Assessing Sediment Transport In Alamo River

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

ii

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

iii

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.

iv

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.

v

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

vi

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

vii

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

1

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

2

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.

3

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,

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.

Figure 1. Salton Sea (red line) and Alamo Reach (blue line) are shown.

4

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

5

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

discharge in stream. This relationship was expressed as a power function Qs =a Qwb

(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-

Transport Curve tested was Qs =0.1889 Qw1.1981. 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

6

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:

Qs =a Qwb (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)

7

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

Regional Water Quality Control Board,

Colorado River Basin Region (Date:

05/03/2002)

Flow-Sediment Load Data

Depth of Water Data at the Garst

Road

Field Surveying (Date: 02/06/2020)

8

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:

∆𝑥 ≤0.15𝐷

𝑆𝑜 (Eq.3)

Where ∆𝑥 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).

9

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.

Z2 + Y2 +a2V2

2

2g= Z1 + Y1 +

a1V12

2g+ he (Eq. 4)

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).

Fr = 𝑉

√(𝑔∗𝐷) (Eq. 5)

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 ft3/s

10

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):

(1- λp) B𝜕𝜂

𝜕𝑡= −

𝜕𝑄𝑠

𝜕𝑥 (Eq. 6)

Where B is the channel width; 𝜆𝑝 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-

11

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

12

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

13

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.

14

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 6 th,

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.

15

Table 3. Various elevations and gauge height data recorded at the Garst Road at different period.

Flowrate

(1) (ft3/s)

Invert

Elevation (ft)

Depth of Water

(ft)

Water

Surface

Elevation

(ft)

Gauge

Height

(ft)

01/30/1999

(UC Davis

Study

Team)

782 (2) -234 6.0 -228 NA

11/09/2010

(USGS

DEM

National

Map

Viewer)

820 NA NA -229 1.0(5)

10/3/2016

(Federal

Fish and

Wildlife

Service)

780 -242 10.4 -233(3) 68.6(4)

02/06/2020

(CPP

Field

Survey)

740 -242.6

9.3 -233.3 66.7(6)

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

16

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.

17

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.

18

R² = 0.9985

-231.55

-231.5

-231.45

-231.4

-231.35

-231.3

-231.25

-231.2

-231.15

-231.1

-231.45-231.4 -231.35-231.3 -231.25-231.2 -231.15-231.1 -231.05 -231

Sim

ula

ted

Wate

r S

urf

ace

Ele

va

tio

n (

ft)

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.

19

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

20

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.

21

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.

22

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.

23

Table 5. Average value computed in steady flow simulations

Flow

(ft3/s)

Average Depth of

Water (ft)

Average

Velocity (ft/s)

Average Top

Width (ft)

Froude

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.

24

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

25

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.

26

27

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.

28

Table 6. Simulated result of invert elevation changes in average and cumulative sediment mass capacity by eight different sediment input-combinations

Inert Elevation

Change (ft) Cumulative Sediment Mass

Capacity of the Alamo Reach

from 2010 to 2020 (tons)

Sediment Mass Capacity

of the Alamo Reach

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

29

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

30

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.

31

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20

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F T a

32

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).

33

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.

34

REFERENCES

(1) Rosgen, D. (1994) A Classification of Natural Rivers [online] Available at:

<http://pages.geo.wvu.edu/~kite/Rosgen1994ClassificationRivers.pdf >

[Accessed 27 March. 2019]

(2) Fondriest Environmental, Inc., (2014) Sediment Transport and Deposition

[Online]

availableat<https://www.fondriest.com/environmentalmeasurements/parameters/h

ydrology/sediment-transport-deposition> [Accessed 28 Mar.2019].

(3) Joshi, N., Lamichhane, G., Rahaman, M., Kalra, A., Ahamd, S., (2019).

Application of HEC-RAS to Study the Sediment Transport Characteristics of

Manmee River in Ohio

[Online]availableat:<https://ascelibrary.org/doi/10.1061/9780784482353.024>[Ac

cessed 26 July.2019].

(4) Zavattero, E., Du, M., Ma, Q., Delestre, O., Gourbesville, P., (2016). 2D Sediment

Transport Modelling in High Energy River- Application to Var River, France

[Online]

availableat:<https://reader.elsevier.com/reader/sd/pii/S1877705816319385?token

=3FAC5AFE17E6C345D917254E9DFE0232A916579B428985E2651845658FD

76DACF5327F6AA8B53992725699FAB4C7863A> [Accessed 26 July.2019].

(5) Normant, C., (2000) Three-Dimensional Modelling of Cohesive Sediment

Transport in the Loire Estuary [Online] available at<https://doi.org/10.1002/1099-

1085(200009)14:13<2231::AID- HYP25>3.0.CO;2-%23> [Accessed 26

July.2019].

35

(6) Formann, E., Habersack, H.M., Schober, S.T. (2007). Morphodynamic River

Processes and Techniques for Assessment of Channel Evolution in Alpine Gravel

Bed River. 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:

<https://www.researchgate.net/publication/317073446_New_One-

Dimensional_Sediment_Features_in_HEC-RAS_50_and_51> [Accessed 26

July.2019].

36

(12) Tetra Tech,Inc., Wetlands Management Services., (2007) New and Alamo River

Wetlands Master Plan. [online]availableat:

<https://www2.usgs.gov/saltonsea/docs/SSA/New_and_Alamo_River_Master_Pl

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

<https://www.waterboards.ca.gov/coloradoriver/water_issues/programs/tmdl/docs

/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:<https://www.researchgate.net/publication/310465314_Application_of_HEC-

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:

<https://onlinelibrary.wiley.com/doi/full/10.1111/lre.12142> [Accessed 27 Mar.

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.

37

[online] available

at:<https://www.tandfonline.com/doi/pdf/10.1080/19475705.2016.1181340?need

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:

<https://www.freese.com/sites/default/files/files/HEC-

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:<https://onlinelibrary.wiley.com/doi/full/10.1111/j.17521688.2012.006

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

<https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/Engineering-

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

38

SFSI Monitoring at NEES permanently Instrumented Field Sites [Online]

available at

<https://www.pwri.go.jp/eng/ujnr/tc/a/ssi_w3/Contributions/Youd.pdf> [Accessed

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

<https://www.waterboards.ca.gov/rwqcb7/water_issues/programs/tmdl/docs/alam

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].

39

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”

40

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

41

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).

42

Quasi Unsteady Flow Series:

Figure B.2.2 Plot for Quasi- Unsteady flow Hydrograph

43

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

(ft)

Water Surface

Elevation (ft)

Depth of

Water (ft)

Froude

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

44

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

(ft)

Water Surface

Elevation (ft)

Depth of

Water (ft)

Froude

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

45

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

46

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

47

Table B.3.3 Steady Flow Simulation Output for flowrate 2040 ft3/s

River Station Flowrate (ft3/s) Invert Elevation

(ft)

Water Surface

Elevation (ft)

Depth of

Water ft)

Froude

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

48

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

49

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

50

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

51

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

Cum: All (tons)

3377 (06FEB2020 00:00:00)-Mass In

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

52

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

53

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

54

B.5 Simulation Output and Observed Data Comparison

Table B.5.1 Model Calibration with n value.

Flowrate

(ft3/s)

Observed Water Surface Elevation

(ft)

Simulated Water Surface

Elevation (ft)

Difference

(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

55

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.

56

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

(ft)

Measured

(ft)

10/3/2016

(ft)

10/3/2016

(ft)

Difference

(ft))

02/06/2020

(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

(ft)

Measured

(ft)

10/3/2016

(ft)

10/3/2016

(ft)

Difference

(ft)

02/06/2020

(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

57

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

(ft)

Measured

(ft)

10/3/2016

(ft)

10/3/2016

(ft)

Difference

(ft)

02/06/2020

(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

58

Group #4: Rate Curve Sediment Load (Toffaleti-Active)

Station Measured

(ft)

Measured

(ft)

10/3/2016

(ft)

10/3/2016

(ft)

Difference

(ft)

02/06/2020

(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

(ft)

Measured

(ft)

10/3/2016 (ft) 10/3/2016

(ft)

Difference

(ft)

02/06/2020

(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

59

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

(ft)

Measured

(ft)

10/3/2016

(ft)

10/3/2016

(ft)

Difference

(ft)

02/06/2020

(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

60

Group #7: Rate Curve Suspended Solid Concentration (Toffaleti-Copeland)

Station Measured

(ft)

Measured

(ft)

10/3/2016

(ft)

10/3/2016

(ft)

Difference

(ft)

02/06/2020

(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

(ft)

Measured

(ft)

10/3/2016 (ft) 10/3/2016 (ft) Difference

(ft)

02/06/2020

(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

61

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