OCS Study

BOEM 2020-035

U.S. Department of the Interior

Bureau of Ocean Energy Management

New Orleans Office

Cooperative Agreement

Coastal Marine Institute

Louisiana State University

Coastal Marine Institute

Economic and Geomorphic Comparison

of Outer Continental Shelf Sand

and Nearshore Sand for 

Coastal Restoration Projects
OCS Study

BOEM 2020-035

Published by

U.S. Department of the Interior

Bureau of Ocean Energy Management

New Orleans Office

Cooperative Agreement

Coastal Marine Institute

Louisiana State University

Coastal Marine Institute

Economic and Geomorphic Comparison

of Outer Continental Shelf Sand

and Nearshore Sand for 

Coastal Restoration Projects

Authors

Rex Caffey

Daniel Petrolia

Ioannis Georgiou

Michael Miner

Hua Wang

Brittany Kime

June 2020

Prepared under BOEM Cooperative Agreement M15AC00013

by

Louisiana State University

Coastal Marine Institute

Baton Rouge, Louisiana 70803
DISCLAIMER

Study concept, oversight, and funding were provided by the US Department of the Interior, Bureau of 

Ocean Energy Management (BOEM), Environmental Studies Program, Washington, DC, under 

Cooperative Agreement Number M15AC00013. This report has been technically reviewed by BOEM, 

and it has been approved for publication. The views and conclusions contained in this document are those 

of the authors and should not be interpreted as representing the opinions or policies of the US 

Government, nor does mention of trade names or commercial products constitute endorsement or 

recommendation for use.

REPORT AVAILABILITY

To download a PDF file of this report, go to the US Department of the Interior, Bureau of Ocean Energy 

Management Data and Information Systems webpage (http://www.boem.gov/Environmental-StudiesEnvData/), click on the link for the Environmental Studies Program Information System (ESPIS), and 

search on 2020-035. The report is also available at the National Technical Reports Library at 

https://ntrl.ntis.gov/NTRL/.

CITATION

Caffey RH, Petrolia D, Georgious IY, Miner M, Wang H, Kime B. 2020. Economic and geomorphic 

comparison of Outer Continental Shelf sand and nearshore sand for coastal restoration projects. New 

Orleans (LA): US Department of the Interior, Bureau of Ocean Energy Management. 54 p. Contract 

No.: M15AC00013. Report No.: OCS Study BOEM 2020-035.

ABOUT THE COVER

The cutter suction dredge E.W. Ellefsen (Weeks Marine) on location in September 2016 mining Ship 

Shoal sand for the Caminada Headland Project. Photograph by Hua Wang.

ACKNOWLEDGMENTS

The study team would like to thank the BOEM program office of Gulf of Mexico OCS Region for 

financial support of this project through the Coastal Marine Institute at Louisiana State University. 

Additional appreciation is extended to members of the project advisory panel and to the Louisiana Coastal 

Protection and Restoration Authority for the provision of the detailed project data required for economic 

model development.
i

Contents

List of Figures .............................................................................................................................................................iii

List of Tables...............................................................................................................................................................iii

List of Abbreviations and Acronyms.........................................................................................................................iv

List of Authors..............................................................................................................................................................v

1 Introduction .........................................................................................................................................................6

1.1 Background ..................................................................................................................................................6

1.2 Problem Statement.......................................................................................................................................6

1.3 Objectives.....................................................................................................................................................7

1.4 Data and Methods ........................................................................................................................................7

1.5 Rationale ......................................................................................................................................................8

2 Conceptual Framework.....................................................................................................................................10

2.1 Advisory Panel............................................................................................................................................10

2.2 Trajectory Model.........................................................................................................................................11

2.3 Boundary Model .........................................................................................................................................13

2.3.1 Sand Quantity.........................................................................................................................................14

2.3.2 Standing and Classification of Benefits ..................................................................................................14

2.4 Mathematical Model....................................................................................................................................15

2.5 Summary ....................................................................................................................................................16

3 Project Benefit Modeling ..................................................................................................................................17

3.1 Geomorphic Data Synthesis and Simulation ..............................................................................................17

3.1.1 Sand Quality...........................................................................................................................................17

3.1.2 Dredging Impacts ...................................................................................................................................17

3.1.3 Project Outcomes...................................................................................................................................18

3.1.4 Sediment Type Suitability.......................................................................................................................18

3.2 Model Domain and Set-up..........................................................................................................................18

3.3 Model Scenarios and Semi-Empirical Results ............................................................................................19

3.3.1 Baseline Project Scenario ......................................................................................................................21

3.3.2 Early Storm Scenario .............................................................................................................................22

3.3.3 Late Storm Scenario...............................................................................................................................23

3.3.4 Larger Sand Scenario ............................................................................................................................24

4 Project Cost Modeling ......................................................................................................................................25

4.1 Data for the analysis...................................................................................................................................25

4.1.1 Project Reports.......................................................................................................................................25

4.1.2 Bid Data .................................................................................................................................................25

4.2 Cost Modeling.............................................................................................................................................29

4.2.1 Potential Variables .................................................................................................................................29

4.2.2 Empirical Results....................................................................................................................................31

4.2.3 Effects of Quantity and Distance ............................................................................................................33

5 INTEGRATED MODEL METHODOLOGIES ......................................................................................................35

5.1 Converting Quantities to Benefits ...............................................................................................................35

5.2 Present Value of Benefits ...........................................................................................................................35

5.3 Present Value of Costs...............................................................................................................................37
ii

5.4 Net Present Value Model............................................................................................................................37

5.4.1 Benefit-Cost Ratio ..................................................................................................................................37

5.4.2 Ecosystem Service Valuation Challenges ..............................................................................................38

5.4.3 Break-Even Approach ............................................................................................................................38

6 RESULTS ...........................................................................................................................................................40

6.1 Model Scenarios and Semi-Empirical Results ............................................................................................40

6.1.1 Baseline Project Scenario ......................................................................................................................41

6.1.2 Early Storm Scenario .............................................................................................................................42

6.1.3 Late Storm Scenario...............................................................................................................................43

6.1.4 Larger Sand Scenario ............................................................................................................................44

7 SUMMARY AND CONCLUSIONS......................................................................................................................45

7.1 Recap of Context and Approach.................................................................................................................45

7.2 Primary Findings.........................................................................................................................................45

7.3 Limitations and Additional Research ..........................................................................................................47

7.4 Policy Implications ......................................................................................................................................48

8 REFERENCES....................................................................................................................................................49

Appendix A: Project Bid Data ...................................................................................................................................51
iii

List of Figures

Figure 1. Conceptual trajectory of dredge-based reclamation stages on a coastal barrier island. ...............................12

Figure 2. Conceptual trajectories for dredge-based reclamation on a barrier island nourished with nearshore (NS) and 

outer continental shelf (OCS) sediments, including a storm event...............................................................................13

Figure 3. Standard components and processes of sand transport for dredge-based reclamation of a barrier island 

system comprised of individual sites............................................................................................................................14

Figure 4. Model domain and system components used in geophysical simulations.....................................................19

Figure 5. Simulated trajectories of barrier boundaries receiving sand dredged from nearshore and outer continental 

shelf sources (baseline scenario).................................................................................................................................21

Figure 6. Simulated trajectories of barrier boundaries receiving sand dredged from nearshore and outer continental 

shelf sources (early storm scenario) ............................................................................................................................22

Figure 7. Simulated trajectories of barrier boundaries receiving sand dredged from nearshore and outer continental 

shelf sources (late storm scenario). .............................................................................................................................23

Figure 8. Simulated trajectories of barrier boundaries receiving sand dredged from nearshore and outer continental 

shelf sources (larger sand class scenario)...................................................................................................................24

Figure 9. Geographic locations of candidate projects (NS- and OC-sourced) for development of a dedicated dredging 

cost model for barrier shoreline and barrier island restoration in in Louisiana, 1997–2018..........................................27

Figure 10. Model-estimated effects of distance and cut-to-fill ratio on construction cost under the baseline project 

scenario .......................................................................................................................................................................34

Figure 11. Ecosystem break-even values (EBEV) for NS- and OCS-soured projects at various boundaries, distances 

and cut-to-fill ratios (baseline scenario)........................................................................................................................41

Figure 12. Ecosystem break-even values (EBEV) for NS- and OCS-sourced projects at various boundaries, distances 

and cut-to-fill ratios (early storm scenario, Year 5).......................................................................................................42

Figure 13. Ecosystem break-even values (EBEV) for NS- and OCS-sourced projects at various boundaries, distances 

and cut-to-fill ratios (late storm scenario, Year 20).......................................................................................................43

Figure 14. Ecosystem break-even values (EBEV) for NS- and OCS-sourced projects at various boundaries, distances 

and cut-to-fill ratios (larger sand scenario)...................................................................................................................44

List of Tables

Table 2. Dredge and Target Volumes for NS- and OCS-sources for the Baseline Project Scenario Under Various Cutto-fill Ratios ..................................................................................................................................................................20

Table 3. Baseline and Post-nourishment Starting Areas for the Geomorphic Simulations...........................................20

Table 4. Candidate Projects for Development of a Representative Cost Model of Dedicated-dredging for Barrier 

Island and Shoreline Restoration in Coastal Louisiana (1990–2018)...........................................................................26

Table 5. Data from Candidate Projects for Development of a Representative Cost Model of Dedicated-dredging for 

Barrier Island and Shoreline Restoration in Coastal Louisiana (1990–2018)...............................................................28

Table 6. Project Cost Variables and Descriptive Statistics...........................................................................................30

Table 7. Project Construction Cost Model Parameter Estimates..................................................................................32

Table 8. Percent of Variation Explained by Individual Predictors in the Project Cost Model ........................................33

Table 9. Scenarios and Assumptions of the Ecosystem Break-even Value (EBEV) Model .........................................40

Table A2. Commercial Bid Data for OCS Sources Dedicated-dredging Projects on Barrier Shorelines and Island in 

Louisiana 1997–2018 (n=35) .......................................................................................................................................52
iv

List of Abbreviations and Acronyms

Short Form Long Form

BICM Barrier Island Comprehensive Modeling

BCA benefit cost analysis

BCM billion cubic meters

BCR benefit: cost ratio

BERM berm to barrier

BEV break even value

BOEM Bureau of Ocean Energy Management

CC construction cost 

CIAP Coastal Impact Assistance Program

CMI Cooperative Marine Institute

CPRA Coastal Protection and Restoration Authority

CTF cut-to-fill 

CWCCIS Civil Works Construction Cost Index System

CWPPRA Coastal Wetland Planning, Protection, and Restoration Act

ESV ecosystem service value

EBEV ecosystem break even value

FFC fully funded costs

CPRA Coastal Protection and Restoration Authority

LCA Louisiana Coastal Area

MRD Mississippi River Delta plain

MBIIP Mississippi Barrier Island Improvement Program

NFWF National Fish and Wildlife Foundation

NOAA National Oceanic and Atmospheric Administration

NPV net present value

NRDA National Resource Damage Assessment

NS nearshore

OCS Outer Continental Shelf

STATE state only projects

TBC total bid cost

TCC total construction cost

USACE US Army Corps of Engineers

USGS US Geological Survey

WIS wave information system

yd3 cubic yard 

YOD year of disappearance 
v

List of Authors

Author Affiliations

Rex Caffey Louisiana State University, Department of Agricultural Economics, Center 

for Natural Resource Economics & Policy

Daniel Petrolia Mississippi State University, Department of Agricultural Economics

Ioannis Georgiou University of New Orleans, Department of Earth and Environmental 

Sciences and Pontchartrain Institute for Environmental Sciences

Michael Miner Bureau of Ocean Energy Management, Gulf of Mexico Region, Marine 

Minerals Program, New Orleans, Louisiana

Hua Wang Louisiana State University, Department of Agricultural Economics, Center 

for Natural Resource Economics & Policy

Brittany Kime University of New Orleans, Department of Earth and Environmental 

Sciences and Pontchartrain Institute for Environmental Sciences
6

1 Introduction

1.1 Background

Coastal land loss processes are a significant threat to United States (US) coastal shoreline counties, a 

region that comprises less than 10 percent of the national land area and more than 40 percent of the 

nation’s population (NOAA 2015). Such threats are especially prominent in the Gulf of Mexico region, 

and Louisiana in particular, where barrier islands and shorelines are subject to both climatic and geologic 

forcing (Morton 2008). The US Geological Survey (USGS) determined that about 1,883 mi2 of land 

became open water between 1932 and 2010 (25% of Louisiana’s coastal land area). Analyses conducted 

in support of Louisiana’s 2017 Coastal Master Plan found that the state could experience annual damages 

from flooding coast-wide totaling $7.7 to $23.4 billion over the next 50 years, depending on future coastal 

conditions. Due to the sediment-starved character of the Mississippi River delta plain, sediment suitability 

and availability are limiting factors that have historically constrained large scale projects. However, the 

demand for addressing Louisiana’s coastal land loss crisis means that the portfolio of rapid land building 

projects (dedicated dredging) will increase, where large quantities (more than 90 million yd3 ) of sediment 

will be needed for coastal restoration in the next 50 years (Khalil and Finkl 2009).

For dedicated dredging projects, coastal managers must choose between nearshore sediment and sediment 

sourced from outside of the active coastal system, such as Outer Continental Shelf (OCS) sand or modern 

Mississippi River sediment load, for inputs. High quality sand (similar to native beach) is required for 

beach and dune barrier habitat restoration whereas sandy muds are required to rebuild coastal marshes 

(Khalil and Finkl 2009). Availability of suitable sediment resources is a vital factor in restoration efforts, 

with almost 80% of the restoration budget allocated to exploration, dredging, and emplacement of 

sediment (Khalil et al. 2010, Wang 2011). Sand resources in state waters are frequently of lower quality

(smaller grain size and higher organic fraction than OCS sand), and dredging within the littoral zone can 

potentially alter wave climate, negatively affecting the landward shoreline. Moreover, excavation of 

nearshore sand often occurs within the active coastal system, compromising long term effectiveness of 

projects and failing to address the need to supplement a deficit in the coastal sand budget. Using OCS 

sand resources minimizes alterations to wave climate and introduces new sand from outside of the active 

coastal system, decreasing the coastal sand deficit and improving project sustainability and geomorphic 

function.

1.2 Problem Statement

To date, there has been no analysis comparing the contributions of OCS sediment compared to nearshore

(NS) sediment toward long term project effectiveness, lifespan, cost, and contribution to system function 

as a whole. Better quantification of the quality and value of OCS sand for coastal restoration projects 

relative to alternative sources is important for federal, state, and local stakeholders to accurately estimate 

long term economic and ecosystem benefits of these projects. 

Within the 2017 Coastal Master Plan, over $22 billion (of an estimated $50 billion) will be needed to fund 

those restoration projects requiring mechanical placement of sediment inputs (CPRA 2017). From 1991to 

2012, projects authorized by the Coastal Wetlands Planning, Protection, and Restoration Act averaged 

$289,686 and $100,795 per acre for barrier island restoration and marsh creation projects, respectively 

(Wang et al. 2012). Yet the costs of more recent projects have exceeded this range, and the costs of future 

projects is expected to be even greater as distance between borrow areas and project footprints increase. 

Material transport is a limiting factor, and using OCS sand further increases project cost due to the 

increased distance and specialized equipment required for work in offshore environments. In Louisiana’s 
7

coastal plain, however, nearshore sediment is a component of a sediment-starved system, and its use on 

projects within the system does not fully address the long term need to supplement a deficit of barrier 

island compatible sand.

1.3 Objectives

The goal of this study was to provide a better understanding of the geomorphic and economic benefits and 

costs of using OCS sediment compared to nearshore sediment for coastal restoration projects on the basis 

of sediment textural properties and the capital required to employ various project construction methods. 

Specific objectives include:

1) Develop a conceptual framework for standardizing site- and system-level assessments of dredgebased renourishment projects on barrier islands.

2) Construct a geomorphic sub-model of sediment transport for a proxy barrier template and 

simulate nearshore- and OCS-sourced sand transport under a range of project scenarios.

3) Construct an economic sub-model for assessing project costs related to harvest, transport and 

deposition of nearshore- and OCS-sourced sediment under a range of project scenarios.

4) Integrate the geomorphic and economic sub-models within coupled frameworks for evaluating 

the benefits and costs of dedicated dredging projects on Louisiana’s coastal barrier islands.

5) Develop case studies to examine the economic tradeoffs associated with sediment location, 

quantity, quality, and meteorological forcing over time.

6) Summarize findings and identify future applications and analyses based on the integrated 

framework. 

1.4 Data and Methods

Because of the dual nature of the study, integration of physical and economic data and analysis required a 

combination of nested and parallel construction of models throughout the study period. This process 

began in year 1 of the project with the convening of an advisory panel for the purpose of identifying 

relevant projects for the analysis and for refining a conceptual framework (Section 2). The framework 

outlines standardized components for the analysis and the temporal and spatial scales required for 

developing comparable sub-models of benefits and costs. 

Technical inputs to the geomorphic analysis (Section 3) were obtained from existing literature (i.e. 

scientific manuscripts and technical reports), geodatabases and federal and/or state-owned sources related 

to coastal sediment inventories and dynamics. Examples of such work included citations of the location 

and extent of relict delta deposits, their proximity to the coastal zone, the potential availability of these 

deposits relative to the ongoing transgression of the Louisiana coast, chief drivers of nearshore sediment 

transports processes within the delta plain, and the role of coastal sediment sinks (Nairn et al. 2004, Miner 

et al. 2009a, Georgiou et al. 2011). Project performance parameters were developed from postconstruction outcome monitoring and from consultation with project managers and engineers in the public 

and private sector. Such information included, but was not limited to: geotechnical and geophysical 

surveys, site- and technology-specific analyses of sediment delivery alternatives, and data from coast-
8

wide reference monitoring systems and other systems with similar capabilities. This information informed 

construction of a morphodynamic model for a proxy barrier island system. Three-dimensional modeling

with the Delft #D modeling suite (with coupled waves, tidal currents, and full sediment transport and 

morphology) was used to simulate cumulative erosion and deposition, with and without project-based 

nourishments.

A sub-model for estimating project costs (Section 4) was generated from data on dedicated dredging

projects in coastal Louisiana sponsored by state and federal restoration programs from 1990-2018. 

Sources of project data included the US Army Corps of Engineers (USACE), the Louisiana Coastal 

Protection and Restoration Authority (CPRA), the Coastal Wetland Planning, Protection, and Restoration 

Act (CWPPRA), the Coastal Impact Assistance Program (CIAP), and the Louisiana Coastal Area (LCA) 

Comprehensive Ecosystem Study. To a lesser extent, direct communications with coastal engineering 

firms were used to provide additional costs and benefits data. Data were analyzed for more than 20 

project-specific variables obtained from 71 private sector bids representing 22 constructed projects. 

Multiple regression analysis was used to construct generic models (Wang 2011) in which construction 

costs were described as a function of sediment quantity and quality, borrow source distance, sponsor 

program, and other project-specific variables.

Section 5 describes a variety of mathematical methods for the integration of benefit and cost sub-models 

with an economic efficiency framework. The various approaches described are based on a benefit-cost 

analysis (BCA) framework in which physical quantities of land (simulated by the geomorphic sub-model)

are combined with output from the project costs model. A benefit-transfer approach is described in which 

ecological service values are extrapolated from existing literature on non-market valuation to yield 

estimates of Net Present Value (NPV) over a 20–50 year project life (Woodward and Wui 2001, Smith 

2018). A variation of BCA is described in which an Ecosystem Break-Even Value (EBEV) can be used to 

derive monetized values for ecosystem services as a function of simulated physical benefits and project 

costs over time (Caffey et al. 2014).

Case studies using the EBEV approach are developed to assess the performance of NS- and OCS-sourced 

sediments under single project comparisons (Section 6). Results are depicted in terms of direct effects 

(site-level) and total effects (system-level) through estimates of EBEV. These simulations support general 

findings and conclusions regarding the economic trade-offs associated with dredge transport distance, 

sediment quality and meteorological risk (Section 7).

1.5 Rationale

Sediment distribution maps developed by Finkl and Freeman (2014) estimate the total volume of 

Louisiana-adjacent OCS sand deposits at ~100 Billion yd3

, primarily from offshore shoals and Paleolithic 

stream channels such as the Sabine Bank, the Tiger and Trinity Shoal Complex, Ship Shoal Complex, and 

St. Bernard Shoal. Approximately three-fourths of this material is dredgeable under current technological 

and regulatory constraints. Previous economic analyses of these two source types (NS and OCS) have 

been piecemeal, and focused on narrow range of cost factors. Comprehensive, performance-based 

comparisons of sediment performance have yet to be developed. Economic and environmental trade-offs 

between alternative sediment sources are expected to be project- and location-specific, and influenced by 

a wide range of constraints related to geomorphic characteristics (material quantity, quality, and 

mobility), technological limitations (dredge capacity and transport distance), seasonal risks (average sea 

state and seasonal weather risks), and environmental policy (operational constraints related to threatened 

and endangered species). To date, no attempts have been made to systematically characterize these 

constraints and to integrate them into a comprehensive economic model useful for informing decision 

making related to dedicated dredging projects. 
9

While the need for such analysis is especially critical in Louisiana, development of an integrated 

geophysical and economic analytical approach would have potentially positive implications for all coastal 

regions. An integrated model developed in Louisiana and tested in the Gulf region could provide the 

foundation for more comprehensive approaches to restoration planning and could support coastal 

resiliency initiatives within along the Atlantic seaboard and other US coastlines.
10

2 Conceptual Framework

2.1 Advisory Panel 

Initial meetings of the researchers involved in this project (study team) were heavily focused on the 

identification of relevant data and development of a common structure and language for the integrated 

analysis. The preliminary approach that resulted from those exchanges was presented to a project 

advisory committee convened at the University of New Orleans in year 1. The meeting consisted of 18

attendees, including 6 members of the study team and 13 external advisors from the public and private 

sector with expertise in coastal geomorphology, environmental engineering and management of state and 

federal dredging projects (Table 1). 

During the meeting, the study team presented alternative frameworks for the study and a list of candidate 

projects under consideration as data sources for an integrated analysis. At the time of the meeting, 16 

candidate projects had been identified for which relevant nearshore (NS) and Outer Continental Shelf 

(OCS) data were available for coastal Louisiana. Project advisors provided input that would lead to the 

identification of an additional 6 relevant projects for guiding the development of the geomorphic and 

economic sub-models. In terms of the analytical framework, the panel offered guidance on temporal and 

spatial scales and discussed key variables most likely to affect sediment-related performance and costs. 

Some of the more salient points that emerged from the integrated framework discussion are provided 

below.

 It is appropriate to simulate a standardized barrier island template and develop geomorphic and 

economic projections based on data from previous projects using NS and OCS sediment sources.

 Sediment dynamics should be modeled at the particle level, as opposed to total volume approach, 

given that sand quality will be highly variable between source locations

.

 Geomorphic simulations should focus on how sand quality affects project longevity at the site-level 

and the system-level. Simulations should address both chronic and acute forcing (storms).

 Monetized benefits should derive from physical outputs (volume/area) of the geomorphic model and 

estimated on an annual net-basis (future-with minus future-without project).

 Monetized cost estimates should be based on a statistical model derived from relevant data (e.g. final 

reports, contractor bids, and input from industry representatives).

 Economic efficiency comparisons should not be based solely on sand as a commodity, but also on 

the flow of ecosystems services generated by that sand throughout the project lifetime.

 Different projects have different goals. Consider using alternative metrics of project performance 

and benefits (e.g. measuring project response at the site and system level and at subaerial and

subaqueous contours).

 Transferability of the knowledge base on this project is important. A valuable outcome would be the

development of an integrated framework for decision-support that could be replicated in other 

coastal regions.
11

Table 1. Advisory Panel Attendees for the Outer Continental Shelf Sand Economic and 

Geomorphic Working Group

Name Title and Affiliation

Biven, Megan Project Manager, Bureau of Ocean Energy Management, New Orleans, Louisiana

Caffey, Rex† Professor, Natural Resource Economics, Louisiana State University, Baton Rouge, 

Louisiana

Childs, John Engineer, US Army Corps of Engineers Engineer Research and Development Center, 

Vicksburg, Mississippi

Duplantis, Bridgette Marine Minerals Information System Lead, Bureau of Ocean Energy Management, 

New Orleans, Louisiana

Flocks, Jim Research Geologist, US Geological Survey, St. Petersburg, Florida

Georgiou, Ioannis† Associate Professor, Department of Earth and Environmental Science, University of 

New Orleans, New Orleans, Louisiana

Grandy, Greg Senior Manager, Coastal Engineering Consultants, Inc. Baton Rouge, Louisiana

Kulp, Mark Associate Professor, Department of Earth and Environmental Science, University of 

New Orleans, New Orleans, Louisiana

Kime, Brittany† Graduate Assistant, Department of Earth and Environmental Science, University of 

New Orleans, New Orleans, Louisiana

Lee, Darin Operations Manager, Coastal Protection & Restoration Authority, Baton Rouge, 

Louisiana

Mallindine, Jessica Marine Biologist, Marine Minerals Program, Bureau of Ocean Energy Management, 

New Orleans, Louisiana

McDonald, Justin Lead Engineer, US Army Corps of Engineers Mobile District Civil Works, Mobile, 

Alabama

Miller, Bradford Project Manager, Coastal Protection & Restoration Authority, Baton Rouge, Louisiana

Miner, Mike† Geologist and Project Manager, Bureau of Ocean Energy Management, New Orleans, 

Louisiana 

Petrolia, Daniel† Associate Professor, Natural Resource Economics, Mississippi State University, 

Mississippi State, Mississippi

Thompson, Gordon Coastal and Civil Engineer, Baird & Associates, Woodlands, Texas

Waldner, Jeff Physical Scientist and Oceanographer, Bureau of Ocean Energy Management, 

Sterling, Virginia

Wang, Hua† Postdoctoral Assistant, Natural Resource Economics & Policy, Louisiana State 

University, Baton Rouge, Louisiana

Professional affiliations as of April 12, 2016, † Study team member

2.2 Trajectory Model

The conceptual framework for this study builds on advisory input and previous geomorphic and economic 

studies of coastal restoration in Louisiana (Georgiou et al. 2011, Wang 2011). These large-scale 

environmental projects are typically characterized by three distinct stages: engineering and design; 

construction; and operation and monitoring. A graphic depiction helps to illustrate the timing, costs, and 

activities associated with these stages for a generic trajectory of dedicated dredging projects (Figure 1). 

Engineering and design (Stage I) is the initial stage in which geotechnical surveys and pre-project 

assessment are used to evaluate sediment availability and dynamics for a proposed template. At this stage, 

feasibility decisions are based on “future-with-project” and “future-without-project” comparisons, 

typically expressed in terms of subaerial land surface over the project life. This phase typically accounts 

for 10 percent of a project’s fully funded costs (FFC) and can last 3–10 years, depending on site- and 

source-specific requirements for geotechnical surveying, development of operational plans for sediment 

transport, permitting and regulatory compliance, and dredge vessel availability.
12

Project construction (Stage II) is a relatively brief period that accounts for the majority of FFC (85%). 

During this phase, an initial quantity of sediment from a designated source (Dredgeq) is mechanically 

transported to the project site and deposited within a bounded template to achieve a target level of postsettlement elevation (Targetq) per sponsor agency specifications. Construction is typically completed 

within a single year, although longer periods can be required depending on various factors, including:

distance between source material and project footprint; project size and design; dredge capacity 

limitations; weather; and, critical habitat constraints that might limit operations during certain seasons.

Figure 1. Conceptual trajectory of dredge-based reclamation stages on a coastal barrier island. 

Project operation and monitoring (Stage III) is the longest period and can range from 20–50 years 

depending on sponsor. During this phase, public benefits derive from an expanded barrier platform. A

range of benefits have been used as justification for these projects; however, storm surge attenuation and 

provision of coastal habitat are two of the most frequently cited ecosystem services for coastal barrier

systems (Petrolia and Kim 2009, Feagin et al. 2010, Barbier et al. 2011). And though there is some 

potential for volumetric and surface area increases of sediment due to longshore sediment transport 

processes at the site and system level, in a transgressive coast, most of these materials (and their 

associated benefits) are expected to diminish over time as restored land succumbs to physical forcing. 

Thus, the basic expectation is that project benefits will exceed project costs and the renourishment will 

sustain a subaerial template (Projectq) that have otherwise been lost over time (Controlq). Despite 

accounting for the lowest portion of FFC (5%), monitoring is critical for collecting the data needed to

refine expectations and to improve the design and construction of future projects. 

Figure 2 expands the basic trajectory with hypothesized responses for projects using NS- and OCSsourced sediments. These curves approximate the observations of project managers and reflect two

important tradeoffs with potential economic implications. First, while nearshore sediment sources may be 

less expensive to harvest and transport (given their proximity to project sites), they often contain a higher 

fraction of organic fines (mud) than OCS sources. Thus, for any given Targetq of sand, the volume of NS

sediment dredged will typically exceed the volume of OCS sediment dredged, i.e. NSq> OCSq. Secondly, 

managers assert that OCS-sourced projects are typically more resilient over time than NS-source ones, 

thus OCSq’ > NSq’. In other words, increased resilience is attributed to the larger diameter of OCS sands, 

which can make them more resistant to the physical forces of coastal transport, erosion and storms (i.e.,

more energy is required for mobilization and transport). Less understood, however, is the degree to which 

these differences translate into economic efficiencies, and the extent to which any source-dependent 

trade-offs are affected by prolonged forcing and major storms events. Examining these questions requires 

the delineation of site and system boundaries and a mathematical framework for quantifying sediment 

dynamics within those boundaries.
13

Figure 2. Conceptual trajectories for dredge-based reclamation on a barrier island nourished with 

nearshore (NS) and outer continental shelf (OCS) sediments, including a storm event.

2.3 Boundary Model

Figure 3 delineates component boundaries and sand dynamics at the site and system level for dredgebased renourishment of a barrier template. For the purpose of this analysis, a “site” is defined as a distinct 

barrier island. Transport of sand into and out of the site affects the 2-dimensional area of the site, as 

defined by some vertical contour. Thus, for any given site, measures of surface area (e.g., acreage) 

increase as depth increases. 

A project site may have one or more adjacent up-drift and down-drift sites associated with it, each with its 

own distinct boundary. A “barrier system” is defined as a set of one or more sites that stand in relation 

(up-drift or down-drift) to one another. Barrier systems are located within an active littoral zone 

characterized by subtidal transport of coastal sediments.

A unit of sand located at a position adjacent to, but external from, the vertical contour of a given site is 

considered to be outside of the site boundary. This designation is necessary for assessing which units of 

sand are to be counted as beneficial in terms of determining standing, discussed in more detail below.

Note that although a unit of sand outside a given boundary is not considered beneficial in a given period, 

that unit of sand may be transported (mechanically or naturally) at some later period to a location inside 

the boundary, at which point it would have standing, and would count as beneficial. 

Note also that site boundaries allow for benefits to accrue at both subaerial and subaqueous contours, and 

that the value of the benefits attributed to a unit of sand at each of these two levels may differ. 
14

Figure 3. Standard components and processes of sand transport for dredge-based reclamation of 

a barrier island system comprised of individual sites.

2.3.1 Sand Quantity

The quantity of sand in Stage III at a given site in a given period is the sum of the quantity of sand at the 

site in the previous period, any sand mechanically dredged from NS or OCS sources outside the system 

and placed within the site during the current period, the quantity of sand “captured” from adjacent sites in 

the current period due to natural transport, and the quantity of sand “lost” due to natural transport.

There is some set of functions that dictate how much sand accumulates (or sloughs off) at the site, and 

how much is recaptured from adjacent sites.

Note that the above description of “dredged” sand is expressed in terms of the quantity of sand placed, 

not the total quantity of sediment dredged, which is composed of some fraction of sand (beneficial) and 

mud (zero benefit) that varies by source location (see Targetq and Dredgeq designationsin Stage II, Figure 

1).

1

2.3.2 Standing and Classification of Benefits

Only sand located within the benefit boundary of a given site is considered to have standing, where 

“standing” dictates which units of sand are counted as beneficial in a given time period. Standing is 

defined at both the site level and at the system level. 

To facilitate policy discussion, benefits are divided into two classes. At the site level, benefits associated 

with pre-existing sand (i.e., sand present at a given site at period t = 0) and sand placed mechanically from 

outside the site boundary are classified as “direct” benefits. Benefits associated with sand recaptured at 

the site from outside the site boundary via natural transport are classified as “indirect” benefits.

At the system level, the classification of benefits is somewhat modified. For example, if sand were 

moved, either mechanically or naturally, from one site within the system to another within the system, 

1 Dredge and. target quantities are addressed in Sections 3 and 4. If desirable, the model can be amended to include mud as also 

beneficial, with its own respective benefit values.
15

this would not result in any change of benefits, because the sand would move from one site with standing 

to another site also with standing. 

Thus, at the system level, benefits associated with pre-existing sand and sand placed mechanically from 

outside the system boundary are classified as “direct” benefits. Benefits associated with the net quantity of 

sand recaptured across the entire system from outside the system via natural transport are classified as 

“indirect” benefits.

2.4 Mathematical Model

Formally, let the change in quantity of sand at site s at time t, st ∆q , be expressed as the sum of the 

quantity of sand added mechanically in the current period, mst , and the net difference between the 

quantity of sand added and lost via natural transport, st n . As Figure 3 indicates, sand added or lost via 

natural transport can originate either from other sites within the barrier system (up-drift or down-drift) or 

“vagrant” sand, i.e., sand from outside the barrier system, either from nearshore (littoral zone) or OCS 

sources. Thus, we may write

~s v

st st st nn n = + (1)

where ~s

st n represents the share originating from other sites within the barrier system ( ~ s indicating “not 

s”) and v

st n but these cannot be individually identified at the site level; we observe only a net gain or loss 

of sand at a site at each period. Thus, we have: 

st st st ∆= + q mn (2)

Summing expression (2) over all sites within the system, we have system quantity of sand at time t as:

( ) 1 1

S S

t st st st

s s

Q q mn = =∆= ∆= + ∑ ∑ (3)

Within a defined barrier system, the sum of sand change via natural transport between all sites, is

necessarily zero, i.e.:

~

1

0

S s

st

s

n

=

∑ = (4)

Thus, at the barrier system level, any net change in sand quantities via natural transport is necessarily 

attributable to vagrant sand exchange with the larger littoral boundary and/or offshore zone (Figure 3).

Specification for Project Evaluation

Renourishment projects are typically conducted at a single site, with mechanical placement of sand at that 

site only. Further, project evaluation is typically based on the sand accrued at the project site only, 

ignoring any changes in sand accrual at other sites in the barrier system. Defining site s =1 as the project 

site, and recognizing that 0 mst = for all s ≠ 1, we may rewrite (3) to separate what is typically 

evaluated, called here “direct”, from what is typically ignored, called here “indirect”. 
16

 1 1

1 2

S S

t st t t st

s s Direct

Indirect

Q q mn n = =∆= ∆= + + ∑ ∑ 

(5)

Accounting for Subaqueous Quantities

If we assume that the benefits of a unit of sand are dependent upon whether that unit is subaerial or 

subaqueous, we may expand (1) into:

aa bb

st st st st st

Subaerial Subaqueous

∆= + + + q mnmn

 

(6)

where the “a” superscript indicates “above the surface” (subaerial) and “b” indicates “below the surface” 

(subaqueous). 

At the system level, substituting (6) into (5), we get:

  11 11

1 2 2 

 

S S S

aa bb a b

t st t t t t st st

s s s Direct Subaerial Direct Subaqueous

Indirect Subaerial Indirect Subaqueous

Q q mn mn n n = = =

∆= ∆= + + + + + ∑   ∑ ∑ (7)

where the first set of terms, “Direct Subaerial”, is what is included in a typical Stage III project 

performance evaluation, with all others ignored.

2.5 Summary

The graphical and mathematical framework outlined above establishes a conceptual model for examining 

the performance of barrier island renourishment projects in terms of sand quantity dynamics at the site 

and system level. Modeling the performance of that sand over time, however, requires a more specific 

delineation of the barrier island template, and geomorphic simulations to depict how sand of different 

quality responds to chronic and acute forcing.
17

3 Project Benefit Modeling 

3.1 Geomorphic Data Synthesis and Simulation

Data for the development of a sub-model of project benefits were obtained from extant literature (i.e. 

scientific manuscripts and technical reports), geodatabases, and federal and/or state-owned sources related 

to coastal sediment inventories and dynamics. Examples of such work included citations of the location 

and extent of relict delta deposits, their proximity to the coastal zone, the potential availability of these 

deposits relative to the ongoing transgression of the Louisiana coast, chief drivers of nearshore sediment 

transports processes within the delta plain, and the role of coastal sediment sinks (Nairn et al. 2004, Miner 

et al. 2009a, Georgiou et al. 2011). Project performance parameters were developed from postconstruction outcome monitoring and from consultation with project managers and engineers in the public 

and private sector. Such information included, but was not limited to: geotechnical and geophysical 

surveys, site- and technology-specific analyses of sediment delivery alternatives, and data from coastwide reference monitoring systems and other systems with similar capabilities. 

3.1.1 Sand Quality 

The sediment characteristics of the Mississippi River Delta (MRD) region vary spatially as a function of 

geomorphology. Sand quality was accessed from Outer Continental Shelf (OCS) locations (e.g., Ship 

Shoal, Trinity Shoal, St. Bernard Shoals, etc.) using available borings and/or other available geophysical 

data. Information used in the analysis and comparison include among others median grain size diameter 

(d50), sorting, shape factor, kurtosis where available, mud content, etc. Similar analysis was used for 

sediment characteristics of sand in nearshore environments used previously for restoration projects, and to

develop normalized plots comparing nearshore compared to OCS sediment quality. Because beachface, 

shoreface, and dune slopes are proportional to the sediment characteristics (Dean 1974, Dean and 

Darlymple 2002) an inventory of slopes in areas where restoration took place was developed to identify 

any correlations with the corresponding grain size diameter (James 1975).

3.1.2 Dredging Impacts

The presence (and geometry) of nearshore bars imposes a control on the available wave energy that 

arrives at a beach, as these bars often induced breaking of larger waves and hence limit the wave energy 

transmission for higher waves (Short 1992). Dredging immediately in front of barrier islands or beaches, 

is not very common in Louisiana, although several borrow pits where nearshore sediments were used are 

proximal to barriers, located within the active shoreface. Review of literature review and information 

synthesis from other states, and in particular Florida, was used to examine cases in which dredging takes 

place routinely following storms. 

Kennedy et al. (2011) reported that, for open coast pits with large alongshore lengths, cross-shore infilling 

appeared to dominate over longshore infilling, but both processes may be of comparable importance in 

shorter pits. Infilling of three borrow pits adjacent to ebb shoals was found to be considerably larger than 

on open coasts, and, finally, the offshore pits experience more rapid bathymetric changes compared to 

nearshore pits. Kennedy et al. (2011) also reported that hurricanes have a significant effect on infilling 

rates, as did Miner et al. (2009b) during a survey of an ebb tidal delta along the Timbalier shoreline in 

2004–2005. These findings were supplemented with a selection of wave simulations using the wave 

model (SWAN) in both stationary and non-stationary mode in a domain previously developed and 

validated by Georgiou et al. (2014). A series of hypothetical dredge pits were examined by adjusting the 

bathymetry at key environments, and performing simulations to compare with the baseline results.
18

3.1.3 Project Outcomes

An inventory of relevant projects that used both NS and OCS sands was developed to guide the 

development of time-dependent quantity estimates for the economic analysis. Quantity measures were 

reported volumetrically and in terms of subaerial land and the proximal subaqueous platform. The project 

database provided a 25–50 year window of historical bathymetry and topography from the corresponding 

period of each project, incorporated shoreline erosion rates (from BICM or other source, e.g., Barrier 

Shoreline Atlas), seafloor change analysis maps (Miner et al. 2012), cut-to-fill ratios, and other metrics of 

performance obtained from project monitoring reports. The challenge was to establish a continuous area 

function that accounted for the role of the shoreface and storm activity and reflected performance trends 

related to differential shoreface response, using BICM bathymetry (Miner et al. 2009c) and surface 

textural characteristics of the sediment (Kindinger et al. 2014).

3.1.4 Sediment Type Suitability

Based on results from the project inventory and data recovered from the literature and synthesis, a matrix 

was developed to categorize sediment type based on suitability for, or project type. Various grain sizes 

were evaluated for renourishment suitability for dunes, beach and back barrier platform. Because different 

templates produce different geomorphologies (given similar forcing), preferred sediment types can be 

determined based on the suitability of restoration targets and habitats. Geomorphic results provided

baseline data to draw the first dependencies and state to complete the matrix. To ensure that model 

behavior is constrained, these simulations are supplemented with literature and results from locations 

where these sediment types are present with their respective habitat.

3.2 Model Domain and Set-up

A final model grid was developed around a proxy barrier system based on the Isle Dernieres island chain 

using National Oceanic and Atmospheric Administration (NOAA) bathymetry from the 1980s. The 

system boundary consist of a 360 ha (subaerial) central barrier island with a large section (898 hecatares 

[ha]) of an up-drift barrier to the east and a smaller section (166 ha) of a down-drift barrier to the west. 

Additional components include tidal inlets, spit platforms, and areas ebb-delta, surf zone, and nearshore 

deposition. The bounded area represents a 50 km2 domain for the application of three-dimensional 

modeling with coupled waves, tidal currents and full sediment transport and morphology. The model is 

constructed with the Delft3D modeling suite and can be used to simulate cumulative erosion and 

deposition, with and without project-based nourishments at subaerial and subaqueous boundaries (Figure 

4). 

The domain is transected by 192 x 384 grid consisting of cells of varying resolution (~20m nearshore to 1 

km offshore). Water is forced at offshore and lateral boundaries (~ 6 hours for waves, ~ 1 hour for water 

level) with a Neumann condition lateral using information from the Wave Information System (WIS) of 

the US Army Corps of Engineers and Port Fourchon NOAA tidal gauge. Changes in relative sea level are 

incorporated into the simulation based on forecast estimates provided by the Coastal Protection and 

Restoration Authority (CPRA) (2017). Sediment dynamics are depicted by a combined 

bedload/suspended load transport function (van Rijn 1984a and 1984b) using different sand classes to 

depict bathymetry updating (NS=156μm, OCS=160 μm, 165 μm, and 200μm). Morphodynamic 

upscaling was used which allows the model to extend bed-load and suspended load transport for washover, breaching, lateral migration, and sediment bypassing. The set-up simulates sand placement in terms 

of direct effects (central barrier) and total system effects (west, central, and east barriers) at elevation and 

depth contours of 1.0, 0.0, and -0.5 meters.
19

Figure 4. Model domain and system components used in geophysical simulations.

3.3 Model Scenarios and Semi-Empirical Results 

All model scenarios are based on variations of a single, hypothetical renourishment project in which 

nearshore (NS)- or OCS-sourced sediment is mechanically transported into the proxy barrier island model 

template. The “baseline project scenario” assumes the target placement of 10,700,000 m3 (13,995,072 y3

) 

of sand. Because of the differences in sediment quality by source and the associated losses due to 

handling and fines, additional sediment would need to be cut (Dredgeq) to fill the desired restoration 

template (Targetq). Using regional geotechnical surveys as a basis, the following cut-to-fill (CTF) ratios 

were applied for NS sources: 1.2 (high quality), 1.3 (average quality), and 1.52 (low quality); and, for 

OCS sources: 1.02 (high quality), 1.1 (average quality), and 1.18 (low quality). These ratios translate to 

initial dredge volumes of 12.8–16.2 million m3 for NS sources and 11–13 million m3 for OCS sources 

(Table 2). This material is deposited on the central barrier to yield a target restoration template of 726 ha 

(1,794 acres) of subaerial land at the 0.0m contour and 942 ha (2327 acres) of subaqueous land at -0.5m 

contour. Baseline quantities for geomorphic simulations are provided for the project site, up-drift and 

down-drift sits, and the barrier system in Table 3.
20

Table 2. Dredge and Target Volumes for NS- and OCS-sources for the Baseline Project Scenario 

Under Various Cut-to-fill Ratios

Targetq

Baseline Project 

Scenario

Dredgeq

High Quality 

Dredgeq

Medium 

Quality 

Dredgeq

Low Quality

Nearshore

CTF ratio 1.00 1.20 1.30 1.50

Dredgeq (m3) 10,700,00 12,840,000 13,910,000 16,050,000

Dredgeq (yd3) 13,995,072 16,794,086 18,193,593 20,992,607

OCS

CTF ratio 1.00 1.05 1.10 1.20

Dredgeq (m3) 10,700,00 11,235,000 11,770,000 12,840,000

Dredgeq (yd3) 13,995,072 14,694,825 15,303,022 16,794,086

Table 3. Baseline and Post-nourishment Starting Areas for the Geomorphic Simulations 

Down-Drift 

Site (q~st) 

West Barrier

Project Site 

(qst) 

Central 

Barrier

Up-Drift 

Site

(q~st) 

East Barrier

Barrier System

(Q) 

West, Central, East

Barriers

Starting area (Subaerial

contour)[hectares/acres @ 0.0m] 166/410 360/892 898/2218 1429/3530

Starting area (Subaqueous contour) 

[hectares/acres @ -0.5m] 263/651 623/1540 1122/2772 2008/4963

Post-nourishment (Subaerial contour) 

[hectares/acres @ 0.0m] 166/410 726/1794 898/2218 1790/4422

Post-nourishment (Subaqueous contour) 

[hectares/acres @ -0.5m] 263/651 942/2327 1122/2772 2327/5750

Four scenarios were developed to evaluate NS- and OCS-sourced project performance: a baseline 

scenario, two storm scenarios, and a scenario different sand classes under chronic and acute forcing.

Within each of the four scenarios, simulations are presented within four boundaries: central barrier 

subaerial, central barrier subaqueous, barrier system subaerial, and barrier system subaqueous. Within 

these boundaries, a total of 44 unique response trajectories are simulated for treatments and controls. All 

trajectories are reported in acres2 and assumed to begin post-construction, immediately after required 

post-settlement elevation is achieved (i.e., stage III @ y0). 3 The scenarios are described in greater detail 

below, along with some preliminary results and observations.

2 English units (yd3 and acres) are used here forward to facilitate integration with economic models (Sections 5 and 6). 

3 The range of CTF ratios described here are derived from previous projects and geotechnical surveying. The effect of these 

ratios on initial construction volume (Dredgeq) is addressed in project cost-modeling (Section 4).
21

3.3.1 Baseline Project Scenario

This scenario assumes a median diameter grain size of 156μm for the nearshore sand source and a 160 

μm grain size for the OCS source. Though this is a relatively minor size difference (~ 2.5% of D50), the 

intent of this baseline scenario is to identify the lower-bound from which small differences in sand class 

might become manifested over time. 

Figure 5 (panel a) shows a divergence in project performance for the two sand sources in the central 

barrier beginning at year 10 and expanding through year 50. This divergence is somewhat muted given 

the larger areas reported when measuring benefits at the subaqueous contour (panel b), and even more 

when measured at the system level (panels c and d). Nevertheless, a performance advantage appears to 

emerge towards the middle to end of the OCS trajectory in each of the four panels. Moreover, the 

response curves for both the NS and OCS projects both exceed the no-action projection (control), in 

which the subaerial land of the central barrier (and the barrier system) is completely lost at a year of 

disappearance (YOD) between 40 and 45 (panels a and c). In terms of final subaerial land, the central 

barrier at year 50 (panel a) ends with a net quantity (treatment-control) of 489 acres for the OCS-sourced 

project, compared to 325 net acres for the nearshore-sourced project. Though not a particularly large 

acreage difference, it is important to reiterate that performance comparisons are not based on terminal 

quantities of land, but on the flow of ecosystems services generated by land throughout the project 

lifetime. 

Figure 5. Simulated trajectories of barrier boundaries receiving sand dredged from nearshore and 

outer continental shelf sources (baseline scenario). 
22

3.3.2 Early Storm Scenario

In this scenario, baseline simulations (i.e., NS sand at 156μm, OCS sand at 160 μm) are punctuated by a

major hurricane of Category 2 intensity on the Saffir-Simpson hurricane wind scale (National Hurricane 

Center 2020). Termed the “early storm scenario”, the intent is to examine how a major storm occurring 

early (year 5) in the 50-year trajectory would affect the performance of NS- and OCS-sourced projects. 

Acreage reductions are based on historical losses resulting from storms impacting the Isle Dernieres island 

chain, most notably Hurricane Lili in 2002 and Hurricane Gustav in 2008.

Figure 6 depicts notable acreage reductions in year 5 within all four boundaries. And though there 

continues to be a slim advantage for the OCS-sourced project compared to the NS-sourced project on the 

central barrier (panel a), the divergence it is diminished, and is barely perceptible at the system level (panel 

c). Moreover, the terminal areas at year 50 on the central barrier are 130 and 225 net acres of subaerial 

land for NS and OCS- and sourced projects, respectively. This equates to a 60% and 54% reduction in 

remnant land remaining in the baseline scenario. 

Despite these impacts, the subaqueous projections for the central barrier (panel b) indicates that a 

considerable amount of sediment remains above the -0.5m contour, as indicated by terminal quantities of 

690 and 828 net acres for NS- and OCS-sourced projects. And both projects remain effective in sustaining 

subaerial land compared to the no-action scenario (panel a) in which control trajectory is completely lost, 

with a YOD between years 30 and 35 (panels a & c)–10 years sooner than observed in the baseline

scenario.

Figure 6. Simulated trajectories of barrier boundaries receiving sand dredged from nearshore and 

outer continental shelf sources (early storm scenario). 
23

3.3.3 Late Storm Scenario

This set-up replicates the same conditions of the previous two scenarios (i.e., NS sand at 156μm, OCS 

sand at 160 μm, category 2 hurricane) but moves storm landfall to year 20. Figure 7 depicts notable 

acreage reductions in year 20 within all four boundaries. The terminal quantities of subaerial land at year 

50 on the central barrier (panel a) is 250 and 360 net acres for NS- and OCS-sourced projects, respectively. 

This equates to 48% and 38% reductions in remnant land from the baseline, a reduction that is not quite as

dramatic as seen in the early storm scenario. In each case (early and late storm), the OCS-sourced projects 

continued to outperform the NS-source projects. This result appears to confirm manager assertions that 

OCS-sourced projects perform better not only under chronic forcing, but also in terms of storm resilience. 

It is interesting to note, however, that this scenario results in the most grave outcome for a control 

simulation. The subaerial land of the central barrier is completely lost by year 30 (panel a) in the absence 

of restoration, indicating the potential for a looming threshold effect for non-restored barrier islands. And 

while a small recovery is evident between years 40 and 45 for the control simulation for all boundaries, it 

is short-lived. This apparent rebound likely reflects a simulated reworking the reworking of system 

sediments dispersed by the storm. 

Figure 7. Simulated trajectories of barrier boundaries receiving sand dredged from nearshore and 

outer continental shelf sources (late storm scenario). 
24

3.3.4 Larger Sand Scenario

This scenario expands the original baseline set-up by adding two additional OCS sand classes, one slightly 

larger (165 μm) and one much larger (200 μm). The intent of this scenario is to examine how modest to 

large increases (3%–20%) in sand diameter affect the long-term performance in OCS-sourced projects. 

Figure 8 depicts five trajectories in each boundary panel: the original three baseline simulations plus two 

additional simulations reflecting projects sourced with larger OCS sands. Because of the modest increase

in size, the 165 μm project trajectory is difficult to discern from the baseline 160 μm trajectory. The 200 

μm class, however, represents a much larger increase in sand quality (diameter) that translates to 

performance advantages clearly evident in all four boundary panels. For the 200 μm sand, there are 825 net 

acres of remnant subaerial land remaining in year 50 on the central barrier (panel a). This represents a near 

70% increase over the baseline OCS-sourced project performance.

The extent to which such performance advantages are possible is a function of the availability of, and 

feasibility of access to, large diameter sand deposits. Sands of 165–200 μm are not uncommon in the 

offshore shoals and Paleolithic stream channels of the Sabine Bank, Tiger and Trinity Shoal Complex, 

Ship Shoal Complex, and St. Bernard Shoal. Sediment distribution maps have estimated the total volume 

of these sources at OCS sand deposits sand at ~75 billion cubic meters (BCM), primarily (Finkl and 

Freeman 2014).

Figure 8 Simulated trajectories of barrier boundaries receiving sand dredged from nearshore and 

outer continental shelf sources (larger sand class scenario). 
25

4 Project Cost Modeling 

4.1 Data for the analysis

4.1.1 Project Reports

Data for the development of a sub-model of project costs were obtained from previously constructed 

restoration projects in coastal Louisiana. A list of “candidate projects” was developed with advisory panel 

input and with a focus on dedicated dredging efforts in the region similar to the proxy barrier system. A 

list of 22 barrier renourishment initiatives were identified for which nearshore (NS) (n=12) or Outer 

Continental Shelf (OCS) (n=10) sediments provided the primary source of dredge material for project

construction efforts from 1997 to 2018 (Table 4).4

Most of these candidate projects (64%) were implemented with federal funds provided through the 

Coastal Wetland Planning Preservation and Restoration Act (CWPPRA). The remainder were funded by 

the Coastal Impact Assistance Program (CIAP), National Resource Damage Assessment (NRDA), 

National Fish and Wildlife Foundation (NFWF), State-Only projects (STATE),5 and Berm to Barrier 

(BERM)6 initiative. 

Figure 9 provides a depiction of the locations of these candidate projects and borrow sites in coastal 

Louisiana. The graphic depicts that 20 of the 22 candidate projects are equally distributed within the 

coastal waters of the Barataria basin (10 projects) and the Terrebonne basin (10 projects). Restoration 

costs are captured for projects on Isles Dernieres (TE 20 and TE 24) - the basis for the proxy barrier 

island template described in Section 3. Similar projects to the east and west of Isles Dernieres provide 

additional sources of spatially-relevant costs data for economic modeling. Note that the borrow sites of 

OCS-sourced projects do not all appear to fall outside the state’s territorial waters. Candidate project 

designation in this study (i.e., NS or OCS) is delineated not only by distance, but also sand quality. Some 

of these projects have used relic deposits of large diameter, OCS-quality sand found relatively close to 

shore. Geotechnical surveys indicate, however, that such deposits are increasingly limited and that future 

sourcing of large-diameter sand will be reliant on deposits within shoals, channel and banks located well 

offshore.

4.1.2 Bid Data

Because of the large scale and budget of barrier island restoration projects, few candidate projects are 

available as the basis for predictive modeling. Additional information on project costs can be obtained 

through surveys of coastal dredging and engineering firms. The time required to collect such information

(and its sensitivity), however, suggests that surveying would be unlikely to yield a sufficient amount of 

reliable information. 

4 Costs data were extracted only from barrier shoreline and barrier island renourishment projects in coastal Louisiana. Interior, 

“marsh creation” projects were not included in the candidate project dataset. 5 In the years 2007, 2008, and 2009, the Louisiana State Legislature allocated $790 million in State surplus funds for use in 

coastal protection and restoration activities. This included both cost-sharing in other federal programs as well as the 

implementation of projects without a federal partner.

6 During the oil spill crisis in 2010, emergency dredging was used in an attempt to build sand berms to block oil from entering 

Louisiana’s coastal marshes. The CPRA has used material from those berms to renourish barrier island chains in the southeastern 

coast of the state.
26

Table 4. Candidate Projects for Development of a Representative Cost Model of Dedicateddredging for Barrier Island and Shoreline Restoration in Coastal Louisiana (1990–2018)

ID Name Program Source*

BA-38-1 Pelican Island Restoration CWPPRA OCS

BA-40 Riverine Sand Mining/Scofield Island Restoration BERM/CWPPRA OCS

BA-45 Caminada Headland Beach and Dune Restoration CIAP OCS

BA-110 Shell Island East BERM Restoration NRDA OCS

BA-111 Shell Island West NRDA Restoration NRDA OCS

BA-143 Caminada Headland Beach and Dune Restoration INCR2 NFWF OCS

CS-31 Holly Beach Sand Management CWPPRA OCS

CS-33 Cameron Parish Shoreline Restoration CWPPRA OCS

TE-48-2 Raccoon Island Shoreline Protection and Marsh Creation CWPPRA OCS

TE-100 Caillou Lake Headlands Restoration NRDA OCS

BA-30 East Grand Terre Island Restoration CIAP NS

BA-35 Pass Chaland to Grand Bayou Pass Restoration CWPPRA NS

BA-38-2 Chaland headland Restoration CWPPRA NS

BA-76 Cheniere Ronquille Barrier Island Restoration CWPPRA NS

TE-20 Isles Dernieres Restoration East Island CWPPRA NS

TE-24 Isles Dernieres Restoration Trinity Island CWPPRA NS

TE- East Timbalier Island Sediment Restoration CWPPRA NS

TE-27 Whiskey Island Restoration CWPPRA NS

TE-37 New Cut Dune and Marsh Restoration CWPPRA NS

TE-40 Timbalier Island Dune and Marsh Creation CWPPRA NS

TE-50 Whiskey Island Back Barrier Marsh Creation CWPPRA NS

TE-52 West Belle Pass Barrier Headland Restoration CWPPRA NS

* Categorization based on source material location and type 

Previous economic research on coastal restoration in Louisiana has used commercial bid data as a means 

of expanding the number of usable observations for predictive modeling (Wang 2012, Caffey et al. 2014). 

State and federal agencies solicit formal bids from the private sector during the design, construction, and 

operation phases of coastal restoration projects. In responding to these public solicitations, private 

dredging and engineering firms develop competitive bids containing highly-detailed physical and 

financial projections. If accepted, a contractor’s bid is legally binding. Thus, the veracity of bid data is 

grounded in legal and economic consequences. 

Appendix A contains commercial bids obtained from Louisiana Coastal Protection and Restoration 

Authority (CPRA) for the 22 candidate projects. The lists include 71 unique bids: 35 for OCS-sourced 

projects and 36 for NS-sourced projects. The average number of bids is 3 per project, with a range of 2–8 

bids overall. Combined with final project data for the 22 candidate projects, this information expands the 

dataset to 93 useable observations.
27

Figure 9. Geographic locations of candidate projects (NS- and OC-sourced) for development of a 

dedicated dredging cost model for barrier shoreline and barrier island restoration in in Louisiana, 

1997–2018.

Table 5 provides a more in-depth view of the physical characteristics and costs for the candidate projects. 

The NS- and OCS-sourced project types share some similarities in terms of dredge volumes and project 

size (e.g., 3.3–3.7 million y3 and 396–409 acres, respectively). Yet these similarities do not extend to 

project costs. At $59.7 million, the average OCS-sourced projects costs more than twice that of the 

average NS-sourced project.7 Because of their similar volumes and acreage, this translates to higher unit 

costs for sediment handling, such as a $17.20/y3 transport cost for OCS sediment compared to $8.05/y3

for NS sediment.

8 The higher cost of OCS-sourced projects is due to the longer transport distances 

between projects and borrow sites. At 17.0 miles, the average transport distance of OCS-dredged 

sediment is more than five times that of NS-sourced projects (3.31 miles). Some of this difference is 

driven by recently constructed OCS projects with very long transport distances (e.g., 31 miles for BA-45 

and 34.5 miles for BA-143). 

7 Note the relatively small differences in average construction costs and project bids: +8% for NS bids and +2% for 

OCS bids. 

8 All costs data contained in bids and project reports are reported in 2016 dollars as adjusted by the Civil Works 

Construction Cost Index System (CWCCIS).
28

Project 

ID Name

Sediment 

Quantity (y3

)

Distance 

(miles)

Marsh 

(acres)

Beach/dune 

(acres)

Net 

Acres

Average Bid † 

($)

Construction* 

Cost ($) $/acre $/y3

East Grand Terre Island 3,144,250 4 455 165 620 36,862,153 34,430,503 55,533 10.95

Pass Chaland to Grand Bayou Pass 5,098,651 8.5 226 124 350 44,184,104 39,725,976 113,503 7.79

Chaland headland Restoration 2,483,649 2 254 230 484 28,931,950 19,842,857 40,998 7.99

Cheniere Ronquille Barrier Island 2,631,400 2 274 137 411 30,948,091 39,725,976 96,657 15.1

Isles Dernieres Restoration East Island 3,900,000 1 40 202 242 14,352,760 15,105,896 62,421 3.87

Isles Dernieres Restoration Trinity Island 4,886,000 1 205 148 353 18,317,588 13,174,156 37,321 2.7

East Timbalier Island Sediment 2,643,437 2.5 161 56 217 17,834,696 14,970,412 68,988 5.66

Whiskey Island 2,338,632 3.5 269 254 523 18,365,011 12,115,143 23,165 5.18

New Cut Dune and Marsh 844,540 3 171 68 239 14,239,068 12,392,490 51,851 14.67

Timbalier Island Dune and Marsh Creation 4,600,000 2.7 264 209 473 17,852,837 19,007,027 40,184 4.13

Whiskey Island Back Barrier Marsh Creation 2,536,784 3.65 319 0 319 28,370,939 26,360,162 82,634 10.39

West Belle Pass Barrier Headland 4,161,226 5.9 334 183 517 36,140,135 33,834,071 65,443 8.13

3,272,381 3.31 248 148 396 25,533,278 23,390,389 61,558 8.05

BA-38-1 3,653,853 8.8 398 180 586 47,560,996 48,961,971 83,553 13.4

BA-40 3,587,081 22 273 261 534 67,565,293 54,741,557 102,512 15.26

BA-45 2,883,800 31 0 246 246 65,088,536 69,104,642 280,913 23.96

BA-110 2,576,000 17 136 141 277 34,756,177 49,186,764 177,570 19.09

BA-111 4,497,500 15.6 265 381 646 63,498,135 93,982,461 145,484 20.9

BA-143 4,941,900 34.5 0 489 489 142,445,762 121,367,379 248,195 24.56

CS-31 2,143,318 5 0 320 320 22,046,463 19,479,809 60,874 9.09

CS-33 1,932,470 21.2 0 267 267 50,785,300 42,507,050 159,202 22

TE-48-2 735,340 4 58 0 58 9,516,021 10,802,970 186,258 14.69

TE-100 9,691,800 7.1 150 512 662 104,106,209 87,304,094 131,879 9.01

3,664,306 17.00 128 280 409 60,736,889 59,743,870 157,644 17.2

*All costs in 2016 dollars, †

Average of 3 bids per project

Nearshore (NS) sourced projects

Average NS

Outer Continental Shelf (OCS) sourced projects

Pelican Island 

BA-76

TE-20

TE-24

TE-25&30

TE-27

TE-37

BA-30

BA-35

Cameron Parish Shoreline 

Raccoon Island Shoreline and Marsh Creation

Caillou Lake Headlands Restoration

Average OCS

BA-38-2

Riverine Sand Mining/Scofield Island 

Caminada Headland Beach and Dune 

Shell Island East BERM 

Shell Island West NRDA 

Caminada Headland Beach and Dune INCR2

Holly Beach Sand Management

TE-40

TE-50

TE-52

Table 5. Data from Candidate Projects for Development of a Representative Cost Model of Dedicated-dredging for Barrier Island and 

Shoreline Restoration in Coastal Louisiana (1990–2018)
29

It is important to note that the net acres constructed by the two project types are substantially different.

The net acreage of the NS-sourced projects is marsh-dominated (63%), and the OCS-sourced projects are 

primarily for beach and dune renourishment (68%). This distinction is noteworthy in terms of the 

economics of sediment quantity (i.e., dredge volumes and cut-to-fill ratios) and sediment quality (i.e.,

sand performance and resilience of various sand grain sizes). Further analysis of these cost and benefit 

tradeoffs requires development of a predictive cost model based on the candidate project dataset.

4.2 Cost Modeling

4.2.1 Potential Variables

Project costs for dedicated dredging can vary considerably depending upon the location, quantity, quality,

and transportation method and distance of source material. Additional cost considerations include 

dredging and placement restrictions pertaining to archeological concerns, endangered species, essential 

fish habitat; and challenges in working around existing oil and gas infrastructure (Michel et al. 2013). 

Costs can also be substantially dependent on dredge availability and capacity. Biven (2014) points out 

that only a small number of seaworthy reclamation vessels are available for US operations under the 

Jones Act of 1920 (P.L. 66-261). Most of these are hopper dredges (15), with a capacity less than 4,000 

m3

. Only three US dredges have capacities greater than 8,000 m3. Mobilizing large vessels within this 

small US fleet can be time consuming and result in high overhead costs. Finally, project costs can also be 

significantly affected by unique contractual details required by individual sponsor agencies (e.g., target 

elevation requirements, project timing limitations and payment mechanisms). 

With these considerations in mind, potential variables for a multiple-regression model of project costs

were identified through consultation with coastal scientists and restoration project managers. Potential 

factors examined by the study team are listed below, along with a brief description and the expected sign 

for all independent variables. Descriptive statistics for these variables are reported in Table 6. 

Dependent Variables

• Project Construction Cost (CC): Stage II construction expenditures (2016 $) for completing the 

built portion of the restoration project. For candidate projects, CC has historically accounted for 

85% of a project's fully funded cost.

• Project Fully Funded Cost (FFC): The total cost (2016 $) of a coastal restoration project 

(Stages I, II, and III). For candidate projects, FFC encompasses the costs of: engineering and 

design (~10%), construction (~85%) and operations and monitoring (~5%). 

Independent Variables

• Dredge Volume (Dredgeq): The initial quantity (million y3

) of sediment dredged (at a given cutto-fill ratio) to achieve a post-settlement target volume of restoration (Targetq). Its expected sign 

is positive, given the more volume needed the higher construction cost is expected to be.

• Net Acres (Net acres): The net acres resulting from mechanically-placed sediment at a project 

site (see mst, equation 2). The expected sign is positive, given the more area needed the higher 

construction cost is expected to be.
30

Table 6. Project Cost Variables and Descriptive Statistics

Variable Description (units) Mean Std.Dev

Dependent 

FFC Fully funded cost of a project ($)* 45,975,433 35,521,510

CC Construction cost of a project ($)* 40,400,000 32,600,000

Independent 

Dredgeq Total dredged sediment (million y3) 3.66 1.79

NA Net Acres in project boundary @ y0 (acres) 398 160

Mob Overhead costs of equipment mobilization ($)* 5,266,983 3,709,377

Distance Borrow sites to project site (miles) 9.41 10.14

Distancesq Distance square (mile2) 190 337

Dune Average dune elevation (feet) 6.42 1.21

AD Access Dredging/Channels (y3) 76,237 218,618

OCS Sediment from outer-continental shelf (yes=1) 0.44 0.5

RH Deposited and harvested sediment (yes=1) 0.24 0.433

TES Endangered and Threatened Species (yes=1) 0.46 0.50

Payonfiill Payment Type ( Fill=1, Cut=0) 0.62 0.49

Basin Barataria (Reference group) 0.45 0.50

Calcasieu-Sabine 0.06 0.25

Terrebonne 0.48 0.50

Program BERM 0.03 0.18

CIAP 0.10 0.30

CWPPRA (Reference group) 0.61 0.49

NFWF 0.05 0.23

NRDA 0.17 0.38

STATE 0.03 0.18

* 2016 dollars

• Mobilization (Mob): Overhead expenditures (in 2016 $) usually occurring in stage I and II and 

encompassing a wide range of activities associated with the transporting of large-scale dredge 

equipment to and from a project site, including the installation and removal of all on-site support 

facilities. Mob is expected to have a positive relationship with costs.

• Distance (Distance): The average distance (miles) from a sediment borrow site(s) to a project 

site. Its expected relationship with costs is positive. The longer the transport distance, the higher 

the project costs are expected to be.

• Distance Square (Distancesq): A square of distance (miles2

) to examine a possible non-linear

relationship between distance and project cost. Specifically, this variable considers whether 

construction cost increases (with distance) at a decreasing rate. In general, if the coefficient on 

Distance is expected to be positive, then Distancesq would be expected to be negative.

• Dune elevation (Dune): Most barrier island restoration projects include a dune element in 

construction. Dune elevation varies among projects based on a wide range of factors such as 

location, project size, forces of wind and waves, seasons. Dune elevation is measured as an 

average across a given site and measured in feet based on the standard North American Vertical 

Datum 1988 (NAVD88). Dune is expected have a positive relationship with cost in that higher 
31

dune elevations require more sediment, which would result in higher project costs. 

• Access Dredging (Access): The volume of dredging (y3

) for a given project location required to 

open a corridor for heavy equipment or to provide a conduit for the distribution of sediment and 

nutrients. The expected effect on cost is positive.

• Outer-Continental Shelf (OCS): Projects in which the dredged sediment comes from an offshore 

borrow site beyond the littoral zone. OCS is treated as a binary variable (yes=1, otherwise 0) and 

is expected to be positively related to project cost.

• Rehandling (RH): Indicates whether the sediment dredged for a project was deposited in a 

temporary location and then re-suspended for transport to the project site (1=yes, otherwise 0). 

This variable is expected to increase project costs.

• Threatened or Endangered Species (TES): The presence of threatened or endangered species 

within or adjacent to a project site boundary can result in regulatory compliance costs associated 

with protection, abatement, mitigation and/or seasonal delays associated with migration and 

reproduction. The presence of any documented TES compliance activity is treated as a binary 

variable (yes=1) and is expected to be positively related to project costs.

• Payment Type (Pay-on-fill): For dredge projects, contractors usually receive payment in one of 

two ways: they are paid on either the cut or the fill. If they are paid on the cut, compensation is 

based on the amount of sediment removed from the borrow site (Dredgeq). If they are paid on the

fill, compensation is based on a post-settlement elevation for the target project site (Targetq). For 

purposes of this analysis, Pay-on-fill is coded 1 if the contractor received payment on the fill and 

0 otherwise. In general, payment on the fill is more costly because of sediment transport losses 

and sediment losses.

• Coastal Basin (Basin): The coastal basin from which candidate project data were derived 

(Barataria (Base), Terrebonne, or Calcasieu-Sabine). This categorical variable examines whether 

geographical location is related to project cost. The expected relationship between Basin and cost 

is unknown.

• Coastal Program (Program): Candidate projects were sponsored by six different coastal 

programs. For analysis purposes, coastal programs were categorized into six groups: (1) BERM; 

(2) CIAP; (3) CWPPRA (base), (4) NFWF, (5) NRDA, and (6) STATE. The expected 

relationship between Program and cost is unknown.

4.2.2 Empirical Results

Ordinary least squares (OLS) analysis was employed to estimate project construction costs from a total of 

93 observations from NS- and OCS-sourced project data. For purpose of this study, the project 

construction costs are assumed to be associated with those potential variables mentioned in previous subsection. The choice of independent variable was determined through a lengthy selection procedure. The 

preliminary regression model was estimated using all potential independent variables and variables were 

sequentially deleted until no further improvements in the model (i.e., variables that had the most 

explanatory power with economic considerations and no multicollinearity). The final multiple regression 

model was estimated with six variables. Thus, the conceptual cost relationship is given by
32

( ,, , , , ) CC f Dredge Dist Distsq Dune Payonfill Program q = (8)

where, Pcc is the project construction costs for a project (NS- or OCS-sourced) expressed in 2016 dollars 

based on both commercial bids and actual project cost data. 

Data for the NS and OCS construction costs model were imported and analyzed in the statistical program 

Stata 13.1. The OLS model was estimated using Stata's “regress” routine. Results are presented in Table 7 

with associated standard errors, t values, and p-values.

Table 7. Project Construction Cost Model Parameter Estimates

Variable Parameter

Estimate

Robust 

Standard

Error

t Value Pr > |t|

Dredgeq 5854.34*** 1041.42 5.62 0.000

Distance 3302.00*** 969.75 3.40 0.001

Distancesq -59.89** 28.56 -2.10 0.039

Dune 820.10 1037.75 0.79 0.432

Paytype 7983.27** 3580.62 2.23 0.029

Program

BERM -10240.96 6852.88 -1.49 0.139

CIAP 5697.69* 3112.83 1.83 0.071

CWPPRA (Reference group)

NFWF 64210.22*** 12233.62 5.25 0.000

NRDA 8693.61** 3377.58 2.57 0.012

STATE -3931.34 4514.04 -0.87 0.386

Constant -15971.52 6636.24 -2.41 0.018

R-squared 0.93

Number of obs. 93

Note: Asterisks indicate levels of significance: *=0.1; **=0.05; ***=0.01.

The estimated marginal effect of an additional million cubic yards of sediment on total construction cost 

is highly significant and equal to $5,854, holding all other variables fixed. The significant coefficients of 

Distance and Distancesq indicate that construction costs increase an estimated $2,115 when the distance 

from the sediment borrow site to project site increases by one mile further than the mean distance of 9.41 

miles. The negative and significant coefficient of distance square indicated that distance has a diminishing 

effect on total construction cost. The marginal effect with respect to Dune was positive but not significant. 

The positive and statistically significant marginal effects with respect to payment type indicated that 

Payonfill plays an important role on the total construction cost. Contractors paid on the fill were found to 

receive $7,983 more than contractors who were paid on the cut. The marginal effect with respect to 

Program indicated that the coastal restoration projects initiated by BERM and STATE programs have 

lower construction cost relative to the base program category CWPPRA program, while CC is higher for 

those projects funded by CIAP, NFWF, and NRDA programs. The final equation is given by:
33

15971.52 5854.34* 3302* 59.89*

820.10* 7983.27* 10240.96* 5697.69*

64210.22* 8693.61* 3931.34*

CC Dredge Distance Distancesq q

Dune Payonfill BERM CIAP

NFWF NRDA STATE

=− + + −

++ − +

+ +−

(9)

4.2.3 Effects of Quantity and Distance

Table 8 includes output from a Relative Importance Metrics routine in R used to estimate the amount of 

variance in the dependent variable CC explained by the uncorrelated predictors of a multiple regression. 

The quantity of sediment (Dredgeq) and the length that it is transported (Distance), account for a combined 

83% of the model’s variation (29% and 55%, respectively). 9

Table 8. Percent of Variation Explained by Individual Predictors in the Project Cost Model

Df Sum Sq Mean Sq F value Pr(>F) PctExp

Dredgeq 1 27,739,516,731 27,739,516,731 329.21 0 28.64

Dist 1 52,843,112,871 52,843,112,871 627.14 0 54.55

Distsq 1 164,386,776 164,386,776 1.95 0.1663 0.17

Program 5 8,434,949,063 1,686,989,813 20.02 0 8.71

Dune 1 187,574,861 187,574,861 2.23 0.1395 0.19

Payonfill 1 590,119,877 590,119,877 7 0.0098 0.61

Residuals 82 6,909,334,025 84,260,171 NA NA 7.13

Figure 10 illustrates the influence of these two dominant variables on construction costs as estimated by 

the regression model. Starting with the fill volume of the baseline scenario (Targetq = 13,995,072 yd3

), 

the effects of volume increases on quantity (Dredgeq) can be observed for low, medium, and high cut-tofill (CTF) ratios at various transport distances. Because of the significant and positive result for Payonfill

in the model, we assume here that payment is on the fill. Thus, for the baseline scenario (Targetq= 

13,995,072yd3), the initial volume of sand to be dredged (Dredgeq) must be adjusted upward to account 

for material losses due sediment quality differences. Inset values are used to highlight project costs for 

different CTF ratios at the borrow site distances of 3–5 miles and 15–20 miles for NS- and OCS-sourced 

projects, respectively. Construction costs for these inset examples ranges from $115–$145 million for NSsourced projects and $129–$148 million for OCS-sourced projects. 

9 PctExp provides only a general indication of the portion of variability explained by orthogonal predictors. 
34

Figure 10. Model-estimated effects of distance and cut-to-fill ratio on construction cost under the 

baseline project scenario.

As indicated in Table 2, “low” quality source materials result in higher CTF ratios, but the difference 

between low and high material quality is borrow source-dependent. For this analysis, a CTF of 1.20 is 

simultaneously the highest quality for NS-sourced sediment (lowest CTF) and the lowest quality for OCSsourced sediment (highest CTF). At this common ratio, an OCS-sourced project with a sediment transport 

distance of 20 miles costs $148 million compared to $115 million for the NS-source project at 3 miles 

(see black line common to both in Figure 10). Under this hypothetical case one might conclude, ceteris 

paribus, that the project with the shortest transport distance (lowest $/yd3

) is the most efficient option. 

Such a conclusion would seem logical given that the NS-sourced project yields the same starting volume 

(See y0 in Figure 2) at a costs that is $33 million, or approximately 30% less than the OCS-sourced project 

of similar scale. 

Project selection regimes within many large-scale restoration programs have historically been driven by 

the basic approach to cost-efficacy described above. Recall, however, that this study seeks to examine not 

only the economics of sediment quantity, but also the economics of sediment performance. To reiterate 

advisory panel guidance: Economic efficiency comparisons should not be based solely on sand as a 

commodity, but also on the flow of ecosystems services generated by that sand throughout the project 

lifetime. Developing a trajectory-based approach to project evaluation requires integration of the project 

cost model developed in this section with the benefit model of the previous section.
35

5 INTEGRATED MODEL METHODOLOGIES

The sub-model developed in Section 3 depicts physical quantities of sediment that can be expected over 

time under difference scenarios. The sub-model of Section 4 depicts the associated costs of that sediment 

under various factors associated with project construction. Such information alone has historically been 

evaluated separately during project selection. Examining the economics of project performance, however, 

requires an alternative framework, one that integrates geomorphic simulations and costs predictions over 

the project’s trajectory. 

This section expands the mathematical framework initiated in Section 2 by introducing alternative models 

for examining economic efficiency in terms of monetized ecosystem services over time. Specifically, the 

basic model is expanded to yield estimates of present value, net present value, benefit-cost ratios and 

break-even value. Each of these measures can be used to examine efficiency tradeoffs between and within 

nearshore (NS)- and Outer Continental Shelf (OCS)-sourced projects. 

5.1 Converting Quantities to Benefits

Adding prices (that is, per-unit values) to expression (6) (continued from Section 2.4) yields the change in 

benefits at site s at time t:

( ) ( ) aa a bb b

st st st st st ∆= + + + b pm n pm n (10)

where a p and b

p are the respective prices (values) per unit of sand placed subaerially and subaqueously, 

respectively. A simplifying but reasonable assumption is that the value of the benefits of a unit of 

subaqueous sand is a fraction of that of a unit of subaerial sand. In this case we may write: 

b a

p p =α (11)

where 0 1 ≤ ≤ α . Substituting (11) into (10), we have:

( ) ( ) a aa bb

st st st st st ∆= + + + b pmn mn   α   (12)

At the system level, the change in benefits at time t can be expressed as:

( 11 11 ) ( ) ( ) ( )

1 2 2 

S S S

aa a ab b a a a b

t st t t t t st st

s s s Direct Subaerial Direct Subaqueous Indirect Subaerial Indirect Subaqueous

B b pm n pm n p n p n α α = = =∆= ∆ = + + + + + ∑ ∑ ∑

 

 

(13)

5.2 Present Value of Benefits

Given (12), the present value of the change in benefits at site s over time, from initial period t = 0 to 

terminal period t = T, can be expressed as:

( ) ( )

0

T

a t aa bb

s st st st st

t

bp mn mn δ α

=

∆= + + +   ∑   (14)
36

where 1

1 r

δ = +

is the discount factor, and r is the discount rate. 10

At the system level, the present value of the change in benefits (PVB) over time, from initial period t = 0 

to terminal period t = T, can be expressed as:

( 1 1 ) ( 1 1 )

1 0 0 0

 

2 0 2 0

 

S T T T a ta a a tb b

st t t t t

s t t t

Direct Subaerial Direct Subaqueous

S T S T

a ta a tb

st st

s t s t

Indirect Subaerial Indir

PVB b p m n p m n

p np n

δ αδ

δα δ

= = = =

= = = =

= ∆= + + +

+ +

∑∑ ∑ ∑

∑∑ ∑∑

 



ect Subaqueous



(15)

If one is not interested in tracking changes in direct and indirect benefits separately, the above expression 

simplifies to:

1 1 ( )

1 0 1 0

S T S T

a ta a b b

st t st t st

s t s t

PVB b p m n m n δ α = = = =

  = ∆=     + + +     ∑∑ ∑∑ (16)

again, recognizing that, at the system level, expression (4) holds, so that any change in sand quantities via 

natural transport is necessarily attributable to vagrant sand. Typical projects place sand in period t = 0

only, such that in expressions (15) and (16), 1

k

m t , k ab = , , can be replaced with 10

k

m .

Based on geomorphic modeling barrier island area projections, the quantity of sand benefits could be 

converted to acreage basis. In this case, the present value of benefit can be expressed as

1 1 ( )

1 0

S T

a ta a b b

t st t st

s t

PVB p ma na ma na δ α = =

  =     ++ +     ∑∑ (18)

where 1

a ma t represents direct subaerial net acres with mechanical placement in the current period at the 

project site. The expression of a

st na stands for indirect subaerial net acres via natural transport in the 

current period from other sites within the system. And 1

b

ma t represents direct subaqueous net acres with 

mechanical placement in the current period at the project site and b

st na stands for indirect subaqueous net 

acres via natural transport in the current period from other sites within the system. 

10 If we wish to induce an added element of risk associated with future flows of benefits above and beyond those 

captured explicitly in the simulation model, we can inflate the discount rate, which, short of having specific info on 

risk, is, in effect, accounted for this way. This approach is used, for example, in the National Resource Damage 

Assessment (NRDA).
37

5.3 Present Value of Costs

As described in Figure 1, the associated project costs of engineering and design and operation and 

maintenance typically account for 10% and 5%, respectively, of total project costs. Although specific data 

for these two costs are unavailable, they can be derived algebraically as a function of construction costs

(CC), which accounts on average for 85% of a projects fully funded cost. In turn, construction costs are 

estimated from multiple-regression analysis of cost factors for OCS and NS projects (Section 4). The 

corresponding cost in period t for NS and OCS projects is given by the function:

( ) ( ) ( ) 1.18 ( ) FFC C ED C CC C OM C CC tt t t = + + =∗ t (19)

where FFC is the fully funded annual costs of a NS or OCS project in year t, inclusive of engineering and 

design (ED), construction costs (CC), and operation and maintenance (OM), Therefore, the present value 

of cost (PVC) function for NS and OCS projects can be expressed as:

0 0

1.18 ( )

T T t t

t t

t t

PVC FFC δ δ C CC = =

= ∗ = ∗∗ ∑ ∑ (20)

where t stands for the number of years of a project and range from 0 to 50. PVC is the total discounted 

costs (in $) of a NS or OCS project during the project life. FFCt is the total annual costs of a NS or OCS 

project in year t and δ is the discount factor.

5.4 Net Present Value Model 

As stated in Section 1, the goal of this study is to provide a better understanding and quantification of the 

economic, ecologic, and geomorphic long-term benefits and costs of using OCS sediment compared to

nearshore sediment for coastal restoration projects. The benefits and associated costs functions defined in 

Sections 3 and 4 can be integrated into a net present value (NPV) analysis for the two restoration methods 

over a given time period (50 years). The equation is given by:

( )

0

1 1

1 0 1

( )

1.18 ( )

T t

t t

t

S T T

a ta a b b t

t st t st t

s t t

NPV B C PVB PVC

p ma na ma na C CC

δ

δα δ

=

= = =

= −= −

  =     + + + − ∗∗    

∑

∑∑ ∑

(21)

where Bt is the sum of benefit in time t, Ct is the sum of cost in time t, δ is the discount factor and t is the 

year.

5.4.1 Benefit:Cost Ratio

The present value formulas for benefit and cost models in the net present valuation (Eq. 21) can be 

rewritten in as a benefit:cost ratio (BCR). This ratio allows for an alternative examination of project 

efficiency, with the underlying assumption that decision-makers should strive for projects in which 

benefits exceed costs, or BCR>1.0.
38

0

0

1.0

T t

t

t

T t

t

t

B

BCR

C

δ

δ

=

=

= >

∑

∑

(22)

5.4.2 Ecosystem Service Valuation Challenges

The ecosystem services cited in association with barrier islands and shorelines typically include storm 

surge attenuation (disturbance regulation), habitat provision, and recreation. The integrated models 

depicted in equations 10–22 require an expression of those services in monetary terms. But the time and 

effort required for valuation of these benefits can be substantial. It is often infeasible to conduct targeted 

valuation studies in support of environmental policy analysis. Thus, under the PV, nre present value 

(NPV) and BCR approaches, ecosystem service values (ESV) must be specified from pre-existing studies. 

Within environmental economics, this extrapolation process is referred to as “benefit transfer”.

Richardson et al. (2015) provide an overview of the growing demand for monetized ESV estimates and 

the increasing use of benefit transfer within environmental policy. Guidelines are cited for facilitating 

more valid transfer of values between a study site and policy site, including: 1) the need for comparable 

scope, scale, and population; 2) recognizing differences in intermediate and final services; 3) and 

aggregation approaches to avoid double counting. The authors reference web-based databases that have 

emerged as a repository for monetized ESV estimates. Though these sites are increasingly used for

benefit-cost analysis, the values they contain often vary by orders of magnitude for a given service. For 

example, the Gulf of Mexico Ecosystem Valuation Database cites ESV estimates ranging from $2.40–

$13,360 per acre/year (US $2012 dollars) as the value of disturbance regulation from barrier islands and 

shorelines. Likewise, studies of the habitat provision of coastal wetlands are cited with estimates ranging 

from $1.77 to $7,854/acre/year (GecoServ 2019). Such large value ranges reflect the variety and 

complexity of non-market valuation methodology, which serves to compound the challenges of benefits 

transfer.

5.4.3 Break-Even Approach

Caffey et al. (2014) describe an alternative approach in which a break-even value for ecosystem services 

(EBEV) can be derived by setting the BCR ratio equal to 1.0 and solving for the average annual value that 

equates project benefits to costs over a given time period. 

0

0

1.0

T t

t

t

T t

t

t

B

BCR

C

δ

δ

=

=

= =

∑

∑

(23)

Substituting the present value of project costs and surface benefits into equation 23 yields: 

0

1

1 0

1.18 ( )

T t

t

t

a S T a ta a

t st

s t

C CC

EBEV

p ma na

δ

δ

=

= =

∗ ∗

=

  +  

∑

∑∑

(24)
39

where EBEVa is the annual break-even ecosystem service value for subaerial land. This condensed 

approach reflects the historic programmatic focus on surface-level project performance. Alternatively, if 

we want to examine performance of all mechanically placed material (both subaerial and subaqueous 

land), we can write:

( )

0

1 1

1 0

1.18 ( )

T t

t

t

ab S T

a ta a b b

t st t st

s t

C CC

EBEV

p ma na ma na

δ

δ

=

= =

∗ ∗

=

  ++ +  

∑

∑∑

(25)

where EBEVab is the annual break-even ecosystem service value for subaerial and subaqueous land.

Though equation 25 assumes no difference in the value of land above and below the surface, it yields a 

comparative metric for depicting project efficiency along a broader contour. With additional economic 

valuations (or sensitivity analysis) future iterations of the model could be parametrized to delineate 

separate values for subaqueous land (see equation 11-13).

Equations 24 and 25 allow for the estimation of project efficiency at two contours: direct effects 

(subaerial land above 0.0 m) and total effects (subaerial and subaqueous above -0.5m). This simple, yet 

effective method avoids the potential pitfalls of benefits transfer. Rather than specifying values from a 

suite of external studies, a project-specific EBEV is derived as a function of physical benefits and project 

costs over given time period.The estimate is based on a BCR of 1.0; it constitutes a useful efficiency 

metric–i.e., the minimum dollar value of ecosystem service benefits required to offset project costs. As an 

analytical metric, break-even value has long been used for examining economic efficiencies in the 

production of market-based goods and services. Its application in environmental policy can likewise yield 

valuable information on the relative efficiencies of a wide range of project alternatives (i.e., temporal, 

spatial, physical, technological, risk, etc.).
40

6 RESULTS

6.1 Model Scenarios and Semi-Empirical Results 

In this section, the ecosystem break even value (EBEV) framework outlined in equations 24 and 25 is 

used to compare the economic performance of nearshore (NS)- and Outer Continental Shelf (OCS)-

sourced projects in terms of monetized ecosystem services over a 50-year time span. Benefits are derived 

from net acreage calculations obtained from the geomorphic sub-model at the site and system level 

(future with project minus future without project). These benefits are combined with estimates from the 

project cost model as estimated for various sediment quantities, qualities, and transport distances. A range 

of EBEVs ($ per acre per year) is derived for each of the geomorphic simulations outlined in Section 3, 

including a baseline project scenario, two storm scenarios, and a larger sand class scenario. An “optional 

scenario” is added to better illustrate the economic benefits of sediment quality (sand diameter) under 

chronic and acute forcing. Table 9 contains a description of model assumptions for each of these 

scenarios in terms sediment quantity, quality, transport distance, and other factors related to project cost.

Table 9. Scenarios and Assumptions of the Ecosystem Break Even Value (EBEV) Model

Assumptions Baseline 

Scenario

Early Storm 

Scenario

Late Storm 

Scenario

Larger Sand 

Scenario

Targetq (million yd3) 14 14 14 14

Dredgeq Cut-To-Fill (ratio)

NS 

OCS 

1.2, 1.3, 1.5

1.05, 1.1, 1.2

1.2, 1.3, 1.5

1.05, 1.1, 1.2

1.2, 1.3, 1.5

1.05, 1.1, 1.2

1.2, 1.3, 1.5

1.05, 1.1, 1.2

Sand size (μm)

NS 

 OCS 

156

160

156

160

156

160

156

200

Boundaries:

 Direct & Indirect

 (Figure 3 areas)

Central, 

System

(qst , Qt)

Central, 

System

(qst , Qt))

Central, 

System

(qst , Qt)

Central, 

System

(qst , Qt)

Contours:

 Subaerial, subaqueous 

(m) 0.0, -0.5 0.0, -0.5 0.0, -0.5 0.0, -0.5

Sediment transport (miles)

NS 

OCS 

3–5

15–20

3–5

15–20

3–5

15–20

3–5

15–20

Project life (years) 50 50 50 50

Discount rate (%) 4 4 4 4

Payonfill (fill=1) 1 1 1 1

Dune elevation (feet) 6.4 6.4 6.4 6.4

Program (1-6) averaged averaged averaged averaged
41

6.1.1 Baseline Project Scenario

The baseline scenario of section 3.3.1 is revisited below in Figure 11 to examine how small differences in 

sediment grain size (NS=156μm, OCS=160μm) interact with sediment quantity, distance and other 

variables to influence economic performance. The order of EBEV curves is similar in all four panels (a, b,

c, d), indicating that increases in distance tend to increase cost, and increases in depth (contour) typically

serve to decrease unit costs. The effects of changes in boundary level (central to system) are less notable, 

given that EBEV are calculated on a net-acre basis, and the underlying system is deteriorating. Therefore, 

the brunt of net change at for all boundaries is levels is primarily driven by the project.

At common distances, NS-sourced projects with medium to high cut-tp-fill (CTF) ratios (1.3–1.5) are less

efficient than OCS projects. Inset values are provided; however, to facilitate BEV comparisons at more 

relevant ranges of sediment transport. At a 3–5 mile range, NS projects with a CTF of 1.2–1.3 are more 

cost-effective than OCS projects with 10–15 miles transport and CTF of 1.1–1.2 (panel a). However, OCS

projects with the highest quality borrow sites (CTF=1.05) yield a BEV range of $6,959-$6,982 at 15–10 

miles, which is more efficient than the $7,527–$7,843 range at 3–5 miles from NS projects using the 

lowest quality borrow sites (CTF=1.50).

Figure 11. Ecosystem break-even values (EBEV) for NS- and OCS-soured projects at various 

boundaries, distances and cut-to-fill ratios (baseline scenario).
42

6.1.2 Early Storm Scenario

The disturbance regulation function of barrier islands is a primary focus of restoration mangers. Under 

typical meteorological conditions, these landforms serve as a buffer to chronic physical forcing (waves, 

tides, salinity), which helps in the protection of leeward shorelines and wetlands. Under more acute 

conditions, their role often described as “sacrificial”.

The panels of Figure 12 depict the economic aspects of simulations described in section 3.3.2. Recall that 

under the “early storm scenario”, the project site is assumed to take a direct hit from a Category 2 

hurricane at y5. This impact reduces the amount of remnant subaerial land at y50 by 60% for the NSsourced project, and 54% for the OCS-sourced project. As a result, the EBEV curves for subaerial land at 

the project site (panel a) increase by a range of $3000 to $4000 compared to the baseline scenario. As 

expected, efficiency losses are less pronounced at broader and deeper boundaries given the amount of 

sand remaining beneath the surface. As a result, the subaqueous contour of the barrier system (panel d) 

depicts a smaller efficiency loss, an increase of only $2700–$3000 in EBEV in panels b and d. 

Figure 12. Ecosystem break-even values (EBEV) for NS- and OCS-sourced projects at various 

boundaries, distances and cut-to-fill ratios (early storm scenario, year 5).
43

6.1.3 Late Storm Scenario

Under the late storm scenario (section 3.3.3), the hurricane makes landfall on the central barrier in year 

20. Similar to the early storm scenario, the storm reduces the amount of remnant subaerial land at y50, 

though to a lesser extent (48% NS and 38% OCS). The main difference seen is in terms of economic 

performance. Because the storm occurs later in the trajectory, there is more time for benefit accrual. 

In Figure 13, the EBEV curves for subaerial land at the project site (panel a) increase by a range of only 

$900 to $1300 compared to the baseline scenario. The associated efficiency loss at the subaqueous 

contour of the barrier system (panel d) is even less, with increases in EBEV of only $700–$1000 (panels b 

and d). These results clearly illustrate the time value of benefits in terms of economic performance, but 

the estimates depicted in these panels are driven primarily by source material quality (CTF) at various 

distances. 

Figure 13. Ecosystem break-even values (EBEV) for NS- and OCS-sourced projects at various 

boundaries, distances and cut-to-fill ratios (late storm scenario, year 20).
44

6.1.4 Larger Sand Scenario

This scenario is based on the simulations of section 3.3.4 which examine the resiliency of a project sourced 

with large diameter sands (OCS= 200 μm) to a smaller, nearshore-sourced project (NS=156 μm). Recall 

from section 3.3.4 that in those simulations, projects sourced with the OCS sand yielded 70% more 

remnant subaerial land. From an economic standpoint, this physical advantage is manifest in two distinct 

ways (Figure 14). 

First, the range of EBEV for OCS sand is approximately 10% lower than observed in the baseline scenario. 

Second, the tradeoff between quantity and distance has narrowed. At 15–20 miles, OCS projects with a 

CTF of 1.05 and 1.10 are now either more competitive or somewhat equal in efficiency to NS-sourced 

projects at 3–5 miles with CTF of 1.50–1.30, respectively. The most pronounced differences are evident in 

panel d, in which the much larger 200 μm sand at 15–20 miles outperforms nearly all of the BEVs for NS 

projects at 3–5 miles. In some comparisons, the OCS advantage holds up for distances beyond 30 miles.

Figure 14. Ecosystem break-even values (EBEV) for NS- and OCS-sourced projects at various 

boundaries, distances and cut-to-fill ratios (larger sand scenario).
45

7 SUMMARY AND CONCLUSIONS

7.1 Recap of Context and Approach

Forty percent of the US population resides in a contiguous band of counties at increasing risk from coastal 

land loss and storms. This threat is especially prominent in the Mississippi River delta plain (MRDP), 

where nearly 2,000 square miles of coastal land has been lost in the past century alone, primarily due to

hydrologic modification, navigation canals, sediment starvation, subsidence, sea level rise, and climatic 

and geologic forcing. The outer boundary of this plain is bordered by a thin network of remnant delta

lobes, the region’s barrier shorelines and islands. 

In the past 30 years, more than $1 billion has been spent on projects designed to sustain this barrier 

system. Accordingly, the availability and suitability of sediment for renourishment projects has emerged 

as a major focus of restoration managers. Sediment for these projects has historically come from one of 

two primary sources. Nearshore (NS) sediment offers the economic advantage of proximity, but at the 

performance cost of smaller diameter sands with more organic fines. Outer Continental Shelf (OCS)

sediments offers better performing, larger diameter sands with lower fines, but at higher transport costs

due to distance. Previous economic comparisons of these sources have been piecemeal, focusing 

primarily on terminal quantities of sediment over a limited range of cost factors.

The goal of this project was to develop a more comprehensive framework for the assessment of NS and 

OCS sediment performance over time. A sub-model of project benefits was developed for a proxy barrier 

template based on the Isle Dernieres island chain. This geomorphic model simulates sediment transport 

within NS- and OCS-sourced projects under various scenarios of sediment quantity, quality, and 

meteorological forcing. A parallel sub-model of project cost was developed using bid and project data 

(n=93) for 22 barrier renourishment projects constructed in the Louisiana Coastal Zone. This statistical 

model estimates project costs, primarily as a function of sediment transport distance, sediment quantity, 

and authorizing program. 

Options for integration of the geomorphic and cost sub-models are described mathematically for a variety 

of economic frameworks, each of which rely on the monetization of ecosystem service values (ESV). 

Because of the challenges associated with the valid transfer of values between a study site and policy site, 

ESVs in this study are not specified, but rather derived within a break-even value (BEV) framework. A 

series of five case scenarios is developed from which BEV curves are estimated at the site and system 

level, at subaerial and subaqueous contours, and for various combinations of sediment quality, quantity,

and transport distance. 

7.2 Primary Findings

Geomorphic simulations of sand transport within the proxy barrier template were developed to examine 

the performance of a 14 million yd3 renourishment project using NS sand of 156 μm and OCS sand of 

160 μm. Under baseline conditions, the trajectory of subaerial land for the central barrier (project site) 

indicates a small advantage in resilience (increased volume and area) for the OCS sand. This divergence 

appears evident at year 10 and slowly expands through year 50. The advantage is less evident when 

measured at the system level and at subaqueous contours, primarily because of a dilution effects and the 

net export of vagrant sand from the proxy system over time.

In terms of final subaerial land, the central barrier at y50 ends with a net quantity of 489 acres for the 

OCS-sourced project, compared to 325 net acres for the nearshore-sourced project. These remnant areas 

are reduced by 60% and 54%, respectively, under an early storm scenario (y5); and by 48% and 38% 

under a late storm scenario (y20). Performance advantages are clearly evident for larger OCS sands, 
46

ranging from marginal improvements at 165 μm to substantial improvements at 200 μm. Projects sourced 

with 200 μm sand depict the largest divergence from the baseline, with 825 net acres of remnant subaerial 

land remaining in y50 on the central barrier, a near 70% increase over the performance compared to 160 

μm sand. In all simulations, the project trajectories successfully maintain subaerial land, compared to the 

no action (control) in which the year of disappearance (YOD) ranged from 30 to 40 years.

Cost modelling for the baseline project yielded estimates ranging from $115 to $148 million for the target 

placement of 14 million y3 of material in y0. This cost range reflects a combination of two types of 

sediment quality (NS at 156 μm and OCS at 160 μm), 6 CTF ratios (1.05, 1.1, 1.2, 1.3, 1.4, and 1.5), and 

two transport distance ranges (3-5 miles for NS-sourced projects and 15-20 miles for OCS sourced 

projects). At 3–5 miles of transport, NS-sourced projects with a CTF of 1.2–1.5 of were 15–30% less 

expensive than the OCS projects with a CTF of 1.05–1.2 and distances of 15–20 miles. On a per unit 

basis, these costs equate to sediment placement costs of $8.20–$10.60/yd3

. 

Though placement costs is a metric that often dominates project selection, it reflects the value of sand as a 

commodity, and fails to fully account for the services generated throughout a project’s lifetime. An 

examination of project performance yields different results. Integration of benefit and cost sub models 

within a EBEV framework indicate that, despite having higher construction costs, the OCS-sourced 

projects actually out-perform NS-sourced projects in a number of cases, some of which involve large 

transport distances. Holding transport distance constant (3–5 miles for NS and 15–20 miles for OCS)

allows for an assessment of the sediment quantity changes resulting from different CTF ratios. Under the 

baseline scenario, NS projects with a CTF of 1.2–1.3 were more cost-effective than OCS projects with a 

CTF of 1.1–1.2. However, OCS projects with the highest quality borrow sites (CTF=1.05) yielded a BEV 

range of $6,959–$6,982 at 15–10 miles, which is more efficient than the $7,527–$7,843 range at 3–5 

miles from NS projects using the lowest quality borrow sites (CTF=1.50).

Break-even values increased for all boundaries and contours under storm-punctuated. This reduction in 

efficiency is due to the net export of sand, and was found to be 20% greater for earlier (Y5) compared to

later occurring storms (Y20). In short, the earlier a storm occurs in the trajectory, the less time there is for 

benefit accrual and the manifestation of any performance advantages due to sand quality (grain size). As

expected, storm-induced efficiency losses were less noticeable beneath the surface, with subaqueous 

BEVs averaging 10% less than subaerial BEVs, compared to 5% lower in the baseline scenario. This 

outcome reflects an important finding from the geomorphic model: a substantial amount of remnant, 

subaqueous sand remains at subsurface contours after a storm. In each case (early and late storm), the 

OCS-sourced projects continued to outperform the NS-source projects in terms of physical resilience. 

This result appears to confirm manager assertions that OCS-sourced projects perform better under both 

chronic forcing and acute conditions. 

Isolating the effects of small differences in sediment quality (grain zize) on project performance requires 

holding sediment quantity constant. With both NS- and OCS-sourced projects modeled at a common CTF 

of 1.20, any economic effects of a 4 μm (2.5% larger) advantage from OCS sand are offset by the shorter 

transport distance (lower costs) for NS-sourced projects. This NS advantage narrows; however, under 

storm-punctuated scenarios, given the increased resilience of larger diameter sand.

The economic implications of larger sands are more pronounced. The increased resilience of 200 μm sand

(28% larger) results in two distinct advantages: 1) EBEVs that are 10% lower than OCS-sourced projects 

at 160 μm; and, 2) a substantial narrowing of the tradeoff between source material quality (CTF) and 

distance, with superior efficiency for all OCS projects at a moderate CTF of 1.10, and in some cases at a 

CTF of 1.20. The simulated performance of projects with OCS-sourced sand (200 μm, 15–20 miles)

outperform nearly all of the NS projects at 3–5 miles. In some of the comparisons, this OCS efficiency 

advantage holds for distances exceeding 30 miles.
47

7.3 Limitations and Additional Research 

Most of this analysis examines the physical and economic performance of a relatively small difference (4 

μm) in sand diameter. It is important to note, however, that considerable time is required for quality 

differences to manifest at this range. Fifty-year trajectories are at the outer limit of coastal restoration 

planning, and many programs set the useful life of projects at 20 years. And while clear advantages are 

evident for OCS sands on the larger end of the D50 spectrum (200 μm), less is known about the 

performance of projects sourced with intermediate-sized sands (170–180 μm range). As a result, the 

current analysis constitutes a lower and upper bound of the economics of sand performance. Additional 

simulations would be required to for examining project performance over a wider range of sand classes 

and for alternative project periods.

Data for development of the cost model were limited and highly variable. The use of contractor bids 

expanded a dataset of 22 candidate projects into 93 useable observations. Though some might question 

the potential redundancies introduced by this process, no viable alternatives exist for this analysis. State 

and federal restoration mangers face similar limitations in the budgeting and allocation of funds for largescale ecosystem restoration projects. Management of large-scale restoration projects requires systematic 

analysis of available costs and benefits, despite these limitations.

As currently structured, this analysis does not attempt to assign different ESVs for land above and 

beneath the water’s surface. The mechanics for this delineation; however, are described within the 

mathematical framework of section 5. The derivation of EBEV at subaerial and subaqueous boundaries 

(as used in this analysis) is merely intended to capture any areal effects resulting from net-transport of 

sand into or out of the proxy barrier system. Moreover, no attempt was made to assign (or derive) ESVs 

based on functional differences associated with disturbance regulation (surge attenuation) or habitat. 

Additional analysis would be required to examine how project efficiency varies with more depth-specific 

and material-specific delineations of ecosystem services.

Readers may question how the range of EBEVs estimated in the four scenarios compare to published ESV 

estimates for disturbance regulation and beach habitat. The insets highlighted in Figures 11–14 feature 

EBEVs ranging from a low of $5,459 (OCS, 200 μm, 1.05 CTF, 15 miles) to a high of $12,004 (NS, 156 

μm, 5 miles, storm at y5). Though this is wide range, it is well within the bounds of published estimates 

of ecosystem values for any one service, and even more so with service aggregation. It is important to 

reiterate; however, the difference between EBEV estimates and net present value (NPV) estimates 

developed via benefit transfer. As derived estimates, EBEVs do not indicate whether a project should be 

built, or not. Instead, they serve as metrics of relative efficiency between and within project alternatives. 

Finally, the integrated framework developed in this study could be replicated for the examination of 

important challenges facing state and federal restoration programs. With a modest amount of updating11, 

the model could be used to address a number of pertinent questions, including: What are the economic 

trade-offs of more regular maintenance compared to a sacrificial approach to barrier island 

renourishment? How do large scale dedicated dredging projects compare (economically) to more 

frequent renourishment efforts via smaller dredges? and What feasibility thresholds for restoration might 

exist given YOD projections for specific coastal barriers? 

11 The time required for recalibration of sub-models of benefits and costs would depend on the context of any 

subsequent analysis, data availability, and processing capacity needed for updating geomorphic simulations.
48

7.4 Policy Implications

Nationwide, billions of dollars are being allocated for barrier island, shoreline, and beach renourishment. 

Since 1995, the Bureau of Ocean Energy Management (BOEM) has conveyed access to 162 million yd3 

of sand for 56 projects in eight states. These projects have restored 343 miles of US shoreline and 

protected billions of dollars of coastal infrastructure and habitat. However, the availability and suitability 

of coastal sediments for dedicated dredging is a growing concern. This concern is especially prominent in 

the Gulf of Mexico region and in Louisiana in particular, where an estimated 90 million yd3 will be 

required in the next 50 years to address the state’s coastal land loss crisis. This demand is reflected in 

Louisiana’s 2017 Coastal Master Plan, which calls for more than $22 billion in expenditures for dedicated 

dredging projects during this same time period. 

Sediment resource maps estimate the total volume of Louisiana-adjacent offshore surficial sand at nearly 

two billion cubic meters, with 60% of this material considered recoverable under current technological 

and regulatory constraints. Until recently, access to these OCS deposits was considered economically 

infeasible in comparison to lower quality, proximal sources. Traditional approaches to project evaluation 

have centered on the value of sediment as a commodity, with a focus on placement cost. The geomorphic 

and economic findings of this study, however, indicate that grain-size matters, and that a more 

comprehensive accounting of project performance beyond the fill template is required to maximize the 

return on coastal restoration spending

This study has provided a decision support tool for managers seeking more in-depth information on the 

economic trade-offs of various alternatives for dediciated dredging. The extent to which the framework is 

used will depend ultimately on its utility, which, in turn, hinges on the availability (and validity) of 

performance data from existing projects. Long-term monitoring of project performance, however, has 

historically received less than 5% of restoration budgeting. Project monitoring must be prioritized to 

ensure for adaptive management and improved efficiency of restoration programs in Louisiana, the Gulf 

of Mexico region and the coastlines of the US.
49

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51

Appendix A: Project Bid Data

Table A1. Commercial Bid Data for NS-sources Dedicated-dredging Projects on Barrier Shorelines 

and Island in Louisiana 1997–2018 (n=36)

Project ID Project Name Bid TBC ($) MM CYD

BA-30 East Grand Terre Island Restoration 1 32,942,178 3.34

BA-30 East Grand Terre Island Restoration 2 35,787,671 3.34

BA-30 East Grand Terre Island Restoration 3 41,856,610 3.34

BA-35 Pass Chaland to Grand Bayou Pass Barrier Shoreline Restoration 1 49,004,367 5.21

BA-35 Pass Chaland to Grand Bayou Pass Barrier Shoreline Restoration 2 39,363,841 5.21

BA-38-2 Chaland headland Restoration 1 20,758,927 2.74

BA-38-2 Chaland headland Restoration 2 37,104,973 2.74

BA-76 Cheniere Ronquille Barrier Island Restoration 1 30,731,186 2.63

BA-76 Cheniere Ronquille Barrier Island Restoration 2 34,632,114 2.63

BA-76 Cheniere Ronquille Barrier Island Restoration 3 27,480,973 2.63

TE-20 Isles Dernieres Restoration East Island 1 11,847,227 3.60

TE-20 Isles Dernieres Restoration East Island 2 12,880,745 3.60

TE-20 Isles Dernieres Restoration East Island 3 12,173,192 3.60

TE-20 Isles Dernieres Restoration East Island 4 16,560,793 3.60

TE-20 Isles Dernieres Restoration East Island 5 13,670,979 3.60

TE-20 Isles Dernieres Restoration East Island 6 15,384,221 3.60

TE-20 Isles Dernieres Restoration East Island 7 16,152,462 3.60

TE-20 Isles Dernieres Restoration East Island 8 16,152,462 3.60

TE-24 Isles Dernieres Restoration Trinity Island 1 16,809,376 4.89

TE-24 Isles Dernieres Restoration Trinity Island 2 20,129,728 4.89

TE-24 Isles Dernieres Restoration Trinity Island 3 16,484,144 4.89

TE-24 Isles Dernieres Restoration Trinity Island 4 18,835,680 4.89

TE-24 Isles Dernieres Restoration Trinity Island 5 19,329,011 4.89

TE-27 Whiskey Island Restoration 1 15,271,243 3.70

TE-27 Whiskey Island Restoration 2 17,974,673 3.70

TE-27 Whiskey Island Restoration 3 19,050,007 3.70

TE-27 Whiskey Island Restoration 4 21,164,123 3.70

TE-37 New Cut Dune and Marsh Restoration 1 12,228,870 0.83

TE-37 New Cut Dune and Marsh Restoration 2 16,249,265 0.83

TE-40 Timbalier Island Dune and Marsh Creation 1 15,351,093 3.60

TE-40 Timbalier Island Dune and Marsh Creation 2 16,985,098 3.60

TE-40 Timbalier Island Dune and Marsh Creation 3 21,222,319 3.60

TE-50 Whiskey Island Back Barrier Marsh Creation 1 26,176,788 2.53

TE-50 Whiskey Island Back Barrier Marsh Creation 2 30,565,090 2.53

TE-52 West Belle Pass Barrier Headland Restoration 1 30,654,289 4.80

TE-52 West Belle Pass Barrier Headland Restoration 2 41,625,982 4.80
Table A2. Commercial Bid Data for OCS Sources Dedicated-dredging Projects on Barrier 

Shorelines and Island in Louisiana 1997–2018 (n=35)

Project ID Project Name Bid Bid Amount ($) MM cyds

BA-38-1 Pelican Island Restoration 1 46,309,424 3.75

BA-38-1 Pelican Island Restoration 2 48,812,567 3.75

BA-40 Riverine Sand Mining/Scofield Island Restoration 1 48,792,190 3.39

BA-40 Riverine Sand Mining/Scofield Island Restoration 2 86,338,395 3.39

BA-45 Caminada Headland Beach and Dune Restoration 1 58,337,338 2.88

BA-45 Caminada Headland Beach and Dune Restoration 2 71,809,850 2.88

BA-45 Caminada Headland Beach and Dune Restoration 3 70,282,383 2.88

BA-45 Caminada Headland Beach and Dune Restoration 4 59,924,573 2.88

BA-110 Shell Island East BERM Restoration 1 34,400,181 1.70

BA-110 Shell Island East BERM Restoration 2 35,112,173 1.70

BA-111 Shell Island West NRDA Restoration 1 58,011,365 4.50

BA-111 Shell Island West NRDA Restoration 2 65,365,519 5.20

BA-111 Shell Island West NRDA Restoration 3 66,236,430 5.28

BA-111 Shell Island West NRDA Restoration 4 61,766,918 5.05

BA-111 Shell Island West NRDA Restoration 5 59,036,659 4.50

BA-111 Shell Island West NRDA Restoration 6 67,360,151 5.20

BA-111 Shell Island West NRDA Restoration 7 68,102,500 5.28

BA-111 Shell Island West NRDA Restoration 8 62,105,540 5.05

BA-143 Caminada Headland Beach and Dune Restoration INCR2 1 147,261,118 5.40

BA-143 Caminada Headland Beach and Dune Restoration INCR2 2 150,253,154 5.40

BA-143 Caminada Headland Beach and Dune Restoration INCR2 3 155,410,084 5.40

BA-143 Caminada Headland Beach and Dune Restoration INCR2 4 116,858,691 5.40

CS-31 Holly Beach Sand Management 1 24,563,544 2.27

CS-31 Holly Beach Sand Management 2 19,529,382 2.27

CS-33 Cameron Parish Shoreline Restoration 1 50,212,174 2.33

CS-33 Cameron Parish Shoreline Restoration 2 51,358,425 2.33

TE-25&30 East Timbalier Island Sediment Restoration 1 14,830,264 2.27

TE-25&30 East Timbalier Island Sediment Restoration 2 17,455,629 2.27

TE-25&30 East Timbalier Island Sediment Restoration 3 18,499,911 2.27

TE-25&30 East Timbalier Island Sediment Restoration 4 20,552,979 2.27

TE-48-2 Raccoon Island Shoreline Protection and Marsh Creation 1 5,889,249 0.64

TE-48-2 Raccoon Island Shoreline Protection and Marsh Creation 2 7,688,697 0.64

TE-48-2 Raccoon Island Shoreline Protection and Marsh Creation 3 14,970,118 0.64

TE-100 Caillou Lake Headlands Restoration 1 104,102,226 10.45

TE-100 Caillou Lake Headlands Restoration 2 104,110,192 10.45
Department of the Interior (DOI)

The Department of the Interior protects and manages the Nation's natural 

resources and cultural heritage; provides scientific and other information about 

those resources; and honors the Nation’s trust responsibilities or special 

commitments to American Indians, Alaska Natives, and affiliated island 

communities.

Bureau of Ocean Energy Management (BOEM)

The mission of the Bureau of Ocean Energy Management is to manage 

development of US Outer Continental Shelf energy and mineral resources in an 

environmentally and economically responsible way.

BOEM Environmental Studies Program

The mission of the Environmental Studies Program is to provide the 

information needed to predict, assess, and manage impacts from offshore 

energy and marine mineral exploration, development, and production activities 

on human, marine, and coastal environments. The proposal, selection, research, 

review, collaboration, production, and dissemination of each of BOEM’s 

Environmental Studies follows the DOI Code of Scientific and Scholarly 

Conduct, in support of a culture of scientific and professional integrity, as set 

out in the DOI Departmental Manual (305 DM 3).