ASSESSING SEDIMENT TRANSPORT IN ALAMO RIVER
A Project
Presented to the
Faculty of
California State Polytechnic University, Pomona
In Partial Fulfillment
Of the Requirements for the Degree
Master of Science
In
Civil Engineering
By
Wensheng Hu
2021
SIGNATURE PAGE
PROJECT: ASSESSING SEDIMENT TRANSPORT IN ALAMO RIVER
AUTHOR: Wensheng Hu
DATE SUBMITTED: Spring 2021
Department of Civil Engineering
Dr. Omar Mora, PhD Project Committee Chair Assistant Professor of Geospatial Engineering
Dr. Monica Palomo, PhD Professor of Civil Engineering
ii
ACKNOWLEDGEMENTS
I would like to express my deepest appreciation to my advisors Dr. Monica
Palomo and Dr. Omar Mora for continuous support. Their guidance and patience helped me in all the time of research and writing of this paper and increased my interest in water resources engineering. My goal to fully commit into water resources engineering for my future career was sparked by their immense knowledge, enthusiasm, and encouragement when doing this research with them.
My sincere gratitude also goes to Dr. Seema Shah-Fairbank who always provides valuable and insightful advice when I was taking a river engineering course with her.
This study cannot be completed without the support from Imperial Valley Water District and Federal Fish and Wildlife Service for providing observed data and historic information on Alamo River. Moreover, I would like to thank the civil engineering senior project team
2020 from Cal Poly Pomona for conducting the geotechnical field surveying and generously providing the soil distribution analysis data to this study.
Last but not least, I would like to thank my family for constantly encouraging me to pursuit more advanced educational degree and knowledge in civil engineering and supporting me, spiritually and financially, throughout my life.
iii
ABSTRACT
The Salton Sea, the largest lake in California, is facing serious environmental challenges, such as hydrological imbalance and water impairment due to high salinity and high inorganic and organic chemical concentration. In addition, water losses have also degraded water quality and negatively affected the ecological system of the surrounding area. The size of the watershed and the uncountable non-point source and point source pollution have made it exceedingly difficult for authorities to monitor, manage and support decision making to remediate the impaired Salton Sea watershed. To support local authorities with the management of data for decision making, this study has developed a hydraulic and sediment transport model to evaluate sediment load of the
Alamo River, which is a tributary on the south rim of the Salton Sea and contributes with significant amounts of sediment with contaminants to the Salton Sea. The 3D surface model of the river reach was developed using the USGS LiDAR data acquired in 2010.
The model boundary locates at downstream of the Alamo River and has a channel length of 9.45 km. The model development included the collection of regional geospatial and meteorological data, and its processing with ArcGIS, and Civil 3D. A 1D HEC-RAS simulation was run to model the hydraulic and sediment transport simulation in the
Alamo Reach in the study period of 2010 to 2020. The steady flow analysis showed that the Alamo Reach is a subcritical flow channel. The sediment transport simulation results revealed that the stream had an average channel invert decrease by 3.88 feet and approximately a total of 230,300 tons (25,000 tons per year) sediment discharge to the
Salton Sea from 2010 to 2020.
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TABLE OF CONTENTS
Signature Page...... ii
Acknowledgements ...... iii
Abstract ...... iv
List Of Tables...... vi
List Of Figures ...... vii
Section 1 Introduction ...... 1
Section 2 Study Area...... 3
Section 3 Methodolgy ...... 4
Section 4 Results and Discussion...... 22
Section 5 Conclusion and Recommendation ...... 32
References ...... 34
Appendix A ...... 39
Appendix B ...... 40
v
LIST OF TABLES
Table 1. Data Used in the Model Simulations and Data Rource ...... 6
Table 2. The Rosgen Stream Classification for the Alamo River...... 12
Table 3. Various Elevations and Gauge Height Data Recorded at the Garst Road at Different Period...... 15
Table 4. A Set of Mximum Depth in the Sediment Model Simulation Input ...... 21
Table 5. Average Value Computed in Steady Flow Simulation ...... 23
Table 6. Simlated Result of Invert Elevation Changes in Average and Cumulative
Sediment Mass Capacity by Eight Different Sediment Input-Combinations ...... 28
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LIST OF FIGURES
Figure 1. Salton Sea and Alamo Reach Location ...... 3
Figure 2.Workflow Diagram in this Study...... 4
Figure 3. The Garst Road and Stream Flows Contributing to the Alamo Reach...... 17
Figure 4. Plot of Measured Water Surface Elevation and Simulated Water Surface
Elevation by Selecting Optimal Manning’s Value ...... 18
Figure 5.The Steady Flow Simulation Profiles of the Alamo Reach ...... 23
Figure 6. Simulated Results by Eight Different Sediment Input-Combinations...... 25
Figure 7.The Plot of Coefficient of Correlation for Eight Different Sediment Input-
Combinations by Simulated Results and Observed Data on the Station 0+00 to Station
65+00 ...... 29
Figure 8. The Invert Elevation Profile of the Alamo Reach in 2010,2012,2016,2018 and
2020...... 31
vii
SECTION 1: INTRODUCTION
In river engineering, sediment transport is the movement of solid particles and can be classified into three forms, including bed load, suspended load, and wash load. The imbalance of sediment in a river system, whether natural or artificial, can cause serious depositional or erosional problems on riverbed and riverbanks. With different features of a river, such as flowrate, channel slope, soil particle size, and sediment types, the problem of deposition and erosion might result in drastic change of river geomorphology over time (Rosgen, 1994). Furthermore, these geomorphological variations can lead to navigational or environmental problems (Fondriest Environmental, Inc, 2014).
With the rapid development of computer science, lots of one- and multi- dimensions computational models have been developed to assess the sediment transport in a river and have been applied to different studies worldwide. These studies, for example, included utilizing HEC-RAS (1-Dimension model) to study the sediment transport characteristics of Maumee River in Ohio (Joshi et. Al., 2019), Mike 21 Sand
Transport (2-Dimension model) was used to analyze sediment movement in Var River,
France (Zavattero et al., 2016), and TELEMAC 3D (3-Dimension model) modeled cohesive sediment transport in the Loire estuary, France (Normant, 2000). From these examples above, with sufficient and optimal input data, it successfully proved that computational models were able to give high reliable results to achieve their goals.
Higher dimensional models consider complex empirical equations, require more input data and needs longer processing time. The selection of model should be based on the site conditions, the goal of the project, spatial and temporal scales of interest and the accuracy required. Generally, 1D models are most applicable in running long-term
1 simulations for the channel geometry that has minimal variation and limited hydraulic complexity (Formann et al., 2007; Waddle et al., 2000); 2D models are most applicable to the channels that have complex morphology and varied hydraulic (Lane et al., 1999); and 3D models are most applicable at the estuary, braided channel, or complex flow fields (Wu, 2008).
Hydrologic Engineering Center’s River Analysis System (HEC-RAS) one- dimensional model that requires less input data and short computation time, compared to
2D and 3D models (Johnson, 2008). It is well applicable to rivers that have a single reach and that historically have a relatively steady flowrate. The advantage of HEC-RAS is that it couples sediment transport to unsteady flow, which provides several powerful features including flow networks, mix flow, and sediment based operational parameters (Gibson et.al., 2017).
In this study, the morphological change of a channel length of 9.45 km of the
Alamo River is analyzed using HEC-RAS to provide a decision-making tool that can be used for environmental monitoring and water management. A1-D model was developed using a simulation period from November 9th, 2010 to February 6th, 2020.
Multiple factors could affect sediment transport in rivers, including hydrology, climate change, geology, geomorphology, wind, organic elements as well as human activities. Due to limiting accessible data, the proposed model largely considers the factors of rainfall precipitation, agricultural effluent and sediment load caused by human activities.
2
SECTION 2: STUDY AREA
The Alamo River is a tributary of the Salton Sea, annually contributing 180,000 tons of sediments that contain pesticides and other salts from irrigational effluents and it is the river reported with the highest sediment transport out of all the tributaries of the
Salton Sea (WQCP, 2017; TETRA Tech, 2007). Shown in Figure 1 is the location of the
Salton Sea and the segment of the Alamo River evaluated. The Alamo
River runs south to north from the Mexicali Valley in Baja California, Mexico into Imperial Valley,
California with a length of
84 kilometers. The Alamo
River is a perennial river, Figure 1. Salton Sea (red line) and Alamo Reach (blue line) are shown. with flows at the outlet ranging from a maximum of about 2,040 ft3/s to a minimum of about 320 ft3/sand averaging about 760 ft3/s based on flow data in the study period from November 9th, 2010 to February 6th, 2020. The study area is a segment of the Alamo River, a reach (hereafter named as Alamo Reach) of 9.45 kilometers, starting adjacent to Brandt Cattle Co Ranch
(Latitude: 33o09’11.85’’ N, 115o34’15.86’’W) and ending at the bifurcation of the Salton
Sea estuary area (Latitude: 33o12’23.46’’ N, 115o36’51.59’’W). The riverbank along the
Alamo River is densely vegetated by shrubs, grasses, and bushes.
3
SECTION 3: METHODOLGY
Preliminary evaluation of the Alamo River was conducted by literature review and field measurements. Based on the research recommendations, the following data were collected: Digital Elevation Model (DEM), flowrate data, sediment loading data, suspended solid concentration data, water temperature data, gauge height data, and bed gradation data. The HEC-RAS 5.0.3 was selected as model software. The steady flow simulation was firstly developed to calculate the water surface profiles with optimal manning’s coefficient n value. Then, the sediment transport simulation was processed by applying 1D hydraulic properties as well as sediment continuity theory. The conclusion for the study was drawn by comparing the observed and simulated invert elevation.
Figure 2 shows the flow diagram in this study.
Figure 2. Workflow diagram in this study
4
3.1 Data Collection
This study required input data for model development and result analysis. The data used were from three different distinct sources, including field survey, data provided by the authorities and agencies, and data researched and collected from reports. Table 1 shows the data collected and its respective sources. The gauge height data, surveying data and documented data were used as observed data to calibrate the model, whereas the rest of data were used for model development. Due to the difficulty of accessing bed gradation data, in-stream riverbank soil data was used instead.
To access a continuous sediment loading data was difficult due to insufficient measurement in the Alamo River. The sediment loading data in the study was calculated by two mathematic approaches. The first approach was Sediment- Transport Curve
Method, which is a relationship between flowrate and its corresponding sediment
b discharge in stream. This relationship was expressed as a power function Qs =a Qw
(Equation 1) (Gray et al., 2008). The Sediment- Transport Curve in this study was derived from a historical analysis of flow and sediment load allocation from different drains on the Alamo River reported in 2002 by California Regional Water Quality
Control Board, Colorado River Basin Region (CRWQCB, 2002). The derived Sediment-
1.1981 Transport Curve tested was Qs =0.1889 Qw . The second approach test was the
Suspended-Sediment Concentration Interpolation Method, which was calculated based on the direct measurement of suspended solid concentration with related flowrate. The relationship was expressed as a linear function Qs =Qw *Cs *k (Equation 2) (Gray et al.,
2008). The Suspended- Sediment Concentration Interpolation Method in this study was
5 derived based on the water quality filed data from Imperial Valley Irrigation District available from 2016 to 2020.
Equation 1 is Sediment- Transport Curve Equation:
b Qs =a Qw (Eq.1)
Where Qs is suspended-sediment discharge in tons per day; Qw is water discharge in cubic feet per second; a is the intercept and b is the slope.
Equation 2 is Sediment Concentration Interpolation Equation:
Qs =Qw *Cs *k (Eq.2)
Where Qs is suspended-sediment discharge in tons per day; Qw is water discharge in cubic feet per second; Cs means suspended solid concentration in milligram per liter; k is a coefficient of 0.0027.
Table 1. Data used in the model simulation and data sources
Data Sources
1/9 arc-second DEM Data USGS National Map Viewer (Date:
11/09/2010)
Flowrate Data and Gauge Height Data USGS 10254730 Alamo River NR at Niland
Gauge Station (Date: 02/06/2020)
Bed Gradation Data Field Sampling at the Riverbanks (Date:
10/26/2019)
Sediment Data USGS Sediment Data Portal (Date:1988 to
2002)
6
Surveying Data US and Federal Fish and Wildlife Service
(Date; 10/03/2016)
Water Temperature data Imperial Valley Water District (Date: 2016 to
2018)
Sediment Loading Data California Regional Water Quality Control
Board & State Water Resources Control
Board (Date: 08/2017)
Suspended Solid Concentration Data USGS Sediment Portal (Date:1988-2002)
Documented Data #1 Previous Research Project on the Alamo River
done by UC Davis in 1999 (Date: 01/30/1999)
Documented Sediment Load Data #2 Sedimentation Data Reported by California
Flow-Sediment Load Data Regional Water Quality Control Board,
Colorado River Basin Region (Date:
05/03/2002)
Depth of Water Data at the Garst Field Surveying (Date: 02/06/2020)
Road
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3.2 Tools Description
3.2.1 DEM, ArcGIS, &Civil 3D
A significant step in developing a computational model for the Alamo Reach is to collect topographic data. In this study, the Digital Elevation Model (DEM) data acquired from the United States Geology and Survey (USGS) National Map Viewer Servers. The
USGS DEM data was developed from Light Detection and Ranging (LiDAR) that was acquiring data by plane.
Before importing the DEM data into the computational model, two additional steps were needed. The first step was to generate a contour layer in vertical interval of 1-foot with ArcGIS. In addition, the ArcGIS also was used to delineate the sub-watershed within the study area. The next step is to process the DEM data with contours in the Civil 3D. In
Civil 3D, an alignment and cross-sections at an interval of 500 feet were developed on the
Alamo Reach. The cross-section interval was determined by Samuel’s equation (Equation
3). Therefore, sixty-two cross-sections were developed for this model.
Equation 3: Samual’s Equation:
0.15퐷 ∆푥 ≤ (Eq.3) 푆표
Where ∆푥 is cross-section spacing in feet; D is average bank full depth of the channel; So is average bed slope (ft/ft).
3.2.2 Hydrologic Engineering Center’s River Analysis System (HEC-RAS)
HEC-RAS is a professional hydraulic modeling software developed by the United
States Army Corps of Engineering. This software is very robust and widely used in the water management industry (Thol et al., 2016; Mohammad et al., 2016; Huo et al., 2016).
8
A study team used HEC-RAS to successfully process the 1D steady flow and sediment transport simulation on a reach’s length of 8,534 km for the Maumee River in Ohio (Joshi et al., 2019). The HEC-RAS model successfully simulated the morphological variation of the Maumee River with proper data and sediment transport equation. Its reliability was validated by achieving high coefficient of determination (R2) of 0.9. Therefore, HEC-
RAS was selected to build the 1D steady flow model and sediment transport on the
Alamo Reach.
3.2.2.1 1-Dimensional Steady Flow Simulation
For 1D steady flow simulation, HEC-RAS applies the energy conservation equation (Equation 4) to compute iterations for each cross section and calculating the
Froude number (Equation 5) to determine hydraulic condition for each cross section.
a V2 a V2 Z + Y + 2 2 = Z + Y + 1 1 + h (Eq. 4) 2 2 2g 1 1 2g e
Where Z1 and Z2 are the elevation of the main channel inverts; Y1 and Y2 are the depth of water at cross sections; V1 and V2 are the average velocities; a1 and a2 are the velocity weighting coefficients; g is gravitational constant, and he is the energy loss (HEC-RAS
Hydraulic Reference Manual, 2016).
푉 Fr = (Eq. 5) √(푔∗퐷)
Where Fr is Froude number; v is the average velocity of the liquid in a channel; g is the gravitational which is 32.17 ft/s2 in this study; and D is the hydraulic depth. (HEC-RAS
Hydraulic Reference Manual, 2016).
The water surface profiles were generated for the minimum flow, the average flow, and the maximum flow in the study period. The minimum flow was 322 ft 3/s
9 recorded on December 12th, 2014, the maximum flow was 2,040 ft3/s recorded on August
8th, 2013, and the average flow in study period was 800 ft3/s. The input data for the sediment transport simulation are demonstrated and the simulated results are demonstrated in the Appendix B.
3.2.3 Sediment Transport Simulation
Multiple factors can affect sediment transport in river. However, the mathematical concept behind it is sediment continuity in a control volume between each cross section.
Generally, the channel is deposited when control volume inflow sediment is greater than control volume outflow sediment, whereas the channel is eroded when the control volume inflow sediment is less than control volume outflow sediment. The conservation of mass equation used in HEC-RAS for sediment transport computation is known as the
Exner equation (Equation 6):
휕휂 휕푄푠 (1- λp) B = − (Eq. 6) 휕푡 휕푥
Where B is the channel width; 휆푝 is the active layer porosity; η is channel invert; t is time; x is distance; and Qs is transported sediment load.
First, the quasi- unsteady flow data was required in HEC-RAS sediment simulation.
The quasi-unsteady flow data was to subdivide the flow series into a sequence of steady flow computation and represents continuous hydrograph with a series of discrete steady in the format of histogram (HEC-RAS User Manual, 2018). Unlike 1D steady flow simulation to run with a constant flow, the sediment transport needs to run with flow series for a study period, which would increase the probability to produce unacceptable output or model crashing due to model instability (HEC-RAS User Manual, 2018). The idea of quasi-
10 unsteady flow to turn complex flow series into multiple constant flow for easier calculation is similar to the mathematic concept of calculating the area of a bell curve with integration.
By subdividing the bell curve into multiple rectangles and summing it up back to a whole bell curve, it is to make the complex calculation easier. A study proved that using quasi- unsteady flow had better sediment transport simulation performance on a river that is without considering additional hydrologic conditions, such as, groundwater interflow, pumps, and others. (Hummel et al., 2012).
3.3 Model Calibration
3.3.1 Rosgen Stream Classification Method
With the Rosgen Stream Classification method, a stream can be easily and preliminarily assessed for stream stability, hydraulic relations, erosion risk based on stream’s characteristics. These characteristics include entrenchment ratio, ratio of width and depth, sinuosity index, channel slope, and riverbed soil (Rosgen, 1996). By applying the Rosgen Stream Classification Method on the Alamo Reach, it provided the basic understanding of the potential behavior of the Alamo Reach before running model simulations, aiding in model development and results analysis. The Rosgen Stream
Classification Chart is demonstrated in the Appendix A.
The Alamo Reach was categorized as E6 type Channel in Rosgen Stream
Classification based on its stream characteristics. An E6 channel is described as a stable channel with constant channel width/depth ratio, meaning it does not have much transformation on riverbed and riverbank. But it would undergo dramatic physical change into other stream types in rapid stream flow and sediment discharge variation (Rosgen,
1996). The Alamo Reach is a stable channel system but extremely sensitive to
11 disturbance. Table 2 shows the Rosgen Stream Classification parameters for Alamo
Reach.
Table 2. The Rosgen Stream Classification for the Alamo Reach
Rosgen Stream Classification
Entrenchment Ratio 2.33
Width/Dept Ratio 10
Sinuosity 1.82
Channel Slope 0.007
Soil Material Silt/Clay
Channel Classification E6
Note: The Alamo Reach is classified as E6, which indicates a highly stable natural channel.
3.3.2 Observed Data
In general, the observed data is required for the model calibration and validation
(Hummel et al., 2012). As mentioned above, the most difficult task in this study was to access data, including the observed data collection. The observed data used in this study included: (1) the flowrate and gauge height data obtained from USGS gauge station, (2) a real topographic surveying data of the downstream Alamo River provided by the U. S.
Fish & Wildlife Service, (3) field surveying activity to measure the depth of water by the study team, and (4) existing documented data.
The USGS gauge station (Gauge Station ID: 10254730 Alamo River NR at
Niland, 33o11’57.06’’ N, 115o35’49.49’’W) was installed 500 feet upstream from the
Garst Road and its location corresponded to Station 70+00 in the geometry data. The water surface elevation of the gauge station was used as observed data to compare with
12 simulated water surface elevation for model calibration. The gauge height data recorded the height of water in the stream referenced within a specific datum system. It does not represent the depth of water, but the water surface elevation (USGS NWIF, 2018). The elevations in Table 2 were in the North American Vertical Datum of 1988 (NAVD88).
Due to the Salton Sea and its vicinity below sea level, the Imperial Valley Irrigation
District Datum (IID datum) is used in the area, which added 1,000 feet to NAVD 88 elevation (USGS SN, 2012; IID, 2010). Since 2012, the gauge height at the USGS
Gauge Station (Gauge Station ID: 10254730) had increased from 1 foot to 71 feet while the flowrate was steady. According to the station notes for Gauge Station 10254730, with greatly affected by the drop in the water surface elevation of the Salton Sea, the gauge height was dropped to 0 feet. To avoid confusion of having a negative number, 70 feet was added to the previous datum (USGS SN, 2012). Thus, the gauge height currently, for example, 71 feet is equivalent to 1 foot before 2012, and equivalent to 771 feet in IID datum. The corresponding measured water surface elevation can be calculated by the conversion of subtracting the 300 feet from the current gauge height data.
The U. S. Fish & Wildlife Service had conducted a survey on the Alamo River for an engineering project in October 2016. The range of the surveying started at the Garst
Road, going downstream toward the estuary to the Salton Sea. The surveying report recorded the water surface elevation and riverbed elevation on downstream of the Alamo
River. The Cross-Section 0+00 to Cross-Section 65+00 in this study inversely overlapped on the Cross-Section 65 +00 to Cross- Section 0+00 in the surveying report 2016. Table 3 shows the invert elevation, water surface elevation, and the gauge height measured at the
Garst Road.
13
A simple technique to measure the depth of water on the Alamo River from the
Garst Road was documented in a study by a UC Davis study team in 1999, using a rope with 0.1-meter incremental marking and tied with a 15 pound-lb weight at the end of rope, then lowered it to the bottom of the river. (Huston et al., 2000). By referencing this technique, a field surveying activity was conducted by the study team on February 6th,
2020 at the same location, measuring the depth of water with an engineering level rod and a hemp rope with 8-pound bricks at the end, lowering the brick to the channel at the middle of a cross-section, then using the engineering level rod to measure the rope that has water mark on. Fifteen measures were done and the average of it was taken as the depth of water on that day. The estimated depth of water on the field day was roughly 9.3 feet.
Lastly, a historical sediment load data from various drains into the Alamo River, published by California Regional Water Quality Control Board and State Water
Resources Control Board, was used as observed data to compare the simulated result on the mass capacity within the Alamo Reach (CRWQB, 2017). The reported reach started at the Rockwood drain and ended at the mouth of the Alamo River to the Salton Sea, which covers the entire Alamo Reach in this study and is approximately 2 miles longer in length. So far, this was only accessible document that is closed to the Alamo Reach in this study, indicating approximately 28,200 tons of sediment was discharged from the reach to the Salton Sea every year. This data was used to compare with simulated sediment mass discharged from the Alamo Reach.
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Table 3. Various elevations and gauge height data recorded at the Garst Road at different period.
Flowrate Invert Depth of Water Water Gauge (1) (ft3/s) Elevation (ft) (ft) Surface Height Elevation (ft) (ft) 01/30/1999 782 (2) -234 6.0 -228 NA (UC Davis Study Team) 11/09/2010 820 NA NA -229 1.0(5) (USGS DEM National Map Viewer) 10/3/2016 780 -242 10.4 -233(3) 68.6(4) (Federal Fish and Wildlife Service) 02/06/2020 740 -242.6 9.3 -233.3 66.7(6) (CPP Field Survey)
Notes: NA means the data is not available; (1) The flowrate was obtained from USGS National Water Information System. (2) There was no flowrate recorded in the USGS on that day, the flowrate was estimated by the UC Davis study team. (3) This water surface elevation was converted from the USGS gauge height data. The water surface elevation in the surveying report from the Federal Fish and Wildlife Service was -231.4 feet. (4) The water surface elevation was -231.4 feet for the USGS gauge height. (5) 1.0 feet gauge height was equivalent to -228 feet for the water surface elevation. (6)66.7 feet gauge height was equivalent to -233.3 feet for the water surface elevation.
3.3.2 Observed Data
According to the DEM data description, the DEM data was collected on
November 9th, 2010, the elevation in the DEM data was -228 feet. The gauge height on the same day showed that the water surface elevation was -229 feet with the flowrate of
840 ft3/s. Hypothetically, if the DEM data accurately scanned the invert elevation of the
Alamo River and the gauge height truly displayed the water surface elevation of the
15
Alamo River, it means that the depth of water was 1 foot. However, hydraulically, 1 foot depth of water unlikely was generated by the flowrate of 840 ft3/s.
Furthermore, presumptively, all the data in Table 3 above were representing the invert elevation and accurate in a consistent datum system. The Alamo Reach had dramatic elevation variation from 1999 to 2016. It had been through depositional issue by
5 feet from 1999 to 2010 and then erosional issue by 13 feet from 2010 to 2016. By the
Rosgen Channel Classification, the Alamo Reach was classified as a stable channel under steady flow conditions. It is impossible that the Alamo Reach had this dramatic elevation change while the historical flowrate was steady. Therefore, the elevation scanned in the
DEM data can be conclusively considered the water surface elevation.
The DEM data was not able to capture the reliable invert elevation of the Alamo
Reach, thus, a modification on the DEM data was made. In the meanwhile, the model calibration by selecting the optimal Manning’s n value was done at this step. For obtaining a reliable model result, the HEC-RAS model needed to be calibrated by adjusting the Manning’s coefficient n value (Joshi et al., 2019; Hummel et al., 2012). The model was considered well calibrated by comparing the simulated water surface elevation and observed water surface elevation if the difference is equal or less than 0.1 feet (HEC-
RAS VVT. 2018). A sensitivity analysis was applied with the gauge height data on
November 9th, 2010, when was the date the DEM data was collected, to check the consistency between the simulated water surface elevation and the observed water surface elevation at the Garst Road (shown in Figure 3), which is the Station 65+00 in this study. Thirty-three different flows, in range of 766 ft3/s to 840 ft3/s, obtained from
USGS gauge station on the Alamo River (Gauge Station ID: 10254730) were analyzed.
16
The values of Manning’s coefficients 0.05, 0.021, and 0.05 shows good performance for left over bank, channel and right of the Alamo Reach, respectively. The coefficient of determination (R2) statistical method was applied throughout this study to compare the simulated results to the measured values. The closer the R2 is to 1, the better data fits the model. Figure 4 shows the plot of R2 of the simulated water surface elevation to the measured water surface elevation. The R2 of 0. 9985 and average different of water surface elevation of 0.095 feet indicated the model was well calibrated by using adopted
Manning’s n value. With optimal Manning’s n values, the elevation was determined by dropping 10 feet. This assumption was applied to the entire Alamo Reach.
Figure 3. The Garst Road (star point) and stream flows (blue line) contributing to the Alamo Reach (red line) are shown.
17
-231.1
-231.15 R² = 0.9985 -231.2 -231.25 -231.3 -231.35
Elevation Elevation (ft) -231.4 -231.45
Simulated Water Simulated Water Surface -231.5 -231.55 -231.45-231.4-231.35-231.3-231.25-231.2-231.15-231.1-231.05 -231 Measured Water Surface Elevation (ft)
Figure 4. Plot of measured water surface elevation and simulated water surface elevation by selecting optimal Manning’s n value of 0.05 for overbank and 0.021 for main channel.
3.4 Model Development
3.4.1 Data Required in Sediment Transport Simulation
With sufficient flowrate data the upstream boundary condition in the quasi-steady flow input was set with flow series data from USGS and the downstream boundary condition was determined as the normal depth of 0.0007 feet based on the elevation change over cross section interval. Furthermore, the water temperature was uniformly assumed to 80-degrees Fahrenheit between April to October and 67-degrees Fahrenheit between November to March based on the water quality report provided by Imperial
Valley Irrigation District. To produce the realistic sediment transport phenomena, the
HEC-RAS sediment simulation needed to select the proper equation for sediment transport function, sorting method, and particle fall velocity based on the basic characteristics of the stream. It also required to determine the bed gradation, sediment load series, and maximum depth that potential layer can be scoured at each cross section.
18
The overall sediment particle diameter in the Alamo River was 1.52 mm from the
USGS Sediment Portal. Based on the basic characteristic of sediment particle size, hydraulics and geography of the Alamo Reach, the Yang and Tofelatti equation were selected for sediment transport function, the Copeland and Active Layer mixing equation were selected for the armoring and sorting method, and the Ruby was selected for fall velocity method. These selections have been reported to accurately model fine sand and silt/clay bed (HEC-RAS Hydraulic Reference Manual, 2016).
In this study, multiple transport functions, sorting methods, and sediment load calculation methods were applied in the sediment model development. In total eight different model input combinations were trialed to select the one that is most suitable for the Alamo Reach. Each combination consists of two different sediment load calculation methods, two different sediment transport functions, and two sorting methods. It is listed below:
Group 1: Sediment- Transport Curve +Yang + Copeland
Group 2: Sediment- Transport Curve +Yang + Active Layer
Group 3: Sediment- Transport Curve +Tofaletti + Copeland
Group 4: Sediment- Transport Curve +Tofaletti+ Active Layer
Group 5: Sediment Concentration Interpolation +Yang + Copeland
Group 6: Sediment Concentration Interpolation +Yang+ Active Layer
Group 7: Sediment Concentration Interpolation +Toffaleti+ Copeland
Group 8: Sediment Concentration Interpolation +Toffaleti + Active Layer
19
In the rest of the study, the combination hereafter would be represented with its corresponding group number. The input data for the sediment transport simulation are demonstrated in the Appendix B.
3.4.2 Model Assumption
Due to lack of precise geotechnical data for the invert of the Alamo Reach, the assumptions for the maximum depth and bed gradation were made in the sediment transport development.
3.4.2.1 Maximum Depth
To define an appropriate maximum depth is critical, because the HEC-RAS would incorrectly estimate the deposition and erosion for the channel due to insufficient maximum depth or over-defined maximum depth (HEC-RAS UM, 2018). The maximum depth was determined using the reported data in 1999 and surveying data reported in
2016, and it varied along the Alamo Reach. Since the surveying data from both reports were measured in the same datum system, the invert elevation at the Garst Road was roughly decreased by 8 feet in comparison from 1999 to 2016, equivalently, decreasing
0.47 feet every year. The maximum depth was mathematically assumed to a constant value of 5 feet calculated by 0.47 feet each year multiplying 10 years. Since the observed data for the Station 0+00 to Station 65+00 was available, the maximum depth was determined by adding the invert elevation difference between the modified geometry data and the surveying data in 2016 to value of 1.6 feet. The value of 1.6 was calculated by
0.47 feet multiplying 3.5 years which is a time span from October 2016 to February 2020.
20
Table 4. A set of maximum depth in the sediment model simulation input
Station Maximum Depth (ft) 7000 -31000 5 6500 6.3 6000 4.1 5500 1 5000 2.9 4500 1.7 4000 1.6 3500 2.5 3000 4.2 2500 2 2000 1.3 1500 0.6 1000 2.5 500 0.1 0 0.4
Note: From Station 70+00 to Station 310+00, the maximum depth is constant for 5 feet. From Station 0+00 to Station 65+00, the maximum depth varies at each cross-section.
3.4.2.2 Riverbed Gradation
Due to lack of soil size distribution analysis data of the Alamo riverbed, the soil samples collected on the riverbank were used in bed gradation. According to the previous studies, it explicitly stated that the Alamo Riverbed soil was largely consisted of silty sand and clay (Hultgren- Tillis Engineers, 2017; Youd et al., 2014). The soil distribution analyses on the riverbank samples indicated that 17% was clay, 24% was silt, and 59 % was fine sand, which has similar soil materials of the riverbed.
21
SECTION 4: RESULTS AND DISCUSSION
This section analyzes the steady flow simulation result in Sub-Section 4.1 and sediment transport simulation result in Sub-Section 4.2. The detailed simulation output will be demonstrated in the Appendix B.
4.1 Steady Flow Simulation
In total sixty-two cross-section were drawn throughout 9.45 km each. The steady flow model assessed the energy state of the flowrate in the Alamo Reach based on the relationship between flow condition and channel geometry. Figure 5 shows the water surface profiles with selected flowrates in the Alamo Reach. The simulated results indicated that the Alamo Reach was classified to subcritical flow channel because the
Froude numbers were less than 1, meaning the tranquil flow dominated in the Alamo
Reach in the study period. Hydraulically, the flow in the Alamo Reach was slow and the depth of water was relatively high. Table 5 lists the average simulated values obtained of steady flow simulation on the Alamo Reach.
The steady flow simulation hydraulically validated the channel geometry data in the Alamo Reach to assure the model accuracy before running the sediment transport simulation. In addition, the original DEM data cannot reflect the invert elevation of the
Alamo Reach was found in the steady flow simulation because the channel was constantly overflowed, even though with lower flowrate data. This situation was not consistent with what happened on the Alamo River.
22
Table 5. Average value computed in steady flow simulations
Flow Average Depth of Average Average Top Froude (ft3/s) Water (ft) Velocity (ft/s) Width (ft) Number 322 3.04 2.41 54.86 0.26 800 5.33 3.21 59.98 0.27 2040 9.73 4.24 71.72 0.28 Note: Froude Number is less than 1 when the flowrate is less than 2000 ft3/s, meaning the Alamo Reach is a subcritical flow channel.
Profile 3: 2040 ft3/s
Profile 2: 800 ft3/s
Profile 1: 322 ft3/s
Figure 5. The steady flow simulation profiles of the Alamo Reach. The largest depth of water occurs at the Cross-Section 85+00 and the smallest depth of water is occurs at the Cross-Section 0+00. Profile1, Profile2, and Profile3 are the profiles for water surface and the green line represents the energy grade line. The red line represents critical flow occurred.
23
4.2 Sediment Transport Simulation
Eight different sediment input combinations were analyzed because they were all applicable on the Alamo Reach. Each combination would be referred with its assigned number in this section. The results from eight different input combinations consistently suggest that predominantly erosion occurred along the Alamo Reach. Figure 6 graphically showed that the invert elevation in 2010 (solid line) is higher than the invert elevation in 2020 (dash line), which means the Alamo Reach invert elevation was decreasing. Table 6 demonstrates the average invert elevation change and cumulative sediment mass capacity change for eight input combinations. The negative numbers, for the invert elevation, means the elevation is decreasing, and, for the cumulative sediment mass capacity, indicates the sediment outflow is higher than the sediment inflow within the system. Even though all the combinations gave the similar results on the Alamo
Reach, each result needed to be compared with the observed data in order to achieve the goal of the study for selecting the most optimal input combination to model Alamo
Reach.
To select the most optimal sediment input combination for the Alamo Reach, the coefficient of determination (R2) was also applied by comparing observed and simulated values. However, one of the limitations of this study was the restricted amount of measured data along the length of the Alamo Reach under study. Thus, with the limited observed data, the idea was to largely focus on a short reach then expanding the conclusion to the entire Alamo Reach. The simulated invert elevation for the Station
0+00 to Station 65+00 was compared to the surveying data recorded in 2016. Figure 7 are the plots that show the R2 for each sediment input combination. It shows that Group 3 has
24 the highest R2 of 0.8, indicating that applying Tofaletti’s equation for sediment transport function and Copeland for sorting method with sediment load data calculated by using
Sediment- Transport Curve equation was suitable to model the Alamo Reach.
25
26
Figure 6. Simulated results by eight different sediment input-combinations. Each chart title represents the group number of different input-combinations assigned in the Sub-Section “3.3.2 Sediment Transport Simulation”. Each input-combination was consisted of different calculation method of sediment load, sediment transport equation, and mixing equation. The blue line represents the invert elevation on November 09,2010 and the orange dash line represents the invert elevation on February 06, 2020. All the simulations showed that the invert elevation in 2020 lower than in 2010, indicating that the Alamo River was eroded.
27
Table 6. Simulated result of invert elevation changes in average and cumulative sediment mass capacity by eight different sediment input-combinations
Inert Elevation Cumulative Sediment Mass Sediment Mass Capacity Change (ft) Capacity of the Alamo Reach of the Alamo Reach from 2010 to 2020 (tons) annually (tons/year) Group 1 -3.82 -223,763 -24,185 Group 2 -3.85 -219,984 -23,777 Group 3 -3.88 -230,284 -23,028 Group 4 -3.83 -213,337 -23,058 Group 5 -3.59 -198,869 -21,495 Group 6 -3.56 -190,868 -20,630 Group 7 -3.84 -222,840 -24,086 Group 8 -3.85 - 217,131 -23,468 Note: The first column of table is the group number of different input-combinations assigned in the Sub- Section “3.3.2 Sediment Transport Simulation”. The second column shows the Alamo Reach invert elevation change in the study period. The negative number indicates the invert elevation dropping from 2010 to 2020. The third column shows the cumulative sediment change in the study period. The negative number in this column meaning sediment deficit, indicating more sediment discharged from the Alamo Reach than entering the Alamo Reach. The fourth column shows the sediment mass capacity of the Alamo Reach every year. Based on the R2 value, the Group 3 of Sediment- Transport Curve +Toffaleti +
Copeland combination exhibited the best fit with the observed data, it was chosen to better represent the sediment transport behavior in the Alamo Reach. The Group 3 combination indicated that the invert elevation of the Alamo Reach decreased 3.88 feet on average, and there was accumulative 230,284 tons sediment discharged from the
Alamo Reach into the Salton Sea from November 09, 2010 to February 06, 2020, which was losing sediment approximately 23,030 tons per year. An existing data indicated that there were approximately 27,300 tons sediment allocation through a similar magnitude reach starting from the Rockwood Drainage to the outlet of the Alamo River every year.
(WQCP, 2017), which is about 3 kilometers longer than the Alamo Reach in this study.
This was only accessible data that specifically recorded the sediment load within a reach which is close to the Alamo Reach. Given that two reaches were not completely identical and the input data for the model was mathematically estimated, the simulated sediment
28 discharge less 4,200 tons per year than the existing data is acceptable when accurate input data was insufficient. Thus, the combination of Sediment- Transport Curve +Toffaleti +
Copeland combination was considered the most optimal selection in this study and it can be used on the same channel for future study.
Figure 7. The plot of coefficient of correlation (R2) for eight different sediment input-combinations by simulated results to the observed data on the Station 0+00 to Station 65+00. The Y-axis represents the
29 simulated invert elevation, and the X-axis represents the observed invert elevation. The Group #3, Sediment Transport Curve+Toffaleti+Copeland, has the highest R2 of 0.80. Figure 8 shows the profiles of the Alamo Reach for every two years change in the study period. Besides the Station 80+00 occurred deposition from 2010 to 2018, the profiles depict the Alamo Reach was dominated by erosion and the erosion rate reduced since 2016. The indication of the Alamo River being eroded are indirectly supported with an existing report which states 13 weirs were built along the Alamo River to slow down water velocity and reduce erosion rate (RBSWPB, 2002). Thus, the model results agree with engineering and management actions done to date to reduce the erosion of the riverbed. This is encouraging because it indicates that the model could be used to help managers to make sediment control decisions in the future.
However, it is important to recognize the limitations of the model because it could affect the accuracy of the model prediction. The assumptions made in the HEC-RAS sediment input limited the model accuracy in some way. The soil distribution analysis on the riverbank cannot completely represent the overall grain-size distribution for the material of river bottom. Furthermore, the data of sediment load related with flowrate was not a continuous and uniform. The sediment data cannot precisely and realistically reflect the boundary condition of the Alamo Reach. Developing an accurate sediment model needs intensive field investigation, in order to refine the sediment model cross- section by cross-section, a future study needs more field surveying to increase the amount of input data.
30
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31
SECTION 5: CONCLUSION AND RECOMMENDATION
A sediment transport simulation for the Alamo Reach was developed at a timeline from November 9th, 2010 to February 6th, 2020. Hydraulically, the HEC-RAS Steady
Flow Model provides a good result, because the simulated water surface elevation is in accord with the observed water surface elevation from the USGS Gauge Station (Gauge
Station ID: 10254730) on the Alamo Reach. The simulated result from the Sediment
Transport Model indicated that the Alamo Reach invert elevation is decreasing about 0.4 feet every year. The entire stream has been slightly eroded in subcritical flow condition.
The simulated result conforms to the Alamo Reach in the Rosgen Stream Classification, which is stable and would not change dramatically in steady flow condition.
The Alamo Reach had been slightly eroded every year. Nevertheless, the dense vegetation along the Alamo Reach is effective to protect the channel from riverbank erosion and the weirs on the Alamo River slow the water velocity resulting in erosion reduction. The recommendations for sediment control management practices on the
Alamo Reach include (1) tailwater drop box with raised grade board, (2) improved drop box with widened weir and raised grade board, (3) pan ditch, (4) check dams, (5) irrigation land leveling, (6) filter strips, (6) irrigation water management, (7) sprinkler irrigation, (8) drip irrigation, (9) reduced tillage, (10) furrow dikes, and (11) sediment basins. The cost for the management practices and monitoring plan varies depending on the program. The cost for the monitoring plan of the Imperial Valley Irrigation District was estimated $25,000 for preparation, $20,000 for the dredging impacts, and $70,000 per year for monitoring plan (WQCP, 2017).
32
The model simulated result on the Alamo Reach can be extended to suspect the entire Alamo River, from the international boundary to the Salton Sea, which is experiencing sediment erosion. Also, with affective data and appropriate functions, the working strategy in this study also can be applied to develop another computational hydraulic and sediment transport model in other tributaries of the Salton Sea, such as
New River. This study intends to provide a helpful tool for public agencies’ managers to make predictions and sediment management decisions. The holistic view of the Alamo
Rivers and region could provide valuable information to support their efforts of improving the Salton Sea and its environment issues.
33
REFERENCES
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Geomorphology, Volume 90, Page 340-355. [Online] available at https://www.sciencedirect.com/science/article/abs/pii/S0169555X07001377[Acce ssed 26 July.2019]. (7) Waddle, T., Steffler, P., Ghanem, A., Katopodis, C., Locke, A. (2000). Comparison of One- and Two-Dimensional Open Channel Flow Models for a Small Habitat Stream. Rivers, Volume 3, Page 205-220. (8) Lane, S.N., Bradbrook, K.F., Richards, K.S., Biron, P.A., Roy, A.G. (1999). The Application of Computational Fluid Dynamics to Natural River Channels: Three Dimensional Versus Two-Dimensional Approaches. Geomorphology, Volume 20, Page 1-20. (9) Wu, Weiming. (2008). Computational River Dynamics. Taylor & Francis, London. (10) Johnson, D.H. (2008). The Application of a Two-Dimensional Sediment Transport Model in a Cumberland Plateau Mountainous Stream Reach with Complex Morphology and Coarse Substrate. Master Theses, The University of Tennessee, Knoxville. (11) Gibson, S., Sanchez, A., Piper, S., Brunner, G., (2017). New One-Dimensional Sediment Features in HEC-RAS 5.0 and 5.1. [online] Available at: Dimensional_Sediment_Features_in_HEC-RAS_50_and_51> [Accessed 26 July.2019]. 35 (12) Tetra Tech,Inc., Wetlands Management Services., (2007) New and Alamo River Wetlands Master Plan. [online]availableat: an_v2_print.pdf > [Accessed 27 Mar. 2019]. (13) Gray, J., Simoes, F.J.M. (2008) Estimating Sediment Discharge [online] Available at: https://water.usgs.gov/osw/techniques/Gray_Simoes.pdf [Accessed 27 Mar. 2019]. (14) California Regional Water Quality Control Board, Colorado River Basin Region., (2002) Appendix D: Load Allocation Calculations [Online] available at /alamo/ar_silt_append.pdf> [Accessed 26 July.2019]. (15) Thol, T., Kim, L., Ly, S., Heng, H., (2016). Application of HEC-RAS for a Flood Study of a River Reach in Cambodia. [online] available at: RAS_for_a_flood_study_of_a_river_reach_in_Cambodia> [Accessed 27 Mar. 2019]. (16) Mohammad, M., Al-Ansari., N, Issa, I., Knutsson, S., (2016). Sediment in Mosul Dam Reservoir Using the HEC-RAS Model. [online] available at: 2019]. (17) Huo, A.D., Guan, W.K., Dang, J., Wu, T.Z., Shantai, H., Wang, W., Van, M. (2016) Submerged Area of Typical Torrential Flood and Debris-Flow Disasters in Mengzong Gully, China, Geomatic, Natural Hazards and Risk, Volume 7, 18-24. 36 [online] available at: Access=true&> [Accessed 27 Mar. 2019]. (18) US Army Corps of Engineers Hydrologic Engineering Center. (2016). HEC-RAS River Analysis System-Hydraulic Reference Manual. [online] available at: RAS_4.1_Reference_Manual.pdf> [Accessed 27 Mar. 2019]. (19) US Army Corps of Engineers Hydrologic Engineering Center. (2016). HEC-RAS River Analysis System-Hydraulic User Manual. [online] available at:< https://www.hec.usace.army.mil/software/hec-ras/documentation/HEC- RAS%205.0%20Users%20Manual.pdf> [Accessed 27 Mar. 2019]. (20) Hummel, R., Duan, J., Zhang,S., (2012). Comparison of Unsteady and Quasi- Unsteady Flow Models in Simulating Sediment Transport in an Ephemeral Arizona Stream. [online] availableat: 63.x>[Accessed 27 Mar. 2019]. (21) Hultgren-Tillis Engineers., (2017) Geotechnical Investigation Salton Sea Species Conservation Habitat Project Salton Sea, California [Online] available at And-Construction/Files/Design- Build/SCH_New_River_Geotechnical_Report_Revision_3-8- 2017.pdf >[Accessed 26 July.2019]. (22) Youd,T., Steidl,J., Nighor,R. (2014) Ground Motion, Pore Water Pressure and 37 SFSI Monitoring at NEES permanently Instrumented Field Sites [Online] available at 26 July.2019]. (23) Huston,D., Cook,C., Orlob,G., (2000) New and Alamo Rivers Project Preliminary Data Collection and Analysis for Development of Hydrodynamic and Water Quality River Models, University of California, Davis. [Accessed 26 July.2019]. (24) US Army Corps of Engineers Hydrologic Engineering Center. (2018). HEC-RAS Verification and Validation Tests. [online] available at:< https://www.hec.usace.army.mil/software/hec-ras/documentation/RD-52_HEC- RAS%20Verification%20and%20Validation.pdf> [Accessed 26 July.2019]. (25) Regional Board Staff Watershed Protection Branch., (2002) California Environmental Protection Agency Regional Water Quality Control Board Colorado River Basin Region [Online] available at o/ar_silttmdl5_3_02.pdf> [Accessed 26 July.2019]. (26) California Regional Water Quality Control Board., State Water Resources Control Board. (2017). Water Quality Control Plan Colorado River Basin-Region 7, August 2017, California Regional Water Quality Control Board., State Water Resources Control Board, California. [Online] available at:< https://www.epa.gov/sites/production/files/2015-03/documents/ca7-plan- colorado-river-basin.pdf> [Accessed 26 July.2019]. 38 APPENDIX A A.1 Rosgen Stream Classification System Sources: Stream Restoration Design Nation Engineering Handbook. https://wildlandhydrology.com/resources/docs/River%20Restoration%20and%20Natural%20Channel%20 Design/Rosgen_Geomorphic_Channel_Design.pdf” 39 APPENDIX B B.1 Steady Flow Simulation Input Upstream Flow and Downstream Flow Condition: Normal Depth=0.000313 (Source: HEC-RAS Reference Manual) Flowrate (cfs): 322/800/2040, the minimum flowrate, average flowrate, and the maximum flowrate in the study period (Source: USGS 10254730 Alamo River NR at Niland Gauge Station) B.2 Sediment Transport Simulation Input Transport Function: Toffaleti Sorting Method: Copeland Fall Velocity Method: Ruby Boundary Condition: Table B.2.1 Sediment Transport Simulation Boundary Defined by Sediment Loading Rate Curve Sediment loading Flow(ft3/s) 504 508 657 680 723 764 766 859 Total load(tons/day) 327 330 450 468 503 538 540 620 Clay (.002-.004) 0.75 0.59 0.64 0.55 0.61 0.36 0.61 0.59 VFM (.004-.008) 0.09 0.09 0.09 0.09 0.08 0.07 0.08 0.08 FM (.008-.016) 0.08 0.08 0.08 0.09 0.08 0.07 0.08 0.08 MM (.016-.032) 0.04 0.08 0.06 0.08 0.06 0.06 0.09 0.11 CM (.032-.0625) 0.02 0.07 0.05 0.07 0.06 0.11 0.07 0.09 VFS (.0625-.125) 0.02 0.01 0.06 0.1 0.07 0.29 0.06 0.03 FS (.125-.25) 0.01 0.01 0.02 0.02 0.04 0.4 0.01 0.01 40 Bed Gradation: Figure B.2.1 Input Panel for Bed Gradation (Source: Field surveying by the Cal Poly Pomona Alamo River Senior Project Team 2019). 41 Quasi Unsteady Flow Series: Figure B.2.2 Plot for Quasi- Unsteady flow Hydrograph 42 B.3 Steady Flow Simulation Output Table B.3.1 Steady Flow Simulation Output for flowrate 300 ft3/s River Station Flowrate (ft3/s) Invert Elevation Water Surface Depth of Froude (ft) Elevation (ft) Water (ft) Number 31000 322 -230 -227.36 2.64 0.2 30500 322 -230.2 -227.69 2.51 0.41 30000 322 -230.4 -227.8 2.6 0.23 29500 322 -230.6 -227.94 2.66 0.25 29000 322 -230.8 -227.99 2.81 0.16 28500 322 -231 -228.06 2.94 0.17 28000 322 -231.04 -228.15 2.89 0.2 27500 322 -231.09 -228.29 2.8 0.25 27000 322 -231.13 -228.41 2.72 0.23 26500 322 -231.17 -228.59 2.58 0.29 26000 322 -231.22 -228.7 2.52 0.22 25500 322 -231.26 -228.96 2.3 0.35 25000 322 -231.3 -229.12 2.18 0.26 24500 322 -232 -229.25 2.75 0.22 24000 322 -232.16 -229.35 2.81 0.21 23500 322 -232.32 -229.49 2.83 0.25 23000 322 -232.49 -229.59 2.9 0.21 22500 322 -232.65 -229.68 2.97 0.2 22000 322 -231.81 -230.02 1.79 0.49 21500 322 -233 -230.16 2.84 0.23 21000 322 -233.1 -230.35 2.75 0.3 20500 322 -233.19 -230.47 2.72 0.22 20000 322 -233.29 -230.55 2.74 0.19 19500 322 -233.39 -230.68 2.71 0.24 19000 322 -233.49 -230.77 2.72 0.2 18500 322 -233.58 -230.86 2.72 0.19 18000 322 -233.68 -230.92 2.76 0.17 17500 322 -233 -231.06 1.94 0.28 17000 322 -234 -231.23 2.77 0.25 16500 322 -234.01 -231.26 2.75 0.14 16000 322 -234.02 -231.35 2.67 0.21 15500 322 -234.04 -231.39 2.65 0.14 15000 322 -234.05 -231.81 2.24 0.54 14500 322 -234.06 -232.08 1.98 0.33 14000 322 -235 -232.28 2.72 0.27 43 13500 322 -235.2 -232.39 2.81 0.22 13000 322 -235.4 -232.5 2.9 0.22 12500 322 -235.6 -232.62 2.98 0.22 12000 322 -235.8 -232.76 3.04 0.25 11500 322 -236 -232.83 3.17 0.18 11000 322 -236.3 -232.9 3.4 0.18 10500 322 -236.7 -233.03 3.67 0.23 10000 322 -237 -233.01 3.99 0.1 9500 322 -237.17 -233.05 4.12 0.12 9000 322 -237.33 -233.18 4.15 0.24 8500 322 -237.5 -233.33 4.17 0.26 8000 322 -237.67 -233.29 4.38 0.08 7500 322 -237.83 -234.16 3.67 0.71 7000 322 -238 -234 4 0.24 6500 322 -238.17 -234.34 3.83 0.38 6000 322 -238.29 -234.28 4.01 0.13 5500 322 -238.42 -234.58 3.84 0.37 5000 322 -238.54 -234.61 3.93 0.19 4500 322 -238.67 -234.74 3.93 0.24 4000 322 -238.8 -234.85 3.95 0.22 3500 322 -238.92 -235.59 3.33 0.61 3000 322 -239.05 -236.37 2.68 0.59 2500 322 -239.17 -236.45 2.72 0.26 2000 322 -239.67 -236.71 2.96 0.33 1500 322 -239.8 -236.93 2.87 0.32 1000 322 -239.9 -237.21 2.69 0.35 500 322 -240 -238.06 1.94 0.72 0 322 -240 -238.12 1.88 0.25 Table B.3.2 Steady Flow Simulation Output for flowrate 800 ft3/s River Station Flowrate (ft3/s) Invert Elevation Water Surface Depth of Froude (ft) Elevation (ft) Water (ft) Number 31000 800 -230 -225.33 4.67 0.21 30500 800 -230.2 -225.77 4.43 0.42 30000 800 -230.4 -225.8 4.6 0.24 29500 800 -230.6 -225.94 4.66 0.26 44 29000 800 -230.8 -225.96 4.84 0.17 28500 800 -231 -226.05 4.95 0.19 28000 800 -231.04 -226.16 4.88 0.22 27500 800 -231.09 -226.34 4.75 0.28 27000 800 -231.13 -226.45 4.68 0.24 26500 800 -231.17 -226.65 4.52 0.3 26000 800 -231.22 -226.73 4.49 0.22 25500 800 -231.26 -227 4.26 0.34 25000 800 -231.3 -227.07 4.23 0.23 24500 800 -232 -227.18 4.82 0.23 24000 800 -232.16 -227.29 4.87 0.23 23500 800 -232.32 -227.45 4.87 0.26 23000 800 -232.49 -227.55 4.94 0.23 22500 800 -232.65 -227.64 5.01 0.22 22000 800 -231.81 -227.93 3.88 0.37 21500 800 -233 -228.02 4.98 0.24 21000 800 -233.1 -228.24 4.86 0.3 20500 800 -233.19 -228.31 4.88 0.23 20000 800 -233.29 -228.37 4.92 0.2 19500 800 -233.39 -228.5 4.89 0.24 19000 800 -233.49 -228.57 4.92 0.2 18500 800 -233.58 -228.63 4.95 0.19 18000 800 -233.68 -228.68 5 0.17 17500 800 -233 -228.77 4.23 0.21 17000 800 -234 -228.91 5.09 0.24 16500 800 -234.01 -228.91 5.1 0.13 16000 800 -234.02 -229.01 5.01 0.2 15500 800 -234.04 -229.02 5.02 0.13 15000 800 -234.05 -229.44 4.61 0.43 14500 800 -234.06 -229.43 4.63 0.22 45 14000 800 -235 -229.55 5.45 0.23 13500 800 -235.2 -229.61 5.59 0.19 13000 800 -235.4 -229.67 5.73 0.19 12500 800 -235.6 -229.75 5.85 0.19 12000 800 -235.8 -229.86 5.94 0.22 11500 800 -236 -229.89 6.11 0.16 11000 800 -236.3 -229.95 6.35 0.17 10500 800 -236.7 -230.09 6.61 0.22 10000 800 -237 -230.04 6.96 0.1 9500 800 -237.17 -230.08 7.09 0.13 9000 800 -237.33 -230.28 7.05 0.25 8500 800 -237.5 -230.42 7.08 0.27 8000 800 -237.67 -230.33 7.34 0.08 7500 800 -237.83 -231.88 5.95 0.78 7000 800 -238 -231.39 6.61 0.27 6500 800 -238.17 -231.82 6.35 0.4 6000 800 -238.29 -231.68 6.61 0.15 5500 800 -238.42 -232.18 6.24 0.41 5000 800 -238.54 -232.13 6.41 0.22 4500 800 -238.67 -232.29 6.38 0.27 4000 800 -238.8 -232.44 6.36 0.26 3500 800 -238.92 -233.91 5.01 0.77 3000 800 -239.05 -234.64 4.41 0.64 2500 800 -239.17 -234.57 4.6 0.28 2000 800 -239.67 -234.94 4.73 0.39 1500 800 -239.8 -235.22 4.58 0.38 1000 800 -239.9 -235.57 4.33 0.41 500 800 -240 -237.1 2.9 0.93 0 800 -240 -236.78 3.22 0.27 46 Table B.3.3 Steady Flow Simulation Output for flowrate 2040 ft3/s River Station Flowrate (ft3/s) Invert Elevation Water Surface Depth of Froude (ft) Elevation (ft) Water ft) Number 31000 2040 -230 -221.17 8.83 0.2 30500 2040 -230.2 -221.7 8.5 0.38 30000 2040 -230.4 -221.62 8.78 0.23 29500 2040 -230.6 -221.75 8.85 0.25 29000 2040 -230.8 -221.73 9.07 0.17 28500 2040 -231 -221.84 9.16 0.19 28000 2040 -231.04 -221.94 9.1 0.22 27500 2040 -231.09 -222.13 8.96 0.27 27000 2040 -231.13 -222.21 8.92 0.23 26500 2040 -231.17 -222.39 8.78 0.27 26000 2040 -231.22 -222.41 8.81 0.21 25500 2040 -231.26 -222.66 8.6 0.29 25000 2040 -231.3 -222.66 8.64 0.2 24500 2040 -232 -222.77 9.23 0.22 24000 2040 -232.16 -222.87 9.29 0.22 23500 2040 -232.32 -223.01 9.31 0.25 23000 2040 -232.49 -223.09 9.4 0.23 22500 2040 -232.65 -223.14 9.51 0.21 22000 2040 -231.81 -223.37 8.44 0.28 21500 2040 -233 -223.42 9.58 0.22 21000 2040 -233.1 -223.62 9.48 0.27 20500 2040 -233.19 -223.62 9.57 0.2 20000 2040 -233.29 -223.67 9.62 0.18 19500 2040 -233.39 -223.77 9.62 0.21 19000 2040 -233.49 -223.8 9.69 0.18 18500 2040 -233.58 -223.85 9.73 0.17 18000 2040 -233.68 -223.88 9.8 0.16 47 17500 2040 -233 -223.93 9.07 0.17 17000 2040 -234 -224.07 9.93 0.21 16500 2040 -234.01 -224.01 10 0.12 16000 2040 -234.02 -224.14 9.88 0.18 15500 2040 -234.04 -224.11 9.93 0.11 15000 2040 -234.05 -224.53 9.52 0.33 14500 2040 -234.06 -224.45 9.61 0.18 14000 2040 -235 -224.56 10.44 0.21 13500 2040 -235.2 -224.6 10.6 0.18 13000 2040 -235.4 -224.63 10.77 0.17 12500 2040 -235.6 -224.69 10.91 0.18 12000 2040 -235.8 -224.83 10.97 0.21 11500 2040 -236 -224.83 11.17 0.16 11000 2040 -236.3 -224.9 11.4 0.17 10500 2040 -236.7 -225.1 11.6 0.23 10000 2040 -237 -224.98 12.02 0.1 9500 2040 -237.17 -225.05 12.12 0.14 9000 2040 -237.33 -225.35 11.98 0.26 8500 2040 -237.5 -225.47 12.03 0.27 8000 2040 -237.67 -225.3 12.37 0.09 7500 2040 -237.83 -227.8 10.03 0.8 7000 2040 -238 -226.86 11.14 0.3 6500 2040 -238.17 -227.36 10.81 0.41 6000 2040 -238.29 -227.1 11.19 0.17 5500 2040 -238.42 -227.94 10.48 0.45 5000 2040 -238.54 -227.74 10.8 0.24 4500 2040 -238.67 -227.9 10.77 0.28 4000 2040 -238.8 -228.13 10.67 0.29 3500 2040 -238.92 -231.35 7.57 1 3000 2040 -239.05 -231.68 7.37 0.68 48 2500 2040 -239.17 -231.4 7.77 0.32 2000 2040 -239.67 -232 7.67 0.46 1500 2040 -239.8 -232.33 7.47 0.44 1000 2040 -239.9 -232.8 7.1 0.48 500 2040 -240 -235.05 4.95 1.01 0 2040 -240 -234.35 5.65 0.29 B.4 Sediment Transport Simulation Output Table B.4.1 Channel Invert Elevation Output (Sediment Load-Tofaletti- Copeland) River Station 1 (09NOV2010 00:00:00)-Ch Invert El (ft) 3377 (06FEB2020 00:00:00)-Ch Invert El (ft) 31000 -230 -234.2813 30500 -230.2 -235.2 30000 -230.4 -234.838 29500 -230.6 -235.5925 29000 -230.8 -235.1715 28500 -231 -235.991 28000 -231.04 -235.6922 27500 -231.09 -236.0829 27000 -231.13 -235.8171 26500 -231.17 -236.1634 26000 -231.22 -235.8397 25500 -231.26 -236.2538 25000 -231.3 -235.9264 24500 -232 -236.9586 24000 -232.16 -237.0752 23500 -232.32 -237.2458 23000 -232.49 -237.4888 22500 -232.65 -237.3522 49 22000 -231.81 -236.7346 21500 -233 -237.6693 21000 -233.1 -238.0917 20500 -233.19 -237.8611 20000 -233.29 -237.7668 19500 -233.39 -238.3813 19000 -233.49 -237.8863 18500 -233.58 -237.924 18000 -233.68 -237.5835 17500 -233 -237.2337 17000 -234 -238.996 16500 -234.01 -236.7595 16000 -234.02 -238.6894 15500 -234.04 -236.6779 15000 -234.05 -239.0425 14500 -234.06 -238.3447 14000 -235 -239.9913 13500 -235.2 -239.6012 13000 -235.4 -239.5925 12500 -235.6 -240.1431 12000 -235.8 -240.7967 11500 -236 -239.6777 11000 -236.3 -240.2379 10500 -236.7 -241.7 10000 -237 -238.2769 9500 -237.17 -239.7931 9000 -237.33 -242.3262 8500 -237.5 -242.5 8000 -237.67 -238.2484 7500 -237.83 -242.8259 50 7000 -238 -242.5383 6500 -238.17 -244.4014 6000 -238.29 -240.849 5500 -238.42 -239.4057 5000 -238.54 -241.2115 4500 -238.67 -240.3858 4000 -238.8 -240.3064 3500 -238.92 -241.4049 3000 -239.05 -243.1328 2500 -239.17 -240.9625 2000 -239.67 -241.0031 1500 -239.8 -240.3422 1000 -239.9 -242.2669 500 -240 -240.0957 0 -240 -240.1859 Table B.4.2 Channel Mass Balance Cumulative Output (Sediment Load- Tofaletti-Copeland) River Station 3377 (06FEB2020 00:00:00)-Mass Out 3377 (06FEB2020 00:00:00)-Mass In Cum: All (tons) Cum: All (tons) 31000 1793558 1791191 30500 1797165 1793558 30000 1801870 1797165 29500 1806788 1801870 29000 1812794 1806788 28500 1818202 1812794 28000 1823192 1818202 27500 1827645 1823192 27000 1832255 1827645 26500 1836705 1832255 51 26000 1841690 1836705 25500 1846145 1841690 25000 1851427 1846145 24500 1856445 1851427 24000 1861144 1856445 23500 1865593 1861144 23000 1870129 1865593 22500 1874903 1870129 22000 1879416 1874903 21500 1883869 1879416 21000 1887953 1883869 20500 1892857 1887953 20000 1897912 1892857 19500 1902973 1897912 19000 1908167 1902973 18500 1913485 1908167 18000 1918533 1913485 17500 1924396 1918533 17000 1929370 1924396 16500 1934150 1929370 16000 1939499 1934150 15500 1944151 1939499 15000 1947784 1944151 14500 1952684 1947784 14000 1957313 1952684 13500 1961762 1957313 13000 1966372 1961762 12500 1970685 1966372 12000 1974867 1970685 11500 1978810 1974867 52 11000 1982747 1978810 10500 1986475 1982747 10000 1988250 1986475 9500 1991231 1988250 9000 1994326 1991231 8500 1997459 1994326 8000 1998139 1997459 7500 1999735 1998139 7000 2002449 1999735 6500 2005653 2002449 6000 2008294 2005653 5500 2008782 2008294 5000 2010769 2008782 4500 2011997 2010769 4000 2012959 2011997 3500 2013893 2012959 3000 2016331 2013893 2500 2017992 2016331 2000 2018941 2017992 1500 2019362 2018941 1000 2021160 2019362 500 2021233 2021160 0 2021475 2021233 53 B.5 Simulation Output and Observed Data Comparison Table B.5.1 Model Calibration with n value. Flowrate Observed Water Surface Elevation Simulated Water Surface Difference (ft3/s) (ft) Elevation (ft) (ft) 766 -231.41 -231.4 0.01 768 -231.4 -231.39 0.01 770 -231.39 -231.38 0.01 773 -231.38 -231.37 0.01 775 -231.37 -231.36 0.01 777 -231.36 -231.35 0.01 779 -231.35 -231.34 0.01 781 -231.34 -231.33 0.01 783 -231.33 -231.32 0.01 785 -231.32 -231.31 0.01 787 -231.31 -231.3 0.01 790 -231.3 -231.29 0.01 792 -231.29 -231.28 0.01 794 -231.28 -231.27 0.01 796 -231.27 -231.26 0.01 798 -231.26 -231.25 0.01 800 -231.25 -231.24 0.01 802 -231.24 -231.23 0.01 804 -231.23 -231.22 0.01 807 -231.22 -231.21 0.01 809 -231.21 -231.2 0.01 811 -231.2 -231.19 0.01 813 -231.19 -231.18 0.01 817 -231.17 -231.16 0.01 819 -231.16 -231.15 0.01 54 821 -231.15 -231.14 0.01 824 -231.14 -231.13 0.01 826 -231.13 -231.12 0.01 830 -231.11 -231.1 0.01 832 -231.1 -231.09 0.01 834 -231.09 -231.08 0.01 836 -231.08 -231.08 0 840 -231.06 -231.06 0 Note: The water surface comparison was analyzed with flowrates recorded on November 9th, 2010 at the Garst Road, which is the Station 65+00 in this study. 55 B.5.2 Sediment Transport Simulation Comparison with Surveying Data on October 3rd, 2016 at Station 0+00 to Station 65+00. Group #1: Rate Curve Sediment Load (Yang-Copeland) Station Measured Measured 10/3/2016 10/3/2016 Difference 02/06/2020 (ft) (ft) (ft) (ft) (ft)) (ft) 6500 757.0 -243 755.6 -244.4057 1.4 -244.4005 6000 759.0 -241 759.6 -240.3503 -0.6 -240.6086 5500 762.0 -238 760.6 -239.4034 1.4 -239.4034 5000 760.0 -240 758.8 -241.2013 1.2 -241.2013 4500 761.0 -239 759.6 -240.3747 1.4 -240.3747 4000 761.0 -239 759.7 -240.3104 1.3 -240.3104 3500 760.0 -240 758.6 -241.4054 1.4 -241.4054 3000 758.2 -241.8 756.9 -243.1293 1.3 -243.1293 2500 760.2 -239.8 759.0 -240.9691 1.2 -240.9691 2000 761.4 -238.6 759.0 -241.0038 2.4 -241.0038 1500 762.0 -238 759.7 -240.3418 2.3 -240.3418 1000 759.0 -241 757.7 -242.2689 1.3 -242.2689 500 761.8 -238.2 759.9 -240.0921 1.9 -240.0921 0 762.0 -238 759.7 -240.2845 2.3 -240.2845 Average 1.4 Group #2: Rate Curve Sediment Load (Yang-Active) Station Measured Measured 10/3/2016 10/3/2016 Difference 02/06/2020 (ft) (ft) (ft) (ft) (ft) (ft) 6500 757.0 -243 755.6 -244.4012 1.4 -244.4009 6000 759.0 -241 760.1 -239.9067 -1.1 -240.5305 5500 762.0 -238 760.6 -239.4017 1.4 -239.4017 5000 760.0 -240 758.8 -241.2024 1.2 -241.2024 4500 761.0 -239 759.6 -240.385 1.4 -240.385 4000 761.0 -239 759.7 -240.307 1.3 -240.307 56 3500 760.0 -240 758.6 -241.406 1.4 -241.406 3000 758.2 -241.8 756.9 -243.1323 1.3 -243.1323 2500 760.2 -239.8 759.0 -240.9774 1.2 -240.9774 2000 761.4 -238.6 759.0 -241.0018 2.4 -241.0018 1500 762.0 -238 759.7 -240.3403 2.3 -240.3403 1000 759.0 -241 757.7 -242.2676 1.3 -242.2676 500 761.8 -238.2 759.9 -240.0921 1.9 -240.0921 0 762.0 -238 759.7 -240.2828 2.3 -240.2916 Average 1.4 Group #3: Rate Curve Sediment Load (Toffaleti-Copeland) Station Measured Measured 10/3/2016 10/3/2016 Difference 02/06/2020 (ft) (ft) (ft) (ft) (ft) (ft) 6500 757.0 -243 755.6 -244.4014 1.4 -244.4014 6000 759.0 -241 759.5 -240.4621 -0.5 -240.849 5500 762.0 -238 760.6 -239.4057 1.4 -239.4057 5000 760.0 -240 758.8 -241.2115 1.2 -241.2115 4500 761.0 -239 759.6 -240.3858 1.4 -240.3858 4000 761.0 -239 759.7 -240.3064 1.3 -240.3064 3500 760.0 -240 758.6 -241.4049 1.4 -241.4049 3000 758.2 -241.8 756.9 -243.1328 1.3 -243.1328 2500 760.2 -239.8 759.0 -240.9625 1.2 -240.9625 2000 761.4 -238.6 759.0 -241.0031 2.4 -241.0031 1500 762.0 -238 759.7 -240.3422 2.3 -240.3422 1000 759.0 -241 757.7 -242.2669 1.3 -242.2669 500 761.8 -238.2 759.9 -240.0957 1.9 -240.0957 0 762.0 -238 759.9 -240.0859 2.1 -240.1859 Average: 1.4 57 Group #4: Rate Curve Sediment Load (Toffaleti-Active) Station Measured Measured 10/3/2016 10/3/2016 Difference 02/06/2020 (ft) (ft) (ft) (ft) (ft) (ft) 6500 757.0 -243 755.6 -244.4023 1.4 -244.4023 6000 759.0 -241 760.3 -239.7017 -1.3 -240.5911 5500 762.0 -238 760.6 -239.4091 1.4 -239.4091 5000 760.0 -240 758.8 -241.2368 1.2 -241.2368 4500 761.0 -239 759.6 -240.3951 1.4 -240.3951 4000 761.0 -239 759.7 -240.3034 1.3 -240.3034 3500 760.0 -240 758.6 -241.409 1.4 -241.409 3000 758.2 -241.8 756.9 -243.1419 1.3 -243.1509 2500 760.2 -239.8 759.0 -240.9861 1.2 -240.9861 2000 760.4 -239.6 759.0 -241.007 1.4 -241.007 1500 762.2 -237.8 759.7 -240.3411 2.5 -240.3411 1000 759.0 -241 757.7 -242.274 1.3 -242.274 500 761.4 -238.6 760.0 -240 1.4 -240 0 761.8 -238.2 759.7 -240.2837 2.1 -240.2837 Average: 1.3 Group #5: Rate Curve Suspended Solid Concentration (Yang- Copeland) Station Measured Measured 10/3/2016 (ft) 10/3/2016 Difference 02/06/2020 (ft) (ft) (ft) (ft) (ft) 6500 757.0 -243 755.6 -244.4065 1.4 -244.4074 6000 759.0 -241 760.3 -239.7179 -1.3 -239.5436 5500 762.0 -238 760.6 -239.4021 1.4 -239.4021 5000 760.0 -240 758.8 -241.2206 1.2 -241.2251 4500 761.0 -239 759.6 -240.3732 1.4 -240.3732 4000 761.0 -239 759.7 -240.3104 1.3 -240.3104 58 3500 760.0 -240 758.6 -241.4058 1.4 -241.4058 3000 758.2 -241.8 756.9 -243.1333 1.3 -243.1333 2500 760.2 -239.8 759.0 -240.9736 1.2 -240.9736 2000 761.4 -238.6 759.0 -241.0058 2.4 -241.0058 1500 762.0 -238 759.7 -240.3419 2.3 -240.3419 1000 759.0 -241 757.7 -242.2726 1.3 -242.2726 500 761.8 -238.2 759.9 -240.0921 1.9 -240.0921 0 762.0 -238 759.7 -240.2845 2.3 -240.2845 1.4 Group #6: Rate Curve Suspended Solid Concentration (Yang- Active) Station Measured Measured 10/3/2016 10/3/2016 Difference 02/06/2020 (ft) (ft) (ft) (ft) (ft) (ft) 6500 757.0 -243 755.6 -244.4001 1.4 -244.4087 6000 759.0 -241 760.4 -239.6442 -1.4 -239.7663 5500 762.0 -238 760.6 -239.401 1.4 -239.401 5000 760.0 -240 758.8 -241.1883 1.2 -241.1883 4500 761.0 -239 759.6 -240.3921 1.4 -240.3921 4000 761.0 -239 759.7 -240.3076 1.3 -240.3076 3500 760.0 -240 758.6 -241.404 1.4 -241.404 3000 758.2 -241.8 756.9 -243.1338 1.3 -243.1338 2500 760.2 -239.8 759.0 -240.9639 1.2 -240.9639 2000 761.4 -238.6 759.0 -241.0018 2.4 -241.0018 1500 762.0 -238 759.7 -240.3405 2.3 -240.3405 1000 759.0 -241 757.7 -242.2633 1.3 -242.2633 500 761.8 -238.2 759.9 -240.0921 1.9 -240.0921 0 762.0 -238 759.7 -240.2836 2.3 -240.2836 Average: 1.4 59 Group #7: Rate Curve Suspended Solid Concentration (Toffaleti-Copeland) Station Measured Measured 10/3/2016 10/3/2016 Difference 02/06/2020 (ft) (ft) (ft) (ft) (ft) (ft) 6500 757.0 -243 755.6 -244.4035 1.4 -244.4009 6000 759.0 -241 759.8 -240.2357 -0.8 -240.2505 5500 762.0 -238 760.6 -239.4053 1.4 -239.4053 5000 760.0 -240 758.8 -241.219 1.2 -241.219 4500 761.0 -239 759.6 -240.3849 1.4 -240.3849 4000 761.0 -239 759.7 -240.3028 1.3 -240.3028 3500 760.0 -240 758.6 -241.4047 1.4 -241.4047 3000 758.2 -241.8 756.9 -243.1281 1.3 -243.1281 2500 760.2 -239.8 759.0 -240.9679 1.2 -240.9679 2000 761.4 -238.6 759.0 -241.0091 2.4 -241.0091 1500 762.0 -238 759.7 -240.3421 2.3 -240.3421 1000 759.0 -241 757.7 -242.2701 1.3 -242.2701 500 761.8 -238.2 759.9 -240.0957 1.9 -240.0957 0 762.0 -238 759.7 -240.2859 2.3 -240.2859 Average: 1.4 Group #8: Rate Curve Suspended Solid Concentration (Toffaleti-Active) Station Measured Measured 10/3/2016 (ft) 10/3/2016 (ft) Difference 02/06/2020 (ft) (ft) (ft) (ft) 6500 757.0 -243 755.6 -244.4049 1.4 -244.4047 6000 759.0 -241 759.6 -240.3878 -0.6 -240.8791 5500 762.0 -238 760.6 -239.4097 1.4 -239.4097 5000 760.0 -240.2 758.8 -241.2337 1.0 -241.2337 4500 761.0 -239 759.6 -240.392 1.4 -240.392 4000 761.0 -239 759.7 -240.304 1.3 -240.304 60 3500 760.0 -240 758.6 -241.4005 1.4 -241.4005 3000 758.2 -241.8 756.9 -243.131 1.3 -243.131 2500 760.2 -241.8 759.0 -240.9762 -0.8 -240.9762 2000 761.4 -237.8 759.0 -241.0085 3.2 -241.0085 1500 762.0 -241 759.7 -240.342 -0.7 -240.342 1000 759.0 -238.6 757.7 -242.2622 3.7 -242.2622 500 761.8 -238.2 759.9 -240.0914 1.9 -240.0914 0 762.0 -237 759.7 -240.2834 3.3 -240.2834 Average: 1.4 61