APPENDIX A. Watershed Delineation and Stream Network Defined from WMS

Similar documents
Hydrologic Characterization of Goodwin Creek

Chapter 10 - Sacramento Method Examples

Big Wood River. General Information

Squaw Creek. General Information

12 SWAT USER S MANUAL, VERSION 98.1

5/4/2017 Fountain Creek. Gage Analysis. Homework 6. Clifton, Cundiff, Pour, Queen, and Zey CIVE 717

Chapter 13. Editing Intermediate Files. Overview

ARTICLE 5 (PART 2) DETENTION VOLUME EXAMPLE PROBLEMS

INFLOW DESIGN FLOOD CONTROL SYSTEM PLAN 40 C.F.R. PART PLANT YATES ASH POND 2 (AP-2) GEORGIA POWER COMPANY

Lab 2: Slope Aspect Lab

Physical Geology Horton s Laws

TABLE OF CONTENTS. 3.1 Synoptic Patterns Precipitation and Topography Precipitation Regionalization... 11

GIS in Water Resources Midterm Quiz Fall There are 5 questions on this exam. Please do all 5. They are of equal credit.

Probability Distribution

An analysis of energy expenditure in Goodwin Creek

INTRODUCTION TO HEC-HMS

Why Data Transformation? Data Transformation. Homoscedasticity and Normality. Homoscedasticity and Normality

REDWOOD VALLEY SUBAREA

unadjusted model for baseline cholesterol 22:31 Monday, April 19,

THE PRINCIPLES AND PRACTICE OF STATISTICS IN BIOLOGICAL RESEARCH. Robert R. SOKAL and F. James ROHLF. State University of New York at Stony Brook

Drainage Analysis. Appendix F

Applied Econometrics (QEM)

Section 4: Model Development and Application

Rick Faber CE 513 Watershed and Streamwork Delineation Lab # 3 4/24/2006

Appendix D. Model Setup, Calibration, and Validation

Chapter 5 CALIBRATION AND VERIFICATION

Model Integration - How WEPP inputs are calculated from GIS data. ( ArcGIS,TOPAZ, Topwepp)

Tables Table A Table B Table C Table D Table E 675

Contents. Acknowledgments. xix

The Iowa Watershed Approach

Jones Creek Case Study

Session 3 The proportional odds model and the Mann-Whitney test

At a certain moment the following temperatures are measured with a psychrometer: 25 C and 17,5 C

OBJECTIVES. Fluvial Geomorphology? STREAM CLASSIFICATION & RIVER ASSESSMENT

City of Thornton Attn: Tim Semones Development Engineeering 9500 Civic Center Dr. Thornton, CO 80229

Bushkill Creek 3 rd Street Dam Removal Analysis

STREUVER FIDELCO CAPPELLI, LLC YONKERS DOWNTOWN DEVELOPMENT PHASE 1. DRAFT ENVIRONMENTAL IMPACT STATEMENT For: PALISADES POINT

APPENDIX B HYDROLOGY

HYDROLOGIC AND WATER RESOURCES EVALUATIONS FOR SG. LUI WATERSHED

Study Sheet. December 10, The course PDF has been updated (6/11). Read the new one.

Volatility. Gerald P. Dwyer. February Clemson University

Chapter 10: Inferences based on two samples

EXAMPLE WATERSHED CONFIGURATIONS

A GIS-based Approach to Watershed Analysis in Texas Author: Allison Guettner

PECKMAN RIVER BASIN, NEW JERSEY FLOOD RISK MANAGEMENT FEASIBILITY STUDY. Hydrology Appendix. New York District

Solutions. Some of the problems that might be encountered in collecting data on check-in times are:

The Stochastic Event Flood Model Applied to Minidoka Dam on the Snake River, Idaho

FHWA - HIGHWAY HYDROLOGY

Institute of Actuaries of India

Hydrological modelling of the Lena River using SWIM

Illinois State Water Survey Division

Chapter 11 Sampling Distribution. Stat 115

MARMOT CREEK BASIN: MANAGING FORESTS FOR WATER

Influence of spatial variation in precipitation on artificial neural network rainfall-runoff model

Workshop: Build a Basic HEC-HMS Model from Scratch

Objectives: After completing this assignment, you should be able to:

ASSESSMENT OF STORM DRAIN SOURCES OF CONTAMINANTS TO SANTA MONICA BAY VOLUME I ANNUAL POLLUTANTS LOADINGS TO SANTA MONICA BAY FROM STORMWATER RUNOFF

BRANDON LAKES AVENUE PRE AND POST CONDITIONS DRAINAGE REPORT

Chris Lenhart, John Nieber, Ann Lewandowski, Jason Ulrich TOOLS AND STRATEGIES FOR REDUCING CHANNEL EROSION IN MINNESOTA

10/8/ W01. Daniel Catiglione First Gulf Corporation 3751 Victoria Park Avenue Toronto, ON M1W 3Z4

DEVELOPMENT AND APPLICATION OF A HYDROCLIMATOLOGICAL STREAM TEMPERATURE MODEL WITHIN SWAT

Introduction to Statistical Analysis

Applied Statistics I

Las Colonias Subdivision September 2010 Flood Study

Dover- Sherborn High School Mathematics Curriculum Probability and Statistics

Characterization of Streamflow Variability Within the Upper Gila River Basin

4 Precipitation. 4.1 Rainfall characteristics

S The Over-Reliance on the Central Limit Theorem

Stage Discharge Tabulation for Only Orifice Flow

GIS Enabled Automated Culvert Design

2 Development of a Physically Based Hydrologic Model of the Upper Cosumnes Basin

B805 TEMPORARY EROSION AND SEDIMENT CONTROL MEASURES - OPSS 805

StreamStats: Delivering Streamflow Information to the Public. By Kernell Ries

Sediment Trap. A temporary runoff containment area, which promotes sedimentation prior to discharge of the runoff through a stabilized spillway.

Ch. 7. One sample hypothesis tests for µ and σ

The Effect of Stormwater Controls on Sediment Transport in Urban Streams

Practical Statistics for the Analytical Scientist Table of Contents

Lower Tuolumne River Accretion (La Grange to Modesto) Estimated daily flows ( ) for the Operations Model Don Pedro Project Relicensing

HEC-HMS for the Sacramento and San Joaquin River Basins Comprehensive Study

Technical Memorandum No Sediment Model

ENGINEERING HYDROLOGY

Rosgen Classification Unnamed Creek South of Dunka Road

Bivariate Rainfall and Runoff Analysis Using Entropy and Copula Theories

Lake Tahoe Watershed Model. Lessons Learned through the Model Development Process

(3) Sediment Movement Classes of sediment transported

Field Observations and One-Dimensional Flow Modeling of Summit Creek in Mack Park, Smithfield, Utah

Confidence Intervals for the Process Capability Index C p Based on Confidence Intervals for Variance under Non-Normality

Creating Watersheds and Stream Networks. Steve Kopp

Hydrography Webinar Series

A Near Real-time Flood Prediction using Hourly NEXRAD Rainfall for the State of Texas Bakkiyalakshmi Palanisamy

MAT2377. Ali Karimnezhad. Version December 13, Ali Karimnezhad

Mathematical statistics

Evaluation of the two stage ditch as a best management practice. A. Hodaj, L.C. Bowling, C. Raj, I. Chaubey

9. PROBABLE MAXIMUM PRECIPITATION AND PROBABLE MAXIMUM FLOOD

Glossary. The ISI glossary of statistical terms provides definitions in a number of different languages:

Overview of fluvial and geotechnical processes for TMDL assessment

Regional Flood Estimation for NSW: Comparison of Quantile Regression and Parameter Regression Techniques

USGS National Hydrography Dataset (NHD) and NHDPlus

Flow Control Threshold Analysis for the San Diego Hydrograph Modification Management Plan. Prepared for. San Diego County and Copermittees

3.2 CRITICAL DEPTH IN NONRECTANGULAR CHANNELS AND OCCUR- RENCE OF CRITICAL DEPTH

Transcription:

APPENDIX A Watershed Delineation and Stream Network Defined from WMS

Figure A.1. Subbasins Delineation and Stream Network for Goodwin Creek Watershed

APPENDIX B Summary Statistics of Monthly Peak Discharge Data (cfs)

Table B.1 Summary Statistics for Subbasin 1 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 799.1 683.5 611.6 231 2.2E+8.96.56.77 2 185.6 922. 146.9 3739 1.8E+9 1.6 1.92.96 3 1227.9 192. 8.9 2967 4.6E+8.89 -.5.65 4 142.9 982. 83.8 3596 9.3E+8 1.62 4.43.8 5 1129.1 466.5 168.8 527 8.4E+9 1.77 1.83 1.49 6 646. 463.5 641.7 212 3.2E+8 1.21.43.99 7 583.9 179. 917.2 366 1.9E+9 2.53 6.91 1.57 8 465.2 12.5 675.5 2516 5.9E+8 1.9 3.99 1.45 9 441.2 16.5 99.6 3758 2.7E+9 2.81 7.83 2.25 1 327.5 13.5 619. 1954 4.6E+8 1.94 2.47 1.89 11 659.7 468.5 62.7 2146 2.4E+8 1.1.29.94 12 16. 77.5 92.1 3383 9.4E+8 1.21 1.34.87

Table B.2 Summary Statistics for Subbasin 2 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 674.9 589. 462. 1593 5.8E+7.59 -.54.68 2 893.6 74. 779.7 2695 6.4E+8 1.36 1.26.87 3 1155.7 989. 761. 2774 3.7E+8.84 -.25.66 4 889.6 813.5 587.2 192 3.1E+7.15-1.18.66 5 117. 441.5 152.9 4686 6.1E+9 1.79 2.2 1.48 6 647.2 451.5 666.3 2423 4.3E+8 1.45 1.77 1.3 7 557.4 196. 829.3 373 1.2E+9 2.18 4.65 1.49 8 436.4 115.5 68.8 2171 3.8E+8 1.67 2.64 1.4 9 43.9 18. 866.6 3194 1.7E+9 2.64 6.64 2.15 1 311.9 12. 592.5 1982 4.3E+8 2.5 3.24 1.9 11 638.7 416.5 625. 231 2.4E+8 1. -.7.98 12 971.3 717.5 928.5 3256 1.2E+9 1.45 1.76.96

Table B.3 Summary Statistics for Subbasin 3 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 426.6 383.5 33.6 1219 3.7E+7 1.2.54.77 2 6.6 393. 735.8 2925 9.4E+8 2.35 5.65 1.23 3 633.1 58. 46.2 147 5.8E+7.86.6.64 4 545.9 512.5 451.6 1975 1.7E+8 1.86 5.3.83 5 121.7 248. 1851.2 5695 1.2E+1 1.96 2.36 1.81 6 447.9 364. 433.1 1681 1.2E+8 1.42 2.6.97 7 38.3 16.5 721.5 27 9.9E+8 2.64 6.64 1.9 8 265.7 38.5 443.9 1739 2.1E+8 2.43 6.83 1.67 9 423.7 14.5 168. 439 4.2E+9 3.44 12.5 2.52 1 237.9 2. 489.7 1739 2.7E+8 2.3 4.8 2.6 11 353.4 243.5 317.2 151 2.4E+7.75 -.43.9 12 624.6 421.5 7.6 2832 7.6E+8 2.21 5.47 1.12

Table B.4 Summary Statistics for Subbasin 4 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 196.8 185.5 151.6 577 3.4E+6.96.77.77 2 262. 193.5 26.4 955 3.E+7 1.67 2.42.99 3 33.2 287.5 198.1 723 5.6E+6.72 -.51.6 4 273.7 264.5 21.1 829 8.3E+6 1.2 2.18.74 5 348.6 132.5 521.6 1691 2.6E+8 1.83 2.31 1.5 6 29.1 124.5 214. 689 1.2E+7 1.21.32 1.2 7 217.8 48.5 326.9 141 6.1E+7 1.74 1.95 1.5 8 18.3 47. 252. 793 2.3E+7 1.41.91 1.4 9 131.3.5 298.3 113 7.2E+7 2.69 6.98 2.27 1 86.6 2.5 165.1 531 8.9E+6 1.98 2.75 1.91 11 166.2 126.5 151.1 442 2.2E+6.65-1.2.91 12 256.1 218.5 29.2 686 7.3E+6.8 -.2.82

Table B.5 Summary Statistics for Subbasin 5 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 229.4 164. 192.6 686 7.2E+6 1.1.25.84 2 38. 236.5 339.8 1289 7.7E+7 1.95 3.64 1.1 3 393.8 353.5 259. 895 9.8E+6.57 -.69.66 4 346.4 328. 291.2 1264 4.5E+7 1.83 5.19.84 5 42.2 126.5 61.3 182 4.E+8 1.77 1.85 1.52 6 291. 26. 321.1 132 6.8E+7 2.5 5.6 1.1 7 184.1 85. 296.1 1176 6.8E+7 2.62 7.37 1.61 8 136.3 21.5 191.2 585 8.5E+6 1.22.19 1.4 9 16.7 7.5 346.1 14 1.3E+8 3.9 1.37 2.15 1 126.1 9.5 242.5 71 2.7E+7 1.89 2.4 1.92 11 243.7 149. 222.5 61 5.9E+6.54-1.42.91 12 318.4 233. 291.7 1 3.1E+7 1.23.89.92

Table B.6 Summary Statistics for Subbasin 6 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 84.1 71.5 69. 272 4.3E+5 1.3 1.94.82 2 111.1 73. 129.6 499 4.7E+6 2.17 4.45 1.17 3 14.4 17.5 96.4 316 6.3E+5.7 -.84.69 4 115.1 98.5 85.7 313 6.E+5.96.45.74 5 132.1 48. 196. 56 1.3E+7 1.67 1.35 1.48 6 121.2 71. 148.3 593 7.2E+6 2.21 5.62 1.22 7 64.4 18. 99.6 281 1.6E+6 1.6 1.6 1.55 8 42.9 4. 68.5 215 6.1E+5 1.89 2.86 1.6 9 78.2 4. 14.9 477 5.2E+6 1.86 2.79 1.8 1 38.7.5 77.5 276 1.1E+6 2.29 4.79 2. 11 73.4 5. 81.1 271 6.6E+5 1.24.63 1.11 12 13.7 73.5 98.4 336 1.1E+6 1.12.5.95

Table B.7 Summary Statistics for Subbasin 7 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 113.9 13.5 89.9 344 8.3E+5 1.14 1.32.79 2 143.4 12.5 143.7 494 4.6E+6 1.54 1.68 1. 3 198.7 165. 117.3 43 1.E+6.62 -.8.59 4 157.3 163. 123.2 516 2.4E+6 1.3 3.17.78 5 253.7 76. 416.5 1346 1.4E+8 1.93 2.52 1.64 6 141.8 97. 163.3 632 8.7E+6 1.99 4.11 1.15 7 21.6 3. 394.7 1222 1.4E+8 2.2 3.75 1.87 8 127.6 19.5 22.2 712 1.5E+7 1.87 3.16 1.58 9 1.3 2. 239.7 947 4.2E+7 3.5 9.8 2.39 1 44.7 2. 84.2 283 1.2E+6 2.3 3.2 1.88 11 118.2 67. 126.8 439 2.7E+6 1.33 1.23 1.7 12 153.1 123.5 146. 496 3.8E+6 1.24.93.95

Table B.8 Summary Statistics for Subbasin 8 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 111.3 16. 79.3 278 2.4E+5.48 -.71.71 2 135.6 99.5 132.4 519 4.1E+6 1.75 3.32.98 3 196.2 17.5 113.3 397 5.9E+5.41-1.5.58 4 174.2 151.5 131.3 532 2.7E+6 1.19 1.96.75 5 171.3 78. 239. 761 2.2E+7 1.63 1.54 1.4 6 17.8 18.5 213.3 95 2.5E+7 2.57 8.36 1.25 7 1.4 54. 145.2 552 6.9E+6 2.25 5.23 1.45 8 57.4 12.5 78.9 227 5.8E+5 1.18 -.4 1.37 9 87.3 1.5 159.8 585 9.1E+6 2.23 4.96 1.83 1 7. 2.5 13.9 43 4.3E+6 1.92 2.43 1.87 11 131.6 12. 114.9 344 9.5E+5.63 -.9.87 12 143. 113. 121.6 436 2.E+6 1.8.66.85

Table B.9 Summary Statistics for Subbasin 9 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 21.1 17. 18.3 6 4.9E+3.8 -.21.87 2 21.1 18. 17.4 59 3.6E+3.68 -.29.83 3 37.1 31.5 25.9 84 7.9E+3.45 -.84.7 4 34.4 35.5 23. 77.E+. -.79.67 5 36.7 1.5 53.5 197 2.9E+5 1.91 3.72 1.46 6 27.1 22.5 26. 87 1.7E+4.97.2.96 7 22.1 8.5 27.4 91 2.7E+4 1.33 1.9 1.24 8 17.8 2. 28.1 87 3.5E+4 1.57 1.38 1.57 9 24.8 4.5 45.4 16 2.1E+5 2.19 4.17 1.83 1 15.1.5 26.8 86 3.6E+4 1.88 2.5 1.77 11 21.9 15. 21.4 7 8.5E+3.87 -.21.97 12 26.3 23. 21.5 79 8.2E+3.82.49.82

Table B.1 Summary Statistics for Subbasin 1 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 1.7 1. 2. 6 1.2E+1 1.48 1.49 1.19 2 3.7 2. 5.2 18 2.9E+2 2.4 3.56 1.4 3 5.3 3. 4.6 14 9.2E+1.98 -.42.87 4 4.7 3. 5. 18 1.8E+2 1.49 2.53 1.7 5 4.7. 8.7 28 1.3E+3 1.97 2.98 1.84 6 2.2. 5. 19 3.9E+2 3.5 9.87 2.29 7 2.1. 4.8 16 2.6E+2 2.39 5.22 2.3 8.8. 1.7 6 1.2E+1 2.65 7.31 2.7 9 1.. 3.6 14 1.8E+2 3.84 14.81 3.61 1 1 1.3. 3. 9 6.E+1 2.17 3.5 2.26 11 1.5. 2. 6 7.9E+.99 -.11 1.36 12 3.5 1. 4.5 14 1.2E+2 1.35.83 1.29 1 Statistics based on a record period of 15 years.

Table B.11 Summary Statistics for Subbasin 11 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 18.4 15. 12.7 44 9.2E+2.46 -.82.69 2 2.8 18. 17.1 63 5.3E+3 1.7.93.82 3 35.4 24.5 24.6 78 1.E+4.68-1.19.69 4 27.6 3.5 18.4 74 3.2E+3.51.96.67 5 31.3 9. 49.3 173 2.4E+5 2.2 3.51 1.57 6 41.8 19.5 74.9 326 1.5E+6 3.55 13.8 1.79 7 21.8 3.5 38. 135 1.3E+5 2.31 4.84 1.74 8 11.2 1. 16.6 51 6.3E+3 1.38.65 1.48 9 16.2.5 29. 98 4.9E+4 1.99 3.15 1.79 1 14.2.5 28.3 9 4.6E+4 2.1 2.75 2. 11 24.1 15.5 23.4 71 1.2E+4.96 -.28.97 12 24.1 18.5 22.9 83 1.6E+4 1.36 1.31.95

Table B.12 Summary Statistics for Subbasin 12 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 32.7 33. 22.7 66 2.E+3.17-1.42.69 2 41.7 23.5 6.9 267 7.5E+5 3.3 12.17 1.46 3 66.7 49.5 5.8 187 1.7E+5 1.33 1.26.76 4 52.6 47. 4.2 169 8.8E+4 1.35 3.17.76 5 88.4 14. 168.2 521 1.1E+7 2.21 3.83 1.9 6 16.1 34.5 298.6 1294 1.1E+8 4.14 17.4 2.81 7 4.3 21.5 65.8 267 8.E+5 2.79 8.6 1.63 8 17.9 7.5 25. 77 2.4E+4 1.55 1.45 1.39 9 24.7 1. 39.2 116 8.5E+4 1.42.53 1.59 1 18. 2.5 34.8 134 1.1E+5 2.61 7.19 1.93 11 41.7 33.5 38.4 122 5.1E+4.91 -.23.92 12 43.7 32. 43.7 176 1.5E+5 1.8 4.3 1.

Table B.13 Summary Statistics for Subbasin 13 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 63.8 66.5 45.8 187 9.9E+4 1.3 1.69.72 2 88.8 68.5 78.1 255 6.1E+5 1.28.76.88 3 17.8 99. 59.4 224 1.2E+5.56 -.37.55 4 9.7 98.5 56.7 194-1.5E+4 -.8 -.92.63 5 149.1 59. 236.9 721 2.4E+7 1.81 1.85 1.59 6 63.4 29. 76.2 245 5.1E+5 1.16.24 1.2 7 67.9 1. 125.1 516 5.9E+6 3.2 1.33 1.84 8 59.9 23. 115.9 489 5.2E+6 3.34 12.25 1.94 9 51.2. 122.2 465 5.2E+6 2.87 8.21 2.39 1 29.8.5 53.5 177 3.E+5 1.98 3.2 1.79 11 65.6 53.5 54.9 176 9.6E+4.58 -.64.84 12 98.7 78. 91. 346 1.E+6 1.39 2.1.92

Table B.14 Summary Statistics for Subbasin 14 Month Average Median Standard Deviation Maximum Skewness Skewness Coefficient Kurtosis Variation Coefficient 1 97.7 11.5 68.1 25 1.9E+5.59 -.1.7 2 147.2 11.5 152.4 597 7.E+6 1.97 4.3 1.4 3 187.7 156. 117.1 431 1.2E+6.76 -.44.62 4 133.4 131. 16.1 367 8.6E+5.72 -.1.8 5 179.9 44.5 276. 852 3.6E+7 1.7 1.65 1.53 6 97.2 36.5 129.7 45 3.E+6 1.38.71 1.33 7 117.2 23. 194.8 719 1.6E+7 2.23 4.84 1.66 8 127.4 31. 212.7 876 2.7E+7 2.81 9.38 1.67 9 65.6 3. 142.8 54 8.2E+6 2.8 7.7 2.18 1 55.2 3.5 18.4 326 2.5E+6 1.94 2.29 1.96 11 114.3 92.5 19.8 315 8.4E+5.63 -.99.96 12 139.1 13. 125.8 397 1.8E+6.92 -.12.9

APPENDIX C Streamflow Duration Curves

6 5 Monthly Peak Flow (cfs) 4 3 2 1 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.1. Streamflow Duration Curve for Subbasin 1 Data

5 4 Monthly Peak Flow (cfs) 3 2 1 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.2. Streamflow Duration Curve for Subbasin 2 Data

6 5 Monthly Peak Flow (cfs) 4 3 2 1 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.3. Streamflow Duration Curve for Subbasin 3 Data

2 16 Monthly Peak Flow (cfs) 12 8 4 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.4. Streamflow Duration Curve for Subbasin 4 Data

2 16 Monthly Peak Flow (cfs) 12 8 4 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.5. Streamflow Duration Curve for Subbasin 5 Data

7 6 Monthly Peak Flow (cfs) 5 4 3 2 1 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.6. Streamflow Duration Curve for Subbasin 6 Data

14 12 Monthly Peak Flow (cfs) 1 8 6 4 2 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.7. Streamflow Duration Curve for Subbasin 7 Data

1 8 Monthly Peak Flow (cfs) 6 4 2 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.8. Streamflow Duration Curve for Subbasin 8 Data

25 2 Monthly Peak Flow (cfs) 15 1 5 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.9. Streamflow Duration Curve for Subbasin 9 Data

3 25 Monthly Peak Flow (cfs) 2 15 1 5 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.1. Streamflow Duration Curve for Subbasin 1 Data

35 3 Monthly Peak Flow (cfs) 25 2 15 1 5 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.11. Streamflow Duration Curve for Subbasin 11 Data

14 12 Monthly Peak Flow (cfs) 1 8 6 4 2 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.12. Streamflow Duration Curve for Subbasin 12 Data

8 7 6 Monthly Peak Flow (cfs) 5 4 3 2 1 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.13. Streamflow Duration Curve for Subbasin 13 Data

1 8 Monthly Peak Flow (cfs) 6 4 2 1 2 3 4 5 6 7 8 9 1 Exceedance Probability (%) Figure C.14. Streamflow Duration Curve for Subbasin 14 Data

APPENDIX D Streamflow Frequency Histograms

18 15 12 Frequency 9 6 3 6 12 18 24 3 36 42 48 54 6 Monthly Peak Flow (cfs) Figure D.1. Frequency Histogram for Subbasin 1 Streamflow Data with Fitted Distribution

18 15 12 Frequency 9 6 3 6 12 18 24 3 36 42 48 Monthly Peak Flow (cfs) Figure D.2. Frequency Histogram for Subbasin 2 Streamflow Data with Fitted Distribution

18 15 12 Frequency 9 6 3 6 12 18 24 3 36 42 48 54 6 Monthly Peak Flow (cfs) Figure D.3. Frequency Histogram for Subbasin 3 Streamflow Data with Fitted Distribution

15 12 Frequency 9 6 3 2 4 6 8 1 12 14 16 18 2 Monthly Peak Flow (cfs) Figure D.4. Frequency Histogram for Subbasin 4 Streamflow Data with Fitted Distribution

15 12 Frequency 9 6 3 2 4 6 8 1 12 14 16 18 2 Monthly Peak Flow (cfs) Figure D.5. Frequency Histogram for Subbasin 5 Streamflow Data with Fitted Distribution

12 9 Frequency 6 3 2 4 6 Monthly Peak Flow (cfs) Figure D.6. Frequency Histogram for Subbasin 6 Streamflow Data with Fitted Distribution

15 12 Frequency 9 6 3 15 3 45 6 75 9 15 12 135 15 Monthly Peak Flow (cfs) Figure D.7. Frequency Histogram for Subbasin 7 Streamflow Data with Fitted Distribution

15 12 Frequency 9 6 3 1 2 3 4 5 6 7 8 9 1 Monthly Peak Flow (cfs) Figure D.8. Frequency Histogram for Subbasin 8 Streamflow Data with Fitted Distribution

12 9 Frequency 6 3 2 4 6 8 1 12 14 16 18 2 Monthly Peak Flow (cfs) Figure D.9. Frequency Histogram for Subbasin 9 Streamflow Data with Fitted Distribution

12 9 Frequency 6 3 3 6 9 12 15 18 21 24 27 3 Monthly Peak Flow (cfs) Figure D.1. Frequency Histogram for Subbasin 1 Streamflow Data with Fitted Distribution

18 15 12 Frequency 9 6 3 35 7 15 14 175 21 245 28 315 35 Monthly Peak Flow (cfs) Figure D.11. Frequency Histogram for Subbasin 11 Streamflow Data with Fitted Distribution

21 18 15 Frequency 12 9 6 3 15 3 45 6 75 9 15 12 135 15 Monthly Peak Flow (cfs) Figure D.12. Frequency Histogram for Subbasin 12 Streamflow Data with Fitted Distribution

18 15 12 Frequency 9 6 3 1 2 3 4 5 6 7 8 9 1 Monthly Peak Flow (cfs) Figure D.13. Frequency Histogram for Subbasin 13 Streamflow Data with Fitted Distribution

12 9 Frequency 6 3 1 2 3 4 5 6 7 8 9 1 Monthly Peak Flow (cfs) Figure D.14. Frequency Histogram for Subbasin 14 Streamflow Data with Fitted Distribution

APPENDIX E Chi-Square Goodness of Fit Test Results

Subbasin ID 2 χ c Table E.1 Results for the Chi-Square Goodness of Fit Test Degrees 2 χ 1 α, ν for Selected Confidence Intervals of Freedom 9% 95% 97.5% 99% 99.5% 1 21.1 13 19.8 22.4 24.7 27.7 29.8 2 16. 14 21.1 23.7 26.1 29.1 31.3 3 15.1 13 19.8 22.4 24.7 27.7 29.8 4 7.2 12 18.5 21. 23.3 26.2 28.3 5 8.6 13 19.8 22.4 24.7 27.7 29.8 6 6.4 12 18.5 21. 23.3 26.2 28.3 7 1.7 12 18.5 21. 23.3 26.2 28.3 8 5.8 12 18.5 21. 23.3 26.2 28.3 9 1. 9 14.7 16.9 19. 21.7 23.6 11 5.8 9 14.7 16.9 19. 21.7 23.6 12 7.4 1 16. 18.3 2.5 23.2 25.2 13 1.2 12 18.5 21. 23.3 26.2 28.3 14 7.4 12 18.5 21. 23.3 26.2 28.3

APPENDIX F Regression Plots for Other Subbasin Geomorphic Attributes

1 8 Flow (cfs) 6 4 2 4 5 6 7 8 9 1 11 L o (ft) Figure F.1. Regression Analysis Results on Average Overland Flow Length (L o )

1 8 Flow (cfs) 6 4 2.25.3.35.4.45.5 So (ft/ft) Figure F.2. Regression Analysis Results on Basin Overland Slope (S o )

1 8 Flow (cfs) 6 4 2..5 1. 1.5 2. 2.5 3. L (mi) Figure F.3. Regression Analysis Results on Basin Length along Main Channel from Outlet to Upstream Boundary (L)

1 8 Flow (cfs) 6 4 2..5.1.15.2.25 S (mi/mi) Figure F.4. Regression Analysis Results on Basin Slope along Main Channel from Outlet to Upstream Boundary (S)

9 8 7 6 Flow (cfs) 5 4 3 2 1..2.4.6.8 1. 1.2 Lca (mi) Figure F.5. Regression Analysis Results on Basin Length along Main Channel from Outlet to Point Opposite Centroid (L ca )

1 8 Flow (cfs) 6 4 2..5.1.15.2.25 Sca (mi/mi) Figure F.6. Regression Analysis Results on Basin Slope along Main Channel from Outlet to Point Opposite Centroid (S ca )

1 8 Flow (cfs) 6 4 2..5 1. 1.5 2. 2.5 Lc (mi) Figure F.7. Regression Analysis Results on Maximum Flow (watercourse) Length (L c )

1 8 Flow (cfs) 6 4 2..5.1.15.2.25 Sc (mi/mi) Figure F.8. Regression Analysis Results on Maximum Flow (watercourse) Slope (S c )

Determination of Variance for APPENDIX G qˆ p to Construct Confidence Intervals

The variance of the predicted flow values as expressed in Equation (11) must be estimated in order to construct the confidence bands. The variance operator is applied to the expression for predicted flows: VAR 1 ( qˆ ) VAR ln P( q > q ) p λ [ ] = o Noting that 1 = λ Q, it is obtained 2 ( qˆ ) = VAR( Q) ln P( q q ) p [ ] VAR > o and VAR ( Q) = VAR( Q). The regression equation relating Q to catchment properties is nonlinear. To find the variance of the mean flow the regression equation must be linearized. This is achieved by taking the logarithm on both sides of the power function expressed in Equation (1), yielding log Q = log c + blog A d The regression parameters, c and b, are known. The drainage area is available for most of the basins or it can be easily obtained by different methods. Therefore, the value of logq can be determined. Likewise, the variance of logq can be calculated.

Then, a relation between the VAR (logq ) and VAR (Q ) should be established. This relationship was performed developing a Taylor expansion for logq. The results of the Taylor series were expressed as VAR 2 ( Q) = ( Q ln 1) VAR ( log Q) Finally, the variance of can be calculated using the following equation. VAR 2 2 ( qˆ ) = ln [ P( q > q )] ( Q ln 1) VAR ( log Q) p o