Novato Creek Watershed: Existing Conditions Model Report
LIST OF APPENDICES (May 2014) Page
Appendix A: Bayland Model Analysis A.1: EC Bayland Model Simulation Results: Q10,Q50 and Q100…………………………………….3 A.2: Bayland Model Sensitivity Analysis……………………………………………………………………....47
Appendix B: Bayland Basin Characteristics……………………………………………………………………………….65
Appendix C: Novato Creek Survey and Terrain Data Synthesis C.1: Kruse Imaging Bare Earth DEM Metadata Summary (Kruse, 2012)………………………71 C.2 KHE Survey Data Synthesis……………………………………………………………………………………85 C.3: Novato Creek Control Point Network and Channel Surveys (CPI, 2012)…………….142
Appendix D: Hydrology and Watershed Modeling Data Sets D.1: Draft Novato Hydrology (HEC HMS) Memorandum (MCFC, 2013)……………………..152 D.2: HMS Model Review Technical Memorandum (WRECO, 2013)……………………………174 D.4 : MCFC Pump Operations Guidelines (MCFC, 2010)……………………………………………187 D.5 BMKCSD Lagoon Operations Guidelines (CLE, 2012)…………………………………………..191
Appendix E: Nave Gardens: SWMM Urban Drainage Model (WRECO, 2014)…………………………194
Appendix F: Pacheco Pond HECRAS Analysis (KHE, 2014)………………………………………………………223
Appendix G: Sediment Loading and Transport Characteristics ……………………………………………..243
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
Appendix A: EC Bayland Model Analysis: A.1: EC Bayland Model Simulation Results: Q10, Q50 and Q100 A.2: Bayland Model Sensitivity Analysis
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
A.1: EC Bayland Model Simulation Results: Q10, Q50 and Q100
Novato Bayland EC_Q10 16
15 Legend CABMK Breach 14 BMKCSD No. Lock DS Hwy37 13 Mid DIB NWPRR 12 Nave Gardens
11
10
9
8
7 WS Elevation (ft)WS Elevation (ft) 6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com Novato Bayland EC:Q10_SLR16 16
15 Legend
14 CABMK Breach BMKCSD No. Lock 13 DS Hwy37 Mid DIB 12 NWPRR Nave Gardens 11
10
9
8
7 WS Elevation (ft)WS Elevation (ft) 6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com Novato Bayland EC: Q10_SLR36 16 Legend 15 CABMK Breach BMKCSD No. Lock 14 DS Hwy37
13 Mid DIB NWPRR 12 Nave Gardens
11
10
9
8
7 WS Elevation (ft) 6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com Novato Bayland EC_Q10 5.50 75
Legend 5.00 53 CABMK Breach BMKCSD No. Lock 4.50 DS HWY37 30 Mid. DIB NWPRR 4.00 Nave Gardens 8 Bay Tide (Ebb=+) 3.50 -15
3.00
-38
2.50 Velocity (m/s)Velocity (m/s) -60 Direction (Degrees) 2.00
-83 1.50
-105 1.00
0.50 -128
0.00 -150 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com Novato Bayland EC_Q10_SLR16 5.50 Legend 75 CABMK Breach BMKCSD No. Lock 5.00 53 DS HWY37 Mid. DIB 4.50 NWPRR 30 Nave Gardens Tide Direction (Ebb=+) 4.00 8
3.50 -15
3.00
-38
2.50 Velocity (ft/s)Velocity (ft/s) -60 Direction (Degrees) 2.00
-83 1.50
-105 1.00
0.50 -128
0.00 -150 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com Novato Bayland EC_Q10_SLR36 5.50 75
5.00 Legend 50 CABMK Breach BMKCSD No. Lock 4.50 DS HWY37 Mid. DIB 25 4.00 NWPRR Nave Gardens Bay Tide Direction (Ebb=+) 0 3.50
3.00 -25
2.50 -50 Velocity (ft/s)Velocity (ft/s) Direction (Degrees) 2.00 -75
1.50
-100 1.00
-125 0.50
0.00 -150 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com Knoxville, TN 37933 www.ds-international.biz
Novato Bayland EC Q50 16 Legend 15 CABMK Breach BMKCSD No. Lock 14 DS Hwy37 Mid DIB 13 NWPRR 12 Nave Gardens
11
10
9
8
7 WS Elevation (ft) 6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com Novato Bayland EC_Q50_SLR16 16
15 Legend CABMK Breach 14 BMKCSD No. Lock DS Hwy37 13 Mid DIB NWPRR 12 Nave Gardens
11
10
9
8
7 WS Elevation (ft)WS Elevation (ft) 6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com 3109: Novato Bayland EC, Run22_01_Q50_02SLR36 16
15 Legend CABMK Breach 14 BMKCSD No. Lock DS Hwy37 13 Mid DIB NWPRR 12 Nave Gardens
11
10
9
8
7 WS Elevation (ft)WS Elevation (ft) 6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com Knoxville, TN 37933 www.ds-international.biz Novato Bayland EC Q50 5.50 75 Legend 5.00 CABMK Breach BMKCSD No. Lock 50 DS Hwy37 4.50 Mid. DIB NWPRR 25
4.00 Nave Gardens Bay Tide Direction (Ebb=+) 0 3.50
3.00 -25
2.50 -50 Velocity (m/s)Velocity (m/s) Direction (Degrees) 2.00 -75
1.50
-100 1.00
-125 0.50
0.00 -150 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com Novato Bayland EC_Q50_SLR16 5.50 75
5.00 Legend 50 CABMK Breach
4.50 BMKCSD N. Lock DS Hwy37 25 Mid. DIB. 4.00 NWPRR Nave Gardens SPB Tide Direction (Ebb=+) 0 3.50
3.00 -25
2.50 -50 Velocity (ft/s)Velocity (ft/s) Direction (Degrees) 2.00 -75
1.50
-100 1.00
-125 0.50
0.00 -150 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com Novato Bayland EC_Q50_SLR36 5.50 75
5.00 Legend 50 CABMK Breach
4.50 BMKCSD No. Lock DS HWY37 25 Mid. DIB 4.00 NWPRR Nave Gardens 0 Tide Direction (Ebb=+) 3.50 -25
3.00
-50
2.50 Velocity (ft/s)Velocity (ft/s) -75 Direction (Degrees) 2.00
-100 1.50
-125 1.00
0.50 -150
0.00 -175 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 Time (days)
KHE Inc www.KHE-Inc.com Knoxville, TN 37933 www.ds-international.biz
Novato Bayland EC:Q100 16 Legend 15 CABMK Breach BMKCSD No. Lock 14 DS Hwy37 Mid DIB 13 NWPRR Nave Gardens 12
11
10
9
8
7 WS Elevation (ft) 6
5
4
3
2
1
0 0 1 2 3 4 Time (days)
KHE Inc www.KHE-Inc.com Novato Bayland EC_Q100_SLR16 16 Legend 15 CABMK Breach 14 BMKCSD No. Lock DS Hwy37 13 Mid DIB NWPRR 12 Nave Gardens
11
10
9
8
7 WS Elevation (ft)WS Elevation (ft) 6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com 3109: Novato Bayland EC_Q100_SLR36 Legend 16 CABMK Breach
15 BMKCSD No. Lock DS Hwy37 14 Mid DIB NWPRR 13 Nave Gardens
12
11
10
9
8
7 WS Elevation (ft) 6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com Novato Bayland EC:Q100 5.50 75 Legend CABMK Breach Site 5.00 BMKCSD No. Lock 50 DS HWY37 Mid. DIB 4.50 NWPRR NAve Gardens 25 4.00 Tide Direction (Ebb=+)
0 3.50
3.00 -25
2.50 -50 Velocity (ft/s) Direction (Degrees) 2.00 -75
1.50
-100 1.00
-125 0.50
0.00 -150 0 1 2 3 4 Time (days)
KHE Inc www.KHE-Inc.com Novato Bayland EC_Q100_SLR16 5.50 75
5.00 Legend 50 CMBMK Breach 4.50 BMKCSD No. Lock DS Hwy37 25 Mid. DIB 4.00 NWPRR NAVE GARDENS 0 Bay Tide (+=Ebb) 3.50
3.00 -25
2.50 -50 Velocity (m/s)Velocity (m/s) Direction (Degrees) 2.00 -75
1.50
-100 1.00
-125 0.50
0.00 -150 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com 3109: Novato Bayland EC_Q100_SLR36 5.50 Legend 75 CABMK Breach BMKCSD No. Lock 4.95 DS Hwy37 50 Mid. DIB NWPRR 4.40 Nave Gardens 25 Tide Direction (Ebb Positive)
3.85 0
3.30
-25
2.75
-50 Velocity (ft/s)Velocity (ft/s) 2.20 Direction (Degrees)
-75 1.65
-100 1.10
-125 0.55
0.00 -150 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
KHE Inc www.KHE-Inc.com
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
A.2: Bayland Model Sensitivity Analysis
Novato Bayland Sensitivity - Q50: WSE Spring Tide and Sensitivity Runs 20.00
Pre-Dredge
18.00 Spring Low Tide Neap Tide Low Roughness 16.00 High Roughness Base Q50 Spring Tide
14.00
12.00
10.00 WS Elevation (ft) 8.00
6.00
4.00
2.00
0.00 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 River Stationing (feet) Novato Bayland Sensitivity - Bed Sheer Stress vs. Long Profile 60
Pre-Dredge Spring Low Tide Neap Tide 50 Low Roughness High Roughness Base Q50 Spring Tide
40
30 Sheer Stres Stres Sheer (Pa)
20
10
0 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 River Stationing (feet) Novato Bayland Sensitivity - Bed Sheer Stress vs. Long Profile 200
Neap Tide
180 Pre-Dredge Spring Low Tide Low Roughness 160 High Roughness Base Q50 Spring Tide
140
120
100 Sheer Stres Stres Sheer (Pa) 80
60
40
20
0 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 River Stationing (feet) Novato Bayland Sensitivity - Bed Sheer Stress vs. Long Profile 30
Neap Tide Pre-Dredge Spring Low Tide 25 Low Roughness High Roughness Base Q50 Spring Tide
20
15 Sheer Stres Stres Sheer (Pa)
10
5
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 River Stationing (feet) Novato Bayland - WSE with Variation in Roughness 16 High Roughness - Nave Gardens 15 Baseline Roughness (Q50 Spring Tide) - Nave Gardens Low Roughness - Nave Gardens 14 High Roughness - Mid DIB Baseline Roughness (Q50 Spring Tide) - Mid DIB 13 Low Roughness - Mid DIB High Roughness - BMKCSD No. Lock 12 Baseline Roughness (Q50 Spring Tide) - BMKCSD No. Lock Low Roughness - BMKCSD No. Lock 11
10
9
8
7 WS Elevation (ft)
6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) Novato Bayland - Velocity with Variation in Roughness 7 High Roughness - Nave Gardens Baseline Roughness (Q50 Spring Tide) - Nave Gardens Low Roughness - Nave Gardens High Roughness - Mid DIB 6 Baseline Roughness (Q50 Spring Tide) - Mid DIB Low Roughness - Mid DIB High Roughness - BMKCSD No. Lock Baseline Roughness (Q50 Spring Tide) - BMKCSD - No. Lock Low Roughness - BMKCSD No. Lock 5
4
Velocity (ft/s) Velocity 3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) Novato Bayland Q50 Spring Tide 16 LegendSeries8 15 Nave Gardens NWPRR Mid DIB 14 DS Hwy37 BMKCSD No. Lock CABMK Breach 13 Series15 BaselineSeries6(Q50 Spring Tide) 12 Nave Gardens NWPRR Mid DIB 11 DS Hwy37 BMKCSD No. Lock 10 CABMK Breach
9
8
7 WS Elevation (ft)
6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) Novato Bayland Q50 Spring Tide 16 LegendSeries8 15 Nave Gardens NWPRR Mid DIB 14 DS Hwy37 BMKCSD No. Lock CABMK Breach 13 Baseline (Q50Series6 Spring Tide) Nave Gardens 12 NWPRR Mid DIB DS Hwy37 11 BMKCSD No. Lock CABMK Breach 10
9
8
7 WS Elevation (ft)
6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) Novato Bayland Q50 Spring Tide 16 Series8Legend 15 Nave Gardens NWPRR Mid DIB 14 DS Hwy37 BMKCSD No. Lock 13 CABMK Breach Baseline Series6(Q50 Spring Tide) Nave Gardens 12 NWPRR Mid DIB DS Hwy37 11 BMKCSD No. Lock CABMK Breach 10
9
8
7 WS Elevation (ft)
6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) Novato Bayland Q50 Spring Tide 16 LegendSeries8 Nave Gardens 15 NWPRR Mid DIB 14 DS Hwy37 BMKCSD No. Lock CABMK Breach 13 BaselineSeries6(Q50 Spring Tide) Nave Gardens 12 NWPRR Mid DIB DS Hwy37 11 BMKCSD No. Lock CABMK Breach 10
9
8
7 WS Elevation (ft)
6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) Novato Bayland Q50 Spring Tide 16 LegendSeries8 Nave Gardens 15 NWPRR Mid DIB 14 DS Hwy37 BMKCSD No. Lock CABMK Breach 13 Baseline (Q50Series6 Spring Tide) Nave Gardens 12 NWPRR Mid DIB DS Hwy37 11 BMKCSD No. Lock CABMK Breach 10
9
8
7 WS Elevation (ft)
6
5
4
3
2
1
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) Pre Dredge Scenario Velocity Time Series 7.00 100 Nave Gardens NWPRR 6.50 Mid DIB DS Hwy37 BMKCS No. Lock 6.00 CABMK Breach BaselineSeries6(Q50 Spring Tide): 50 Nave Gardens 5.50 NWPRR Mid DIB DS Hwy37 5.00 BMKCS No. Lock CABMK Breach 0 Bay Tide (Ebb=+) 4.50
4.00
3.50 -50
Velocity (ft/s) Velocity 3.00 Direction (Degrees) Direction
2.50 -100
2.00
1.50
-150 1.00
0.50
0.00 -200 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) Pre Dredge Scenario Velocity Time Series 7.00 100 Nave Gardens NWPRR 6.50 Mid DIB DS Hwy37 BMKCS No. Lock 6.00 CABMK Breach BaselineSeries6(Q50 Spring Tide): 50 Nave Gardens 5.50 NWPRR Mid DIB DS Hwy37 5.00 BMKCS No. Lock CABMK Breach 0 Bay Tide (Ebb=+) 4.50
4.00
3.50 -50
Velocity (ft/s) Velocity 3.00 Direction (Degrees) Direction
2.50 -100
2.00
1.50
-150 1.00
0.50
0.00 -200 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) Neap Tide Scenario Velocity Time Series 7.00 100 Nave Gardens NWPRR 6.50 Mid DIB DS Hwy37 BMKCS No. Lock 6.00 CABMK Breach BaselineSeries6(Q50 Spring Tide): 50 Nave Gardens 5.50 NWPRR Mid DIB DS Hwy37 5.00 BMKCS No. Lock CABMK Breach 0 4.50
4.00
3.50 -50
Velocity (ft/s) Velocity 3.00 Direction (Degrees)
2.50 -100
2.00
1.50
-150 1.00
0.50
0.00 -200 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) Low Roughness Scenario Velocity Time Series 7.00 100 Nave Gardens NWPRR 6.50 Mid DIB DS Hwy37 BMKCS No. Lock 6.00 CABMK Breach Baseline (Q50Series6 Spring Tide): 50 Nave Gardens 5.50 NWPRR Mid DIB DS Hwy37 5.00 BMKCS No. Lock CABMK Breach 0 4.50
4.00
3.50 -50
Velocity (ft/s) Velocity 3.00 Direction (Degrees)
2.50 -100
2.00
1.50
-150 1.00
0.50
0.00 -200 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) High Roughness Scenario Velocity Time Series 7.00 100 Nave Gardens NWPRR 6.50 Mid DIB DS Hwy37 BMKCS No. Lock 6.00 CABMK Breach BaselineSeries6(Q50 Spring Tide): 50 Nave Gardens 5.50 NWPRR Mid DIB DS Hwy37 5.00 BMKCS No. Lock CABMK Breach Bay Tide (Ebb=+) 0 4.50
4.00
3.50 -50
Velocity (ft/s) Velocity 3.00 Direction (Degrees)
2.50 -100
2.00
1.50
-150 1.00
0.50
0.00 -200 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days) Novato Bayland Q50 Spring Tide 7.00 LegendSeries6 6.50 Nave Gardens NWPRR
6.00 Mid DIB DS Hwy37
5.50 BMKCS No. Lock CABMK Breach
5.00
4.50
4.00
3.50
Velocity (ft/s) Velocity 3.00
2.50
2.00
1.50
1.00
0.50
0.00 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Time (days)
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
Appendix B: Bayland Basin Characteristics
Lower Novato Creek: Bayland Basins – Volume vs. Elevation
Source:( NHC, NHC,2007) 2007 Lower Novato Creek: Bayland Basin Volumes
( NHC, 2007) Lower Novato Creek: Bayland Basins – Potential Tidal Prism (2011,2050)
( NHC, 2007) Lower Novato Creek: Bayland Basins – Surface Area vs. Elevation
Source:( NHC, NHC,2007) 2007
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
Appendix C: Novato Creek Survey and Terrain Data Synthesis
C.1: Kruse Imaging Bare Earth DEM Metadata Summary (Kruse, 2012) C.2: Survey Data Synthesis Technical Memorandum (KHE 2013) C.3: Novato Creek Control Network Analysis Report (CPI, 2012)
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
C.1: Kruse Imaging Bare Earth DEM Metadata Summary (Kruse, 2012)
Marin Watersheds – Updated GGLP Aerial LiDAR DEMs ArcGIS Metadata Summary Prepared by: Kruse Imaging – 7/23/12
OVERVIEW
Output Products:
UTM 10N, NAD83, NAVD88 (EPSG:26910) Native Projection Primary Products - DEM Bare Earth Elevation Data, raster tiles and mosaic - DEM Bare Earth Elevation Hillshade, raster tiles and mosaic - DEM Bare Earth Elevation Data – Filtered, raster mosaic - DEM 0.3048 meter (1 ft) Contours, vector - DEM 1 meter Contours, vector - DEM 5 meter Contours, vector - DEM 10m Contours, vector - GGLP Tile Scheme, vector
California State Plane (EPSG:2872) - DEM Bare Earth Elevation Data, raster mosaic
USA Contiguous Albers Equal Area Conic USGS (SR-ORG:7301) - DEM Bare Earth Elevation Data, raster mosaic
ArcGIS V10.0 File Geodatabase - Loaded with UTM data sets
Key Words:
Marin, watershed, DEM, LiDAR, GGLP, Golden Gate LiDAR Project
Summary:
The DEM (Digital Elevation Model) represents the bare earth surface elevation at each raster grid point’s geographic coordinates. The DEM provides an important GIS elevation reference layer supporting spatial analysis.
Description:
Data Source: The data source is the 2010 Golden Gate LiDAR Project’s binary LAS V1.2 classified and tiled point cloud files. This data set is in the public domain, is available online from USGS and was created to provide a new ~3m resolution portion of the USGS National Map.
UTM Rasters: The UTM native projection DEM provides an improved 0.5m resolution bare earth surface based upon the reclassification and interpolation of the original point cloud. A hillshade image of this terrain is included to support visualization. Raster DEM (32bit floating point) and hillshade (8bit grayscale) tiles (1.5 X 1.5 km each) cover the watershed and are base upon the original GGLP tile and naming scheme. These tiled products are also provided as full watershed mosaics. A smoothed and reduced version of the original DEM mosaic is provided at 1m resolution to support the creation of vector contours. All raster data files are provided in GeoTiff format that can be used by all GIS software.
UTM Vectors: The GGLP tile scheme and DEM contours are provided as shape files that can be used by all GIS software. DEM contours are created from the smoothed 1m DEM mosaic in 0.3048, 1, 5 and 10 meter versions.
State Plane Raster: The California State Plane DEM is created from the source UTM 0.5m resolution DEM mosaic. The source DEM is reprojected and rescaled to 2 ft resolution to create a GeoTiff raster file. The reprojection rotates the raster leaving nodata areas near the edges of the DEM. The nodata value is provided in the GeoTiff header.
Albers Raster: The USA Continuous Albers Equal Area Conic USGS version DEM is created from the source UTM 0.5m resolution DEM mosaic. The source DEM is reprojected and rescaled to 1.75m resolution to create a GeoTiff raster file. The reprojection rotates the raster leaving nodata areas near the edges of the DEM. The nodata value is provided in the GeoTiff header.
Credits:
- LiDAR reprocessing by Kruse Imaging www.kruseimaging.com Bill Kruse
- LiDAR data set created by the Golden Gate LiDAR Project for ARRA/USGS bss.sfsu.edu/ehines/arra_golden_gate_lidar_project.htm Ellen Hines, Bill Kruse, Eli Waggoner
- Marin County GIS, Watershed, Flood Control and Water Conservation www.marincounty.org Roger Leventhal, Laurie William, Brian Quinn
Use Limitations:
Creation of the improved Marin Watershed DEMs was funded by Marin County. The new products are derived from public domain LiDAR data available from USGS and are intended for use by Marin County and their Contractors. Permission for other uses may be obtained by contacting Marin County.
Contact:
Originator: Bill Kruse, Owner Kruse Imaging 3230 Ross Road Palo Alto, CA 94303 650-843-1124 [email protected] www.kruseimaging.com
Point of Contact: Laurie Williams Marin County Watershed GIS 3501 Civic Center Drive, San Rafael, CA 94903 415-473-4301 [email protected] www.marincounty.org
Alternate Point of Contact: Roger Leventhal Marin County Flood Control & Water Conservation District 3501 Civic Center Drive, Room 304 San Rafael, CA 94903 415-473-3249 [email protected] www.marincounty.org
RESOURCE
Quality:
Horizontal Accuracy:
LiDAR point cloud: Horizontal Accuracy < 1.0 meter RMSE From the SFSU LiDAR Project Report page 7 – October 3, 2011
Vertical Accuracy:
LiDAR point cloud: Vertical Accuracy < 9.25 cm RMSE From the SFSU LiDAR Project Report page 7 – October 3, 2011
Lineage Statement:
The LiDAR derived DEM representing the bare ground surface was created from the original LiDAR source point cloud data using a sequence of classification and interpolation algorithms. The ground classification was recreated from scratch using all non-noise points and the resulting higher density ground points were interpolated using custom tuned scripting of non-commercial software tools outside of ArcGIS. The raster and vector products were prepared in industry standard file formats (GeoTiff, Shape) and also imported into an ArcGIS file geodatabase to provide for broad compatibility and support robust digital archiving. Sufficient detail is provided in the following data source and process sections to document the workflow.
Data Source:
The original source data for the LiDAR derived DEM products is the 2010 Golden Gate LiDAR Project (GGLP) data sets acquired by GeoEye. This acquisition was managed by San Francisco Statue University and funded by an ARRA/USGS grant. The acquisition includes ~30% of Northern San Mateo County, all of San Francisco and Marin Counties and ~10% of Southern Sonoma County. The LiDAR data set is in the public domain and available from USGS (http://lidar.cr.usgs.gov/).
For each Marin watershed, the selection GGLP source tiles were assembled to cover the watershed.
Ground Control:
The GGLP ground control included 47 points, 27 of which are located in Marin County. These points were intentionally coordinated to be consistent with those used for the NOAA Coastal LiDAR acquisition that occurred in the same time frame during the Spring and Summer of 2010.
Data acquisition flights took place from April 23 to July 14, 2010. Specifically, the acquisitions occurred on April 23rd, 24th, 25, 29th, 30th, May 5th, 6th, 7th, 8th, 11th, 12th, 23rd, 29th, 30th, 31st, June 5th, 6th, 7th, 9th, 10th, and July 14th.
Survey point collection Compliance with the accuracy standard was ensured through the collection of GPS ground control during the acquisition of aerial LiDAR and the establishment of a GPS base station operating at the Gnoss Field airport in Novato. In addition to the base station, CORS bases may have been used to supplement the solutions. The following criteria were adhered to during control point collection. 1. Each point was collected during periods of very low (<2) PDOP. 2. No point was collected with a base line greater than 25 miles. 3. Each point was collected at a place of constant slope so as to minimize any errors introduced through LiDAR triangulation. 4. Each point was collected at moderate intensity surfaces so any intensity based anomalies could be avoided.
The base station equipment used was a Trimble R7 with a Zephyr geodetic model 2 antenna. The control points were collected with a Trimble R8 integrated receiver and antenna unit.
Figure 1 - GGLP Ground Control Point Distribution
Figure 2 - GGLP Ground Control Vertical Accuracy Table
Process Description:
Summary:
The LiDAR points are reclassified to aggressively identify those points representing the ground. These points are interpolated to a 0.5m resolution raster floating point elevation grid. The elevation grid is used, after filtering, to generate the vector contours. Each derived product is then imported into ArcCatalog to create a file geodatabase.
The reclassification and interpolation computations can be completed faster using tile based parallelism to process as many tiles at a time as there are available computer cores on the network. This approach was used on the Marin watershed datasets.
Software:
The following list represents the key software tools used for creating the improved LiDAR derived DEM products.
Lastools (build 4/21/12) – LAS format data manipulation utilities lasstools.org
mcc-lidar (V1.0) – LAS multi-scale curvature ground classification utility sourceforge.net/projects/mcclidar
GRASS (V6.4) – Open Source GIS Software grass.osgeo.org
GDAL (V1.8) – Open Source Geospatial Data Abstraction Library & utilities www.gdal.org
ArcGIS (V10.0) – Commercial GIS Software www.esri.com
LP360 (V11) – Commercial LiDAR Software www.qcoherent.com
Windows 7 Professional 64bit – Commercial Operating System Software www.microsoft.com
Processing Details:
The following steps describe an idealized workflow that represents how the watershed DEM products were created for the Marin watersheds. Some steps have been fully scripted and some remain manual for now. Except for the unique classification and interpolation steps, the rest of the processing and product preparation (data management, mosicing, reprojection, filtering, contouring, etc.) can be accomplished in equivalent ways using the software and procedures of your choice.
UTM DEM processing:
1) Assemble the source data tiles
Use the watershed boundary to identify all of the 1.5 x 1.5 km GGLP source tiles needed to support the watershed. Then add a one tile deep boundary to buffer the processing area. This buffer makes it possible for the processed tiles to mesh seamlessly with adjacent areas that have been processed in the same way.
2) Create buffered subtiles for processing
Use the Lastools utility lastile to create a new set of 500m tiles with a 25m buffer to support the next processing steps. These new tiles have their LL corner UTM coordinates embedded in the new tilename which simplifies batch file scripting. The smaller tiles also overcome some processing software point count limitations and provide finer grain control over the parallel implementation of the batch processing.
3) Classify the ground points
Use mcc-lidar to classify the ground points. This involves the manipulation of the point classes using Lastools and several classification steps using different scale and limit parameters in sequence to obtain the optimized ground points.
Typical batch file script for the ground reclassification performed on each edge buffered 500 x 500 m LAS subtile:
for %%f in (%1) do (
rem filter the high points using existing class 2 surface lasheight -i %%~nf.las -o gnd_0_%%~nf.las -class 2 -drop_above 1.0
rem convert classes all to 0 for new classification using mcc-lidar las2las -i gnd_0_%%~nf.las -o gnd_1_%%~nf.las -keep_class 1 2 4 - change_classification_from_to 1 0 -change_classification_from_to 2 0 - change_classification_from_to 4 0 -last_only
rem remove xyz duplicates lasduplicate -i gnd_1_%%~nf.las -o gnd_2_%%~nf.las -lowest_z
rem rough filter for ground mcc-lidar -t 0.5 -s 1.0 gnd_2_%%~nf.las gnd_3_%%~nf.las
rem convert rough ground class to 0 las2las -i gnd_3_%%~nf.las -o gnd_4_%%~nf.las -keep_class 2 - change_classification_from_to 2 0
rem fine filter for ground mcc-lidar -t 0.03 -s 0.4 gnd_4_%%~nf.las gnd_5_%%~nf.las
rem filter the low points
rem invert z value and convert ground class to 0 again las2las -i gnd_5_%%~nf.las -o gnd_6_%%~nf.las -keep_class 2 - change_classification_from_to 2 0 -scale_z -1.0
rem run mcc-lidar to clean any negative blunders mcc-lidar -t 0.6 -s 0.7 gnd_6_%%~nf.las gnd_7_%%~nf.las
rem reinvert z values and keep existing classifications las2las -i gnd_7_%%~nf.las -o gnd_8_%%~nf.las -scale_z -1.0 -keep_class 2 copy gnd_8_%%~nf.las gnd_%%~nf.las del gnd_*_%%~nf.las )
4) Manually QA and edit the ground points
Use LP360 in ArcMap to interactively review the TIN interpolation display of the ground points and edit/reclassify any unusual high or low points as noise.
5) Interpolate the ground points
Use the GRASS utility v.surf.rst to interpolate the ground points in each tile into a 0.5m raster after they have been imported into GRASS as 3D vector points. A hillshade of the raster DEM tile is created. Non-buffered versions of these rasters are also created for export.
Typical batch file script applied to each edge buffered 500 x 500 m LAS subtile for loading and interpolating the ground points, creating the hillshade and trimming the buffered products to the subtile boundary: rem load and interplate points rem %1 = column_start, %2 = column_stop, %3 = row_start, %4 = row_stop - (all in meters) rem reduced npmin from 450 to 250 for efficiency, added column range rem update npmin to 300 and segmax to 30 to reduce interpolation artifacts but make it slower.... rem Bill Kruse - 7/12/12 for /L %%C in (%1,500,%2) do ( for /L %%A in (%3,500,%4) do ( if exist gnd_b25_%%C_%%A.las ( echo %%A g.region -p region=b25_%%C_%%A g.region -p res=0.25 las2txt -i gnd_b25_%%C_%%A.las -o gnd_b25_%%C_%%A.csv -keep_class 2 -sep comma -parse xyz r.in.xyz in=gnd_b25_%%C_%%A.csv out=gnd_b25_%%C_%%A_pts fs=, x=1 y=2 z=3 type=FCELL method=min --overwrite --verbose rem read gnd points into vector r.to.vect -z -b feature=point in=gnd_b25_%%C_%%A_pts out=gnd_b25_%%C_%%A_pts --overwrite --verbose rem interpolate gnd points g.region -p region=b25_%%C_%%A v.surf.rst layer=0 in=gnd_b25_%%C_%%A_pts elev=gnd_b25_%%C_%%A_rst slope=gnd_b25_%%C_%%A_slp aspect=gnd_b25_%%C_%%A_asp segmax=30 npmin=300 tension=20 smooth=0.1 dmin=0.2 --overwrite --verbose rem create hillshade r.shaded.relief map=gnd_b25_%%C_%%A_rst shadedmap=gnd_b25_%%C_%%A_rst_shade --overwrite --verbose rem create unbuffered tiles g.region -p region=b00_%%C_%%A r.mapcalc gnd_b00_%%C_%%A_rst = gnd_b25_%%C_%%A_rst r.mapcalc gnd_b00_%%C_%%A_rst_shade = gnd_b25_%%C_%%A_rst_shade r.mapcalc gnd_b00_%%C_%%A_slp = gnd_b25_%%C_%%A_slp r.mapcalc gnd_b00_%%C_%%A_asp = gnd_b25_%%C_%%A_asp rem cleanup temporary buffered tiles del gnd_b25_%%C_%%A.csv g.remove -f rast=gnd_b25_%%C_%%A_pts endlocal ) ) )
6) QA the interpolation using the hillshade image
Visually inspect each hillshade tile or the hillshade tile mosaic for any remaining artifacts that require additional noise point editing. Reinterpolate any edited tiles and review the new hillshade results before proceeding.
7) Export the UTM DEM and hillshade GGLP tiles
Use GRASS to patch the processed 500 x 500m non-buffered DEM and hillshade tiles into the original GGLP 1.5 x 1.5 km tile scheme for export. Export them using the GGLP tile naming scheme to Geotiff files.
UTM Contour processing:
1) Filter and rescale the DEM
Low pass filter the 0.5m DEM using a 9x9 circular average filter. Then resample to filtered file to 1m resolution which reduces the contour computation and doesn’t loose any of the filtered information. These steps help reduce contour jaggies (noise) so they are easier to interpret. A small amount of detail is lost with the filtering but it becomes much easier to interpret the resulting vector contours.
2) Create the contours
Create separate vector contours for 0.3048, 1, 5 and 10 meter intervals in GRASS, ArcGIS or using GDAL. Depending upon the software tools used, an additional step may be necessary to remove small closed contours below a reasonable threshold. This and other contouring parameters can be used to reflect the individual requirements of each user.
3) Export the contours
If created with a GIS, export the contours to a shape file for each contour interval. If created by GDAL, the shape file(s) already exist.
ArcGIS Geodatabase:
1) Import each UTM raster and vector product into the ArcCatalog file geodatabase if it was not previously loaded.
DEM Reprojection Processing:
1) Reproject, rescale and export the State Plane DEM mosaic
GDAL can be used to reproject from the UTM DEM mosaic to State Plane. State Plane uses feet so a destination resolution of 2ft was selected since it is close to the source resolution of 0.5m.
If the UTM DEM is already in ArcGIS it can be reprojected and rescaled to State Plane during export to a GeoTiff file.
2) Reproject, rescale and export the non-standard Albers DEM mosaic
GDAL can be used to reproject from the UTM DEM mosaic but does natively support the US Contiguous Albers Equal Area Conic USGS version projection. However, it can be custom configured to add support for this projection.
If the UTM DEM is already in ArcGIS it can be reprojected and rescaled to this custom Albers projection during export to a GeoTiff file. A rescaled resolution of 1.75 m was selected to keep the file size under 600MB for compatibility with the GeoHMS hydrographic modeling software.
Rational:
Evaluation of the GGLP point cloud suggested that additional true ground points existed in the data set that could prove useful for increasing the quality and resolution (or fidelity) of the LiDAR derived DEM.
The LiDAR data vendor (Earth Eye) provided a product that included a base level of ground classification created using industry standard TerraScan software. The Golden Gate LiDAR Project team manually edited the classified ground points to remove obvious noise, buildings and vegetation points. An additional automated software filtering step was applied to reduce the significant amount of near- ground low-vegetation points. The resulting ground points and TIN interpolated surface met the specifications and intent of USGS to produce a 1m resolution DEM that would be rescaled to 3m for the National Map.
To improve upon the products provided to USGS, an approach to ground classification was developed using non-commercial software (lastools, mcc-lidar) to improve upon competing commercial software capabilities under dense vegetation and where the terrain slope changes quickly. The average classified ground point density can be typically increased by more than 1.5 times in most areas while reducing the impact of near-ground vegetation. This supports improved terrain detail in those areas where more points exist.
The resulting reclassification process reevaluates all non-noise points within 1 meter of the original GGLP ground surface including previous unclassified and above ground points. An iterative, multi-scale minimum curvature process is then applied to identify ground points conforming to the assumption that the terrain can be well represented by a minimum curvature surface.
The next step interpolates the irregularly spaced ground points into a raster grid using an approach that adapts to the point density. This spline based interpolation model is conceptually similar to the multi-scale minimum curvature classification described above. As a result, the interpolated DEM closely represents the surface defined by the classified ground points.
The reclassification and interpolation computational cost is significantly higher than competing methods and with current software requires up to 2 CPU core hours of computer time per square kilometer at the 0.5m resolution DEM resolution created from the GGLP data set. However, the cost of CPU time is low, continues to drop and the improved ground classification is useful when the best possible LiDAR derived terrain model is needed.
Classification Software Information:
The ground classification uses a “Multiscale Curavature Algorithm for Classifying Discrete Return LiDAR in Forested Environments” that was originally implemented in AML using Workstation ArcInfo but since ported to C++, open sourced and can be run independent of GIS software. The software uses three scales (0.5x, 1x, 2x) based upon a configurable LiDAR point spacing and a configurable threshold to identify positive local deviations from surrounding points and then iteratively classifies them as non-ground. A script extends algorithm to five scales for positive local deviations to find more points and also three scales of negative local deviation to remove below ground noise that exists in this LiDAR data set. Additional background information can be found using the following links.
sourceforge.net/projects/mcclidar www.cnr.uidaho.edu/watershed/owlx/papers/evans_2007_ieee.pdf www.mdpi.com/2072-4292/3/3/638
Interpolation Software Information:
The interpolation method used is Regularized Spline with Tension (RST) and is implemented in the GRASS module v.surf.rst. The interpolation function is a sum of a trend function and a radial basis function with an explicit form which depends on the choice of the measure of smoothness. Adaptive segmentation allows local processing of very large point data sets and dynamically adjusts to highly variable point densities. For more information see Mitasova and Mitas (1993) and Mitasova et. al. (2005). This interpolation method is considered by some to create raster surfaces with fewer artifacts and more consistent curvature than the fast TIN or the more traditional geostatistical Kriging interpolation algorithms.
http://www.osgeo.org/grass http://grass.osgeo.org/grass64/manuals/html64_user/v.surf.rst.html http://skagit.meas.ncsu.edu/~helena/gmslab/papers/MG-I-93.pdf http://skagit.meas.ncsu.edu/~helena/gmslab/papers/IEEEGRSL2005.pdf
Contours: The DEM is filtered before contouring to reduce the DEM noise and produces smoother contours. Contour filtering in the vector domain is more difficult and computationally expensive. The slightly reduced contour fidelity has limited impact on the overall contour accuracy and usefulness since the DEM itself is limited by the sparseness and number of points used for the interpolation.
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
C.2: Survey Data Synthesis Technical Memorandum (KHE 2013)
DRAFT Kamman Hydrology & Engineering, Inc. Memorandum Date: May 5, 2014 To: Roger Leventhal, Project Manager Laurie Williams, Contract Manager Marin County Department of Public Works – Flood Control (MCFC)
From: Rachel Kamman, KHE Inc. Cc: Subject: Novato Creek Watershed Hydraulics Study: Survey Data Synthesis
This memorandum summarizes and serves as transmittal for watershed survey and facilities data gathered to support the Novato Creek Watershed Hydraulics Assessment. The description and sources of data are summarized below. Data files are provided on an accompanying external hard drive labeled “Novato Survey Synthesis 2014”.
A: Data Sources and Projections: To support the Novato Creek hydraulics assessment KHE gathered and reviewed available survey data. The data incorporated consists of pre‐project survey and facilities data provided to KHE by MCFC, Bare Earth LIDAR and survey data developed by the KHE team to support the watershed study, and data contributed by participating stakeholders including the City of Novato (City), Novato Sanitary District (NSD), Bel Marin Keys Community Services District (BMKCSD), and the USACE/SCC Bel Marin Keys restoration team (BMK). Survey information utilized in the study included ground surface elevations, levee crest elevations, channel bathymetric information, and critical elevations of water control infrastructure throughout the study area.
The following project projection and datum, utilized for data synthesis, are standard for Marin County Departments of Public Works, Parks and Open Space and Community Development Agency and the City of Novato: Datum: North American Datum 1983, HARN Linear Unit: Foot_US ArcGIS Coordinate System Name: NAD_1983_StatePlane_California_III_FIPS_0403_Feet Vertical Datum: ft, NAVD88 NGVD29 Datum Conversion: +2.69 ft (100.0 ft NGVD29 = 102.69 ft NAVD88)
In compiling and reviewing survey point and metadata from various sources, KHE found conversion factors between NAVD88 and NGVD29 ranging from 2.61 to 2.75 ft. For the purposes of this study, KHE adopted the MarinMap datum conversion factor of +2.69 ft (100.0 ft NGVD29 = 102.69 ft NAVD88). Marin County adopted this constant conversion factor, applied throughout Marin County, in accordance with FEMA map adjustment standards (FEMA, 2002). This conversion factor has been applied for all Marin County sourced pre‐project data, as well as data sets provided by Novato Sanitary District (NSD)
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DRAFT Kamman Hydrology & Engineering, Inc. and the Bel Marin Keys Community Services District (BMKCSD). KHE also identified City of Novato control points within the study area as reported in CA State Plane Zone3 coordinates and NAVD881. References for datum conversions are provided in the directory: “Datums_Docs.”
Marin County Flood Control and Stakeholder Pre‐Project Data KHE reviewed available MCFC and Stakeholder survey data for numerous sites and facilities. Table A‐1 identifies the pre‐project ground topography data sets reviewed in data synthesis. Digital files in the original format and re‐projected (as needed) to project shape files are provided in the directories identified in Table A‐1. Pre‐project and project control points are presented in Figure
Table A‐1: Pre‐project Topographic and Bathymetric Survey Data Sources and Coverage Source/(Directory) Description Format Location(s) Date(s) MCDPW Levee Survey: CAD/GIS Levee top: Novato County Survey Staff PRE2012_DPW_Topo/Levees (NGVD29) Cr. Diablo Blvd to 2004,2007,2008,2009 San Pablo Bay MCDPW Ground Points: CAD Novato Cr. : Kristen 2009 PRE2012_DPW_Topo/Site Channel (NGVD29) La. Surveys MCDPW Channel XSs and CAD Arbor Circle to KHE Inc.,2011 Novato_TopoGIS_201213 Bed Profile (NAVD88) McClay Ave MCDPW Bathymetric Paper Lynwood ponds 2009 PRE2012_DPW_Topo/Bayland Contours Pacheco Pond 1982 Ponds As‐Built Drawings (NGVD29)
BMKCSD via Gridded yxz Lower Novato Cr. CLE Engineering, CLE Engineering bathymetric pt channel ‐ North 2003,5,7,8 – 11 BMKCSD_Data surveys Lock to San Pablo Bay BMKCSD via Ground points & CAD BMK South Levee CLE Engineering, 2009‐ CLE Engineering Levee Surveys top and structures 2011 BMKCSD_Data (NGVD29) BMK_SCC/USACE Ground and xyz text Perimeter levee top Tucker Surveying, 2009 BMKV_Data facilities pts. and transects (NAVD88) CALTRANS As built drawings Paper Bridges, road and CALTRANS (no digital files) creek crossings (multiple) City of Novato Bathymetric CAD Scottsdale pond City of Novato, 2011 City_Topo&Facilities_Pre2012 contours: As built City of Novato As Built Drawings CAD/PDF Bridge, creek and City of Novato, 2011 (no digital files, see City GIS) (NGVD29 storm drain projects Novato Sanitary District Ground points, CAD Novato Cr. left bank (NSD_Data) Facilities alignments Levees, field pts. and 2004, 2005 and unknown Levee surveys Irrigation alignments (NGVD29)
1 City of Novato conversion factors ranged from 2.66 to 2.74, as determined using local point coordinates and the USGS CORPSCON conversion software (NGS/USACE, version 6.0).
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DRAFT Kamman Hydrology & Engineering, Inc. in basins 1.1.1,2,3
State Lands FUDS Ground points and CAD State lands parcel USACE,SCC PRE2012_DPW_Topo/State facilities (NAVD88) Lands
KHE rectified pre‐project data sets to the extent possible using available metadata and project survey control. Rectification consisted of defining bay and watershed vertical and horizontal control networks and linking pre‐project survey data sets to the control network based on common controls points. Where no common control points existed, existing data was ported to the project projection/datum and reviewed for general agreement with other nearby data points. Working with MCFC staff, KHE identified and reoccupied the Marin County survey control points most commonly used as part of the project ground control survey2.
Pre‐Project Survey Control: Table A‐2 and identifies the pre‐project survey control point data sets evaluated in data synthesis. Source digital files compiled in their original and rectified formats are provided in the directory: SurveyControl.
Table A‐2: Pre‐project Topographic Survey Control Point Data Sources and Coverage Source Description Format Location(s) Date(s) MCDPW County Control Points CAD/GIS NWP RR ROWs, DPW Multiple (NGVD29) facilities, Lower Novato (1982‐2011) Cr. and Vineyard Cr. Bridges City of Novato Control Points in Watershed GIS Recorded point elevation Multiple NAVD88 = NGVD29+local (1965‐2000) roads, curbs & structures. Vertcon correction factor BMKCSD via BMKCSD Survey CAD South Lock CLE Engineering, 2011 CLE Engineering Control (NGVD29) BMK_SCC/USACE HPGN Control Points XYZ txt BMKV Interior Tucker Surveying, 2009 NAVD88 SMART HPGN Control Points XYZ txt ROW and Hwy Cinquini & Passarino, NAVD88 Interchanges Inc., Unknown
KHE recommends that future engineering work in the watershed be conducted based on the MCFC 2013 watershed wide vertical control survey and other available HPGN control points. (See Section B: Project Vertical Control Surveys)
B: Project Surveys Following review of available survey information, KHE identified and gathered the additional topographical and survey data necessary to support the Novato Creek watershed hydraulic assessment. Under contract to KHE, licensed surveyors collected topographic data throughout the watershed between October 2012 and February 2013. Data collection presented below focused on the following five information needs:
2 See Appendix A.1 for conversion factors for primary MCFC survey controls
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DRAFT Kamman Hydrology & Engineering, Inc. 1. A watershed wide Survey Control Network utilizing a NAVD88 vertical datum, with sufficient points to serve as a basis for conversion of existing County surveys, and support hydraulic modeling, monitoring and watershed/geomorphic assessment 2. LIDAR DEM: Watershed wide coverage based on a single acquisition period and filtering procedure. 3. Bridge Geometry: Upstream and downstream, soffit heights, deck and bed elevations and adjacent channel cross sections at the nine (9) bridges in the Novato creek corridor 4. Channel Cross‐section and longitudinal thalweg profiles at selected representative locations throughout the watershed. 5. Intertidal Channel and Pond Bathymetry: A single contiguous snapshot of existing conditions to support model calibration/validation.
The directory for the digital data sets that support each information area is identified below.
B.1: Survey Control Network: (Directory: SurveyControl)
B.1.1 Bayland Control Network: Highway 101 to San Pablo Bay In October 2012, surveyors from Oberkamper and Associates Civil Engineers, Inc. (OACE) established a survey control point network in the Novato bayland. OACE gathered existing HPGN controls in the area (USACE/SMART/NGS), and adding new points along the Novato Creek corridor from San Pablo bay to Hwy 101. Coordinates and elevations were determined utilizes a GPS RTK survey. The control point survey reoccupied frequently used Marin County control points near and upstream of Hwy 37, to facilitate rectification of pre‐project County surveys of levee crest elevations and other flood control facilities. (See figure B‐1 for the Bayland Control Network, and Attachment A for surveyor documentation).
B.1.2: Watershed Control Network: Stafford Lake to Highway 101 Surveyors Cinquini and Passarino Inc., PLS (CPI) established a survey control point network utilizing Static GPS to tie points together along the Novato Creek corridor between Stafford Lake and Highway 101. The Novato Creek watershed control network utilized high precision (HPGN) survey knowns established by CPI in 2008 for SMART at the Bayland limit of the project. Near the upstream limit of the project, GPS survey control was based on HPGN knowns at Stafford Lake set by CPI for North Marin Water District (NMWD) in 2011. New watershed control points were established using static GPS and a least squares analysis for closure which utilized supplemental data from available Continuously Operating Reference Stations (CORS). See Attachment A for the surveyor’s supporting documentation. See Table B‐1 for a listing of Novato Creek Watershed control point (CP) locations. (See Figure B‐1 on the following page for the Watershed Control Point network, and Attachment A for surveyor documentation).
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DRAFT Figure B-1: Novato Watershed Survey Control Network Kamman Hydrology & Engineering, Inc.
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DRAFT Kamman Hydrology & Engineering, Inc. Table B‐1: Control Point Locations (See Location on Map KHE‐1):
Control Point Location Control Pt. # CP,BR Novato Creek: Diablo Ave. Bridge NC10 CP,BR Novato Creek: Seventh Street Bridge: NC20 CP,BR Novato Creek: Grant Avenue Bridge: NC 30 CP,BR Novato Creek: Simmonds Ln Bridge NC 40 CP Novato Creek: Pioneer Park NC 41 CP Novato Creek: Near Poco Vista NC 42 CP Novato Creek: Near Olivia Ct. NC 43 CP Novato Creek: Miwok Pk. Foot Bridge NC 44 CP,BR Novato Creek: Novato Blvd Bridge NC 50 CP,BR Novato Creek: Thorsson Ct. Bridge NC 60 CP Novato Creek: Novato Blvd: btwn Thorsson and Sutro NC 61 CP,BR Novato Creek: Sutro Ave Bridge NC 70 CP Novato Creek: O’Hare Park 1 @ MSF NC 71 CP Novato Creek: O’Hare Park 2 @ MSF NC 72 CP Novato Creek: O’Hare Park 3 @ Footbridge NC 73 CP Novato Creek: O’Hare Park 4 @ Dog Park NC 74 CP Novato Creek: Novato Blvd. Below Bowman Cyn NC 80 CP Novato Creek: Novato Blvd. Above Bowman Cyn NC 81 CP Novato Creek: Novato Blvd. @ Stafford Lake Outlet NC 82 CP,BR Warner Creek: Diablo Avenue Bridge WC 11 CP,BR Warner Creek: Tamalpias Avenue Bridge WC 21 CP Warner Creek: Meyers Ct WC31 CP, Vineyard Creek: McClay Ave VC32
B.2: Watershed LIDAR Terrain Model
The Novato Watershed digital elevation model (DEM) (Figure B‐2) defines a single continuous ground surface for the study area. The four hydraulic models are integrated across this surface. Model accuracy is strongly dependent on the accuracy and consistency of the DEM. Golden Gate Lidar (2010) served as the starting point for the model DEM. The raw data set consists of binary LAS classified and tiled point cloud files. The data set is in the public domain and available online from USGS. To improve the accuracy of the LIDAR based terrain for this hydraulic study, Kruse Imaging (KI) was contracted to post process the Golden Gate LIDAR data and generate “bare earth” coverage of the Novato Creek watershed. A metatdata summary for the KI analysis is provided in Appendix B. Generation of bare earth terrain utilizes a series of filters to remove above ground pixels associated with vegetation, structures and
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DRAFT Kamman Hydrology & Engineering, Inc.
Figure B‐2: Novato Watershed DEM (Cropped to Valley Bottom)
other non‐terrain features, and re‐interpolates the data surface utilizing only those pixels representative of ground surface elevations. Overall DEM accuracy as reported by Kruse (2012) is +‐10cm. The bare earth DEM is developed to maintain this accuracy under dense vegetative cover found throughout the Novato creek riparian corridor, in hillside drainages and bay marshland. The DEM coverage extends to the upper limits of the watershed. Shading in Figure B‐2 is truncated at approximately 20 ft. above the valley floor to focus on grades on the area of hydraulic assessment. KI developed bare earth DEMs in a variety of projections and formats. KI data sets are provided in the directory: KRUSE_DEM_Bare Earth. One foot contours developed based on the project DEMs for both watershed and baylands areas are provided in directory: Novato_TopoGIS_201213\Watershed Contours.
KHE undertook a rigorous comparison between topographic point data (focused on levee crests and bay basins) and the LIDAR DEM to determine if vegetative cover biased the bayland terrain surface (Appendix C). Although the accuracy of individual survey data sets varied widely, on average the DEM over‐ estimated bayland elevations by +4‐6 inches (Figure B‐3). However, KHE does not recommend downward adjustment of the DEM since the offset is within recognized error limits for the data.
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DRAFT Kamman Hydrology & Engineering, Inc. Figure B‐3: Comparison between KI Bare Earth Lidar and MCFC Levee Survey Points (Subset)
Lidar does not capture data below the water surface. To characterize channel and pond bathymetry, KHE supplemented the LIDAR data with bathymetric surveys (CLE, 2011/2012) in the Novato Creek channel and as built surveys of Lynwood Basin and Pacheco Pond (See Section B.5). The final project Bayland DEM (Figure B‐4) integrates KI bare earth Lidar with channel and pond DEMs developed by KHE from available subsurface data. The final 5‐ft resolution Bayland DEM, and the associated 1 ft. contours are provided in directory: NovatoBayland_DEM_201213.
B.3: Watershed Bridge Surveys Fifteen bridges cross Novato, Warner and Vineyard creeks within the watershed study area. Accurate representation of current conditions and critical elevations at each structure is required for modeling accuracy because the multiple structures in the corridor strongly influence the elevation and slope of the channel, and in turn the flow and sediment conveyance capacity. CPI (2013) conducted surveys of nine of these structures, identified as BR in Table B‐1, defining deck elevations, soffit height, conveyance cross section and adjacent bed elevation. CPI survey data are provided in directory: Novato_TopoGIS_201213.
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DRAFT Kamman Hydrology & Engineering, Inc. Figure B-4: Bayland DEM
In addition to delineating the structures, survey points defined upstream and downstream channel cross sections and longitudinal bed profiles in the vicinity of each structure. Each local survey originates with points from the watershed survey control network. The bridge characterization data collected is utilized in hydraulic model development. Pre‐project survey data was utilized to characterize Vineyard Creek bridges at Center Rd and McClay Rd. (MCFC/KHE, 2012), and CalTrans as built survey data provided by MCFC was utilized to describe bayland bridges at Hwy 101, Redwood Blvd and Hwy 37. Figure B‐5a/b identifies the location of project bridge surveys. Detailed survey documentation is provided in Attachment A.
B‐4: Watershed Channel Cross Sections and Longitudinal Profiles (Upstream of Diablo Ave) In addition to the bridge surveys, CPI and KHE surveyed cross‐sectional channel geometry and adjacent longitudinal profiles at 22 locations along Novato and Warner Creeks in November 2012 and January 2013 (Figure B‐5a/b). Surveys upstream of Diablo Blvd originated with watershed control network points and consisted of channel cross sections from top of bank to top of bank, and upstream and downstream longitudinal profiles of the channel thalweg and toe of bank. To support hydraulic modeling KHE selected channel cross section locations in the field that were representative of
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DRAFT Kamman Hydrology & Engineering, Inc.
Figure B-5a and B-5b: Watershed Surveys: Control Point, Bridge and Channel Cross Sections
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DRAFT Kamman Hydrology & Engineering, Inc. channel characteristics, and/or delineate changes in channel geometry, geomorphic form or bedrock grade controls. In addition to project specific surveys, KHE also incorporated 2012 post‐construction monitoring surveys on Vineyard Creek between Arbor Circle and McClay Ave., and 2009 DPW surveys at Kristin Lane which supported a local bank stabilization project (shown in black). Watershed channel survey data is provided in directory: Novato_TopoGIS_201213.
B.5 Intertidal Channel and Pond Bathymetry
B.5.1 Dredge Channel Geometry: Novato Cr. /Warner Cr. Channels: Diablo & So. Novato Ave to HWY 37 Following completion of maintenance dredging in October 2012, Oberkamper and Associated (OACE) conducted an as‐built survey of the Novato Creek dredged channel bottom, in the dewatered Novato Creek channel from Diablo Ave to the NWPRR Crossing, and in Warner Creek from South Novato Blvd to the Novato Creek confluence. In addition to the channel bottom surveys, OACE surveyed channel cross‐ sections (top‐of‐bank to top‐of‐bank) at 17 locations within the dewatered reach. In 2013, OACE extended terrestrial channel surveys from NWPRR (the downstream limit of the Novato Dredge project) to Hwy 37. Twelve (12) cross sections were surveyed from the interior basin, across the levee top to the limit of exposed bank at low tide. The low tide, land based survey of the undredged reach, was combined with high tide bathymetric surveys described below to delineate geometry and depth in this high intertidal reach. Figure B‐6/B7 presents the location of OACE’s 2013 dredge channel terrestrial surveys. GIS and CAD files can be found in directories: Novato_TopoGIS_201213 and Survey_Data_CAD\OACE_* respectively.
Figure B‐6: Novato Creek Channel Surveys: Dredge Reach
B.5.2 Bathymetric Channel Surveys: Novato Creek: NWPRR to HWY 37 Accurate characterization of tidal hydraulic in the Novato Creek baylands is strongly dependent on the elevation slope and geometry of the tidal channel network. In January 2012, CLE conducted high tide bathymetric surveys of the reach from NWPRR to Hwy37 to characterize low flow channel conditions in the reach. This methodology was consistent with prior CLE bathymetric surveys of the lower Novato Creek channel from North Lock to San Pablo Bay. Since 2003, CLE has conducted seven bathymetric
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DRAFT Kamman Hydrology & Engineering, Inc. Figure B‐6: 2013 Novato Creek Channel Surveys: NWPRR to Hwy37
surveys downstream of Hwy 37in lower Novato Creek. BMKCSD conducts frequent surveys to support navigational maintenance dredging and optimization of lock operation. CLE’s bathymetric surveys utilize a project control point at the South Lock, RTK GPS to provide continuous measurement of boat elevation, and a single beam hydrographic echo sounder to measure water depth. Bay KHE utilized CLE/BMKCSD’s most recent (2011) bathymetric survey to define channel conditions in this most bayward reach. KHE integrated CLE’s gridded (5‐ft) bathymetric survey results with Lidar data and pond bathymetry data to create Bayland DEM (Figure B‐4). The associated GIS and CAD files are located in directories: BMKCSD_Data and Survey_Data_CAD\CLE_Data respectively.
B.5.3 Pond Bathymetry: To complete the Bayland DEM, KHE defined below water surface conditions in Lynwood Basin (2009), Pacheco Pond (1982) and Scottsdale Pond (2003) based on as built and design drawings provided by MCFC and City of Novato. Paper maps of pond contours were scanned, digitized and visually rectified with the project data set. DEM of pond bathymetry was generated from as‐built contours and integrated in the project data base. KHE did not incorporate these surfaces into the project Bayland DEM because the data accuracy is lower than that of the overall surface. Future phases of work should gather this data to maintain a consistent level of accuracy in technical analysis. These data sets were incorporated into the domain of the numerical model, as the best available data at the time of the study. The DEMs for Lynwood Basin and Pacheco Pond are shown in Figure B‐7. The associated GIS tiff and contour files are located in directory: NovatoBayland_DEM_201213\Ponds.
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Figure B‐7: Novato Bayland Pond DEMs
C: Additional Data of Interest: In additional to topographic data KHE examined and developed other data sets to support watershed assessment. The following is a list of additional data of interest included to provide MCFC with resources anticipated to be of value in ongoing watershed study, planning and design. Datum_Docs: County, CDA, City and FEMA documents describing various datum conventions and conversions for the study area EC Data: Non‐ topo watershed data supporting the Existing Conditions Assessment (FEMA maps, hydrology basins, bayland basins, watershed reaches, etc.) Imagery: 2009 NAIP and 2011 USGS high resolution imagery used by KHE in analysis and presentation. KHE_LayerFiles: Formats and color scales for shape files, contour maps and DEMs NSD_Facilities and Infrastructure: Alignments, easements, mitigation site parcel, irrigation, pumps PRE2012_DPW_Facilities and Infrastructure: Rectified GIS/CAD data provided by MCFC to support the study (land use, ownership, facilities and infrastructure).
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Attachments:
Attachment A: Survey notes and metadata files: CPI/OACE/CLE Attachment B: Bare Earth DEM Metadata Summary: Kruse Imaging Attachment C: Comparison of Lidar and Ground Survey Data: KHE Inc.
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Attachments:
Attachment A: Survey notes and metadata files: CPI/OACE/CLE Attachment B: Bare Earth DEM Metadata Summary: Kruse Imaging Attachment C: Comparison of Lidar and Ground Survey Data: KHE Inc.
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CINQUINI & PASSARINO INC.
BOUNDARY TOPOGRAPHIC CONSTRUCTION SUBDIVISIONS LAND SURVEYING
Novato Creek Watershed December 2012
Project Units: U.S Survey Feet
Project Horizontal Datum: California Coordinate System of 1983, Zone 3, epoch 2008.00
Project Vertical Datum: North American Vertical Datum of 1988
Field Notes for the Novato Creek Single Beam Survey Date: January, 02 2013 Weather: Sunny, Calm wind and no seas Equipment Sounder: Odom CVM Frequency: 200KHX GPS Trimble 5700 Sound Velocity Reson SVP 14 Offsets Sounder Draft: 1.0 ft entered in sounder Speed of sound: 4889 entered in sounder GPS Forward: 0 Starboard: 0 Tide: RTK GPS via point 1006 located on the South lock. Measuring the water level and comparing it to the tide measurement computed by the survey software determined the tide level. Adjustments to the antenna height were made to obtain the correct tide reading. Bar check: Due to the variable draft of the vessel due to weight shifts, the vessel was moored on the south lock in order to check the draft. First the average sound velocity was measured using a sound velocity profiler and the value was entered in to the sounder. The depth to the lock floor was measured using a grade rod and the draft of the vessel was adjusted to read the measured reading. Benchmark: Project Bench Mark is Point # 101 brass disc set at the entrance to the south lock, EL= 8.05 ft MLLW (Coordinates: N 2223120.90, E 5984338.24)
MEMORANDUM Kamman Hydrology & Engineering, Inc. 7 Mt. Lassen Dr., Ste. B250, San Rafael, CA 94903 Telephone: (415) 491-9600 Facsimile: (415) 680-1538 E-mail: [email protected]
Date: August 09, 2013 To: Rachel Kamman P.E. From: Corey Hayes Subject: Novato Creek CPI Surveys, Bridge and Intermittent Cross Sections
Introduction/Methods: The following results are compiled from on-site surveys of upland sections along Novato Creek (NC), conducted by Cinquinni and Passarino Inc. (CPI). A total of 7 Bridges were surveyed along NC at streets including Sutro Ave., Thorson Ct., Novato Blvd. near Eucalyptus, Simmons Ln., Grant Ave., 7th St and Diablo Ave. Cross sectional areas for bridges were determined from representative survey points closest to each bridge face. The horizontal projection used for this analysis was in NAD_1983_StatePlane/ California_III_FIPS_0403_Feet and the vertical datum was in NAVD88-ft.
A total of 18 cross sections were surveyed at various intermittent locations between bridges. This report contains information such as: areas for each cross section, average “flowline” (FL) elevations as well as average slopes between bridges and between each intermittent cross section. Distances were calculated by creating a route along the NC thalweg. This route starts at 5946435.786 E 2236908.937 N at the beginning of the Stafford Lake Spillway. Relative distances are taken downstream of NC from this location.
Results/Conclusions:
• The largest bridge cross sections occurred at approximately 12,000 ft downstream of NC at the Thorson Ct. Bridge and the Novato Blvd. Bridge near Eucalyptus. These resulting areas were in the range of approximately 800-925 ft2. • The remaining bridge cross sections (Sutro Ave., Simmons Ln., Grant Ave., 7th St. and Diablo Ave.) remained within a constant range of 300-450 ft2. • The largest intermittent cross section occurred farthest upstream near Stafford Lake at XS-1 and XS-3. These areas were 579 ft2 and 607 ft2. • Cross sections downstream of XS-3 (XS-4 through XS-18) remained at a relatively constant level within the range of 262-446 ft2. • The range of average slopes occurring between all cross sections was between 0.0015-0.0079 ft/ft. • The greatest average slope overall, occurred between XS-4 and XS-5 at 0.0079 ft/ft. • The greatest slope between bridge cross sections occurred between Thorson Ct. Bridge and the Novato Blvd. Bridge at 0.0052 ft/ft. • The slope between Novato Blvd. Bridge near Eucalyptus and the Simmons Ln. Bridge was 0.0048 ft/ft.
Kamman Hydrology & Engineering, Inc. 1
(Nad83, SP, Cal zone III, ). (Refer to Figure 1 for Map) Thalweg. from the start of the Stafford Lake Spillway (5989944.084 E 2228880.229 N) **Distance from start is rela ve to distance downstream, along the Novato Creek Average Slope (%)
Average Flowline Elevation (NAVD88) (ft) 0.00% 0.20% 0.40% 0.60% 0.80% 1.00% 100 120 140 20 40 60 80 0
0 0
XS-1
XS-1 to XS-2 XS-2 XS-2 to XS-3 XS-3
XS-3 to XS-4
XS-4 5,000 5,000
XS-4 to XS-5
XS-5
XS-5 to XS-6
XS-6
XS-6 to XS-7
XS-7 10,000 10,000 XS-7 to Sutro Ave Bridge Sutro Ave. Bridge Sutro Ave. Bridge to XS-8 Distance FromStartingPoint(ft)** XS-8
XS-8 - Thorson Ct. Bridge
Thorson Ct. Bridge Thorson Ct. Bridge to Novato Blvd. Bridge near Eukalyptus Nov. Blvd. Bridge near Eukalyptus
Nov. Blvd. Bridge near Eukalyptus to XS-9
XS-9
XS-9 to XS-10
15,000 XS-10 15,000
XS-10 to XS-11
XS-11
XS-11 to XS-12
XS-12 Simmons Lane Bridge XS-12 to Simmons Ln. Bridge Simmons Lane Bridge to XS-13 XS-13
XS-13 to XS-14 20,000 20,000
XS-14 XS-14 to Grant Ave. Bridge Grant Ave Bridge Grant Ave Bridge to XS-15 XS-15 XS-15 to XS-16 XS-16 XS-16 to 7th St. Bridge 7th St. Bridge
7th St. Bridge to XS-17
XS-17 XS-17 to XS-18 25,000 XS-18 25,000 XS-18 to Diablo Ave. and S Novato Blvd. Bridge Diablo and S Novato Blvd Bridge (Nad83, SP, Cal zone III, ). (Refer to Figure 1 for Map) Thalweg. from the start of the Stafford Lake Spillway (5989944.084 E 2228880.229 N) **Distance from start is rela ve to distance downstream, along the Novato Creek Cross Sec onal Area ( 2) 1000
Average Flowline Elevation (NAVD88) (ft) 100 200 300 400 500 600 700 800 900 100 120 140 20 40 60 80 0 0
0 0
XS-1 XS-1
XS-2 XS-2
XS-3 XS-3
XS-4 XS-4 5,000 5,000
XS-5 XS-5
XS-6 XS-6
XS-7 10,000 XS-7 10,000 Sutro Ave. Bridge Sutro Ave. Bridge Distance FromStartingPoint(ft)** XS-8 XS-8
Thorson Ct. Bridge Thorson Ct. Bridge
Nov. Blvd. Bridge near Eukalyptus Nov. Blvd. Bridge near Eukalyptus
XS-9 XS-9
15,000 XS-10 15,000 XS-10
XS-11 XS-11
XS-12 XS-12 Simmons Lane Bridge Simmons Lane Bridge
XS-13 XS-13
20,000 20,000
XS-14 XS-14
Grant Ave Bridge Grant Ave Bridge
XS-15 XS-15
XS-16 XS-16
7th St. Bridge 7th St. Bridge
XS-17 XS-17
25,000 XS-18 25,000 XS-18
Diablo and S Novato Blvd Bridge Diablo and S Novato Blvd Bridge 2 Cross Sec onal Area ( ) 1000 100 200 300 400 500 600 700 800 900 0 **Distance from start is rela ve to distance downstream, along III ) Spillway (5989944.084 E 2228880.229 N) (Nad83, SP, Cal zone the Novato Creek Thalweg., from the start of the Stafford Lake 0
XS-1
XS-2 XS-3 5,000 XS-4
XS-5
XS-6 10,000
XS-7 Sutro Ave. Bridge XS-8 Distance From Start ( )**
Thorson Ct. Bridge Nov. Blvd. Bridge near Eukalyptus
XS-9 15,000
XS-10
XS-11
Simmons Lane Bridge XS-12 XS-13 20,000
XS-14 Grant Ave Bridge XS-15 XS-16 7th St. Bridge Novato Creek Bridges Intermediate Cross Sec on 25,000 XS-17 XS-18 Diablo and S Novato Blvd Bridge 30,000 Novato Creek Bridges - CPI Survey US/DS Profile Plots and Cross Sectional Areas
90 90
85 85
80 80
75 75
70 70 Elevation (NAVD88) (ft) (NAVD88) Elevation Elevation (NAVD88) (ft) (NAVD88) Elevation 65 65
60 60 0 102030405060708090100110120 0 102030405060708090100110120
Upstream Streambed Elevations Upstream Streambed Elevations
Distance (ft) Upstream Soffit Elevations Distance (ft) Upstream Soffit Elevations
2 2 US Area (ft ) 408 Sutro Ave. Bridge Cross Sections DS Area (ft ) 369 Bridge Distance from Start (ft)** = 10,488
90 90
85 85
80 80
75 75
70 70 Elevation (NAVD88) (ft) (NAVD88) Elevation (ft) (NAVD88) Elevation
65 65
60 60 0 102030405060708090100110120 0 102030405060708090100110120
Upstream Streambed Elevations Upstream Streambed Elevations
Distance (ft) Upstream Soffit Elevations Distance (ft) Upstream Soffit Elevations
2 2 US Area (ft ) 810 Thorson Ct. Bridge Cross Sections DS Area (ft ) 901 Bridge Distance from Start (ft)** = 12,046
90 90
85 85
80 80
75 75
70 70 Elevation (NAVD88) (ft) (NAVD88) Elevation Elevation (NAVD88) (ft) (NAVD88) Elevation 65 65
60 60 0 102030405060708090100110120 0 102030405060708090100110120
Upstream Streambed Elevations Upstream Streambed Elevations
Distance (ft) Upstream Soffit Elevations Distance (ft) Upstream Soffit Elevations
2 2 US Area (ft ) 927 Novato Blvd. Bridge near Eukalyptus Cross Sections DS Area (ft ) 861 Bridge Distance from Start (ft)** = 12,720 **Relative distances are taken downstream along Novato Creek Thalwig from start point located at 5946435.786 E 2236908.937 N at the beginning of the Stafford Lake Spillway Novato Creek Bridges - CPI Survey US/DS Profile Plots and Cross Sectional Areas
65 65
60 60
55 55
50 50
45 45 Elevation (NAVD88) (ft) Elevation (NAVD88) (ft) 40 40
35 35 0 102030405060708090100110120 0 102030405060708090100110120
Upstream Streambed Elevations Upstream Streambed Elevations
Distance (ft) Upstream Soffit Elevations Distance (ft) Upstream Soffit Elevations
2 2 US Area (ft ) 367 Simmons Ln. Bridge Cross Sections DS Area (ft ) 440 Bridge Distance from Start (ft)** = 18,193
50 50
45 45
40 40
35 35
30 30 Elevation (NAVD88) (ft) (NAVD88) Elevation Elevation (NAVD88) (ft) (NAVD88) Elevation 25 25
20 20 0 102030405060708090100110120 0 102030405060708090100110120
Upstream Streambed Elevations Upstream Streambed Elevations
Distance (ft) Upstream Soffit Elevations Distance (ft) Upstream Soffit Elevations
2 2 US Area (ft ) 397 Grant Ave. Bridge Cross Sections DS Area (ft ) 307 Bridge Distance from Start (ft)** = 21,310
40 40
35 35
30 30
25 25
20 20 Elevation (NAVD88) (ft) (NAVD88) Elevation Elevation (NAVD88) (ft) (NAVD88) Elevation
15 15
10 10 0 102030405060708090100110120 0 102030405060708090100110120
Upstream Streambed Elevations Upstream Streambed Elevations
Distance (ft) Upstream Soffit Elevations Distance (ft) Upstream Soffit Elevations
2 2 US Area (ft ) 386 7th Street Bridge Cross Sections DS Area (ft ) 413 Bridge Distance from Start (ft)** = 23,148 **Relative distances are taken downstream along Novato Creek Thalwig from start point located at 5946435.786 E 2236908.937 N at the beginning of the Stafford Lake Spillway Novato Creek Bridges - CPI Survey US/DS Profile Plots and Cross Sectional Areas
30 30
25 25
20 20
15 15
10 10 Elevation (NAVD88) (ft) Elevation (NAVD88) (ft) 5 5
0 0 0 102030405060708090100110120 0 102030405060708090100110120
Upstream Streambed Elevations Upstream Streambed Elevations
Distance (ft) Upstream Soffit Elevations Distance (ft) Upstream Soffit Elevations
2 2 US Area (ft ) 330 Diablo Ave. and S. Novato Blvd. Bridge Cross Sections DS Area (ft ) 493 Bridge Distance from Start (ft)** = 25,641 **Relative distances are taken downstream along Novato Creek Thalwig from start point located at 5946435.786 E 2236908.937 N at the beginning of the Stafford Lake Spillway Warner Creek Bridges - CPI Survey US/DS Profile Plots and Cross Sectional Areas
30 30
25 25
20 20
15 15
10 10 Elevation (NAVD88) (ft) (NAVD88) Elevation (ft) (NAVD88) Elevation 5 5
0 0 0 102030405060708090100110120 0 102030405060708090100110120
Upstream Streambed Elevations Upstream Streambed Elevations
Distance (ft) Upstream Soffit Elevations Distance (ft) Upstream Soffit Elevations
2 2 US Area (ft ) 378 Tamalpias Ave. Bridge Cross Section Along Warner Creek DS Area (ft ) 352
30 30
25 25
20 20
15 15
10 10 Elevation (NAVD88) (ft) (NAVD88) Elevation (ft) (NAVD88) Elevation 5 5
0 0 0 102030405060708090100110120 0 102030405060708090100110120
Upstream Streambed Elevations Upstream Streambed Elevations
Distance (ft) Upstream Soffit Elevations Distance (ft) Upstream Soffit Elevations
2 2 US Area (ft ) 263 Diablo Ave. Bridge Cross Section Along Warner Creek DS Area (ft ) 251 **Relative distances are taken downstream along Novato Creek Thalwig from start point located at 5946435.786 E 2236908.937 N at the beginning of the Stafford Lake Spillway MEMORANDUM Kamman Hydrology & Engineering, Inc. 7 Mt. Lassen Dr., Ste. B250, San Rafael, CA 94903 Telephone: (415) 491‐9600 Facsimile: (415) 680‐1538 E‐mail: greg@khe‐inc.com
Date: July 22, 2013, rev 2/12/14 To: Novato Watershed Project File From: Corey Hayes, Rachel Kamman Subject: Novato Creek Survey Synthesis: Marin County and Novato Sanitary District Levee Surveys Versus LiDAR
KHE evaluated multiple methodologies to identify trends and determine if there was a significant difference between County and NSD levee surveys and the KI bare earth LiDAR DEM. The methodology which proved most insightful in organizing the scattered data examines the difference between points on a route along the levee. This methodology captured difference in accuracy associated with distance from survey control, and potentially differences between survey sets and data collection events. KHE used the ‘create a route’ and the ‘locate features along route’ tool in ArcGIS 10.1 to extract distances from a given starting point for all levee survey points along Novato Creek. The starting point for the NC route was located at the Stafford Lake Spillway1 and continues in a downstream towards the mouth of NC (Figure 10). The horizontal projection for all data used in this analysis was NAD83 State Plane CA Zone III and the vertical datum was NAVD88‐ft.
By comparing survey point elevations with LiDAR DEM 5‐ft cell values, KHE evaluated the difference between surveys points collected along segments of the lower Novato Creek (NC) levee crest and adjacent baylands and the Kruse LiDAR elevations. Point survey data included NC and BMK crest elevations determined by Marin County (MCFC) and Novato Sanitary District (NSD), and MCFC field surveys on the State Lands parcel north of Bel Marin Keys (BMK).
Figure 1 presents a map of lower Novato creek and the associated Marin County and NSD levee and field surveys points evaluated, as well as the route origin below Stafford Lake. Using distance from an upstream point as a metric to sort the data, KHE generated plots of the elevation and difference between point surveys and LiDAR data (DEM minus Survey). Figure 2 shows MCFC levee crest points evaluated between Diablo Ave and San Pablo Bay. Survey and LiDAR points along the alignment are plotted together in Figure 2a, and the difference between the values is presented in Figures 2b. Based on differences plots (Figure 2b), KHE defined four zones (groups of points) for the MCFC levee crest survey data shown in difference colors on Figure 2. Zone 1, which falls between NWPRR and Hwy37,
1 Stafford Lake start of route: 5,989,977.1’ E 2,228,880.2’ N
Kamman Hydrology & Engineering, Inc. has the smallest error between the LiDAR and the survey points. We hypothesize that this is because there are CalTrans maintains survey control at both the upstream and downstream limit of area. Zones 2‐4 show greatest difference between survey point and LiDAR Data. The reasons for the differences are unknown and may be attributed to differences in survey control, survey event/operator, vegetation cover, DEM terrain averaging over cell area, or DEM filtering technique. These observations suggested that zone 1 surveys exhibited higher amount of accuracy compared to other survey locations, and provides the most consistent basis for comparison of trends between LiDAR and ground survey data. Figure 2c shows an expanded scale plot of the Zone 1 points, as well as the mean of the differences of 0.13‐ft.
The same graphical method were used for difference plots between NSD levee top elevations and LiDAR data (Figures 3), State Lands ground survey points and LiDAR (Figures 4, 4a‐c) and BMKV perimeter levees (Figures 5, 5a‐c). A separate route2 was established for the BMK surveys and new zones (Zones 5 through 8) were defined according to similarities observed by the difference plots. The NSD levee crest comparison, by zone, demonstrates a point deviation trend and magnitude similar to that observed in MCFC data. In contrast, MCFC field survey points (Figures 4, 4a and 4b) show a larger magnitude of deviation, and a stronger tendency for LiDAR to overestimate ground elevations from 0.25 to 0.75 ft. The magnitude of the deviation suggest that some of the survey points may not be representative of ground elevations on this flat but partially developed parcel The comparison between BMK levee crest elevations and LiDAR again shows zones of varying agreement, and it is interesting to note that the observed deviation increases with the survey point density. This suggests that the resolution of the DEM, defined in terms of averaging cell size, may influence the predictive accuracy.
Overall, this analysis indicates that the bare earth LiDAR has varying levels of agreement with ground survey data, and that localized accuracy can be demonstrated in areas where there is well founded and consistent survey ground control.
To test LiDAR accuracy on a more localized scale and relative to project surveys, KHE compared LiDAR based elevations on a 2‐ft averaging area with ground survey utilizing a more rigorous filter to eliminate non‐ground points from the survey data set. Non‐ground points were eliminated based on point attributes or labels included in the original data sets. Overall average point vs DEM variation was on the order of 0.4 ft., with a range of mean deviation values for a data set of from 0.02 to 1.02ft. These reported deviations are generally greater than the DEM’s 0.3 ft. (10 cm) reported accuracy. Presented below are a summary of statistical variation for project and pre‐project data sets.
1. Obercamper (OACE) Cross‐Sections between NWPRR and Hwy 37. OACE completed cross sectional surveys above the wetted channel at 12 locations in the reach. In order to compare the OACE survey elevations with Kruse LiDAR‐based elevations, the LiDAR elevations were
2 BMK start of route: 5,979,755.4’ E 2,218,619.4’ N
Kamman Hydrology & Engineering, Inc. extracted from a DEM (2‐foot grid) generated from the Kruse LiDAR‐derived elevation contours. The statistical results of this comparison (Obercamper v. LiDAR), presented below:
Count: 259 Mean: 1.02 Maximum: 7.11 Minimum: ‐7.81 Range: 14.92 Variance: 2.91 Standard Deviation: 1.71
indicate that, on average, the LiDAR elevations derived by Kruse are a little over 1.0‐foot higher than the Obercamper survey elevations. This data comparison displays a relatively large standard deviation of 1.71‐feet.
2. Novato Sanitary District Survey of fields and levee tops. NSD survey elevations were provided in NGVD29 datum. These elevations were converted to NAVD88 datum by adding 2.69‐feet to each NGVD29 elevation. The statistical results of a direct comparison (NSD v. LiDAR) are as follows:
Count: 923 Mean: 0.02 Maximum: 5.36 Minimum: ‐4.04 Range: 9.40 Variance: 1.05 Standard Deviation: 1.02
The statistical results of a direct comparison (NSD v. LiDAR) points on the levee tops only are as follows: Count: 633 Mean: 0.29 Maximum: 5.36 Minimum: ‐4.04 Range: 9.40 Variance: 1.10 Standard Deviation: 1.05
The statistical results of a direct comparison (NSD v. LiDAR) points in spray fields and Bel Marin Keyes area are as follows: Count: 290 Mean: 0.57 Maximum: 4.99 Minimum: ‐2.73 Range: 7.72 Variance: 0.42 Standard Deviation: 0.65
Kamman Hydrology & Engineering, Inc.
3. Comparison with Survey Control. There are numerous survey bench marks installed historically by the County, City and others in the project bayland area. The following sections provide a statistical tabulation on how well these data agree with the Kruse LiDAR‐ based DEM.
The statistical results of a direct comparison (CPI survey control v. LiDAR) are as follows: Count: 11 Mean: 0.49 Maximum: 2.79 Minimum: ‐0.09 Range: 2.88 Variance: 0.65 Standard Deviation: 0.80
The statistical results of a direct comparison (Marin County survey control v. LiDAR) are as follows: Count: 40 Mean: 0.91 Maximum: 7.07 Minimum: ‐0.64 Range: 7.71 Variance: 2.46 Standard Deviation: 1.57
The statistical results of a direct comparison (City of Novato survey control v. LiDAR) are as follows: Count: 73 Mean: 0.46 Maximum: 8.84 Minimum: ‐3.20 Range: 12.04 Variance: 3.36 Standard Deviation: 1.83
Kamman Hydrology & Engineering, Inc. Figure 1: Map displaying lower NC surveys, NC route and the start of the NC route at the Stafford Lake Spillway (5,989,977.1 E 2,228,880.3 N).
Kamman Hydrology & Engineering, Inc.
Figure 2: County (MCFC) survey data of Novato Creek levees.
Kamman Hydrology & Engineering, Inc. 25.0
Lidar Elevations
County Levee Surveys
20.0
15.0
10.0
Elevation (NAVD88) (ft) Elevation (NAVD88) Zone 1 Zone 2 Zone 3 Zone 4
5.0
0.0 25,000 30,000 35,000 40,000 45,000 50,000 55,000 60,000 65,000
**Distance from start is relative to distance downstream, along the Distance From Starting Point (ft)** See map for locations of zones 1 through 4. Novato Creek Thalweg. from the start of the Stafford Lake Spillway (5,946,435.9 E,2,236,906.8 N) (Nad83, SP, Cal zone III ft) Figure 2a:MCFC levee surveys versus LiDAR DEM elevations. 5.0 Difference Between Kruse Lidar and County Surveys (Lidar- County Surveys) 4.0
3.0
2.0
1.0
0.0
-1.0
-2.0 Zone 1 Zone 2 Zone 3 Zone 4 Elevation Difference (ft) DifferenceElevation (ft) -3.0
-4.0
-5.0 25,000 30,000 35,000 40,000 45,000 50,000 55,000 60,000 65,000
**Distance from start is relative to distance downstream, along the See map for locations of zones 1 through 4. Novato Creek Thalweg. from the start of the Stafford Lake Spillway Distance From Starting Point (ft)** (5,946,435.9 E,2,236,906.8 N) (Nad83, SP, Cal zone III ft) Figure 1b: Difference plot: LiDAR minus MCFC Levee Crest Surveys
Kamman Hydrology & Engineering, Inc. 5.0 Difference Between Kruse Lidar and County Surveys (Lidar-County Surveys) Mean Difference = 0.128 ft 4.0
3.0
2.0
1.0
0.0
-1.0
-2.0 Elevation Difference (ft) (ft) Difference Elevation -3.0
-4.0
-5.0 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000
**Distance from start is relative to distance downstream, along the See map for locations of zones 1 through 4. Novato Creek Thalweg. from the start of the Stafford Lake Spillway Distance From Starting Point (ft)** (5,946,435.9 E,2,236,906.8 N) (Nad83, SP, Cal zone III ft) Figure 2c: Zoomed extent of Zone 1: LiDAR DEM minus MCFC levee crest survey data
Kamman Hydrology & Engineering, Inc.
Figure 3: Novato Sanitary District survey point data for Novato Creek levees.
Kamman Hydrology & Engineering, Inc. 25.0
Lidar Elevations
NSD Levee Surveys
20.0
15.0
10.0
Elevation (NAVD88) (ft) Elevation (NAVD88) Zone 2 Zone 3 Zone 4
5.0
0.0 25,000 30,000 35,000 40,000 45,000 50,000 55,000 60,000 65,000 **Distance from start is relative to distance downstream, along the Distance From Starting Point (ft)** Novato Creek Thalweg. from the start of the Stafford Lake Spillway See map for locations of zones 2 through 4. (5,946,435.9 E,2,236,906.8 N) (Nad83, SP, Cal zone III ft)
Figure 3a: Novato Sanitary District levee surveys versus LiDAR DEM elevations.
5.0 Difference Between Kruse Lidar and NSD Surveys (Lidar-NSD Surveys)
4.0
3.0
2.0
1.0
0.0
-1.0
-2.0 Zone 2 Zone 3 Zone 4 Elevation Difference (ft) (ft) Difference Elevation
-3.0
-4.0
-5.0 25,000 30,000 35,000 40,000 45,000 50,000 55,000 60,000 65,000 **Distance from start is relative to distance downstream, along the See map for locations of zones 2 through 4. Novato Creek Thalweg. from the start of the Stafford Lake Spillway Distance From Starting Point (ft)** (5,946,435.9 E,2,236,906.8 N) (Nad83, SP, Cal zone III ft)
Fig ure 3b: Difference between LiDAR and NSD levee surveys (LiDAR – NSD Survey).
Kamman Hydrology & Engineering, Inc.
Figure 4: MCFC Field surveys State Lands Parcel
Kamman Hydrology & Engineering, Inc.
Figure 4a: DPW field survey elevations versus Kruse LiDAR data.
Figure 4b: Elevation difference plot between DPW field survey elevations and Kruse LiDAR data.
Kamman Hydrology & Engineering, Inc.
Figure 5: Map of BMK levee surveys and levee route (Start of route: 5,979,755.4 E 2,218,619.4 N Fee
Kamman Hydrology & Engineering, Inc.
Figure 5a: BMK levee survey elevations versus Kruse LiDAR.
Figure 5b: Elevation difference plot between BMK levee-top surveys and Kruse LiDAR.
Kamman Hydrology & Engineering, Inc. Appendix B: Appendix B:
Marin Watersheds – Updated GGLP Aerial LiDAR DEMs ArcGIS Metadata Summary Prepared by: Kruse Imaging – revised 7/29/12
OVERVIEW
Output Products:
UTM 10N, NAD83, NAVD88 (EPSG:26910) Native Projection Primary Products - DEM Bare Earth Elevation Data, raster tiles and mosaic - DEM Bare Earth Elevation Hillshade, raster tiles and mosaic - DEM Bare Earth Elevation Data – Filtered, raster mosaic - DEM 0.3048 meter (1 ft) Contours, vector - DEM 1 meter Contours, vector - DEM 5 meter Contours, vector - DEM 10m Contours, vector - GGLP Tile Scheme, vector
California State Plane (EPSG:2872) - DEM Bare Earth Elevation Data, raster mosaic
USA Contiguous Albers Equal Area Conic USGS version (SR-ORG:7301) - DEM Bare Earth Elevation Data, raster mosaic
ArcGIS V10.0 File Geodatabase - Loaded with UTM data sets
Key Words:
Marin, watershed, DEM, LiDAR, GGLP, Golden Gate LiDAR Project
Summary:
The DEM (Digital Elevation Model) represents the bare earth surface elevation at each raster grid point’s geographic coordinates. The DEM provides an important GIS elevation reference layer supporting spatial analysis.
Description:
Data Source: The data source is the 2010 Golden Gate LiDAR Project’s binary LAS V1.2 classified and tiled point cloud files. This data set is in the public domain, is available online from USGS and was created to provide a new ~3m resolution portion of the USGS National Map.
UTM Rasters: The UTM native projection DEM provides an improved 0.5m resolution bare earth surface based upon the reclassification and interpolation of the original point cloud. A hillshade image of this terrain is included to support visualization. Raster DEM (32bit floating point) and hillshade (8bit grayscale) tiles (1.5 X 1.5 km each) cover the watershed and are base upon the original GGLP tile and naming scheme. These tiled products are also provided as full watershed mosaics. A smoothed and reduced version of the original DEM mosaic is provided at 1m resolution to support the creation of vector contours. All raster data files are provided in GeoTiff format that can be used by all GIS software.
UTM Vectors: The GGLP tile scheme and DEM contours are provided as shape files that can be used by all GIS software. DEM contours are created from the smoothed 1m DEM mosaic in 0.3048, 1, 5 and 10 meter versions.
State Plane Raster: The California State Plane DEM is created from the source UTM 0.5m resolution DEM mosaic. The source DEM is reprojected and rescaled to 2 ft resolution to create a GeoTiff raster file. The reprojection rotates the raster leaving nodata areas near the edges of the DEM. The nodata value is provided in the GeoTiff header.
Albers Raster: The USA Continuous Albers Equal Area Conic USGS version DEM is created from the source UTM 0.5m resolution DEM mosaic. The source DEM is reprojected and rescaled to 1.75m resolution to create a GeoTiff raster file. The reprojection rotates the raster leaving nodata areas near the edges of the DEM. The nodata value is provided in the GeoTiff header.
Credits:
- LiDAR reprocessing by Kruse Imaging www.kruseimaging.com Bill Kruse
- LiDAR data set created by the Golden Gate LiDAR Project for ARRA/USGS bss.sfsu.edu/ehines/arra_golden_gate_lidar_project.htm Ellen Hines, Bill Kruse, Eli Waggoner
- Marin County GIS, Watershed, Flood Control and Water Conservation www.marincounty.org Roger Leventhal, Laurie William, Brian Quinn
Use Limitations:
Creation of the improved Marin Watershed DEMs was funded by Marin County. The new products are derived from public domain LiDAR data available from USGS and are intended for use by Marin County and their Contractors. Permission for other uses may be obtained by contacting Marin County.
Contact:
Originator: Bill Kruse, Owner Kruse Imaging 3230 Ross Road Palo Alto, CA 94303 650-843-1124 [email protected] www.kruseimaging.com
Point of Contact: Laurie Williams Marin County Watershed GIS 3501 Civic Center Drive, San Rafael, CA 94903 415-473-4301 [email protected] www.marincounty.org
Alternate Point of Contact: Roger Leventhal Marin County Flood Control & Water Conservation District 3501 Civic Center Drive, Room 304 San Rafael, CA 94903 415-473-3249 [email protected] www.marincounty.org
RESOURCE
Quality:
Horizontal Accuracy:
LiDAR point cloud: Horizontal Accuracy < 1.0 meter RMSE From the SFSU LiDAR Project Report page 7 – October 3, 2011
Vertical Accuracy:
LiDAR point cloud: Vertical Accuracy < 9.25 cm RMSE From the SFSU LiDAR Project Report page 7 – October 3, 2011
Lineage Statement:
The LiDAR derived DEM representing the bare ground surface was created from the original LiDAR source point cloud data using a sequence of classification and interpolation algorithms. The ground classification was recreated from scratch using all non-noise points and the resulting higher density ground points were interpolated using custom tuned scripting of non-commercial software tools outside of ArcGIS. The raster and vector products were prepared in industry standard file formats (GeoTiff, Shape) and also imported into an ArcGIS file geodatabase to provide for broad compatibility and support robust digital archiving. Sufficient detail is provided in the following data source and process sections to document the workflow.
Data Source:
The original source data for the LiDAR derived DEM products is the 2010 Golden Gate LiDAR Project (GGLP) data sets acquired by GeoEye. This acquisition was managed by San Francisco Statue University and funded by an ARRA/USGS grant. The acquisition includes ~30% of Northern San Mateo County, all of San Francisco and Marin Counties and ~10% of Southern Sonoma County. The LiDAR data set is in the public domain and available from USGS (http://lidar.cr.usgs.gov/).
For each Marin watershed, the selection GGLP source tiles were assembled to cover the watershed.
Ground Control:
The GGLP ground control included 47 points, 27 of which are located in Marin County. These points were intentionally coordinated to be consistent with those used for the NOAA Coastal LiDAR acquisition that occurred in the same time frame during the Spring and Summer of 2010.
Data acquisition flights took place from April 23 to July 14, 2010. Specifically, the acquisitions occurred on April 23rd, 24th, 25, 29th, 30th, May 5th, 6th, 7th, 8th, 11th, 12th, 23rd, 29th, 30th, 31st, June 5th, 6th, 7th, 9th, 10th, and July 14th.
Survey point collection Compliance with the accuracy standard was ensured through the collection of GPS ground control during the acquisition of aerial LiDAR and the establishment of a GPS base station operating at the Gnoss Field airport in Novato. In addition to the base station, CORS bases may have been used to supplement the solutions. The following criteria were adhered to during control point collection. 1. Each point was collected during periods of very low (<2) PDOP. 2. No point was collected with a base line greater than 25 miles. 3. Each point was collected at a place of constant slope so as to minimize any errors introduced through LiDAR triangulation. 4. Each point was collected at moderate intensity surfaces so any intensity based anomalies could be avoided.
The base station equipment used was a Trimble R7 with a Zephyr geodetic model 2 antenna. The control points were collected with a Trimble R8 integrated receiver and antenna unit.
Figure 1 - GGLP Ground Control Point Distribution
Figure 2 - GGLP Ground Control Vertical Accuracy Table
Process Description:
Summary:
The LiDAR points are reclassified to aggressively identify those points representing the ground. These points are interpolated to a 0.5m resolution raster floating point elevation grid. The elevation grid is used, after filtering, to generate the vector contours. Each derived product is then imported into ArcCatalog to create a file geodatabase.
The reclassification and interpolation computations can be completed faster using tile based parallelism to process as many tiles at a time as there are available computer cores on the network. This approach was used on the Marin watershed datasets.
Software:
The following list represents the key software tools used for creating the improved LiDAR derived DEM products.
Lastools (build 4/21/12) – LAS format data manipulation utilities lasstools.org mcc-lidar (V1.0) – LAS multi-scale curvature ground classification utility sourceforge.net/projects/mcclidar
GRASS (V6.4) – Open Source GIS Software grass.osgeo.org
GDAL (V1.8) – Open Source Geospatial Data Abstraction Library & utilities www.gdal.org
ArcGIS (V10.0) – Commercial GIS Software www.esri.com
LP360 (V11) – Commercial LiDAR Software www.qcoherent.com
Windows 7 Professional – Commercial Operating System Software www.microsoft.com
Processing Details:
The following steps describe an idealized workflow that represents how the watershed DEM products were created for the Marin watersheds. Some steps have been fully scripted and some remain manual for now. Except for the unique classification and interpolation steps, the rest of the processing and product preparation (data management, mosicing, reprojection, filtering, contouring, etc.) can be accomplished in equivalent ways using the software and procedures of your choice.
UTM DEM processing:
1) Assemble the source data tiles
Use the watershed boundary to identify all of the 1.5 x 1.5 km GGLP source tiles needed to support the watershed. Then add a one tile deep boundary to buffer the processing area. This buffer makes it possible for the processed tiles to mesh seamlessly with adjacent areas that have been processed in the same way.
2) Create buffered subtiles for processing
Use the Lastools utility lastile to create a new set of 500m tiles with a 25m buffer to support the next processing steps. These new tiles have their LL corner UTM coordinates embedded in the new tilename which simplifies batch file scripting. The smaller tiles also overcome some processing software point count limitations and provide finer grain control over the parallel implementation of the batch processing.
3) Classify the ground points
Use mcc-lidar to classify the ground points. This involves the manipulation of the point classes using Lastools and several classification steps using different scale and limit parameters in sequence to obtain the optimized ground points.
Typical batch file script for the ground reclassification performed on each edge buffered 500 x 500 m LAS subtile:
for %%f in (%1) do (
rem filter the high points using existing class 2 surface lasheight -i %%~nf.las -o gnd_0_%%~nf.las -class 2 -drop_above 1.0
rem convert classes all to 0 for new classification using mcc-lidar las2las -i gnd_0_%%~nf.las -o gnd_1_%%~nf.las -keep_class 1 2 4 - change_classification_from_to 1 0 -change_classification_from_to 2 0 - change_classification_from_to 4 0 -last_only
rem remove xyz duplicates lasduplicate -i gnd_1_%%~nf.las -o gnd_2_%%~nf.las -lowest_z
rem rough filter for ground mcc-lidar -t 0.5 -s 1.0 gnd_2_%%~nf.las gnd_3_%%~nf.las
rem convert rough ground class to 0 las2las -i gnd_3_%%~nf.las -o gnd_4_%%~nf.las -keep_class 2 - change_classification_from_to 2 0
rem fine filter for ground mcc-lidar -t 0.03 -s 0.4 gnd_4_%%~nf.las gnd_5_%%~nf.las
rem filter the low points
rem invert z value and convert ground class to 0 again las2las -i gnd_5_%%~nf.las -o gnd_6_%%~nf.las -keep_class 2 - change_classification_from_to 2 0 -scale_z -1.0
rem run mcc-lidar to clean any negative blunders mcc-lidar -t 0.6 -s 0.7 gnd_6_%%~nf.las gnd_7_%%~nf.las
rem reinvert z values and keep existing classifications las2las -i gnd_7_%%~nf.las -o gnd_8_%%~nf.las -scale_z -1.0 -keep_class 2 copy gnd_8_%%~nf.las gnd_%%~nf.las del gnd_*_%%~nf.las )
4) Manually QA and edit the ground points
Use LP360 in ArcMap to interactively review the TIN interpolation display of the ground points and edit/reclassify any unusual high or low points as noise.
5) Interpolate the ground points
Use the GRASS utility v.surf.rst to interpolate the ground points in each tile into a 0.5m raster after they have been imported into GRASS as 3D vector points. A hillshade of the raster DEM tile is created. Non-buffered versions of these rasters are also created for export.
Typical batch file script applied to each edge buffered 500 x 500 m LAS subtile for loading and interpolating the ground points, creating the hillshade and trimming the buffered products to the subtile boundary: rem load and interplate points rem %1 = column_start, %2 = column_stop, %3 = row_start, %4 = row_stop - (all in meters) rem reduced npmin from 450 to 250 for efficiency, added column range rem update npmin to 300 and segmax to 30 to reduce interpolation artifacts but make it slower.... rem Bill Kruse - 7/12/12 for /L %%C in (%1,500,%2) do ( for /L %%A in (%3,500,%4) do ( if exist gnd_b25_%%C_%%A.las ( echo %%A g.region -p region=b25_%%C_%%A g.region -p res=0.25 las2txt -i gnd_b25_%%C_%%A.las -o gnd_b25_%%C_%%A.csv -keep_class 2 -sep comma -parse xyz r.in.xyz in=gnd_b25_%%C_%%A.csv out=gnd_b25_%%C_%%A_pts fs=, x=1 y=2 z=3 type=FCELL method=min --overwrite --verbose rem read gnd points into vector r.to.vect -z -b feature=point in=gnd_b25_%%C_%%A_pts out=gnd_b25_%%C_%%A_pts --overwrite --verbose rem interpolate gnd points g.region -p region=b25_%%C_%%A v.surf.rst layer=0 in=gnd_b25_%%C_%%A_pts elev=gnd_b25_%%C_%%A_rst slope=gnd_b25_%%C_%%A_slp aspect=gnd_b25_%%C_%%A_asp segmax=30 npmin=300 tension=20 smooth=0.1 dmin=0.2 --overwrite --verbose rem create hillshade r.shaded.relief map=gnd_b25_%%C_%%A_rst shadedmap=gnd_b25_%%C_%%A_rst_shade --overwrite --verbose rem create unbuffered tiles g.region -p region=b00_%%C_%%A r.mapcalc gnd_b00_%%C_%%A_rst = gnd_b25_%%C_%%A_rst r.mapcalc gnd_b00_%%C_%%A_rst_shade = gnd_b25_%%C_%%A_rst_shade r.mapcalc gnd_b00_%%C_%%A_slp = gnd_b25_%%C_%%A_slp r.mapcalc gnd_b00_%%C_%%A_asp = gnd_b25_%%C_%%A_asp rem cleanup temporary buffered tiles del gnd_b25_%%C_%%A.csv g.remove -f rast=gnd_b25_%%C_%%A_pts endlocal ) ) )
6) QA the interpolation using the hillshade image
Visually inspect each hillshade tile or the hillshade tile mosaic for any remaining artifacts that require additional noise point editing. Reinterpolate any edited tiles and review the new hillshade results before proceeding.
7) Export the UTM DEM and hillshade GGLP tiles
Use GRASS to patch the processed 500 x 500m non-buffered DEM and hillshade tiles into the original GGLP 1.5 x 1.5 km tile scheme for export. Export them using the GGLP tile naming scheme to Geotiff files.
UTM Contour processing:
1) Filter and rescale the DEM
Low pass filter the 0.5m DEM using a 11x11 circular average filter. Then resample to filtered file to 1m resolution which reduces the contour computation and doesn’t loose any of the filtered information. These steps help reduce contour jaggies (noise) so they are easier to interpret. A small amount of detail is lost with the filtering but it becomes much easier to interpret the resulting vector contours.
2) Create the contours
Create separate vector contours for 0.3048, 1, 5 and 10 meter intervals in GRASS, ArcGIS or using GDAL. Depending upon the software tools used, an additional step may be necessary to remove small closed contours below a reasonable threshold. This and other contouring parameters can be used to reflect the individual requirements of each user.
3) Export the contours
If created with a GIS, export the contours to a shape file for each contour interval. If created by GDAL, the shape file(s) already exist.
ArcGIS Geodatabase:
1) Import each UTM raster and vector product into the ArcCatalog file geodatabase if it was not previously loaded.
DEM Reprojection Processing:
1) Reproject, rescale and export the State Plane DEM mosaic
GDAL can be used to reproject from the UTM DEM mosaic to State Plane. State Plane uses feet so a destination resolution of 2ft was selected since it is close to the source resolution of 0.5m. The projected output raster is aligned with the output coordinates. A sample command line illustrates use of the necessary parameters.
gdalwarp -dstnodata -1e+037 -r bilinear -tap -tr 2.0 2.0 -co "TFW=YES" -t_srs EPSG:2872 Input_watershed_DEM_05m_EPSG26910.tif Output_watershed_DEM_2ft_EPSG2872.tif
If the UTM DEM is already in ArcGIS it can be reprojected and rescaled to State Plane during export to a GeoTiff file.
2) Reproject, rescale and export the non-standard Albers DEM mosaic
GDAL can be used to reproject from the UTM DEM mosaic but does natively support the US Contiguous Albers Equal Area Conic USGS version projection. However, GDAL can use ESRI projection files that can be found in the ArcGIS coordinate systems directory tree. The projected output raster is aligned with output coordinates. A sample command line illustrates use of the necessary parameters and assumes the projection file is in the current directory.
gdalwarp -dstnodata -1e+037 -r bilinear -tap -tr 1.75 1.75 -co "TFW=YES" -t_srs "ESRI::USA Contiguous Albers Equal Area Conic USGS.prj" Input_watershed_DEM_05m_EPSG26910.tif Output_watershed_DEM_175m_SR_ORG6703.tif
If the UTM DEM is already in ArcGIS it can be reprojected and rescaled to this custom Albers projection during export to a GeoTiff file. A rescaled resolution of 1.75 m was selected to keep the file size to keep the files size for the Novato Creek watershed under 600MB for compatibility with the GeoHMS hydrographic modeling software.
Rational:
Evaluation of the GGLP point cloud suggested that additional true ground points existed in the data set that could prove useful for increasing the quality and resolution (or fidelity) of the LiDAR derived DEM.
The LiDAR data vendor (Earth Eye) provided a product that included a base level of ground classification created using industry standard TerraScan software. The Golden Gate LiDAR Project team manually edited the classified ground points to remove obvious noise, buildings and vegetation points. An additional automated software filtering step was applied to reduce the significant amount of near- ground low-vegetation points. The resulting ground points and TIN interpolated surface met the specifications and intent of USGS to produce a 1m resolution DEM that would be rescaled to 3m for the National Map.
To improve upon the products provided to USGS, an approach to ground classification was developed using non-commercial software (lastools, mcc-lidar) to improve upon competing commercial software capabilities under dense vegetation and where the terrain slope changes quickly. The average classified ground point density can be typically increased by more than 1.5 times in most areas while reducing the impact of near-ground vegetation. This supports improved terrain detail in those areas where more points exist.
The resulting reclassification process reevaluates all non-noise points within 1 meter of the original GGLP ground surface including previous unclassified and above ground points. An iterative, multi-scale minimum curvature process is then applied to identify ground points conforming to the assumption that the terrain can be well represented by a minimum curvature surface.
The next step interpolates the irregularly spaced ground points into a raster grid using an approach that adapts to the point density. This spline based interpolation model is conceptually similar to the multi-scale minimum curvature classification described above. As a result, the interpolated DEM closely represents the surface defined by the classified ground points.
The reclassification and interpolation computational cost is significantly higher than competing methods and with current software requires up to 2 CPU core hours of computer time per square kilometer at the 0.5m resolution DEM resolution created from the GGLP data set. However, the cost of CPU time is low, continues to drop and the improved ground classification is useful when the best possible LiDAR derived terrain model is needed.
Classification Software Information:
The ground classification uses a “Multiscale Curavature Algorithm for Classifying Discrete Return LiDAR in Forested Environments” that was originally implemented in AML using Workstation ArcInfo but since ported to C++, open sourced and can be run independent of GIS software. The software uses three scales (0.5x, 1x, 2x) based upon a configurable LiDAR point spacing and a configurable threshold to identify positive local deviations from surrounding points and then iteratively classifies them as non-ground. A script extends algorithm to five scales for positive local deviations to find more points and also three scales of negative local deviation to remove below ground noise that exists in this LiDAR data set. Additional background information can be found using the following links.
sourceforge.net/projects/mcclidar www.cnr.uidaho.edu/watershed/owlx/papers/evans_2007_ieee.pdf www.mdpi.com/2072-4292/3/3/638
Interpolation Software Information:
The interpolation method used is Regularized Spline with Tension (RST) and is implemented in the GRASS module v.surf.rst. The interpolation function is a sum of a trend function and a radial basis function with an explicit form which depends on the choice of the measure of smoothness. Adaptive segmentation allows local processing of very large point data sets and dynamically adjusts to highly variable point densities. For more information see Mitasova and Mitas (1993) and Mitasova et. al. (2005). This interpolation method is considered by some to create raster surfaces with fewer artifacts and more consistent curvature than the fast TIN or the more traditional geostatistical Kriging interpolation algorithms. http://www.osgeo.org/grass http://grass.osgeo.org/grass64/manuals/html64_user/v.surf.rst.html http://skagit.meas.ncsu.edu/~helena/gmslab/papers/MG-I-93.pdf http://skagit.meas.ncsu.edu/~helena/gmslab/papers/IEEEGRSL2005.pdf
Contours: The DEM is filtered before contouring to reduce the DEM noise and produces smoother contours. Contour filtering in the vector domain is more difficult and computationally expensive. The slightly reduced contour fidelity has limited impact on the overall contour accuracy and usefulness since the DEM itself is limited by the sparseness and number of points used for the interpolation.
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
C.3: Novato Creek Control Network Analysis Report (CPI, 2012)
Final Network Analysis Report Novato Creek Watershed Control Network December 28, 2012
Table of Contents:
Project Overview ...... 2 Project Control Requirements ...... 2 Project Monuments ...... 2 Observation Methods ...... 2 Data Processing ...... 3 Post Processing / Office Methods ...... 4 Minimally Constrained Adjustment ...... 5 Fully Constrained Adjustment ...... 6 Horizontal Control ...... 6 Vertical Control ...... 6 Conclusion ...... 7
APPENDIX 1 Network Diagram APPENDIX 2 Final Point Listing APPENDIX 3 Monument Record Forms APPENDIX 4 GPS Loop Closure Report APPENDIX 5 Minimally Constrained Network Report APPENDIX 6 Fully Constrained Network Report APPENDIX 7 Digital Level Loops APPENDIX 8 Final Report – Central Coast Height Modernization Project 2007-2008
Novato Creek Control Network 1 of 8 Final Report Cinquini & Passarino, Inc. CPI # 6518-12
Project Overview
In December 2012 Kamman Hydrology and Engineering retained Cinquini & Passarino, Inc. to perform a survey to establish control that will be used for creek cross sections and bridge surveys along Novato Creek in Marin County. High quality control was needed for the project to ensure the vertical accuracy of the cross sections and subsequent surveys that would be performed. The survey network that we established should be used for all subsequent surveys along the creek corridor.
Project Control Requirements
The vertical component of the Novato Creek Watershed control network was based on the North American Vertical Datum of 1988 (NAVD 88). This datum is consistent with the Federal Emergency Management Agency’s (FEMA) National Flood Insurance Program update to the National Flood Insurance Rate Maps.
The horizontal component of the Novato Creek Watershed control network is based upon the California Coordinate System of 1983, Zone 3 (CCS 83 Zone 3 as referenced by epoch 2008.00). U.S. Survey Feet were utilized for the units on this project.
All Coordinates derived from this survey are grid values and based upon the Central Coast Height Modernization Project 2007-2008(CENCHM2007), as published by the California Spatial Reference Center (CSRC), entitled “Final Coordinates Central Coast Height Modernization Project 2007-2008 (CENCHM2007) California Spatial Reference Center (CSRC)/Towill Inc., March 4, 2009.” The final report for the CENCHM2007 project states the project accuracy as an “Order B GPS network that qualifies as a 2-cm Height Modernization project.”
Project Monuments
The project established 30 control points along the project corridor. Each of these control points is marked by either a Mag Nail with a 2 inch stainless steel washer stamped “Cinquini & Passarino, Control) or a 1/2 inch iron pipe with a red plastic cap marked “CPI Control”. In addition point 32 utilized an existing street well monument, a description of this monument is included in the monument record form. Swing ties and a sketch are included for each of these control points and are included in the monument record forms.
Observation Methods
Static Global Positioning System (GPS) observation methods were used for each session of the survey. Each session consisted of three receivers tracking at least five GPS satellites for a minimum of fifteen minutes. The GPS equipment utilized included four Trimble R8 units with Trimble TSC3 data collectors. Data collectors connected by cables and bluetooth were used to log all of the data. Using a data collector allowed for real time monitoring of the number of satellites and the Precise Dilution of Precision (P DOP). All receivers were set over control
Novato Creek Control Network 2 of 8 Final Report Cinquini & Passarino, Inc. CPI # 6518-12 points on fixed-height tripods of 2.00 meters (6.56 feet). Utilization of fixed height rods ensures that errors for each of these points are minimized and that we are able to provide the highest possible control information.
Data Processing
Field observations were downloaded at the end of each day. The data was processed using a masking angle of 10 degrees. To determine the number of independent base lines which were observed during each session the following formula was used: Number of GPS Receivers – 1 = Number of Independent Baselines A Precise ephemeris was downloaded from the International GPS Service (IGS) to help process the data. Reports for processed baselines were reviewed and analyzed. Baselines that did not process were analyzed for error, corrected and then reprocessed. Cycle slips or signal loss during a GPS session can greatly affect the results of processed baselines or can preclude baselines from processing entirely. Segments of satellite data which contained cycle slips were removed resulting in an average baseline ratio of 49.5, during processing ratios are required to be above 2.0 and a higher number is better. All processing of data was performed using Trimble Business Center, version 2.81. During the baseline processing, vectors were rejected if the following tolerances were not met: Reference Variance > 1.0 Baseline Ratio > 2.0 RMS > 0.05’
The Reference Variance, Ratio and RMS are defined as follows:
Reference Variance: A measure of how well the baseline processor estimates the expected error. Ideally, the Reference Variance should be approximately 1. A Reference Variance greater than 1 indicates that more error was encountered than expected.1
Baseline Ratio: During the initialization process, the receiver determines the integer number of wavelengths between each satellite and the GPS antenna phase center. For a particular set of integers, the ratio is the probability of correctness of the currently-best set of integers to the probability of correctness of the next-best set. A high ratio indicates that the best set of integers is much better than any other set. This gives us confidence that it is correct. The ratio must be above 2. 2
RMS (Root Mean Square): The RMS is used to express the accuracy of a point measurement. RMS is the radius of the error circle within which approximately 70% of position fixes are to be found. It can be expressed in distance units or in wavelength cycles. 3
1 Trimble Postprocessed Surveying Training Guide, Revision E, November 2004, page 301 & 302. 2 Trimble Postprocessed Surveying Training Guide, Revision E, November 2004, page 301 & 302. 3 Trimble Postprocessed Surveying Training Guide, Revision E, November 2004, page 301 & 302.
Novato Creek Control Network 3 of 8 Final Report Cinquini & Passarino, Inc. CPI # 6518-12
Post Processing / Office Methods
All dependent or “trivial” baselines were removed from the network prior to any loop closure analysis or network adjustments. A loop closure is computed in all three coordinate components, northing, easting and elevation, and expressed as a ratio to the total distance of the loop. This internal accuracy can be expressed in parts per million of the total baseline length. Loop closures were reviewed and analyzed in order to help determine the source of any baseline error. The average loop length for the network was 136,089.85 feet with an average error of 0.022 feet horizontally and 0.057 feet vertically, resulting in an average 3.508 parts per million closure. The worst loop, control points 16, 17 and 18 had a closure of 0.061 feet horizontally, 0.181 feet vertically and 77.704 parts per million of which is still considered an acceptable tolerance within the network, the error was due to the short length of the baselines. Below is a description of the loop closure report values ΔHoriz = The calculated horizontal error in the GNSS loop ΔVert = The calculated vertical error in the GNSS loop PPM = The error as calculated by a function of the error parts per million distance, where length of the GNSS loop is divided by the total calculated error. ΔX = The calculated difference in closure along the X axis based on ECEF values ΔY = The calculated difference in closure along the Y axis based on ECEF values ΔZ = The calculated difference in closure along the Z axis based on ECEF values Δ3D = The calculated difference in closure as calculated using ECEF coordinate values for the beginning and ending of the GNSS loop. ECEF coordinates are Earth Centered Earth Fixed coordinates which use the center of the earth for the basis of the coordinate values. Below is a diagram which shows how ECEF coordinates originate.
Novato Creek Control Network 4 of 8 Final Report Cinquini & Passarino, Inc. CPI # 6518-12
Minimally Constrained Adjustment
Once acceptable baseline solutions and loop closures were achieved a minimally constrained adjustment was performed. This adjustment is a quality control check which allows for an analysis of the internal consistency of the network independent of the CCS 83 control network. It is used to evaluate the weighting of adjustments and observation standard errors assigned to the observations which make up the network. In other words, it uses statistical theory and least squares principles to show how well all baselines in a network fit together. CORS Station SVIN was used as the single constraint to the World Geodetic System of 1984 (WGS 84) datum. Since all GPS observations are made on the WGS-84 datum, the adjustment of the observations should be tied closely to the WGS-84 datum. A statistical analysis of the free adjustment revealed the baseline residuals and error ellipses at the 1.96 sigma confidence level fell well within a two centimeter (0.065 feet) accuracy level.
Minimally Constrained Adjustment Statistics Network Reference 1.00 Factor A Priori Scalar 2.24 Degrees of Freedom 189
Network Reference Factor: Is a measure of the magnitude of observational residuals in an adjusted network. As compared to estimated preadjustment observational errors. If observational errors have been accurately estimated, you can expect that, on average, the residual received by each observation will be about the same size as its estimated error. In any adjustment, some observations will receive corrections smaller than their estimated errors, and some will receive larger. It can be demonstrated mathematically that if the estimated errors for an observation have been accurately estimated, the reference factor will be about 1.00.4
A Priori Scalar: Is an estimation of the observational errors in the network. A scalar value is used to apply an estimation of the errors within a network. Applying estimation allows the network reference factor to remain close to 1.00 while a scalar estimates what the errors in the network are. Generally, in an unconstrained adjustment, a scalar of 1.00 indicates a properly weighted network, free from errors. A scalar value between 3.00 and 5.00 is typically considered reasonable. Any scalar with a value between 1.00 and 3.00 is considered very good and means that the network was well planned and the occupations were in good order.
Degrees of Freedom: Is the number of independent observations beyond the minimum required to uniquely define the unknown quantities. The strength of and confidence in the solution increases as the degrees of freedom increase.5
4 Trimble Postprocessed Surveying Training Guide, Revision E, November 2004, page 250. 5 Trimble Postprocessed Surveying Training Guide, Revision E, November 2004, page 249.
Novato Creek Control Network 5 of 8 Final Report Cinquini & Passarino, Inc. CPI # 6518-12
Fully Constrained Adjustment Horizontal Control
Performing a fully constrained adjustment is the last step in creating a GPS network. It transforms the network of observations to the control points in the CCS 83 network. Once network is fixed to the control points in the CCS 83 datum, adjusted coordinates for all other points in the network can be determined.
The horizontal control is based on NAD 83 latitude and longitude values which were used to establish the CCS83, Zone 2, Epoch 2008.00 coordinate values for the local control monuments. This projection and epoch were chosen for the horizontal control due to the recent publication of the previously mentioned CENCHM 2007. To establish the project coordinates on CCS83, the following horizontal control points were held fixed. Horizontal Control SVIN P198 201 (SMART Control) Vertical Control Recently many local agencies have decided to use NAVD 88 to establish and define the vertical component of their projects. This is largely due to the inconsistencies in the vertical benchmarks established and published on NGVD 29. To eliminate the NGVD 29 inconsistencies, NAVD 88 was chosen for the vertical component of this project. This will allow a more consistent vertical model over the project area and vertically relate to the Federal Emergency Management Agency’s flood insurance program. The recently published CENCHM 2007 benchmark values prepared by the CSRC were utilized to aid in establishing a consistent vertical model.
The points established within the network were held as the vertical control for this project. A precise differential level loop was performed using a Trimble® Dini Digital Level as part of the vertical element for this project. The level loop included selected points to ensure that there was no tilt within the project. The included points were, 1, 2, 14, 16, 17, 18, 19 and 20. These control points were not leveled due to the agreed scope of work and the reasoning that by leveling through the selected points we would be able to check and ensure that there was not undue vertical error. The total length of the level loops was 1.68 miles.
The following points were held to control the vertical component of the network in the final adjustment.
Vertical Control SVIN P198 201 (SMART) P194 P193 205 (SMART)
Novato Creek Control Network 6 of 8 Final Report Cinquini & Passarino, Inc. CPI # 6518-12
Fully Constrained Adjustment Statistics Network Reference 2.28 Factor Priori Scalar 1.02 Degrees of Freedom 202
The network was adjusted using a 1.96 sigma or 95% confidence ellipse. A 95% confidence ellipse is centered on the horizontal coordinates of a point; the true position is unknown and can only be estimated through measurements. The 95% confidence ellipse describes the uncertainty or random error in this estimated position, resulting from the random errors in the measurements. There is a 95% probability that, in the absence of other biases or other systematic errors, the true position will fall within the region bounded by the ellipse. The error ellipses produced by the fully constrained adjustment resulted in an average error of 0.027 feet in the semi major axis and 0.021 in the semi minor axis.
The results of the fully constrained adjustment produced a Network Reference Factor of 1.04 and a Priori Scalar of 2.28. The network reference factor indicates how well the observations fit together. If the network errors have been properly estimated a network reference factor should be close to 1.00. If the factor exceeds 1.00 then a larger adjusted scalar value must be applied to the network, provided there were no outliers contained within the adjustment. A scalar of 1.0 would be desirous in a fully constrained adjustment, but is rarely, if ever, achieved. A more realistic scalar value for the network would be 3.0 or less but never above 5.0.
Conclusion
Cinquini & Passarino, Inc. has provided the Kamman Hydrology and Engineering with a local control network consisting of 29 high quality points which will benefit future analysis of the Novato Creek Watershed by providing a quality horizontal and vertical basis for surveys. The accuracy classification of the network at the 95% confidence level is less than or equal to a horizontal and vertical accuracy of 2 centimeters as referenced by the Federal Geographic Data Committee, Part 2, Standards for Geodetic Networks (FDGC-STD-007.2-1998). By incorporating the latest and innovative adjustment techniques as well as guidelines provided by CSRC, CalTrans and the California Geodetic Control Committee, Cinquini & Passarino, Inc. was able to achieve excellent results for the County of Marin
Note: This project is a based on the CCS 83 Zone II projection and all coordinates are expressed as grid values. The appropriate combined grid factor should be applied when converting from grid to ground.
The combined grid factors (combination scale factors) and convergence or mapping angles listed in this report were obtained by exporting the data from the Trimble Business Center version 2.81. The values are a standard item which can be exported for each point. When future work is done using this information the average scale factor for the coordinates being used can be
Novato Creek Control Network 7 of 8 Final Report Cinquini & Passarino, Inc. CPI # 6518-12 applied to the measurements taken between the points if it is requested that the project utilize ground values. For most projects within the watershed it is recommended that all survey work be completed using grid values. If grid values are utilized that the appropriate adjustment programs and routines should be utilized to ensure that the ground survey measurements are reduced to Grid values.
Novato Creek Control Network 8 of 8 Final Report Cinquini & Passarino, Inc. CPI # 6518-12
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
Appendix D: Hydrology and Operations
D.1: Draft Novato Hydrology (HEC HMS) Memorandum (MCFC, 2013) D.2: HMS Model Review Technical Memorandum (WRECO, 2013) D.3: MCFC Pump Operations Guidelines (MCFC, 2010) D.4: BMKCSD Lagoon Operations Guidelines (CLE,2012)
TECHNICAL MEMORANDUM DEPARTMENT OF PUBLIC WORKS FLOOD CONTROL WATERSHED GROUP
DATE: May 24, 2013
TO: Novato Creek Flood Study Project File
FROM: Roger Leventhal, P.E.
RE: ***DRAFT*** Summary of HEC-HMS Hydrologic Analysis of the Novato Creek Watershed
______
Introduction
This memo describes the methodology and results for development of a hydrology model for the Novato Creek Watershed. This work was conducted by Marin County Flood Control Division staff as part of the Novato Creek Hydraulics Study with the primary goal to generate design flow hydrographs for incorporation into the hydraulic models in development by Consultants to the County looking at flood studies and sediment transport in the Lower Novato watershed. However, this work is applicable to all future hydrology and hydraulics studies in the watershed looking at a variety of goals from fish passage to dam break analysis. This model will be updated periodically as new rainfall and stream gage data becomes available and can be made available to consultants and others working in the watershed.
The software chosen for this study is the HEC-HMS (hydrology modeling software) developed by the Corps of Engineers. HMS is a powerful modeling package and is considered a standard hydrology software package that has been used for numerous studies of this type. HEC-HMS is open source software and therefore does not require special licensing agreements and is thus more readily available for consultants or groups interested in watershed studies.
Model Development
General Approach to Hydrologic Modeling
This section briefly describes the process of watershed modeling using specialized software to simulate hydrology. Hydrologic modeling using HEC-HMS or any other program first consists of building a basin model of the specific sub-basin physical characteristics combined with a meteorological model of rainfall across the sub-basins for the storm event of interest across the watershed that accounts for variations in rainfall by locations. The
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model is then calibrated to specific storm events by adjusting the various model calibration parameters (described below) until the model results best match the shape and volume of the measured hydrograph at the calibration point (for Novato creek at the USGS gage site). If possible, its best to use several different storms for model calibration since the calibration parameters may not be unique and one set of parameter adjustments may not match the values required to match the hydrograph from another event. Note that since the focus of the Novato Creek Hydraulics Study is flooding this study focused on event-flow modeling of the higher flood flow events (the specific events are described below).
Therefore as discussed in more detail below, the modeling work in the study produces three sets of model results:
Calibration Runs – Calibration runs are the model runs with parameters adjusted to best-fit the individual design storms being modeled. The various calibration model parameters are adjusted to best match the shape and volume of the event hydrograph.
Validation Runs - The results of the calibration runs typically result in separate model parameters for each storm event. This was particularly true for this Study where one event (the New Years 2006 storm) required model parameters to be set very differently from the two other calibration storms. During validation runs, a single set of model parameters is developed by averaging the calibration parameters and then the model is rerun to assess its goodness of fit to these averaged calibration parameters.
Design Runs - As discussed in more detail below, the validation runs calibrated to the three individual events did not produce enough flow to match the USGS flood frequency curve for Q50 and Q100 flows. Therefore, I adjusted calibration parameters within reason to increase flows to match the flood frequency curve values from the USGS stream gage for the design runs. This was required in order to generate enough flow to match the required flow runoff rates for the Hydraulic Study flood modeling design hydrographs actually used by the Consultants performing the Novato Creek Hydraulics Study modeling.
Important Note for this Study: The goal of this study was to supply hydrographs at required locations for the Hydraulics Study. All of these locations are above the limit of tidal influence and as such not subject to tidal backwater effects. In addition, many of the lower lying subbasins in the flatter gradient wetlands areas as within the hydrology model but the results of this model are not used for these lower gradient sub-basins which are part of the hydraulics models. The Novato Creek Watershed Existing Conditions Hydraulics Study Report should be available by the end of June 2013 and will contain more details on the hydraulics studies (KHE 2013 in-progress).
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Rainfall Gages
This analysis used tipping bucket raingage data from three Marin County ALERT rain gages located in the Novato Creek Watershed, the Kentfield raingage data located in the Corte Madera creek watershed and the ACMdP raingage located in the ACMdP watershed in Mill Valley. The rainfall amounts and intensities are automatically adjusted in the model by distance from the calibration site using the inverse-distance method so that the Novato gage which is the closest to the gage site is the most heavily weighted.
Flow Gage
Flow data were obtained from the USGS stream gage (#11459500) located in the Novato watershed. The period of record for this is 1987 to the present. This is a period of record of approximately 25 years which is a fairly good stream gage period of record for model calibration.
Topographic Data and Basin Model Development
The basin model was built from detailed topographic data for the watershed developed from the recent Golden Gate LiDAR-based Bare Earth Digital Elevation Model (DEM) with additional analysis by Bill Kruse for Marin County to improve the LiDAR classification scheme. This LiDAR model provided the basis for defining routing reaches, slopes, and sub- basin boundaries described in greater detail below. Soil information was available from the US Department of Agriculture internet portal for soil survey data1.
The HEC’s Geospatial Hydrologic Modeling extension HEC–GeoHMS was used to define the basic stream network and catchments for the model, on the basis of the LiDAR dataset mentioned above. The Geospatial Hydrologic Modeling Extension (HEC-GeoHMS) is a software package for use with the ArcGIS system to analyze digital terrain information (e.g., digital elevation model) for delineating drainage sub-basins and quantifying other pertinent data inputs that are to be used in the HEC-Hydrologic Modeling System (HEC- HMS). Specifically, HEC-GeoHMS transforms the drainage paths and watershed boundaries into a hydrologic data structure including the HEC-HMS basin model, physical watershed and stream characteristics, and background map file. The resulting numerical model predicts the watershed response to precipitation. Subsequent development of the basin model was carried out using generic GIS tools to create and edit shapefiles for the network and sub-basins. The GeoHMS work was conducted with assistance from David Ford Consulting Engineers.
1 http://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx
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Figure 1: Novato Creek Watershed HMS Basin Model
Stafford Lake Dam Storage
The North Marin Water District (NMWD) maintains a water supply reservoir in the Upper Novato Creek Watershed called Stafford Lake. For this study, NMWD staff provided two dam operations curves which were combined by County staff into a single stage-storage curve that was entered as a time-series into the model for the various basin runs. For the calibration runs, County staff provided NMWD staff with a list of storm events and times for which NMWD provided the initial water surface elevation for each event. This initial water surface elevation was converted in HMS via the stage-storage curve into a starting lake storage value. As the model routes water into the lake, at approximately elevation 4600 acre-ft of storage, the dam spillway is activated and flow from the dam enters mainstepm Novato Creek below the dam.
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Table 1: Starting Water Surface Elevations at Stafford Lake for Various Storm Events
Summary Table for Model Stafford Initial Conditions Runs date Elev (ft initial storage NGVD) (acre-ft) 12/31/92 182.9 1830 1/4/1993 184.95 2124 12/30/1994 183.4 1902 1/6/1995 186.6 4260 3/7/95 196.15 4300 12/31/97 188.5 2721.5 1/27/1998 196.55 4160 12/15/2002 187.35 2505 12/26/04 185.7 2505 1/1/2005 196.5 4387 12/29/2005 196.4 4390 1/24/2008 184.25 2000 3/18/2011 197.11 4510 11/30/2012 181.5 1629
HMS MODEL PARAMETERS
HEC-HMS conceptualizes the runoff process in a sub-basin in two separate simulation elements, a loss model and a transform model. The loss model has the function of partitioning the input rainfall into the portion that runs off during the modeled event and the portion that infiltrates to the soil (and is lost from the point of view of the model for the runoff event) while the transform model converts the runoff volume into a hydrograph on the basis of watershed characteristics. There are also methods of accounting separately for canopy and surface storage, but these are used primarily in continuous simulation applications and were not utilized for this model application.
Loss Method
For this model we used the initial and constant loss methodology. Under this method there are three parameters, two of them used for model calibration.
Initial Loss – This parameter represents initial model losses due to surface storage or other factors that remove water from the system. This is a calibration parameter that was adjusted during the calibration steps (described below) as needed to match the hydrograph.
Constant Rate (in/hr) – This is the rate at which rainfall infiltrates into the ground and is removed from the modeled system. For this approach, the loss rate is set at a constant value for the storm event under calibration. This is a calibraiotn parameter
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that is adjusted for various storm events.
Percent Impervious (%) – The percent impervious was estimated from County of Marin Map values and was not adjusted during model calibration.
A summary of the loss method parameter used for the final flood frequency curve design runs is shown below. Note that these are the values for the final Design Runs and the values shown vary from the calibration runs:
Figure 2: HMS Loss Method Input Table for Design Runs
Baseflow
The baseflow modeling method used for this study was the Baseflow Recession method which has three adjustment parameters. Baseflow methods are typically used to model the shape of the limbs of the hydrograph both before and after the event of interest. This helps to better match the overall volume of the storm hydrograph.
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The following figure shows the values used for baseflow recession for the Design Runs. As previously noted, values for the calibration and validation runs may be different.
Figure 3: HMS Model Baseflow Method Input Parameters Design Runs
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Transform Method
For this study, the Clark Unit Hydrograph Transform was used to transform rainfall to runoff. The Clark Method uses the time of concentration but also adds a second parameter, the storage coefficient “R”, which is a calibration parameter defining a linear reservoir to account for storage effects. This method was selected for the present study because it allowed for more flexibility during calibration and because modified Clark is the required transform method to do gridded hydrology which may be useful during subsequent studies in the watershed.
The initial time of concentration was calculated using USDA NRCS equations to calculate the time of concentration for all sub-basins in the model (USDA, 2009). The resulting values for the Design Runs are shown below. As expected, peak discharge was found to be insensitive to changes in time of concentration consequently, time of concentration was not considered a useful calibration parameter, and the “R” value of the Clark method was used for primary calibration. Initial values of R were estimated using an equation that develops first cut estimates of R = 5 times time of concentration. Extensive adjustment of this R parameter was made during the calibration process.
Figure 4 shows the input table for the Design Runs (theses are design runs even though the name says validation run) for the Clark Transform method.
Figure 4: Input Table for Clark Method Parameters for Design Runs
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Routing Method
When two subbasins connect, it requires a routing method to transport from the upstream watershed down through the downstream watershed to a junction location. For this analysis, the Muskingham-Cunge routing method was selected since all of the hydrograph input location into the hydraulics model and the location of the USGS calibration point are all upstream of the tidal backwater location. In future watershed studies where the hydrographs are required at location subject to tidal backwater, a different routing method that allows for backwater may be required but was not required for this study.
For this study, the values used for the Design Runs are as follows. These parameters were based upon physical parameters from site surveys and not adjusted during calibration.
Figure 5: Routing Parameters Used for Design Runs Calibration Runs
Calibration runs were performed to best match the shape of the hydrographs for three of the largest storm events in the Novato Creek watershed for which there was both flow and raingage data.
The list of potential calibration events were selected from the largest storms:
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Table 2: Novato Creek HMS Model Calibration Event Candidates
Event Approximate comments peak from USGS Gage (cfs) Jan 1993 1090 Jan-Feb 1995 1826 Used for model calibration Mar-Apr 1995 1650 Jan-Mar 1998 3365 Used for model calibration Dec 2002 1740 Dec 2004-Jan 2005 952 Dec 2005-Jan 2006 3175 Used for model calibration Jan-Feb 2008 1442 Feb-Apr 2011 1218 Dec-2012 1250
*According to the USGS, the flood of record occurred in January 1982. Here, 3,800 cfs was measured at the gage and 1,200 cfs broke out 1 mi upstream and flowed into Warner Creek.
As noted, the three largest design storms of record with raingage data are the Jan-Feb 1995 event, the Jan-Feb1998 event and the New Year’s 2006 storm event were used for model calibration.
In the HMS model, the three simulation runs (combination of basin model and met model) used for calibration are named with just date of the simulation. For example, “Jan-Feb95” is the calibration run for January-February 1995 storm event. Similarly, for the Jan-Mar98 and NY2006 are the model runs for the 1998 and New Year’s 2006 storm events, respectively.
The HMS model was initially calibrated to the USGS Novato stream gage (#11459500) for the following three historic storm events which are around the three largest events that were recorded at the USGS gage for the period of record (1987-present).
January 8th through January 20th, 1995 (recorded 15-minute peak flow at USGS approx. = approximately 1,826 cfs)
January 27th through February 10th, 1998 (recorded 15-minute peak flow at USGS approx. = approximately 3,365 cfs)
December 15th through January 13th, 2006 (New Years 2006 storm event with a recorded 15-minute peak flow at USGS approx. = approximately 3,175 cfs)
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During model calibration the 1995 and 1998 events calibrated somewhat opposite from the 2006 storm event (i.e. the R values in the Clark transform method were adjusted up for 2006 and down for 1995 and 1998).
The results for each calibration run are as follows:
Calibration Run for the Jan-Feb 1995 Event
The modeled hydrograph for the January through February 1995 storm event is shown below. There were issues with matching the recession limb of the hydrograph as well as some of the lesser peaks before and after the main peak on January 9, 1995. Given that he goal of the modeling is to simulate flood flows, the emphasis of this calibration was matching the highest peak flow for the storm event.
Figure 6: Calibration Run for Jan-Feb95 event
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Calibration Run for the January-February 1998 Event
Similarly with the 1995 event, the calibration runs do not match the lesser peaks both before and after the main event but the focus of this study was matching the main event peak.
Figure 7: Model Calibration Run Hydrograph for Jan-Feb1998 Event
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Calibration Run for New Year’s 2006 Event
The leak flow for this event matches the flow at the USGS gage fairly well in terms of both timing and magnitude.
Figure 8: Model Calibration Run Hydrograph for NY2006 Storm Event
Validation Runs
The previously noted, a single set of calibration parameters were developed by weighted average based on percentage of event peak flow from each of the calibration runs to create a single set of parameters that are then rerun n the model and compared ot the calibration runs. These runs are called validation runs.
The three validation run hydrographs are shown below:
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Validation runs for Jan 1995
Figure 9: Model Validation Run Hydrograph for Jan-Feb1995 Event
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Validation run for Jan 1998
Figure 10: Model Validation Run Hydrograph for Jan-Feb1998 Event
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Validation run for NY2006
Figure 11: Model Validation Run Hydrograph for NY2006 Event
Discussion of Validation Run Results
The results of the validation runs are not as good as hoped. The validation runs underestimate the peak flows quite substantially and also do not match the recession limb volumes for each of the storm events. The cause of this is that the NY2006 storm event calibrated quite differently from the 1995 and 1998 storm events. The R value in particular had to be increased from the base R values for the NY2006 storm and decreased for the 1995/1998 events. Some of the other calibration parameters were similar. An excel spreadsheet “Novato_HMS_calibration.xlsx” is available that contains the specific parameter values and adjustments made for the calibration, validation and design runs.
Note that the original validaiton runs are no longer part of the current HMS model files(although these could be easily recreated if required). These original validaiton model runs were updated during the design run process (described in the next section) and not kept as the original files.
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Design Runs
The goal of the design runs is to produce design hydrographs that match the various reutrn interval flow values from the USGS flood frequency curve (Figure X below) for use in the hydraulics models performing the Hydrualics Study for the County.
Flood Frequency Curve
The first step was for County staff to develop an updated flood frequency curve for the Novato creek gage using all data through 2012. For this work we used HEC-SSP the statistical analysis program developed by the Corps of Engineers ansd methods contained in the original Bulliten 17B for analysis of stream gage data. The updated FFC for Novato creek is shown below.
The flood frequency curve provides the flowrates for the larger storm events of interest in this Study. For example, for Novato Creek the 50-year design flow is more accurately referred to the flow with the annual exceedeance probability (AEP) of 2% (=1/50). It is more accurater to refer to this flow as a 2% AEP since this flow has a 2% chance of occuring in any given year. In order to generate a hydrpgraph with this peak flow, a 2% AEP (or 50-year design storm event is required.
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Bulletin 17B Plot for Novato FFC Return Period
1.0 1.1 2 5 10 50 200 1000 10000 100000.0
10000.0
1000.0 Flow (cfs)Flow
100.0
10.0 0.9999 0.999 0.99 0.9 0.5 0.2 0.1 0.02 0.005 0.0001
Probability Computed Curve Expected Probability Curve 5 Percent Confidence Limit 95 Percent Confidence Limit Observed Events (Median plotting positions)
Figure 12: Updated USGS Novato Creek Gage Flood Frequency Curve (FFC)
Development of Design Storm
In order to generate the flows in HMS that match those of the FFC, There is no single methodology for generating design storms and different design storm approaches will generate different hydrographs. The method used in this report is the Balanced Storm or Frequency Storm Approach which uses actual site values for rainfall depths as developed by NOAA and contained in Atlas 14 for California. The design rainfall depths used were derived from NOAA Atlas 14 (Volume 6, Version 2.0) that succeeded the previous NOAA Atlas for California in 2011, for which data are available from the convenient online Precipitation Frequency Data Server (http://hdsc.nws.noaa.gov/hdsc/pfds/).
Under this method, rainfall hyetographs were derived for a 7-day duration storm event using NOAA Atlas 14 values fdor various durations from 5 minutes to 7 days. The rainfall depth values used for the 1% AEP event (i.e. the Q100 storm event) is shown below:
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Table 3: Sample Frequency Storm Distribution Rainfall Depths for Q100 Hyetograph Input into HEC-HMS
Different AEP storms will have different rainfall storm depths. The HMS model files contain the frequency storm values for each design storm event.
Note that we use day 7 day storm for the frequency storm event but because the rainflal is distributed over a longer period of time, the length of rainfall over 24 hours does not make any significant difference in the model results.
Discussion of Design Run Results
Table 4 contains the results of the initial validation runs and the final design runs for the project. As shown, the initial validation runs for the 1% AEP event (the so-called 100-year storm event) produced less then half the flow of the FFC curve generated form actual streamgage data.
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Table 4: Summary of FFC Flows and Comparison to Initial Validartion Run Flows and Final Design Run Flows (all flows in cfs)
Model Runs 10% (10 yr) 2% (50 yr) 1% (100 yr) 0.5% (200 yr) 0.2% (500 yr)
Flows from Updated FFC 2052 3866 4828 5914 7558
Model Results from Initial Validation Runs original NY2006_validation 1673 2581 2103 3392 3958 runs original 1648 2545 2942 3348 3911 FebMar1998_validation_runs original 1636 2509 2882 3286 3822 JanFeb1995_validation_runs
Model Results from Final Design Runs (see below for details)
NY2006_design_runs 2799 4201 4856 5598 6644
JanFeb1998_design_runs 2737 4106 4741 5464 6475
JanFeb1995_design_runs 2816 4232 4895 5644 6691
Design Run Calibration Process
The design runs consisted of a series of model adjustments to produce design hydrographs that match the peak flow values of the USGS stream gage FFC.
Without going into all the details of the adjustment process, the following general steps were taken as part of this final design process:
Increased the intitial dam storage value to immediately begin spilling flow from Stafford Lake into Novato Creek. We believe this assumption to be valid given that for larger flow events such as the 2% AEP (50-year storm event) or the 1% AEP event (the 100-year storm event) it is likely that the dam would be full of water and immediately begin spilling. This adjustment helps increase the modeled flow at the USGS stream gage calibration point.
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We then lowered both the intitial loss rate and the R value to match the 1995 storm event values which were the lowest. This change made a large impact to increase runoff flows.
Finally, we increased the rainfall depths from Atlas 14 by 4% from the median values in the range. Note that a 4% increase in rainfall depths is still well within the range of published values in Atlas 14.
This final adjustment produced the values shown in Table 4 optimized to match the 1% AEP flows (100-yr event) and the results are within a few percent of the FFC values for the 1% AEP event. These runs were provided to the Consultants working on the Novato Creek Hydrualics Study for their modeling work. For the 2% AEP design flow hydrographs, the final step of increasing the rainfall depths should be reduced since for these tuns, the flows generated for this event exceed the FFC values.
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9. REFERENCES
Hines, Ellen PhD, Final Report: Golden Gate LiDAR Project San Francisco State University DRAFT, San Francisco State University, Ca. 2011.
Perica, Sanja et al. NOAA Atlas 14: Precipitation-Frequency Atlas of the United States, Volume 6 Version 2.0 California. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 2011.
Rantz, S. E. et al. Measurement and Computaton of Streamflow: Volume 2. Computation of Discharge. Geological Survey Water-Supply Paper 2175, Washington: 1982.
USACE-HEC. Hydrologic Modeling System HEC-HMS Technical Reference Manual. 2000.
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1243 Alpine Road, Suite 108 Walnut Creek, CA 94596 Phone: 925.941.0017 Fax: 925.941.0018 www.wreco.com
Memorandum Date: August 7, 2013 To: Roger Leventhal, Marin County Department of Public Works From: David Mueller – WRECO Subject: HEC-HMS Model Review
Introduction
The following is a summary of the review of the HEC-HMS model provided by Marin County Department of Public Works Flood Control for the Novato Creek watershed. Precipitation
WRECO obtained localized precipitation data from Weather Underground for stations in and around the Novato Creek watershed. Table 1 lists these stations. Table 1. Rain Gauges Owner Data Name Type/Hardware MAP (in) LAT LONG Data begin Private Davis Vantage Pleasant Valley Pro 2 33.4 38.11 -122.61 2012 Private Electronic Data San Marin Collector 33.3 38.12 -122.6 2011 County Davis Vantage Nicasio Pro 38.1 38.07 -122.7 2005 Big Rock County RAWS 42.1 38.04 -122.57 2007 Novato (Airport) Airport METAR 29.5 38.14 -122.56 2007 Novato (ALERT) County ALERT 30.5 38.12 -122.54 1986 Oregon Scientific Ignatio WM918 42.2 38.06 -122.57 2005 Private Davis Weather Red Hill Monitor II 39.1 37.98 -122.56 2000 Private Electronic Data Bel Marin Keys Collector 27.4 38.08 -122.52 2011 Source: Weather Underground, USGS
Stations at San Marin, Bel Marin Keys, Ignatio, and Pleasant Valley are located within the Novato Creek watershed. Stations at the Marin County DPW Corporation Yard at Nicasio Reservoir (Nicasio), Red Hill, Novato (airport), and Big Rock are outside of the watershed. See Figure 1 for the locations of the rain gauges.
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Figure 1. Rain Gauge Locations
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Storm Selection WRECO looked at three storms: November/December 2012, January 2008, and December/January 2006. We only looked at the 5 day period where the peak runoff occurred during these storms. Precipitation values for these events are presented in Table 2. I also located the corresponding observed flows at the United States Geological Survey (USGS) stream flow gauge in Novato Creek for these storms. Table 2. Precipitation Values Storm Rain Gauge Name 2012 2008 2006 Precip. (in) Precip. (in) Precip. (in) Pleasant Valley 6.23 N/A N/A San Marin 5.81 N/A N/A Nicasio 7.43 5.14 7.6 Big Rock 6.13 N/A N/A Novato (Airport) 5.76 9.61 N/A Novato (ALERT) 5.08 N/A 10.18 Ignatio N/A 5.73 7.91 Red Hill 6.34 5.35 6.66 Bel Marin Keys 6.16 N/A N/A Note: N/A indicated values are not available for this time period. Source: Weather Underground
Basin Modification Before running the storms, the constant loss rates were re-evaluated using the hydrologic soil group for the soil types within each basin. Soil types were found from the Natural Resources Conservation Service (NRCS). Loss rate parameters are presented in Table 3. Table 3. Loss Rate Parameters Hydrologic Soil Group Loss rate (in/hr) A B C D Low 0.30 0.15 0.05 0.00 High 0.45 0.30 0.15 0.05 Source: NRCS
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2012 Storm
The initial results from the 2012 storm showed peaks much higher than observed flows. However, the timing of the peak flows matched very well. We suspected the loss rates in the model were not reflecting the antecedent moisture conditions. Also, the observed flow rates showed almost no flows before and after the storm. So, the baseflow option was turned off in the model, an initial loss parameter was added, and constant loss parameters were revised based on an optimization run. After these adjustments, the simulated results more closely matched the observed flows.
Figure 2. Observed and Calculated Hydrographs at USGS Gauge, 2012 Storm
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Because little to no baseflow was observed during the 2012 event, we believe the assumption that antecedent soil moisture conditions were abnormally dry and losses in the watershed were greater than normal. Because the model is ultimately intended to model peak flow rates for large storm events, the antecedent moisture conditions in the 2012 model are not anticipated to relate to future events. Therefore, revisions made to antecedent soil moisture conditions in the 2012 calibration model were not carried over to subsequent model scenarios.
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2008 Storm
Initial results from the 2008 run indicated higher peak flows than observed data at the USGS gauge. The precipitation at the Novato Airport during this storm was much higher than other values used in the model. We suspected that this high value was an outlier and was adversely influencing the results. Therefore, the index value for the Novato airport gauge was revised upward to 54.12 from the original 29.5 (corresponding to the mean annual precipitation at the gauge location). The revision to this single gauge resulted in a peak flow location and value that closely resembled the observed flow:
Figure 3. Observed and Calculated Hydrographs at USGS Gauge, 2008 Storm
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2006 Storm
The 2006 storm had three distinct peak locations from the observed USGS flow. Initial results from the 2006 storm closely matched the observed peak location, but peak values were slightly higher. An optimization run was performed to revise the constant loss rate parameters so that the peak flows matched more closely. The results of the optimization run are shown in Figure 4:
Figure 4. Observed and Calculated Hydrographs at USGS Gauge, 2006 Storm
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Design Storms An attempt was made to use a frequency storm meteorologic model to “match” the peak flows found in the flood frequency curve (FFC) analysis performed by Marin County. However, when the 100- year National Oceanic and Atmospheric Administration (NOAA) NOAA Atlas 14 Point Precipitation estimates were used, the peak flow rates were significantly lower than the FFC values. Unfortunately, we do not have either local precipitation values or observed flow rates for the 1982 storm, which most closely represents the 100-year (1% annual chance) flow rate in Novato Creek at the USGS gauge. However, the 2006 storm is the 3rd highest peak on record, and we have shown that the model closely represents watershed conditions for this storm. Therefore, 2006 model was used as a template model to develop inflow hydrographs for use in hydraulic modeling. To create design storm input hydrographs, constant loss parameters were revised so that the peak flow rate from calculated hydrographs matched the peak flow rates from the FFC analysis performed by Marin County. Precipitation Unit precipitation hyetographs were developed for each basin based on the amount of rainfall each basin received during the 2006 storm. It was assumed that only the first large peak in the 2006 storm hydrograph would represent the peak flow rate that would match the FFC values. Therefore, HEC- DSS was used to extract the precipitation hydrographs at each basin and truncate the precipitation values contributing to the lesser peaks in the 2006 storm hydrograph. Unit hyetographs were calculated by dividing each precipitation value by the total precipitation from the 2006 storm. Figure 5 shows the unit hyetograph calculated for basin W1250. The duration of the unit precipitation hyetographs was approximately a 48-hour period. WRECO obtained the NOAA Atlas 14 Precipitation Frequency estimates in GIS compatible format for the 10-year, 50-year, and 100-year recurrence intervals for 2-day durations and calculated the point precipitation values at the centroid of each basin for each design storm event. Optimization For each design storm, constant loss rates were optimized so that the peak flow rates from the HMS model matched the FFC peak flow values obtained by Marin County at the USGS gauge. First, the model was run using the parameters calculated in the calibration effort. The peak flow rate determined in the HMS model at the USGS basin was divided by the FFC peak flow rate to determine an adjustment factor to apply to the HMS hydrograph. HEC-DSS was used to adjust the hydrograph so that the peak flow rate matched the FFC value. This adjusted hydrograph was then used in an optimization trial to revise the constant loss rate parameters for upstream basins contributing to the resultant hydrograph at the USGS gauge. Thus, inflow hydrographs were created based on the 2006 event that match the peak flow rates from the FFC analysis. Table 4 shows the adjustment factors applied to input hydrographs for the 1%, 2%, and 10% design storms.
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Figure 5. Unit Hyetograph Calculated for Basin W1250.
Design Storm Method 10% 2% 1% FFC Peak Flow 2052 3866 4828 Rate (cfs) HEC-HMS Peak 2457.2 3847 4469.2 Flow Rate (cfs) Adjustment Factor 0.835 1.005 1.080 (FFC/HEC-HMS) Table 4. Adjustment Factor Applied to HMS Hydrographs
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Figure 6. 1% Annual Chance Hydrograph at USGS Gauge using 2006 Base Storm
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Figure 7. 2% Annual Chance Hydrograph at USGS Gauge using 2006 Base Storm
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Figure 8. 10% Annual Chance Hydrograph at USGS Gauge using 2006 Base Storm
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Discussion
It is recommended that the hydraulic models for Novato Creek watershed studies carry forward the 1%, 2%, and 10% hydrographs generated from the design storm analysis based on the 2006 storm presented in this memo. We believe the parameters calculated by Marin County and modified slightly in the subject analysis adequately represent the watershed as evidenced by the results of the three historical storms reviewed in this memo. Conversely, the frequency storm method is not appropriate for the subject analysis. When NOAA Atlas 14 Point Precipitation values are input into the frequency storm model the resulting peak flow rates do not correspond well to the FFC values calculated by Marin County Department of Public Works Flood Control. Observation of the three historical storms shows the initial parameters calculated by Marin County Department of Public Works Flood Control adequately represent the watershed characteristics for these three storms when more localized precipitation data is used in the analysis. Rather than modify the basin parameters to force the frequency model to match results of the FFC analysis, we suggest the 2006 model adequately represents the watershed, and modified hydrographs from the design storm analysis should be used in subsequent analyses.
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Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
Appendix E:
Nave Gardens SWMM Urban Drainage System Model (WRECO, 2014)
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Draft Memorandum Date: May 7, 2014 To: Roger Leventhal From: David Mueller, Kazuya Tsurushita – WRECO Project: Novato Watershed Hydraulic Study Subject: SWMM Urban Drainage System Model for Nave Gardens
Introduction
This memorandum summarizes the hydrologic and hydraulic analysis of the Nave Gardens neighborhood in the City of Novato, California using the Environmental Protection Agency’s (EPA) storm water management model (SWMM). The SWMM model was developed as part of the Novato Watershed Hydraulic Study (Project).
Background The Nave Gardens neighborhood is located along South Novato Boulevard at Novato Creek. The boundaries extend from Nave Court at the north (at the Novato Fair Shopping Center) to Lauren Avenue along the south, with South Novato Boulevard along the west and Novato Creek in the east (see Figure 1). Warner Creek, Arroyo Avichi, and Novato Creek converge at the eastern end of the Nave Gardens neighborhood. The Baccaglio Basin, which provides stormwater detention and routing of peak flows from Arroyo Avichi to Scottsdale Pond, is located south of Lauren Avenue.
Storm drain systems within Nave Gardens drain to Novato Creek, Warner Creek, Arroyo Avichi, or Baccaglio Basin. Novato Creek is under the influence of tides at Nave Gardens. Tidal backwater limits the conveyance capacity of the storm drain systems within Nave Gardens, and contributes to flood hazards throughout the watershed.
Localized flooding within Nave Gardens is due to backwater ponding of storm drains and direct creek overflows from Novato Creek, Warner Creek, and Arroyo Avichi. Overflows from Novato Creek flow into Warner Creek in the vicinity of Novato Boulevard and Simmons Lane. Warner Creek overflows its banks beginning near Tamalpais Avenue. Significant street flows occur in Center Road due to overflows from Warner Creek. Flows from Center Road eventually drain into the Nave Gardens via Garden Court.
The goal of this task is to build a model of the existing system, determine the existing level of protection, and identify potential improvements to reduce flooding impacts.
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Figure 1. Location Map: Nave Gardens neighborhood Source Google Earth
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SWMM Model Software
PCSWMM is one of the spatial decision support systems for the EPA’s SWMM. In addition to the features available in the EPA’s SWMM, PCSWMM can perform hydraulic analysis of two- dimensional surface flows with one-dimensional hydraulic features. This software was used to develop the hydraulic model to simultaneously simulate the overland flow (2D), storm drain flow (1D), and stream flow (1D) in the Nave Gardens neighborhood during design storm events.
Model Development
Pipe Network Figure 2 presents the pipe network within Nave Gardens. Pipe locations and sizes were determined based on GIS information provided by the City of Novato. Marin County Flood Control (MCFC) provided the invert, and rim elevations based on for the pipe network within Nave Gardens. Subcatchment WRECO delineated subcatchment areas, widths, and determined imperviousness within the Nave Gardens Neighborhood based on aerial imagery obtained from USGS. Subbasin slopes were assumed to be 1%. The roughness coefficient for pervious surfaces was assumed to be 0.15, corresponding to short grass. The roughness coefficient for impervious surfaces was assumed to be 0.015. Depression storages were assumed to be 0.05 inches for impervious surfaces and 0.15 inches for pervious surfaces. The percent of impervious area without depression storage was assumed to be 25%, as recommended in the SWMM user manual. The Curve Number model was chosen as the infiltration model. Soils Review of United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) soils data showed no known hydrologic soil group (HSG) information for the Nave Gardens area. The average lot size in the Nave Gardens neighborhood is approximately 1/5 acre. Therefore, a curve number of 87 corresponding to residential districts with an average lot size of 1/4 acre and HSG D was chosen for each subcatchment.
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Figure 2. Pipe network
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Design Storms WRECO developed precipitation distributions corresponding to the 10-year, 50-year, and 100-year design storms during our review of the HEC-HMS modeling performed by the County. The Nave Gardens area is mostly within Basin W1030 in the HEC-HMS model. Therefore, precipitation distributions for the 10-year, 50-year, and 100-year SWMM model were referenced from precipitation distributions for Basin W1030 in the HEC-HMS model. Figure 3 presents the precipitation distributions referenced from the HEC-HMS model.
Figure 3. Precipitation distributions for design storms
Stream Flows The Nave Gardens neighborhood is bounded by Novato Creek, Warner Creek, and Arroyo Avichi. Overflows from these creeks both locally and upstream in the watershed are known to cause flooding in Nave Gardens. These creek flows were included in the SWMM model. The creek geometry used to create the FLO-2D model1 was imported into the SWMM model. Cross sections were truncated at the channel banks, so that inside the channel banks a one-dimensional model was implemented.
1 A FLO-2D Model was developed as a part of the Novato Creek Watershed Study to determine the magnitude of flows escaping and co-mingling between Novato Creek and Warner Creek.
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The watershed digital elevation model (DEM) (KRUSE, 2013) was modified to “flatten” the DEM at the top of the channel banks so that the channel section below the top of banks was not represented in the DEM. This was done to eliminate a “double counting” of the conveyance and storage within the channel in the 2D portion of the model that is already represented in the 1D model. Therefore, flows within the channel banks are accounted for in the 1D model, and flows that overflow the banks are accounted for in the 2D model. Figure 4 shows a representation of the 1D-2D interaction for a typical channel section.
Figure 4. Typical section showing 1D-2D interaction
Hydrographs from the HEC-HMS model were input into junctions in the SWMM model at the same locations as the FLO-2D model. Figure 5 shows input hydrograph locations used in the SWMM model.
Downstream Boundary Conditions Tidal downstream boundary conditions for the three design storm events were provided by Kamman Hydrology & Engineering, Inc. (KHE) based on a hydrodynamic model of the Novato baylands. The tidal boundary conditions represent a spring high tide event concurrent with the storm peak, which incorporates tidal backwater in Novato Creek. This approach provides a worst case scenario for determining flood hazards for each design storm event.
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Figure 5. Input hydrograph locations
2D Model Development WRECO used the watershed DEM (KRUSE, 2013) to model creek overbank flows and street flows within the Nave Gardens neighborhood. We created bounding boxes for areas of unique roughness to develop the 2D grid of links and nodes throughout the watershed where overbank flows are present. The 2D grid is based on point elevations extracted from the DEM to create a 2D nodes layer. The grid resolution was varied depending on land use. We varied the roughness coefficients in each bounding box depending on the surface. Roughness coefficients ranged from 0.016 for roadways to 0.08 for high density developments. The 2D grid was attached to the 1D creek model at each junction. Figure 6 presents bounding boxes and roughness coefficients used to create the 2D grid.
West Side Drainage After initial simulations identified a significant volume of overbank flow between Nave Gardens and Scottsdale Pond, WRECO consulted with MCFC and expanded the domain of the model to include Scottsdale Pond and the downstream outfall from Scottsdale Pond to Lynwood Basin. We used AutoCAD Civil 3D to modify the DEM, incorporating as-built drawings of Scottsdale Pond provided by the County. The outfall structure encompasses four 12-foot by 10-foot culverts under US 101. Lynwood Basin is represented by a storage area downstream of the 12-foot by 10-foot culverts. The starting water surface elevation of Lynwood basin at the downstream end of the west side drainage portion of the SWMM model was set at 1 foot NAVD based on MCFC guidelines (P. Balderama, 2012).
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Figure 6. Bounding boxes and roughness coefficients
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Calibration with HEC-HMS, FLO-2D WRECO calibrated the HEC-HMS model based on a November 2012 storm event, using input hydrographs from the 2012 storm, and observed water levels in Novato Creek. The FLO-2D model was also calibrated using the 2012 storm event. WRECO compared the results of the SWMM model with the FLO-2D output at common points in the watershed for the 2012 storm event. Figure 7 shows the resulting hydrographs for Novato Creek just downstream of Diablo Boulevard. Based on visual observation of figure 7, the location and magnitude of peak flow rates developed for the SWMM model correlate with the peak flow rates from the calibrated FLO-2D model.
Figure 7. Calibration results at Diablo Boulevard
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Existing Condition Results
10-Year Storm Event Figure 8 presents the maximum depth of flooding within the Nave Gardens neighborhood in the 10- year storm event. Local pipe networks are overwhelmed because of the high starting water surface elevation in adjacent creeks, due to Novato Creek being under tidal influence. The streets within Nave Gardens flood in a 2 hour period, resulting in ponding depths of up to 2 feet in Joan Avenue and Nave Court. These streets remain flooded for approximately 3 hours after the peak flooding depth occurs.
Figure 8. Maximum depth, 10-year event
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50-year Storm Event Figure 9 presents the maximum depth of flooding within the Nave Gardens neighborhood in the 50- year storm event. As in the 10-year event, local pipe networks are overwhelmed because of the high starting water surface elevation in adjacent creeks, due to Novato Creek being under tidal influence.
Significant storm flows can be anticipated in the streets in and around the Nave Gardens neighborhood during the 50-year storm event. Peak flows of up to 700 cfs were calculated in Center Road in the 50-year storm event. This large magnitude of flow is caused by overflows from Novato Creek spilling over to Warner Creek, and overflows from Warner Creek eventually spilling onto Center Road. Storm flows in Center Road spill onto Nave Gardens at Garden Court, resulting in increased flooding throughout Nave Gardens.
The streets within Nave Gardens flood in a 3 hour period, resulting in ponding depths of up to 2.5 feet in Joan Avenue and Nave Court. The additional flows from offsite streets cause the Nave Gardens area to remain flooded with at least 0.5 feet of water for approximately 8 hours after the peak flooding depth occurs.
Figure 10 presents overbank flow depths, street flows in Novato Boulevard adjacent to Nave Gardens, and cross sections showing the maximum depth of flow in Novato Boulevard and in Nave Gardens in the 50-year event. Figure 11 presents the corresponding South Novato Boulevard profile, and Figure 12 presents the corresponding Nave Gardens profile.
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Figure 9. Nave Gardens flow depths, 50-year event
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Figure 10. Nave Gardens flow depths and street flows, 50-year event
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Figure 11. South Novato Boulevard Max. Water Surface profile, 50-year storm event
Figure 12. Nave Gardens profile, 50-year storm event
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Detention Basins Table 1 presents the hydraulic characteristics of the Baccaglio Basin, Scottsdale Pond, and Lynwood basin in the 50-year storm event.
Table 1. Detention Basin Hydraulic Characteristics Developed from SWMM Model Detention Basin Maximum depth (feet) Maximum inflow (cfs) Maximum outflow (cfs) Baccaglio Basin 5.2 221 64 Scottsdale Pond 7.2 526 413 Lynwood Basin 4.3 413 NA
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100-year Storm Event Figure 12 presents the maximum depth in the Nave Gardens area in the 100-year flood event. The 100-year storm event results in the same general distribution of flooding as the 50- year storm event. The maximum flooded depths in the 100-year event are approximately 10-20% larger than the depths in the 50-year event. The Nave Gardens is inundated with at least 0.5 feet of water for 9 hours.
Figure 13. Maximum depth, 100-year storm event
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Potential Solutions
Tidal gates Novato Creek and Warner Creek are under tidal influence in the vicinity of Nave Gardens. Based on discussions with MCFC, the storm drain pipes systems in Nave Gardens do not have functioning tidal gates. To evaluate the potential benefit of adding flood gates, we repeated the 10-year simulation assuming tidal gates prevented Novato Creek backflows into the Nave Gardens storm drain. Therefore, a proposed conditions scenario was run for the 10-year event assuming tidal gates for pipes in Nave Gardens.
The results of the model show flooding did not decrease with the addition of tidal gates. Figures 13 and 14 show the maximum water surface elevation in the storm drain systems in Nave Court and Joan Avenue remain approximately the same with and without flap gates at the outlet of each storm drain system.
Local Pump Stations Pump Stations in Joan Avenue, Lauren Avenue, and Nave Court may reduce flood risks within Nave Gardens. The model shows local gravity pipe systems are not able to convey flows against the tidally influenced Novato Creek. West Side Drainage Improvements The culvert at Arroyo Avichi may be improved to facilitate additional flows conveyed past the Arroyo Avichi overflow weir. Also, drainage improvements adjacent to the Baccaglio Basin may allow for additional storm flows to be conveyed through to the Scottsdale Pond/Lynwood Basin detention basins.
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Figure 14. Nave Court storm drain, with and without flap gates
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Figure 15. Joan Avenue storm drain, with and without flap gates
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Interceptor Lines/Pump Station in Center Road/South Novato Boulevard A series of interceptor storm drain lines in South Novato Boulevard and/or Center Road to route street flows to Baccaglio Basin may be a potential solution to reduce flooding in Nave Gardens. Figure 16 shows a profile view of Center Road extracted from the global project DEM. Figure 17 shows the profile location and water surface elevations in Center Road in the 50-year storm event. A low point in Figure 18 includes flow depths in Center Road in the 50-year storm event. The flood depths in Center Road are up to 3 feet deep in the 50-year event, because of a localized low point in Center Road at Plaza Amapola.
If sufficient room is available within the right-of-way of Novato Boulevard and Center Road, additional storm drain lines may help reduce flood risks for Nave Gardens. Pump stations may also be necessary to reduce flood risks due to the high magnitude of flows in these streets.
Figure 16. Center Road profile
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Figure 17. Center Road profile, street flows, and 50-year WSE
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Figure 18. Center Road flow depths, street flows, 50-year storm event
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Remove overflow weir at Arroyo Avichi The overflow weir at Arroyo Avichi approximately 400 feet upstream of Novato Boulevard directs runoff below the weir through three 36-inch culverts under Novato Boulevard to Arroyo Avichi. The weir is approximately 55 feet long. Flows over the weir are directed to Baccaglio Basin. Novato Creek tidal backwater severely limits the amount of flow conveyed by Arroyo Avichi during high tide events. Modifications to this structure would alter the volume of water directed to Scottsdale Pond, which is not under tidal influence. See Figure 19 for a photo and aerial view of the Arroyo Avichi overflow weir. Flows to Scottsdale Pond follow a gravity outfall to Lynwood basin where MCFC pumps discharge to Novato Creek adjacent to the North Deer Island Basin.
Figure 19. Arroyo Avichi overflow weir overview Source: WRECO
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Photo 1. Arroyo Avichi culvert inlet structure Source: WRECO
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Photo 2. Arroyo Avichi overflow weir Source: WRECO
Utilize Scottsdale/Lynwood Pond Capacity Based on review of as-built drawings, Scottsdale Pond has an existing capacity of approximately 13.9 acre-feet. A stage-storage curve was provided by MCFC that showed Lynwood Basin has a capacity of over 4,000 acre-feet.
The PCSWMM model was revised for the IRWMP alternative analysis to link the Scottsdale Pond with the Lynwood Basin. These two detention basins are connected by four 12-foot by 10-foot RCB culverts. The outlet elevation of the culverts is -5.9 feet NAVD88. The SWMM model assumed a starting water surface elevation in the Lynwood Basin of 2 feet NAVD88.
Figure 20 shows the flow rates in and out of the Scottsdale Basin in a 50-year storm event. The SWMM model showed the peak flow rate into Scottsdale Pond was reduced from 550 cfs to 414 cfs. The total volume into Scottsdale Pond in the 50-year storm event was 552 acre-feet. The volume out of Scottsdale Pond was 493 acre-feet. Therefore, the amount of flow entering Lynwood Basin in the
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50-year storm event is less than 15% of the capacity of Lynwood Basin. If an interceptor line from Center Road and/or Novato Boulevard to Baccaglio Basin were to be constructed, Lynwood Basin would have capacity to store this additional runoff. Further analysis is required to determine if the pipe network in the vicinity of Scottsdale Pond is sufficient to convey this additional runoff from Baccaglio Basin through to Lynwood Basin. For example, a pump station may be required in Baccaglio Basin to convey flows south, and a pump station may be required in Scottsdale Pond to convey flows through the four culverts to Lynwood Basin.
Figure 20. 50-year Input and Output Hydrographs at Scottsdale Pond/Lynwood Basin.
Watershed Alternatives Other alternatives to reduce flood risks for Nave Gardens include increased storage from Stafford Lake, detention storage basins within the watershed, diversion of flows from Novato Creek to Rush Creek through Grant Avenue, and reversing the flow path of the storm drain system from Novato Creek to Machin Avenue. These alternatives within the watershed would reduce peak flows in the creeks adjacent to Nave Gardens and thus reduce flood risks in Nave Gardens.
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Alternatives Analysis The SWMM model was modified to include several alternatives discussed in this memo. An ideal pump was added to the low point in Center Road at Plaza Amapola. This pump will simulate the required volume and peak flow rate needed to reduce flood risks at this low point. An additional 8- foot x 4-foot RCB culvert was added under South Novato Boulevard. A trapezoidal channel was added in the Baccaglio Basin from the RCB under South Novato the 48-inch Baccaglio Basin outlet pipe. The Baccaglio Basin outlet pipe was upsized to a dual 60-inch RCP.
The results of the analysis show a reduction in flood depths in Center Road of up to 2 feet, and flood depths in Nave Gardens of up to 1 foot. Flood depths in Baccaglio Basin increased up to 0.5 feet due to the additional flows from Center Road. Figure 21 shows the reduction in flooding from these alternatives in the 50-year event. The ideal pump in Center Road conveyed 67 acre-feet of flow, with a peak flow rate of 190 cfs.
Figure 21. Flooding Reduction in 50-year event due to Alternatives
| Civil Engineering | Water Resources | Environmental Compliance | Geotechnical Engineering | 27 Storm Water Management Model (SWMM)
The EPA Storm Water Management Model (SWMM) is a hydraulic and water quality simulations, and viewing the re- dynamic rainfall-runoff simulation model used for single event sults in a variety of formats. These include color-coded drain- or long-term (continuous) simulation of runoff quantity and age area maps, time series graphs and tables, profile plots, and quality from primarily urban areas. The runoff component of statistical frequency analyses. SWMM operates on a collection of subcatchment areas on which rain falls and runoff is generated. The routing portion This latest re-write of EPA SWMM was produced by the of SWMM transports this runoff through a conveyance sys- Water Supply and Water Resources Division of the U.S. En- tem of pipes, channels, storage/treatment devices, pumps, and vironmental Protection Agency’s National Risk Management regulators. SWMM tracks the quantity and quality of runoff Research Laboratory with assistance from the consulting firm generated within each subcatchment, and the flow rate, flow of CDM, Inc. depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. SWMM was first developed back in 1971 and has undergone Visit the several major upgrades since then. The current edition, Ver- Watershed & Water Quality Modeling sion 5, is a complete re-write of the previous release. Run- Technical Support Center Website ning under Windows, EPA SWMM 5 provides an integrated http://www.epa.gov/athens/wwqtsc/index.html environment for editing drainage area input data, running
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
Appendix F:
Pacheco Pond HECRAS Analysis (KHE 2014)
MEMORANDUM Kamman Hydrology & Engineering, Inc. 7 Mt. Lassen Dr., Ste. B250, San Rafael, CA 94903 Telephone: (415) 491-9600 Facsimile: (415) 680-1538 E-mail: [email protected]
Date: June 1, 2014 To: R. Leventhal, Marin County Dept. of Public Works From: Stephanie Lapine, KHE Inc
Subject: Novato Creek HEC-RAS Modeling
A previously developed HEC‐RAS model (NHI, 2005) of the lower reach of Novato Creek was modified by KHE to simulate hydraulic exchange between Pacheco Pond and Novato Creek for selected design storm scenarios. During both high tide and storm events, Pacheco Pond outflows are limited by the water levels in Novato creek. As a result, storm inflows to Pacheco Pond from Arroyo San Jose and Pacheco Creek are stored during peak tide and storm events, and discharged to Novato creek during flood and ebb tide recession periods.
Model elements retained for the PP analysis include: Novato Creek channel from downstream of HWY 37 bayward to a cross sections near the midpoint of the CABMK restoration site; Pacheco Pond (PP), two adjacent bayland parcels (Leveroni and CABMK) and the levees connecting them; and levees connecting Novato Creek to the three basins (Figure 5.6). The PP outlet structure consists of a levee (10.6‐ft. crest elevation) and six (6) culverts (invert elevations of 1.73‐ft (NAVD88)) which are equipped with flap gates that prevent flow of Novato Creek into Pacheco Pond. All simulations assume a starting water surface elevation in PP of 2.6 ft., and that Leveroni and CABMK parcels are dry. MCFC’s rainfall runoff (HEC HMS) model defined inflows to PP for each storm event. Design simulations conservatively assume current high tide at the peak of the storm which limits outflows from Pacheco pond.
An iterative approach, similar to that described above for FLO‐2D, is used to integrate EFDC and HECRAS models for each simulation scenario. A starting PP model simulation is conducted assuming a mean tide water surface elevation at the outflow boundary. HEC‐ RAS generates a time series of inflows from PP to Novato Creek. EFDC is run with the starting PP inflow time series, and used to generate boundary conditions (upstream inflows and downstream water surface elevations on Novato) for HECRAS. KHE iterates, running simulations and passing results between the models until they converge on a single solution. Model convergence typically required 2‐3 iterations. The Q10/50/100 simulation results presented in the attached figures illustrates model outputs which include inflows
Kamman Hydrology & Engineering, Inc. 1 and outflows to Pacheco pond, pond and adjacent basin water levels, and mass movement across levees.
The dashed lines represent stage elevations in PP, and Novato Creek downstream of Hwy 37 and near the inlet to San Pablo Bay. Solid lines represent inflows and outflows from PP to NC and the adjacent BMK and Private basins. The dotted lines represent typical elevations of levee crests in between parcels.
Pacheco pond discharges (purple line) are prevented during high flows due to backwater from main‐ stem Novato Creek, and resume during period of low tide during flood recession. In the absence of a NC outfall during large storm events, Pacheco pond overtops the adjacent BMK and Leveroni levees. As a result, Pacheco pond discharges to Novato Creek are larger in Q10 events then during larger design storms. Sea Level rise decrease the volume and duration of PP discharges.
Figure 1. Modified HEC‐RAS model geometry.
The three storage areas are characterized by elevation‐volume curves developed from existing site topography. No water is ponded in any storage area at the start of each model run.
Kamman Hydrology & Engineering, Inc. 2
KHE developed flow files containing boundary condition hydrographs at three locations. The EFDC Model was used to define the Novato Creek upstream inflow hydrograph, which extended over a four‐day time period for Q10, Q50, and Q100 storm events. Corresponding four day Q10, Q50, and Q100 inflows entered into Pacheco Pond. EFDC was also used to predict the tidal water level at the most downstream cross section of the model. Modeling analysis utilized a stage‐hydrograph for a spring tidal cycle. Additionally, two sea level rise scenarios were run, assuming 16‐inch and 36‐inch sea level rise as the downstream boundary condition.
An outlet structure connects Pacheco Pond to Novato Creek. Each of six rectangular culverts within the outlet structure is 4‐ft x 4‐ft, with an invert elevation of 1.73‐ft (NAVD88). Manning’s n is 0.013. The culverts are equipped with flap gates that prevent backflow of Novato Creek into Pacheco Pond whenever stage in Novato Creek is greater than stage in Pacheco Pond. The crest elevation of the levee containing the culverts is 10.6‐ ft.
Three additional levees are represented in the model. The south bank of Novato Creek connects to Leveroni Basin. The Leveroni Levee has an average elevation of 9 feet and ranges from 8.5 to 11.5 feet. The levee which connects Leveroni Basin with Pacheco Pond has an average elevation of 8.25 feet and an elevation range of 8 to 10 feet. The levee connecting Pacheco Pond with Bel Marin Keys storage area has an average elevation of 8 feet and an elevation range of 7 to 12.5 feet.
Flow in the downstream direction for Novato Creek is considered positive (ebb tide); reversal of flow in Novato Creek is negative (flood tide). Flow entering Pacheco Pond is positive, while all flow exiting Pacheco Pond is negative.
KHE ran the models for a four day period, and the results were plotted into a series of figures. The following model plans were run:
1a. Q10 inflow Novato Creek, Q10 inflow Pacheco Pond, Q10 stage at downstream boundary 1b. Q10 inflow Novato Creek, Q10 inflow Pacheco Pond, Q10 + 16‐inch SLR stage at downstream boundary 1c. Q10 inflow Novato Creek, Q10 inflow Pacheco Pond, Q10 + 36‐inch SLR stage at downstream boundary
2a. Q50 inflow Novato Creek, Q50 inflow Pacheco Pond, Q50 stage at downstream boundary 2b. Q50 inflow Novato Creek, Q50 inflow Pacheco Pond, Q50 + 16‐inch SLR stage at downstream boundary 2c. Q50 inflow Novato Creek, Q50 inflow Pacheco Pond, Q50 + 36‐inch SLR stage at downstream boundary
Kamman Hydrology & Engineering, Inc. 3
3a. Q100 inflow Novato Creek, Q100 inflow Pacheco Pond, Q100 stage at downstream boundary 3b. Q100 inflow Novato Creek, Q100 inflow Pacheco Pond, Q100 + 16‐inch SLR stage at downstream boundary 3c. Q100 inflow Novato Creek, Q100 inflow Pacheco Pond, Q100 + 36‐inch SLR stage at downstream boundary
For each model run, the following results were plotted into a series of 9 graphs for each plan described above (Figure pages 1‐9):
1. Inflow to Pacheco Pond (cfs) 2. Inflow to Novato Creek at Hwy 37 (cfs) 3. Novato Creek flow upstream of culverts from Pacheco Pond (cfs) 4. Novato Creek flow downstream of culverts from Pacheco Pond (cfs) 5. Flow through culverts from Pacheco Pond to Novato Creek (cfs) {this flow is only negative, due to flap gates on the culverts} 6. Flow over Pacheco Pond ‐ Leveroni Basin levee (cfs) {flow is negative when leaving Pacheco Pond, positive when flowing into Pacheco Pond} 7. Flow over Pacheco Pond ‐ Bel Marin Keys Basin levee (cfs) {flow is negative when leaving Pacheco Pond} 8. Stage in Pacheco Pond (ft NAVD88) 9. Stage in Novato Creek (ft NAVD88) 10. Stage of Tide at tidal boundary location (downstream Novato Creek, ft NAVD88) 11. Average elevation of Pacheco Pond – Leveroni Basin (8.25‐ft NAVD88) 12. Average elevation of Pacheco Pond – Bel Marin Keys Basin Levee (8‐ft NAVD88) 13. Average elevation of Novato Creek – Leveroni Basin Levee (9‐ft NAVD88)
Four Cumulative Volume plots were developed for Q10, Q50, and Q100 flows, and for these same flows with sea level rise scenarios (Figure pages 10‐13). Existing conditions is illustrated on page 10, sea level rise of 16‐inches is illustrated on page 11, and sea level rise of 36‐inches is illustrated on page 12. Figure page 13 has all time series on the same graph.
Pacheco Pond Culvert Flow into Novato Creek is illustrated on Figure page 14 for Q10, Q50, and Q100 flows, and for these same flows with 16 and 36 inch sea level rise conditions.
Observations:
Q10 flow with no sea level rise tops the Pacheco Pond – Leveroni Basin levee and causes Pacheco Pond to spill into Leveroni Basin.
Q10 flow with sea level rise of 36 inches causes Leveroni Basin to spill into Pacheco Pond.
Kamman Hydrology & Engineering, Inc. 4 Q50 flows with any sea level rise causes Leveroni Basin to spill into Pacheco Pond.
All Q100 flows, existing conditions as well as sea level rise conditions, cause Leveroni Basin to spill into Pacheco Pond.
Flow from Pacheco Pond entering Novato Creek decrease with increasing storm magnitude and sea level rise.
Cumulative volume in a basin is somewhat misleading because negative flow out of Pacheco Pond is cancelled by positive flow back into Pacheco Pond over the Leveroni Basin‐Pacheco Pond levee.
With higher water surface elevations in Pacheco Pond, the outflow rate through the culverts could potentially increase. However, water is generally unable to exit via the culverts due to longer periods of increased water surface elevation within Novato Creek. Higher water surfaces in Novato Creek is caused both from increased flow generated by the storm event, and from higher tides taking longer to ebb and water elevations not staying below the threshold level1 for a longer period of time due to the shorter duration of low water due to sea level rise.
1 In this case, threshold level is when Pacheco Pond water surface elevation is higher than Novato Creek water surface elevation, creating negative flow (i.e. flow exiting from culvert into Pacheco Pond).
Kamman Hydrology & Engineering, Inc. 5 Q10 V2 NEGATIVE FLOW VALUES INDICATES FLOW MOVING UPSTREAM IN NOVATO CREEK OR IS FLOWING OUT FROM PACHECO POND 7000 12 Inflow HMS Pacheco Pond (cfs)
6000 10 Inflow at Hwy 37 (cfs)
5000 8 Flow Upstream of Culverts in Novato Creek (cfs) Flow Downstream of 4000 6 Culverts in Novato Creek (cfs) Flow Through Culverts from Pacheco to 3000 4 Novato Creek (cfs)
Stage Flow to Leveroni Basin (cfs)
2000 2 (ft Flow to Bel Marin Keys
(cfs) NAVD88) Basin (cfs)
Flow 1000 0 Stage Pacheco Pond (ft NAVD88)
0 ‐2 Stage Novato Creek (ft NAVD88)
Stage Tide (ft NAVD88) ‐1000 ‐4
Leveroni Basin Levee (ft ‐2000 ‐6 NAVD88)
BMK Basin Levee (ft NAVD88) ‐3000 ‐8 Novato‐Leveroni Basin Levee (ft NAVD88) ‐4000 ‐10 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Q10 + SEA LEVEL RISE OF 16‐INCHES NEGATIVE FLOW VALUES INDICATES FLOW MOVING UPSTREAM IN NOVATO CREEK OR IS FLOWING OUT FROM PACHECO POND 7000 12 Inflow HMS Pacheco Pond (cfs)
6000 10 Inflow at Hwy 37 (cfs)
Flow Upstream of 5000 8 Culverts in Novato Creek (cfs) Flow Downstream of 4000 6 Culverts in Novato Creek (cfs) Flow Through Culverts from Pacheco to 3000 4 Novato Creek (cfs) Flow to Leveroni Basin Stage (cfs)
2000 2 (ft Flow to Bel Marin Keys
(cfs) NAVD88) Basin (cfs)
Flow 1000 0 Stage Pacheco Pond (ft NAVD88)
0 ‐2 Stage Novato Creek (ft NAVD88)
Stage Tide (ft NAVD88) ‐1000 ‐4
Leveroni Basin Levee (ft ‐2000 ‐6 NAVD88)
BMK Basin Levee (ft NAVD88) ‐3000 ‐8 Novato Creek‐Leveroni Basin Levee (ft ‐4000 ‐10 NAVD88) 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Q10 + SEA LEVEL RISE OF 36‐INCHES NEGATIVE FLOW VALUES INDICATES FLOW MOVING UPSTREAM IN NOVATO CREEK OR IS FLOWING OUT FROM PACHECO POND 7000 12 Inflow HMS Pacheco Pond (cfs)
6000 10 Inflow at Hwy 37 (cfs)
Flow Upstream of 5000 8 Culverts in Novato Creek (cfs) Flow Downstream of 4000 6 Culverts in Novato Creek (cfs) Flow Through Culverts from Pacheco to Novato 3000 4 Creek (cfs) Flow to Leveroni Basin Stage (cfs)
2000 2 (ft Flow to Bel Marin Keys
(cfs) NAVD88) Basin (cfs)
Flow 1000 0 Stage Pacheco Pond (ft NAVD88)
0 ‐2 Stage Novato Creek (ft NAVD88)
Stage Tide (ft NAVD88) ‐1000 ‐4
Leveroni Basin Levee (ft ‐2000 ‐6 NAVD88) BMK Basin Levee (ft NAVD88) ‐3000 ‐8 Novoto Creek‐Leveroni Levee (ft NAVD88) ‐4000 ‐10 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Q50 V2 NEGATIVE FLOW VALUES INDICATES FLOW MOVING UPSTREAM IN NOVATO CREEK OR IS FLOWING OUT FROM PACHECO POND 7000 12 Inflow HMS Pacheco Pond (cfs)
6000 10 Inflow at Hwy 37 (cfs)
Flow Upstream of 5000 8 Culverts in Novato Creek (cfs) Flow Downstream of 4000 6 Culverts in Novato Creek (cfs) Flow Through Culverts from Pacheco to Novato 3000 4 Creek (cfs)
Stage Flow to Leveroni Basin (cfs)
2000 2 (ft Flow to Bel Marin Keys
(cfs) NAVD88) Basin (cfs)
Flow 1000 0 Stage Pacheco Pond (ft NAVD88)
0 ‐2 Stage Novato Creek (ft NAVD88)
Stage Tide (ft NAVD88) ‐1000 ‐4
Leveroni Basin Levee ‐2000 ‐6 (NAVD88)
BMK Basin Levee (ft NAVD88) ‐3000 ‐8 Novato Creek‐Leveroni Basin Levee (ft NAVD88) ‐4000 ‐10 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Q50 + SEA LEVEL RISE OF 16‐INCHES NEGATIVE FLOW VALUES INDICATES FLOW MOVING UPSTREAM IN NOVATO CREEK OR IS FLOWING OUT FROM PACHECO POND 7000 12 Inflow HMS Pacheco Pond (cfs)
6000 10 Inflow at Hwy 37 (cfs)
Flow Upstream of 5000 8 Culverts in Novato Creek (cfs) Flow Downstream of 4000 6 Culverts in Novato Creek (cfs) Flow Through Culverts from Pacheco to Novato 3000 4 Creek (cfs)
Stage Flow to Leveroni Basin (cfs)
2000 2 (ft Flow to Bel Marin Keys
(cfs) NAVD88) Basin (cfs)
Flow 1000 0 Stage Pacheco Pond (ft NAVD88)
0 ‐2 Stage Novato Creek (ft NAVD88)
Stage Tide (ft NAVD88) ‐1000 ‐4
Leveroni Basin Levee (ft ‐2000 ‐6 NAVD88)
BMK Basin Levee (ft NAVD88) ‐3000 ‐8 Novato Creek‐Leveroni Basin Levee (ft NAVD88) ‐4000 ‐10 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Q50 + SEA LEVEL RISE OF 36‐INCHES NEGATIVE FLOW VALUES INDICATES FLOW MOVING UPSTREAM IN NOVATO CREEK OR IS FLOWING OUT FROM PACHECO POND 7000 12 Inflow HMS Pacheco Pond (cfs)
6000 10 Inflow at Hwy 37 (cfs)
Flow Upstream of 5000 8 Culverts in Novato Creek (cfs) Flow Downstream of 4000 6 Culverts in Novato Creek (cfs) Flow Through Culverts from Pacheco to Novato 3000 4 Creek (cfs) Flow to Leveroni Basin Stage (cfs)
2000 2 (ft Flow to Bel Marin Keys
(cfs) NAVD88) Basin (cfs)
Flow 1000 0 Stage Pacheco Pond (ft NAVD88)
0 ‐2 Stage Novato Creek (ft NAVD88)
Stage Tide (ft NAVD88) ‐1000 ‐4
Leveroni Basin Levee (ft ‐2000 ‐6 NAVD88) BMK Basin Levee (ft NAVD88) ‐3000 ‐8 Novato Creek‐Leveroni Basin Levee (ft NAVD88) ‐4000 ‐10 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Q100 V2 NEGATIVE FLOW VALUES INDICATES FLOW MOVING UPSTREAM IN NOVATO CREEK OR IS FLOWING OUT FROM PACHECO POND 7000 12 Inflow HMS Pacheco Pond (cfs)
6000 10 Inflow at Hwy 37 (cfs)
Flow Upstream of Culverts 5000 8 in Novato Creek (cfs)
Flow Downstream of 4000 6 Culverts in Novato Creek (cfs) Flow Through Culverts from Pacheco to Novato 3000 4 Creek (cfs) Flow to Leveroni Basin Stage (cfs)
2000 2 (ft Flow to Bel Marin Keys
(cfs) NAVD88) Basin (cfs)
Flow 1000 0 Stage Pacheco Pond (ft NAVD88)
0 ‐2 Stage Novato Creek (ft NAVD88)
Stage Tide (ft NAVD88) ‐1000 ‐4
Leveroni Basin Levee (ft ‐2000 ‐6 NAVD88)
BMK Basin Levee (ft NAVD88) ‐3000 ‐8 Novato Creek‐Leveroni Basin Levee (ft NAVD88) ‐4000 ‐10 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Q100 + SEA LEVEL RISE OF 16‐INCHES NEGATIVE FLOW VALUES INDICATES FLOW MOVING UPSTREAM IN NOVATO CREEK OR IS FLOWING OUT FROM PACHECO POND 7000 12 Inflow HMS Pacheco Pond (cfs)
6000 10 Inflow at Hwy 37 (cfs)
Flow Upstream of Culverts 5000 8 in Novato Creek (cfs)
Flow Downstream of 4000 6 Culverts in Novato Creek (cfs) Flow Through Culverts from Pacheco to Novato 3000 4 Creek (cfs)
Stage Flow to Leveroni Basin (cfs)
2000 2 (ft Flow to Bel Marin Keys
(cfs) NAVD88) Basin (cfs)
Flow 1000 0 Stage Pacheco Pond (ft NAVD88)
0 ‐2 Stage Novato Creek (ft NAVD88)
Stage Tide (ft NAVD88) ‐1000 ‐4
Leveroni Basin Levee (ft ‐2000 ‐6 NAVD88)
BMK Basin Levee (ft NAVD88) ‐3000 ‐8 Novato Creek‐Leveroni Basin Levee (ft NAVD88) ‐4000 ‐10 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Q100 + SEA LEVEL RISE OF 36‐INCHES NEGATIVE FLOW VALUES INDICATES FLOW MOVING UPSTREAM IN NOVATO CREEK OR IS FLOWING OUT FROM PACHECO POND 7000 12 Inflow HMS Pacheco Pond (cfs)
6000 10 Inflow at Hwy 37 (cfs)
Flow Upstream of Culverts 5000 8 in Novato Creek (cfs)
Flow Downstream of 4000 6 Culverts in Novato Creek (cfs) Flow Through Culverts from Pacheco to Novato 3000 4 Creek (cfs)
Stage Flow to Leveroni Basin (cfs)
2000 2 (ft Flow to Bel Marin Keys
(cfs) NAVD88) Basin (cfs)
Flow 1000 0 Stage Pacheco Pond (ft NAVD88)
0 ‐2 Stage Novato Creek (ft NAVD88)
Stage Tide (ft NAVD88) ‐1000 ‐4
Leveroni Basin Levee (ft ‐2000 ‐6 NAVD88)
BMK Basin Levee (NAVD88) ‐3000 ‐8 Novato Creek‐Leveroni Basin Levee (ft NAVD88) ‐4000 ‐10 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Cumulative Volume (ac‐ft) 700,000
Q10 Vol. Exiting Culverts
600,000 Q10 Vol. into Leveroni
Q10 Vol. into BMK 500,000
Q50 Vol. Exiting Culverts ft) ‐ (ac
400,000 Q50 Vol. into Leveroni Voume
300,000 Q50 Vol into BMK Cumulative
Q100 Vol. Exiting Culverts 200,000
Q100 Vol. into Leveroni
100,000 Q100 Vol into BMK
0 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Cumulative Volume + Sea Level Rise 16‐inches (ac‐ft) 700,000
Q10SLR16 Vol. Exiting Culverts
600,000 Q10SLR16 Vol. into Leveroni
Q10SLR16 Vol. into BMK 500,000
Q50SLR16 Vol. Exiting Culverts ft) ‐ (ac
400,000 Q50SLR16 Vol. into Leveroni Voume
300,000 Q50SLR16 Vol. into BMK Cumulative
Q100SLR16 Vol. Exiting Culverts 200,000
Q100SLR16 Vol. into Leveroni
100,000 Q100SLR16 Vol. into BMK
0 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Cumulative Volume + Sea Level Rise 36‐inches (ac‐ft) 700,000
Q10SLR36 Vol. Exiting Culverts
600,000 Q10SLR36 Vol. into Leveroni
Q10SLR36 Vol. into BMK 500,000
Q50SLR36 Vol. Exiting Culverts ft) ‐ (ac
400,000 Q50SLR36 Vol. into Leveroni Voume
300,000 Q50SLR36 Vol. into BMK Cumulative
Q100SLR36 Vol. Exiting Culverts 200,000
Q100SLR36 Vol. into Leveroni
100,000 Q100SLR36 Vol. into BMK
0 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Cumulative Volume (ac‐ft) 700,000 Q10 Vol. Exiting Culverts Q10 Vol. into Leveroni Q10 Vol. into BMK Q50 Vol. Exiting Culverts 600,000 Q50 Vol. into Leveroni Q50 Vol into BMK Q100 Vol. Exiting Culverts Q100 Vol. into Leveroni 500,000 Q100 Vol into BMK Q10SLR16 Vol. Exiting Culverts Q10SLR16 Vol. into Leveroni ft) ‐ Q10SLR16 Vol. into BMK (ac 400,000 Q50SLR16 Vol. Exiting Culverts Q50SLR16 Vol. Exiting Leveroni
Voume Q50SLR16 Vol. Exiting BMK Q100SLR16 Vol. Exiting Culverts 300,000 Q100SLR16 Vol. into Leveroni Q100SLR16 Vol. into BMK Cumulative Q10SLR36 Vol. Exiting Culverts Q10SLR36 Vol. into Leveroni 200,000 Q10SLR36 Vol. into BMK Q50SLR36 Vol. Exiting Culverts Q50SLR36 Vol. into Leveroni Q50SLR36 Vol. into BMK Q100SLR36 Vol. Exiting Culverts 100,000 Q100SLR36 Vol. into Leveroni Q100SLR36 Vol. into BMK
0 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes) Pacheco Pond Culvert Flow into Novato Creek (cfs) Negative Flow = Flow Exiting Pacheco Pond 200 Q10 Flow (cfs) Q10 SLR16 Flow Q10 SLR36 Flow Q50 Flow Q50 SLR16 Flow Q50 SLR36 Flow Q100 Flow Q100 SLR16 Q100 SLR36 Flow 100
0
‐100 (cfs)
Flow
‐200 Culvert
‐300
‐400
‐500 0 720 1440 2160 2880 3600 4320 5040 5760 Time (minutes)
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02
Appendix G:
Sediment Loading and Transport Characteristics
a) Cross Sectional Profile b) Flow Rating Curve
c) Grain Size Distribution d) Bedload Transport Curve
FIGURE 1: Bedload transport estimates (Parker, 1990 and Wilcox‐Crowe, 2003) for Novato Creek below Stafford Lake and above confluence with Bowman Canyon (WP#705). Appendix G, Page‐1 a) Cross Sectional Profile b) Flow Rating Curve
c) Grain Size Distribution d) Bedload Transport Curve
FIGURE 2: Bedload transport estimates (Parker, 1990 and Wilcox‐Crowe, 2003) for Novato Creek below confluence with Bowman Canyon (WP#613). Appendix G, Page‐2 a) Cross Sectional Profile b) Flow Rating Curve
c) Grain Size Distribution d) Bedload Transport Curve
FIGURE 3: Bedload transport estimates (Parker, 1990 and Wilcox‐Crowe, 2003) for Novato Creek above Sutro Ave. (WP#507). Appendix G, Page‐3 a) Cross Sectional Profile b) Flow Rating Curve
c) Grain Size Distribution d) Bedload Transport Curve
FIGURE 4: Bedload transport estimates (Parker, 1990 and Wilcox‐Crowe, 2003) for Novato Creek at Miwock Park (WP#321). Appendix G, Page‐4 a) Cross Sectional Profile b) Flow Rating Curve
c) Grain Size Distribution d) Bedload Transport Curve
FIGURE 5: Bedload transport estimates (Parker, 1990 and Wilcox‐Crowe, 2003) for Novato Creek at Olivia Court (WP#314). Appendix G, Page‐5 a) Cross Sectional Profile b) Flow Rating Curve
c) Grain Size Distribution d) Bedload Transport Curve
FIGURE 6: Bedload transport estimates (Parker, 1990 and Wilcox‐Crowe, 2003) for Novato Creek upstream of Simmons Lane (WP#309). Appendix G, Page‐6 a) Cross Sectional Profile b) Flow Rating Curve
c) Grain Size Distribution d) Bedload Transport Curve
FIGURE 7: Bedload transport estimates (Parker, 1990 and Wilcox‐Crowe, 2003) for Novato Creek downstream of Simmons Lane (WP#209). Appendix G, Page‐7 a) Cross Sectional Profile b) Flow Rating Curve
c) Grain Size Distribution d) Bedload Transport Curve
FIGURE 8: Bedload transport estimates (Parker, 1990 and Wilcox‐Crowe, 2003) for Novato Creek upstream of Grant Avenue (WP#205). Appendix G, Page‐8 a) Cross Sectional Profile b) Flow Rating Curve
c) Grain Size Distribution d) Bedload Transport Curve
FIGURE 9: Bedload transport estimates (Parker, 1990 and Wilcox‐Crowe, 2003) for Novato Creek upstream of 7th Avenue (WP#199). Appendix G, Page‐9 a) Cross Sectional Profile b) Flow Rating Curve
c) Grain Size Distribution d) Bedload Transport Curve
FIGURE 10: Bedload transport estimates (Parker, 1990 and Wilcox‐Crowe, 2003) for Novato Creek downstream of 7th Avenue (WP#195). Appendix G, Page‐10 a) Cross Sectional Profile b) Flow Rating Curve
c) Grain Size Distribution d) Bedload Transport Curve
FI GURE 11: Bedload transport estimates (Parker, 1990 and Wilcox‐Crowe, 2003) for Novato Creek upstream of Diablo Blvd. (WP#191). Appendix G, Page‐11 a) Upper Reach b) Middle Reach
c) Lower Reach d) All Reaches
FIGURE 12: Bedload transport estimates (Parker, 1990) for Novato Creek reaches. Appendix G, Page‐12 a) Upper Reach b) Middle Reach
c) Lower Reach d) All Reaches
FIGURE 13: Bedload transport estimates (Wilcox‐Crowe, 2003) for Novato Creek reaches. Appendix G, Page‐13
Draft Hydraulic Assessment of Existing Conditions: Novato Creek Watershed Project DRAFT 2014_06_02