will be used to compare model predictions and observations in the second calibration stage. For the intended uses of the model, determining the relative contributions of different source areas and the relative performance of different management measures is of greatest importance, while obtaining a precise estimate of loading time series is of less direct interest. Ideally, the models should attain tight calibration to observed data; however, a less precise calibration can still provide useful information.

In light of these uses of the models, it is most informative to specify performance target ranges of precision that characterize the model results as ―very good,‖ ―good,‖ ―fair,‖ or ―poor.‖ These characterizations inform appropriate uses of the model: Where a model achieves an excellent fit it can assume a strong role in evaluating management options. Conversely, where a model achieves only a fair or poor fit it should assume a much less prominent role in the overall weight-of-evidence evaluation of management options.

The general acceptance criterion for models to be applied in this project is to achieve a quality of fit of ―good‖ or better. In the event that this level of quality is not achieved on some or all measures the model may still be useful; however, a detailed description of its potential range of applicability will be provided. The LSPC model is functionally identical to U.S. EPA’s HSPF model. For hydrologic calibration of HSPF, a variety of performance targets have been specified, including Donigian et al. (1984), Lumb et al. (1994), and Donigian (2000). Based on these, the performance targets for simulation of the water balance components are summarized in Table 24. Model performance will be deemed acceptable where a performance evaluation of ―good‖ or ―very good‖ is attained.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 60 Table 24. Performance Targets for HSPF Hydrologic Simulation (Magnitude of Annual and Seasonal Relative Mean Error (RE); Daily and Monthly R2)

Model Component Very Good Good Fair Poor

1. Error in total volume ≤ 5% 5 - 10% 10 - 15% > 15% 2. Error in 50% lowest ≤ 10% 10 - 15% 15 - 25% > 25% flow volumes

3. Error in 10% highest ≤ 10% 10 - 15% 15 - 25% > 25% flow volumes 4. Error in storm ≤ 10% 10 - 15% 15 - 25% > 25% volume

5. Winter volume error ≤ 15% 15 - 30% 30 - 50% > 50% 6. Spring volume error ≤ 15% 15 - 30% 30 - 50% > 50%

7. Summer volume ≤ 15% 15 - 30% 30 - 50% > 50% error

8. Fall volume error ≤ 15% 15 - 30% 30 - 50% > 50% 9. R2 daily values > 0.80 > 0.70 > 0.60 ≤ 0.60

10. R2 monthly values > 0.85 > 0.75 > 0.65 ≤ 0.65

It is important to clarify that the tolerance ranges are intended to be applied to mean values, and that individual events or observations may show larger differences and still be acceptable (Donigian, 2000). General performance targets for water quality simulation with HSPF/LSPC are also provided by Donigian (2000) and are shown in Table 25. These are observed to be calculated from observed and simulated daily values, and should only be applied in cases where there are a minimum of 20 observations. As noted previously, the model will be deemed acceptable where a performance evaluation of ―good‖ or ―very good‖ is attained.

Table 25. Performance Targets for HSPF Water Quality Simulation (Magnitude of Annual and Seasonal Relative Average Error (RE) on Daily Values)

Model Component Very Good Good Fair Poor

1. Suspended ≤ 20% 20 - 30% 30 - 45% > 45% Sediment

2. Nutrients ≤ 15% 15 - 25% 25 - 35% > 35%

5.1.4 Model Validation Validation is defined as the comparison of modeled results with calibrated parameters applied to an independent set of. Model validation is in reality an extension of the calibration process. Its purpose is to assure that the calibrated model properly assesses the range of variables and conditions that are expected within the simulation. Although there are several approaches to validating a model, perhaps the most effective procedure is to use only a portion of the available record of observed values for calibration. The rest is used for validation. Once final calibration parameters are developed, simulation is performed for

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 61 the remaining period of observed values and the goodness-of- fit between recorded and simulated values is reassessed. This type of split-sample calibration and validation procedure will be used for the Flathead modeling project, and the results of the validation will be assessed using the same approach as that described for the model calibration.

5.2 Uncertainty Analysis From a decision context, the primary function of the calibrated water quality model is to predict the response of sediment and nutrient loads to changes in management. As such, an important input to the decision-making process is information on the degree of uncertainty that is associated with model predictions. In some cases, the risks or costs of not meeting water quality standards could be substantially greater than the costs of over-protection, creating an asymmetric decision problem in which there is a strong motivation for risk avoidance. Further, if two scenarios produce equivalent predicted results, the scenario that has the smaller uncertainty is often preferable. Therefore, an uncertainty analysis of model predictions is essential.

As with any mathematical approximation of reality, a water quality model is subject to significant uncertainties. Computed values differ from observed ones, and the magnitude and frequency of these differences characterize the uncertainty of the best model estimate (Beard 1994).

The major sources of model uncertainty include the mathematical formulation, boundary conditions data uncertainty, calibration data uncertainty, and parameter specification (CREM 2009). In many cases, a significant amount of the overall prediction uncertainty is due to boundary conditions (e.g., uncertainty in estimation of rainfall from point gauge measurements, uncertainty in specifying point source loading time series) and uncertainty in the observed data used for calibration and validation. These sources of uncertainty are largely unavoidable, but do not invalidate the use of the model for decision purposes. Uncertainties in the mathematical formulation and model parameters are usually of greater concern for decision purposes as these describe the cause and effect relationships in the calibrated model.

For the Flathead watershed model, the model code (LSPC) and its parent model (HSPF) have a long history of testing and application, so outright errors in the coding of the models are unlikely. A simulation model, however, is only a simplified representation of the complexities of the real world. The question is not whether the model is ―right‖ in the sense that it represents all processes, but rather whether it is useful, in the sense that it represents the important processes to a sufficiently correct degree to be useful in answering the principal study questions. This type of uncertainty is most effectively addressed through peer review of the model application. For this project, it is anticipated that peer review will be provided by modelers and scientists at U.S. EPA, DEQ, and the Flathead Lake watershed Technical Advisory Group (TAG).

Uncertainty related to parameter specification can be evaluated numerically. The most widely applied parameter uncertainty analysis approach for complex simulation models is sensitivity analysis. Sensitivity analysis is implemented by perturbing model parameter values one at a time (or in combination) and evaluating the model response. This method is useful in identifying key parameters and processes in a water quality system, and the interpretation of the result is straightforward and meaningful. Sensitivity analysis, however, is limited in its ability to evaluate nonlinear interactions among multiple parameters.

Finally, an estimate of the total uncertainty in model performance can be provided through the model validation analyses that compare observations to model predictions. A set of basic statistical methods will be used to compare predictions and observations, including the mean error statistic, the absolute mean error, the root-mean-square error, the relative error, the coefficient of determination, and the Nash-

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 62 Sutcliffe coefficient of model fit efficiency. While each of these statistics will be reported, model acceptance criteria are defined on a specific subset of these measures, as described above.

Mean Error Statistic. The mean error between model predictions and observations is defined as

(O  P) E   , n where E = mean error O = observations P = model prediction at the same time as the observations n = number of observed-predicted pairs A mean error of zero is ideal. A non-zero value is an indication that the model might be biased toward either over- or under-prediction. However, an important consideration of the mean error approach is that it can severely penalize the model for small phase shifts in timing. One approach that can be used to address this is to establish a time window, calculate the range of model predictions for the time window, then count a deviation from prediction only if the observation falls outside this range.

Absolute Mean Error Statistic. The absolute mean error between model predictions and observations is defined as (O  P) E   , abs n where

Eabs = absolute mean error. An absolute mean error of zero is ideal. The magnitude of the absolute mean error indicates the average deviation between model predictions and observed data. Unlike the mean error, the absolute mean error cannot give a false zero.

Root-Mean-Square Error Statistic. The root-mean-square error (Erms) is defined as (O  P)2 E   , rms n A root-mean-square error of zero is ideal. The root-mean-square error is an indicator of the deviation between model predictions and observations. The Erms statistic is an alternative to (and is usually larger than) the absolute mean error.

Relative Error Statistics. The relative error statistics (RE) between model predictions and observations can be calculated by dividing the mean error and absolute mean error statistics by the mean of the observations. A relative error statistic of zero is ideal. When it is non-zero, it represents the percentage of deviation between the model prediction and observation.

Coefficient of Determination. The coefficient of determination (R2) is defined as:

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 63

n 2 Pi  O R2  i1 , n 2 Oi  O i1 where the overbar indicates the mean of the n observed values. The coefficient of determination varies between 0 and 1 and indicates the proportion of the total variation in observations explained by the model.

Coefficient of Model Fit Efficiency. The coefficient of model fit efficiency or Nash-Sutcliffe coefficient (ENS) is particularly useful for evaluating model fit to continuously observed data, taking into account both the difference between model and observation and the variance of the observations. The statistic is defined as n 2 Oi  Pi  i1 ENS  1.0  n . 2 Oi O i1 The resulting coefficient ranges from minus infinity to 1.0, with higher values indicating better agreement. At a value of zero, the test indicates that the observed mean is as good a predictor as the model, while negative values indicate that the observed mean is a better predictor than the model.

5.3 Reconciliation with User Requirements Specific numeric acceptance criteria are not specified for the model. Instead, appropriate uses of the model will be determined by the project team based on assessment of the types of decisions to be made, the model performance, and the available resources.

If the project team determines that the quality of the model calibration is insufficient to address the principal study questions, Tetra Tech will consult with U.S. EPA and other team members, as appropriate, as to whether the levels of uncertainty present in the models can allow user requirements to be met, and, if not, the actions needed to address the issue.

A detailed evaluation of the ability of the modeling tools to meet user requirements will be provided in the modeling report.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 64 6.0 Nondirect Measurements (Secondary Data Acquisition Requirements) Nondirect measurements (also referred to as secondary data) are data previously collected under an effort outside this contract that are used for model development and calibration. Sources of key secondary data are summarized in Sections 4.0and 5.0. Details regarding how relevant secondary data will be used for this task are provided below.

6.1 Quality Control for Nondirect Measurements The majority of the nondirect measurements will be obtained from quality assured sources. Tetra Tech will assume that data obtained from USGS, DEQ, or U.S. EPA documents and databases have been screened and meet specified measurement performance criteria. These criteria might not be reported for the parameters of interest in the documents or databases. Tetra Tech will determine how much effort should be made to find reports or metadata that might contain that information. Tetra Tech will perform general quality checks on the transfer of data from any source databases to another database, spreadsheet, or document. Where data are obtained from sources lacking an associated quality report, Tetra Tech will evaluate data quality of such secondary data before use. Additional methods that might be used to determine the quality of secondary data include: . Verifying values and extracting statements of data quality from the raw data, metadata, or original final report . Comparing data to a checklist of required factors (e.g., analyzed by an approved laboratory, used a specific method, met specified DQOs, validated) If it is determined that such searches are not necessary or that no quality requirements exist or can be established, however these data must be used in the task, Tetra Tech will add a disclaimer to the deliverable indicating that the quality of the secondary data is unknown.

6.2 Data Management and Hardware/Software Configuration No sampling (primary data collection) will be conducted by Tetra Tech for this task. Secondary data collected as part of this task will be maintained as hard copy only, both hard copy and electronic, or electronic only, depending on their nature. The modeling software to be used for this project consists primarily of the LSPC model, for which both code and executables are publicly available from U.S. EPA. The Tetra Tech TOL will maintain and provide the final version of the model input, output, and executables to U.S. EPA and DEQ for archiving at the completion of the task. Electronic copies of the data, GIS, and other supporting documentation will be supplied to U.S. EPA with the final report. Tetra Tech will maintain copies in a task subdirectory (subject to regular system backups) and on disk for a maximum period of 3 years after task termination, unless otherwise directed by the client. Most work conducted by Tetra Tech for this task requires the maintenance of computer resources. Tetra Tech’s computers are either covered by onsite service agreements or serviced by in-house specialists. When a problem with a microcomputer occurs, in-house computer specialists diagnose the problem and correct it if possible. When outside assistance is necessary, the computer specialists call the appropriate vendor. For other computer equipment requiring outside repair and not currently covered by a service contract, local computer service companies are used on a time-and-materials basis. Routine maintenance of microcomputers is performed by in-house computer specialists. Electric power to each microcomputer flows through a surge suppressor to protect electronic components from potentially damaging voltage spikes. All computer users have been instructed on the importance of routinely archiving work

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 65 assignment data files from hard drive to compact disc or server storage. The office network server is backed up nightly during the week. Screening for viruses on electronic files loaded on microcomputers or the network is standard company policy. Automated screening systems have been placed on all of Tetra Tech’s computer systems and are updated regularly to ensure that viruses are identified and destroyed. Annual maintenance of software is performed to keep up with evolutionary changes in computer storage, media, and programs. 7.0 Model Uses and Scenarios The previous sections of this document have discussed set-up and parameterization of the model for the purposes of calibration/validation for the time period extending between October 1, 1979 and September 30, 2008. However, the intended uses of the model have not yet been discussed.

The purpose of the Flathead Lake Basin LSPC modeling exercise is to develop a tool to support the following goals:

1. Complete the Phase II Allocation Strategy for the Flathead Basin as specified in the 2001 Nutrient Management Plan and Total Maximum Daily Loads for Flathead Lake. 2. Support development of all necessary nutrient and sediment-related TMDLs for all impaired waters in the Flathead Lake Basin. 3. Develop an overall, basin-scale plan for managing nutrients in the Flathead Lake Basin.

Ultimately, it is envisioned that a number of modeling scenarios will need to be developed and run to support these goals. Conceptually, model scenarios to support these goals will likely include an existing condition and natural occurring scenario, one or more scenarios reflecting compliance with the applicable water quality standards and variances, and a number of watershed or subwatershed-scale Best Management Practice (BMP) scenarios. Additionally, scenarios may be developed in the future, in consultation with the TAG and Flathead Lake Basin stakeholders, examining a variety of alternatives that may exist for managing nutrients in the Flathead Lake Basin. For example, scenarios could be developed to assess the magnitude of nutrient reduction associated with municipal wastewater treatment, septic system, and/or sewer system alternatives. The LSPC model could also potentially be used to evaluate nutrient trading options and assist with MPDES permit development.

The model scenarios that will likely be developed to support the above goals are briefly described below. Detailed scenario descriptions are not presented at this time.

7.1 Existing Condition Scenario Model simulations of the existing condition will attempt to replicate the actual hydrologic influences and pollutant generation, fate, and transport associated with the land uses and activities affecting pollutant load generation at the present time. For example, the model will be parameterized to reflect: current WWTP discharge flow rates, nutrient concentrations, and loads; current operation practices at Hungry Horse and Kerr Dams; the current number and spatial distribution of septic systems; etc.

Given a lag time between the collection of data and the time when it becomes available, the existing condition scenario will be developed for the year 2011. The existing condition scenario will be the baseline from which all other scenarios are compared. Also, the current nutrient and sediment loads from each of the potentially significant pollutant sources will be quantified based on the results of this scenario.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 66 7.2 Natural Occurring Scenario The applicable Montana water quality standards for sediment are narrative and linked to a definition of ―naturally occurring‖ found in 75-5-306, MCA. ―Naturally occurring‖ means conditions or material present from runoff or percolation over which man has no control or from developed land where all reasonable land, soil, and water conservation practices have been applied. ―Reasonable land, soil, and water conservation practices‖ means methods, measures, or practices that protect present and reasonably anticipated beneficial uses. These practices include but are not limited to structural and nonstructural controls and operation and maintenance procedures.

Defining the naturally occurring condition is necessary for sediment TMDL development. As such, a naturally occurring scenario will be developed for the model subbasins draining to the sediment impaired reaches in the Flathead Lake Basin (Table 4). This scenario will be developed in consultation with DEQ.

7.3 Water Quality Standards Compliance Because many of the Montana’s draft base numeric nutrient standards are stringent and may be difficult for MPDES permit holders to meet in the short term, Montana’s legislature adopted laws (e.g., §75-5-313, MCA) allowing for the achievement of the standards over time through variances. This approach should allow time for nitrogen and phosphorus removal technologies to improve and become less costly, and to allow time for nonpoint sources of nitrogen and phosphorus pollution to be better addressed.

The details of the variance process have not yet been formally adopted. However, for the purposes of this document, it is envisioned that MPDES permit nutrient concentration limits will decrease following the approach outlined in Part B of the draft DEQ Department Circular DEQ-12 (DEQ, 2011).

Tetra Tech will work with DEQ and U.S. EPA to develop one or more scenarios that allow the model to examine the water quality effects of decreasing nitrogen and phosphorus discharge concentrations in MPDES permit limits over time.

7.4 BMP Scenarios It is likely that nonpoint sources throughout the watershed will need to reduce nutrient loading to meet the TMDLs. The model will be used to determine how much nutrient loading is contributed from the various nonpoint sources (existing condition scenario). If reductions are needed, scenarios will be run to determine how much load reduction can be achieved from a variety of BMP options for nonpoint sources. It is likely that a variety of options will be available to reduce nonpoint source loads, and Tetra Tech will work with U.S EPA, DEQ, and the stakeholder group to determine what type of scenarios to run. LSPC will not explicitly model BMPs nor will BMPs be modeled at the scale of an individual structural feature. Rather the model will be parameterized at the model subwatershed-scale using either locally derived BMP pollutant removal efficiency values or values obtained from the literature.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 67 8.0 Assessment and Response Actions and Reports to Management

8.1 Assessment and Response Actions The QA program under which this task order will operate includes surveillance and internal and external testing of the software application. The essential steps in the QA program are as follows:

1) Identify and define the problem 2) Assign responsibility for investigating the problem 3) Investigate and determine the cause of the problem 4) Assign and accept responsibility for implementing appropriate corrective action 5) Establish the effectiveness of and implement the corrective action 6) Verify that the corrective action has eliminated the problem

Many technical problems can be solved on the spot by the staff members involved; for example, by modifying the technical approach, correcting errors in input data, or correcting errors or deficiencies in documentation. Immediate corrective actions are part of normal operating procedures and are noted in records for the task. Problems not solved this way require formalized, long-term corrective action. If quality problems that require attention are identified, Tetra Tech will determine whether attaining acceptable quality requires short- or long-term actions. If a failure in an analytical system occurs (e.g., performance requirements are not met), the appropriate QC Officer will be responsible for corrective action and will immediately inform the Tetra Tech TOL or QA Officer, as appropriate. Subsequent steps taken will depend on the nature and significance of the problem.

The Tetra Tech TOL (or designee) has primary responsibility for monitoring the activities of this task and identifying or confirming any quality problems. Significant quality problems will also be brought to the attention of the Tetra Tech QA Officer, who will initiate the corrective action system described above, document the nature of the problem, and ensure that the recommended corrective action is carried out. The Tetra Tech QA Officer has the authority to stop work if problems affecting data quality that will require extensive effort to resolve are identified.

Corrective actions may include the following: . Reemphasizing to staff the task objectives, the limitations in scope, the need to adhere to the agreed-upon schedule and procedures, and the need to document QC and QA activities . Securing additional commitment of staff time to devote to the task . Retaining outside consultants to review problems in specialized technical areas . Changing procedures

The assigned QC Officer (or designee) will perform or oversee the following qualitative and quantitative assessments of model performance to ensure that models are performing the required tasks while meeting the quality objectives: . Data acquisition assessments . Secondary data quality assessments . Model testing studies . Model evaluations . Internal peer reviews

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 68 8.1.1 Model Development Quality Assessment This QAPP and other supporting materials will be distributed to all personnel involved in the work assignment. The designated QC Officer will ensure that all tasks described in the work plan are carried out in accordance with the QAPP. Tetra Tech will review staff performance throughout each development phase of each case study to ensure adherence to task protocols.

Quality assessment is defined as the process by which QC is implemented in the model development task. All modelers will conform to the following guidelines: . All modeling activities including data interpretation, load calculations, or other related computational activities are subject to audit or peer review. Thus, the modelers are instructed to maintain careful written and electronic records for all aspects of model development. . If historical data are used, a written record on where the data were obtained and any information on their quality will be documented in the final report. A written record on where this information is on a computer or backup media will be maintained in the task files. . If new theory is incorporated into the model framework, references for the theory and how it is implemented in any computer code will be documented. . All modified computer codes will be documented, including internal documentation (e.g., revision notes in the source code), as well as external documentation (e.g., user’s guides and technical memoranda supplements).

The QC Officer will periodically conduct surveillance of each modeler’s work. Modelers will be asked to provide verbal status reports of their work at periodic internal modeling work group meetings. The Tetra Tech TOL or his assigned deputy will make detailed modeling documentation available to members of the modeling work group on a monthly basis.

8.1.2 Software Development Quality Assessment The QC Officer (or designee) will also conduct surveillance on any needed software development activities to ensure that all tasks are carried out in accordance with the QAPP and satisfy user requirements. Staff performance will be reviewed throughout the life cycle to ensure adherence to task procedures and protocols. All task staff will conform to the following guidelines: . All software development activities, including data compilation, processing, and analysis, are subject to audit or peer review. Thus, the programmers are instructed to maintain careful written and electronic records for all aspects of software development. . As computer programs are modified (e.g., hand calculation checks, checks against other models), the code will be checked and a written record made as to how the code is known to work. . If historical data are used, a written record of where the data were obtained and any information on the quality of the data will be documented in the final report. A written record of where this information is on a computer or backup medium will be maintained in the task files. . All new and modified computer codes will be documented, including internal documentation (e.g., revision notes in the source code) as well as external documentation (e.g., user guides and technical memoranda supplements). The QC Officer or his designee will conduct periodic surveillance of each programmer’s work. Programmers will also adhere to a variety of practices and protocols in addition to the guidelines listed above. Programmers will follow development practices and use a software testing plan that includes internal testing, error tracking, and external testing.

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 69 Depending on the scale of the software task, one or more developers might need to collaborate or concurrently work with the same software source code. In these situations, it is important that all changes to the code be tracked and easily reconstructed as the various versions are reassembled into the single, monolithic code base. To assist with version control and management, Tetra Tech uses a concurrent version control system (CVS) during development. CVS is a source control or revision control tool designed to keep track of source changes made by groups of developers working on the same files, allowing them to stay in sync. Version control and tracking also enables a particular snapshot of a development process to be recovered at some stage in the future (after the development has moved beyond the snapshot).

8.1.3 Surveillance of Project Activities Internal peer reviews will be documented in the project file and QAPP file. Documentation will include the names, titles, and positions of the peer reviewers; their report findings; and the project management’s documented responses to their findings. The Tetra Tech TOL may replace a staff member if it is in the best interest of the task to do so.

Performance audits are quantitative checks on different segments of task activities. The Tetra Tech QC Officer or his designees will be responsible for overseeing work as it is performed and for periodically conducting internal assessments during the data entry and analysis phases of the task. The Tetra Tech TOL will perform surveillance activities throughout the duration of the task to ensure that management and technical aspects are being properly implemented according to the schedule and quality requirements specified in the data review and technical approach documentation. These surveillance activities will include assessing how task milestones are achieved and documented, corrective actions are implemented, budgets are adhered to, peer reviews are performed, and data are managed, and whether computers, software, and data are acquired in a timely manner.

8.2 Reports to Management The TOL (or designee) will provide monthly progress reports to U.S. EPA. As appropriate, these reports will inform U.S. EPA of the following: . Adherence to project schedule and budget . Deviations from approved QAPP, as determined from project assessment and oversight activities . The impact of these deviations on model application quality and uncertainty . The need for and results of response actions to correct the deviations . Potential uncertainties in decisions based on model predictions and data . Data Quality Assessment findings regarding model input data and model outputs

DRAFT REPORT – DO NOT CITE OR DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 70 9.0 Model Documentation The modeling team will develop a central file repository for the information and data used in the preparation of any reports and the following information will be included: . Reports and documents prepared as part of the project. . The project QAPP. . Contract and work assignment information. . Copies of e-mail correspondence with critical information or that document important project decisions. . Technical review correspondence, data quality assessments, and performance audits. . Significant communications (technical memoranda, internal notes, telephone conversation records, letters, meeting minutes, and all written correspondence between DEQ, the contractor, and other modeling team members). . Maps, photographs, and drawings. . Studies, reports, documents, and newspaper articles pertaining to the project. . Special data compilations. The records of receipt, and information on source and description of documentation shall be filed along with the original data to ensure traceability. Records of such actions and subsequent findings will be kept for processing. Examples include unit conversions, data gap interpolation, and data extrapolation. Recordkeeping shall also include example calculations and conversions, and software references for data processing (e.g., name of software, provider, version, etc.).

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10.0 Schedule A tentative schedule of modeling steps and TAG meetings is presented below in Table 26.

Table 26. Tentative schedule Modeling Task Anticipated Timeframe TAG meeting to discuss draft QAPP February 21, 2012 Finalize QAPP March, 2012 Phase I model set-up , calibration, and validation April – July, 2012 TAG meeting to discuss Phase I results and July, 2012 proposed approach for Phase II modeling Finalize Phase I model set-up, calibration, and August, 2012 validation Phase II model set-up, calibration, and validation August – December, 2012 TAG meeting to discuss Phase II results and January, 2013 scenarios Model Scenarios and TMDL Support February - April, 2013 Model Report May, 2013

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11.0 References Beard, L., 1994. Anatomy of Best Estimate. Journal of Hydraulic Engineering 120: 679-692.

Bicknell, B.R., J.C. Imhoff, J. Kittle, A.S. Donigian, and R.C. Johansen. 1996. Hydrological Simulation ProgramFORTRAN, User's Manual for Release H. U.S. Environmental Protection Agency, Environmental Research Laboratory, Athens, GA.

Boyd, K., T. Thatcher, and B. Swindell. 2010. Flathead River Channel Migration Zone Mapping. Prepared for the Flathead Lakers.

Burton, A.G., and R.E. Pitt. 2001. Stormwater Effects Handbook – A Toolbox for Watershed Managers, Scientists, and Engineers. Lewis Publishers. Boca Raton, Florida.

Caltrans. 2003. Caltrans Tahoe Highway Runoff Characterization and Sand Trap Effectiveness Studies. 2000-03 Monitoring Report. California Department of Transportation.

Chapra, S.C. 2002. Surface Water-Quality Modeling. McGraw-Hill. Boston, MA.

Costnick, L.A. 1996. Indexing Current Watershed Conditions Using Remote Sensing and GIS. Final report to Congress, vol. II, Assessments and scientific basis for management options

Crawford, N.H. and R.K. Linsley. 1966. Digital Simulation in Hydrology: The Stanford Watershed Model IV. Palo Alto, CA: Dept. of Civil Engineering, Stanford University; Tech. Rep. No. 39.

CREM. 2009. Guidance on the Development, Evaluation, and Application of Environmental Models. EPA/100/K-09/003. Office of the Science Advisor, Council for Regulatory Environmental Modeling, U.S. Environmental Protection Agency, Washington, DC.

DEQ. 2002. Nutrient Management Plan and Total Maximum Daily Load for Flathead Lake, Montana. Montana Department of Environmental Quality. December 28, 2001.

DEQ. 2008. Literature Review of the Flathead Lake Watershed (unpublished). Montana Department of Environmental Quality, Water Quality Planning Bureau, Data Management Section. Helena, Montana.

DEQ. 2011. Draft Department Circular DEQ-12, Parts A and B. Montana Base Numeric Nutrient Standards and Nutrient Standards Variences. Montana Department of Environmental Quality. Version 5.4.

DNRC. 2006. Draft Montana Watershed Boundaries 6th Code HUCs (Dated November 2006) [Computer File]. Montana Department of Natural Resources and Conservation [Producer] and Montana Natural Resource Information System (NRIS) [Distributor]. Helena, Montana. Available online at http://nris.state.mt.us/nsdi/watershed/index.html (Accessed December 12, 2006).

Donigian, A.S., Jr. 2000. HSPF Training Workshop Handbook and CD. Lecture #19. Calibration and Verification Issues. Prepared for and presented to the U.S. Environmental Protection Agency, Office of Water, Office of Science and Technology, Washington, DC.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 74 Donigian, A. S., Jr., J. C. Imhoff., B.R. Bicknell, and J. L. Kittle, Jr. 1984. Application Guide for the Hydrological Simulation Program - FORTRAN. EPA 600/3-84-066. U. S. EPA, Environmental Research Laboratory, Athens, Georgia.

Ellis, B.K. and J.A. Craft. 2008. Trophic status and trends in water quality for Volunteer Monitoring Program lakes in northwestern Montana, 1993-2007. FLBS Report #200-08. Prepared for Flathead Basin Commission, Kalispell, Montana by Flathead Lake Biological Station. The University of Montana, Polson, MT. 43pp.

Flathead Lake Biological Station. 2001. Flathead Lake Facts. Available online at http://www.umt.edu/biology/flbs/facts/default.html.

Hollister, J. and W. B. Milstead. 2010. Using GIS to estimate lake volume from limited data. Lake and Reservoir Management. 26: 3, 194 — 199.

Homer C, Dewitz J, Fry J, Coan M, Hossain N, Larson C, Herold N, McKerrow A, Van Driel, JN, Wickham J. 2007. Completion of the 2001 National Land Cover Database for the conterminous United States.(PDF) (5pp, 2MB) Photogrammetric Engineering and Remote Sensing 73:337-341.

Kendall, Craig. 2011. Personal communications, Flathead National Forest, December 6, 2011.

King, K.W., R.D. Harmel, H.A. Torbert, and J.C. Balogh. 2001. Impact of a turfgrass system on nutrient loadings to surface water. J. Amer. Water Resources Assoc. 37(3):629-640.

Land & Water Consulting. 2005. Analysis of Eroding Banks along Swift Creek. A technical memo prepared for the Swift Creek Coalition. May 20, 2005.

Lumb, A.M., R.B. McCammon, and J.L. Kittle, Jr. 1994. Users Manual for an Expert System (HSPEXP) for Calibration of the Hydrological Simulation Program–Fortran. Water-Resources Investigations Report 94-4168. U.S. Geological Survey. Reston, VA.

McGurk, B.J., and D.R. Fong, 1995. Equivalent roaded area as a measure of cumulative effect of logging. Environmental Management 19: 609-621.

National Research Council. 2008. Urban Stormwater Management in the United States. Committee on Reducing Stormwater Discharge Contributions to Water Pollution. Water Science and Technology Board, Division on Earth and Life Studies. Washington, D.C. Available online at http://cfpub.epa.gov/npdes/home.cfm?program_id=6.

Sugden, Brian D. 2012. Personal communication. January 3, 2012.

Sugden, Brian D. and Scott W. Woods, 2007. Sediment Production from Forest Roads in Western Montana. Journal of the American Water Resources Association (JAWRA) 43(1):193-206.

U.S. Census Bureau. 2009. 2009 Population Estimates [Computer File]. U.S. Census Bureau – American Factfinder (Producer and Distributor). Available online at http://factfinder.census.gov/ (Accessed June 16, 2009).

USDA Forest Service. 1976. Forest Hydrology Part II. Hydrologic effects of vegetation manipulation.

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U.S. EPA. 1997. Technical Guidance Manual for Developing Total Maximum Daily Loads: Book 2, Rivers and Streams; Part 1 - Biochemical Oxygen Demand/Dissolved Oxygen & Nutrient Eutrophication EPA 823/B-97-002.

U.S. EPA. 2002. Guidance for Quality Assurance Project Plans for Modeling EPA QA/G-5M. U.S. Environmental Protection Agency Office of Environmental Information. EPA/240/R-02/007. Washington, D.C.

U.S. EPA. 2003a. Draft Guidance on the Development, Evaluation, and Application of Regulatory Environmental Models. Prepared by The Council for Regulatory Environmental Modeling. U.S. Environmental Protection Agency, Office of Science Policy, Office of Research and Development. Washington, D.C.

U.S. EPA. 2003b. ―What is Stormwater Runoff?‖ [Online]. U.S. Environmental Protection Agency, Office of Wetlands, Oceans, and Watersheds. EPA 833-B-03-002. Washington, D.C.

U.S. EPA. 2009. Flathead Basin TMDLs Technical Memo – Septic Systems – Internal Review Draft. Prepared by the U.S. Environmental Protection Agency, Region 8, Montana Operations Office. Helena, MT. March 2, 2009.

U.S. EPA. 2010a. Summary of Urban Stormwater Sources in the Flathead Lake Basin– Public Review Draft. Prepared by Tetra Tech for the U.S. Environmental Protection Agency, Region 8, Montana Operations Office. Helena, MT. March 1, 2010.

U.S. EPA. 2010b. Flathead Basin TMDL Technical Report – Forest Fires – Stakeholder Review Draft. Prepared by Tetra Tech for the U.S. Environmental Protection Agency, Region 8, Montana Operations Office. Helena, MT. March 1, 2010.

U.S. EPA. 2010c. Summary of Road Network in the Flathead Lake Basin – Public Review Draft. Prepared by Tetra Tech for the U.S. Environmental Protection Agency, Region 8, Montana Operations Office. Helena, MT. March 1, 2010.

U.S. EPA. 2011a. Summary of the Groundwater System of the Flathead Lake Basin – Stakeholder Review Draft. Prepared by Tetra Tech for the U.S. Environmental Protection Agency, Region 8, Montana Operations Office. Helena, MT. September 30, 2011.

U.S. EPA. 2011b. Summary of Permitted Point Sources in the Flathead Lake Basin – Public Review Draft. Prepared by Tetra Tech for the U.S. Environmental Protection Agency, Region 8, Montana Operations Office. Helena, MT. September 28, 2011.

U.S. EPA. 2011c. Summary of Timber Harvest in the Flathead Lake Basin – Public Review Draft. Prepared by Tetra Tech for the U.S. Environmental Protection Agency, Region 8, Montana Operations Office. Helena, MT. September 28, 2011.

U.S. EPA. 2011d. Summary of Lakes and Reservoirs in the Flathead Lake Basin – Public Review Draft. Prepared by Tetra Tech for the U.S. Environmental Protection Agency, Region 8, Montana Operations Office. Helena, MT. September 28, 2011.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 76 U.S. EPA. 2011e. Summary of Available Nutrient Data for Impaired Waters in the Flathead Basin – Public Review Draft. Prepared by Tetra Tech for the U.S. Environmental Protection Agency, Region 8, Montana Operations Office. Helena, MT. September 28, 2011.

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Watershed Consulting, LLC. 2009. Flathead-Stillwater TMDL Planning Area Sediment and Habitat Assessment Summary Report. Prepared for Montana Department of Environmental Quality. March 15, 2009.

Wendt, Alan R. 2011. The Flathead Valley Agricultural Impacts Report. Prepared by Alan R. Wendt for the Montana Department of Environmental Quality and Flathead River Commission. April 2011.

Winter J., K.M. Somers, P.J. Dillon, C. Paterson, and R. A. Reid. Impacts of Golf Courses on Macroinvertebrate Community Structure in Precambrian Shield Streams. Journal of Environmental Quality. 31 no6 N/D 2002.

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 77 Appendix A Meteorological Data for Flathead Lake Basin LSPC Modeling

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 78 Appendix B Precipitation Data

DRAFT REPORT – DO NOT DISTRIBUTE Flathead Lake Basin LSPC Modeling Approach and QAPP Revision 2 Date: 2/9/2012 Page 79 Appendix C Temperature Data

DRAFT REPORT – DO NOT DISTRIBUTE