Coastal wetland restoration for blue carbon in Australia - work package to identify restoration sites, carbon abatement and co-benefits, and undertake cost-benefit and cost-effectiveness analyses (NESP MaC 1.15, The University of Queensland)

This dataset contains the spatial analysis workflow, data derived from the spatial analysis, and R code to identify the blue carbon restoration opportunity in three case study regions in Australia: 
1) Fitzroy Basin, QLD; 
2) Peel-Harvey and northern part of South West Catchments, WA; and 
3) Ord River, WA. 

It uses a multi-stage approach to identify: 
1) potential coastal wetland restoration sites across case study regions based on biophysical suitability; 
2) their carbon abatement following the Australian Government Tidal restoration of blue carbon ecosystems method; 
3) their economic feasibility under a carbon market using cost-benefit analysis with variations of carbon abatement, restoration cost, discount rate, carbon price, and farm gross margin; and 
4) their co-benefits for biodiversity, fisheries, water quality, and coastal protection, and evaluation of their cost-effectiveness considering profitability, restoration feasibility, and provision of co-benefits. 
Potential cultural benefits were also evaluated. This work package supports the regional approach developed for selecting coastal wetland restoration sites for blue carbon and co-benefits in Australia (Hagger et al. 2024 Journal of Environmental Management).

This work package utilises publicly-available spatial datasets from Geoscience Australia, the Queensland Government (QSpatial), the Western Australian Government (Landgate) and the National Native Title Tribunal to identify coastal wetland restoration sites in case study regions in Queensland and Western Australia. Spatial datasets were analysed across the sites in ArcMap 10.8 (ESRI, 2019) or QGIS (Open-source software, 2002) to extract data required to estimate carbon abatement, co-benefit indicators, cultural benefits, and restoration feasibility, and undertake the cost-benefit analysis, economic prioritisation analysis, and statistical analysis in R 4.0.2 (R Core Team, 2020).

Methods:

The methods to perform the analyses for each case study region are provided in Hagger et al. (2024). The approach integrates spatial analysis with R data analysis. The spatial analysis workflows provided in this work package provide details on the methodology for the extraction of data. The R scripts are annotated with methods for the data analysis. 
The methods to perform the analyses for each case study region are provided in Hagger et al. (2024). The approach integrates spatial analysis with R data analysis. The spatial analysis workflows provided in this work package provide details on the methodology for the extraction of data. The R scripts are annotated with methods for the data analysis. 

For example, the steps for Fitzroy Basin are:
•   Identify potential restoration sites (section 2.3):
    =&gt; Follow the spatial analysis workflow steps to map (1) the study area, (2) the restorable land-uses, (3) historic coastal wetlands, and (4) Highest Astronomical Tide impact area under (a) current sea-level and (b) sea-level rise scenarios. Intersect these data layers to identify (5) potential restoration sites under current sea-level, (6) potential restoration sites with sea-level rise scenarios. 
•   Estimate carbon abatement (section 2.4):
    =&gt; Follow the spatial analysis workflow to perform intersections of spatial data required to inform the carbon abatement calculations, including:
        - (8) land-uses, pre-clear regional ecosystems, intertidal zone, drainage basins to inform CO2 removals and CH4 and N2O emissions from restoration scenario;
        - (9) hydrologically modified wetlands, (10) water storage points and reservoirs, and (11) soil carbon stocks to inform avoided CO2, CH4, and N2O emissions from ceasing baseline scenario;
        - (12) existing wetlands to inform CO2 removals and CH4 and N2O emissions from baseline scenario.
    =&gt; Follow the R script “1_wetland_restoration_bluecarbon”.
•   Undertake the cost-benefit analysis (section 2.5):
    =&gt; Follow the spatial analysis workflow to perform intersections of spatial data required to inform the calculation of farm gross margins:
        - (13) grazing land management land types.
    =&gt; Follow the R script “2_wetland_restoration_economic”.
•   Estimate co-benefits (section 2.6):
    =&gt; Follow the spatial analysis workflow to perform intersections of spatial data required to inform the co-benefit indicators:
        - (14) biodiversity
        - (15) fisheries
        - (16) water quality
        - (17) coastal protection
•   Estimate cultural benefits (section 2.7):
    =&gt; Follow the spatial analysis workflow to perform intersections of spatial data required to inform the cultural benefit indicators:
        - (18) cultural heritage
•   Estimate restoration feasibility (section 2.8)
    =&gt; Follow the spatial analysis workflow to perform intersections of spatial data required to inform the restoration feasibility indicators:
        - (19) drains, barriers and tidal zone, 
•   Undertake the economic prioritisation analysis (section 2.9)
    =&gt; Follow the R script “3_wetland_restoration_cobenefits”
•   Statistical analysis performed (section 2.10) for the cost-benefit analysis and economic prioritisation analysis.

Detailed methods for calculation of the carbon abatement, biodiversity, fisheries, water quality, coastal protection, and cultural benefits, farm gross margins, and restoration feasibility are provided in the Supplementary Data to Hagger et al. 2024:
•   Table S6 Methods for estimating avoided greenhouse gas emissions and CO2 removals from ceasing agricultural land use, and associated equations, emission factors (EF), accumulation rate (AR), conversion factors (CF), and global warming potentials (GWP).
•   Table S7 Methods for estimating CO2 removals and greenhouse gas emissions in coastal wetland ecosystems, and associated equations, carbon accumulation rates (AR), emission factors (EF), conversion factors (CF) and global warming potential (GWP) applied.
•   Table S11 Co-benefit indicators and measures for each case study region and the weightings applied under the different scenarios.
•   Table S12 Restoration feasibility probability method.
•   Section S2 Farm gross margins.

Limitations:

Complete work packages (spatial analysis workflow, data generated, and R scripts) are provided for the Fitzroy Basin and Peel-Harvey case study regions to reproduce the results provided in Hagger et al. (2024). These work packages can also be used to apply the approach to a new region. 
The spatial data on the identified coastal wetland restoration sites has not been provided due to the sensitivity in revealing the locations of priority areas for restoration. 
The work package for the Ord River case study region only identifies the restoration opportunity. Cost-benefit and economic prioritisation analyses were not conducted for the Ord region, because of limited opportunity for tidal restoration and no available data to estimate degraded wetland area and no current carbon method for avoiding disturbance to coastal wetlands from grazing.

Format:

This dataset consists of work packages for three case study regions:
1)  Fitzroy Basin, Queensland
2)  Peel-Harvey and northern part of South West catchments, Western Australia
3)  Ord River, Western Australia.

Fitzroy Basin and Peel-Harvey case studies contain the following:
1)  Spatial analysis workflow (word document) with detailed instructions in ArcMap 10.8 on identifying potential restoration sites and extracting data for estimating carbon abatement, co-benefit indicators, and restoration feasibility.
2)  Data generated from the spatial analysis for input into the R scripts (excel or csv files).
3)  R scripts to undertake the multi-staged approach for assessing blue carbon restoration opportunity:
    - 1_wetland_restoration_bluecarbon = estimates the carbon abatement (baseline land-use avoided emissions and removals and restored land-use removals and emissions).
    - 2_wetland_restoration_economic = cost-benefit analysis using net present value (NPV), considering financial benefit from carbon abatement, annual opportunity cost from agricultural production (farm gross margin), and restoration and maintenance costs.
    - 3_wetland_restoration_cobenefits = estimates the co-benefit indicators for biodiversity, fisheries, water quality and coastal protection using the defined indicators and undertakes the economic prioritisation analysis, considering NPV, restoration feasibility, and the provision of co-benefits.

Ord River case study contains the following:
1)  Spatial analysis workflow (word document) with detailed instructions on identifying potential restoration sites in ArcMap 10.8 and Purple-crowned fairy-wren habitat in QGIS.
2)  Data generated from the QGIS spatial analysis for the co-benefit indicators for biodiversity, fisheries, and coastal protection (none for water quality) for assessment of co-benefits (csv files). Detailed instructions for extraction of data in QGIS have not been provided, however follows methods in Hagger et al. (2024).
3)  R script to identify preliminary blue carbon restoration opportunity:
    - 1_wetland_restoration_prelim = calculates restoration area and preliminary carbon abatement estimates.

Data Dictionary:

AEha = heads per hectare
ag = agriculture
AGB = above ground biomass
ALUM = Australian land use management
ANR = assisted natural regeneration
BC = blue carbon
BD = biodiversity
BGB = below ground biomass
buf = buffer
BVG = broad vegetation group
C = carbon
CAPAD = Collaborative Australian Protected Areas Database
CBA = cost-benefit analysis
CE = cost-effectiveness analysis
CF = conversion factor
CH = cultural heritage
CH4 = methane
CLUM = catchment scale land use management
CO2 = carbon dioxide
CP = carbon price
CPI = consumer price index
CYC = Capricorn Yellow Chat
DBVG = dominant broad vegetation group
DCF = discounted cash flow
deg = degraded
DEM = digital elevation model
df = dataframe
DIN = dissolved inorganic nitrogen
dis = dissolved
EF = emissions factor
EVNT = endangered, vulnerable, near threatened species
FGM = farm gross margin
FHA = fish habitat area
GLM = grazing land management
Gm = gross margin
GWP = global warming potential
HAT = highest astronomical tide
hydromod = hydrologically modified wetlands
LU = land use
mang = mangrove
Mg = metric tonne
MHWN = mean high water neaps
MHWS = mean high water springs
MSL = mean sea level
N2O = nitrous oxide
NNTT = National Native Title Tribunal
NPV = net present value
NRM = natural resource management
PHSW = Peel-Harvey South West
prob = probability
prof = profit
prop = proportion
QLD = Queensland
RCP = Representative Concentration Pathway
RE = regional ecosystem
RE = regional ecosystems
regrem = regrowth and remnant vegetation
rsites = restoration sites
salt = saltmarsh
se = standard error
sec = secondary
SLR = sea level rise
SOC = soil organic carbon
sup = supratidal
supra = supratidal
ter = tertiary
tid = tidal
TSS = total suspended solids
var = variance
veg = vegetation
WA = Western Australia
yr = year

Location of the dataset:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2021-2022-NESP-MaC-1\1.15_Coastal-wetland-blue-carbon\data\received-2024-09-01

References:

Hagger, V., Stewart-Sinclair, P., Rossini, R., Waltham, N.J., Ronan, M., Adame, M.F., Lavery, P., Glamore, W. and Lovelock, C.E. (2022) Coastal wetland restoration for blue carbon in Australia. Values-based approach for selecting restoration sites. Report to the National Environmental Science Program. The University of Queensland. https://www.nespmarinecoastal.edu.au/technical-reports-2/ 

Hagger, V., Stewart-Sinclair, P. Rossini, R.A., Adame, M.F., Glamore, W., Lavery, P., Waltham, N.J. and Lovelock, C.E. (2024) Lessons learned on the feasibility of coastal wetland restoration for blue carbon and co-benefits in Australia. Journal of Environmental Management.

Tags: MARINE, National Environmental Science Program (NESP) Marine and Coastal Hub, marine
Organization: Australian Ocean Data Network