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Predicting Channel Stability in Colorado Mountain Streams

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Predicting Channel Stability in Colorado Mountain Streams AN ABSTRACT OF THE DISSERTATION OF SteDhanie L. Moret for the degree of Doctor of PhilosoDhy in Environmental Sciences presented on March 16, 2001. Title: Predictinc Channel Stability in Colorado Mountain Streams UsinQ HydrobioQeomorDhic and Land Use Data: A Cost-Sensitive Machine Learnincj Aprroach to ModelinQ RaDid Assessment Protocols. Abstract aPPro Redacted for Privacy Peter cMlincieman Natural resource data are typically non-linear and complex, yet modeling methods oftenutilizestatisticalanalysis techniques, such as regression, that are insufficient for use on such data. This research proposes an innovative modeling method based on pattern recognition techniques borrowed from the field of machine learning. These techniques make no data distribution assumptions, can fit non-linear data, can be effective on a small data set, and can be weighted to include relative costs of different predictive errors. Rapid Assessment Protocols (RAPs) are commonly used to collect, analyze, and interpret stream data to assist diverse management decisions. A modeling method was developed to predict the outcome of a RAP in an effort to improve accurate prediction, weighted for cost-effectiveness and safety, while prioritizing investigations and improving monitoring.This method was developed using channel stability data collected from 58 high-elevation streams in the Upper Colorado River Basin. The purpose of the research was to understand the relationships of channel stability to several hydrobiogeomorphic features, easily derived from paper or electronic maps, in an effort to predict channel stability. Given that the RAP used was developed to evaluate channel stability, the research determined:1) relationships between channelstability and major land-use and hydrobiogeomorphic features, and 2) if a predictive model could be developed to aid in identifying unstable channel reaches while minimizing costs, for the purpose of land management. ThisresearchusedPearson's andchi-squaredcorrelationsto determine associative relationships between channel stability and major land- use and hydrobiogeomorphic features. The results of the Pearson's correlations were used to build and test classification models using randomly selected training and test sets.The modeling techniques assessed were regression, single decision trees, and bagged (bootstrap aggregated) decision trees. A cost analysis / prediction (CAP) model was developed to incorporate cost-effectiveness and safety into the models. The models were compared based ontheir1)performance and2)operationaladvantages and disadvantages. A reliable predictive model was developed by integrating a CAP model, receiving operator characteristic curves, and bagged decision trees. This system can be used in conjunction with a GIS to produce maps to guide field investigations. © Copyright by Stephanie L. Moret March 16, 2001 All Rights Reserved Predicting Channel Stability in Colorado Mountain Streams Using Hydrobiogeomorphic and Land Use Data: A Cost-Sensitive Machine Learning Approach to Modeling Rapid Assessment Protocols by Stephanie L. Moret A DISSERTATION submitted to Oregon State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy Completed March 16, 2001 Commencement June 2001 Doctor of Philosophy dissertation of Stephanie L. Moret presented on March 16, 2001 APPROVED: Redacted for Privacy Major Professor representing Environmental Redacted for Privacy Chair of Environmental Sciences Graduate Program Redacted for Privacy Dean of Graduate I understand that my dissertation will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my dissertation to any reader upon request. Redacted for Privacy Stephanie L. Moret, Author ACKNOWLEDGMENT "Nobody sees a flower really, it is so small. We haven't time, and to see takes timelike to have a friend takes time." - Georgia O'Keeffe I am a fortunate soul; I've been loved, nurtured, understood, and educated- and thus am able to love, nurture, understand, and teach.My parents,RickandMarilynFligerprovidedthestrength,loveand encouragement, which enable me to believe in myself, see beyond myself, and persevere when the going gets tough.I've been supported by many of the folk from my hometowns of St. Helena (Mr. Senter) and Lake Cachuma (rangers, CBR), California as well as family and friends: Rick, Linda, Andrea, Jim, Peg, Dave, Terry Joseph, Bobbie, Hans, Eric, Sooz, Brad, Heidi, Laura, Billie Ann, Krissy, Stacy, Chip, Ken and Estelle Johnson, Dr. Heresco, Pastor Carlo Petersen, the Rudds,Fligers,Likes,Caros, Readys, Linghams, McConnells, Defreses, Dunlaps, Swanger-Borns, Langfords, and Fazzinis. Academically, I've been fortunate to have a number of inspiring professors and mentors: Dr's Beschta, Buckhouse, Frey (SDSU), Gregory, Hofmeister (UCD), Jones, Klingeman, McClain (UCD), Milstein, Mount (UCD), Rosenfeld, Singer (UCD), Swanson, Urquhart, and Winner. My PhD committee was wonderful: Dr. Peter Klingeman served as my major professor and provided remarkable editing and enthusiasm for the product. Dr. Randy Milstein provided friendship, encouragement, and editing. Dr. Lori Cramer inspired me to explore sociology, and Dr. Dave Bella shared his great thoughts relating to systems and life. Dr. John Bliss was a conscientious, wise, and trustworthy graduate representative. Dr. John Buckhouse was everything wonderful: my mentor, my friend, my task keeper, and a great source of wisdom, encouragement and kindness.Dr. Bill Winner, the chair of the Environmental Sciences Graduate Program, has been a great source of encouragement, and provided me with knowledge and opportunity. The fieldwork was funded by a grant from the Geological Society of America and an equipment grant from Chevron. The research was produced by the labor of several people.I was inspired by Maria Morisawa's book, 'Streams'. Charles Rosenfeld, my master's and initial PhD advisor, introduced me to channel stability and inspired me to apply my PhD to a useful purpose. Doug Holdt spent a long time, far away from home, in freezing rivers at high altitudes, traveling through treacherous terrain, to help me collect stream data. In addition to being a great field researcher, he's a great friend; clever, upbeat, and fun to be with. My brother, Rick, helped Doug and I set up a base camp and lent me both his 4-wheel drive and his dog, Ebony. Ebony (aka 'bear bait') provided comic relief. John Almy, a hydrologist at the US Forest Service in Delta, provided office space, project support, and materials.Our travel plans and safety checks were diligently monitored by the Fish and Wildlife Service in Paonia. Celia Moret helped me to enter the data and provided field assistance for the pilot study.Bill Langford provided bright ideas; the trip to and from Colorado; mathematical and computer support; many machine learningandstatisticsexplanationsandinterpretations;andsupport, friendship, and comfort for much of the journey. Dragos Margineantu provided the coding and genius behind the machine learning techniques used in this research.Steve Hoakim provided professional statistical services.Michael Fernandez, in addition to his warm friendship, read and pre-edited the machine learning sections, to see if a 'normal person' could understand it. Ken Williamson provided both encouragement and financial support, and Peter Klingeman edited, and re-edited. Skye Moret visited my office frequently and left happy notes for me, which made me smile. As always, there are the special people who encourage one in life. For me, these are my mother, Marilyn; cousin, Cheryl Caro; great friends, Karen Swanger and Franziska Whelan; and my amazing, wonderful, intelligent, kind, warm, beautiful, supportive daughters, Skye and Celia. My love and thanks to you all! TABLE OF CONTENTS Paae Chapter 1 INTRODUCTION I 1.1 The Research Problem I 1.2 The Research Problem Expressed as a Channel Stability Problem 3 1.3 Research Questions and Objectives 8 1.4 Scope of Research 9 1.4.1 Channel Stability Evaluation Method 9 1.4.2 Selected Research Field Location 10 1.4.3 Associated Mapped Features and Analyses 11 Chapter 2 CHANNEL STABILITY LITERATURE REVIEW 13 2.1 Larger Scale Stream Channel Processes 13 2.2 Smaller Scale Stream Channel Processes 14 2.3 Anthropogenic Effects of Channel Erosion, Stability and Equilibrium 18 2.4 Channel Stability Studies 21 2.5 Assessing Channel Stability and Change 26 2.6 Assessing Channel Stability Using the USDA FS Stream Reach Inventory and Channel Stability Evaluation Method 28 2.6.1Interpretation of Pfankuch's Definition of Channel Stability 29 2.6.2 Purpose and Use 30 2.6.3 Channel Stability Evaluation Design 30 2.6.4 Channel Stability Indicator Items 34 TABLE OF CONTENTS Pafle Chapter 3 THE RESEARCH HYPOTHESES 40 3.1 Hypotheses for Research Question 1 40 3.1.1Topographic Stream Gradient 41 3.1.2Channel Sinuosity 42 3.1.3Elevation 43 3.1.4Precipitation 43 3.1.5 Geology 44 3.1.6Land Use and Land Cover 45 3.2 Hypotheses for Research Question 2 46 3.3 Hypothesis Testing 46 Chapter 4 THE RESEARCH SITE AND HYDROLOGIC SETTING 48 4.1 The Upper Colorado River Basin 48 4.2 The Hydrologic Unit Categories, Sub-Basins and Streams Evaluated 50 4.2.1Colorado Headwaters-Eagle Basin 52 4.2.2Roaring Fork Basin 55 4.2.3North Fork Gunnison Basin 57 4.2.4Gunnison Basin 59 4.2.5San Miguel Basin 62 4.3 Watershed Hydrology and Snow Dominance 63 TABLE OF CONTENTS Pafle Chapter 5 RESEARCH METHODOLOGY 66 5.1 Sampling Design for USFS Survey 66 5.2
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