ERDC/CHL T

R-20-17 

Dredging Innovations Group

Utilizing Stream Flows to Forecast Dredging

Requirements 

Coastal and Hydraulics Laboratory 

Elissa M. Yeates, Ahmad A. Tavakoly, Kenneth N. Mitchell,

Gregory W. Dreaper, Shahab Afshari

August 2020

Approved for public release; distribution is unlimited
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Cover photo: USACE Marine Design Center
Dredging Innovations Group ERDC/CHL TR-20-17 

August 2020 

Utilizing Stream Flows to Forecast Dredging 

Requirements 

Elissa M. Yeates, Ahmad A. Tavakoly, Kenneth N. Mitchell, and Gregory W. Dreaper 

Coastal and Hydraulics Laboratory

US Army Engineer Research and Development Center

3909 Halls Ferry Road

Vicksburg, Mississippi 39180-6199

Shahab Afshari 

Climate System Research Center Laboratory

Department of Geosciences

University of Massachusetts 

Amherst, Massachusetts 01003

Final report

Approved for public release; distribution is unlimited

Prepared for US Army Corps of Engineers

Washington, DC 20314-1000

Under Funding Account No. 476553, “Watershed Informed Shoal Forecasting,”

Dredging Innovations Group
ERDC/CHL TR-20-17 ii

Abstract

In recent years, the United States Army Corps of Engineers (USACE) has 

spent an average of approximately a billion dollars annually for navigation 

channel maintenance dredging. To execute these funds effectively, USACE 

districts must determine which navigation channels are most in need of 

maintenance dredging each year. Traditionally, dredging volume estimates 

for Operations and Maintenance budget development are based on 

experiential knowledge and historic averages, with the effects of upstream, 

precipitation-driven streamflows considered via general-rule

approximations. This study uses the Streamflow Prediction Tool, a 

hydrologic routing model driven by global weather forecast ensembles, 

and dredging records from the USACE Galveston District to explore 

relationships between precipitation-driven inland channel flow and 

subsequent dredged volumes in the downstream coastal channel reaches. 

Spatially based regression relationships are established between 

cumulative inland flows and dredged volumes. Results in the test cases of 

the Houston Ship Channel and the Sabine-Neches Waterway in Texas 

indicate useful correlations between the computed streamflow volumes 

and recorded dredged volumes. These relationships are stronger for 

channel reaches farther inland, upstream of the coastal processes that are 

not included in the precipitation-driven hydrologic model. 

DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. 

Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. 

All product names and trademarks cited are the property of their respective owners. The findings of this report are not to 

be construed as an official Department of the Army position unless so designated by other authorized documents.

DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR.
ERDC/CHL TR-20-17 iii

Contents

Abstract.................................................................................................................................... ii

Figures and Tables...................................................................................................................v

Preface.....................................................................................................................................vi

Executive Summary...............................................................................................................vii

1 Introduction ...................................................................................................................... 1

1.1 Background........................................................................................................1

1.2 Objective ............................................................................................................2

1.3 Approach ............................................................................................................3

2 Watershed Scale Hydrologic Modeling.........................................................................4

2.1 Routing Application for Parallel computatIon of Discharge (RAPID) 

hydrologic routing model.............................................................................................4

2.2 NHDPlus stream network..................................................................................5

2.3 Streamflow Prediction Tool (SPT)......................................................................6

3 Methodology..................................................................................................................... 8

3.1 Study area of interest........................................................................................8

3.2 Data processing.................................................................................................9

3.3 Models applied for analysis ............................................................................15

4 Results.............................................................................................................................17

4.1 Analysis overview.............................................................................................17

4.2 Single-variate linear model results.................................................................17

4.2.1 Single variate linear model results – Sabine-Neches Waterway............................18

4.2.2 Single variate linear model results — Houston Ship Channel.................................24

4.3 Nonlinear and multivariate model analyses ................................................. 28

4.3.1 Nonlinear and multivariate model results — Sabine-Neches Waterway ................29

4.3.2 Nonlinear and multivariate model results — Houston Ship Channel .....................31

5 Conclusion and Future Work ........................................................................................34

5.1 Study limitations............................................................................................. 34

5.2 Conclusion from analysis ............................................................................... 34

5.3 Future work ..................................................................................................... 35

References.............................................................................................................................36

Appendix A: Hydrographs for Analyzed Streams...............................................................38

Appendix B: Regression Model Results.............................................................................43

Unit Conversion Factors.......................................................................................................54
ERDC/CHL TR-20-17 iv

Acronyms and Abbreviations...............................................................................................55

Report Documentation Page
ERDC/CHL TR-20-17 v

Figures and Tables

Figures

Figure 1. The NHDPlus representation of the Texas Gulf Coast Region. .............................. 5

Figure 2. Selecting a stream reach through the SPT Tethys portal. ...................................... 7

Figure 3. Viewing historical flow time series in the SPT Tethys portal................................... 7

Figure 4. Data processing work flow.......................................................................................11

Figure 5. Time series output of cumulative streamflow and dredged volumes at 

reach 1440511 in the Houston Ship Channel. .....................................................................13

Figure 6. Hydrograph output of the SPT flow rates for stream reach 1440511 in 

the Houston Ship Channel.......................................................................................................14

Figure 7. Examined COMIDs and dredged channel areas in the Sabine-Neches 

Waterway. ..................................................................................................................................19

Figure 8. Model 1 Results for COMIDs 1112455, 1115825, and 1477515 in the 

Sabine-Neches Waterway........................................................................................................21

Figure 9. Results for COMIDs 1477725, 1477713, and 1477589 in the SabineNeches Waterway.....................................................................................................................22

Figure 10. Results for COMIDs 1477595, 1481563, and 24719331 in the 

Sabine-Neches Waterway........................................................................................................23

Figure 11. Examined COMIDs and dredged channel areas in the Houston Ship 

Channel. ....................................................................................................................................25

Figure 12. Results for COMIDs 1440525, 1440539, and 1440511 in the 

Houston Ship Channel. ............................................................................................................26

Figure 13. Results for COMIDs 1440521 and 1440485 in the Houston Ship 

Channel. ....................................................................................................................................27

Figure 14. Percentage error of predictions by model, Sabine-Neches Waterway..............29

Figure 15. Estimated versus observed volumes of dredged material for Reach 

1477725 in the Sabine-Neches Waterway............................................................................ 31

Figure 16. Percentage error of predictions by model, Houston Ship Channel. ..................32

Figure 17. Estimated versus observed volumes of dredged material for Reach 

1440525 in the Houston Ship Channel.................................................................................33

Tables

Table 1. Summary of stream reaches analyzed...................................................................... 9

Table 2. Description of variables used in regression models ..............................................15

Table 3. Equations of tested multi-level models....................................................................16

Table 4. Summary of single variable linear regression Model 1 fit. ....................................18

Table 5. R-squared and p-value by model for selected reaches, Sabine-Neches 

Waterway. ..................................................................................................................................30

Table 6. R-squared and p-value by model for selected reaches, Houston Ship 

Channel. ....................................................................................................................................33
ERDC/CHL TR-20-17 vi

Preface

Funding for this study was provided by the US Army Corps of Engineers 

(USACE) Navigation Business Line, Dredging Innovations Group under 

Funding Account No. 476553, “Watershed Informed Shoal Forecasting.” 

The technical monitor was Dr. Kenneth N. Mitchell. The Program Manager 

for Dredging Operations Technical Support was Dr. Burton Suedel.

Mr. Michael E. Ott was Chief of the Headquarters, USACE Navigation 

Branch and the Navigation Business Line Manager.

The work was performed by the Hydrologic Systems, River Engineering, 

and Coastal Engineering branches of the Flood & Storm Protection and 

Navigation Divisions, US Army Engineer Research and Development 

Center, Coastal and Hydraulic Laboratory (ERDC-CHL). At the time of 

publication, Dr. Hwai-Ping Cheng was Chief, Hydrologic Systems 

Branch; Dr. Cary A. Talbot was Chief, Flood and Storm Protection 

Division; and Mr. Charles E. Wiggins, CHL, was the Technical Director 

for Navigation. The Deputy Director of ERDC-CHL was Mr. Jeffrey R. 

Eckstein, and the Director was Dr. Ty V. Wamsley.

COL Teresa A. Schlosser was the Commander of ERDC, and the Director 

was Dr. David W. Pittman.
ERDC/CHL TR-20-17 vii

Executive Summary

The US Army Corps of Engineers (USACE) navigation mission requires 

periodic dredging of channels nationwide to maintain sufficient navigable 

depths for safe, reliable, and cost-effective shipping. Presently, USACE 

dredging managers lack tools to account explicitly for the impact of inland, 

precipitation-driven streamflow on near-term channel dredging 

requirements. Researchers at the US Army Engineer Research and 

Development Center, Coastal and Hydraulics Laboratory, have developed 

the Streamflow Prediction Tool (SPT), which uses global atmospheric and 

land-surface modeling products and a watershed-scale hydrologic routing 

scheme to produce estimated stream flow projections. This report 

documents the analytical steps used to estimate predictive relationships 

between modeled stream flows from the SPT and subsequent channel 

dredging requirements for the historical period of 1980–2014 for two 

federal navigation projects along the Texas coast. This analysis seeks to 

leverage existing dredging datasets and tools maintained by the USACE

towards insights useful in the channel maintenance process. 

Eight different single and multivariate, linear and nonlinear regression 

models were tested on the dredging event and streamflow datasets by 

stream reach in the Sabine-Neches Waterway and the Houston Ship 

Channel. Among the models tested, model performance is highest for a 

linear model that uses cumulative in-channel base flow volumes and high 

flow volumes between localized dredging events as predictors of 

subsequent dredged volumes. R-squared values indicate that this model 

captures 66%–94% of the variance in the dredged amounts in upstream 

reaches. This simple linear model can easily be applied to any reach for 

which historical, date-stamped dredging volumes are available. 

For the river systems examined, inland riverine flow appears highly 

correlated with subsequent dredging loads at reaches inland of bays and 

coastlines. Dredging loads in stream reaches where coastal processes 

impact flow are not well-predicted by these models. Recognizing those 

limitations, precipitation-driven flow volumes can provide additional 

information about channel maintenance needs without the deployment of 

data-intensive, high-fidelity sedimentation models. As streamflow 

forecasting improves to give longer lead times on flow variability, 

techniques such as this can inform dredging managers of expected channel 

maintenance requirements over the upcoming seasons. 
ERDC/CHL TR-20-17 1

1 Introduction

1.1 Background

The US Army Corps of Engineers (USACE) navigation mission is to provide 

safe, reliable, efficient, and environmentally sustainable waterborne 

transportation systems (channels, harbors, and waterways) for movement 

of commerce, national security needs, and recreation. Dredging to navigable 

depths and the placement and management of dredged materials are crucial 

in meeting this mission. Periodic dredging of harbors and rivers nationwide 

is required for safe transit of ships, barges, ferries, and other vessels. In 

fiscal years 2009 through 2018, USACE spent an average of $984 million 

annually on contracts for maintenance channel dredging in the contiguous 

United States, resulting in a total of 1.96 billion cy* of material dredged 

(Dredging Information System 2018). 

Currently, the standard in most USACE districts is for dredging managers 

and engineers to utilize their institutional knowledge gained from years of 

experience to estimate dredging patterns from year to year. USACE 

districts budget for channel dredging contract needs up to 2 years in 

advance. Providing these managers and engineers with additional 

information on stream conditions and projected sediment loads will 

improve the efficiency with which dredging contracts are designed and 

scheduled. Other current USACE efforts to improve dredging decision 

support include the Corps Shoaling Analysis Tool (CSAT) (Dunkin et al. 

2018). CSAT processes historical channel survey data to produce channelspecific volumetric rates of change, which are then extrapolated to project 

near-term (i.e., 1-3 years) sediment loads. As CSAT is entirely empirical, it 

does not explicitly account for the inter-annual effects of coastal processes 

or runoff from inland hydrologic systems on channel sediment loads.

The Streamflow Prediction Tool (SPT) was developed by US Army Engineer 

Research and Development Center (ERDC) and university partners to 

generate historic streamflows and projected future streamflows for tens to 

 

* For a full list of the spelled-out forms of the units of measure used in this document, please refer to US 

Government Publishing Office Style Manual, 31st ed. (Washington, DC: US Government Publishing 

Office 2016), 248-52, https://www.govinfo.gov/content/pkg/GPO-STYLEMANUAL-2016/pdf/GPOSTYLEMANUAL-2016.pdf.
ERDC/CHL TR-20-17 2

hundreds of thousands of reaches within watersheds that may not have 

available gage data (Snow et al. 2016). By incorporating a large spatial 

extent and the converging flow volumes of streams and tributary systems, 

the SPT accounts for unique watershed responses to precipitation in 

estimating stream flows. SPT output includes reconstructed 3-hourly flow 

rates for each stream reach in a given watershed from 1980 to the present 

day, and continually updated 2-week forecasted flow rates. The tool 

estimates flows for every channel in a dense hydrologic network, including 

locations for which gauge data are unavailable. SPT was originally 

developed to inform in-theatre military logistics and deployment questions 

but is applied here to benefit the USACE Civil Works mission area, 

including the estimation of future dredging needs and more efficient 

allocations of limited dredging resources. 

This technical report expands on the findings presented in Yeates et al. 

(2019). That conference paper described results of this analysis for the 

Sabine-Neches study area using the first four of the eight models 

presented in this paper. Those limited results were presented at the 

Federal Interagency Sedimentation and Hydrology meeting in June 2019. 

1.2 Objective

The objective of this study was to estimate predictive relationships 

between precipitation-driven inland streamflow and subsequent channel 

dredging requirements near the base of the respective watershed. This 

analysis leverages existing dredging datasets and tools maintained by the 

USACE for insights useful in the channel maintenance process. The

relationship between flow rate outputs from the SPT (Snow et al. 2016) 

and historic dredged volume records for two large deep-draft Navigation 

projects along the Texas coast were investigated. Uncovering correlative 

relationships between precipitation-driven inland streamflows and 

subsequent channel dredging requirements will provide USACE dredging 

managers with additional information to develop plans and specifications 

for maintenance dredging contracts, out-year budget requests, and 

sequencing of nearby channel maintenance activities. This is similar to 

methods applied by Dahl et al. (2017) in assessing the potential impact of 

climate-varying future precipitation on dredging load requirements.
ERDC/CHL TR-20-17 3

1.3 Approach

In Section 2, Watershed scale hydrologic modeling, an overview of the SPT

is presented. The approach employed in the SPT to watershed-scale inland 

hydrology enables the generation of decades of historical simulated stream 

flows for thousands of stream reaches in the study area. 

Section 3, Methodology, gives an overview of the study area of interest and 

explains the data sources and processing used in the study. Data were 

processed to spatially connect dredging histories to reconstructed 

streamflows in the corresponding watershed. Then the historic dredged 

volumes were analyzed as a function of cumulative flow volumes to 

characterize the relationship between precipitation-driven flow and 

subsequent dredging volumes in each reach. 

In Section 4, Results, the Sabine-Neches and Houston Ship Channel 

navigation project results are discussed. Visualizations are provided of the 

data analyzed and findings for the regression relationships for cumulative 

flow volumes and dredged volumes in each of the test reaches. 

Section 5 presents conclusions and the path for future work in this effort. 
ERDC/CHL TR-20-17 4

2 Watershed Scale Hydrologic Modeling

To explore the impact of recent inland watershed-scale hydrologic 

processes on the subsequent near-term dredging load requirements of 

coastal waterways, hindcast streamflow simulation results from the SPT 

were used. This section details the hydrologic routing model components 

used in this analysis. 

2.1 Routing Application for Parallel computatIon of Discharge 

(RAPID) hydrologic routing model 

The RAPID model is an open-source model for routing precipitation 

runoff overland into river networks. RAPID uses blue lines on the map for 

river reaches and a grid network for river networks with an automated 

parameter estimation procedure (http://rapid-hub.org/index.html). Hydrologic 

routing is done via a matrix-based version of the Muskingum flow-routing 

method, which computes flow rates in river networks containing many 

thousands of reaches (David et al. 2011b). 

Inputs to RAPID are the gridded surface runoff time series, obtained from 

user-selected input atmospheric and land surface models and the calibrated 

Muskingum parameters for each river reach, which describe storage and 

flow behavior (David et al. 2011b). RAPID also requires catchment IDs to 

link individual river reaches to catchment areas. The specific input file types 

and preprocessing routines are described on http://rapid-hub.org/documents.html

(David 2020). The output file of a RAPID run is a time series of flow rates (a 

synthetic hydrograph) for all river reaches in the examined river network. In 

the continental United States, the National Hydrography Dataset Plus

(NHDPlus) dataset can be used to determine the location of a river reach as 

a blue line and to derive the Muskingum K and X parameters for RAPID 

input file preparation (Tavakoly et al. 2016a). 

The performance of the RAPID model and application of the NHDPlus 

network for hydrologic routing have been described by: David et al. 

2011a,b, 2013; Follum et al. 2016; Tavakoly et al. 2016a,b. David et al. 

(2013) found the hindcast RAPID model outflows to perform similarly to 

observed gage data in the Texas Gulf Hydrologic Region, which is the 

domain to which the RAPID model output in this study is applied.
ERDC/CHL TR-20-17 5

2.2 NHDPlus stream network 

The NHDPlus, version 2, is a major geospatial dataset used in this study. 

This dataset is a horizontal integration of the medium-resolution National 

Hydrography Dataset (NHD), the National Elevation Dataset, and the 

National Watershed Boundary Dataset (McKay et al. 2012; USGS USDA 

NRCS 2013). NHDPlus provides locations of perennial streams (i.e., blue 

lines) and the catchments that surround them. The attribute table of the 

NHDPlus flowline feature shows that the Texas Gulf Coast Region has a 

total of 68,901 river reaches with an average length of 3 km and an average 

catchment area of 6.8 km2. Figure 1 shows the NHDPlus river network for 

the entire Texas Gulf Coast Region. The flow lines depicted are those with 

a Strahler stream order of three or higher (Pierson et al. 2008). The 

Strahler order is a methodology for ranking stream size; upmost 

headwater streams have an order of one (Pierson et al. 2008). The 

Hydrologic Unit Code of 12 refers to the Texas Gulf Coast Region in the 

NHDPlus Dataset.

Figure 1. The NHDPlus representation of the Texas Gulf Coast Region. 
ERDC/CHL TR-20-17 6

2.3 Streamflow Prediction Tool (SPT)

The SPT is a continuously updated instance of RAPID models run for 

watersheds near globally and an interface for accessing RAPID model 

results by watershed and stream reach. Hindcast 3-hourly flow rates 

from 1980 through 2014 and 2-week streamflow forecasts are available 

via the USACE Model Interface Portal (UMIP) at 

https://umip.erdc.dren.mil/apps/streamflow-prediction-tool/ (UMIP access requires a 

government common access card login). 

At the time of this analysis, the RAPID model results produced for the SPT

were driven by atmospheric and land surface runoff data from the 

European Centre for Medium Range Weather Forecasts (ECMWF)

(Balsamo et al. 2009). The ECMWF Reanalysis-Interim (ERA-I) runoff 

data needed to run RAPID are retrieved from the Meteorological Archive 

and Retrieval System, and covers the 1980–2014 time period at a 3-hourly 

time step (Berrisford et al. 2011). These simulated runoff volume estimates 

are one product of a complex coupled atmospheric-ocean-wave model, 

detailed in Balsamo et al. (2009) and Berrisford et al. (2011). 

This hydrologic modeling framework takes advantage of over 30 years of 

the ERA-I runoff estimates available globally to reconstruct stream flows 

at ungauged locations. The atmospheric and land surface modeling inputs 

use energy and mass balancing to produce estimated gridded global 

surface water runoff. This runoff is routed into streamflow using the 

RAPID hydrological model. The benefit of this framework is the ability to 

calculate streamflow anywhere in a river network without a dependency on 

nearby available rainfall or streamflow gauges. Hence, the calculated 

streamflow can be obtained for any location of interest in any watershed.

To date, SPT development has been primarily funded by the ERDC 

military engineering program to support Warfighter Support via 

Reachback and Outside Contiguous United States operational demands. 

The US Army, ERDC, and university partners have developed the method 

for hindcasting and forecasting streamflow by routing globally available 

runoff estimates over continental-scale stream networks. 

The RAPID streamflow results are stored in an open source data 

management portal, the Comprehensive Knowledge Archive Network 

(CKAN). Users download model results for a stream reach through a 

Tethys Platform web-based application connected to CKAN. Users can 
ERDC/CHL TR-20-17 7

select and view results for specific stream reaches by NHD Common ID 

(COMID), a river reach identifier) as shown in Figure 2. 

Figure 2. Selecting a stream reach through the SPT Tethys portal. 

Users can then display and download the historic calculated streamflow 

time series for that reach, as shown in Figure 3.

Figure 3. Viewing historical flow time series in the SPT Tethys portal. 
ERDC/CHL TR-20-17 8

3 Methodology

For this analysis, historic dredging records from the USACE Dredging 

Information System and reconstructed stream flows from the SPT to 

connect them spatially by stream reach were processed. Dredged volumes 

at each reach were analyzed as a function of the cumulative flow volumes 

in the same reach to evaluate relationships between precipitation-driven 

flow and subsequent dredging requirements. The study area, data 

processing, and analytical methodology are detailed in this section. 

3.1 Study area of interest

For initial investigation of the link between cumulative streamflow and 

dredging volumes, two study areas in Texas Gulf Coast Regions were 

selected: the Sabine-Neches Waterway and the Houston Ship Channel. 

These test sites represent a range of Texas coastal riverine systems and 

have available dredging records and calculated stream flow time series. 

Within these study areas, specific channels were chosen for analysis. Maps 

of the selected channels are presented in the Results section. 

Fourteen river-reach COMIDs within the study area for analysis were 

selected: nine in the Sabine-Neches Waterway and five in the Houston 

Ship Channel. COMIDs were chosen to represent a variety of reach 

conditions in the regions of interest: those closer to inland riverine 

systems, closer to the outlet to bay areas, man-made channelized areas, 

and reaches before and after various stream confluences in each region. 

Furthermore, stream reaches with spatially matched robust historical 

dredging records were chosen. 

It was determined that stream reaches with fewer than five dredging 

events in the study time period should be excluded from analysis, due to 

having too few data points to perform regression. This resulted in more 

eligible stream reaches in the Sabine-Neches Waterway than in the 

Houston Ship Channel system. Table 1 summarizes the stream reaches 

included in the analysis, including COMID, drainage area, and number of 

dredging events from 1980 through 2014. 
ERDC/CHL TR-20-17 9

Table 1. Summary of stream reaches analyzed. 

Study Area Stream ID Drainage Area, 

km2 

Number of 

Dredging Events 

Sabine-Neches 

Waterway

1112455 25,931 11

1115825 26,058 9

1477515 26,064 15

1477595 26,220 15

1477713 26,204 12

1477589 26,215 16

1477725 26,201 11

1481563 27,705 23

24719331 53,730 12

Houston Ship Channel

1440485 1,192 8

1440511 2,012 8

1440521 2,578 6

1440525 1,205 9

1440539 1,578 10

Consecutive stream reaches have drainage areas very similar in size 

because they are part of the same system, with the downstream reach 

having only the additional directly adjacent drainage area. The stream 

reaches in the Sabine-Neches Waterway have much larger drainage areas 

than those in the Houston Ship Channel. The stream reaches with the 

largest drainage areas are the outlets into the bay draining each of those 

basins. Maps of each river system are included in the Results subsections. 

3.2 Data processing 

For this analysis, several existing USACE data sets and tools were used. 

The streamflow time series for selected reaches were downloaded from the 

SPT online portal interface. The historic dredging data records were 

provided by the USACE Galveston District, and dredging data were plotted 

spatially using the USACE National Channel Framework Geographic 

Information System database (USACE 2019). These inputs were processed

via R scripting to match streamflow time series with dredged volume time 
ERDC/CHL TR-20-17 10

series at the river reach level. Regression analyses of dredged volumes as a 

function of precipitation-driven streamflow in R were performed, and an 

online mapping tool to store the results spatially was created. Figure 4

illustrates this work flow. 

To create spatial datasets of the dredging records used in this project, 

three main source datasets were used: the National Channel Framework 

(NCF) feature class, the National Hydrographic Dataset Plus Version 2 

(NHDPlusV2) and the dredge histories dataset for the Galveston District, 

which includes all harbors in the study area.

The NCF (https://catalog.data.gov/dataset/national-channel-framework) is a set of 

enterprise Geographic Information System feature classes that provide 

geospatial locations of the congressionally authorized navigation channels 

maintained by the USACE. Details include reaches, channels, and 

quarters. The Galveston District dredge histories database includes 

channel reaches, river stations, dredged quantities in cubic yards, and 

project date end for each dredging event in the District in the last 40 years. 

This district dataset encompasses the two regions examined in this study, 

Sabine-Neches Waterway and the Houston Ship Channel.

To conduct this analysis, both the NHDPlusV2 stream reaches and the 

local dredge history records were connected spatially to the channel 

features in the NCF. Where there were several NHDPlusV2 reaches 

relating to a single NCF channel, the most downstream reach was 

associated with the feature. Associating each dredge history record with a 

channel feature allowed the relating of dredging events and amounts with 

historical streamflow at that location. These connections were all 

performed using ArcGIS software. 
ERDC/CHL TR-20-17 11

Figure 4. Data processing work flow. 
ERDC/CHL TR-20-17 12

To correlate dredged amounts in each reach with the reconstructed 

streamflow produced by the SPT for that reach, a number of data 

manipulation measures were performed in an R script. Steps to relate 

dredged volumes and streamflows were performed as follows:

1. Link the streamflow reach COMIDs and dredging channel locations 

spatially.

2. Export the reconstructed streamflow time series for each stream reach 

of interest from the Streamflow Prediction Tool. The 3-hourly 

streamflow time series from 1980 to 2014 were retrieved in commaseparated values format.

3. Import streamflow data and dredging record data into R and reformat 

the date and time-stamp fields to be the same for both files.

4. Link streamflow data and all dredging data for that particular river 

reach by the common COMID. 

5. Connect dredging project data to the streamflow time series by the date 

listed in the dredge project history as “Work Complete.” Dredging 

records with work completion dates before 1981 were discarded to 

ensure sufficient overlap with the SPT time series output. 

6. Aggregate dredging records with multiple project volumes reported for 

the same reach location and same day into a single project volume. 

7. Perform a return period analysis on the flow time history for each 

reach to determine the magnitude of the 1.5-year flow at that reach. 

The 1.5-year return period interval was chosen as a proxy for bankfull 

discharge (Copeland et al. 2005). 

8. Code all streamflow values in the time history for each reach as being 

either less than or greater than the 1.5-year flow value. 

9. Partition streamflow data into time history sets separated by the 

dredging event dates for each reach. This enabled the calculation of 

cumulative stream flow between each dredging event. 

10. Convert streamflow rates into volumes by multiplying each streamflow 

rate in the SPT output by the 3-hour time-step for which it was 

estimated. 

11. Sum up streamflow volumes within the hydrograph sets partitioned by 

dredging events, producing a cumulative flow volume preceding each 

dredged volume. 

12. Analyze these data by producing regression models relating dredged 

volume as the dependent variable and cumulative streamflow volume 

as the driving variable. Regression models used included linear and 
ERDC/CHL TR-20-17 13

exponential models, and are further described in the Results section of 

this technical report. 

The cumulative streamflow volumes between dredging events were plotted 

as a time series, with the subsequent dredged volumes in cubic yards 

plotted on a secondary axis (an example is shown in Figure 5; the 

streamflow cumulative volume time series is in blue lines and the dredging 

event volumes are shown as red dots). This provides an intuitive way to 

relate cumulative flow volumes to corresponding subsequent dredged 

sediment volumes. It also represents both flow and dredged volumes as 

time series, allowing for visualization of changes in the dredging practices 

in each stream reach over time. Volume units are different following 

convention for stream flow and for dredged material.

Figure 5. Time series output of cumulative streamflow and dredged volumes at reach 

1440511 in the Houston Ship Channel. 

Additionally, hydrographs of the stream flow rate over the entire time 

period of interest for each reach were produced. This provided the ability 

to view the seasonality of the flow for that reach and to see any dramatic 

spikes in precipitation rates. Volumes associated with dredging events at 

that reach were also graphed in these figures. An example is shown in 
ERDC/CHL TR-20-17 14

Figure 6, and the hydrographs for each analyzed reach are available in 

Appendix A. 

Figure 6. Hydrograph output of the SPT flow rates for stream reach 1440511 in the 

Houston Ship Channel. 
ERDC/CHL TR-20-17 15

3.3 Models applied for analysis

To conduct the analysis, single and multivariate, linear and nonlinear 

regressions were performed on the dredging event and streamflow 

datasets by reach. Table 2 details all variables used in the regression 

models. Note that flow volumes are in cubic meters and sediment volumes 

are in cubic yards, following convention. 

Table 2. Description of variables used in regression models. 

Variable Description 

VVVVVVdd

Total dredged volume from one dredging event 

(cubic yards)

QQtttttt

Total cumulative flow since the prior dredging event 

(cubic meters)

QQbbbbbbbb

Cumulative in-channel flow (flow rate less than the 1.5-year flow rate 

for that stream) since the prior dredging event (cubic meters)

QQii

Cumulative high flow (flow rate greater than the 1.5-year flow rate for 

that stream) since the prior dredging event (cubic meters)

QQtttttt−1

Total cumulative flow between the two prior dredging events (cubic 

meters)

QQbbbbbbbb−1

Cumulative in-channel flow (flow rate less than the 1.5-year flow rate 

for that stream) between the two prior dredging events (cubic meters)

QQii−1

Cumulative high flow (flow rate greater than the 1.5-year flow rate for 

that stream) between the two prior dredging events (cubic meters)

kk Regression intercept (cubic yards)

aa, bb, cc, dd Regression coefficients

Eight different models were tested with the dredging data for the Houston 

Ship Channel and Sabine-Neches cases. These are described in Table 3 

below. The simplest is a single-variate model to predict the next dredged 

volume as a linear function of the total cumulative flow in the reach since 

the last time the channel was dredged (Model 1). Model 2 is a multi-variate 

function which disaggregates that cumulative flow since the last dredging 

into in-channel flow and high flow. Models 3 and 4 mirror the variables 

tested in 1 and 2 but are nonlinear. Models 5–8 mirror the first four but 

incorporate the cumulative flow before the prior dredging event in an 

attempt to capture hysteresis in the sediment transport system.
ERDC/CHL TR-20-17 16

Table 3. Equations of tested multi-level models. 

Model Equation 

Linear models using the cumulative flow since the prior dredging event:

1 VVVVVVdd = kk + aaQQtttttt

2 VVVVVVdd = kk + aaQQbbbbbbbb + bbQQii

Exponential models using the cumulative since the prior dredging event:

3 VVVVVVdd = kk(QQtttttt )

4 VVVVVVdd = kk(QQbbbbbbbb )(QQii

bb)

Linear models using the cumulative flow since the prior dredging event and the cumulative 

flow before the prior dredging event:

5 VVVVVVdd = kk + aaQQtttttt + bbQQtttttt−1

6 VVVVVVdd = kk + aaQQbbbbbbbb + bbQQii + ccQQbbbbbbbb−1 + ddQQii−1

Exponential models using the cumulative flow since the prior dredging event and the 

cumulative flow before the prior dredging event:

7 VVVVVVdd = kk(QQtttttt )(QQtttttt−1

bb)

8 VVVVVVdd = kk(QQbbbbbbbb )(QQii

bb)(QQbbbbbbbb−1cc

)(QQii−1

dd)

Matched pairs of cumulative streamflow volume and subsequent dredged 

material volume were plotted and fitted with linear lines of best fit 

(Model 1), including the regression equations and R squared values. A 5% 

confidence interval was selected to evaluate model significance. The 

plotted results for all analyzed reaches are presented in the Results 

section of this report. 
ERDC/CHL TR-20-17 17

4 Results

4.1 Analysis overview

Coefficients of determination (R-squared) values for many of the reaches 

across some of the tested models were sufficiently high to indicate that the 

incorporation of precipitation-driven inland hydrology into forecasts of 

future dredging load requirements can prove useful. The regression 

models performed best in channelized reaches upstream of coastal outlets, 

and found little to no relationships in reaches connected to coasts and bays 

where coastal sedimentation processes likely dominate over sediment 

delivered by the upstream watershed. These results strengthen the case for 

limiting application of these watershed hydrology based regression models 

to upper portions of coastal navigation channels where influences from 

coastal processes can be expected to be minimal. Results by model type 

and study area are presented in this section. 

4.2 Single-variate linear model results

In the reaches with the best performance, Model 1 captured up to 63% of 

the variance in dredging amounts in the Sabine-Neches Waterway and up 

to 93% of the variance in dredging amounts in the Houston Ship Channel 

using total cumulative flow since the prior dredging event as the 

predictive variable. For reaches closer to the bays or coasts, this fell to 

0% in Sabine-Neches and 11% in the Houston Ship Channel, with low 

confidence in the estimate.

Table 4 shows goodness of fit results (R-squared and p-value) for the 

single-variable linear regression model comparing dredged volumes to 

cumulative streamflow prior to each dredged event for nine stream 

reaches in the Sabine-Neches Waterway and five stream reaches in the 

Houston Ship Channel. R-squared is a measure of how close the observed 

data values are to the fitted regression line of the model; an R-squared 

value of 1.0 indicates a perfect fit of data to the model. An R-squared value 

close to 0.0 indicates that the variables used in the regression model do 

not explain the variance in the data. The p-value is the probability of the 

observed data occurring if the proposed regression model is not valid. A 

low p-value (for example, less than 0.05) indicates high confidence in the 

validity of the proposed model.
ERDC/CHL TR-20-17 18

Table 4. Summary of single variable linear regression Model 1 fit. 

Study Area Stream ID R-squared 

Value P-value 

Sabine-Neches

1112455 0.38 0.03

1115825 0.30 0.10

1477515 0.04 0.50

1477595 0.16 0.10

1477713 0.48 0.04

1477589 0.16 0.10

1477725 0.63 0.01

1481563 0.20 0.02

24719331 0.00 0.89

Houston Ship 

Channel

1440485 0.12 0.36

1440511 0.32 0.15

1440521 0.11 0.52

1440525 0.93 0.00

1440539 0.33 0.08

4.2.1 Single variate linear model results – Sabine-Neches Waterway

For the Sabine-Neches study area, reaches upstream, along the canal, and 

close to the outlet (Figure 7) were selected. Stream reaches are highlighted 

in light blue with the corresponding COMID displayed along the stream 

reach. Streams were filtered for analysis to include only those with more 

than five dredging event records from 1980 to 2014. 

Linear relationships were found in inland, upstream stream reaches, such 

as the most upstream COMID 1112455 (R-squared of 0.38 with a p-value 

of 0.03), and in channelized reaches, such as COMID 1477725 (R-squared 

of 0.63 with a p-value of 0.01). No linear relationships were found in 

stream reaches that directly connected to bay and coastal outlets, 

including 1477515 (R-squared of 0.04 with a p-value of 0.50) and 

247719331 (R-squared of 0.00 with a p-value of 0.89). COMID 1477515 is 

actually in Sabine Lake — the NHDPlus Streamlines dataset automatically 

represents lake bodies as series of streamlines. Although dredging occurs 

in this location, the dredged volumes are not well correlated with the 

associated streamline cumulative flow. This demonstrates the failure of the 
ERDC/CHL TR-20-17 19

SPT to accurately represent flows in areas like lakes, and the pitfall of 

using this association in an area like Sabine Lake.

Unsurprisingly, streamflow and dredged volumes were not correlated at 

the coastal outlet, at COMID 24719331. Sedimentation here is 

predominantly governed by coastal effects, not by upstream streamflow. It 

is not recommended to use this approach to predict dredging needs at 

coastal outlets. 

Figure 7. Examined COMIDs and dredged channel areas in the 

Sabine-Neches Waterway. 
ERDC/CHL TR-20-17 20

Time series of the cumulative flow volumes and subsequent dredged 

amounts and the linear regression model results are reported on the 

following pages for each reach. Shown are the results for the nine analyzed 

COMIDs, from the most upstream (Figure 8) through the middle of the 

channel (Figure 9) to the most downstream near the coastal outlet (Figure

10). Note that the dredged amounts used for COMIDs 1477589 and 

1477595 are the same as the location indicators in the dredging records 

were not specific enough to delineate between these reaches.

Regression coefficients, intercepts, and variable p-values are presented by 

reach in Appendix B. 
ERDC/CHL TR-20-17 21

Figure 8. Model 1 results for COMIDs 1112455, 1115825, and 1477515 in the Sabine-Neches Waterway. 

 

 

 

 

 
ERDC/CHL TR-20-17 22

Figure 9. Results for COMIDs 1477725, 1477713, and 1477589 in the Sabine-Neches Waterway. 

 
ERDC/CHL TR-20-17 23

Figure 10. Results for COMIDs 1477595, 1481563, and 24719331 in the Sabine-Neches Waterway. 

 
ERDC?CHL TR-20-17 24

4.2.2 Single variate linear model results — Houston Ship Channel

Five stream reaches along the Houston Ship Channel were selected for 

analysis because they spatially matched dredging project areas and had at 

least five dredging records available for the period of analysis (shown in the 

map in Figure 11). These stream reaches ranged from well upstream of the 

bay system (COMID 1440525) to the outlet from the ship channel to the bay 

(COMID 1440485). In the upstream reaches, cumulative flow volume was a 

more skilled predictor of dredged volumes than in the downstream reaches. 

The linear relationship between cumulative flow volume and dredged 

sediment volume was found to be strongest in the most upstream reach, 

COMID 1440525, with an R-squared of 0.93 and a p-value of 0.00 

indicating an almost perfect correlation and very high confidence in the 

model. The correlation had a strong positive trend between precipitationdriven flow and dredged volumes. This is the most upstream reach in the 

system for which dredging records were available. 

The correlation for this single-variate linear model was weaker for the 

remaining reaches, with the next two downstream reaches (COMIDs 

1440539 1440511) demonstrating R-squared values of 0.33 and 0.32 and 

p-values outside the selected 5% confidence level (0.08 and 0.15) and the 

two farthest downstream reaches, COMIDs 1440521 and 0440485 

demonstrating R-squared values of 0.11 and 0.12. This mirrors the trend 

found for the Sabine-Neches system in which the dredging load 

requirements in the upstream reaches can be more accurately predicted 

using precipitation-driven riverine hydrology. This indicates that other 

factors than precipitation-driven sediment transport affected dredging 

volumes here.
ERDC?CHL TR-20-17 25 

Figure 11. Examined COMIDs and dredged channel areas in the Houston Ship Channel. 

Individual reach results are reported on the following pages. The 

cumulative streamflow and dredged volume time series and the linear 

regression model results for the five analyzed COMIDs are shown, from the 

most upstream (Figure 12) to the most downstream near the coastal outlet 

(Figure 13). Regression coefficients, intercepts, and variable p-values are 

presented by reach in Appendix B. 
ERDC/CHL TR-20-17 26

Figure 12. Results for COMIDs 1440525, 1440539, and 1440511 in the Houston Ship Channel. 

 
ERDC/CHL TR-20-17 27

Figure 13. Results for COMIDs 1440521 and 1440485 in the Houston Ship Channel. 
ERDC/CHL TR-20-17 28

4.3 Nonlinear and multivariate model analyses

Seven additional models were applied to the dredging and streamflow 

datasets for reaches in Sabine-Neches Waterway and the Houston Ship 

Channel systems, as outlined in Table 3 in the Methodology section. 

Several of these models disaggregated the cumulative flow into in-channel

flows (below the 1.5-year flow rate) and high flows (above the 1.5-year flow 

rate) (Models 2, 4, 6, and 8); applied non-linear frameworks (Models 3, 4, 

7, and 8); and incorporated prior event flow volumes to account for system 

lag (Models 5, 6, 7, and 8). 

Model performance varied widely across model, harbor system, and 

river-reach COMID. For most of the tests, the percentage error in the 

predicted volumes was within one order of magnitude of the actual dredged 

volume from the record. Figure 14 and Figure 16 show the percentage error 

of prediction by dredging event for each reach and model for the SabineNeches and Houston Ship Channel systems, respectively. Errors were 

predominantly positive — the models tended to over predict dredging loads 

at lower volumes and under predict loads at higher volumes. The modeled 

and observed dredged volumes for each event are shown in Figure 15 for the 

Sabine-Neches and Figure 17 for the Houston Ship Channel.

The model with the highest R-squared values and highest number of 

reaches for which the model demonstrated significance was Model 2. This 

model is a simple linear regression equation that predicts dredged 

sediment volumes as a function of cumulative in-channel flow volumes 

and high flow volumes since the prior dredging event. It is interesting that 

in most reaches to which Model 2 was applied, the in-channel flow volume 

had a positive coefficient and the high flow volume had a negative 

coefficient, indicating that periods of high flow reduce sediment shoaling. 

This is possibly due to high flows scouring or flushing sediment from the 

channel, reducing the need for subsequent dredging. Regression 

coefficients, intercepts, variable p-values, and model performance metrics 

for each model are presented by reach in Appendix B.
ERDC/CHL TR-20-17 29

4.3.1 Nonlinear and multivariate model results — Sabine-Neches 

Waterway

Figure 14 shows the percentage error of prediction for the eight tested 

models for 129 dredging events in the Sabine-Neches harbor system 

during the study period of record. Half of the model estimates were 

within a 40% error band of the actual magnitude of sediment dredged, 

and 26% of the estimates were within 20% of the magnitude. Model 

estimates were more accurate for events with higher volumes of dredged 

sediment, with error magnitude dropping significantly for dredging 

events above 3 million cy of sediment. 

Figure 14. Percentage error of predictions by model, Sabine-Neches Waterway. 

Among the eight models tested, model performance was highest for Model 

2 and Model 8, which both captured 32% of the events within a 20% error 

band of the sediment magnitudes. Model 2 is a model of linear fit using 

cumulative in-channel flow and high flow input variables, and Model 8 is a 

nonlinear model of fit using in-channel and high flows and flows before 

the prior dredging event (equations presented in Table 3). For most of 

these reaches, there were a low number of dredging events in the history, 

so Model 8 is likely overfit. 

-200%

-100%

0%

100%

200%

300%

400%

500%

600%

0.00E+00 1.00E+06 2.00E+06 3.00E+06 4.00E+06 5.00E+06 6.00E+06

Percentage error of prediction

Cubic yards of dredged material (observed)

1

2

3

4

5

6

7

8
ERDC/CHL TR-20-17 30

Table 5 summarizes model results by COMID for the Sabine-Neches 

system with best model performance. Across these, the models with higher 

R-squared values, indicating stronger model fit, and p-values below the 

significance threshold of 0.05 (shown in bold), were Models 1, 2, 3, and 4. 

Models 5–8, which incorporate the cumulative flows prior to the last 

dredging event as a predictive variable for the next dredged volume, had 

higher p-values indicating low confidence in the model. This may be due to 

model overfit. Among the first four models, Model 2 performed the 

strongest across these four reaches, with R-squared values indicating that 

the model captured 66%–77% of the variance in the dredged amounts and 

p-values indicating high confidence. Model 2 is a linear regression model 

that uses cumulative in-channel flows (defined here as below the 1.5-year 

flow) and high flows (above the 1.5-year flow) as separate input variables. 

Table 5. R-squared and p-value by model for selected reaches, Sabine-Neches Waterway. 

COMID 

Model 

1477725 1477713 1115825 1112455 

R2 p-val R2 p-val R2 p-val R2 p-val

1 0.63 0.01 0.48 0.04 0.30 0.10 0.38 0.03 

2 0.77 0.01 0.66 0.04 0.72 0.01 0.68 0.01 

3 0.62 0.01 0.54 0.02 0.04 0.56 0.18 0.17

4 0.68 0.03 0.57 0.08 0.07 0.76 0.18 0.41

5 0.65 0.07 0.58 0.12 0.33 0.30 0.48 0.08

6 0.85 0.13 0.79 0.22 0.87 0.05 0.72 0.07

7 0.64 0.08 0.65 0.07 0.06 0.83 0.18 0.46

8 0.78 0.22 0.74 0.27 0.36 0.71 0.17 0.86

Figure 15 below shows the estimated versus observed volumes of dredged 

material for each dredging event and model for one reach in the SabineNeches system. This reach, COMID 1477725, had the highest model 

performance across the nine reaches examined in the Sabine-Neches. This 

figure shows the spread of estimate accuracy by model across the seven 

dredging events in the 1980–2014 study period. 
ERDC/CHL TR-20-17 31

Figure 15. Estimated versus observed volumes of dredged material for Reach 1477725 

in the Sabine-Neches Waterway. 

4.3.2 Nonlinear and multivariate model results — Houston Ship Channel

Figure 16 shows the percentage error of prediction for the eight tested 

models for 42 dredging events in the Houston Ship Channel harbor system 

during the study period of record. The figure presents that 74% of the 

model estimates were within a 40% error band of the actual magnitude of 

sediment dredged and 49% of the total estimates were within 20% of the 

observed dredged material magnitude. Model estimates were more 

accurate for events with higher volumes of dredged sediment, with error 

magnitude dropping significantly for dredging events above 200,000 cy of 

sediment. For larger dredged volumes, models tended to under predict the 

dredged volume.

-5.00E+05

0.00E+00

5.00E+05

1.00E+06

1.50E+06

2.00E+06

2.50E+06

0.00E+00 5.00E+05 1.00E+06 1.50E+06 2.00E+06 2.50E+06

Cubic yards of dredged material - estimated

Cubic yards of dredged material - observed

1

2

3

4

observed

5

6

7

8
ERDC/CHL TR-20-17 32

Figure 16. Percentage error of predictions by model, Houston Ship Channel. 

Table 6 shows the R-squared and p-values by model for three of the 

reaches for which inland precipitation-driven flow demonstrated some 

skill in predicting dredged sediment loads. Two of these reaches, the most 

upstream in the system, had high model performance for all eight tested 

models. This indicates that precipitation-driven flow generally was tightly 

correlated with dredging load requirements in these reaches. The third 

reach had much worse performance across most of the models, with the 

exception of Model 2 (a linear regression using in-channel flow and high 

flow cumulative volumes as the predictive variables). Model 2 had an 

R-squared value of 0.87 and a p-value within the 5% significance threshold 

for this reach. 

-200%

-100%

0%

100%

200%

300%

400%

500%

600%

700%

0.00E+00 2.00E+05 4.00E+05 6.00E+05 8.00E+05 1.00E+06 1.20E+06 1.40E+06 1.60E+06

Percentage error of prediction

Cubic yards of dredged material (observed)

1

2

3

4

5

6

7

8
ERDC/CHL TR-20-17 33

Table 6. R-squared and p-value by model for selected reaches, 

Houston Ship Channel. 

COMID 

Model

1440525 1440539 1440521 

R2 p-val R2 p-val R2 p-val

1 0.93 0.00 0.33 0.08 0.11 0.52

2 0.94 0.00 0.33 0.25 0.87 0.05 

3 0.94 0.00 0.47 0.03 0.05 0.67

4 0.96 0.00 0.56 0.06 0.73 0.14

5 0.96 0.00 0.36 0.26 0.32 0.68

6 0.97 0.01 0.66 0.60 NA NA

7 0.96 0.00 0.44 0.04 0.42 0.58

8 0.97 0.01 0.70 0.22 NA NA

Figure 17 below shows the estimated versus observed volumes of dredged 

material for each dredging event and model for one reach in the Houston 

Ship Channel system. This reach, COMID 1440525, had the highest model 

performance across the five reaches examined in the Houston Ship 

Channel. This figure shows the spread of estimate accuracy by model 

across the nine dredging events in the 1980–2014 study period. For this 

reach, each model showed a tight correlation between precipitation-driven 

stream flow and subsequent dredging requirements. 

Figure 17. Estimated versus observed volumes of dredged material for Reach 

1440525 in the Houston Ship Channel.
ERDC/CHL TR-20-17 34

5 Conclusion and Future Work

In this study, USACE hindcasted streamflow values with historical 

dredging records were connected for two federal navigation projects 

regions in Texas to investigate a causal relationship between inland 

hydrology and maintenance dredging needs. Results suggest that there is a 

correlation between higher precipitation-driven streamflows and greater 

volumes of dredged sediment in these managed channels. The relationship 

was stronger in river reaches farther upstream from the coastal outlet; 

near bays, lakes, and the coast there was not a strong correlation between 

streamflow and dredged loads. The coastal effects in these reaches either 

dominate sedimentation or are enough of a driver to conceal whatever 

correlation may exist for inland hydrology. 

5.1 Study limitations

The dredging history datasets used made no distinction between 

maintenance channel dredging and special channel widening or deepening 

projects, which would not be well-correlated with inland precipitation. 

Additionally, channel maintenance dredging volumes are tied at least in 

part to budget availability, which is not captured in these models. Another 

limitation is the low number of dredging events for any one reach in a 

system over the 34-year period of reconstituted streamflows. This resulted 

in model overfit for the regression models with more than two input 

variables. Since the dredging record datasets are small, it was not possible

to reserve data for testing outside of the regression process. This could be 

addressed in future studies by aggregating like reaches together or 

applying analysis to regions with more robust dredging records. 

5.2 Conclusion from analysis

This approach demonstrates enough skill to be considered as potentially 

useful in estimating downstream dredging requirements. For the river 

systems examined, inland riverine flow seems highly correlated with 

subsequent dredging loads at reaches inland of bays and coasts. 

Recognizing those limitations, flow volumes can provide additional 

information about channel maintenance needs without deploying dataintensive, high-fidelity sedimentation models. The best-performing model, 

Model 2, used as input variables the streamflow volumes disaggregated 

into those above and below the 1.5-year flow by reach. This is a simple 
ERDC/CHL TR-20-17 35

linear model that could easily be applied to any reach for which 

reconstituted flow data are available. 

As streamflow forecasting improves to give longer lead times on flow 

variability, techniques like this can alert channel managers to the need for 

upcoming maintenance. The development of regional model equations and 

additional model validation would improve the approach. 

5.3 Future work

This analysis could be extended to different hydrologic regions within the 

United States, particularly those dominated by inland flows. Accurate 

spatial information for dredging records is critical for this type of analysis, 

and it may be challenging to obtain long-term detailed dredge histories for 

some locations. 

There is potential to incorporate reach-level shoaling rates as calculated in 

the USACE CSAT tool. Connecting the work done for the SPT and CSAT 

could leverage USACE capabilities to improve the information available to 

dredging managers and to optimize dredging activities. 

Future work includes production of sub-seasonal scale streamflow forecasts 

in the SPT, which could provide USACE project managers and Operations 

personnel with a sense of the magnitude and timing of streamflow peaks 

several months in advance. This capability could enhance the usefulness of 

the results presented here for estimating near-term dredging requirements 

based on recent and ongoing inland flow events. 
ERDC/CHL TR-20-17 36

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USACE (US Army Corps of Engineers). 2019. National Channel Framework. 

https://catalog.data.gov/dataset/national-channel-framework

USGS USDA NRCS (US Geological Survey and US Department of Agriculture, Natural 

Resources Conservation Service). 2013. Federal Standards and Procedures for 

the National Watershed Boundary Dataset (WBD), 4th ed. US Geological Survey 

Techniques and Methods 11–A3. http://pubs.usgs.gov/tm/tm11a3/

Yeates, E. M., A. A. Tavakoly, G. Dreaper, K. Mitchell, and S. Afshari. 2019. “Use of a 

Gridded Runoff Flow Routing Model to Estimate Sedimentation and Dredging 

Burdens.” Federal Interagency Sedimentation and Hydrologic Modeling 

Conference, June 2019. 
ERDC/CHL TR-20-17 38

Appendix A: Hydrographs for Analyzed 

Streams

Hydrographs for the included reaches were produced in the course of this

analysis. These hydrographs are disaggregated into the base, in-channel

flow (below the 1.5-year flow in this study, shown in blue), and higher 

flows (above 1.5-year flow, shown in red). Hydrographs run from 1980 to 

the year of the last dredging event in the record used for each stream 

reach. In the following figures, the hydrographs are presented by harbor 

system. 

Sabine-Neches Waterway
ERDC/CHL TR-20-17 39
ERDC/CHL TR-20-17 40
ERDC/CHL TR-20-17 41

Houston Ship Channel
ERDC/CHL TR-20-17 42
ERDC/CHL TR-20-17 43

Appendix B: Regression Model Results

The following tables in this appendix present the regression analysis 

results for all 8 models and 14 stream reaches described in this report. 

Intercepts, coefficient values, p-values for individual variables, and the Rsquared and p-values for each model and reach are tabled.
ERDC/CHL TR-20-17 44

Model 1. Linear, using cumulative total flow since prior dredging event 

VVVVVVdd = kk + aaQQtttttt

Sabine-Neches Waterway

COMID 1481563 1112455 1115825 1477515 1477589 1477595 1477713 1477725 24719331 

k 1.01E+06 9.87E+05 5.24E+05 1.77E+06 1.13E+06 1.06E+06 1.80E+05 1.30E+04 1.37E+06

a 7.40E-05 1.04E-04 4.79E-05 -2.37E-05 3.32E-05 3.42E-05 3.19E-05 3.44E-05 1.45E-06

p-value, k 8.43E-03 3.17E-01 4.45E-01 4.63E-03 5.15E-03 1.02E-02 6.40E-01 9.66E-01 3.67E-02

p-value, a 2.06E-02 3.33E-02 1.03E-01 4.70E-01 9.84E-02 1.04E-01 3.90E-02 1.09E-02 8.89E-01

adjusted R-squared 1.71E-01 3.16E-01 2.10E-01 -2.89E-02 1.09E-01 1.04E-01 4.04E-01 5.74E-01 -1.22E-01

multiple R-squared 2.04E-01 3.78E-01 2.98E-01 3.54E-02 1.61E-01 1.57E-01 4.78E-01 6.27E-01 2.58E-03

p-value, model 2.06E-02 3.33E-02 1.03E-01 4.70E-01 9.84E-02 1.04E-01 3.90E-02 1.09E-02 8.89E-01

Houston Ship Channel

COMID 1440485 1440511 1440521 1440525 1440539 

k 5.52E+05 1.47E+05 3.23E+05 7.65E+04 1.67E+05

a 3.14E-05 7.61E-05 8.63E-05 4.00E-04 9.29E-05

p-value, k 4.70E-03 2.61E-01 5.74E-01 2.06E-01 7.60E-02

p-value, a 3.56E-01 1.42E-01 5.21E-01 2.65E-05 8.43E-02

adjusted R-squared -3.21E-03 2.09E-01 -1.13E-01 9.21E-01 2.43E-01

multiple R-squared 1.22E-01 3.22E-01 1.10E-01 9.30E-01 3.27E-01

p-value, model 3.56E-01 1.42E-01 5.21E-01 2.65E-05 8.43E-02
ERDC/CHL TR-20-17 45

Model 2. Linear, using cumulative base and high flows since prior dredging event

VVVVVVdd = kk + aaQQbbbbbbbb + bbQQii

Sabine-Neches Waterway

COMID 1481563 1112455 1115825 1477515 1477589 1477595 1477713 1477725 24719331 

k 8.98E+05 1.68E+06 9.67E+05 1.56E+06 8.32E+05 7.01E+05 1.14E+05 -2.86E+04 1.30E+06

a 1.11E-04 -8.30E-05 -6.10E-05 2.42E-05 9.19E-05 1.05E-04 7.23E-05 6.62E-05 -1.54E-06

b -5.40E-05 1.10E-03 5.99E-04 -2.33E-04 -1.49E-04 -1.81E-04 -1.69E-04 -1.33E-04 2.02E-05

p-value, k 2.75E-02 5.18E-02 7.33E-02 1.37E-02 8.65E-02 1.63E-01 7.34E-01 9.13E-01 7.12E-02

p-value, a 6.73E-02 2.79E-01 1.49E-01 6.42E-01 1.55E-01 1.30E-01 2.66E-02 1.25E-02 9.15E-01

p-value, b 7.58E-01 1.10E-02 9.51E-03 2.11E-01 4.26E-01 3.58E-01 1.79E-01 1.84E-01 7.31E-01

adjusted R-squared 1.55E-01 6.07E-01 6.43E-01 -3.76E-05 1.10E-01 1.20E-01 5.52E-01 6.90E-01 -2.62E-01

multiple R-squared 2.23E-01 6.78E-01 7.22E-01 1.25E-01 2.14E-01 2.23E-01 6.64E-01 7.67E-01 1.86E-02

p-value, model 5.51E-02 6.07E-03 1.13E-02 3.93E-01 1.64E-01 1.50E-01 3.80E-02 1.26E-02 9.36E-01

Houston Ship Channel

COMID 1440485 1440511 1440521 1440525 1440539 

k 5.61E+05 1.45E+05 3.64E+05 7.69E+04 1.67E+05

a 1.51E-04 9.03E-05 2.84E-04 4.85E-04 8.55E-05

b -9.09E-04 -1.32E-05 -1.53E-03 -9.07E-05 1.33E-04

p-value, k 6.35E-03 3.15E-01 2.23E-01 2.13E-01 9.92E-02

p-value, a 2.93E-01 2.79E-01 3.04E-02 2.60E-03 5.50E-01

p-value, b 3.98E-01 9.72E-01 3.11E-02 8.65E-01 8.53E-01

adjusted R-squared -1.97E-02 6.32E-02 7.75E-01 9.20E-01 1.35E-01

multiple R-squared 2.35E-01 3.31E-01 8.65E-01 9.40E-01 3.27E-01

p-value, model 4.47E-01 3.66E-01 4.95E-02 2.19E-04 2.50E-01
ERDC/CHL TR-20-17 46

Model 3. Exponential, using cumulative total flow since prior dredging event

VVVVVVdd = kk(QQtttttt )

Sabine-Neches Waterway

COMID 1481563 1112455 1115825 1477515 1477589 1477595 1477713 1477725 24719331 

k 1.07E+01 7.98E+00 9.98E+00 1.49E+01 8.30E+00 8.57E+00 -9.59E-01 -9.05E-01 9.87E+00

a 1.52E-01 2.83E-01 1.66E-01 -4.60E-02 2.52E-01 2.36E-01 6.10E-01 6.04E-01 1.72E-01

p-value, k 5.72E-04 9.86E-02 1.59E-01 1.45E-02 3.28E-02 8.14E-02 8.53E-01 8.33E-01 1.07E-01

p-value, a 2.18E-01 1.69E-01 5.63E-01 8.47E-01 1.22E-01 2.56E-01 2.40E-02 1.12E-02 4.62E-01

adjusted R-squared 2.35E-02 9.83E-02 -7.59E-02 -6.39E-02 8.96E-02 2.24E-02 4.75E-01 5.71E-01 -4.67E-02

multiple R-squared 6.26E-02 1.80E-01 4.36E-02 2.57E-03 1.43E-01 8.00E-02 5.41E-01 6.25E-01 6.96E-02

p-value, model 2.18E-01 1.69E-01 5.63E-01 8.47E-01 1.22E-01 2.56E-01 2.40E-02 1.12E-02 4.62E-01

Houston Ship Channel

COMID 1440485 1440511 1440521 1440525 1440539 

k 1.38E+01 5.90E+00 1.98E+00 -1.30E+00 7.10E+00

a -2.38E-02 3.11E-01 5.04E-01 6.95E-01 2.60E-01

p-value, k 1.74E-03 5.45E-01 9.38E-01 3.71E-01 7.71E-03

p-value, a 8.63E-01 4.97E-01 6.69E-01 1.53E-05 2.98E-02

adjusted R-squared -1.38E-01 -7.31E-02 -1.87E-01 9.32E-01 3.98E-01

multiple R-squared 4.56E-03 8.02E-02 5.05E-02 9.41E-01 4.65E-01

p-value, model 8.63E-01 4.97E-01 6.69E-01 1.53E-05 2.98E-02
ERDC/CHL TR-20-17 47

Model 4. Exponential, using cumulative base and high flows since prior dredging event

VVVVVVdd = kk(QQbbbbbbbb )(QQii

bb)

Sabine-Neches Waterway

COMID 1481563 1112455 1115825 1477515 1477589 1477595 1477713 1477725 24719331 

k 1.02E+01 8.13E+00 7.40E+00 1.16E+01 4.98E+00 1.27E+00 -4.65E+00 -7.72E+00 9.99E+00

a 1.77E-01 2.78E-01 2.78E-01 9.48E-02 3.96E-01 5.51E-01 7.70E-01 8.95E-01 1.38E-01

b -1.57E-04 2.41E-04 -2.81E-03 -1.44E-03 -8.25E-04 -2.17E-03 -1.22E-03 -2.76E-03 3.30E-02

p-value, k 2.35E-02 2.88E-01 4.32E-01 2.07E-01 4.36E-01 8.75E-01 6.85E-01 4.08E-01 1.26E-01

p-value, a 3.43E-01 3.94E-01 4.91E-01 8.07E-01 1.60E-01 1.28E-01 1.48E-01 5.09E-02 6.90E-01

p-value, b 9.14E-01 9.52E-01 6.58E-01 7.22E-01 6.42E-01 3.42E-01 7.93E-01 4.66E-01 9.14E-01

adjusted R-squared -1.89E-02 -3.34E-03 -1.95E-01 -1.32E-01 6.80E-02 4.28E-02 4.25E-01 5.70E-01 -1.99E-01

multiple R-squared 6.26E-02 1.79E-01 7.02E-02 9.42E-03 1.78E-01 1.55E-01 5.68E-01 6.77E-01 6.77E-02

p-value, model 4.75E-01 4.11E-01 7.75E-01 9.36E-01 2.31E-01 2.82E-01 8.04E-02 3.36E-02 7.82E-01

Houston Ship Channel

COMID 1440485 1440511 1440521 1440525 1440539 

k 1.01E+01 3.92E+00 -2.23E+01 -3.76E+00 2.79E+00

a 1.47E-01 5.01E-01 2.32E+00 8.21E-01 4.68E-01

b -3.18E-03 -1.07E-01 -7.92E-01 -1.35E-03 -4.55E-03

p-value, k 1.07E-02 7.20E-01 2.66E-01 1.96E-01 4.99E-01

p-value, a 8.92E-11 4.55E-01 7.95E-02 6.40E-04 4.28E-02

p-value, b 3.01E-01 7.58E-01 8.48E-02 4.41E-01 2.54E-01

adjusted R-squared 2.71E-01 -2.23E-01 5.52E-01 9.41E-01 4.35E-01

multiple R-squared 4.53E-01 1.26E-01 7.31E-01 9.56E-01 5.60E-01

p-value, model 1.64E-01 7.14E-01 1.39E-01 8.46E-05 5.64E-02
ERDC/CHL TR-20-17 48

Model 5. Linear, using cumulative flow since the prior dredging event, and the cumulative flow before the prior dredging event

VVVVVVdd = kk + aaQQtttttt + bbQQtttttt−1

Sabine-Neches Waterway

COMID 1481563 1112455 1115825 1477515 1477589 1477595 1477713 1477725 24719331 

k 8.65E+05 -2.38E+05 1.67E+05 6.36E+05 1.39E+06 1.28E+06 6.27E+05 3.05E+05 1.24E+06

a 8.29E-05 1.19E-04 4.92E-05 -9.71E-06 3.33E-05 3.42E-05 2.75E-05 2.77E-05 1.53E-06

b -1.02E-08 5.42E-05 1.46E-05 6.83E-05 -1.89E-05 -1.60E-05 -1.83E-05 -1.02E-05 3.62E-06

p-value, k 6.21E-02 8.69E-01 8.74E-01 3.75E-01 8.21E-03 1.82E-02 1.59E-01 3.42E-01 2.49E-01

p-value, a 1.54E-02 2.95E-02 1.49E-01 7.48E-01 1.12E-01 1.22E-01 7.25E-02 3.06E-02 8.99E-01

p-value, b 1.00E+00 2.64E-01 6.38E-01 3.86E-02 3.51E-01 4.55E-01 1.52E-01 2.74E-01 7.72E-01

adjusted R-squared 1.81E-01 3.44E-01 1.05E-01 2.09E-01 1.02E-01 7.55E-02 4.08E-01 5.14E-01 -3.12E-01

multiple R-squared 2.49E-01 4.75E-01 3.29E-01 3.15E-01 2.14E-01 1.91E-01 5.77E-01 6.53E-01 1.57E-02

p-value, model 4.29E-02 7.59E-02 3.03E-01 8.59E-02 1.85E-01 2.27E-01 1.16E-01 7.11E-02 9.54E-01

Houston Ship Channel

COMID 1440485 1440511 1440521 1440525 1440539 

k 2.28E+05 7.21E+05 -3.17E+03 1.40E+05 2.28E+05

a 7.05E-05 9.11E-05 4.02E-04 9.07E-05 7.05E-05

b -2.98E-05 -1.16E-04 5.94E-05 9.32E-06 -2.98E-05

p-value, k 3.48E-01 5.81E-01 9.63E-01 2.37E-01 3.48E-01

p-value, a 2.79E-01 6.69E-01 1.01E-04 1.24E-01 2.79E-01

p-value, b 6.49E-01 5.35E-01 1.61E-01 8.61E-01 6.49E-01

adjusted R-squared 2.52E-03 -3.66E-01 9.46E-01 1.49E-01 2.52E-03

multiple R-squared 3.35E-01 3.17E-01 9.62E-01 3.61E-01 3.35E-01

p-value, model 4.42E-01 6.83E-01 2.88E-04 2.60E-01 4.42E-01
ERDC/CHL TR-20-17 49

Model 6. Linear, using base and high flows since the prior dredging event, and the base

and high flows before the prior dredging event

VVVVVVdd = kk + aaQQbbbbbbbb + bbQQii + ccQQbbbbbbbb−1 + ddQQii−1

Sabine-Neches Waterway

COMID 1481563 1112455 1115825 1477515 1477589 1477595 1477713 1477725 24719331 

k 8.02E+05 1.59E+06 1.64E+06 4.70E+05 1.13E+06 8.68E+05 1.58E+06 9.05E+05 1.34E+06

a 1.22E-04 -8.89E-05 -9.77E-05 4.10E-05 7.92E-05 9.89E-05 -6.29E-05 -2.91E-05 1.39E-06

b -3.97E-05 1.18E-03 8.45E-04 -9.38E-05 -1.09E-04 -1.63E-04 4.04E-04 2.57E-04 3.83E-05

c -2.70E-05 -3.92E-05 -5.86E-05 -7.96E-06 -2.01E-05 -1.02E-05 -1.01E-04 -6.60E-05 1.50E-05

d 1.01E-04 1.99E-04 9.03E-05 4.19E-04 -5.62E-07 -1.29E-05 2.43E-04 1.97E-04 -8.47E-05

p-value, k 1.32E-01 3.24E-01 7.99E-02 4.51E-01 2.01E-01 3.55E-01 1.27E-01 1.53E-01 2.49E-01

p-value, a 5.46E-02 4.55E-01 9.74E-02 3.36E-01 3.22E-01 2.58E-01 4.15E-01 5.39E-01 9.36E-01

p-value, b 8.30E-01 5.27E-02 1.28E-02 5.43E-01 6.54E-01 5.31E-01 2.53E-01 2.56E-01 5.89E-01

p-value, c 6.80E-01 6.85E-01 2.61E-01 8.52E-01 8.01E-01 9.07E-01 1.43E-01 1.24E-01 4.07E-01

p-value, d 5.82E-01 6.96E-01 6.98E-01 2.40E-02 9.98E-01 9.58E-01 2.11E-01 1.52E-01 2.81E-01

adjusted R-squared 1.39E-01 5.33E-01 7.42E-01 4.20E-01 -5.26E-03 -1.35E-02 5.01E-01 6.56E-01 -3.67E-01

multiple R-squared 2.83E-01 7.20E-01 8.71E-01 5.75E-01 2.46E-01 2.40E-01 7.86E-01 8.53E-01 3.16E-01

p-value, model 1.38E-01 6.93E-02 4.55E-02 3.78E-02 4.55E-01 4.71E-01 2.16E-01 1.29E-01 7.63E-01
ERDC/CHL TR-20-17 50

Model 6. Linear, using base and high flows since the prior dredging event, and the base

and high flows before the prior dredging event

VVVVVVdd = kk + aaQQbbbbbbbb + bbQQii + ccQQbbbbbbbb−1 + ddQQii−1

Houston Ship Channel

COMID 1440485 1440511 1440521 1440525 1440539 

k 8.45E+05 -8.53E+04 7.11E+05 -5.38E+03 1.49E+05

a 2.21E-04 1.82E-04 2.59E-04 5.04E-04 -1.23E-06

b -2.44E-03 -1.21E-04 -1.92E-03 -1.68E-04 6.16E-04

c -4.96E-04 2.27E-04 -4.39E-05 1.21E-04 -1.04E-04

d 4.00E-03 -1.13E-03 3.14E-04 -2.45E-04 5.74E-04

p-value, k 2.00E-01 7.60E-01 NA 9.46E-01 2.93E-01

p-value, a 2.29E-01 1.57E-01 NA 2.55E-02 9.95E-01

p-value, b 2.94E-01 7.66E-01 NA 8.11E-01 5.38E-01

p-value, c 3.70E-01 2.52E-01 NA 3.59E-01 6.19E-01

p-value, d 3.12E-01 1.86E-01 NA 6.96E-01 5.79E-01

adjusted R-squared 4.11E-01 3.27E-01 NA 9.30E-01 -1.29E-01

multiple R-squared 7.47E-01 7.76E-01 NA 9.70E-01 6.57E-01

p-value, model 2.69E-01 3.99E-01 NA 1.27E-02 5.96E-01
ERDC/CHL TR-20-17 51

Model 7. Exponential, using cumulative flow since the prior dredging event, and the cumulative flow

before the prior dredging event

VVVVVVdd = kk(QQtttttt )(QQtttttt−1

bb)

Sabine-Neches Waterway

COMID 1481563 1112455 1115825 1477515 1477589 1477595 1477713 1477725 24719331 

k 6.32E+00 7.40E+00 6.90E+00 1.29E+01 1.05E+01 8.85E+00 8.03E+00 4.38E+00 8.50E+00

a 1.80E-01 2.80E-01 1.86E-01 -4.82E-02 2.40E-01 2.32E-01 5.24E-01 5.00E-01 1.79E-01

b 1.64E-01 2.73E-02 1.11E-01 8.54E-02 -8.38E-02 -7.69E-03 -3.02E-01 -1.27E-01 5.13E-02

p-value, k 1.73E-01 3.43E-01 5.72E-01 1.52E-01 8.99E-02 2.60E-01 2.60E-01 4.64E-01 3.85E-01

p-value, a 1.50E-01 2.28E-01 5.82E-01 8.51E-01 1.71E-01 3.05E-01 4.68E-02 3.53E-02 4.95E-01

p-value, b 3.12E-01 9.02E-01 7.41E-01 7.40E-01 6.20E-01 9.72E-01 1.65E-01 4.70E-01 8.44E-01

adjusted R-squared 5.36E-02 -3.06E-02 -2.51E-01 -1.39E-01 3.65E-02 -5.48E-02 5.14E-01 4.90E-01 -2.23E-01

multiple R-squared 1.32E-01 1.76E-01 6.14E-02 1.24E-02 1.57E-01 7.71E-02 6.53E-01 6.36E-01 8.30E-02

p-value, model 2.10E-01 4.62E-01 8.27E-01 9.22E-01 3.03E-01 5.70E-01 7.11E-02 7.99E-02 7.71E-01

Houston Ship Channel

COMID 1440485 1440511 1440521 1440525 1440539 

k 3.08E+00 1.50E+01 6.23E+00 -3.08E+00 4.05E+00

a 1.37E-01 3.10E-01 1.40E+00 6.98E-01 2.44E-01

b 3.40E-01 -4.24E-01 -1.11E+00 8.41E-02 1.65E-01

p-value, k 7.02E-01 3.93E-01 9.05E-01 1.89E-01 1.54E-01

p-value, a 4.46E-01 5.73E-01 4.96E-01 1.38E-04 3.25E-02

p-value, b 1.96E-01 4.80E-01 4.69E-01 2.65E-01 1.12E-01

adjusted R-squared 3.36E-02 -2.12E-01 -1.62E-01 9.39E-01 5.43E-01

multiple R-squared 3.10E-01 1.92E-01 4.19E-01 9.56E-01 4.36E-01

p-value, model 3.96E-01 6.52E-01 5.81E-01 4.01E-04 4.03E-02
ERDC/CHL TR-20-17 52

Model 8. Exponential, using base and high flows since the prior dredging event, and the base

and high flows before the prior dredging event

VVVVVVdd = kk(QQbbbbbbbb )(QQii

bb)(QQbbbbbbbb−1cc

)(QQii−1

dd)

Sabine-Neches Waterway

COMID 1481563 1112455 1115825 1477515 1477589 1477595 1477713 1477725 24719331 

k 1.15E+01 7.26E+00 1.28E+01 1.18E+01 8.33E+00 7.99E+00 1.17E+01 8.28E+00 9.04E+00

a 1.98E-01 2.61E-01 3.05E-01 9.46E-02 3.82E-01 5.18E-01 7.50E-01 7.57E-01 1.27E-01

b 5.82E-04 1.67E-04 -3.15E-03 -1.71E-03 -8.07E-04 -1.75E-03 -1.48E-03 -1.71E-03 1.10E-01

c -7.06E-02 5.31E-02 -2.58E-01 -7.51E-03 -1.31E-01 -2.55E-01 -6.79E-01 -5.43E-01 4.11E-01

d 1.77E-03 -4.75E-04 8.74E-03 8.83E-04 3.27E-04 2.02E-03 3.77E-03 4.26E-03 -4.58E-01

p-value, k 2.65E-01 5.31E-01 4.12E-01 4.32E-01 4.54E-01 5.64E-01 4.92E-01 5.35E-01 4.32E-01

p-value, a 2.94E-01 5.91E-01 5.07E-01 8.29E-01 2.29E-01 1.94E-01 2.30E-01 1.51E-01 7.82E-01

p-value, b 7.46E-01 9.74E-01 6.68E-01 7.12E-01 6.99E-01 5.05E-01 7.72E-01 6.75E-01 7.94E-01

p-value, c 8.50E-01 9.08E-01 5.80E-01 9.87E-01 6.78E-01 5.22E-01 2.33E-01 2.28E-01 3.31E-01

p-value, d 4.30E-01 9.44E-01 2.60E-01 8.51E-01 8.78E-01 4.54E-01 4.72E-01 3.26E-01 2.34E-01

adjusted R-squared -1.05E-02 -3.77E-01 -2.84E-01 -3.40E-01 -7.41E-02 -6.77E-02 4.04E-01 4.88E-01 -2.06E-01

multiple R-squared 1.58E-01 1.74E-01 3.58E-01 1.75E-02 1.94E-01 1.99E-01 7.44E-01 7.81E-01 3.97E-01

p-value, model 4.62E-01 8.58E-01 7.07E-01 9.95E-01 5.92E-01 5.79E-01 2.73E-01 2.23E-01 6.52E-01
ERDC/CHL TR-20-17 53

Model 6. Exponential, using base and high flows since the prior dredging event, and the base

and high flows before the prior dredging event

VVVVVVdd = kk(QQbbbbbbbb )(QQii

bb)(QQbbbbbbbb−1cc

)(QQii−1

dd)

Houston Ship Channel

COMID 1440485 1440511 1440521 1440525 1440539 

k 3.99E+00 5.51E+00 -1.88E+01 -4.32E+00 2.61E+00

a 2.93E-01 1.02E+00 2.52E+00 7.98E-01 3.78E-01

b -4.97E-03 -4.83E-01 -1.66E+00 -9.57E-04 -2.76E-03

c 1.43E-01 -4.93E-02 7.25E-01 5.00E-02 1.02E-01

d 3.72E-04 -2.24E-01 -3.13E-01 5.10E-04 9.13E-04

p-value, k 6.22E-01 8.73E-01 NA 3.98E-01 6.54E-01

p-value, a 1.96E-01 4.76E-01 NA 1.29E-02 1.52E-01

p-value, b 1.38E-01 6.15E-01 NA 6.56E-01 5.48E-01

p-value, c 5.73E-01 9.72E-01 NA 7.61E-01 6.53E-01

p-value, d 8.48E-01 7.60E-01 NA 8.08E-01 8.32E-01

adjusted R-squared 3.53E-01 -9.36E-01 NA 9.27E-01 3.87E-01

multiple R-squared 7.23E-01 3.55E-01 NA 9.69E-01 6.94E-01

p-value, model 3.04E-01 8.74E-01 NA 1.35E-02 2.24E-01
ERDC/CHL TR-20-17 54

Unit Conversion Factors

Multiply By To Obtain 

cubic feet 0.02831685 cubic meters

cubic yards 0.764555 cubic meters

feet 0.3048 meters

kilometer 0.6214 miles

miles (US statute) 1,609.347 meters

square miles 2.589998 E+06 square meters
ERDC/CHL TR-20-17 55

Acronyms and Abbreviations

CKAN Comprehensive Knowledge Archive Network 

COMID Common ID 

CSAT Corps Shoaling Analysis Tool 

ECMWF European Centre for Medium Range Weather Forecasts 

ERA-I ECMWF Reanalysis-Interim 

ERDC US Army Engineer Research and Development Center

NCF National Channel Framework 

NHD National Hydrography Dataset

NHDPlus National Hydrography Dataset Plus

NHDPlusV2 National Hydrographic Dataset Plus Version 2 

RAPID Routing Application for Parallel computatIon of Discharge 

SPT Streamflow Prediction Tool 

UMIP USACE Model Interface Portal 

USACE US Army Corps of Engineers 
REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

1. REPORT DATE 

August 2020

2. REPORT TYPE

Final Report

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

Utilizing Stream Flows to Forecast Dredging Requirements

5a. CONTRACT NUMBER

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

Elissa M. Yeates, Ahmad A. Tavakoly, Kenneth N. Mitchell Gregory W. Dreaper, 

Shahab Afshari

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND 

ADDRESS(ES) 

Coastal and Hydraulics Laboratory

US Army Engineer Research and 

Development Center

3909 Halls Ferry Road

 

Climate System Research Center Laboratory

Department of Geosciences

University of Massachusetts 

Amherst, Massachusetts 01003

8. PERFORMING

ORGANIZATION REPORT

NUMBER

ERDC/CHL TR-20-17

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

US Army Corps of Engineers

Washington, DC 20314-1000

10. SPONSOR/MONITOR'S ACRONYM(S)

USACE

11. SPONSOR/MONITOR'S

REPORT NUMBER(S)

12. DISTRIBUTION/AVAILABILITY STATEMENT

Approved for public release; distribution is unlimited.

13. SUPPLEMENTARY NOTES

Funding Account No. 476553

14. ABSTRACT

In recent years, the United States Army Corps of Engineers (USACE) has spent an average of approximately a billion dollars annually 

for navigation channel maintenance dredging. To execute these funds effectively, USACE districts must determine which navigation 

channels are most in need of maintenance dredging each year. Traditionally, dredging volume estimates for Operations and Maintenance 

budget development are based on experiential knowledge and historic averages, with the effects of upstream, precipitation-driven 

streamflows considered via general-rule approximations. This study uses the Streamflow Prediction Tool, a hydrologic routing model 

driven by global weather forecast ensembles, and dredging records from the USACE Galveston District to explore relationships between 

precipitation-driven inland channel flow and subsequent dredged volumes in the downstream coastal channel reaches. Spatially based 

regression relationships are established between cumulative inland flows and dredged volumes. Results in the test cases of the Houston 

Ship Channel and the Sabine-Neches Waterway in Texas indicate useful correlations between the computed streamflow volumes and 

recorded dredged volumes. These relationships are stronger for channel reaches farther inland, upstream of the coastal processes that are 

not included in the precipitation-driven hydrologic model.

15. SUBJECT TERMS 

Dredging—Management, Dredging spoil, Hydrology, Precipitation (Meteorology), Runoff, Streamflow

16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 

ABSTRACT

SAR

18. NUMBER 

OF 

PAGES

65

19a. NAME OF RESPONSIBLE PERSON

Elissa M. Yeates a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified 19b. TELEPHONE NUMBER (Include area code)

601-634-5221

Standard Form 298 (Rev. 8/98)

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